fix: other algorithms are now functional 🎉

lib/DataDrivenLux/src/DataDrivenLux.jl

@@ -14,7 +14,7 @@ using ConcreteStructs: @concrete
 using Setfield: Setfield, @set!
 
 using Optim: Optim, LBFGS
-using Optimisers: Optimisers, ADAM
+using Optimisers: Optimisers, Adam
 
 using Lux: Lux, logsoftmax, softmax!
 using LuxCore: LuxCore, AbstractLuxLayer, AbstractLuxWrapperLayer

lib/DataDrivenLux/src/algorithms/randomsearch.jl

@@ -1,51 +1,26 @@
-"""
-$(TYPEDEF)
+@concrete struct RandomSearch <: AbstractDAGSRAlgorithm
+    options <: CommonAlgOptions
+end
 
-Performs a random search over the space of possible solutions to the 
-symbolic regression problem.
+"""
+$(SIGNATURES)
 
-# Fields
-$(FIELDS)
+Performs a random search over the space of possible solutions to the symbolic regression
+problem.
 """
-@kwdef struct RandomSearch{F, A, L, O} <: AbstractDAGSRAlgorithm
-    # "The number of candidates to track"
-    # populationsize::Int = 100
-    # "The functions to include in the search"
-    # functions::F = (sin, exp, cos, log, +, -, /, *)
-    # "The arities of the functions"
-    # arities::A = (1, 1, 1, 1, 2, 2, 2, 2)
-    # "The number of layers"
-    # n_layers::Int = 1
-    # "Include skip layers"
-    # skip::Bool = true
-    # "Simplex mapping"
-    # simplex::AbstractSimplex = Softmax()
-    # "Evaluation function to sort the samples"
-    # loss::L = aicc
-    # "The number of candidates to keep in each iteration"
-    # keep::Union{Real, Int} = 0.1
-    # "Use protected operators"
-    # use_protected::Bool = true
-    # "Use distributed optimization and resampling"
-    # distributed::Bool = false
-    # "Use threaded optimization and resampling - not implemented right now."
-    # threaded::Bool = false
-    # "Random seed"
-    # rng::AbstractRNG = Random.default_rng()
-    # "Optim optimiser"
-    # optimizer::O = LBFGS()
-    # "Optim options"
-    # optim_options::Optim.Options = Optim.Options()
-    # "Observed model - if `nothing`is used, a normal distributed additive error with fixed variance is assumed."
-    # observed::Union{ObservedModel, Nothing} = nothing
-    # "Field for possible optimiser - no use for Randomsearch"
-    # optimiser::Nothing = nothing
+function RandomSearch(; populationsize = 100, functions = (sin, exp, cos, log, +, -, /, *),
+        arities = (1, 1, 1, 1, 2, 2, 2, 2), n_layers = 1, skip = true, loss = aicc,
+        keep = 0.1, use_protected = true, distributed = false, threaded = false,
+        rng = Random.default_rng(), optimizer = LBFGS(), optim_options = Optim.Options(),
+        observed = nothing, alpha = 0.999f0)
+    return RandomSearch(CommonAlgOptions(;
+        populationsize, functions, arities, n_layers, skip, simplex = Softmax(), loss,
+        keep, use_protected, distributed, threaded, rng, optimizer,
+        optim_options, optimiser = nothing, observed, alpha))
 end
 
 Base.print(io::IO, ::RandomSearch) = print(io, "RandomSearch")
 Base.summary(io::IO, x::RandomSearch) = print(io, x)
 
 # Randomsearch does not do anything
-function update_parameters!(::SearchCache)
-    return
-end
+update_parameters!(::SearchCache) = nothing

lib/DataDrivenLux/src/algorithms/reinforce.jl

@@ -1,60 +1,35 @@
+@concrete struct Reinforce <: AbstractDAGSRAlgorithm
+    reward
+    ad_backend <: AD.AbstractBackend
+    options <: CommonAlgOptions
+end
+
 """
-$(TYPEDEF)
+$(SIGNATURES)
 
-Uses the REINFORCE algorithm to search over the space of possible solutions to the 
+Uses the REINFORCE algorithm to search over the space of possible solutions to the
 symbolic regression problem.
-
-# Fields
-$(FIELDS)
 """
-@kwdef struct Reinforce{F, A, L, O, R} <: AbstractDAGSRAlgorithm
-    "Reward function which should convert the loss to a reward."
-    reward::R = RelativeReward(false)
-    # "The number of candidates to track"
-    # populationsize::Int = 100
-    # "The functions to include in the search"
-    # functions::F = (sin, exp, cos, log, +, -, /, *)
-    # "The arities of the functions"
-    # arities::A = (1, 1, 1, 1, 2, 2, 2, 2)
-    # "The number of layers"
-    # n_layers::Int = 1
-    # "Include skip layers"
-    # skip::Bool = true
-    # "Simplex mapping"
-    # simplex::AbstractSimplex = Softmax()
-    # "Evaluation function to sort the samples"
-    # loss::L = aicc
-    # "The number of candidates to keep in each iteration"
-    # keep::Union{Real, Int} = 0.1
-    # "Use protected operators"
-    # use_protected::Bool = true
-    # "Use distributed optimization and resampling"
-    # distributed::Bool = false
-    # "Use threaded optimization and resampling - not implemented right now."
-    # threaded::Bool = false
-    # "Random seed"
-    # rng::AbstractRNG = Random.default_rng()
-    # "Optim optimiser"
-    # optimizer::O = LBFGS()
-    # "Optim options"
-    # optim_options::Optim.Options = Optim.Options()
-    # "Observed model - if `nothing`is used, a normal distributed additive error with fixed variance is assumed."
-    # observed::Union{ObservedModel, Nothing} = nothing
-    # "AD Backend"
-    # ad_backend::AD.AbstractBackend = AD.ForwardDiffBackend()
-    # "Optimiser"
-    # optimiser::Optimisers.AbstractRule = ADAM()
+function Reinforce(reward = RelativeReward(false); populationsize = 100,
+        functions = (sin, exp, cos, log, +, -, /, *), arities = (1, 1, 1, 1, 2, 2, 2, 2),
+        n_layers = 1, skip = true, loss = aicc, keep = 0.1, use_protected = true,
+        distributed = false, threaded = false, rng = Random.default_rng(),
+        optimizer = LBFGS(), optim_options = Optim.Options(), observed = nothing,
+        alpha = 0.999f0, optimiser = Adam(), ad_backend = AD.ForwardDiffBackend())
+    return Reinforce(reward, ad_backend, CommonAlgOptions(;
+        populationsize, functions, arities, n_layers, skip, simplex = Softmax(), loss,
+        keep, use_protected, distributed, threaded, rng, optimizer,
+        optim_options, optimiser, observed, alpha))
 end
 
 Base.print(io::IO, ::Reinforce) = print(io, "Reinforce")
 Base.summary(io::IO, x::Reinforce) = print(io, x)
 
 function reinforce_loss(candidates, p, alg)
-    (; loss, reward) = alg
-    losses = map(loss, candidates)
-    rewards = reward(losses)
+    losses = map(alg.options.loss, candidates)
+    rewards = alg.reward(losses)
     # ∇U(θ) = E[∇log(p)*R(t)]
-    mean(map(enumerate(candidates)) do (i, candidate)
+    return mean(map(enumerate(candidates)) do (i, candidate)
         return rewards[i] * -candidate(p)
     end)
 end

lib/DataDrivenLux/src/caches/cache.jl

@@ -12,7 +12,7 @@ end
 Base.show(io::IO, cache::SearchCache) = print(io, "SearchCache : $(cache.alg)")
 
 function init_model(x::AbstractDAGSRAlgorithm, basis::Basis, dataset::Dataset, intervals)
-    (; simplex, n_layers, arities, functions, use_protected, skip) = x
+    (; simplex, n_layers, arities, functions, use_protected, skip) = x.options
 
     # Get the parameter mapping
     variable_mask = map(enumerate(equations(basis))) do (i, eq)

lib/DataDrivenLux/test/randomsearch_solve.jl

@@ -27,9 +27,9 @@ dummy_dataset = DataDrivenLux.Dataset(dummy_problem)
 
 @test isempty(dummy_dataset.u_intervals)
 
-for (data, interval) in zip((X, Y, 1:size(X, 2)),
+for (data, _interval) in zip((X, Y, 1:size(X, 2)),
     (dummy_dataset.x_intervals[1], dummy_dataset.y_intervals[1], dummy_dataset.t_interval))
-    @test (interval.lo, interval.hi) == extrema(data)
+    @test isequal_interval(_interval, interval(extrema(data)))
 end
 
 # We have 1 Choices in the first layer, 2 in the last 

lib/DataDrivenLux/test/reinforce_solve.jl

@@ -6,6 +6,7 @@ using Random
 using Distributions
 using Test
 using Optimisers
+using Optim
 using StableRNGs
 
 rng = StableRNG(1234)

lib/DataDrivenLux/test/runtests.jl

@@ -9,21 +9,21 @@ const GROUP = get(ENV, "GROUP", "All")
 
 @time begin
     if GROUP == "All" || GROUP == "DataDrivenLux"
-        @safetestset "Lux" begin
+        @testset "Lux" begin
             @safetestset "Nodes" include("nodes.jl")
             @safetestset "Layers" include("layers.jl")
             @safetestset "Graphs" include("graphs.jl")
         end
 
-        @safetestset "Caches" begin
+        @testset "Caches" begin
             @safetestset "Candidate" include("candidate.jl")  # FIXME
             @safetestset "Cache" include("cache.jl")
         end
 
-        @safetestset "Algorithms" begin
-            @safetestset "RandomSearch" include("randomsearch_solve.jl")  # FIXME
-            @safetestset "Reinforce" include("reinforce_solve.jl")  # FIXME
-            @safetestset "CrossEntropy" include("crossentropy_solve.jl")  # FIXME
+        @testset "Algorithms" begin
+            @safetestset "RandomSearch" include("randomsearch_solve.jl")
+            @safetestset "Reinforce" include("reinforce_solve.jl")
+            @safetestset "CrossEntropy" include("crossentropy_solve.jl")
         end
     end
 end