def METHOD_NAME(rows_count: int, columns_count: int = 200, unique_features_count: int = 1000)\
        -> tp.Tuple[float, float]:
    unique_str_values: tp.List[str] = get_unique_random_strings(unique_features_count)
    assert len(set(unique_str_values)) == unique_features_count
    column_data: tp.Dict[str, tp.List[str]] = {}
    column_names: tp.List[str] = ["str_" + str(column_id) for column_id in range(columns_count)]
    for column_id in range(columns_count):
        column_data[column_names[column_id]] = [unique_str_values[value_id]
                                                for value_id in np.random.randint(unique_features_count,
                                                                                  size=rows_count)]
    frame = pd.DataFrame(column_data)
    return measure_pool_building_with_categorical_data(frame, column_names)