partofthething
Use ACE in a predictive sense?
Follow up on a closed issue I have more questions. I went back and reviewed the literature papers; if I understand those examples correctly, I think that one could use ACE in a predictive sense in a couple of ways:
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use the magnitude of the transforms as a measure of the strength of the relationship between the original independent predictors and the target, even though as you say, there are no functional forms for the transforms
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predict the target given new measurements of the predictors, using the inverse relationship between theta and Y; e.g., using the example from Wang and Murphy:
or in pictorial way using my example from here:
Originally posted by @mycarta in https://github.com/partofthething/ace/issues/11#issuecomment-531837984
Yes that's a great use of this. That's basically how I use it. I run some simulations and build X's and Y's. Then I do continuous optimization problems choosing x's and predicting Y(x) in the way you mention. It works great for that.
Do you have an example that could be added to the documentation? I am happy to help document if needed.
I may be misunderstanding, but isn't this demo exactly what you need for this?
I was thinking of an example of predicting the target y given new measurement of the predictors using an existing transformation
Ah I see. So you want to initialize the myace = model.Model() part by loading transformations from disk rather than re-building them with x,y pairs and then run myace.eval([0.1, 0.2, 0.5, 0.3, 0.5]) without having to run myace.build_model_from_xy(x, y)?
I could add model.save() and model.load() methods to help with that. Meanwhile I think just using pickle on the model would probably work.
Would this help?
Nice example, and yes, picle would do but having a method that is part of your library would be awesome.