Here you will learn about different techniques to infer parameters of a (differentiable) process-based model against data. This is useful to in the context of mechanistic inference, where we want to explain patterns in a system by understanding the processes that generate them, in contrast to purely statistical or empirical inference, which might identify patterns or correlations in data without necessarily understanding the causes. We’ll mostly focus on differential equation models. Make sure that you stick to the end, where we’ll see how we can not only infer parameter values but also functional forms, by parametrizing models’ components with neural networks.

Preliminaries

Wait, what is a differentiable model?

One can usually write a model as a map ℳ mapping some parameters p, an initial state u₀ and a time t to a future state u_t

u_t = ℳ(u₀, t, p).

We call differentiable a model ℳ for which we can calculate its derivative with respect to p or u₀. The derivative $\frac{\partial \mathcal{M}}{\partial \theta}$ expresses how much the model output changes with respect to a small change in θ.

Recall your Calculus class!

$$\frac{df}{dx}(x) = \lim_{h \to 0} \frac{f(x + h) - f(x)}{h}$$

Let’s illustrate this concept with the logistic equation model. This model has an analytic formulation given by:

$$\mathcal{M}(u_0, p, t) = \frac{K}{1 + \big( \frac{K-u_0}{u_0} \big) e^{rt}}$$

Let’s code it

using UnPack
using Plots
using Random
using ComponentArrays
using BenchmarkTools
Random.seed!(0)

function mymodel(u0, p, t)
    T = eltype(u0)
    @unpack r, K = p

    @. K / (one(T) + (K - u0) / u0 * exp(-r * t))
end

p = ComponentArray(;r = 1., K = 1.)
u0 = 0.005

tsteps = range(0, 20, length=100)
y = mymodel(u0, p, tsteps)

plot(tsteps, y)

What is a ComponentArray?

A ComponentArray is a convenient Array type that allows to access array elements with symbols, similarly to a NamedTuple, while behaving like a standard array. For instance, you could do something like

cv = ComponentVector(;a = 1, b = 2)
cv .= [3, 4]

ComponentVector{Int64}(a = 3, b = 4)

This is useful, because you can only calculate a gradient w.r.t a Vector!

Now let’s try to calculate the gradient of this model. While you could in this case derive the gradient analytically, an analytic derivation is generally tricky with complex models. And what about models that can only be simulated numerically, with no analytic expressions? We need to find a more automatized way to calculate gradients.

How about the finite difference method?

Exercise: finite differences

Implement the function ∂mymodel_∂K(h, u0, p, t) which returns the model’s derivative with respect to K, calculated with a small h to be provided by the user.

function ∂mymodel_∂K(h, u0, p, t)
    phat = (; r = p.r, K= p.K + h)
    return (mymodel(u0, phat, t) - mymodel(u0, p, t)) / h
end
∂mymodel_∂K(1e-1, u0, p, 1.)

The gradient of the model is useful to understand how a parameter influences the output of the model. Let’s calculate the importance of the carrying capacity K on the model output:

dm_dp = ∂mymodel_∂K(1e-1, u0, p, tsteps)
plot(tsteps, dm_dp)

As you can observe, the carrying capacity has no effect at small t where population is small, and its influence on the dynamics grows as the population grows. We expect the reverse effect for r.

On the importance of gradients for inference

The ability to calculate the derivative of a model is crucial when it comes to inference. Both within a full Bayesian inference context, where one wants to sample the posterior distribution of parameters θ given data u, p(θ|u), or when one wants to obtain a point estimate $\theta^\star = \text{argmax}_\theta (p(\theta | u))$ (frequentist or machine learning context), the model gradient proves very useful. In a full Bayesian inference context, they are used e.g. with Hamiltonian Markov Chains methods, such as the NUTS sampler, and in a machine learning context, they are used with gradient-based optimizer.

Gradient descent

The best way to grasp the importance of gradients in inference is to understand the gradient descent algorithm.

The following picture illustrates the algorithm in the special case where p is one-dimensional.

Given an initial estimate of the parameter value p₀, $\frac{d \mathcal{M}}{dp}$ is used to suggest a new, better estimate, following

$$p_{n+1} = p_n - \eta \frac{d \mathcal{M}}{dp}(u_0, t, p) $$

where η is the learning rate.

Gradient-based methods are usually very efficient in high-dimensional spaces.

Automatic differentiation

Let’s go back to our method ∂mymodel_∂p. What is the optimal value of h to calculate the derivative? This is a tricky question, because a too small h can lead to round off errors (see more explanations here) while h too large also leads to a bad approximation of the asymptotic definition.

Fortunately, a bunch of techniques referred to as automatic differentiation (AD) allows to exactly differentiate any piece of numerical functions. In practice, your code must be exclusively written within an AD-backend, such as Torch, JAX or Tensorflow. Those libraries do not know how to differentiate code not written in their own language, such as normal Python code.

Fortunately, Julia is an AD-pervasive language! This means that any piece of Julia code is theoretically differentiable with AD.

using ForwardDiff

@btime ForwardDiff.gradient(p -> mymodel(u0, p, 1.), p);

  1.225 μs (12 allocations: 432 bytes)

This is what makes Julia great for model calibration and inference! Write your model in Julia, and any inference method using AD will be able to work with your model!

To learn more about AD in Julia, check-out this cool blog-post and this short presentation.

Now let’s get started with inference.

Mechanistic inference

The mechanistic model and the data

We’ll use a simple dynamical community model, the Lotka Volterra model, to generate data. We’ll then contaminate this data with noise, and try to recover the parameters that have generated the data. The goal of the session will be to estimate those parameters from the data, using a bunch of different techniques.

So let’s first generate the data.

using OrdinaryDiffEq

# Define Lotka-Volterra model.
function lotka_volterra(du, u, p, t)
    # Model parameters.
    @unpack 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

# Define initial-value problem.
u0 = [2.0, 2.0]
p_true = (;α = 1.5, β = 1.0, γ = 3.0, δ = 1.0)
# tspan = (hudson_bay_data[1,:t], hudson_bay_data[end,:t])
tspan = (0., 5.)
tsteps = range(tspan[1], tspan[end], 51)
alg = Tsit5()

prob = ODEProblem(lotka_volterra, u0, tspan, p_true)

saveat = tsteps
sol_true = solve(prob, alg; saveat)
# Plot simulation.
plot(sol_true)

This is the true state of the system. Now let’s contaminate it with observational noise.

Exercise: contaminate data with noise

Create a data_mat array, which consists of the ODE solution contaminated with a lognormally-distributed noise with standard deviation 0.3.

Note

Note that we add lognormally-distributed noise instead of normally-distributed because we are observing population abundance, which can only be positive.

Solution

data_mat = Array(sol_true) .* exp.(0.3 * randn(size(sol_true)))
# Plot simulation and noisy observations.
plot(sol_true; alpha=0.3)
scatter!(sol_true.t, data_mat'; color=[1 2], label="")

Now that we have our data, let’s do some inference!

Mechanistic inference as a supervised learning task

We’ll get started with a very crude approach to inference, where we’ll treat the calibration of our LV model similarly to a supervised machine learning task. To do so, we’ll write a loss function, defining a distance between our model and the data, and we’ll try to minimize this loss. The parameter minimizing this loss will be our best model parameter estimate.

function loss(p)
    predicted = solve(prob,
                        alg; 
                        p, 
                        saveat,
                        abstol=1e-6, 
                        reltol = 1e-6)

    l = 0.
    for i in 1:length(predicted)
        if all(predicted[i] .> 0)
            l += sum(abs2, log.(data_mat[:, i]) - log.(predicted[i]))
        end
    end
    return l, predicted
end

loss (generic function with 1 method)

Note

Notice that we explicitly check whether predictions are > 0, because the log of negative number is not defined and will throw an error!

Let’s define a helper function, that will plot how good does the model perform across different iterations.

losses = []
callback = function (p, l, pred; doplot=true)
    push!(losses, l)
    if length(losses)%100==1
        println("Current loss after $(length(losses)) iterations: $(losses[end])")
        if doplot
            plt = scatter(tsteps, data_mat',  color = [1 2], label=["Prey abundance data" "Predator abundance data"])
            plot!(plt, tsteps, pred', color = [1 2], label=["Inferred prey abundance" "Inferred predator abundance"])
            display(plot(plt, yaxis = :log10, title="it. : $(length(losses))"))
        end
    end
    return false
end

#13 (generic function with 1 method)

And let’s define a wrong initial guess for the parameters

pinit = ComponentArray(;α = 1., β = 1.5, γ = 1.0, δ = 0.5)

callback(pinit, loss(pinit)...; doplot = true)

Current loss after 1 iterations: 251.10349846646116

false

Our initial predictions are bad, but you’ll likely get even worse predictions in a real-case scenario!

We’ll use the library Optimization, which is a wrapper library around many optimization libraries in Julia. Optimization therefore provides us with many different types of optimizers to find parameters minimizing loss. We’ll specifically use the infamous Adam optimizer (187k citations!!!), widely used in ML.

using Optimization
using OptimizationOptimisers
using SciMLSensitivity

adtype = Optimization.AutoZygote()
optf = Optimization.OptimizationFunction((x, p) -> loss(x), adtype)
optprob = Optimization.OptimizationProblem(optf, pinit)

@time res_ada = Optimization.solve(optprob, Adam(0.1); callback, maxiters = 500)
res_ada.minimizer

Current loss after 101 iterations: 8.039887486778179

Current loss after 201 iterations: 7.9094080306025445

Current loss after 301 iterations: 7.806219868794404

Current loss after 401 iterations: 7.74345616951535

Current loss after 501 iterations: 7.712910946192632

 13.731183 seconds (49.62 M allocations: 3.145 GiB, 7.17% gc time, 93.45% compilation time: 8% of which was recompilation)

ComponentVector{Float64}(α = 1.5322556800023097, β = 1.0159023620691514, γ = 2.8926590524331766, δ = 0.9148575218436299)

Nice! It seems that the optimizer did a reasonable job, and that we found a reasonable estimate of our parameters!

Exercise: Hey, this is cheating!

Notice that we use the true u0, as if we were to know exactly the initial state. In a real situation, we need also to infer the true state!

Can you modify the model to infer the true state?

Solution

function loss2(p)
    predicted = solve(prob,
                        alg; 
                        p,
                        u0 = p.u0,
                        saveat,
                        abstol=1e-6, 
                        reltol = 1e-6)
    l = 0.
    for i in 1:length(predicted)
        if all(predicted[i] .> 0)
            l += sum(abs2, log.(data_mat[:, i]) - log.(predicted[i]))
        end
    end
    return l, predicted
end
losses = []
pinit = ComponentArray(;α = 1., β = 1.5, γ = 1.0, δ = 0.5, u0 = data_mat[:,1])
adtype = Optimization.AutoZygote()
optf = Optimization.OptimizationFunction((x, p) -> loss2(x), adtype)
optprob = Optimization.OptimizationProblem(optf, pinit)
@time res_ada = Optimization.solve(optprob, Adam(0.1); callback, maxiters = 1000)
res_ada.minimizer

Current loss after 1 iterations: 416.2139476098838

Current loss after 101 iterations: 8.276915907364208

Current loss after 201 iterations: 7.932781156086005

Current loss after 301 iterations: 7.826220840461579

Current loss after 401 iterations: 7.742200328964401

Current loss after 501 iterations: 7.6847707674856744

Current loss after 601 iterations: 7.649835853033301

Current loss after 701 iterations: 7.6304539871467085

Current loss after 801 iterations: 7.620491408711084

Current loss after 901 iterations: 7.61570935872972

Current loss after 1001 iterations: 7.61357485440162

6.735983 seconds (36.27 M allocations: 2.207 GiB, 4.93% gc time, 72.28% compilation time) ComponentVector{Float64}(α = 1.4627582443041978, β = 0.9327814276650684, γ = 3.084479105946653, δ = 0.9916501731843601, u0 = [1.9639554456506427, 2.145084576010591])

Regularization

In supervised learning, it is common practice to regularize the model to prevent overfitting. Regularization can also help the model to converge. Regularization is done by adding a penalty term to the loss function:

Loss(θ) = Loss_data(θ) + λ Reg(θ)

Exercise: regularization

Add a regularization term to the loss, which penalizes the loss when the inferred initial conditions are less than 0.

Multiple shooting

Another trick that can greatly improve the convergence of the optimization is to break down the prediction task into simpler tasks. Namely, instead of trying to predict in one shot the whole time series, the idea of multiple shooting is to predict for shorter time horizon.

Exercise: multiple shooting

Can you modify your loss function to implement this idea?

Solution

function multiple_shooting_idx(N, length_interval = 10)
    K = N ÷ length_interval
    @assert N % K == 1 "`N - 1` is not a multiple of `length_interval`"
    interval_idxs = [k*length_interval+1:(k+1)*length_interval+1 for k in 0:(K-1)]
    return interval_idxs
end
function loss_multiple_shooting(p)
    interval_idxs = multiple_shooting_idx(length(tsteps))
    l = 0.
    for idx in interval_idxs
        saveat = tsteps[idx]
        # u0_i = sol_true.u[idx[1]] # here we are cheating, using true states for initial conditions!
        u0_i = data_mat[:, idx[1]] # this is not cheating, but it does not work very well
        predicted = solve(prob,
                        alg; 
                        u0 = u0_i,
                        p, 
                        saveat,
                        tspan=(saveat[1], saveat[end]),
                        abstol=1e-6, 
                        reltol = 1e-6)
        for i in 1:length(predicted)
            if all(predicted[i] .> 0)
                l += sum(abs2, log.(data_mat[:, idx[i]]) - log.(predicted[i]))
            end
        end
    end
    predicted = solve(prob,
                    alg; 
                    p,
                    saveat=tsteps,
                    abstol=1e-6, 
                    reltol = 1e-6)
    return l, predicted
end
losses = []
pinit = ComponentArray(;α = 1., β = 1.5, γ = 1.0, δ = 0.5)
adtype = Optimization.AutoZygote()
optf = Optimization.OptimizationFunction((x, p) -> loss_multiple_shooting(x), adtype)
optprob = Optimization.OptimizationProblem(optf, pinit)
@time res_ada = Optimization.solve(optprob, Adam(0.1); callback, maxiters = 500)
res_ada.minimizer

Current loss after 1 iterations: 57.64884717929634

Current loss after 101 iterations: 15.985881478253205

Current loss after 201 iterations: 15.984751300361513

Current loss after 301 iterations: 15.984751280519914

Current loss after 401 iterations: 15.98475128052433

Current loss after 501 iterations: 15.984751280410928

3.995846 seconds (16.45 M allocations: 989.683 MiB, 3.27% gc time, 69.20% compilation time) ComponentVector{Float64}(α = 2.0111356895351227, β = 1.3936359371191127, γ = 2.8416910236613444, δ = 1.031702000687222)

Sensitivity methods

Did you wonder why do we need to load SciMLSensitivity? and why did we specify adtype = Optimization.AutoZygote()?

AD comes in different flavours, with broadly two types of AD methods - forward methods and backward methods -, and a bunch of different implementations.

You can specify which ones Optimization.jl will use to differentiate loss with adtype, see available options here.

But when it comes to differentiating the solve function from OrdinaryDiffEq, you want to use AutoZygote(), because when trying to differentiate solve, a specific adjoint rule provided by the SciMLSensitivity package will be used.

What are adjoint rules?

These are algoirithmic rules that specify to the AD backend how to calculate the derivative of a specific function.

If you want to know more, check-out the ChainRules.jl documentation

These adjoint rules can be specificed by the keyword sensealg when calling solve and have been designed for best performance when differentiating solutions of an ODEProblem. There exists a lot of them (see a review here), and if sensealg is not provided, a smart polyalgorithm is going to pick up one for you.

You can have a look in the documentation here for hints on how to choose an algorithm.

Exercise: benchmarking sensitivity methods

Can you evaluate the performance of ForwardDiffSensitivity() and ReverseDiffAdjoint()?

using Zygote
function loss_sensealg(p, sensealg)
    predicted = solve(prob,
                        alg; 
                        sensealg,
                        p,
                        u0 = p.u0,
                        saveat,
                        abstol=1e-6, 
                        reltol = 1e-6)
    l = 0.
    for i in 1:length(predicted)
        if all(predicted[i] .> 0)
            l += sum(abs2, log.(data_mat[:, i]) - log.(predicted[i]))
        end
    end
    return l
end

loss_sensealg (generic function with 1 method)

pinit = ComponentArray(;α = 1., β = 1.5, γ = 1.0, δ = 0.5, u0 = data_mat[:,1])
@btime Zygote.gradient(p -> loss_sensealg(p, ForwardDiffSensitivity()), pinit);

  1.039 ms (14955 allocations: 896.28 KiB)

@btime Zygote.gradient(p -> loss_sensealg(p, ReverseDiffAdjoint()), pinit);

  4.904 ms (104797 allocations: 4.45 MiB)

For a small number of parameters, forward methods tend to perform best, but with higher number of parameters, the other way around is true.

Well done! Now, let’s jump into the Bayesian world…

Bayesian inference

Julia has a very strong library for Bayesian inference: Turing.jl.

Let’s declare our first Turing model!

This is done with the @model macro, which allows the library to automatically construct the posterior distribution based on the definition of your model’s random variables.

Frequentist (supervised learning) vs. Bayesian approach

The main difference between a frequentist approach and a Bayesian approach is that the latter considers that parameters are random variables. Hence instead of trying to estimate a single value for the parameters, the Bayesian will try to estimate the posterior (joint) distribution of those parameters.

$$ P(\theta | \mathcal{D}) = \frac{P(\mathcal{D} | \theta) P(\theta)}{P(\mathcal{D})} $$

Random variables are defined with the ~ symbol.

Our first Turing model

using Turing
using LinearAlgebra

@model function fitlv(data, prob)
    # Prior distributions.
    σ ~ InverseGamma(3, 0.5)
    α ~ truncated(Normal(1.5, 0.5); lower=0.5, upper=2.5)
    β ~ truncated(Normal(1.2, 0.5); lower=0, upper=2)
    γ ~ truncated(Normal(3.0, 0.5); lower=1, upper=4)
    δ ~ truncated(Normal(1.0, 0.5); lower=0, upper=2)

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

    # Observations.
    for i in 1:length(predicted)
        if all(predicted[i] .> 0)
            data[:, i] ~ MvLogNormal(log.(predicted[i]), σ^2 * I)
        end
    end

    return nothing
end

fitlv (generic function with 2 methods)

Now we can instantiate our model, and run the inference!

model = fitlv(data_mat, prob)

# Sample 3 independent chains with forward-mode automatic differentiation (the default).
chain = sample(model, NUTS(), MCMCThreads(), 1000, 3; progress=true)

Chains MCMC chain (1000×17×3 Array{Float64, 3}):

Iterations        = 501:1:1500
Number of chains  = 3
Samples per chain = 1000
Wall duration     = 26.64 seconds
Compute duration  = 25.3 seconds
parameters        = σ, α, β, γ, δ
internals         = lp, n_steps, is_accept, acceptance_rate, log_density, hamiltonian_energy, hamiltonian_energy_error, max_hamiltonian_energy_error, tree_depth, numerical_error, step_size, nom_step_size

Summary Statistics
  parameters      mean       std      mcse    ess_bulk    ess_tail      rhat   ⋯
      Symbol   Float64   Float64   Float64     Float64     Float64   Float64   ⋯

           σ    0.2796    0.0195    0.0005   1721.3574   1789.3390    1.0013   ⋯
           α    1.4928    0.1501    0.0052    841.9730    862.7118    1.0025   ⋯
           β    0.9902    0.1210    0.0040    907.2968    995.0953    1.0008   ⋯
           γ    2.9967    0.2656    0.0090    863.2374    992.8786    1.0045   ⋯
           δ    0.9592    0.1043    0.0034    939.4457   1141.4992    1.0034   ⋯
                                                                1 column omitted

Quantiles
  parameters      2.5%     25.0%     50.0%     75.0%     97.5% 
      Symbol   Float64   Float64   Float64   Float64   Float64 

           σ    0.2449    0.2656    0.2788    0.2922    0.3212
           α    1.2283    1.3875    1.4784    1.5863    1.8276
           β    0.7748    0.9077    0.9816    1.0661    1.2575
           γ    2.4786    2.8192    2.9973    3.1727    3.5358
           δ    0.7563    0.8869    0.9584    1.0274    1.1650

Threads

How many threads do you have running? Threads.nthreads() will tell you!

Let’s see if our chains have converged.

using StatsPlots
plot(chain)

Data retrodiction

Let’s now generate simulated data using samples from the posterior distribution, and compare to the original data.

function plot_predictions(chain, sol, data_mat)
    myplot = plot(; legend=false)
    posterior_samples = sample(chain[[:α, :β, :γ, :δ]], 300; replace=false)
    for parr in eachrow(Array(posterior_samples))
        p = NamedTuple([:α, :β, :γ, :δ] .=> parr)
        sol_p = solve(prob, Tsit5(); p, saveat)
        plot!(sol_p; alpha=0.1, color="#BBBBBB")
    end

    # Plot simulation and noisy observations.
    plot!(sol; color=[1 2], linewidth=1)
    scatter!(sol.t, data_mat'; color=[1 2])
    return myplot
end
plot_predictions(chain, sol_true, data_mat)

Exercise: Hey, this is cheating!

Notice that we use the true u0, as if we were to know exactly the initial state. In a real situation, we need also to infer the true state!

Can you modify the model to infer the true state?

Solution

@model function fitlv2(data, prob)
    # Prior distributions.
    σ ~ InverseGamma(2, 3)
    α ~ truncated(Normal(1.5, 0.5); lower=0.5, upper=2.5)
    β ~ truncated(Normal(1.2, 0.5); lower=0, upper=2)
    γ ~ truncated(Normal(3.0, 0.5); lower=1, upper=4)
    δ ~ truncated(Normal(1.0, 0.5); lower=0, upper=2)
    u0 ~ MvLogNormal(data[:,1], σ^2 * I)
    # Simulate Lotka-Volterra model but save only the second state of the system (predators).
    p = (;α, β, γ, δ)
    predicted = solve(prob, alg; p, u0, saveat)
    # Observations.
    for i in 2:length(predicted)
        if all(predicted[i] .> 0)
            data[:, i] ~ MvLogNormal(log.(predicted[i]), σ^2 * I)
        end
    end
    return nothing
end
model2 = fitlv2(data_mat, prob)
# Sample 3 independent chains.
chain2 = sample(model2, NUTS(), MCMCThreads(), 3000, 3; progress=true)
plot(chain2)

Here is a small utility function to visualize your results.

`plot_predictions2`

function plot_predictions2(chain, sol, data_mat)
    myplot = plot(; legend=false)
    posterior_samples = sample(chain, 300; replace=false)
    for i in 1:length(posterior_samples)
        ps = posterior_samples[i]
        p = get(ps, [:α, :β, :γ, :δ], flatten=true)
        u0 = get(ps, :u0, flatten = true)
        u0 = [u0[1][1], u0[2][1]]

        sol_p = solve(prob, Tsit5(); u0, p, saveat)
        plot!(sol_p; alpha=0.1, color="#BBBBBB")
    end

    # Plot simulation and noisy observations.
    plot!(sol; color=[1 2], linewidth=1)
    scatter!(sol.t, data_mat'; color=[1 2])
    return myplot
end

plot_predictions2(chain2, sol_true, data_mat)

Mode estimation

Turing allows you to find the maximum likelihood estimate (MLE) or maximum a posteriori estimate (MAP).

$$ \theta_{MLE} = \underset{\theta}{\text{argmax}} \ P(\mathcal{D} | \theta), \qquad \theta_{MAP} = \underset{\theta}{\text{argmax}} \ P(\theta | \mathcal{D}). $$

MAP and regularization in supervised learning

Although Bayesian inference seems very different from the supervised learning approach we developed in the first part, estimating the MAP, which can be still considered as Bayesian inference, transforms in an optimization problem that can be seen as a supervised task.

To see that, we can log-transform the posterior:

log P(θ|𝒟) = log P(𝒟|θ) + log P(θ) − log P(𝒟)

Since the evidence P(𝒟) is independent of θ, it can be ignored when maximizing with respect to θ. Therefore, the MAP estimate simplifies to:

$$ \theta_{MAP} = \underset{\theta}{\text{argmax}} \ \left[\log P(\mathcal{D} | \theta) + \log P(\theta)\right] $$

Here, log P(𝒟|θ) can be seen as our previous non-regularized loss and log P(θ) acts as a regularization term, penalizing unlikely parameter values based on our prior beliefs. Priors on parameters can be seen as regularization term.

MLE and MAP can be obtained by maximum_likelihood and maximum_a_posteriori.

Random.seed!(0)
maximum_a_posteriori(model2, maxiters = 1000)

ModeResult with maximized lp of -104.88
[0.3545376205457767, 1.4695692517420373, 0.9162499950736273, 3.263944963496157, 1.0243607922108577, 2.150749205538098, 2.4795481828054595]

Since Turing uses under the hood the same Optimization.jl library, you can specify which optimizer youd’d like to use.

map_res = maximum_a_posteriori(model2, Adam(0.01), maxiters=2000)

ModeResult with maximized lp of -104.88
[0.35455374965749115, 1.4707686527453756, 0.9171941147556801, 3.2614628620071664, 1.0235193248242322, 2.1506473758409883, 2.4789084651090993]

We can check whether the optimization has converged:

@show map_res.optim_result

map_res.optim_result = retcode: Default
u: [-1.036895323466616, -0.05847935462336067, -0.16599185850450063, 1.1190957778225292, 0.04704732580671333, 0.765768901858866, 0.9078183282523818]
Final objective value:     104.87762402604213

retcode: Default
u: 7-element Vector{Float64}:
 -1.036895323466616
 -0.05847935462336067
 -0.16599185850450063
  1.1190957778225292
  0.04704732580671333
  0.765768901858866
  0.9078183282523818

What’s very nice is that Turing.jl provides you with utility functions to analyse your mode estimation results.

using StatsBase
coeftable(map_res)

Exercise: Partially observed state

Let’s assume the following situation: for some reason, you only have observation data for the predator. Could you still infer all parameters of your model, including those of the prey?

Could be! Because the signal of the variation in abundance of the predator contains information on the dynamics of the whole predator-prey system.

Do it!

You’ll need to assume so prior state for the prey. Just assume that it is the same as that of the predator.

Solution

@model function fitlv3(data::AbstractVector, prob)
    # Prior distributions.
    σ ~ InverseGamma(2, 3)
    α ~ truncated(Normal(1.5, 0.5); lower=0.5, upper=2.5)
    β ~ truncated(Normal(1.2, 0.5); lower=0, upper=2)
    γ ~ truncated(Normal(3.0, 0.5); lower=1, upper=4)
    δ ~ truncated(Normal(1.0, 0.5); lower=0, upper=2)
    u0 ~ MvLogNormal([data[1], data[1]], σ^2 * I)
    # Simulate Lotka-Volterra model but save only the second state of the system (predators).
    p = (;α, β, γ, δ)
    predicted = solve(prob, Tsit5(); p, u0, saveat, save_idxs=2)
    # Observations of the predators.
    for i in 2:length(predicted)
        if predicted[i] > 0
            data[i] ~ LogNormal(log.(predicted[i]), σ^2)
        end
    end
    return nothing
end
model3 = fitlv3(data_mat[2, :], prob)
# Sample 3 independent chains.
chain3 = sample(model3, NUTS(), MCMCThreads(), 3000, 3; progress=true)
plot(chain3)
p = plot_predictions2(chain3, sol_true, data_mat)
plot!(p, yaxis=:log10)

Now you need to realise that up to now, we had a relatively simple model. How would this model scale, should we have a much larger model? Let’s cook-up some idealised LV model. –>

AD backends and sensealg

The NUTS sampler uses automatic differentiation under the hood.

By default, Turing.jl uses ForwardDiff.jl as an AD backend, meaning that the SciML sensitivity methods are not used when the solve function is called. However, you could change the AD backend to Zygote with adtype=AutoZygote().

chain2 = sample(model2, NUTS(), MCMCThreads(), adtype=AutoZygote(), 3000, 3; progress=true)

Chains MCMC chain (3000×19×3 Array{Float64, 3}):

Iterations        = 1001:1:4000
Number of chains  = 3
Samples per chain = 3000
Wall duration     = 57.41 seconds
Compute duration  = 56.94 seconds
parameters        = σ, α, β, γ, δ, u0[1], u0[2]
internals         = lp, n_steps, is_accept, acceptance_rate, log_density, hamiltonian_energy, hamiltonian_energy_error, max_hamiltonian_energy_error, tree_depth, numerical_error, step_size, nom_step_size

Summary Statistics
  parameters      mean       std      mcse    ess_bulk    ess_tail      rhat   ⋯
      Symbol   Float64   Float64   Float64     Float64     Float64   Float64   ⋯

           σ    0.3690    0.0267    0.0003   6634.3954   6080.5757    1.0000   ⋯
           α    1.5026    0.1596    0.0030   2825.0996   3566.4279    1.0004   ⋯
           β    0.9458    0.1303    0.0024   3055.7314   3652.9872    1.0015   ⋯
           γ    3.2448    0.3214    0.0060   2806.7123   2850.3476    1.0009   ⋯
           δ    1.0199    0.1212    0.0022   3167.1794   3679.8852    1.0008   ⋯
       u0[1]    2.1903    0.2017    0.0026   6066.7978   5252.2598    1.0001   ⋯
       u0[2]    2.4814    0.2547    0.0034   5638.9388   5057.7901    1.0007   ⋯
                                                                1 column omitted

Quantiles
  parameters      2.5%     25.0%     50.0%     75.0%     97.5% 
      Symbol   Float64   Float64   Float64   Float64   Float64 

           σ    0.3219    0.3507    0.3675    0.3854    0.4254
           α    1.2290    1.3890    1.4889    1.6003    1.8558
           β    0.7245    0.8536    0.9332    1.0234    1.2380
           γ    2.6101    3.0243    3.2492    3.4636    3.8759
           δ    0.7863    0.9365    1.0169    1.1007    1.2611
       u0[1]    1.8288    2.0508    2.1789    2.3149    2.6257
       u0[2]    2.0080    2.3053    2.4727    2.6454    3.0176

Doing so, you could specify within solve the adtype. It is usually a good idea to try a few different sensitivity algorithm.

See here for more information.

Exercise: benchmark

Can you evaluate the performance of ForwardDiffSensitivity() and ReverseDiffAdjoint()?

Variational Inference

Variational inference (VI) consists in approximating the true posterior distribution P(θ|𝒟) by an approximate distribution Q(θ; ϕ), where ϕ is a parameter vector defining the shape, location, and other characteristics of the approximate distribution Q, to be optimzed so that Q is as close as possible to P. This is achieved by minimizing the Kullback-Leibler (KL) divergence between the true posterior P(θ|𝒟) and the approximate distribution :

$$ \phi^* = \underset{\phi}{\text{argmin}} \ \text{KL}\left(Q(\theta; \phi) \||\ P(\theta | \mathcal{D})\right) $$

The advantage of VI over traditional MCMC sampling methods is that VI is generally faster and more scalable to large datasets, as it transforms the inference problem into an optimization problem.

Let’s do VI in Turing!

import Flux
using Turing: Variational
model = fitlv2(data_mat, prob)
q0 = Variational.meanfield(model)
advi = ADVI(10, 10_000) # first arg is the 

q = vi(model, advi, q0; optimizer=Flux.ADAM(1e-2))

function plot_predictions_vi(q, sol, data_mat)
    myplot = plot(; legend=false)
    z = rand(q, 300)
    for parr in eachcol(z)
        p = NamedTuple([:α, :β, :γ, :δ] .=> parr[2:5])
        u0 = parr[6:7]
        sol_p = solve(prob, Tsit5(); u0, p, saveat)
        plot!(sol_p; alpha=0.1, color="#BBBBBB")
    end

    # Plot simulation and noisy observations.
    plot!(sol; color=[1 2], linewidth=1)
    scatter!(sol.t, data_mat'; color=[1 2])
    return myplot
end

plot_predictions_vi(q, sol_true, data_mat)

The cool thing with VI that we can sample from the resulting q with ease.

q isa MultivariateDistribution

true

rand(q)

7-element Vector{Float64}:
 0.3910702850249754
 1.8261988965103004
 1.169798842596696
 2.80613428184438
 0.8402820867003005
 2.313112765625009
 2.406422925384114

• Learn more on VI in turing here
• VI in general here

Infering functional forms

Up to now, we have been infering the value of the model’s parameters, assuming that the structure of our model is correct. But this is very idealistic, specifically in ecology. As a general trend, we have little idea of how does e.g. the functional response of a species look like.

What if instead of inferring parameter values, we could infer functional forms, or components within our model for which we have little idea on how to express it mathematically?

In Julia, we can do that.

To illustrate this, we’ll assume that we do not know the functional response of both prey and predator, i.e. the terms β * y and δ * x. Instead, we will parametrize this component in our DE model by a neural network, which can be seen as a simple non-linear regressor dependent on some extra parameters p_nn.

We then simply have to optimize those parameters, along with the other model’s parameters!

Let’s get started. To make the neural network, we’ll use the deep learning library Lux.jl, which is similar to Flux.jl but where models are explicitly parametrized. This explicit parametrization makes it simpler to integrate with an ODE model.

To make things simpler, we will define a single layer neural network

using Lux
Random.seed!(2)
rng = Random.default_rng()
nn_init = Lux.Chain(Lux.Dense(2,2, relu))
p_nn_init, st_nn = Lux.setup(rng, nn_init)

nn = StatefulLuxLayer(nn_init, st_nn)

StatefulLuxLayer{true}(
    Dense(2 => 2, relu),                # 6 parameters
)         # Total: 6 parameters,
          #        plus 0 states.

We use a StatefulLuxLayer to not having to carry around st_nn, a struct containing states of a Lux model, which is essentially useless for a multi-layer perceptron.

st_nn

NamedTuple()

We can now evaluate our neural network model as follows:

nn(u0, p_nn_init)

2-element Vector{Float64}:
 0.0
 0.0

instead of

nn_init(u0, p_nn_init, st_nn)

([0.0, 0.0], NamedTuple())

Let’s define a new parameter vectors, which will consist of the ODE model parameters as well as the neural net parameters

pinit = ComponentArray(;σ = 0.3, α = 1., γ = 1.0, p_nn=p_nn_init)

ComponentVector{Float64}(σ = 0.3, α = 1.0, γ = 1.0, p_nn = (weight = [-1.0083649158477783 -0.7284937500953674; -1.219232201576233 0.4427390396595001], bias = [0.0; 0.0;;]))

Exercise: neural network-based Lotka-Volterra model

Define the neural network-based Lotka-Volterra model

function lotka_volterra_nn(du, u, p, t)
    # Model parameters.
    @unpack α, γ, p_nn = p
    # Current state.
    x, y = u
    û = nn(u, p_nn) # Network prediction
    # Evaluate differential equations.
    du[1] = (α - û[1]) * x # prey
    du[2] = (û[2] - γ) * y # predator
    return nothing
end

lotka_volterra_nn (generic function with 1 method)

Let’s check our initial model predictions:

prob_nn = ODEProblem(lotka_volterra_nn, u0, tspan, pinit)
init_sol = solve(prob_nn, alg; saveat)
# Plot simulation.
plot(init_sol)

Now we can define our Turing Model. We’ll need to use a utility function vector_to_parameters that reconstructs the neural network parameter type based on a sampled parameter vector (taken from this Turing tutorial). You do not need to worry about this. Note that we could have used a component vector, but for some reason this did not work at the time of the writing of this tutorial…

using Functors # for the `fmap`
function vector_to_parameters(ps_new::AbstractVector, ps::NamedTuple)
    @assert length(ps_new) == Lux.parameterlength(ps)
    i = 1
    function get_ps(x)
        z = reshape(view(ps_new, i:(i + length(x) - 1)), size(x))
        i += length(x)
        return z
    end
    return fmap(get_ps, ps)
end

vector_to_parameters (generic function with 1 method)

# Create a regularization term and a Gaussian prior variance term.
sigma = 0.2

@model function fitlv_nn(data, prob)
    # Prior distributions.
    σ ~ InverseGamma(3, 0.5)
    α ~ truncated(Normal(1.5, 0.5); lower=0.5, upper=2.5)
    γ ~ truncated(Normal(3.0, 0.5); lower=1, upper=4)

    nparameters = Lux.parameterlength(nn)
    p_nn_vec ~ MvNormal(zeros(nparameters), sigma^2 * I)

    p_nn = vector_to_parameters(p_nn_vec, p_nn_init)

    # Simulate Lotka-Volterra model. 
    p = (;α, γ, p_nn)

    predicted = solve(prob, alg; p, saveat)

    # Observations.
    for i in 1:length(predicted)
        if all(predicted[i] .> 0)
            data[:, i] ~ MvLogNormal(log.(predicted[i]), σ^2 * I)
        end
    end

    return nothing
end


model = fitlv_nn(data_mat, prob_nn)

DynamicPPL.Model{typeof(fitlv_nn), (:data, :prob), (), (), Tuple{Matrix{Float64}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(σ = 1, α = 2, γ = 3, p_nn = ViewAxis(4:9, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2))), bias = ViewAxis(5:6, ShapedAxis((2, 1))))))}}}, ODEFunction{true, SciMLBase.AutoSpecialize, typeof(lotka_volterra_nn), UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardODEProblem}}, Tuple{}, DynamicPPL.DefaultContext}(fitlv_nn, (data = [1.8655845948955276 2.298199048573464 … 4.071164055293614 5.672667515002083; 2.651867857608795 3.2812317734519048 … 1.351784872962806 1.1243450946947573], prob = ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(σ = 1, α = 2, γ = 3, p_nn = ViewAxis(4:9, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2))), bias = ViewAxis(5:6, ShapedAxis((2, 1))))))}}}, ODEFunction{true, SciMLBase.AutoSpecialize, typeof(lotka_volterra_nn), UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardODEProblem}(ODEFunction{true, SciMLBase.AutoSpecialize, typeof(lotka_volterra_nn), UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing, Nothing}(lotka_volterra_nn, UniformScaling{Bool}(true), nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, SciMLBase.DEFAULT_OBSERVED, nothing, nothing, nothing, nothing), [2.0, 2.0], (0.0, 5.0), (σ = 0.3, α = 1.0, γ = 1.0, p_nn = (weight = [-1.0083649158477783 -0.7284937500953674; -1.219232201576233 0.4427390396595001], bias = [0.0; 0.0;;])), Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}(), SciMLBase.StandardODEProblem())), NamedTuple(), DynamicPPL.DefaultContext())

using Optimization, OptimizationOptimisers
@time map_res = maximum_a_posteriori(model, ADAM(0.05), maxiters=3000, initial_params=pinit)
pmap = ComponentArray(;σ=0, pinit...)
pmap .= map_res.values[:]
sol_map = solve(prob_nn, alg;p=pmap, saveat, tspan = (0, 10))
scatter(tsteps, data_mat',  color = [1 2], label=["Predator abundance data" "Prey abundance data"])
plot!(sol_map, color = [1 2], label=["Inferred predator abundance" "Inferred prey abundance"], yscale=:log10)

 12.103192 seconds (31.83 M allocations: 5.634 GiB, 3.78% gc time, 86.93% compilation time: <1% of which was recompilation)

This seems to fail.

Exercise: take some initiative!

What could you do to improve the convergence of the optimization?

Solution

sigma = 0.2
@model function fitlv_nn(data, prob)
    # Prior distributions.
    σ ~ InverseGamma(3, 0.5)
    α ~ truncated(Normal(1.5, 0.5); lower=0.5, upper=2.5)
    γ ~ truncated(Normal(3.0, 0.5); lower=1, upper=4)
    nparameters = Lux.parameterlength(nn)
    p_nn_vec ~ MvNormal(zeros(nparameters), sigma^2 * I)
    p_nn = vector_to_parameters(p_nn_vec, p_nn_init)
    # Simulate Lotka-Volterra model. 
    p = (;α, γ, p_nn)
    interval_idxs = multiple_shooting_idx(length(tsteps))
    for ts_idx in interval_idxs
        saveat = tsteps[ts_idx]
        u0 = sol_true.u[ts_idx[1]]
        predicted = solve(prob_nn,
                            alg; 
                            tspan = (saveat[1], saveat[end]),
                            u0,
                            p, 
                            saveat,
                            abstol=1e-6, 
                            reltol = 1e-6)
        # Observations.
        for i in 1:length(predicted)
            if all(predicted[i] .> 0)
                data[:, ts_idx[i]] ~ MvLogNormal(log.(predicted[i]), σ^2 * I)
            end
        end
    end
    return nothing
end
model = fitlv_nn(data_mat, prob_nn)
@time map_res = maximum_a_posteriori(model, Adam(0.1), maxiters=3000, initial_params=pinit)
pmap = ComponentArray(;σ=0, pinit...)
pmap .= map_res.values[:]
sol_map = solve(prob_nn, alg;p=pmap, saveat, tspan = (0, 10))
plot(sol_map, label=["Inferred predator abundance" "Inferred prey abundance"])
scatter!(sol_map.t, data_mat',  color = [1 2], label=["Predator abundance data" "Prey abundance data"], yscale=:log10)

  6.436015 seconds (53.74 M allocations: 23.768 GiB, 22.96% gc time, 14.78% compilation time: 75% of which was recompilation)

Happy with the convergence? Now let’s investigate what did the neural network learn!

`plot_func_resp`

function plot_func_resp(p, data)
    # plotting prediction of functional response
    u1 = range(minimum(data[1,:]), maximum(data[1,:]), length=100) 
    u2 = range(minimum(data[2,:]), maximum(data[2,:]), length=100) 
    u = hcat(u1,u2)

    func_resp = nn(u', p.p_nn)

    myplot1 = plot(u2,
                    - p_true.β .* u2; 
                    label="True functional form", 
                    xlabel="Predator abundance")
    plot!(myplot1,
                u2,
                - func_resp[1,:]; 
                color="#BBBBBB",
                label="Inferred functional form")

    myplot2 = plot(u1,
                    p_true.δ .* u2; 
                    legend=false, xlabel="Prey abundance")

    plot!(myplot2,
            u1,
            func_resp[2,:]; 
            color="#BBBBBB")

    myplot = plot(myplot1, myplot2)
    return myplot
end

plot_func_resp (generic function with 1 method)

plot_func_resp(pmap, data_mat)

The neural network has well captured the functional forms of the predator and prey.

Exercise: Probabilistic functional forms

Could you try to obtain a bayesian estimate of the functional forms with e.g. VI?

This concludes this tutorial; I hope that it has given you plenty of ideas for your future research projects!

Resources

• https://turinglang.org/docs/tutorials/10-bayesian-differential-equations/
• https://turinglang.org/docs/tutorials/09-variational-inference/