rasa.core.policies.memoization
MemoizationPolicy Objects
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__
Initialize the policy.
Arguments:
featurizer
- tracker featurizerpriority
- the priority of the policymax_history
- maximum history to take into account when featurizing trackerslookup
- a dictionary that stores featurized tracker states and predicted actions for them
AugmentedMemoizationPolicy Objects
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.