X_train_clean = X_train.drop(columns=["post_hoc_flag_count"])

detector = AdversarialDriftDetector(auc_threshold=0.65)
detector.fit(X_train_clean)

X_production = X_train_clean.sample(1000, random_state=1).reset_index(drop=True)
# each column's own range stays plausible on its own - it's the relationship
# between them that flips, which is exactly what a per-feature check misses
shift_mask = X_production["num_transactions_30d"] > X_production["num_transactions_30d"].median()
X_production.loc[shift_mask, "avg_transaction_value"] *= 0.6
X_production.loc[~shift_mask, "num_transactions_30d"] = (
    X_production.loc[~shift_mask, "num_transactions_30d"] * 1.8
).astype(int)

result = detector.check(X_production)
print(result["auc"], result["drift_detected"])
print(result["top_drift_features"])