This is the code repository for NeST, the Neuro-Symbolic Transpiler.
The code herein should compile readily. The intended way of getting your libraries in order is via the haskell tool Stack. You can find it here , or on linux download it like so:
curl -sSL https://get.haskellstack.org/ | sh
From there,
stack run
should run the compiler. Use -- to pass arguments to NeST rather than the build system. You can use
stack run -- -i yourCode.spll compile -o output.py -l python
to generate python inference code from the code in the input file.
You can also run the program directly from the command line using the built-in interpreter:
stack run -- -i yourCode.spll generate
stack run -- -i yourCode.spll probability -x 0.5
stack run -- -i yourCode.spll integrate -l 0 -h 1
to run the program in the forward direction, the prior distribution at x=0.5 and integrate the prior distribution from 0 to 1.
The functions in Prelude.hs provide an easy to use interface for probabilistic programming. The following example declares a program, that represents the sum of two dice, generates a random sample from that program and inferes the probability of that value being produced by the program.
showcase :: IO ()
showcase = do
let twoDice = Program [("main", dice 6 #<+># dice 6)] []
let conf = CompilerConfig {verbose=0, topKThreshold=Nothing, countBranches=False}
gen <- evalRandIO (runGen conf twoDice [])
putStrLn ("Generated value: " ++ show gen)
let VTuple (VFloat prob) (VFloat dim) = runProb conf twoDice [] gen
putStrLn ("Probability of that value occuring: " ++ show prob)
You can also decare continuous distributions using the uniform or normal functions from Prelude.hs. The resulting infered probability is then a density instead of a mass. The following example samples from a normal distribution, with a standart deviation of 2 and a mean of 1. Again the program samples one value from this distribution and outputs the probability density for that value.
showcase2 :: IO ()
showcase2 = do
let dist = Program [("main", normal #*# constF 2 #+# constF 1)] []
let conf = CompilerConfig {verbose=2, topKThreshold=Nothing, SPLL.IntermediateRepresentation.countBranches=False}
gen <- evalRandIO (runGen conf dist [])
putStrLn ("Generated value: " ++ show gen)
let VTuple (VFloat prob) (VFloat dim) = runProb conf dist [] gen
putStrLn ("Probability of that value occuring: " ++ show prob)
Some of the features of NeST we have described are implemented in a Proof of Concept manner and may not work as intended in all cases. If you believe you have encountered such a case, feel free to reach out or create an issue. Contribuations are of course also welcome.
You can cite this work as such: Viktor Pfanschilling, Hikaru Shindo, Devendra Singh Dhami, Kristian Kersting (2022): Sum-Product Loop Programming: From Probabilistic Circuits to Loop Programming. In Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning (KR).