initial prototype (refactoring of policy/tracker featrizer/single state featurizer) #9074
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| That is, this function filters all sparse sequence features and then | ||
| turns each of these sparse sequence features into sparse sentence feature | ||
| by summing over their first axis, respectively. |
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This doesn't always work. For example, the "natural" operation to combine semantic map embeddings is not a sum.
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Since this is something that depends on the kind of Features and their origin (which means this will not make separate state featurizer classes necessary), I think we'll leave this in here as TODO (for a future Feature rework or so?)
| # 1. Would be cool if users would be notified that they're not training | ||
| # the policy based on what their NLU pipeline returns.... (cf. | ||
| # featurize_via_interpreter) |
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I was really confused whether the wrong interpreter might be used by accident. But I guess the reason there's no mismatch (in practice now) is in the .persist of the TrackerFeaturizer which will pickles this StateFeaturizer as a wholeand preserve the "use_regex..." flag.
| self, state: State, interpreter: NaturalLanguageInterpreter | ||
| ) -> Dict[Text, List["Features"]]: | ||
| """Encode the given state with the help of the given interpreter. | ||
| class EntityFeaturizer: |
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Not sure I follow. Why do you want to implement a separate class here?
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It just helped me understand that the functionality that follows is completely independent of the rest (at least the functions itself). The logic seems to overlap and might be merged/solved with some of the above. No?
Co-authored-by: Johannes E. M. Mosig <j.mosig@rasa.com>
ka-bu
changed the title
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