I’m working on developing a system for computing and caching probability models, and am looking for either software that does this (preferably in R or Ruby) or a design pattern to use as I implement my own.
I have a general pattern of the form function C depends on the output of function B, which depends on the output of function A. I have three models, call them 1, 2, and 3. Model 1 implements A, B and C. Model 2 only implements C, and Model 3 implements A and C.
I would like to be able to get the value ‘C’ from all models with minimal recomputation of the intermediate steps.
To make things less abstract, a simple example:
I have a dependency graph that looks like so:
A1 is Model 1’s implementation of A, and A3 is model 3’s implementation of A. C depends on B, and B depends on A in all of the models.
The actual functions are as follows (again, this is a toy example, in reality these functions are much more complex, and can take minutes to hours to compute).
The values should be as follows.
Without caching, this is fine in any framework. I can make a class for model 1, and make model 2 extend that class, and have A,B, and C be functions on that class. Or I can use a dependency injection framework, replacing model 1’s A and C with model 2’s. And similarly for Model 3.
However I get into problems with caching. I want to compute C on all of the models, in order to compare the results.
So I compute C on model 1, and cache the results, A, B and C. Then I compute C on model 2, and it uses the cached version of B from before, since it is extended from model 2.
However when I compute model 3, I need to not use the cached version of B, since even though the function is the same, the function it depends on, A, is different.
Is there a good way to handle this sort of caching with dependency problem?
We ended up writing our own DSL in Ruby to support this problem.