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/**
* Copyright 2020-2023 by XGBoost Contributors
*/
#pragma once
#include <gtest/gtest.h>
#include <cmath>
#include "../../../src/common/survival_util.h"
#include "../helpers.h"
#include "xgboost/metric.h"
namespace xgboost {
namespace common {
inline void CheckDeterministicMetricElementWise(StringView name, int32_t device) {
auto ctx = MakeCUDACtx(device);
std::unique_ptr<Metric> metric{Metric::Create(name.c_str(), &ctx)};
metric->Configure(Args{});
HostDeviceVector<float> predts;
auto p_fmat = EmptyDMatrix();
MetaInfo& info = p_fmat->Info();
auto &h_predts = predts.HostVector();
SimpleLCG lcg;
SimpleRealUniformDistribution<float> dist{0.0f, 1.0f};
size_t n_samples = 2048;
h_predts.resize(n_samples);
for (size_t i = 0; i < n_samples; ++i) {
h_predts[i] = dist(&lcg);
}
auto &h_upper = info.labels_upper_bound_.HostVector();
auto &h_lower = info.labels_lower_bound_.HostVector();
h_lower.resize(n_samples);
h_upper.resize(n_samples);
for (size_t i = 0; i < n_samples; ++i) {
h_lower[i] = 1;
h_upper[i] = 10;
}
auto result = metric->Evaluate(predts, p_fmat);
for (size_t i = 0; i < 8; ++i) {
ASSERT_EQ(metric->Evaluate(predts, p_fmat), result);
}
}
inline void VerifyAFTNegLogLik(DataSplitMode data_split_mode, DeviceOrd device) {
auto ctx = MakeCUDACtx(device.ordinal);
/**
* Test aggregate output from the AFT metric over a small test data set.
* This is unlike AFTLoss.* tests, which verify metric values over individual data points.
**/
auto p_fmat = EmptyDMatrix();
MetaInfo& info = p_fmat->Info();
info.num_row_ = 4;
info.labels_lower_bound_.HostVector()
= { 100.0f, 0.0f, 60.0f, 16.0f };
info.labels_upper_bound_.HostVector()
= { 100.0f, 20.0f, std::numeric_limits<bst_float>::infinity(), 200.0f };
info.weights_.HostVector() = std::vector<bst_float>();
info.data_split_mode = data_split_mode;
HostDeviceVector<bst_float> preds(4, std::log(64));
struct TestCase {
std::string dist_type;
bst_float reference_value;
};
for (const auto& test_case : std::vector<TestCase>{ {"normal", 2.1508f}, {"logistic", 2.1804f},
{"extreme", 2.0706f} }) {
std::unique_ptr<Metric> metric(Metric::Create("aft-nloglik", &ctx));
metric->Configure({ {"aft_loss_distribution", test_case.dist_type},
{"aft_loss_distribution_scale", "1.0"} });
EXPECT_NEAR(metric->Evaluate(preds, p_fmat), test_case.reference_value, 1e-4);
}
}
inline void VerifyIntervalRegressionAccuracy(DataSplitMode data_split_mode, DeviceOrd device) {
auto ctx = MakeCUDACtx(device.ordinal);
auto p_fmat = EmptyDMatrix();
MetaInfo& info = p_fmat->Info();
info.num_row_ = 4;
info.labels_lower_bound_.HostVector() = { 20.0f, 0.0f, 60.0f, 16.0f };
info.labels_upper_bound_.HostVector() = { 80.0f, 20.0f, 80.0f, 200.0f };
info.weights_.HostVector() = std::vector<bst_float>();
info.data_split_mode = data_split_mode;
HostDeviceVector<bst_float> preds(4, std::log(60.0f));
std::unique_ptr<Metric> metric(Metric::Create("interval-regression-accuracy", &ctx));
EXPECT_FLOAT_EQ(metric->Evaluate(preds, p_fmat), 0.75f);
info.labels_lower_bound_.HostVector()[2] = 70.0f;
EXPECT_FLOAT_EQ(metric->Evaluate(preds, p_fmat), 0.50f);
info.labels_upper_bound_.HostVector()[2] = std::numeric_limits<bst_float>::infinity();
EXPECT_FLOAT_EQ(metric->Evaluate(preds, p_fmat), 0.50f);
info.labels_upper_bound_.HostVector()[3] = std::numeric_limits<bst_float>::infinity();
EXPECT_FLOAT_EQ(metric->Evaluate(preds, p_fmat), 0.50f);
info.labels_lower_bound_.HostVector()[0] = 70.0f;
EXPECT_FLOAT_EQ(metric->Evaluate(preds, p_fmat), 0.25f);
CheckDeterministicMetricElementWise(StringView{"interval-regression-accuracy"}, device.ordinal);
}
} // namespace common
} // namespace xgboost
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