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"],
        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)
    sensitive_features_names = automl._models[0].get_sensitive_features_names()
    self.assertEqual(len(sensitive_features_names), 1)
    self.assertTrue("sensitive" in sensitive_features_names)
    self.assertTrue(automl._models[0].get_fairness_metric("sensitive") is not None)
    self.assertTrue(len(automl._models[0].get_fairness_optimization()) > 1)
    self.assertTrue(automl._models[0].get_worst_fairness() is not None)
    self.assertTrue(automl._models[0].get_best_fairness() is not None)