Rasa 海关政策不适用于 cmd 中的 rasa shell

Question

我对 rasa 中的海关政策有一个小问题。我建立了一个随机选择操作的策略，但是当使用命令“rasa shell”并与机器人聊天时，它 return 没有任何回答，只有这个错误：

2022-01-14 20:50:46 警告 rasa.core.processor - 断路器跳闸。已停止预测发件人“36716023155644c88aaca2c245cec779”的更多操作。

我的代码是：

@DefaultV1Recipe.register(DefaultV1Recipe.ComponentType.POLICY_WITHOUT_END_TO_END_SUPPORT,     is_trainable=False      )
class RandomPolicy(Policy):
 def __init__(self, 
    config: Dict[Text, Any],
    model_storage: ModelStorage,
    resource: Resource,
    execution_context: ExecutionContext,
    featurizer: Optional[TrackerFeaturizer] = None) -> None:

    super().__init__(config, model_storage, resource, execution_context, featurizer=featurizer)

@classmethod
def required_components(cls) -> List[Type]:
    """Components that should be included in the pipeline before this component."""
    return []

@staticmethod
def get_default_config() -> Dict[Text, Any]:
    """Returns the component's default config.

    Default config and user config are merged by the `GraphNode` before the
    config is passed to the `create` and `load` method of the component.

    Returns:
        The default config of the component.
    """
    return {
        'enable_feature_string_compression': True,
        'use_nlu_confidence_as_score': False,
        'priority': 1,
        'max_history': None
    }


@classmethod
def create(
    cls,
    config: Dict[Text, Any],
    model_storage: ModelStorage,
    resource: Resource,
    execution_context: ExecutionContext,
) -> RandomPolicy:
    """Creates a new `GraphComponent`.

    Args:
        config: This config overrides the `default_config`.
        model_storage: Storage which graph components can use to persist and load
            themselves.
        resource: Resource locator for this component which can be used to persist
            and load itself from the `model_storage`.
        execution_context: Information about the current graph run.

    Returns: An instantiated `GraphComponent`.
    """
    return cls(config, model_storage, resource, execution_context)


def train(
    self, 
    training_trackers: List[TrackerWithCachedStates], 
    domain: Domain,
    precomputations: Optional[MessageContainerForCoreFeaturization] = None,
    **kwargs: Any) -> Resource:

    print(training_trackers[0].as_dialogue().as_dict())
           
    return self._resource

def process(self, messages: List[Message]) -> List[Message]:
    # This is the method which Rasa Open Source will call during inference.
    #print("msg", messages)
    return messages

def predict_action_probabilities(
    self, 
    tracker: DialogueStateTracker, 
    domain: Domain, 
    rule_only_data: Optional[Dict[Text, Any]] = None, 
    **kwargs: Any) -> PolicyPrediction:


    #print(tracker.past_states(domain))

    action     = random.choice(domain.as_dict()['actions'])
    index_act  = domain.as_dict()['actions'].index(action)
    len_action = len(domain.as_dict()['actions'])

    prediction    = [0.0]*len_action
    prediction[index_act] = 1.0
    
    return self._prediction(prediction)

@classmethod
def load(
    cls,
    config: Dict[Text, Any],
    model_storage: ModelStorage,
    resource: Resource,
    execution_context: ExecutionContext,
    **kwargs: Any,
) -> RandomPolicy:
    """Creates a component using a persisted version of itself.

    If not overridden this method merely calls `create`.

    Args:
        config: The config for this graph component. This is the default config of
            the component merged with config specified by the user.
        model_storage: Storage which graph components can use to persist and load
            themselves.
        resource: Resource locator for this component which can be used to persist
            and load itself from the `model_storage`.
        execution_context: Information about the current graph run.
        kwargs: Output values from previous nodes might be passed in as `kwargs`.

    Returns:
        An instantiated, loaded `GraphComponent`.
    """
    return cls.create(config, model_storage, resource, execution_context)

@staticmethod
def supported_languages() -> Optional[List[Text]]:
    """Determines which languages this component can work with.

    Returns: A list of supported languages, or `None` to signify all are supported.
    """
    return None

@staticmethod
def not_supported_languages() -> Optional[List[Text]]:
    """Determines which languages this component cannot work with.

    Returns: A list of not supported languages, or
        `None` to signify all are supported.
    """
    return None

@staticmethod
def required_packages() -> List[Text]:
    """Any extra python dependencies required for this component to run."""
    return []

Answer 1

问题很好解决，基本上预测就得考虑之前的对话状态。这是一个代码示例：

def predict_action_probabilities(
    self, 
    tracker: DialogueStateTracker, 
    domain: Domain, 
    rule_only_data: Optional[Dict[Text, Any]] = None, 
    **kwargs: Any) -> PolicyPrediction:


    prediction    = self._default_predictions(domain)
    if tracker.past_states(domain)[-1]['prev_action']['action_name'] == 'action_listen':
        list_action   = [
            (index, action) for index, action in enumerate(domain.action_names_or_texts)
            if 'utter' in action
        ]
        index_act     = random.choice(list(range(list_action[0][0], list_action[-1][0])))
        prediction[index_act] = 1.0
        
    else:
        prediction[0] = 1.0
    
    return self._prediction(prediction)

Rasa 海关政策不适用于 cmd 中的 rasa shell

Rasa customs policy do not work with rasa shell in cmd

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