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import os
import sys
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
sys.path.append("tests/python")
import test_basic_models as test_bm
# Don't import the test class, otherwise they will run twice.
import test_callback as test_cb # noqa
rng = np.random.RandomState(1994)
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="hist", device="cuda")
cls.fit(X, y)
cls.get_booster().save_model("test_deterministic_gpu_hist-0.json")
cls = xgb.XGBClassifier(tree_method="hist", device="cuda")
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(self):
self.cpu_test_cb.run_eta_decay("gpu_hist")
@pytest.mark.parametrize(
"objective", ["binary:logistic", "reg:absoluteerror", "reg:quantileerror"]
)
def test_eta_decay_leaf_output(self, objective) -> None:
self.cpu_test_cb.run_eta_decay_leaf_output("gpu_hist", objective)
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
)
with pytest.raises(ValueError, match="ordinal 9999 is invalid"):
cls2.fit(X, y)
cls2 = xgb.XGBClassifier(
tree_method="hist", device="cuda:9999", fail_on_invalid_gpu_id=True
)
with pytest.raises(ValueError, match="ordinal 9999 is invalid"):
cls2.fit(X, y)
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