def METHOD_NAME(self):
    X = np.random.uniform(size=(30, 2))
    y = np.random.randint(0, 2, size=(30,))
    S = pd.DataFrame({"sensitive": ["A", "B"] * 15})
    automl = AutoML(
        results_path=self.automl_dir,
        model_time_limit=10,
        algorithms=["Xgboost"],
        privileged_groups=[{"sensitive": "A"}],
        underprivileged_groups=[{"sensitive": "B"}],
        fairness_metric="demographic_parity_ratio",
        fairness_threshold=0.2,
        explain_level=0,
        train_ensemble=False,
        stack_models=False,
        validation_strategy={"validation_type": "split"},
        start_random_models=1,
    )
    automl.fit(X, y, sensitive_features=S)
    self.assertGreater(len(automl._models), 0)