CompositionalNetworks.jl

A Julia package for Interpretable Compositional Networks (ICN), a variant of neural networks, allowing the user to get interpretable results, unlike regular artificial neural networks.
Author JuliaConstraints
Popularity
6 Stars
Updated Last
5 Months Ago
Started In
December 2020

CompositionalNetworks

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CompositionalNetworks.jl, a Julia package for Interpretable Compositional Networks (ICN), a variant of neural networks, allowing the user to get interpretable results, unlike regular artificial neural networks.

The current state of our ICN focuses on the composition of error functions for LocalSearchSolvers.jl, but produces results independently of it and export it to either/both Julia functions or/and human readable output.

How does it work?

The package comes with a basic ICN for learning global constraints. The ICN is composed of 4 layers: transformation, arithmetic, aggregation, and comparison. Each contains several operations that can be composed in various ways. Given a concept (a predicate over the variables' domains), a metric (hamming by default), and the variables' domains, we learn the binary weights of the ICN.

Installation

] add CompositionalNetworks

As the package is in a beta version, some changes in the syntax and features are likely to occur. However, those changes should be minimal between minor versions. Please update with caution.

Quickstart

# 4 variables in 1:4
doms = [domain([1,2,3,4]) for i in 1:4]

# allunique concept (that is used to define the :all_different constraint)
err = explore_learn_compose(allunique, domains=doms)
# > interpretation: identity ∘ count_positive ∘ sum ∘ count_eq_left

# test our new error function
@assert err([1,2,3,3], dom_size = 4) > 0.0

# export an all_different function to file "current/path/test_dummy.jl" 
compose_to_file!(icn, "all_different", "test_dummy.jl")

The output file should produces a function that can be used as follows (assuming the maximum domain size is 7)

import CompositionalNetworks

all_different([1,2,3,4,5,6,7]; dom_size = 7)
# > 0.0 (which means true, no errors)

Please see JuliaConstraints/Constraints.jl/learn.jl for an extensive example of ICN learning and compositions.

Contributing

Contributions to this package are more than welcome and can be arbitrarily, and not exhaustively, split as follows:

  • Adding (useful) operations in one of the $4$ existing layers
  • Creating other ICNs from scratch or with only some of the original operations
  • Creating an ICN with a layer structure
  • Creating other compositional networks which target other problems
  • Just making stuff better, faster, user-friendlier, etc.

Contact

Do not hesitate to contact me (@azzaare) or other members of JuliaConstraints on GitHub (file an issue), the julialang discourse forum, the julialang slack channel, the julialang zulip server, or the Human of Julia (HoJ) discord server.