Version: 3.x

rasa.core.policies.memoization

MemoizationPolicy Objects

@DefaultV1Recipe.register(
DefaultV1Recipe.ComponentType.POLICY_WITHOUT_END_TO_END_SUPPORT, is_trainable=True
)
class MemoizationPolicy(Policy)

A policy that follows exact examples of max_history turns in training stories.

Since slots that are set some time in the past are preserved in all future feature vectors until they are set to None, this policy implicitly remembers and most importantly recalls examples in the context of the current dialogue longer than max_history.

This policy is not supposed to be the only policy in an ensemble, it is optimized for precision and not recall. It should get a 100% precision because it emits probabilities of 1.1 along it's predictions, which makes every mistake fatal as no other policy can overrule it.

If it is needed to recall turns from training dialogues where some slots might not be set during prediction time, and there are training stories for this, use AugmentedMemoizationPolicy.

get_default_config

| @staticmethod
| get_default_config() -> Dict[Text, Any]

Returns the default config (see parent class for full docstring).

__init__

| __init__(config: Dict[Text, Any], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, featurizer: Optional[TrackerFeaturizer] = None, lookup: Optional[Dict] = None) -> None

Initialize the policy.

recall

| recall(states: List[State], tracker: DialogueStateTracker, domain: Domain, rule_only_data: Optional[Dict[Text, Any]]) -> Optional[Text]

Finds the action based on the given states.

Arguments:

  • states - List of states.
  • tracker - The tracker.
  • domain - The Domain.
  • rule_only_data - Slots and loops which are specific to rules and hence should be ignored by this policy.

Returns:

The name of the action.

predict_action_probabilities

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

Predicts the next action the bot should take after seeing the tracker.

Arguments:

  • tracker - the :class:rasa.core.trackers.DialogueStateTracker
  • domain - the :class:rasa.shared.core.domain.Domain
  • rule_only_data - Slots and loops which are specific to rules and hence should be ignored by this policy.

Returns:

The policy's prediction (e.g. the probabilities for the actions).

persist

| persist() -> None

Persists the policy to storage.

load

| @classmethod
| load(cls, config: Dict[Text, Any], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, **kwargs: Any, ,) -> MemoizationPolicy

Loads a trained policy (see parent class for full docstring).

AugmentedMemoizationPolicy Objects

@DefaultV1Recipe.register(
DefaultV1Recipe.ComponentType.POLICY_WITHOUT_END_TO_END_SUPPORT, is_trainable=True
)
class AugmentedMemoizationPolicy(MemoizationPolicy)

The policy that remembers examples from training stories for max_history turns.

If it is needed to recall turns from training dialogues where some slots might not be set during prediction time, add relevant stories without such slots to training data. E.g. reminder stories.

Since slots that are set some time in the past are preserved in all future feature vectors until they are set to None, this policy has a capability to recall the turns up to max_history from training stories during prediction even if additional slots were filled in the past for current dialogue.

recall

| recall(states: List[State], tracker: DialogueStateTracker, domain: Domain, rule_only_data: Optional[Dict[Text, Any]]) -> Optional[Text]

Finds the action based on the given states.

Uses back to the future idea to change the past and check whether the new future can be used to recall the action.

Arguments:

  • states - List of states.
  • tracker - The tracker.
  • domain - The Domain.
  • rule_only_data - Slots and loops which are specific to rules and hence should be ignored by this policy.

Returns:

The name of the action.