Version: 2.2.x

rasa.core.policies.memoization

MemoizationPolicy Objects

class MemoizationPolicy(Policy)

The policy that remembers exact examples of max_history turns from 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.

__init__

| __init__(featurizer: Optional[TrackerFeaturizer] = None, priority: int = MEMOIZATION_POLICY_PRIORITY, max_history: Optional[int] = MAX_HISTORY_NOT_SET, lookup: Optional[Dict] = None, **kwargs: Any, ,) -> None

Initialize the policy.

Arguments:

  • featurizer - tracker featurizer
  • priority - the priority of the policy
  • max_history - maximum history to take into account when featurizing trackers
  • lookup - a dictionary that stores featurized tracker states and predicted actions for them

AugmentedMemoizationPolicy Objects

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.