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CompositionalNetworks.jl

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.

Public interface

CompositionalNetworks.ICN — Type
ICN(; nvars, dom_size, param, transformation, arithmetic, aggregation, comparison)

Construct an Interpretable Compositional Network, with the following arguments:

  • nvars: number of variable in the constraint
  • dom_size: maximum domain size of any variable in the constraint
  • param: optional parameter (default to nothing)
  • transformation: a transformation layer (optional)
  • arithmetic: a arithmetic layer (optional)
  • aggregation: a aggregation layer (optional)
  • comparison: a comparison layer (optional)
CompositionalNetworks.arithmetic_layer — Method
arithmetic_layer()

Generate the layer of arithmetic operations of the ICN. The operations are mutually exclusive, that is only one will be selected.

CompositionalNetworks.code — Function
code(c::Composition, lang=:maths; name="composition")

Access the code of a composition c in a given language lang. The name of the generated method is optional.

CompositionalNetworks.comparison_layer — Function
comparison_layer(param = false)

Generate the layer of transformations functions of the ICN. Iff param value is set, also includes all the parametric comparison with that value. The operations are mutually exclusive, that is only one will be selected.

CompositionalNetworks.compose — Function
compose(icn, weigths=nothing)

Return a function composed by some of the operations of a given ICN. Can be applied to any vector of variables. If weigths are given, will assign to icn.

CompositionalNetworks.compose_to_file! — Method
compose_to_file!(concept, name, path; domains, param = nothing, language = :Julia, search = :complete, global_iter = 10, local_iter = 100, metric = hamming, popSize = 200)

Explore, learn and compose a function and write it to a file.

Arguments:

  • concept: the concept to learn
  • name: the name to give to the constraint
  • path: path of the output file

Keywords arguments:

  • domains: domains that defines the search space
  • param: an optional paramater of the constraint
  • language: the language to export to, default to :julia
  • search: either :partial or :complete search
  • global_iter: number of learning iteration
  • local_iter: number of generation in the genetic algorithm
  • metric: the metric to measure the distance between a configuration and known solutions
  • popSize: size of the population in the genetic algorithm
CompositionalNetworks.explore_learn_compose — Method
explore_learn_compose(concept; domains, param = nothing, search = :complete, global_iter = 10, local_iter = 100, metric = hamming, popSize = 200, action = :composition)

Explore a search space, learn a composition from an ICN, and compose an error function.

Arguments:

  • concept: the concept of the targeted constraint
  • domains: domains of the variables that define the training space
  • param: an optional parameter of the constraint
  • search: either flexible,:partial or :complete search. Flexible search will use search_limit and solutions_limit to determine if the search space needs to be partially or completely explored
  • global_iter: number of learning iteration
  • local_iter: number of generation in the genetic algorithm
  • metric: the metric to measure the distance between a configuration and known solutions
  • popSize: size of the population in the genetic algorithm
  • action: either :symbols to have a description of the composition or :composition to have the composed function itself
CompositionalNetworks.hamming — Method
hamming(x, X)

Compute the hamming distance of x over a collection of solutions X, i.e. the minimal number of variables to switch in xto reach a solution.

CompositionalNetworks.lazy — Method
lazy(funcs::Function...)

Generate methods extended to a vector instead of one of its components. A function f should have the following signature: f(i::Int, x::V).

CompositionalNetworks.lazy_param — Method
lazy_param(funcs::Function...)

Generate methods extended to a vector instead of one of its components. A function f should have the following signature: f(i::Int, x::V; param).

CompositionalNetworks.learn_compose — Method
learn_compose(;
    nvars, dom_size, param=nothing, icn=ICN(nvars, dom_size, param),
    X, X_sols, global_iter=100, local_iter=100, metric=hamming, popSize=200
)

Create an ICN, optimize it, and return its composition.

CompositionalNetworks.regularization — Method
regularization(icn)

Return the regularization value of an ICN weigths, which is proportional to the normalized number of operations selected in the icn layers.

CompositionalNetworks.transformation_layer — Function
transformation_layer(param = false)

Generate the layer of transformations functions of the ICN. Iff param value is true, also includes all the parametric transformations.