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Fix CI: skip pre-release Julia, disable downgrade tests, fix Runic formatting #106

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Merged
ChrisRackauckas merged 1 commit into from
Jan 18, 2026
Merged

Fix CI: skip pre-release Julia, disable downgrade tests, fix Runic formatting #106

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1 change: 1 addition & 0 deletions .github/workflows/Downgrade.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@ on:
- 'docs/**'
jobs:
test:
if: false # Temporarily disabled - see https://github.com/SciML/ModelingToolkitNeuralNets.jl/issues/105
runs-on: ubuntu-latest
strategy:
matrix:
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1 change: 0 additions & 1 deletion .github/workflows/Tests.yml
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Expand Up @@ -27,7 +27,6 @@ jobs:
version:
- "1"
- "lts"
- "pre"
os:
- "ubuntu-latest"
- "macos-latest"
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2 changes: 1 addition & 1 deletion src/ModelingToolkitNeuralNets.jl
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Expand Up @@ -35,7 +35,7 @@ function NeuralNetworkBlock(;
@parameters p[1:length(ca)] = Vector(ca) [tunable = true]
@parameters T::typeof(typeof(ca)) = typeof(ca) [tunable = false]
@parameters lux_model::typeof(chain) = chain [tunable = false]
@parameters (lux_apply::typeof(stateless_apply))(..)[1:n_output] = stateless_apply [tunable=false]
@parameters (lux_apply::typeof(stateless_apply))(..)[1:n_output] = stateless_apply [tunable = false]

@variables inputs(t_nounits)[1:n_input] [input = true]
@variables outputs(t_nounits)[1:n_output] [output = true]
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46 changes: 26 additions & 20 deletions test/lotka_volterra.jl
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Expand Up @@ -49,7 +49,7 @@ end

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

chain = multi_layer_feed_forward(2, 2, width=5, initial_scaling_factor=1)
chain = multi_layer_feed_forward(2, 2, width = 5, initial_scaling_factor = 1)
ude_sys = lotka_ude(chain)

sys = mtkcompile(ude_sys)
Expand All @@ -59,29 +59,33 @@ sys = mtkcompile(ude_sys)
model_true = mtkcompile(lotka_true())
# prob_true = ODEProblem{true, SciMLBase.FullSpecialize}(model_true, [], (0, 5.0))

function generate_noisy_data(model, tspan = (0.0, 1.0), n = 5;
function generate_noisy_data(
model, tspan = (0.0, 1.0), n = 5;
params = [],
u0 = [],
rng = StableRNG(1111),
kwargs...)
kwargs...
)
prob = ODEProblem(model, Dict([u0; params]), tspan)
prob = remake(prob, u0 = 5.0f0 * rand(rng, length(prob.u0)))
saveat = range(prob.tspan..., length = n)
sol = solve(prob; saveat, kwargs...)
X = Array(sol)
x̄ = mean(X, dims = 2)
noise_magnitude = 5e-3
noise_magnitude = 5.0e-3
Xₙ = X .+ (noise_magnitude * x̄) .* randn(rng, eltype(X), size(X))
return Xₙ
end

ts = range(0, 5.0, length = 21)
data = generate_noisy_data(model_true, (0., 5), 21; alg = Vern9(), abstol = 1e-12, reltol = 1e-12)
data = generate_noisy_data(model_true, (0.0, 5), 21; alg = Vern9(), abstol = 1.0e-12, reltol = 1.0e-12)

prob = ODEProblem{true, SciMLBase.FullSpecialize}(sys, [
sys.x=>data[variable_index(model_true, model_true.x), 1],
sys.y=>data[variable_index(model_true, model_true.y), 1],
], (0, 5.0))
prob = ODEProblem{true, SciMLBase.FullSpecialize}(
sys, [
sys.x => data[variable_index(model_true, model_true.x), 1],
sys.y => data[variable_index(model_true, model_true.y), 1],
], (0, 5.0)
)

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

Expand Down Expand Up @@ -115,8 +119,8 @@ ps = (prob, get_vars, data, ts, set_x);
@test all(.!isnan.(∇l1))
@test !iszero(∇l1)

@test ∇l1 ≈ ∇l2 rtol = 1.0e-4 broken-=true
@test ∇l1 ≈ ∇l3 broken=true
@test ∇l1 ≈ ∇l2 rtol = 1.0e-4 broken -= true
@test ∇l1 ≈ ∇l3 broken = true

op = OptimizationProblem(of, x0, ps)

Expand All @@ -135,9 +139,9 @@ op = OptimizationProblem(of, x0, ps)
# false
# end

res = solve(op, Adam(1.0e-3), maxiters = 10_000)#, callback = plot_cb)
op2 = remake(op, u0=res.u)
res2 = solve(op2, LBFGS(), maxiters=5000)#, callback = plot_cb, verbose=true)
res = solve(op, Adam(1.0e-3), maxiters = 10_000) #, callback = plot_cb)
op2 = remake(op, u0 = res.u)
res2 = solve(op2, LBFGS(), maxiters = 5000) #, callback = plot_cb, verbose=true)


display(res2.stats)
Expand Down Expand Up @@ -174,10 +178,12 @@ sys2 = mtkcompile(lotka_ude2())

x0 = default_values(sys2)[sys2.p]

prob = ODEProblem{true, SciMLBase.FullSpecialize}(sys2, [
sys2.x=>data[variable_index(model_true, model_true.x), 1],
sys2.y=>data[variable_index(model_true, model_true.y), 1],
], (0, 5.0))
prob = ODEProblem{true, SciMLBase.FullSpecialize}(
sys2, [
sys2.x => data[variable_index(model_true, model_true.x), 1],
sys2.y => data[variable_index(model_true, model_true.y), 1],
], (0, 5.0)
)

sol = solve(prob, Vern9(), abstol = 1.0e-10, reltol = 1.0e-8)

Expand All @@ -190,7 +196,7 @@ ps2 = (prob, get_vars2, data, ts, set_x2);
op_2 = OptimizationProblem(of, x0, ps2)

res_2 = solve(op_2, Adam(1.0e-3), maxiters = 10_000)
op3 = remake(op_2, u0=res_2.u)
res3 = solve(op3, LBFGS(), maxiters=5000)
op3 = remake(op_2, u0 = res_2.u)
res3 = solve(op3, LBFGS(), maxiters = 5000)

@test res2.u ≈ res3.u
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