optimization of rule-based model on discrete variables

[Elena]

Hi, I have, say 10 variables (x1 ... x10) which can assume discrete finite 
values, for instance [0,1 or 2].
I need to build a set of rules, such as:

1) if x1==0 and x2==1 and x10==2 then y = 1
2) if x2==1 and x3==1 and x4==2 and x6==0 then y = 0
3) if x2==0 and x3==1 then y = 2
4) if x6==0 and x7==2 then y = 0
...
...
(actually it can be seen as a decision tree classifier).

y can assume the same discrete value [0,1 or 2]
I don't know a-priori anything about the number of rules and the 
combinations of the tested inputs.

Given a dataset of X={(x1... x10)} I can calculate Y=f(X) where f is this 
rule-based function.

I know an operator g that can calculate a real value from Y: e = g(Y)
g is too complex to be written analytically.

I would like to find a set of rules f able to minimize e on X.

I know the problem can become NP-hard, but I would be fine also with a 
suboptimal solution.

What's the best way to approach the problem?
In case, does something already exist in python?


thank you