Problem not finding initial step in Bayesian Differential Equations Turing


I am trying to estimate the parameters of a ODE model with N equations. The model’s output are probabilities, ie, a vector of N probabilities each time step. Each coordinate of the vector defines the probability that patch i is occupied at time t. The observed data is 0/1 which means empty/occupied, this data is yearly. So I am using a Bernoulli distribution for the likelihood.
I have the output of the model which is “continuous” and I compare it with the 0/1 of the observed data. To do that I take t = 15 August year X from the output of the model and compare to the 0/1 of this year X. Also the model is non autonomous so it has a parameter that depends on time, which I interpolate and after in the model ODE it calls this interpolation to compute the value of the parameter. So I have some issues:

  1. There is no multivariate Bernouilli. So I do it in two loops as it can be seen in the code.
  2. This is an example with fake data when I run it with the real data i get many warnings from the SciMLBase:

    I am using rtil = 1e-20 and the DP8() for solving the ODE.
  3. The size of the system is big and it takes ages. Just to solve the ODE it takes minutes. Is there a way to make it faster?

I do not know if there is something that I am doing wrong and this is why it never finish or its just slow due to the cost of the integration method.

This is how I code this using fake input data:

# Code that integrates the Hanski model to obtain the solutions 
# With input: (from code input_Hanski.R)
#     . Distance matrix
#     . Flow matrix
#     . Area vector
#     . R_M vector
# And estimate the parameters of the model from the presence absence data
# Load pkgs and remove data -----------------------------------------------------
import Pkg
using DifferentialEquations
using DataFrames
using CSV
using Plots
using Shapefile
import GeoDataFrames as GDF
using LinearAlgebra
using ODE
using Interpolations
using DiffEqParamEstim
using SparseArrays
using ParameterizedFunctions
#using RecursiveArrayTools
using Optimization
using Distributions

# Constants
m_c = 0.001 # probability of mosquito in a car
alp = 1/200 # average natural dispersal distance

# Create fake data
end_ind = 5
eta = rand(Uniform(0, 1000),end_ind, end_ind)
eta[diagind(eta)] .= 0 

# Set to zero random entraces
eta[1,4] = 0
eta[2,3] = 0
eta[5,1:end] .= 0

N = size(eta, 1) # Number of patches
d_1 = rand(Uniform(0, 100),end_ind, end_ind)
d_1[5,:] .= rand(Uniform(900, 1000),end_ind,1)
mat = exp.(-alp*d_1)
mat[diagind(mat)] .= 0 # Set to zero diagonal of distance

# Choose number of patches and IC
pop_init = zeros(N)
pop_init[1] = 1

# Fake time series
num_years = 6
num_days = 365*num_years
days = 1:num_days
days_per_year = 365
dates = collect(1:num_days)

#  Fake seasonal
R_Ms = zeros(num_days,end_ind)
R_Ms[1:end,1] .= (30 .+ 10 * sin.(2 * π * days / days_per_year) + 2. * randn(num_days))./10
R_Ms[1:end,2] .= (30 .+ 10 * sin.(2 * π * days / days_per_year) + 2. * randn(num_days))./30
R_Ms[1:end,3] .= (30 .+ 10 * sin.(2 * π * days / days_per_year) + 2. * randn(num_days))./40
R_Ms[1:end,4] .= (30 .+ 10 * sin.(2 * π * days / days_per_year) + 2. * randn(num_days))./10
R_Ms[1:end,5] .= (30 .+ 10 * sin.(2 * π * days / days_per_year) + 2. * randn(num_days))./100

plot(days, R_Ms[1:end,1], title="Positive Seasonal Pattern with Random Variation", xlabel="Days", ylabel="Value")
# Create an array to store interpolated functions
interpolated_functions = []
# Perform interpolation for each location
for i in 1:end_ind
    # Extract temperature values for the current location
    R_Ms_val = R_Ms[:, i]
    dates_num = 1:size(dates, 1)

    # Perform linear interpolation
    itp = LinearInterpolation(dates_num, R_Ms_val,extrapolation_bc=Flat())
    # Store the interpolated function
    push!(interpolated_functions, itp)



# Integration non autonomous
function fun_na!(du, u, p, t)
    R_M_vec = [interpolated_functions[i](t) for i in 1:N]
    eta1 = 1.0./(1.0 .+ exp.(-p[4].*eta.+p[5]))
    Cd = p[1].*mat*u # Natural dispersal
    Ch = p[2].*(m_c*eta1)*u # Human mobility
    du.= R_M_vec.*(Cd .+ Ch) .* (1 .- u) - p[3] .* u
# Set parameters
tspan = (t0, tf)
u0 = pop_init
p = [0.001,0.001,0.001,0.5,500]

# Create the ode model
prob = DifferentialEquations.ODEProblem(fun_na!, u0, tspan, p)
alg= DP8() #For low tolerances, Rodas4()

# Test Integrate ODE
@time sol = DifferentialEquations.solve(prob,alg,reltol = rtol)

# Create the vector of times of the observation. Assuming we observe in August 15 (or 16 depending onn leap years)
t_obs = zeros(num_years)
t_obs[1] = 227 
for i = 2:length(t_obs)
  t_obs[i] = t_obs[i-1] + 365

# Create fake obs if p(t)>0.5 then 1
matrix_obs = zeros(end_ind,num_years)

# Loop through unique id_mitma values
for i in 1:end_ind
    # Get years for the current id_mitma
    for j in 1:num_years
        prob = sol(t_obs[j])[i]
        # Convert years to indices in the matrix
        if (prob > 0.5)
           matrix_obs[i,j:end] .= 1 

# Load packages
using Turing
using StatsPlots

# Estimation Bayesian --------------------------------
@model function fitlv(data, prob) 
    # Prior distributions.
    c_d_dist ~ Uniform(0.1e-4, 0.1)
    c_h_dist ~ Uniform(0.1e-4, 0.1)
    e_dist ~ Uniform(0.1e-7, 0.1)
    a_dist ~ Uniform(0.1, 0.8)
    b_dist ~ Uniform(450,550)

    # Simulate Hanski model. 
    p = [c_d_dist, c_h_dist, e_dist,a_dist,b_dist] 
    predicted = DifferentialEquations.solve(prob,alg,reltol = rtol) # Observations. 
    #if length(predicted) > 0
    for i in 1:length(t_obs)
      predicted_value = predicted(t_obs[i])
      for j in 1:length(predicted_value)
          data[j, i] ~ Bernoulli(clamp(predicted_value[j],0,1))  # Ensure it's a probability it goes to -7.e-10 which is zero 
    return nothing 

# Save the Bayesian model to fit with the observed data and the ode model
prob = DifferentialEquations.ODEProblem(fun_na!, u0, tspan, p)
model = fitlv(matrix_obs, prob) 

# Sample 3 independent chains with forward-mode automatic differentiation (the default). 
iterations = 1000
print("Before MCMC")
chain = sample(model, NUTS(), MCMCSerial(),1000, 3, 
init_params = [0.01, 0.01, 0.001,0.45,490] ; progress=false, verbose = false)

Given that the relative size of a Float64 number can hold 16 digits, it seems like it would be rather hard to get 1e-20 relative tolerance…

Hi, but this does not matter before it was 14 and I change it until 1e-20, I got the same error all the times.

Fixing your allocations would help a lot. But it’s a small ODE, so you should just use static arrays. Follow the tutorial:

You’d just use Rodas5P as the solver with static arrays and it should be good.

Hi Chris,

It is a 365 equations ODE, the one that I write on the question is a toy model. Moreover in the future I would like to do the full model with 2300 equation. Also, some parameters depends on time and sometimes the equations are stiff depending on the value for the parameters.

The example that you have send me its for non stiff equations. Should I follow this even though my problem is stiff?


Getting your code allocation-free is always a first step. Has that already been done?

I have done my best to do that but I am new in Julia so for sure it could be done better. I have tried with the Jacobian but it is slower than without the Jacobian.