class TestGPUBasicModels(unittest.TestCase):
cputest = test_bm.TestModels()
def test_eta_decay_gpu_hist(self):
self.cputest.run_eta_decay('gpu_hist')
def test_deterministic_gpu_hist(self):
kRows = 1000
kCols = 64
kClasses = 4
# Create large values to force rounding.
X = np.random.randn(kRows, kCols) * 1e4
y = np.random.randint(0, kClasses, size=kRows)
cls = xgb.XGBClassifier(tree_method='gpu_hist',
deterministic_histogram=True,
single_precision_histogram=True)
cls.fit(X, y)
cls.get_booster().save_model('test_deterministic_gpu_hist-0.json')
cls = xgb.XGBClassifier(tree_method='gpu_hist',
deterministic_histogram=True,
single_precision_histogram=True)
cls.fit(X, y)
cls.get_booster().save_model('test_deterministic_gpu_hist-1.json')
with open('test_deterministic_gpu_hist-0.json', 'r') as fd:
model_0 = fd.read()
with open('test_deterministic_gpu_hist-1.json', 'r') as fd:
model_1 = fd.read()
assert hash(model_0) == hash(model_1)
os.remove('test_deterministic_gpu_hist-0.json')
os.remove('test_deterministic_gpu_hist-1.json')
class TestGPUBasicModels:
cpu_test_cb = test_cb.TestCallbacks()
cpu_test_bm = test_bm.TestModels()
def run_cls(self, X, y):
cls = xgb.XGBClassifier(tree_method='gpu_hist',
single_precision_histogram=True)
cls.fit(X, y)
cls.get_booster().save_model('test_deterministic_gpu_hist-0.json')
cls = xgb.XGBClassifier(tree_method='gpu_hist',
single_precision_histogram=True)
cls.fit(X, y)
cls.get_booster().save_model('test_deterministic_gpu_hist-1.json')
with open('test_deterministic_gpu_hist-0.json', 'r') as fd:
model_0 = fd.read()
with open('test_deterministic_gpu_hist-1.json', 'r') as fd:
model_1 = fd.read()
os.remove('test_deterministic_gpu_hist-0.json')
os.remove('test_deterministic_gpu_hist-1.json')
return hash(model_0), hash(model_1)
def test_custom_objective(self):
self.cpu_test_bm.run_custom_objective("gpu_hist")
def test_eta_decay_gpu_hist(self):
self.cpu_test_cb.run_eta_decay('gpu_hist')
def test_deterministic_gpu_hist(self):
kRows = 1000
kCols = 64
kClasses = 4
# Create large values to force rounding.
X = np.random.randn(kRows, kCols) * 1e4
y = np.random.randint(0, kClasses, size=kRows)
model_0, model_1 = self.run_cls(X, y)
assert model_0 == model_1
@pytest.mark.skipif(**tm.no_sklearn())
def test_invalid_gpu_id(self):
from sklearn.datasets import load_digits
X, y = load_digits(return_X_y=True)
# should pass with invalid gpu id
cls1 = xgb.XGBClassifier(tree_method='gpu_hist', gpu_id=9999)
cls1.fit(X, y)
# should throw error with fail_on_invalid_gpu_id enabled
cls2 = xgb.XGBClassifier(tree_method='gpu_hist',
gpu_id=9999,
fail_on_invalid_gpu_id=True)
try:
cls2.fit(X, y)
assert False, "Should have failed with with fail_on_invalid_gpu_id enabled"
except xgb.core.XGBoostError as err:
assert "gpu_id 9999 is invalid" in str(err)
class TestGPUBasicModels(unittest.TestCase):
cputest = test_bm.TestModels()
def test_eta_decay_gpu_hist(self):
self.cputest.run_eta_decay('gpu_hist')