a5a58102e5
This PR replaces the original RABIT implementation with a new one, which has already been partially merged into XGBoost. The new one features: - Federated learning for both CPU and GPU. - NCCL. - More data types. - A unified interface for all the underlying implementations. - Improved timeout handling for both tracker and workers. - Exhausted tests with metrics (fixed a couple of bugs along the way). - A reusable tracker for Python and JVM packages.
105 lines
3.6 KiB
C++
105 lines
3.6 KiB
C++
/**
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* Copyright 2016-2023 by XGBoost contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/context.h>
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#include <xgboost/objective.h>
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#include "../helpers.h"
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#include "../objective_helpers.h"
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TEST(Objective, UnknownFunction) {
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xgboost::ObjFunction* obj = nullptr;
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xgboost::Context tparam;
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std::vector<std::pair<std::string, std::string>> args;
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tparam.UpdateAllowUnknown(args);
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EXPECT_ANY_THROW(obj = xgboost::ObjFunction::Create("unknown_name", &tparam));
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EXPECT_NO_THROW(obj = xgboost::ObjFunction::Create("reg:squarederror", &tparam));
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if (obj) {
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delete obj;
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}
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}
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namespace xgboost {
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TEST(Objective, PredTransform) {
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// Test that show PredTransform uses the same device with predictor.
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xgboost::Context tparam;
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tparam.UpdateAllowUnknown(Args{{"gpu_id", "0"}});
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size_t n = 100;
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for (const auto& entry : ::dmlc::Registry<::xgboost::ObjFunctionReg>::List()) {
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std::unique_ptr<xgboost::ObjFunction> obj{xgboost::ObjFunction::Create(entry->name, &tparam)};
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if (entry->name.find("multi") != std::string::npos) {
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obj->Configure(Args{{"num_class", "2"}});
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}
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if (entry->name.find("quantile") != std::string::npos) {
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obj->Configure(Args{{"quantile_alpha", "0.5"}});
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}
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HostDeviceVector<float> predts;
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predts.Resize(n, 3.14f); // prediction is performed on host.
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ASSERT_FALSE(predts.DeviceCanRead());
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obj->PredTransform(&predts);
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ASSERT_FALSE(predts.DeviceCanRead());
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ASSERT_TRUE(predts.HostCanWrite());
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}
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}
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class TestDefaultObjConfig : public ::testing::TestWithParam<std::string> {
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Context ctx_;
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public:
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void Run(std::string objective) {
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auto Xy = MakeFmatForObjTest(objective, 10, 10);
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std::unique_ptr<Learner> learner{Learner::Create({Xy})};
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std::unique_ptr<ObjFunction> objfn{ObjFunction::Create(objective, &ctx_)};
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learner->SetParam("objective", objective);
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if (objective.find("multi") != std::string::npos) {
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learner->SetParam("num_class", "3");
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objfn->Configure(Args{{"num_class", "3"}});
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} else if (objective.find("quantile") != std::string::npos) {
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learner->SetParam("quantile_alpha", "0.5");
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objfn->Configure(Args{{"quantile_alpha", "0.5"}});
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} else {
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objfn->Configure(Args{});
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}
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learner->Configure();
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learner->UpdateOneIter(0, Xy);
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learner->EvalOneIter(0, {Xy}, {"train"});
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Json config{Object{}};
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learner->SaveConfig(&config);
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auto jobj = get<Object const>(config["learner"]["objective"]);
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ASSERT_TRUE(jobj.find("name") != jobj.cend());
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// FIXME(jiamingy): We should have the following check, but some legacy parameter like
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// "pos_weight", "delta_step" in objectives are not in metrics.
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// if (jobj.size() > 1) {
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// ASSERT_FALSE(IsA<Null>(objfn->DefaultMetricConfig()));
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// }
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auto mconfig = objfn->DefaultMetricConfig();
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if (!IsA<Null>(mconfig)) {
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// make sure metric can handle it
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std::unique_ptr<Metric> metricfn{Metric::Create(get<String const>(mconfig["name"]), &ctx_)};
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metricfn->LoadConfig(mconfig);
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Json loaded(Object{});
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metricfn->SaveConfig(&loaded);
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metricfn->Configure(Args{});
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ASSERT_EQ(mconfig, loaded);
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}
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}
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};
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TEST_P(TestDefaultObjConfig, Objective) {
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std::string objective = GetParam();
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this->Run(objective);
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}
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INSTANTIATE_TEST_SUITE_P(Objective, TestDefaultObjConfig,
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::testing::ValuesIn(MakeObjNamesForTest()),
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[](const ::testing::TestParamInfo<TestDefaultObjConfig::ParamType>& info) {
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return ObjTestNameGenerator(info);
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});
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} // namespace xgboost
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