Use ACE in a predictive sense?

[mycarta]

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:

1. 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

2. 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 #11 (comment)

[mycarta]

hi
Do you have some insights on this?

[partofthething]

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.

[mycarta]

Do you have an example that could be added to the documentation?
I am happy to help document if needed.

[partofthething]

I may be misunderstanding, but isn't this demo exactly what you need for this?

[mycarta]

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

[partofthething]

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?

[mycarta]

Nice example, and yes, picle would do but having a method that is part of your library would be awesome.