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fix typos in test #79

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fix typos in test #79

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41 changes: 24 additions & 17 deletions test/lotka_volterra.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -15,13 +15,13 @@ using DifferentiationInterface
using SciMLSensitivity
using Zygote: Zygote
using Statistics
using Lux

function lotka_ude()
function lotka_ude(chain)
@variables t x(t)=3.1 y(t)=1.5
@parameters α=1.3 [tunable = false] δ=1.8 [tunable = false]
Dt = ModelingToolkit.D_nounits

chain = multi_layer_feed_forward(2, 2)
@named nn = NeuralNetworkBlock(2, 2; chain, rng = StableRNG(42))

eqs = [
Expand All @@ -36,48 +36,55 @@ end

function lotka_true()
@variables t x(t)=3.1 y(t)=1.5
@parameters α=1.3 β=0.9 γ=0.8 δ=1.8
@parameters α=1.3 [tunable = false] β=0.9 γ=0.8 δ=1.8 [tunable = false]
Dt = ModelingToolkit.D_nounits

eqs = [
Dt(x) ~ α * x - β * x * y,
Dt(y) ~ -δ * y + δ * x * y
Dt(y) ~ -δ * y + γ * x * y
]
return System(eqs, ModelingToolkit.t_nounits, name = :lotka_true)
end

ude_sys = lotka_ude()
rbf(x) = exp.(-(x .^ 2))

chain = Lux.Chain(
Lux.Dense(2, 5, rbf), Lux.Dense(5, 5, rbf), Lux.Dense(5, 5, rbf),
Lux.Dense(5, 2))
ude_sys = lotka_ude(chain)

sys = mtkcompile(ude_sys, allow_symbolic = true)

prob = ODEProblem{true, SciMLBase.FullSpecialize}(sys, [], (0, 1.0))
prob = ODEProblem{true, SciMLBase.FullSpecialize}(sys, [], (0, 5.0))

model_true = mtkcompile(lotka_true())
prob_true = ODEProblem{true, SciMLBase.FullSpecialize}(model_true, [], (0, 1.0))
sol_ref = solve(prob_true, Vern9(), abstol = 1e-10, reltol = 1e-8)
prob_true = ODEProblem{true, SciMLBase.FullSpecialize}(model_true, [], (0, 5.0))
sol_ref = solve(prob_true, Vern9(), abstol = 1e-12, reltol = 1e-12)

ts = range(0, 5.0, length = 21)
data = reduce(hcat, sol_ref(ts, idxs = [model_true.x, model_true.y]).u)

x0 = default_values(sys)[sys.nn.p]

get_vars = getu(sys, [sys.x, sys.y])
get_refs = getu(model_true, [model_true.x, model_true.y])
set_x = setp_oop(sys, sys.nn.p)
set_x = setsym_oop(sys, sys.nn.p)

function loss(x, (prob, sol_ref, get_vars, get_refs, set_x))
new_p = set_x(prob, x)
new_prob = remake(prob, p = new_p, u0 = eltype(x).(prob.u0))
ts = sol_ref.t
function loss(x, (prob, sol_ref, get_vars, data, ts, set_x))
# new_u0, new_p = set_x(prob, 1, x)
new_u0, new_p = set_x(prob, x)
new_prob = remake(prob, p = new_p, u0 = new_u0)
new_sol = solve(new_prob, Vern9(), abstol = 1e-10, reltol = 1e-8, saveat = ts)

if SciMLBase.successful_retcode(new_sol)
mean(abs2.(reduce(hcat, get_vars(new_sol)) .- reduce(hcat, get_refs(sol_ref))))
mean(abs2.(reduce(hcat, get_vars(new_sol)) .- data))
else
Inf
end
end

of = OptimizationFunction{true}(loss, AutoZygote())

ps = (prob, sol_ref, get_vars, get_refs, set_x);
ps = (prob, sol_ref, get_vars, data, ts, set_x);

@test_call target_modules=(ModelingToolkitNeuralNets,) loss(x0, ps)
@test_opt target_modules=(ModelingToolkitNeuralNets,) loss(x0, ps)
Expand All @@ -89,7 +96,7 @@ ps = (prob, sol_ref, get_vars, get_refs, set_x);
@test all(.!isnan.(∇l1))
@test !iszero(∇l1)

@test ∇l1≈∇l2 rtol=1e-5
@test ∇l1≈∇l2 rtol=1e-4
@test ∇l1 ≈ ∇l3

op = OptimizationProblem(of, x0, ps)
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