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import json
import sys
import pytest
import xgboost
from xgboost import testing as tm
from xgboost.testing.metrics import check_precision_score, check_quantile_error
sys.path.append("tests/python")
import test_eval_metrics as test_em # noqa
class TestGPUEvalMetrics:
cpu_test = test_em.TestEvalMetrics()
@pytest.mark.parametrize("n_samples", [4, 100, 1000])
def test_roc_auc_binary(self, n_samples):
self.cpu_test.run_roc_auc_binary("gpu_hist", n_samples)
@pytest.mark.parametrize(
"n_samples,weighted", [(4, False), (100, False), (1000, False), (1000, True)]
)
def test_roc_auc_multi(self, n_samples, weighted):
self.cpu_test.run_roc_auc_multi("gpu_hist", n_samples, weighted)
@pytest.mark.parametrize("n_samples", [4, 100, 1000])
def test_roc_auc_ltr(self, n_samples):
import numpy as np
rng = np.random.RandomState(1994)
n_samples = n_samples
n_features = 10
X = rng.randn(n_samples, n_features)
y = rng.randint(0, 16, size=n_samples)
group = np.array([n_samples // 2, n_samples // 2])
Xy = xgboost.DMatrix(X, y, group=group)
booster = xgboost.train(
{"tree_method": "hist", "eval_metric": "auc", "objective": "rank:ndcg"},
Xy,
num_boost_round=10,
)
cpu_auc = float(booster.eval(Xy).split(":")[1])
booster.set_param({"device": "cuda:0"})
assert (
json.loads(booster.save_config())["learner"]["generic_param"]["device"]
== "cuda:0"
)
gpu_auc = float(booster.eval(Xy).split(":")[1])
assert (
json.loads(booster.save_config())["learner"]["generic_param"]["device"]
== "cuda:0"
)
np.testing.assert_allclose(cpu_auc, gpu_auc)
def test_pr_auc_binary(self):
self.cpu_test.run_pr_auc_binary("gpu_hist")
def test_pr_auc_multi(self):
self.cpu_test.run_pr_auc_multi("gpu_hist")
def test_pr_auc_ltr(self):
self.cpu_test.run_pr_auc_ltr("gpu_hist")
def test_precision_score(self):
check_precision_score("gpu_hist")
@pytest.mark.skipif(**tm.no_sklearn())
def test_quantile_error(self) -> None:
check_quantile_error("gpu_hist")
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