import numpy as np
from xgboost import XGBClassifier
from sklearn.datasets import make_classification
from sklearn.metrics import accuracy_score

print("🚀 Testing XGBoost with CUDA GPU...")
try:
    X, y = make_classification(n_samples=10000, n_features=20, n_classes=2, random_state=42)
    model = XGBClassifier(
        n_estimators=100,
        max_depth=10,
        tree_method="hist",
        device="cuda",
        random_state=42,
        verbosity=1
    )
    print("Training model...")
    model.fit(X, y)
    y_pred = model.predict(X)
    acc = accuracy_score(y, y_pred)
    print(f"✅ Training successful! Accuracy: {acc*100:.2f}%")
except Exception as e:
    print(f"❌ Error during training: {e}")