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[backport] Fix default metric configuration. (#9575) (#9590)

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Jiaming Yuan 2023-09-18 23:40:43 +08:00 committed by GitHub
parent 096047c547
commit ace7713201
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GPG Key ID: 4AEE18F83AFDEB23
6 changed files with 105 additions and 25 deletions
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2
src/learner.cc
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@ -1317,7 +1317,9 @@ class LearnerImpl : public LearnerIO {
if (metrics_.empty() && tparam_.disable_default_eval_metric <= 0) {
metrics_.emplace_back(Metric::Create(obj_->DefaultEvalMetric(), &ctx_));
auto config = obj_->DefaultMetricConfig();
if (!IsA<Null>(config)) {
metrics_.back()->LoadConfig(config);
}
metrics_.back()->Configure({cfg_.begin(), cfg_.end()});
}

7
src/objective/regression_obj.cu
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@ -268,6 +268,13 @@ class PseudoHuberRegression : public FitIntercept {
}
FromJson(in["pseudo_huber_param"], &param_);
}
[[nodiscard]] Json DefaultMetricConfig() const override {
CHECK(param_.GetInitialised());
Json config{Object{}};
config["name"] = String{this->DefaultEvalMetric()};
config["pseudo_huber_param"] = ToJson(param_);
return config;
}
};
XGBOOST_REGISTER_OBJECTIVE(PseudoHuberRegression, "reg:pseudohubererror")

58
tests/cpp/objective/test_objective.cc
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@ -6,6 +6,7 @@
#include <xgboost/objective.h>
#include "../helpers.h"
#include "../objective_helpers.h"
TEST(Objective, UnknownFunction) {
xgboost::ObjFunction* obj = nullptr;
@ -43,4 +44,61 @@ TEST(Objective, PredTransform) {
ASSERT_TRUE(predts.HostCanWrite());
}
}
class TestDefaultObjConfig : public ::testing::TestWithParam<std::string> {
Context ctx_;
public:
void Run(std::string objective) {
auto Xy = MakeFmatForObjTest(objective);
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

31
tests/cpp/objective_helpers.cc Normal file
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@ -0,0 +1,31 @@
/**
* Copyright (c) 2023, XGBoost contributors
*/
#include "objective_helpers.h"
#include "../../src/common/linalg_op.h" // for begin, end
#include "helpers.h" // for RandomDataGenerator
namespace xgboost {
std::shared_ptr<DMatrix> MakeFmatForObjTest(std::string const& obj) {
auto constexpr kRows = 10, kCols = 10;
auto p_fmat = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix(true);
auto& h_upper = p_fmat->Info().labels_upper_bound_.HostVector();
auto& h_lower = p_fmat->Info().labels_lower_bound_.HostVector();
h_lower.resize(kRows);
h_upper.resize(kRows);
for (size_t i = 0; i < kRows; ++i) {
h_lower[i] = 1;
h_upper[i] = 10;
}
if (obj.find("rank:") != std::string::npos) {
auto h_label = p_fmat->Info().labels.HostView();
std::size_t k = 0;
for (auto& v : h_label) {
v = k % 2 == 0;
++k;
}
}
return p_fmat;
};
} // namespace xgboost

4
tests/cpp/objective_helpers.h
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@ -1,6 +1,8 @@
/**
* Copyright (c) 2023, XGBoost contributors
*/
#pragma once
#include <dmlc/registry.h> // for Registry
#include <gtest/gtest.h>
#include <xgboost/objective.h> // for ObjFunctionReg
@ -29,4 +31,6 @@ inline std::string ObjTestNameGenerator(const ::testing::TestParamInfo<ParamType
}
return name;
};
std::shared_ptr<DMatrix> MakeFmatForObjTest(std::string const& obj);
} // namespace xgboost

26
tests/cpp/test_learner.cc
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@ -655,33 +655,11 @@ TEST_F(InitBaseScore, InitWithPredict) { this->TestInitWithPredt(); }
TEST_F(InitBaseScore, UpdateProcess) { this->TestUpdateProcess(); }
class TestColumnSplit : public ::testing::TestWithParam<std::string> {
static auto MakeFmat(std::string const& obj) {
auto constexpr kRows = 10, kCols = 10;
auto p_fmat = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix(true);
auto& h_upper = p_fmat->Info().labels_upper_bound_.HostVector();
auto& h_lower = p_fmat->Info().labels_lower_bound_.HostVector();
h_lower.resize(kRows);
h_upper.resize(kRows);
for (size_t i = 0; i < kRows; ++i) {
h_lower[i] = 1;
h_upper[i] = 10;
}
if (obj.find("rank:") != std::string::npos) {
auto h_label = p_fmat->Info().labels.HostView();
std::size_t k = 0;
for (auto& v : h_label) {
v = k % 2 == 0;
++k;
}
}
return p_fmat;
};
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 = MakeFmat(objective);
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");
@ -705,7 +683,7 @@ class TestColumnSplit : public ::testing::TestWithParam<std::string> {
public:
void Run(std::string objective) {
auto p_fmat = MakeFmat(objective);
auto p_fmat = MakeFmatForObjTest(objective);
std::unique_ptr<Learner> learner{Learner::Create({p_fmat})};
learner->SetParam("tree_method", "approx");
learner->SetParam("objective", objective);