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/**
* Copyright 2016-2023 by XGBoost contributors
*/
#include <gtest/gtest.h>
#include <xgboost/context.h>
#include <xgboost/objective.h>
#include "../helpers.h"
#include "../objective_helpers.h"
TEST(Objective, UnknownFunction) {
xgboost::ObjFunction* obj = nullptr;
xgboost::Context tparam;
std::vector<std::pair<std::string, std::string>> args;
tparam.UpdateAllowUnknown(args);
EXPECT_ANY_THROW(obj = xgboost::ObjFunction::Create("unknown_name", &tparam));
EXPECT_NO_THROW(obj = xgboost::ObjFunction::Create("reg:squarederror", &tparam));
if (obj) {
delete obj;
}
}
namespace xgboost {
TEST(Objective, PredTransform) {
// Test that show PredTransform uses the same device with predictor.
xgboost::Context tparam;
tparam.UpdateAllowUnknown(Args{{"gpu_id", "0"}});
size_t n = 100;
for (const auto& entry : ::dmlc::Registry<::xgboost::ObjFunctionReg>::List()) {
std::unique_ptr<xgboost::ObjFunction> obj{xgboost::ObjFunction::Create(entry->name, &tparam)};
if (entry->name.find("multi") != std::string::npos) {
obj->Configure(Args{{"num_class", "2"}});
}
if (entry->name.find("quantile") != std::string::npos) {
obj->Configure(Args{{"quantile_alpha", "0.5"}});
}
HostDeviceVector<float> predts;
predts.Resize(n, 3.14f); // prediction is performed on host.
ASSERT_FALSE(predts.DeviceCanRead());
obj->PredTransform(&predts);
ASSERT_FALSE(predts.DeviceCanRead());
ASSERT_TRUE(predts.HostCanWrite());
}
}
class TestDefaultObjConfig : public ::testing::TestWithParam<std::string> {
Context ctx_;
public:
void Run(std::string objective) {
auto Xy = MakeFmatForObjTest(objective, 10, 10);
std::unique_ptr<Learner> learner{Learner::Create({Xy})};
std::unique_ptr<ObjFunction> objfn{ObjFunction::Create(objective, &ctx_)};
learner->SetParam("objective", objective);
if (objective.find("multi") != std::string::npos) {
learner->SetParam("num_class", "3");
objfn->Configure(Args{{"num_class", "3"}});
} else if (objective.find("quantile") != std::string::npos) {
learner->SetParam("quantile_alpha", "0.5");
objfn->Configure(Args{{"quantile_alpha", "0.5"}});
} else {
objfn->Configure(Args{});
}
learner->Configure();
learner->UpdateOneIter(0, Xy);
learner->EvalOneIter(0, {Xy}, {"train"});
Json config{Object{}};
learner->SaveConfig(&config);
auto jobj = get<Object const>(config["learner"]["objective"]);
ASSERT_TRUE(jobj.find("name") != jobj.cend());
// FIXME(jiamingy): We should have the following check, but some legacy parameter like
// "pos_weight", "delta_step" in objectives are not in metrics.
// if (jobj.size() > 1) {
// ASSERT_FALSE(IsA<Null>(objfn->DefaultMetricConfig()));
// }
auto mconfig = objfn->DefaultMetricConfig();
if (!IsA<Null>(mconfig)) {
// make sure metric can handle it
std::unique_ptr<Metric> metricfn{Metric::Create(get<String const>(mconfig["name"]), &ctx_)};
metricfn->LoadConfig(mconfig);
Json loaded(Object{});
metricfn->SaveConfig(&loaded);
metricfn->Configure(Args{});
ASSERT_EQ(mconfig, loaded);
}
}
};
TEST_P(TestDefaultObjConfig, Objective) {
std::string objective = GetParam();
this->Run(objective);
}
INSTANTIATE_TEST_SUITE_P(Objective, TestDefaultObjConfig,
::testing::ValuesIn(MakeObjNamesForTest()),
[](const ::testing::TestParamInfo<TestDefaultObjConfig::ParamType>& info) {
return ObjTestNameGenerator(info);
});
} // namespace xgboost
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