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
55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
import sys
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import os
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import numpy as np
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import xgboost as xgb
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import pytest
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sys.path.append("tests/python")
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# Don't import the test class, otherwise they will run twice.
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import test_callback as test_cb # noqa
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rng = np.random.RandomState(1994)
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class TestGPUBasicModels:
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cputest = test_cb.TestCallbacks()
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def run_cls(self, X, y, deterministic):
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cls = xgb.XGBClassifier(tree_method='gpu_hist',
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deterministic_histogram=deterministic,
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single_precision_histogram=True)
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cls.fit(X, y)
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cls.get_booster().save_model('test_deterministic_gpu_hist-0.json')
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cls = xgb.XGBClassifier(tree_method='gpu_hist',
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deterministic_histogram=deterministic,
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single_precision_histogram=True)
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cls.fit(X, y)
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cls.get_booster().save_model('test_deterministic_gpu_hist-1.json')
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with open('test_deterministic_gpu_hist-0.json', 'r') as fd:
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model_0 = fd.read()
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with open('test_deterministic_gpu_hist-1.json', 'r') as fd:
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model_1 = fd.read()
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os.remove('test_deterministic_gpu_hist-0.json')
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os.remove('test_deterministic_gpu_hist-1.json')
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return hash(model_0), hash(model_1)
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def test_eta_decay_gpu_hist(self):
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self.cputest.run_eta_decay('gpu_hist', True)
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self.cputest.run_eta_decay('gpu_hist', False)
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def test_deterministic_gpu_hist(self):
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kRows = 1000
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kCols = 64
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kClasses = 4
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# Create large values to force rounding.
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X = np.random.randn(kRows, kCols) * 1e4
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y = np.random.randint(0, kClasses, size=kRows) * 1e4
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model_0, model_1 = self.run_cls(X, y, True)
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assert model_0 == model_1
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model_0, model_1 = self.run_cls(X, y, False)
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assert model_0 != model_1
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