Class Attack

textattack/attack.py:28–494

An attack generates adversarial examples on text. An attack is comprised of a goal function, constraints, transformation, and a search method. Use :meth:`attack` method to attack one sample at a time. Args: goal_function (:class:`~textattack.goal_functions.GoalFunction`):

class Attack:
    """An attack generates adversarial examples on text.

    An attack is comprised of a goal function, constraints, transformation, and a search method. Use :meth:`attack` method to attack one sample at a time.

    Args:
        goal_function (:class:`~textattack.goal_functions.GoalFunction`):
            A function for determining how well a perturbation is doing at achieving the attack's goal.
        constraints (list of :class:`~textattack.constraints.Constraint` or :class:`~textattack.constraints.PreTransformationConstraint`):
            A list of constraints to add to the attack, defining which perturbations are valid.
        transformation (:class:`~textattack.transformations.Transformation`):
            The transformation applied at each step of the attack.
        search_method (:class:`~textattack.search_methods.SearchMethod`):
            The method for exploring the search space of possible perturbations
        transformation_cache_size (:obj:`int`, `optional`, defaults to :obj:`2**15`):
            The number of items to keep in the transformations cache
        constraint_cache_size (:obj:`int`, `optional`, defaults to :obj:`2**15`):
            The number of items to keep in the constraints cache

    Example::

        >>> import textattack
        >>> import transformers

        >>> # Load model, tokenizer, and model_wrapper
        >>> model = transformers.AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-imdb")
        >>> tokenizer = transformers.AutoTokenizer.from_pretrained("textattack/bert-base-uncased-imdb")
        >>> model_wrapper = textattack.models.wrappers.HuggingFaceModelWrapper(model, tokenizer)

        >>> # Construct our four components for `Attack`
        >>> from textattack.constraints.pre_transformation import RepeatModification, StopwordModification
        >>> from textattack.constraints.semantics import WordEmbeddingDistance
        >>> from textattack.transformations import WordSwapEmbedding
        >>> from textattack.search_methods import GreedyWordSwapWIR

        >>> goal_function = textattack.goal_functions.UntargetedClassification(model_wrapper)
        >>> constraints = [
        ...     RepeatModification(),
        ...     StopwordModification(),
        ...     WordEmbeddingDistance(min_cos_sim=0.9)
        ... ]
        >>> transformation = WordSwapEmbedding(max_candidates=50)
        >>> search_method = GreedyWordSwapWIR(wir_method="delete")

        >>> # Construct the actual attack
        >>> attack = textattack.Attack(goal_function, constraints, transformation, search_method)

        >>> input_text = "I really enjoyed the new movie that came out last month."
        >>> label = 1 #Positive
        >>> attack_result = attack.attack(input_text, label)
    """

    def __init__(
        self,
        goal_function: GoalFunction,
        constraints: List[Union[Constraint, PreTransformationConstraint]],
        transformation: Transformation,
        search_method: SearchMethod,