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Add quantile metric. (#8761)

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This commit is contained in:
Jiaming Yuan
2023-02-13 19:07:40 +08:00
committed by GitHub
parent d11a0044cf
commit 457f704e3d
11 changed files with 313 additions and 4 deletions
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30
tests/cpp/common/test_quantile_utils.cc Normal file
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@@ -0,0 +1,30 @@
/**
* Copyright 2023 by XGBoost contributors
*/
#include <gtest/gtest.h>
#include "../../../src/common/quantile_loss_utils.h"
#include "xgboost/base.h" // Args
namespace xgboost {
namespace common {
TEST(QuantileLossParam, Basic) {
QuantileLossParam param;
auto& ref = param.quantile_alpha.Get();
param.UpdateAllowUnknown(Args{{"quantile_alpha", "0.3"}});
ASSERT_EQ(ref.size(), 1);
ASSERT_NEAR(ref[0], 0.3, kRtEps);
param.UpdateAllowUnknown(Args{{"quantile_alpha", "[0.3, 0.6]"}});
ASSERT_EQ(param.quantile_alpha.Get().size(), 2);
ASSERT_NEAR(ref[0], 0.3, kRtEps);
ASSERT_NEAR(ref[1], 0.6, kRtEps);
param.UpdateAllowUnknown(Args{{"quantile_alpha", "(0.6, 0.3)"}});
ASSERT_EQ(param.quantile_alpha.Get().size(), 2);
ASSERT_NEAR(ref[0], 0.6, kRtEps);
ASSERT_NEAR(ref[1], 0.3, kRtEps);
}
} // namespace common
} // namespace xgboost

35
tests/cpp/metric/test_elementwise_metric.cc
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@@ -1,5 +1,5 @@
/*!
* Copyright 2018-2022 by XGBoost contributors
/**
* Copyright 2018-2023 by XGBoost contributors
*/
#include <xgboost/json.h>
#include <xgboost/metric.h>
@@ -311,5 +311,36 @@ TEST(Metric, DeclareUnifiedTest(MultiRMSE)) {
ASSERT_FLOAT_EQ(ret, loss);
ASSERT_FLOAT_EQ(ret, loss_w);
}
TEST(Metric, DeclareUnifiedTest(Quantile)) {
auto ctx = xgboost::CreateEmptyGenericParam(GPUIDX);
std::unique_ptr<Metric> metric{Metric::Create("quantile", &ctx)};
HostDeviceVector<float> predts{0.1f, 0.9f, 0.1f, 0.9f};
std::vector<float> labels{0.5f, 0.5f, 0.9f, 0.1f};
std::vector<float> weights{0.2f, 0.4f,0.6f, 0.8f};
metric->Configure(Args{{"quantile_alpha", "[0.0]"}});
EXPECT_NEAR(GetMetricEval(metric.get(), predts, labels, weights), 0.400f, 0.001f);
metric->Configure(Args{{"quantile_alpha", "[0.2]"}});
EXPECT_NEAR(GetMetricEval(metric.get(), predts, labels, weights), 0.376f, 0.001f);
metric->Configure(Args{{"quantile_alpha", "[0.4]"}});
EXPECT_NEAR(GetMetricEval(metric.get(), predts, labels, weights), 0.352f, 0.001f);
metric->Configure(Args{{"quantile_alpha", "[0.8]"}});
EXPECT_NEAR(GetMetricEval(metric.get(), predts, labels, weights), 0.304f, 0.001f);
metric->Configure(Args{{"quantile_alpha", "[1.0]"}});
EXPECT_NEAR(GetMetricEval(metric.get(), predts, labels, weights), 0.28f, 0.001f);
metric->Configure(Args{{"quantile_alpha", "[0.0]"}});
EXPECT_NEAR(GetMetricEval(metric.get(), predts, labels), 0.3f, 0.001f);
metric->Configure(Args{{"quantile_alpha", "[0.2]"}});
EXPECT_NEAR(GetMetricEval(metric.get(), predts, labels), 0.3f, 0.001f);
metric->Configure(Args{{"quantile_alpha", "[0.4]"}});
EXPECT_NEAR(GetMetricEval(metric.get(), predts, labels), 0.3f, 0.001f);
metric->Configure(Args{{"quantile_alpha", "[0.8]"}});
EXPECT_NEAR(GetMetricEval(metric.get(), predts, labels), 0.3f, 0.001f);
metric->Configure(Args{{"quantile_alpha", "[1.0]"}});
EXPECT_NEAR(GetMetricEval(metric.get(), predts, labels), 0.3f, 0.001f);
}
} // namespace metric
} // namespace xgboost

6
tests/python-gpu/test_gpu_eval_metrics.py
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@@ -1,8 +1,10 @@
import sys
import pytest
from xgboost.testing.metrics import check_quantile_error
import xgboost
from xgboost import testing as tm
sys.path.append("tests/python")
import test_eval_metrics as test_em # noqa
@@ -58,3 +60,7 @@ class TestGPUEvalMetrics:
def test_pr_auc_ltr(self):
self.cpu_test.run_pr_auc_ltr("gpu_hist")
@pytest.mark.skipif(**tm.no_sklearn())
def test_quantile_error(self) -> None:
check_quantile_error("gpu_hist")

7
tests/python/test_eval_metrics.py
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@@ -1,5 +1,6 @@
import numpy as np
import pytest
from xgboost.testing.metrics import check_quantile_error
import xgboost as xgb
from xgboost import testing as tm
@@ -306,10 +307,14 @@ class TestEvalMetrics:
group=groups,
eval_set=[(X, y)],
eval_group=[groups],
eval_metric="aucpr"
eval_metric="aucpr",
)
results = ltr.evals_result()["validation_0"]["aucpr"]
assert results[-1] >= 0.99
def test_pr_auc_ltr(self):
self.run_pr_auc_ltr("hist")
@pytest.mark.skipif(**tm.no_sklearn())
def test_quantile_error(self) -> None:
check_quantile_error("hist")