def METHOD_NAME():
    """Test of TEASER with different One-Class-Classifier."""
    X_train, y_train, X_test, y_test, indices = load_unit_data()
    # train probability threshold
    teaser = TEASER(
        random_state=0,
        classification_points=[6, 10, 16, 24],
        estimator=TimeSeriesForestClassifier(n_estimators=10, random_state=0),
        one_class_classifier=IsolationForest(n_estimators=5),
        one_class_param_grid={"bootstrap": [True, False]},
    )
    teaser.fit(X_train, y_train)
    X_test = from_nested_to_3d_numpy(X_test)[indices]
    final_probas = np.zeros((10, 2))
    open_idx = np.arange(0, 10)
    for i in teaser.classification_points:
        probas, decisions = teaser.update_predict_proba(X_test[:, :, :i])
        X_test, open_idx, final_idx = teaser.split_indices_and_filter(
            X_test, open_idx, decisions
        )
        final_probas[final_idx] = probas[decisions]
    testing.assert_array_equal(final_probas, teaser_if_unit_test_probas)