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Bayesian inference with probabilistic programming.
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Getting an error on the Bayesian ODE tutorial when running the sample function #1831

Closed johnsonrd58 closed 2 years ago

johnsonrd58 commented 2 years ago

I am using Julia version 1.6.6 and getting the following

Non-standard number type (i.e. not Float32, Float64, ComplexF32, or ComplexF64) detected as the element type for the initial condition or time span. These generic number types are only compatible with the pure Julia solvers which support generic programming, such as OrdinaryDiffEq.jl. The chosen solver does not support this functionality. Please double check that the initial condition and time span types are correct, and check that the chosen solver was correct.

function lotka_volterra(du, u, p, t)

Model parameters.

α, β, γ, δ = p
# Current state.
x, y = u

# Evaluate differential equations.
du[1] = (α - β * y) * x # prey
du[2] = (δ * x - γ) * y # predator

return nothing

end

sol = solve(prob, Tsit5(); saveat=0.1) odedata = Array(sol) + 0.8 * randn(size(Array(sol)))

Define initial-value problem.

u0 = [1.0, 1.0] p = [1.5, 1.0, 3.0, 1.0] tspan = (0.0, 10.0) prob = ODEProblem(lotka_volterra, u0, tspan, p)

Plot simulation.

plot(solve(prob, Tsit5()))

@model function fitlv(data, prob)

Prior distributions.

σ ~ InverseGamma(2, 3)
α ~ truncated(Normal(1.5, 0.5), 0.5, 2.5)
β ~ truncated(Normal(1.2, 0.5), 0, 2)
γ ~ truncated(Normal(3.0, 0.5), 1, 4)
δ ~ truncated(Normal(1.0, 0.5), 0, 2)

# Simulate Lotka-Volterra model. 
p = [α, β, γ, δ]
predicted = solve(prob, Tsit5(); p=p, saveat=0.1)

# Observations.
for i in 1:length(predicted)
    data[:, i] ~ MvNormal(predicted[i], σ^2 * I)
end

return nothing

end

model = fitlv(odedata, prob)

Sample 3 independent chains with forward-mode automatic differentiation (the default).

chain = sample(model, NUTS(0.65), MCMCSerial(), 1000, 3; progress=false)

devmotion commented 2 years ago

I assume this is caused by a recent change in SciML, most likely https://github.com/SciML/DiffEqBase.jl/pull/757.

devmotion commented 2 years ago

Tsit5 is a Julia solver, so the error message seems wrong.

devmotion commented 2 years ago

As a quick workaround you can downgrade DiffEqBase to a recent version without that PR. More concretely, if you use the versions in the Manifest file everything should work. Such unintentional upstream issues are exactly the reason for why the Manifest files are included in the first place.

Maybe you're using an old version of OrdinaryDiffEq that doesn't define the traits used in the upstream checks introduced in DiffEqBase, and hence updating OrdinaryDiffEq might fix the issue as well. Can you post the output of ] st -m?

johnsonrd58 commented 2 years ago

@.***) pkg> st -m Status C:\Users\johnson.rd.1\.julia\environments\v1.6\Manifest.toml [a4c015fc] ANSIColoredPrinters v0.0.1 [621f4979] AbstractFFTs v1.0.1 [80f14c24] AbstractMCMC v3.3.1 [7a57a42e] AbstractPPL v0.1.4 [1520ce14] AbstractTrees v0.3.4 [79e6a3ab] Adapt v3.3.3 [0bf59076] AdvancedHMC v0.3.5 [5b7e9947] AdvancedMH v0.6.7 [576499cb] AdvancedPS v0.2.4 [b5ca4192] AdvancedVI v0.1.5 [dce04be8] ArgCheck v2.3.0 [ec485272] ArnoldiMethod v0.1.0 [7d9fca2a] Arpack v0.4.0 [4fba245c] ArrayInterface v3.2.2 [30b0a656] ArrayInterfaceCore v0.1.2 [a2b0951a] ArrayInterfaceTracker v0.1.1 [4c555306] ArrayLayouts v0.7.5 [13072b0f] AxisAlgorithms v1.0.1 [39de3d68] AxisArrays v0.4.5 [94b1ba4f] AxisKeys v0.1.21 [ab4f0b2a] BFloat16s v0.2.0 [aae01518] BandedMatrices v0.16.13 [198e06fe] BangBang v0.3.36 [9718e550] Baselet v0.1.1 [6e4b80f9] BenchmarkTools v0.7.0 [76274a88] Bijectors v0.9.7 [62783981] BitTwiddlingConvenienceFunctions v0.1.3 [8e7c35d0] BlockArrays v0.16.11 [ffab5731] BlockBandedMatrices 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[8e850ede] nghttp2_jll [3f19e933] p7zip_jll

@.***) pkg>

Robert D. Johnson, Ph.D @. Procter & Gamble Mason Business Center 8700 Mason Montgomery Road @*.**@*.***> (513) 292-6640

From: David Widmann @.> Sent: Wednesday, May 25, 2022 10:34 PM To: TuringLang/Turing.jl @.> Cc: Johnson, Robert @.>; Author @.> Subject: Re: [TuringLang/Turing.jl] Getting an error on the Bayesian ODE tutorial when running the sample function (Issue #1831)

[EXTERNAL]

As a quick workaround you can downgrade DiffEqBase to a recent version without that PR. More concretely, if you use the versions in the Manifest file everything should work. Such unintentional upstream issues are exactly the reason for why the Manifest files are included in the first place.

Maybe you're using an old version of OrdinaryDiffEq that doesn't define the traits used in the upstream checks introduced in DiffEqBase, and hence updating OrdinaryDiffEq might fix the issue as well. Can you post the output of ] st -m?

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devmotion commented 2 years ago

The important parts are

[2b5f629d] DiffEqBase v6.88.0
...
[1dea7af3] OrdinaryDiffEq v6.11.1
...

Ie, you use the latest version of DiffEqBase which includes the check that errors but not the latest version of OrdinaryDiffEq (6.13.0) which is needed for disabling the errors for Julia ODE solvers such as Tsit5.

Thus you should either downgrade DiffEqBase (] add DiffEqBase@6.87) or preferrably update OrdinaryDiffEq (] add OrdinaryDiffEq@6.13.0).

devmotion commented 2 years ago

Feel free to reopen if the error is not fixed by changing the SciML package versions.

BTW the code is a bit more readable if you use Markdown code blocks.

johnsonrd58 commented 2 years ago

It works now

I downgraded DiffEqBase and am using the current version of DifferentialEquations

Robert D. Johnson, Ph.D @. Procter & Gamble Mason Business Center 8700 Mason Montgomery Road @*.**@*.***> (513) 292-6640

From: David Widmann @.> Sent: Thursday, May 26, 2022 2:34 AM To: TuringLang/Turing.jl @.> Cc: Johnson, Robert @.>; Author @.> Subject: Re: [TuringLang/Turing.jl] Getting an error on the Bayesian ODE tutorial when running the sample function (Issue #1831)

[EXTERNAL]

The important parts are

[2b5f629d] DiffEqBase v6.88.0

...

[1dea7af3] OrdinaryDiffEq v6.11.1

...

Ie, you use the latest version of DiffEqBase which includes the check that errors but not the latest version of OrdinaryDiffEq (6.13.0) which is needed for disabling the errors for Julia ODE solvers such as Tsit5.

Thus you should either downgrade DiffEqBase (] add @.**@.>) or preferrably update OrdinaryDiffEq (] add @.**@.>).

— Reply to this email directly, view it on GitHubhttps://urldefense.com/v3/__https:/github.com/TuringLang/Turing.jl/issues/1831*issuecomment-1138205679__;Iw!!NgSi4SaN6ydtRVQ-!0u9XU35GaotdodxCSu10fH85s3Gvpdeii9LRBzTwC_OaKKj_fKssfE5fUNeCcdOBdEOWe3AVWkAF1qSMEnAO6mMu$, or unsubscribehttps://urldefense.com/v3/__https:/github.com/notifications/unsubscribe-auth/AD6MJ6EDN5TWYY5VPL27JK3VL4LL3ANCNFSM5W6Z7AJQ__;!!NgSi4SaN6ydtRVQ-!0u9XU35GaotdodxCSu10fH85s3Gvpdeii9LRBzTwC_OaKKj_fKssfE5fUNeCcdOBdEOWe3AVWkAF1qSMEpzT7lYY$. You are receiving this because you authored the thread.Message ID: @.**@.>>

johnsonrd58 commented 2 years ago

I use jupyter notebooks so it is in code blocks

THX

Robert D. Johnson, Ph.D @. Procter & Gamble Mason Business Center 8700 Mason Montgomery Road @*.**@*.***> (513) 292-6640

From: David Widmann @.> Sent: Thursday, May 26, 2022 2:35 AM To: TuringLang/Turing.jl @.> Cc: Johnson, Robert @.>; Author @.> Subject: Re: [TuringLang/Turing.jl] Getting an error on the Bayesian ODE tutorial when running the sample function (Issue #1831)

[EXTERNAL]

Feel free to reopen if the error is not fixed by changing the SciML package versions.

BTW the code is a bit more readable if you use Markdown code blocks.

— Reply to this email directly, view it on GitHubhttps://urldefense.com/v3/__https:/github.com/TuringLang/Turing.jl/issues/1831*issuecomment-1138208386__;Iw!!NgSi4SaN6ydtRVQ-!1doUX9SSQf7LQp4Gl9B4yZgBVvgkgAjbTXqyHKl861Xjm6z-WbXrcgoOq3g1w3ildlShUSMDPGK2_qyUO12BQyzj$, or unsubscribehttps://urldefense.com/v3/__https:/github.com/notifications/unsubscribe-auth/AD6MJ6EBP32XYF6AJWDIX4DVL4LSPANCNFSM5W6Z7AJQ__;!!NgSi4SaN6ydtRVQ-!1doUX9SSQf7LQp4Gl9B4yZgBVvgkgAjbTXqyHKl861Xjm6z-WbXrcgoOq3g1w3ildlShUSMDPGK2_qyUO-sUzHHd$. You are receiving this because you authored the thread.Message ID: @.**@.>>