722 lines
26 KiB
C++
722 lines
26 KiB
C++
/**
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* Copyright (c) 2017-2023, XGBoost contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/learner.h> // for Learner
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#include <xgboost/logging.h> // for LogCheck_NE, CHECK_NE, LogCheck_EQ
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#include <xgboost/objective.h> // for ObjFunction
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#include <xgboost/version_config.h> // for XGBOOST_VER_MAJOR, XGBOOST_VER_MINOR
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#include <algorithm> // for equal, transform
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#include <cinttypes> // for int32_t, int64_t, uint32_t
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#include <cstddef> // for size_t
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#include <iosfwd> // for ofstream
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#include <iterator> // for back_insert_iterator, back_inserter
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#include <limits> // for numeric_limits
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#include <map> // for map
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#include <memory> // for unique_ptr, shared_ptr, __shared_ptr_...
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#include <random> // for uniform_real_distribution
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#include <string> // for allocator, basic_string, string, oper...
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#include <thread> // for thread
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#include <type_traits> // for is_integral
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#include <utility> // for pair
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#include <vector> // for vector
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#include "../../src/collective/communicator-inl.h" // for GetRank, GetWorldSize
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#include "../../src/common/api_entry.h" // for XGBAPIThreadLocalEntry
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#include "../../src/common/io.h" // for LoadSequentialFile
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#include "../../src/common/linalg_op.h" // for ElementWiseTransformHost, begin, end
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#include "../../src/common/random.h" // for GlobalRandom
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#include "dmlc/io.h" // for Stream
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#include "dmlc/omp.h" // for omp_get_max_threads
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#include "dmlc/registry.h" // for Registry
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#include "filesystem.h" // for TemporaryDirectory
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#include "helpers.h" // for GetBaseScore, RandomDataGenerator
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#include "objective_helpers.h" // for MakeObjNamesForTest, ObjTestNameGenerator
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#include "xgboost/base.h" // for bst_float, Args, bst_feature_t, bst_int
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#include "xgboost/context.h" // for Context, DeviceOrd
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#include "xgboost/data.h" // for DMatrix, MetaInfo, DataType
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#include "xgboost/host_device_vector.h" // for HostDeviceVector
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#include "xgboost/json.h" // for Json, Object, get, String, IsA, opera...
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#include "xgboost/linalg.h" // for Tensor, TensorView
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#include "xgboost/logging.h" // for ConsoleLogger
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#include "xgboost/predictor.h" // for PredictionCacheEntry
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#include "xgboost/string_view.h" // for StringView
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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 = RandomDataGenerator{10, 10, 0.0f}.GenerateDMatrix();
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auto learner = std::unique_ptr<Learner>(Learner::Create({mat_ptr}));
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learner->SetParams(args);
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auto major = XGBOOST_VER_MAJOR;
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auto minor = XGBOOST_VER_MINOR;
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auto patch = XGBOOST_VER_PATCH;
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static_assert(std::is_integral_v<decltype(major)>, "Wrong major version type");
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static_assert(std::is_integral_v<decltype(minor)>, "Wrong minor version type");
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static_assert(std::is_integral_v<decltype(patch)>, "Wrong patch version type");
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}
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TEST(Learner, ParameterValidation) {
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ConsoleLogger::Configure({{"verbosity", "2"}});
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size_t constexpr kRows = 1;
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size_t constexpr kCols = 1;
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auto p_mat = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix();
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auto learner = std::unique_ptr<Learner>(Learner::Create({p_mat}));
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learner->SetParam("validate_parameters", "1");
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learner->SetParam("Knock-Knock", "Who's-there?");
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learner->SetParam("Silence", "....");
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learner->SetParam("tree_method", "exact");
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testing::internal::CaptureStderr();
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learner->Configure();
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std::string output = testing::internal::GetCapturedStderr();
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ASSERT_TRUE(output.find(R"(Parameters: { "Knock-Knock", "Silence" })") != std::string::npos);
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// whitespace
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learner->SetParam("tree method", "exact");
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EXPECT_THROW(learner->Configure(), dmlc::Error);
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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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bst_feature_t constexpr kNumCols = 15;
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std::shared_ptr<DMatrix> p_mat{RandomDataGenerator{kNumRows, kNumCols, 0.0f}.GenerateDMatrix()};
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std::vector<bst_float> weight(kNumGroups, 1);
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std::vector<bst_group_t> 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->SetInfo("weight", static_cast<void *>(weight.data()), DataType::kFloat32, kNumGroups);
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p_mat->SetInfo("group", group.data(), DataType::kUInt32, kNumGroups);
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p_mat->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->SetParams({Arg{"objective", "rank:pairwise"}});
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EXPECT_NO_THROW(learner->UpdateOneIter(0, p_mat));
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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->SetInfo("group", group.data(), DataType::kUInt32, kNumGroups+1);
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EXPECT_ANY_THROW(learner->UpdateOneIter(0, p_mat));
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}
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TEST(Learner, SLOW_CheckMultiBatch) { // NOLINT
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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, 50000);
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std::shared_ptr<DMatrix> dmat(
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xgboost::DMatrix::Load(tmp_file + "?format=libsvm" + "#" + tmp_file + ".cache"));
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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->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->SetParams(Args{{"objective", "binary:logistic"}});
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learner->UpdateOneIter(0, dmat);
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}
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TEST(Learner, Configuration) {
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std::string const emetric = "eval_metric";
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{
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std::unique_ptr<Learner> learner { Learner::Create({nullptr}) };
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learner->SetParam(emetric, "auc");
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learner->SetParam(emetric, "rmsle");
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learner->SetParam("foo", "bar");
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// eval_metric is not part of configuration
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auto attr_names = learner->GetConfigurationArguments();
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ASSERT_EQ(attr_names.size(), 1ul);
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ASSERT_EQ(attr_names.find(emetric), attr_names.cend());
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ASSERT_EQ(attr_names.at("foo"), "bar");
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}
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{
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std::unique_ptr<Learner> learner { Learner::Create({nullptr}) };
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learner->SetParams({{"foo", "bar"}, {emetric, "auc"}, {emetric, "entropy"}, {emetric, "KL"}});
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auto attr_names = learner->GetConfigurationArguments();
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ASSERT_EQ(attr_names.size(), 1ul);
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ASSERT_EQ(attr_names.at("foo"), "bar");
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}
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}
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TEST(Learner, JsonModelIO) {
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// Test of comparing JSON object directly.
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size_t constexpr kRows = 8;
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int32_t constexpr kIters = 4;
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std::shared_ptr<DMatrix> p_dmat{RandomDataGenerator{kRows, 10, 0}.GenerateDMatrix()};
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p_dmat->Info().labels.Reshape(kRows);
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CHECK_NE(p_dmat->Info().num_col_, 0);
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{
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std::unique_ptr<Learner> learner { Learner::Create({p_dmat}) };
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learner->Configure();
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Json out { Object() };
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learner->SaveModel(&out);
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dmlc::TemporaryDirectory tmpdir;
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std::ofstream fout (tmpdir.path + "/model.json");
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fout << out;
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fout.close();
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auto loaded_str = common::LoadSequentialFile(tmpdir.path + "/model.json");
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Json loaded = Json::Load(StringView{loaded_str.data(), loaded_str.size()});
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learner->LoadModel(loaded);
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learner->Configure();
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Json new_in { Object() };
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learner->SaveModel(&new_in);
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ASSERT_EQ(new_in, out);
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}
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{
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std::unique_ptr<Learner> learner { Learner::Create({p_dmat}) };
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for (int32_t iter = 0; iter < kIters; ++iter) {
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learner->UpdateOneIter(iter, p_dmat);
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}
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learner->SetAttr("best_score", "15.2");
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Json out { Object() };
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learner->SaveModel(&out);
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learner->LoadModel(out);
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Json new_in { Object() };
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learner->Configure();
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learner->SaveModel(&new_in);
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ASSERT_TRUE(IsA<Object>(out["learner"]["attributes"]));
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ASSERT_EQ(get<Object>(out["learner"]["attributes"]).size(), 1ul);
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ASSERT_EQ(out, new_in);
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}
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}
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TEST(Learner, ConfigIO) {
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bst_row_t n_samples = 128;
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bst_feature_t n_features = 12;
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std::shared_ptr<DMatrix> p_fmat{
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RandomDataGenerator{n_samples, n_features, 0}.GenerateDMatrix(true, false, 2)};
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auto serialised_model_tmp = std::string{};
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std::string eval_res_0;
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std::string eval_res_1;
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{
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std::unique_ptr<Learner> learner{Learner::Create({p_fmat})};
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learner->SetParams(Args{{"eval_metric", "ndcg"}, {"eval_metric", "map"}});
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learner->Configure();
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learner->UpdateOneIter(0, p_fmat);
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eval_res_0 = learner->EvalOneIter(0, {p_fmat}, {"Train"});
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common::MemoryBufferStream fo(&serialised_model_tmp);
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learner->Save(&fo);
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}
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{
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common::MemoryBufferStream fi(&serialised_model_tmp);
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std::unique_ptr<Learner> learner{Learner::Create({p_fmat})};
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learner->Load(&fi);
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eval_res_1 = learner->EvalOneIter(0, {p_fmat}, {"Train"});
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}
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ASSERT_EQ(eval_res_0, eval_res_1);
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}
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// Crashes the test runner if there are race condiditions.
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//
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// Build with additional cmake flags to enable thread sanitizer
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// which definitely catches problems. Note that OpenMP needs to be
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// disabled, otherwise thread sanitizer will also report false
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// positives.
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//
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// ```
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// -DUSE_SANITIZER=ON -DENABLED_SANITIZERS=thread -DUSE_OPENMP=OFF
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// ```
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TEST(Learner, MultiThreadedPredict) {
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size_t constexpr kRows = 1000;
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size_t constexpr kCols = 100;
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std::shared_ptr<DMatrix> p_dmat{RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix()};
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p_dmat->Info().labels.Reshape(kRows);
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CHECK_NE(p_dmat->Info().num_col_, 0);
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std::shared_ptr<DMatrix> p_data{RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix()};
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CHECK_NE(p_data->Info().num_col_, 0);
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std::shared_ptr<Learner> learner{Learner::Create({p_dmat})};
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learner->Configure();
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std::vector<std::thread> threads;
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for (uint32_t thread_id = 0;
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thread_id < 2 * std::thread::hardware_concurrency(); ++thread_id) {
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threads.emplace_back([learner, p_data] {
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size_t constexpr kIters = 10;
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auto &entry = learner->GetThreadLocal().prediction_entry;
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HostDeviceVector<float> predictions;
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for (size_t iter = 0; iter < kIters; ++iter) {
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learner->Predict(p_data, false, &entry.predictions, 0, 0);
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learner->Predict(p_data, false, &predictions, 0, 0, false, true); // leaf
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learner->Predict(p_data, false, &predictions, 0, 0, false, false, true); // contribs
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}
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});
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}
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for (auto &thread : threads) {
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thread.join();
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}
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}
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TEST(Learner, BinaryModelIO) {
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size_t constexpr kRows = 8;
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int32_t constexpr kIters = 4;
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auto p_dmat = RandomDataGenerator{kRows, 10, 0}.GenerateDMatrix();
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p_dmat->Info().labels.Reshape(kRows);
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std::unique_ptr<Learner> learner{Learner::Create({p_dmat})};
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learner->SetParam("eval_metric", "rmsle");
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learner->Configure();
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for (int32_t iter = 0; iter < kIters; ++iter) {
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learner->UpdateOneIter(iter, p_dmat);
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}
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dmlc::TemporaryDirectory tempdir;
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std::string const fname = tempdir.path + "binary_model_io.bin";
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{
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// Make sure the write is complete before loading.
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std::unique_ptr<dmlc::Stream> fo(dmlc::Stream::Create(fname.c_str(), "w"));
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learner->SaveModel(fo.get());
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}
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learner.reset(Learner::Create({p_dmat}));
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std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r"));
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learner->LoadModel(fi.get());
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learner->Configure();
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Json config { Object() };
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learner->SaveConfig(&config);
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std::string config_str;
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Json::Dump(config, &config_str);
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ASSERT_NE(config_str.find("rmsle"), std::string::npos);
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ASSERT_EQ(config_str.find("WARNING"), std::string::npos);
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}
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#if defined(XGBOOST_USE_CUDA)
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// Tests for automatic GPU configuration.
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TEST(Learner, GPUConfiguration) {
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using Arg = std::pair<std::string, std::string>;
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size_t constexpr kRows = 10;
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auto p_dmat = RandomDataGenerator(kRows, 10, 0).GenerateDMatrix();
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std::vector<std::shared_ptr<DMatrix>> mat {p_dmat};
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std::vector<bst_float> labels(kRows);
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for (size_t i = 0; i < labels.size(); ++i) {
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labels[i] = i;
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}
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p_dmat->Info().labels.Data()->HostVector() = labels;
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p_dmat->Info().labels.Reshape(kRows);
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{
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std::unique_ptr<Learner> learner {Learner::Create(mat)};
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learner->SetParams({Arg{"booster", "gblinear"},
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Arg{"updater", "gpu_coord_descent"}});
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learner->UpdateOneIter(0, p_dmat);
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ASSERT_EQ(learner->Ctx()->Device(), DeviceOrd::CUDA(0));
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}
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{
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std::unique_ptr<Learner> learner{Learner::Create(mat)};
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learner->SetParams({Arg{"tree_method", "gpu_hist"}});
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learner->Configure();
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ASSERT_EQ(learner->Ctx()->Device(), DeviceOrd::CUDA(0));
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learner->UpdateOneIter(0, p_dmat);
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ASSERT_EQ(learner->Ctx()->Device(), DeviceOrd::CUDA(0));
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}
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{
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std::unique_ptr<Learner> learner {Learner::Create(mat)};
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learner->SetParams({Arg{"tree_method", "gpu_hist"},
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Arg{"gpu_id", "-1"}});
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learner->UpdateOneIter(0, p_dmat);
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ASSERT_EQ(learner->Ctx()->Device(), DeviceOrd::CUDA(0));
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}
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{
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// with CPU algorithm
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std::unique_ptr<Learner> learner {Learner::Create(mat)};
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learner->SetParams({Arg{"tree_method", "hist"}});
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learner->UpdateOneIter(0, p_dmat);
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ASSERT_EQ(learner->Ctx()->Device(), DeviceOrd::CPU());
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}
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{
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// with CPU algorithm, but `gpu_id` takes priority
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std::unique_ptr<Learner> learner {Learner::Create(mat)};
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learner->SetParams({Arg{"tree_method", "hist"}, Arg{"gpu_id", "0"}});
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learner->UpdateOneIter(0, p_dmat);
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ASSERT_EQ(learner->Ctx()->Device(), DeviceOrd::CUDA(0));
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}
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}
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#endif // defined(XGBOOST_USE_CUDA)
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TEST(Learner, Seed) {
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auto m = RandomDataGenerator{10, 10, 0}.GenerateDMatrix();
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std::unique_ptr<Learner> learner {
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Learner::Create({m})
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};
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auto seed = std::numeric_limits<int64_t>::max();
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learner->SetParam("seed", std::to_string(seed));
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learner->Configure();
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Json config { Object() };
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learner->SaveConfig(&config);
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ASSERT_EQ(std::to_string(seed),
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get<String>(config["learner"]["generic_param"]["seed"]));
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seed = std::numeric_limits<int64_t>::min();
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learner->SetParam("seed", std::to_string(seed));
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learner->Configure();
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learner->SaveConfig(&config);
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ASSERT_EQ(std::to_string(seed),
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get<String>(config["learner"]["generic_param"]["seed"]));
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}
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TEST(Learner, ConstantSeed) {
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auto m = RandomDataGenerator{10, 10, 0}.GenerateDMatrix(true);
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std::unique_ptr<Learner> learner{Learner::Create({m})};
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// Use exact as it doesn't initialize column sampler at construction, which alters the rng.
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learner->SetParam("tree_method", "exact");
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learner->Configure(); // seed the global random
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std::uniform_real_distribution<float> dist;
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auto& rng = common::GlobalRandom();
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float v_0 = dist(rng);
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learner->SetParam("", "");
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learner->Configure(); // check configure doesn't change the seed.
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float v_1 = dist(rng);
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CHECK_NE(v_0, v_1);
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{
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rng.seed(Context::kDefaultSeed);
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std::uniform_real_distribution<float> dist;
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float v_2 = dist(rng);
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CHECK_EQ(v_0, v_2);
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}
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}
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TEST(Learner, FeatureInfo) {
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size_t constexpr kCols = 10;
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auto m = RandomDataGenerator{10, kCols, 0}.GenerateDMatrix(true);
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std::vector<std::string> names(kCols);
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for (size_t i = 0; i < kCols; ++i) {
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names[i] = ("f" + std::to_string(i));
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}
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std::vector<std::string> types(kCols);
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for (size_t i = 0; i < kCols; ++i) {
|
|
types[i] = "q";
|
|
}
|
|
types[8] = "f";
|
|
types[0] = "int";
|
|
types[3] = "i";
|
|
types[7] = "i";
|
|
|
|
std::vector<char const*> c_names(kCols);
|
|
for (size_t i = 0; i < names.size(); ++i) {
|
|
c_names[i] = names[i].c_str();
|
|
}
|
|
std::vector<char const*> c_types(kCols);
|
|
for (size_t i = 0; i < types.size(); ++i) {
|
|
c_types[i] = names[i].c_str();
|
|
}
|
|
|
|
std::vector<std::string> out_names;
|
|
std::vector<std::string> out_types;
|
|
|
|
Json model{Object()};
|
|
{
|
|
std::unique_ptr<Learner> learner{Learner::Create({m})};
|
|
learner->Configure();
|
|
learner->SetFeatureNames(names);
|
|
learner->GetFeatureNames(&out_names);
|
|
|
|
learner->SetFeatureTypes(types);
|
|
learner->GetFeatureTypes(&out_types);
|
|
|
|
ASSERT_TRUE(std::equal(out_names.begin(), out_names.end(), names.begin()));
|
|
ASSERT_TRUE(std::equal(out_types.begin(), out_types.end(), types.begin()));
|
|
|
|
learner->SaveModel(&model);
|
|
}
|
|
|
|
{
|
|
std::unique_ptr<Learner> learner{Learner::Create({m})};
|
|
learner->LoadModel(model);
|
|
|
|
learner->GetFeatureNames(&out_names);
|
|
learner->GetFeatureTypes(&out_types);
|
|
ASSERT_TRUE(std::equal(out_names.begin(), out_names.end(), names.begin()));
|
|
ASSERT_TRUE(std::equal(out_types.begin(), out_types.end(), types.begin()));
|
|
}
|
|
}
|
|
|
|
TEST(Learner, MultiTarget) {
|
|
size_t constexpr kRows{128}, kCols{10}, kTargets{3};
|
|
auto m = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix();
|
|
m->Info().labels.Reshape(kRows, kTargets);
|
|
linalg::ElementWiseTransformHost(m->Info().labels.HostView(), omp_get_max_threads(),
|
|
[](auto i, auto) { return i; });
|
|
|
|
{
|
|
std::unique_ptr<Learner> learner{Learner::Create({m})};
|
|
learner->Configure();
|
|
|
|
Json model{Object()};
|
|
learner->SaveModel(&model);
|
|
ASSERT_EQ(get<String>(model["learner"]["learner_model_param"]["num_target"]),
|
|
std::to_string(kTargets));
|
|
}
|
|
{
|
|
std::unique_ptr<Learner> learner{Learner::Create({m})};
|
|
learner->SetParam("objective", "multi:softprob");
|
|
// unsupported objective.
|
|
EXPECT_THROW({ learner->Configure(); }, dmlc::Error);
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Test the model initialization sequence is correctly performed.
|
|
*/
|
|
class InitBaseScore : public ::testing::Test {
|
|
protected:
|
|
std::size_t static constexpr Cols() { return 10; }
|
|
std::shared_ptr<DMatrix> Xy_;
|
|
|
|
void SetUp() override { Xy_ = RandomDataGenerator{10, Cols(), 0}.GenerateDMatrix(true); }
|
|
|
|
public:
|
|
void TestUpdateConfig() {
|
|
std::unique_ptr<Learner> learner{Learner::Create({Xy_})};
|
|
learner->SetParam("objective", "reg:absoluteerror");
|
|
learner->UpdateOneIter(0, Xy_);
|
|
Json config{Object{}};
|
|
learner->SaveConfig(&config);
|
|
auto base_score = GetBaseScore(config);
|
|
ASSERT_NE(base_score, ObjFunction::DefaultBaseScore());
|
|
|
|
// already initialized
|
|
auto Xy1 = RandomDataGenerator{100, Cols(), 0}.Seed(321).GenerateDMatrix(true);
|
|
learner->UpdateOneIter(1, Xy1);
|
|
learner->SaveConfig(&config);
|
|
auto base_score1 = GetBaseScore(config);
|
|
ASSERT_EQ(base_score, base_score1);
|
|
|
|
Json model{Object{}};
|
|
learner->SaveModel(&model);
|
|
learner.reset(Learner::Create({}));
|
|
learner->LoadModel(model);
|
|
learner->Configure();
|
|
learner->UpdateOneIter(2, Xy1);
|
|
learner->SaveConfig(&config);
|
|
auto base_score2 = GetBaseScore(config);
|
|
ASSERT_EQ(base_score, base_score2);
|
|
}
|
|
|
|
void TestBoostFromAvgParam() {
|
|
std::unique_ptr<Learner> learner{Learner::Create({Xy_})};
|
|
learner->SetParam("objective", "reg:absoluteerror");
|
|
learner->SetParam("base_score", "1.3");
|
|
Json config(Object{});
|
|
learner->Configure();
|
|
learner->SaveConfig(&config);
|
|
|
|
auto base_score = GetBaseScore(config);
|
|
// no change
|
|
ASSERT_FLOAT_EQ(base_score, 1.3);
|
|
|
|
HostDeviceVector<float> predt;
|
|
learner->Predict(Xy_, false, &predt, 0, 0);
|
|
auto h_predt = predt.ConstHostSpan();
|
|
for (auto v : h_predt) {
|
|
ASSERT_FLOAT_EQ(v, 1.3);
|
|
}
|
|
learner->UpdateOneIter(0, Xy_);
|
|
learner->SaveConfig(&config);
|
|
base_score = GetBaseScore(config);
|
|
// no change
|
|
ASSERT_FLOAT_EQ(base_score, 1.3);
|
|
|
|
auto from_avg = std::stoi(
|
|
get<String const>(config["learner"]["learner_model_param"]["boost_from_average"]));
|
|
// from_avg is disabled when base score is set
|
|
ASSERT_EQ(from_avg, 0);
|
|
// in the future when we can deprecate the binary model, user can set the parameter directly.
|
|
learner->SetParam("boost_from_average", "1");
|
|
learner->Configure();
|
|
learner->SaveConfig(&config);
|
|
from_avg = std::stoi(
|
|
get<String const>(config["learner"]["learner_model_param"]["boost_from_average"]));
|
|
ASSERT_EQ(from_avg, 1);
|
|
}
|
|
|
|
void TestInitAfterLoad() {
|
|
std::unique_ptr<Learner> learner{Learner::Create({Xy_})};
|
|
learner->SetParam("objective", "reg:absoluteerror");
|
|
learner->Configure();
|
|
|
|
Json model{Object{}};
|
|
learner->SaveModel(&model);
|
|
auto base_score = GetBaseScore(model);
|
|
ASSERT_EQ(base_score, ObjFunction::DefaultBaseScore());
|
|
|
|
learner.reset(Learner::Create({Xy_}));
|
|
learner->LoadModel(model);
|
|
Json config(Object{});
|
|
learner->Configure();
|
|
learner->SaveConfig(&config);
|
|
base_score = GetBaseScore(config);
|
|
ASSERT_EQ(base_score, ObjFunction::DefaultBaseScore());
|
|
|
|
learner->UpdateOneIter(0, Xy_);
|
|
learner->SaveConfig(&config);
|
|
base_score = GetBaseScore(config);
|
|
ASSERT_NE(base_score, ObjFunction::DefaultBaseScore());
|
|
}
|
|
|
|
void TestInitWithPredt() {
|
|
std::unique_ptr<Learner> learner{Learner::Create({Xy_})};
|
|
learner->SetParam("objective", "reg:absoluteerror");
|
|
HostDeviceVector<float> predt;
|
|
learner->Predict(Xy_, false, &predt, 0, 0);
|
|
|
|
auto h_predt = predt.ConstHostSpan();
|
|
for (auto v : h_predt) {
|
|
ASSERT_EQ(v, ObjFunction::DefaultBaseScore());
|
|
}
|
|
|
|
Json config(Object{});
|
|
learner->SaveConfig(&config);
|
|
auto base_score = GetBaseScore(config);
|
|
ASSERT_EQ(base_score, ObjFunction::DefaultBaseScore());
|
|
|
|
// since prediction is not used for trianing, the train procedure still runs estimation
|
|
learner->UpdateOneIter(0, Xy_);
|
|
learner->SaveConfig(&config);
|
|
base_score = GetBaseScore(config);
|
|
ASSERT_NE(base_score, ObjFunction::DefaultBaseScore());
|
|
}
|
|
|
|
void TestUpdateProcess() {
|
|
// Check that when training continuation is performed with update, the base score is
|
|
// not re-evaluated.
|
|
std::unique_ptr<Learner> learner{Learner::Create({Xy_})};
|
|
learner->SetParam("objective", "reg:absoluteerror");
|
|
learner->Configure();
|
|
|
|
learner->UpdateOneIter(0, Xy_);
|
|
Json model{Object{}};
|
|
learner->SaveModel(&model);
|
|
auto base_score = GetBaseScore(model);
|
|
|
|
auto Xy1 = RandomDataGenerator{100, Cols(), 0}.Seed(321).GenerateDMatrix(true);
|
|
learner.reset(Learner::Create({Xy1}));
|
|
learner->LoadModel(model);
|
|
learner->SetParam("process_type", "update");
|
|
learner->SetParam("updater", "refresh");
|
|
learner->UpdateOneIter(1, Xy1);
|
|
|
|
Json config(Object{});
|
|
learner->SaveConfig(&config);
|
|
auto base_score1 = GetBaseScore(config);
|
|
ASSERT_EQ(base_score, base_score1);
|
|
}
|
|
};
|
|
|
|
TEST_F(InitBaseScore, TestUpdateConfig) { this->TestUpdateConfig(); }
|
|
|
|
TEST_F(InitBaseScore, FromAvgParam) { this->TestBoostFromAvgParam(); }
|
|
|
|
TEST_F(InitBaseScore, InitAfterLoad) { this->TestInitAfterLoad(); }
|
|
|
|
TEST_F(InitBaseScore, InitWithPredict) { this->TestInitWithPredt(); }
|
|
|
|
TEST_F(InitBaseScore, UpdateProcess) { this->TestUpdateProcess(); }
|
|
|
|
class TestColumnSplit : public ::testing::TestWithParam<std::string> {
|
|
void TestBaseScore(std::string objective, float expected_base_score, Json expected_model) {
|
|
auto const world_size = collective::GetWorldSize();
|
|
auto const rank = collective::GetRank();
|
|
|
|
auto p_fmat = MakeFmatForObjTest(objective);
|
|
std::shared_ptr<DMatrix> sliced{p_fmat->SliceCol(world_size, rank)};
|
|
std::unique_ptr<Learner> learner{Learner::Create({sliced})};
|
|
learner->SetParam("tree_method", "approx");
|
|
learner->SetParam("objective", objective);
|
|
if (objective.find("quantile") != std::string::npos) {
|
|
learner->SetParam("quantile_alpha", "0.5");
|
|
}
|
|
if (objective.find("multi") != std::string::npos) {
|
|
learner->SetParam("num_class", "3");
|
|
}
|
|
learner->UpdateOneIter(0, sliced);
|
|
Json config{Object{}};
|
|
learner->SaveConfig(&config);
|
|
auto base_score = GetBaseScore(config);
|
|
ASSERT_EQ(base_score, expected_base_score);
|
|
|
|
Json model{Object{}};
|
|
learner->SaveModel(&model);
|
|
ASSERT_EQ(model, expected_model);
|
|
}
|
|
|
|
public:
|
|
void Run(std::string objective) {
|
|
auto p_fmat = MakeFmatForObjTest(objective);
|
|
std::unique_ptr<Learner> learner{Learner::Create({p_fmat})};
|
|
learner->SetParam("tree_method", "approx");
|
|
learner->SetParam("objective", objective);
|
|
if (objective.find("quantile") != std::string::npos) {
|
|
learner->SetParam("quantile_alpha", "0.5");
|
|
}
|
|
if (objective.find("multi") != std::string::npos) {
|
|
learner->SetParam("num_class", "3");
|
|
}
|
|
learner->UpdateOneIter(0, p_fmat);
|
|
|
|
Json config{Object{}};
|
|
learner->SaveConfig(&config);
|
|
|
|
Json model{Object{}};
|
|
learner->SaveModel(&model);
|
|
|
|
auto constexpr kWorldSize{3};
|
|
auto call = [this, &objective](auto&... args) { TestBaseScore(objective, args...); };
|
|
auto score = GetBaseScore(config);
|
|
RunWithInMemoryCommunicator(kWorldSize, call, score, model);
|
|
}
|
|
};
|
|
|
|
TEST_P(TestColumnSplit, Objective) {
|
|
std::string objective = GetParam();
|
|
this->Run(objective);
|
|
}
|
|
|
|
INSTANTIATE_TEST_SUITE_P(ColumnSplitObjective, TestColumnSplit,
|
|
::testing::ValuesIn(MakeObjNamesForTest()),
|
|
[](const ::testing::TestParamInfo<TestColumnSplit::ParamType>& info) {
|
|
return ObjTestNameGenerator(info);
|
|
});
|
|
} // namespace xgboost
|