* Re-implement ROC-AUC. * Binary * MultiClass * LTR * Add documents. This PR resolves a few issues: - Define a value when the dataset is invalid, which can happen if there's an empty dataset, or when the dataset contains only positive or negative values. - Define ROC-AUC for multi-class classification. - Define weighted average value for distributed setting. - A correct implementation for learning to rank task. Previous implementation is just binary classification with averaging across groups, which doesn't measure ordered learning to rank.
217 lines
8.8 KiB
C++
217 lines
8.8 KiB
C++
// Copyright by Contributors
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#include <xgboost/metric.h>
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#include "../helpers.h"
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#if !defined(__CUDACC__)
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TEST(Metric, AMS) {
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auto tparam = xgboost::CreateEmptyGenericParam(GPUIDX);
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EXPECT_ANY_THROW(xgboost::Metric::Create("ams", &tparam));
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xgboost::Metric * metric = xgboost::Metric::Create("ams@0.5f", &tparam);
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ASSERT_STREQ(metric->Name(), "ams@0.5");
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EXPECT_NEAR(GetMetricEval(metric, {0, 1}, {0, 1}), 0.311f, 0.001f);
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EXPECT_NEAR(GetMetricEval(metric,
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{0.1f, 0.9f, 0.1f, 0.9f},
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{ 0, 0, 1, 1}),
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0.29710f, 0.001f);
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delete metric;
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metric = xgboost::Metric::Create("ams@0", &tparam);
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ASSERT_STREQ(metric->Name(), "ams@0");
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EXPECT_NEAR(GetMetricEval(metric, {0, 1}, {0, 1}), 0.311f, 0.001f);
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delete metric;
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}
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#endif
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TEST(Metric, DeclareUnifiedTest(AUCPR)) {
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auto tparam = xgboost::CreateEmptyGenericParam(GPUIDX);
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xgboost::Metric *metric = xgboost::Metric::Create("aucpr", &tparam);
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ASSERT_STREQ(metric->Name(), "aucpr");
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EXPECT_NEAR(GetMetricEval(metric, {0, 0, 1, 1}, {0, 0, 1, 1}), 1, 1e-10);
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EXPECT_NEAR(GetMetricEval(metric, {0.1f, 0.9f, 0.1f, 0.9f}, {0, 0, 1, 1}),
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0.5f, 0.001f);
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EXPECT_NEAR(
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GetMetricEval(metric,
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{0.4f, 0.2f, 0.9f, 0.1f, 0.2f, 0.4f, 0.1f, 0.1f, 0.2f, 0.1f},
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{0, 0, 0, 0, 0, 1, 0, 0, 1, 1}),
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0.2908445f, 0.001f);
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EXPECT_NEAR(GetMetricEval(
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metric, {0.87f, 0.31f, 0.40f, 0.42f, 0.25f, 0.66f, 0.95f,
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0.09f, 0.10f, 0.97f, 0.76f, 0.69f, 0.15f, 0.20f,
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0.30f, 0.14f, 0.07f, 0.58f, 0.61f, 0.08f},
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{0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1}),
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0.2769199f, 0.001f);
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EXPECT_ANY_THROW(GetMetricEval(metric, {0, 1}, {}));
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EXPECT_ANY_THROW(GetMetricEval(metric, {0, 0}, {0, 0}));
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EXPECT_ANY_THROW(GetMetricEval(metric, {0, 0}, {1, 1}));
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// AUCPR with instance weights
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EXPECT_NEAR(GetMetricEval(
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metric, {0.29f, 0.52f, 0.11f, 0.21f, 0.219f, 0.93f, 0.493f,
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0.17f, 0.47f, 0.13f, 0.43f, 0.59f, 0.87f, 0.007f},
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{0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0},
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{1, 2, 7, 4, 5, 2.2f, 3.2f, 5, 6, 1, 2, 1.1f, 3.2f, 4.5f}), // weights
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0.694435f, 0.001f);
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// AUCPR with groups and no weights
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EXPECT_NEAR(GetMetricEval(
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metric, {0.87f, 0.31f, 0.40f, 0.42f, 0.25f, 0.66f, 0.95f,
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0.09f, 0.10f, 0.97f, 0.76f, 0.69f, 0.15f, 0.20f,
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0.30f, 0.14f, 0.07f, 0.58f, 0.61f, 0.08f},
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{0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1},
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{}, // weights
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{0, 2, 5, 9, 14, 20}), // group info
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0.556021f, 0.001f);
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// AUCPR with groups and weights
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EXPECT_NEAR(GetMetricEval(
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metric, {0.29f, 0.52f, 0.11f, 0.21f, 0.219f, 0.93f, 0.493f,
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0.17f, 0.47f, 0.13f, 0.43f, 0.59f, 0.87f, 0.007f}, // predictions
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{0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0},
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{1, 2, 7, 4, 5, 2.2f, 3.2f, 5, 6, 1, 2, 1.1f, 3.2f, 4.5f}, // weights
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{0, 2, 5, 9, 14}), // group info
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0.8150615f, 0.001f);
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// Exception scenarios for grouped datasets
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EXPECT_ANY_THROW(GetMetricEval(metric,
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{0, 0.1f, 0.3f, 0.5f, 0.7f},
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{1, 1, 0, 0, 0},
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{},
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{0, 2, 5}));
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delete metric;
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}
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TEST(Metric, DeclareUnifiedTest(Precision)) {
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// When the limit for precision is not given, it takes the limit at
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// std::numeric_limits<unsigned>::max(); hence all values are very small
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// NOTE(AbdealiJK): Maybe this should be fixed to be num_row by default.
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auto tparam = xgboost::CreateEmptyGenericParam(GPUIDX);
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xgboost::Metric * metric = xgboost::Metric::Create("pre", &tparam);
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ASSERT_STREQ(metric->Name(), "pre");
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EXPECT_NEAR(GetMetricEval(metric, {0, 1}, {0, 1}), 0, 1e-7);
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EXPECT_NEAR(GetMetricEval(metric,
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{0.1f, 0.9f, 0.1f, 0.9f},
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{ 0, 0, 1, 1}),
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0, 1e-7);
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delete metric;
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metric = xgboost::Metric::Create("pre@2", &tparam);
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ASSERT_STREQ(metric->Name(), "pre@2");
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EXPECT_NEAR(GetMetricEval(metric, {0, 1}, {0, 1}), 0.5f, 1e-7);
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EXPECT_NEAR(GetMetricEval(metric,
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{0.1f, 0.9f, 0.1f, 0.9f},
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{ 0, 0, 1, 1}),
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0.5f, 0.001f);
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EXPECT_ANY_THROW(GetMetricEval(metric, {0, 1}, {}));
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delete metric;
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}
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TEST(Metric, DeclareUnifiedTest(NDCG)) {
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auto tparam = xgboost::CreateEmptyGenericParam(GPUIDX);
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xgboost::Metric * metric = xgboost::Metric::Create("ndcg", &tparam);
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ASSERT_STREQ(metric->Name(), "ndcg");
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EXPECT_ANY_THROW(GetMetricEval(metric, {0, 1}, {}));
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EXPECT_NEAR(GetMetricEval(metric,
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xgboost::HostDeviceVector<xgboost::bst_float>{},
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{}), 1, 1e-10);
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EXPECT_NEAR(GetMetricEval(metric, {0, 1}, {0, 1}), 1, 1e-10);
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EXPECT_NEAR(GetMetricEval(metric,
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{0.1f, 0.9f, 0.1f, 0.9f},
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{ 0, 0, 1, 1}),
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0.6509f, 0.001f);
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delete metric;
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metric = xgboost::Metric::Create("ndcg@2", &tparam);
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ASSERT_STREQ(metric->Name(), "ndcg@2");
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EXPECT_NEAR(GetMetricEval(metric, {0, 1}, {0, 1}), 1, 1e-10);
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EXPECT_NEAR(GetMetricEval(metric,
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{0.1f, 0.9f, 0.1f, 0.9f},
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{ 0, 0, 1, 1}),
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0.3868f, 0.001f);
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delete metric;
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metric = xgboost::Metric::Create("ndcg@-", &tparam);
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ASSERT_STREQ(metric->Name(), "ndcg-");
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EXPECT_NEAR(GetMetricEval(metric,
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xgboost::HostDeviceVector<xgboost::bst_float>{},
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{}), 0, 1e-10);
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EXPECT_NEAR(GetMetricEval(metric, {0, 1}, {0, 1}), 1, 1e-10);
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EXPECT_NEAR(GetMetricEval(metric,
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{0.1f, 0.9f, 0.1f, 0.9f},
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{ 0, 0, 1, 1}),
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0.6509f, 0.001f);
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delete metric;
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metric = xgboost::Metric::Create("ndcg-", &tparam);
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ASSERT_STREQ(metric->Name(), "ndcg-");
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EXPECT_NEAR(GetMetricEval(metric,
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xgboost::HostDeviceVector<xgboost::bst_float>{},
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{}), 0, 1e-10);
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EXPECT_NEAR(GetMetricEval(metric, {0, 1}, {0, 1}), 1, 1e-10);
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EXPECT_NEAR(GetMetricEval(metric,
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{0.1f, 0.9f, 0.1f, 0.9f},
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{ 0, 0, 1, 1}),
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0.6509f, 0.001f);
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delete metric;
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metric = xgboost::Metric::Create("ndcg@2-", &tparam);
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ASSERT_STREQ(metric->Name(), "ndcg@2-");
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EXPECT_NEAR(GetMetricEval(metric, {0, 1}, {0, 1}), 1, 1e-10);
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EXPECT_NEAR(GetMetricEval(metric,
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{0.1f, 0.9f, 0.1f, 0.9f},
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{ 0, 0, 1, 1}),
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0.3868f, 0.001f);
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delete metric;
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}
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TEST(Metric, DeclareUnifiedTest(MAP)) {
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auto tparam = xgboost::CreateEmptyGenericParam(GPUIDX);
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xgboost::Metric * metric = xgboost::Metric::Create("map", &tparam);
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ASSERT_STREQ(metric->Name(), "map");
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EXPECT_NEAR(GetMetricEval(metric, {0, 1}, {0, 1}), 1, 1e-10);
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EXPECT_NEAR(GetMetricEval(metric,
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{0.1f, 0.9f, 0.1f, 0.9f},
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{ 0, 0, 1, 1}),
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0.5f, 0.001f);
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EXPECT_NEAR(GetMetricEval(metric,
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xgboost::HostDeviceVector<xgboost::bst_float>{},
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std::vector<xgboost::bst_float>{}), 1, 1e-10);
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// Rank metric with group info
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EXPECT_NEAR(GetMetricEval(metric,
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{0.1f, 0.9f, 0.2f, 0.8f, 0.4f, 1.7f},
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{2, 7, 1, 0, 5, 0}, // Labels
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{}, // Weights
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{0, 2, 5, 6}), // Group info
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0.8611f, 0.001f);
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delete metric;
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metric = xgboost::Metric::Create("map@-", &tparam);
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ASSERT_STREQ(metric->Name(), "map-");
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EXPECT_NEAR(GetMetricEval(metric,
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xgboost::HostDeviceVector<xgboost::bst_float>{},
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{}), 0, 1e-10);
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delete metric;
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metric = xgboost::Metric::Create("map-", &tparam);
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ASSERT_STREQ(metric->Name(), "map-");
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EXPECT_NEAR(GetMetricEval(metric,
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xgboost::HostDeviceVector<xgboost::bst_float>{},
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{}), 0, 1e-10);
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delete metric;
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metric = xgboost::Metric::Create("map@2", &tparam);
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ASSERT_STREQ(metric->Name(), "map@2");
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EXPECT_NEAR(GetMetricEval(metric, {0, 1}, {0, 1}), 1, 1e-10);
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EXPECT_NEAR(GetMetricEval(metric,
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{0.1f, 0.9f, 0.1f, 0.9f},
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{ 0, 0, 1, 1}),
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0.25f, 0.001f);
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delete metric;
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}
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