Reflect test fix in docs, update images after Makie update, and up Sc…

…iML compat (#92)
DanielVandH · Dec 5, 2023 · 42d6403 · 42d6403 · DanielVandH · Dec 5, 2023
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diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "ProfileLikelihood"
 uuid = "3fca794e-44fa-49a6-b6a0-8cd45572ba0a"
 authors = ["Daniel VandenHeuvel <danj.vandenheuvel@gmail.com>"]
-version = "0.3.2"
+version = "0.3.3"
 
 [deps]
 ChunkSplitters = "ae650224-84b6-46f8-82ea-d812ca08434e"
@@ -36,7 +36,7 @@ OffsetArrays = "1.12"
 PolygonInbounds = "0.2"
 PreallocationTools = "0.4"
 Requires = "1.3"
-SciMLBase = "1.77, 1.91, 1.94"
+SciMLBase = "=2.9.1"
 SimpleNonlinearSolve = "0.1"
 StatsFuns = "1.1, 1.3"
 julia = "1"

diff --git a/docs/src/figures/linear_exponential_example.png b/docs/src/figures/linear_exponential_example.png
diff --git a/docs/src/figures/logistic_example.png b/docs/src/figures/logistic_example.png
diff --git a/docs/src/figures/logistic_example_prediction.png b/docs/src/figures/logistic_example_prediction.png
diff --git a/docs/src/figures/lokta_example_bivariate_predictions.png b/docs/src/figures/lokta_example_bivariate_predictions.png
diff --git a/docs/src/figures/lokta_example_bivariate_profiles_low_quality.png b/docs/src/figures/lokta_example_bivariate_profiles_low_quality.png
diff --git a/docs/src/figures/lokta_example_bivariate_profiles_smoothed_quality.png b/docs/src/figures/lokta_example_bivariate_profiles_smoothed_quality.png
diff --git a/docs/src/figures/lokta_example_profiles.png b/docs/src/figures/lokta_example_profiles.png
diff --git a/docs/src/figures/lokta_example_univariate_predictions.png b/docs/src/figures/lokta_example_univariate_predictions.png
diff --git a/docs/src/figures/noisy_pde_data.png b/docs/src/figures/noisy_pde_data.png
diff --git a/docs/src/figures/pde_prediction_intervals.png b/docs/src/figures/pde_prediction_intervals.png
diff --git a/docs/src/figures/pde_profiles.png b/docs/src/figures/pde_profiles.png
diff --git a/docs/src/figures/regression_profiles.png b/docs/src/figures/regression_profiles.png
diff --git a/docs/src/heat.md b/docs/src/heat.md
@@ -244,7 +244,7 @@ using CairoMakie, LaTeXStrings
 fig = plot_profiles(prof; nrow=1, ncol=3,
     latex_names=[L"k", L"c", L"u_0"],
     true_vals=[k[1], c, u₀],
-    fig_kwargs=(fontsize=38, resolution=(2109.644f0, 444.242f0)),
+    fig_kwargs=(fontsize=38, size=(2109.644f0, 444.242f0)),
     axis_kwargs=(width=600, height=300))
 scatter!(fig.content[1], get_parameter_values(prof, :k), get_profile_values(prof, :k), color=:black, markersize=9)
 scatter!(fig.content[2], get_parameter_values(prof, :c), get_profile_values(prof, :c), color=:black, markersize=9)
@@ -358,7 +358,7 @@ The confidence intervals contain the true values. We can now visualise.
 fig = plot_profiles(prof; nrow=1, ncol=3,
     latex_names=[L"k", L"u_0"],
     true_vals=[k[1], u₀],
-    fig_kwargs=(fontsize=38, resolution=(1441.9216f0, 470.17322f0)),
+    fig_kwargs=(fontsize=38, size=(1441.9216f0, 470.17322f0)),
     axis_kwargs=(width=600, height=300))
 scatter!(fig.content[1], get_parameter_values(prof, :k), get_profile_values(prof, :k), color=:black, markersize=9)
 scatter!(fig.content[2], get_parameter_values(prof, :u₀), get_profile_values(prof, :u₀), color=:black, markersize=9)
@@ -452,7 +452,7 @@ Now we can visualise the curves. We will also show the mass curve from the exact
 ```julia
 exact_soln = compute_mass_function([k[1], u₀], prediction_data)
 mle_soln = compute_mass_function(get_mle(mle_sol), prediction_data)
-fig = Figure(fontsize=38, resolution=(1360.512f0, 848.64404f0))
+fig = Figure(fontsize=38, size=(1360.512f0, 848.64404f0))
 alp = join('a':'z')
 latex_names = [L"k", L"u_0"]
 for i in 1:2
@@ -619,7 +619,7 @@ using CairoMakie, LaTeXStrings
 fig = plot_profiles(prof; nrow=1, ncol=3,
     latex_names=[L"k", L"c", L"u_0"],
     true_vals=[k[1], c, u₀],
-    fig_kwargs=(fontsize=38, resolution=(2109.644f0, 444.242f0)),
+    fig_kwargs=(fontsize=38, size=(2109.644f0, 444.242f0)),
     axis_kwargs=(width=600, height=300))
 scatter!(fig.content[1], get_parameter_values(prof, :k), get_profile_values(prof, :k), color=:black, markersize=9)
 scatter!(fig.content[2], get_parameter_values(prof, :c), get_profile_values(prof, :c), color=:black, markersize=9)
@@ -674,7 +674,7 @@ prof = profile(likprob_2, mle_sol; ftol_abs=1e-4, ftol_rel=1e-4, xtol_abs=1e-4,
 fig = plot_profiles(prof; nrow=1, ncol=3,
     latex_names=[L"k", L"u_0"],
     true_vals=[k[1], u₀],
-    fig_kwargs=(fontsize=38, resolution=(1441.9216f0, 470.17322f0)),
+    fig_kwargs=(fontsize=38, size=(1441.9216f0, 470.17322f0)),
     axis_kwargs=(width=600, height=300))
 scatter!(fig.content[1], get_parameter_values(prof, :k), get_profile_values(prof, :k), color=:black, markersize=9)
 scatter!(fig.content[2], get_parameter_values(prof, :u₀), get_profile_values(prof, :u₀), color=:black, markersize=9)
@@ -705,7 +705,7 @@ parameter_wise, union_intervals, all_curves, param_range =
 exact_soln = compute_mass_function([k[1], u₀], prediction_data)
 mle_soln = compute_mass_function(get_mle(mle_sol), prediction_data)
 
-fig = Figure(fontsize=38, resolution=(1360.512f0, 848.64404f0))
+fig = Figure(fontsize=38, size=(1360.512f0, 848.64404f0))
 alp = join('a':'z')
 latex_names = [L"k", L"u_0"]
 for i in 1:2

diff --git a/docs/src/logistic.md b/docs/src/logistic.md
@@ -69,6 +69,7 @@ prob = LikelihoodProblem(
     data=(uᵒ, σ),
     ode_parameters=[1.0, 1.0], # temp values for [λ, K]
     ode_kwargs=(verbose=false, saveat=t),
+    f_kwargs=(adtype=Optimization.AutoFiniteDiff(),),
     prob_kwargs=(lb=lb, ub=ub),
     ode_alg=Rosenbrock23()
 )

diff --git a/docs/src/lotka.md b/docs/src/lotka.md
@@ -200,7 +200,7 @@ fig_2 = plot_profiles(prof_2, param_pairs; # param_pairs not needed, but this en
     true_vals=[α, β, a₀, b₀],
     xlim_tuples=[(0.5, 1.5), (0.5, 1.5), (0.5, 1.5), (0.7, 1.3), (0.7, 1.3), (0.5, 1.1)],
     ylim_tuples=[(0.5, 1.5), (0.5, 1.05), (0.1, 0.5), (0.5, 1.05), (0.1, 0.5), (0.1, 0.5)],
-    fig_kwargs=(fontsize=24,))
+    fig_kwargs=(fontsize=17,))
 ```
 
 ```@raw html
@@ -226,7 +226,7 @@ fig_3 = plot_profiles(prof_2, param_pairs;
     interpolation=true,
     xlim_tuples=[(0.5, 1.5), (0.5, 1.5), (0.5, 1.5), (0.7, 1.3), (0.7, 1.3), (0.5, 1.1)],
     ylim_tuples=[(0.5, 1.5), (0.5, 1.05), (0.1, 0.5), (0.5, 1.05), (0.1, 0.5), (0.1, 0.5)],
-    fig_kwargs=(fontsize=24,))
+    fig_kwargs=(fontsize=17,))
 ```
 
 ```@raw html
@@ -269,7 +269,7 @@ prediction_function!(exact_soln, [α, β, a₀, b₀], pred_data)
 prediction_function!(mle_soln, get_mle(sol), pred_data)
 
 # Plot the parameter-wise intervals 
-fig = Figure(fontsize=38, resolution=(2935.488f0, 1392.64404f0))
+fig = Figure(fontsize=38, size=(2935.488f0, 1392.64404f0))
 alp = [['a', 'b', 'e', 'f'], ['c', 'd', 'g', 'h']]
 latex_names = [L"\alpha", L"\beta", L"a_0", L"b_0"]
 for (k, idx) in enumerate((a_idx, b_idx))
@@ -338,7 +338,7 @@ individual_intervals, union_intervals, q_vals, param_ranges =
         q_prototype)
 
 # Plot the intervals 
-fig = Figure(fontsize=38, resolution=(2935.488f0, 1854.64404f0))
+fig = Figure(fontsize=38, size=(2935.488f0, 1854.64404f0))
 integer_param_pairs = ProfileLikelihood.convert_symbol_tuples(param_pairs, prof_2) # converts to the integer representation
 alp = [['a', 'b', 'e', 'f', 'i', 'j'], ['c', 'd', 'g', 'h', 'k', 'l']]
 for (k, idx) in enumerate((a_idx, b_idx))

diff --git a/src/problem_updates.jl b/src/problem_updates.jl
@@ -2,17 +2,40 @@ update_initial_estimate(prob::OptimizationProblem, θ) = remake(prob; u0=θ)
 update_initial_estimate(prob::OptimizationProblem, sol::SciMLBase.OptimizationSolution) = update_initial_estimate(prob, sol.u)
 
 # replace obj with a new obj
-function replace_objective_function(prob::OptimizationProblem{iip,FF,uType,P,B,LC,UC,Sns,K}, obj::F) where {F,iip,AD,G,H,HV,C,CJ,CH,LH,HP,CJP,CHP,LHP,S,S2,O,HCV,CJCV,CHCV,LHCV,EX,CEX,SYS,FF2,FF<:OptimizationFunction{iip,AD,FF2,G,H,HV,C,CJ,CH,LH,HP,CJP,CHP,LHP,S,S2,O,HCV,CJCV,CHCV,LHCV,EX,CEX,SYS},uType,P,B,LC,UC,Sns,K}
-    f = OptimizationFunction{iip,AD,F,G,H,HV,C,CJ,CH,LH,HP,CJP,CHP,LHP,S,S2,O,HCV,CJCV,CHCV,LHCV,EX,CEX,SYS
-    }(obj,
-        prob.f.adtype, prob.f.grad,
-        prob.f.hess, prob.f.hv,
-        prob.f.cons, prob.f.cons_j, prob.f.cons_h, prob.f.lag_h,
-        prob.f.hess_prototype, prob.f.cons_jac_prototype, prob.f.cons_hess_prototype, prob.f.lag_hess_prototype,
-        prob.f.syms, prob.f.paramsyms, prob.f.observed,
-        prob.f.hess_colorvec, prob.f.cons_jac_colorvec, prob.f.cons_hess_colorvec, prob.f.lag_hess_colorvec,
-        prob.f.expr, prob.f.cons_expr, prob.f.sys)
-    return remake(prob; f=f)
+@inline function replace_objective_function(f::OF, new_f::FF) where {iip,AD,F,G,H,HV,C,CJ,CH,HP,CJP,CHP,S,S2,O,EX,CEX,SYS,LH,LHP,HCV,CJCV,CHCV,LHCV,OF<:OptimizationFunction{iip,AD,F,G,H,HV,C,CJ,CH,HP,CJP,CHP,S,S2,O,EX,CEX,SYS,LH,LHP,HCV,CJCV,CHCV,LHCV},FF}
+    # scimlbase needs to add a constructorof method for OptimizationFunction before we can just do @set with accessors.jl. 
+    # g = @set f.f = new_f
+    # return g
+    return OptimizationFunction{iip,AD,FF,G,H,HV,C,CJ,CH,HP,CJP,CHP,S,S2,O,EX,CEX,SYS,LH,LHP,HCV,CJCV,CHCV,LHCV}(
+        new_f,
+        f.adtype,
+        f.grad,
+        f.hess,
+        f.hv,
+        f.cons,
+        f.cons_j,
+        f.cons_h,
+        f.hess_prototype,
+        f.cons_jac_prototype,
+        f.cons_hess_prototype,
+        f.syms,
+        f.paramsyms,
+        f.observed,
+        f.expr,
+        f.cons_expr,
+        f.sys,
+        f.lag_h,
+        f.lag_hess_prototype,
+        f.hess_colorvec,
+        f.cons_jac_colorvec,
+        f.cons_hess_colorvec,
+        f.lag_hess_colorvec,
+    )
+end
+
+@inline function replace_objective_function(prob::F, obj::FF) where {F<:OptimizationProblem,FF}
+    g = replace_objective_function(prob.f, obj)
+    return remake(prob; f=g)
 end
 
 # fix the objective function's nth parameter at θₙ