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import xgboost as xgb
import pytest
import sys
import numpy as np
from xgboost import testing as tm
pytestmark = pytest.mark.skipif(**tm.no_sklearn())
rng = np.random.RandomState(1994)
def test_sycl_binary_classification():
from sklearn.datasets import load_digits
from sklearn.model_selection import KFold
digits = load_digits(n_class=2)
y = digits["target"]
X = digits["data"]
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for cls in (xgb.XGBClassifier, xgb.XGBRFClassifier):
for train_index, test_index in kf.split(X, y):
xgb_model = cls(random_state=42, device="sycl", n_estimators=4).fit(
X[train_index], y[train_index]
)
preds = xgb_model.predict(X[test_index])
labels = y[test_index]
err = sum(
1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]
) / float(len(preds))
print(preds)
print(labels)
print(err)
assert err < 0.1
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