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rl_chain's Issues

featurization defaults

embeddings + raw features with options to turn off embeddings and define which features you want to embed

pydantic checks

If we make all the base classes (ABC's) in base chain also inherit from pydantic's BaseModel we will be able to catch any errors in the parameter passing types

Fix python logger

Some logging can be done via the chain callback run manager feature

rest of logging (e.g. warning when response_validator` not set) might need to be logged outside of the chain callback but logging implementation needs to be revisited

Add ability to freeze a model

So that it is clear that no more training is happening

essentially when there is no response_validator this means that there is no learning, could just be a function called remove_response_validator or invalidate_response_validator or something like that

More intuitive chain input

examples:

  • chain.run(prefix = rl_chain.Embed(['action1', 'action2']))
  • chain.run(this_general_variable = "some other text") {this_general_variable} to be used, for example, by user in prompt but ignored by VW in e.g. slates
  • chain.run(selected = rl_chain.ToSelectFrom(rl_chain.Embed(['action1', 'action2']), based_on = rl_chain.BasedOn("Tom"), a_prompt_value = "Another Tom")))

VW arg defaults

  • default full namespace
  • squarecb with edecay and enforce minimum epsilon default vw args (try this out first)

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