9c9070aea2
* Do not derive from unittest.TestCase (not needed for pytest) * assertRaises -> pytest.raises * Simplify test_empty_dmatrix with test parametrization * setUpClass -> setup_class, tearDownClass -> teardown_class * Don't import unittest; import pytest * Use plain assert * Use parametrized tests in more places * Fix test_gpu_with_sklearn.py * Put back run_empty_dmatrix_reg / run_empty_dmatrix_cls * Fix test_eta_decay_gpu_hist * Add parametrized tests for monotone constraints * Fix test names * Remove test parametrization * Revise test_slice to be not flaky
37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
import xgboost as xgb
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import pytest
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import sys
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import numpy as np
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sys.path.append("tests/python")
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import testing as tm # noqa
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import test_with_sklearn as twskl # noqa
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pytestmark = pytest.mark.skipif(**tm.no_sklearn())
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rng = np.random.RandomState(1994)
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def test_gpu_binary_classification():
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from sklearn.datasets import load_digits
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from sklearn.model_selection import KFold
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digits = load_digits(2)
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y = digits['target']
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X = digits['data']
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kf = KFold(n_splits=2, shuffle=True, random_state=rng)
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for cls in (xgb.XGBClassifier, xgb.XGBRFClassifier):
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for train_index, test_index in kf.split(X, y):
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xgb_model = cls(
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random_state=42, tree_method='gpu_hist',
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n_estimators=4, gpu_id='0').fit(X[train_index], y[train_index])
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preds = xgb_model.predict(X[test_index])
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labels = y[test_index]
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err = sum(1 for i in range(len(preds))
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if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
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assert err < 0.1
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def test_boost_from_prediction_gpu_hist():
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cpu_test = twskl.run_boost_from_prediction('gpu_hist')
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