85 lines
2.7 KiB
Python
85 lines
2.7 KiB
Python
import os
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import sys
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import numpy as np
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import pytest
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import xgboost as xgb
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from xgboost import testing as tm
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sys.path.append("tests/python")
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import test_basic_models as test_bm
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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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cpu_test_cb = test_cb.TestCallbacks()
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cpu_test_bm = test_bm.TestModels()
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def run_cls(self, X, y):
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cls = xgb.XGBClassifier(tree_method='gpu_hist')
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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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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_custom_objective(self):
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self.cpu_test_bm.run_custom_objective("gpu_hist")
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def test_eta_decay(self):
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self.cpu_test_cb.run_eta_decay('gpu_hist')
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@pytest.mark.parametrize(
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"objective", ["binary:logistic", "reg:absoluteerror", "reg:quantileerror"]
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)
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def test_eta_decay_leaf_output(self, objective) -> None:
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self.cpu_test_cb.run_eta_decay_leaf_output("gpu_hist", objective)
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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)
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model_0, model_1 = self.run_cls(X, y)
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assert model_0 == model_1
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_invalid_gpu_id(self):
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from sklearn.datasets import load_digits
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X, y = load_digits(return_X_y=True)
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# should pass with invalid gpu id
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cls1 = xgb.XGBClassifier(tree_method="gpu_hist", gpu_id=9999)
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cls1.fit(X, y)
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# should throw error with fail_on_invalid_gpu_id enabled
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cls2 = xgb.XGBClassifier(
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tree_method="gpu_hist", gpu_id=9999, fail_on_invalid_gpu_id=True
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)
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with pytest.raises(ValueError, match="ordinal 9999 is invalid"):
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cls2.fit(X, y)
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cls2 = xgb.XGBClassifier(
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tree_method="hist", device="cuda:9999", fail_on_invalid_gpu_id=True
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)
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with pytest.raises(ValueError, match="ordinal 9999 is invalid"):
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cls2.fit(X, y)
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