Fix package extensions; Clean up tests; Remove use of threadid; Move figures into docs folder; Add a better, shorter, PDE example; remove GeneralLazyBufferCache (#87)

Author: DanielVandH

.github/workflows/CI.yml

@@ -21,11 +21,6 @@ jobs:
       matrix:
         version:
           - '1'
-          - '1.6'
-          - '1.7'
-          - '1.8'
-          - '1.9'
-          - 'nightly'
         os:
           - ubuntu-latest
         arch:

Project.toml

@@ -23,8 +23,8 @@ DelaunayTriangulation = "927a84f5-c5f4-47a5-9785-b46e178433df"
 Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
 
 [extensions]
-ProfileLikelihoodMakieExt = "Makie"
 ProfileLikelihoodDelaunayTriangulationExt = "DelaunayTriangulation"
+ProfileLikelihoodMakieExt = "Makie"
 
 [compat]
 ChunkSplitters = "1.0"
@@ -42,7 +42,9 @@ StatsFuns = "1.1, 1.3"
 julia = "1"
 
 [extras]
+DelaunayTriangulation = "927a84f5-c5f4-47a5-9785-b46e178433df"
+Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test"]
+test = ["Test", "DelaunayTriangulation", "Makie"]

README.md

@@ -2,8 +2,10 @@
 
 
 [![DOI](https://zenodo.org/badge/508701126.svg)](https://zenodo.org/badge/latestdoi/508701126)
-[![](https://img.shields.io/badge/docs-dev-blue.svg)](https://DanielVandH.github.io/ProfileLikelihood.jl/dev)
-[![](https://img.shields.io/badge/docs-stable-blue.svg)](https://DanielVandH.github.io/ProfileLikelihood.jl/stable)
+[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://DanielVandH.github.io/ProfileLikelihood.jl/dev)
+[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://DanielVandH.github.io/ProfileLikelihood.jl/stable)
+[![Build Status](https://github.com/DanielVandH/ProfileLikelihood.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/DanielVandH/ProfileLikelihood.jl/actions/workflows/CI.yml?query=branch%3Amain)
+[![Coverage](https://codecov.io/gh/DanielVandH/ProfileLikelihood.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/DanielVandH/ProfileLikelihood.jl)
 
 This package defines the routines required for computing maximum likelihood estimates and profile likelihoods. The optimisation routines are built around the [Optimization.jl](https://github.com/SciML/Optimization.jl) interface, allowing us to e.g. easily switch between algorithms, between finite differences and automatic differentiation, and it allows for constraints to be defined with ease. Below we list the definitions we are using for likelihoods and profile likelihoods. This code works for univariate and bivariate profiles.
 

docs/make.jl

@@ -21,8 +21,8 @@ makedocs(
         "Example I: Multiple linear regression" => "regression.md"
         "Example II: Logistic ordinary differential equation" => "logistic.md"
         "Example III: Linear exponential ODE and grid searching" => "exponential.md"
-        "Example IV: Diffusion equation on a square plate" => "heat.md"
-        "Example V: Lotka-Volterra ODE, GeneralLazyBufferCache, and computing bivarate profile likelihoods" => "lotka.md"
+        "Example IV: Lotka-Volterra ODE and computing bivarate profile likelihoods" => "lotka.md"
+        "Example V: Fisher-Stefan PDE" => "stefan.md"
         "Mathematical and Implementation Details" => "math.md"
     ])
 

docs/src/exponential.md

@@ -16,6 +16,7 @@ using LatinHypercubeSampling
 using OptimizationOptimJL
 using OptimizationNLopt
 using Test
+using StableRNGs
 ```
 
 ## Setting up the problem 
@@ -24,7 +25,7 @@ Let us start by defining the data and the likelihood problem:
 
 ```julia
 ## Step 1: Generate some data for the problem and define the likelihood
-Random.seed!(2992999)
+rng = StableRNG(2992999)
 λ = -0.5
 y₀ = 15.0
 σ = 0.5
@@ -33,16 +34,14 @@ n = 450
 Δt = T / n
 t = [j * Δt for j in 0:n]
 y = y₀ * exp.(λ * t)
-yᵒ = y .+ [0.0, rand(Normal(0, σ), n)...]
+yᵒ = y .+ [0.0, rand(rng, Normal(0, σ), n)...]
 @inline function ode_fnc(u, p, t)
-    local λ
     λ = p
     du = λ * u
     return du
 end
 using LoopVectorization, MuladdMacro
-@inline function _loglik_fnc(θ::AbstractVector{T}, data, integrator) where {T}
-    local yᵒ, n, λ, σ, u0
+function _loglik_fnc(θ::AbstractVector{T}, data, integrator) where {T}
     yᵒ, n = data
     λ, σ, u0 = θ
     integrator.p = λ
@@ -90,7 +89,7 @@ We can now use this grid to evaluate the likelihood function at each point, and
 ```julia
 gs = grid_search(prob, regular_grid)
 LikelihoodSolution. retcode: Success
-Maximum likelihood: -547.9579886200935
+Maximum likelihood: -548.3068396174556
 Maximum likelihood estimates: 3-element Vector{Float64}
      λ: -0.612244897959183
      σ: 0.816327448979592
@@ -103,7 +102,7 @@ You could also use an irregular grid, defining some grid as a matrix where each
 using LatinHypercubeSampling
 d = 3
 gens = 1000
-plan, _ = LHCoptim(500, d, gens)
+plan, _ = LHCoptim(500, d, gens; rng)
 new_lb = [-2.0, 0.05, 10.0]
 new_ub = [2.0, 0.2, 20.0]
 bnds = [(new_lb[i], new_ub[i]) for i in 1:d]
@@ -112,20 +111,21 @@ irregular_grid = IrregularGrid(lb, ub, parameter_vals)
 gs_ir, loglik_vals_ir = grid_search(prob, irregular_grid; save_vals=Val(true), parallel = Val(true))
 ```
 ```julia
+julia> gs_ir
 LikelihoodSolution. retcode: Success
-Maximum likelihood: -1729.7407123603484
+Maximum likelihood: -2611.078183576969
 Maximum likelihood estimates: 3-element Vector{Float64}
-     λ: -0.5090180360721444
-     σ: 0.19368737474949904
-     y₀: 15.791583166332664
+     λ: -0.5170340681362726
+     σ: 0.18256513026052107
+     y₀: 14.348697394789578
 ```
 ```julia
 max_lik, max_idx = findmax(loglik_vals_ir)
 @test max_lik == PL.get_maximum(gs_ir)
 @test parameter_vals[:, max_idx] ≈ PL.get_mle(gs_ir)
 ```
 
-(If you just want to try many points for starting your optimiser, see the optimiser in MultistartOptimization.jl.)
+(If you just want to try many points for starting your optimiser, see e.g. the optimiser in MultistartOptimization.jl.)
 
 ## Parameter estimation 
 
@@ -143,10 +143,10 @@ prof = profile(prob, sol; alg=NLopt.LN_NELDERMEAD, parallel = true)
 ```
 ```julia
 ProfileLikelihoodSolution. MLE retcode: Success
-Confidence intervals: 
-     95.0% CI for λ: (-0.51091362373969, -0.49491369219060505)
-     95.0% CI for σ: (0.49607205632240814, 0.5652591835193789)
-     95.0% CI for y₀: (14.98587355568687, 15.305179849533756)
+Confidence intervals:
+     95.0% CI for λ: (-0.5092192953535792, -0.49323747169071175)
+     95.0% CI for σ: (0.4925813447124647, 0.5612815283609663)
+     95.0% CI for y₀: (14.856528827532468, 15.173375766524025)
 ```
 ```julia
 @test λ ∈ get_confidence_intervals(prof, :λ)
@@ -162,90 +162,13 @@ Finally, we can visualise the profiles:
 fig = plot_profiles(prof; nrow=1, ncol=3,
     latex_names=[L"\lambda", L"\sigma", L"y_0"],
     true_vals=[λ, σ, y₀],
-    fig_kwargs=(fontsize=30, resolution=(2109.644f0, 444.242f0)),
+    fig_kwargs=(fontsize=41,),
     axis_kwargs=(width=600, height=300))
+resize_to_layout!(fig)
 ```
 
-![Linear exponential profiles](https://github.com/DanielVandH/ProfileLikelihood.jl/blob/main/test/figures/linear_exponential_example.png?raw=true)
-
-## Just the code
-
-Here is all the code used for obtaining the results in this example, should you want a version that you can directly copy and paste.
-
-```julia 
-## Step 1: Generate some data for the problem and define the likelihood
-using OrdinaryDiffEq, Random, Distributions, LoopVectorization, MuladdMacro
-Random.seed!(2992999)
-λ = -0.5
-y₀ = 15.0
-σ = 0.5
-T = 5.0
-n = 450
-Δt = T / n
-t = [j * Δt for j in 0:n]
-y = y₀ * exp.(λ * t)
-yᵒ = y .+ [0.0, rand(Normal(0, σ), n)...]
-@inline function ode_fnc(u, p, t)
-    local λ
-    λ = p
-    du = λ * u
-    return du
-end
-@inline function _loglik_fnc(θ::AbstractVector{T}, data, integrator) where {T}
-    local yᵒ, n, λ, σ, u0
-    yᵒ, n = data
-    λ, σ, u0 = θ
-    integrator.p = λ
-    ## Now solve the problem 
-    reinit!(integrator, u0)
-    solve!(integrator)
-    if !SciMLBase.successful_retcode(integrator.sol)
-        return typemin(T)
-    end
-    ℓ = -0.5(n + 1) * log(2π * σ^2)
-    s = zero(T)
-    @turbo @muladd for i in eachindex(yᵒ, integrator.sol.u)
-        s = s + (yᵒ[i] - integrator.sol.u[i]) * (yᵒ[i] - integrator.sol.u[i])
-    end
-    ℓ = ℓ - 0.5s / σ^2
-end
-
-## Step 2: Define the problem
-using Optimization
-θ₀ = [-1.0, 0.5, 19.73] # will be replaced anyway
-lb = [-10.0, 1e-6, 0.5]
-ub = [10.0, 10.0, 25.0]
-syms = [:λ, :σ, :y₀]
-prob = LikelihoodProblem(
-    _loglik_fnc, θ₀, ode_fnc, y₀, (0.0, T);
-    syms=syms,
-    data=(yᵒ, n),
-    ode_parameters=1.0, # temp value for λ
-    ode_kwargs=(verbose=false, saveat=t),
-    f_kwargs=(adtype=Optimization.AutoFiniteDiff(),),
-    prob_kwargs=(lb=lb, ub=ub),
-    ode_alg=Tsit5()
-)
-
-## Step 3: Grid search
-regular_grid = RegularGrid(lb, ub, 50) # resolution can also be given as a vector for each parameter
-gs = grid_search(prob, regular_grid)
-
-## Step 4: Compute the MLE, starting at the grid search solution 
-using OptimizationOptimJL
-prob = ProfileLikelihood.update_initial_estimate(prob, gs)
-sol = mle(prob, Optim.LBFGS())
-
-## Step 5: Profile 
-using OptimizationNLopt
-prof = profile(prob, sol; alg=NLopt.LN_NELDERMEAD, parallel=true)
-
-
-## Step 6: Visualise 
-using CairoMakie, LaTeXStrings
-fig = plot_profiles(prof; nrow=1, ncol=3,
-    latex_names=[L"\lambda", L"\sigma", L"y_0"],
-    true_vals=[λ, σ, y₀],
-    fig_kwargs=(fontsize=30, resolution=(2109.644f0, 444.242f0)),
-    axis_kwargs=(width=600, height=300))
-```
+```@raw html
+<figure>
+    <img src='../figures/linear_exponential_example.png', alt'Linear exponential profiles'><br>
+</figure>
+```