d4349426d8
* Support binary/multi-class classification, ranking. * Add documents. * Handle missing data.
59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
import sys
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import xgboost
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import pytest
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sys.path.append("tests/python")
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import test_eval_metrics as test_em # noqa
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class TestGPUEvalMetrics:
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cpu_test = test_em.TestEvalMetrics()
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@pytest.mark.parametrize("n_samples", [4, 100, 1000])
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def test_roc_auc_binary(self, n_samples):
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self.cpu_test.run_roc_auc_binary("gpu_hist", n_samples)
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@pytest.mark.parametrize(
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"n_samples,weighted", [(4, False), (100, False), (1000, False), (1000, True)]
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)
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def test_roc_auc_multi(self, n_samples, weighted):
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self.cpu_test.run_roc_auc_multi("gpu_hist", n_samples, weighted)
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@pytest.mark.parametrize("n_samples", [4, 100, 1000])
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def test_roc_auc_ltr(self, n_samples):
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import numpy as np
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rng = np.random.RandomState(1994)
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n_samples = n_samples
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n_features = 10
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X = rng.randn(n_samples, n_features)
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y = rng.randint(0, 16, size=n_samples)
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group = np.array([n_samples // 2, n_samples // 2])
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Xy = xgboost.DMatrix(X, y, group=group)
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cpu = xgboost.train(
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{"tree_method": "hist", "eval_metric": "auc", "objective": "rank:ndcg"},
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Xy,
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num_boost_round=10,
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)
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cpu_auc = float(cpu.eval(Xy).split(":")[1])
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gpu = xgboost.train(
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{"tree_method": "gpu_hist", "eval_metric": "auc", "objective": "rank:ndcg"},
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Xy,
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num_boost_round=10,
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)
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gpu_auc = float(gpu.eval(Xy).split(":")[1])
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np.testing.assert_allclose(cpu_auc, gpu_auc)
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def test_pr_auc_binary(self):
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self.cpu_test.run_pr_auc_binary("gpu_hist")
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def test_pr_auc_multi(self):
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self.cpu_test.run_pr_auc_multi("gpu_hist")
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def test_pr_auc_ltr(self):
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self.cpu_test.run_pr_auc_ltr("gpu_hist")
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