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test_auc.cc
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test_auc.cc
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#include <xgboost/metric.h>
#include "../helpers.h"
namespace xgboost {
namespace metric {
TEST(Metric, DeclareUnifiedTest(BinaryAUC)) {
auto tparam = xgboost::CreateEmptyGenericParam(GPUIDX);
std::unique_ptr<Metric> uni_ptr {Metric::Create("auc", &tparam)};
Metric * metric = uni_ptr.get();
ASSERT_STREQ(metric->Name(), "auc");
// Binary
EXPECT_NEAR(GetMetricEval(metric, {0, 1}, {0, 1}), 1.0f, 1e-10);
EXPECT_NEAR(GetMetricEval(metric, {0, 1}, {1, 0}), 0.0f, 1e-10);
EXPECT_NEAR(GetMetricEval(metric, {0, 0}, {0, 1}), 0.5f, 1e-10);
EXPECT_NEAR(GetMetricEval(metric, {1, 1}, {0, 1}), 0.5f, 1e-10);
EXPECT_NEAR(GetMetricEval(metric, {0, 0}, {1, 0}), 0.5f, 1e-10);
EXPECT_NEAR(GetMetricEval(metric, {1, 1}, {1, 0}), 0.5f, 1e-10);
EXPECT_NEAR(GetMetricEval(metric, {1, 0, 0}, {0, 0, 1}), 0.25f, 1e-10);
// Invalid dataset
MetaInfo info;
info.labels_ = {0, 0};
float auc = metric->Eval({1, 1}, info, false);
ASSERT_TRUE(std::isnan(auc));
info.labels_ = HostDeviceVector<float>{};
auc = metric->Eval(HostDeviceVector<float>{}, info, false);
ASSERT_TRUE(std::isnan(auc));
EXPECT_NEAR(GetMetricEval(metric, {0, 1, 0, 1}, {0, 1, 0, 1}), 1.0f, 1e-10);
// AUC with instance weights
EXPECT_NEAR(GetMetricEval(metric,
{0.9f, 0.1f, 0.4f, 0.3f},
{0, 0, 1, 1},
{1.0f, 3.0f, 2.0f, 4.0f}),
0.75f, 0.001f);
// regression test case
ASSERT_NEAR(GetMetricEval(
metric,
{0.79523796, 0.5201713, 0.79523796, 0.24273258, 0.53452194,
0.53452194, 0.24273258, 0.5201713, 0.79523796, 0.53452194,
0.24273258, 0.53452194, 0.79523796, 0.5201713, 0.24273258,
0.5201713, 0.5201713, 0.53452194, 0.5201713, 0.53452194},
{0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0}),
0.5, 1e-10);
}
TEST(Metric, DeclareUnifiedTest(MultiAUC)) {
auto tparam = CreateEmptyGenericParam(GPUIDX);
std::unique_ptr<Metric> uni_ptr{
Metric::Create("auc", &tparam)};
auto metric = uni_ptr.get();
// MultiClass
// 3x3
EXPECT_NEAR(GetMetricEval(metric,
{
1.0f, 0.0f, 0.0f, // p_0
0.0f, 1.0f, 0.0f, // p_1
0.0f, 0.0f, 1.0f // p_2
},
{0, 1, 2}),
1.0f, 1e-10);
EXPECT_NEAR(GetMetricEval(metric,
{
1.0f, 0.0f, 0.0f, // p_0
0.0f, 1.0f, 0.0f, // p_1
0.0f, 0.0f, 1.0f // p_2
},
{2, 1, 0}),
0.5f, 1e-10);
EXPECT_NEAR(GetMetricEval(metric,
{
1.0f, 0.0f, 0.0f, // p_0
0.0f, 1.0f, 0.0f, // p_1
0.0f, 0.0f, 1.0f // p_2
},
{2, 0, 1}),
0.25f, 1e-10);
// invalid dataset
float auc = GetMetricEval(metric,
{
1.0f, 0.0f, 0.0f, // p_0
0.0f, 1.0f, 0.0f, // p_1
0.0f, 0.0f, 1.0f // p_2
},
{0, 1, 1}); // no class 2.
EXPECT_TRUE(std::isnan(auc)) << auc;
}
TEST(Metric, DeclareUnifiedTest(RankingAUC)) {
auto tparam = CreateEmptyGenericParam(GPUIDX);
std::unique_ptr<Metric> metric{Metric::Create("auc", &tparam)};
// single group
EXPECT_NEAR(GetMetricEval(metric.get(), {0.7f, 0.2f, 0.3f, 0.6f},
{1.0f, 0.8f, 0.4f, 0.2f}, /*weights=*/{},
{0, 4}),
0.5f, 1e-10);
// multi group
EXPECT_NEAR(GetMetricEval(metric.get(), {0, 1, 2, 0, 1, 2},
{0, 1, 2, 0, 1, 2}, /*weights=*/{}, {0, 3, 6}),
1.0f, 1e-10);
EXPECT_NEAR(GetMetricEval(metric.get(), {0, 1, 2, 0, 1, 2},
{0, 1, 2, 0, 1, 2}, /*weights=*/{1.0f, 2.0f},
{0, 3, 6}),
1.0f, 1e-10);
// AUC metric for grouped datasets - exception scenarios
ASSERT_TRUE(std::isnan(
GetMetricEval(metric.get(), {0, 1, 2}, {0, 0, 0}, {}, {0, 2, 3})));
// regression case
HostDeviceVector<float> predt{0.33935383, 0.5149714, 0.32138085, 1.4547751,
1.2010975, 0.42651367, 0.23104341, 0.83610827,
0.8494239, 0.07136688, 0.5623144, 0.8086237,
1.5066161, -4.094787, 0.76887935, -2.4082742};
std::vector<bst_group_t> groups{0, 7, 16};
std::vector<float> labels{1., 0., 0., 1., 2., 1., 0., 0.,
0., 0., 0., 0., 1., 0., 1., 0.};
EXPECT_NEAR(GetMetricEval(metric.get(), std::move(predt), labels,
/*weights=*/{}, groups),
0.769841f, 1e-6);
}
TEST(Metric, DeclareUnifiedTest(PRAUC)) {
auto tparam = xgboost::CreateEmptyGenericParam(GPUIDX);
xgboost::Metric *metric = xgboost::Metric::Create("aucpr", &tparam);
ASSERT_STREQ(metric->Name(), "aucpr");
EXPECT_NEAR(GetMetricEval(metric, {0, 0, 1, 1}, {0, 0, 1, 1}), 1, 1e-10);
EXPECT_NEAR(GetMetricEval(metric, {0.1f, 0.9f, 0.1f, 0.9f}, {0, 0, 1, 1}),
0.5f, 0.001f);
EXPECT_NEAR(GetMetricEval(
metric,
{0.4f, 0.2f, 0.9f, 0.1f, 0.2f, 0.4f, 0.1f, 0.1f, 0.2f, 0.1f},
{0, 0, 0, 0, 0, 1, 0, 0, 1, 1}),
0.2908445f, 0.001f);
EXPECT_NEAR(GetMetricEval(
metric, {0.87f, 0.31f, 0.40f, 0.42f, 0.25f, 0.66f, 0.95f,
0.09f, 0.10f, 0.97f, 0.76f, 0.69f, 0.15f, 0.20f,
0.30f, 0.14f, 0.07f, 0.58f, 0.61f, 0.08f},
{0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1}),
0.2769199f, 0.001f);
auto auc = GetMetricEval(metric, {0, 1}, {});
ASSERT_TRUE(std::isnan(auc));
// AUCPR with instance weights
EXPECT_NEAR(GetMetricEval(metric,
{0.29f, 0.52f, 0.11f, 0.21f, 0.219f, 0.93f, 0.493f,
0.17f, 0.47f, 0.13f, 0.43f, 0.59f, 0.87f, 0.007f},
{0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0},
{1, 2, 7, 4, 5, 2.2f, 3.2f, 5, 6, 1, 2, 1.1f, 3.2f,
4.5f}), // weights
0.694435f, 0.001f);
// Both groups contain only pos or neg samples.
auc = GetMetricEval(metric,
{0, 0.1f, 0.3f, 0.5f, 0.7f},
{1, 1, 0, 0, 0},
{},
{0, 2, 5});
ASSERT_TRUE(std::isnan(auc));
delete metric;
}
TEST(Metric, DeclareUnifiedTest(RankingPRAUC)) {
auto tparam = xgboost::CreateEmptyGenericParam(GPUIDX);
std::unique_ptr<Metric> metric{Metric::Create("aucpr", &tparam)};
std::vector<float> labels {1.0f, 0.0f, 1.0f, 0.0f, 0.0f};
std::vector<uint32_t> groups {0, 2, 5};
float auc = 0;
auc = GetMetricEval(metric.get(), {1.0f, 0.0f, 1.0f, 0.0f, 0.0f}, labels, {}, groups);
EXPECT_EQ(auc, 1.0f);
auc = GetMetricEval(metric.get(), {1.0f, 0.5f, 0.8f, 0.3f, 0.2f}, labels, {}, groups);
EXPECT_EQ(auc, 1.0f);
auc = GetMetricEval(metric.get(), {0.0f, 1.0f, 0.0f, 1.0f, 1.0f}, labels, {}, groups);
EXPECT_NEAR(auc, 0.189, 1e-3);
}
} // namespace metric
} // namespace xgboost