old code rm

test/problems/jak-stat_model.jl

@@ -117,22 +117,3 @@ function jakstat_obj_component(sim,data,σ)
   end
   return loss_i
 end
-
-#=
-optf = OptimizationFunction(loss_func, Optimization.AutoForwardDiff())
-#optf = OptimizationFunction(lsq_func, Optimization.AutoFiniteDiff())
-_prob = OptimizationProblem(optf, p_best, lb=fill(-Inf,p_len), ub=fill(Inf,p_len))
-
-
-maxiters = 1000
-scan_bounds = (1e-4, 1e4)
-Δθ = 0.2
-
-identity_profiler = IntegrationProfiler(integrator=FBDF(autodiff=false), matrix_type=:identity, integrator_opts=(reltol=1e-3, abstol=1e-6, adaptive=false, dt=0.1), gamma = 100.)
-hess_profiler = IntegrationProfiler(integrator=FBDF(autodiff=false), matrix_type=:hessian, integrator_opts=(reltol=1e-3, abstol=1e-6, adaptive=false, dt=0.1), gamma = 1.)
-opt_profiler = OptimizationProfiler(NLopt.LN_NELDERMEAD, (reltol=1e-3,), :fixed, Δθ, maxiters)
-
-@time opt_prof = profile_parameter(_prob, opt_profiler, 1; scan_bounds, verbose=true)
-@time hess_prof = profile_parameter(_prob, hess_profiler, 1; scan_bounds, threashold=threshold, verbose=true)
-@time identity_prof = profile_parameter(_prob, identity_profiler, 1; scan_bounds, threashold=threshold, verbose=true)
-=#

test/problems/taxol_model.jl

@@ -121,63 +121,3 @@ end
 # https://github.com/marisae/cancer-chemo-identifiability/blob/master/Profile%20Likelihood/testa0_fit.m#L40-L41
 sigmasq = (mean([(Cerr005/C005); (Cerr010/C010); (Cerr040/C040); (Cerr100/C100)]))^2
 
-#=
-pid = 2
-maxiters = 1000
-scan_bounds = ((1 - 0.05*14)*p0[pid], (1 + 0.05*60)*p0[pid])
-Δθ = 0.2
-
-identity_profiler = IntegrationProfiler(integrator=Rodas4(autodiff=false), matrix_type=:identity, integrator_opts=(reltol=1e-3, abstol=1e-6), gamma = 1.)
-hess_profiler = IntegrationProfiler(integrator=Rodas4(autodiff=false), matrix_type=:hessian, integrator_opts=(reltol=1e-3, abstol=1e-6), gamma = 1.)
-
-opt_profiler0 = OptimizationProfiler(NLopt.LN_NELDERMEAD, (reltol=1e-3,), :fixed, Δθ, maxiters)
-opt_profiler1 = OptimizationProfiler(NLopt.LN_NELDERMEAD, (reltol=1e-3,), :linear, Δθ, maxiters)
-
-@time opt_prof0 = profile_parameter(_prob, opt_profiler0, pid; scan_bounds, threashold=threshold, verbose=true)
-@time opt_prof1 = profile_parameter(_prob, opt_profiler1, pid; scan_bounds, threashold=threshold, verbose=true)
-
-@time hess_prof = profile_parameter(_prob, hess_profiler, pid; scan_bounds, threashold=threshold, verbose=true)
-@time identity_prof = profile_parameter(_prob, identity_profiler, pid; scan_bounds, threashold=threshold, verbose=true)
-
-plot(identity_prof, dpi=400, legend=:top, xlabel="ka", ylabel="L(θ)")
-plot!(hess_prof, alpha=0, conf_level=false, steps=false)
-
-plot(opt_prof0, dpi=400, legend = :bottomleft, xlabel="ka", ylabel="L(θ)")
-plot(opt_prof1, dpi=400, legend=:bottomleft, xlabel="ka", ylabel="L(θ)")
-
-endp = LikelihoodProfiler.ProfileSolution(
-  nothing,
-  nothing,
-  hess_prof.θopt,
-  [[0., 4.92292246374492, 0., 0., 0.], [0., 10.778706398866387, 0., 0., 0.]],
-  [hess_prof.flevel, hess_prof.flevel],
-  2,
-  #syms::
-  hess_prof.flevel,
-  nothing,
-  nothing
-
-)
-
-df = CSV.read("./lp_ka.csv", DataFrame)
-rename!(df, :f=>:obj)
-
-ka_full = LikelihoodProfiler.ProfileSolution(
-  nothing,
-  nothing,
-  hess_prof.θopt,
-  [[df.ka[i]] for i in 1:64],
-  [df.obj[i] for i in 1:64],
-  1,
-  #syms::
-  hess_prof.flevel,
-  nothing,
-  nothing
-
-)
-
-plot(endp, xticks=[4, 6,7,8,9,10, 4.99, 10.73, 12], xlims=(4.5,11), 
-dpi=400, legend=:top, xlabel="ka", ylabel="L(θ)", endpoints=true)
-scatter!((8.0959,0.538146855), label="optim")
-plot!(ka_full, alpha=0.2, conf_level=false)
-=#

test/test_jak-stat_model.jl

@@ -69,7 +69,7 @@ end
 
 # TODO Fix
 
-@testset "JAK2-STAT5 model. Fixed-step OptimizationProfiler with derivative-free optimizer" begin
+@testset "JAK2-STAT5 model. Fixed-step OptimizationProfiler with gradient-based optimizer" begin
 
   idxs = 1:9
   profile_step(p0, i) = abs(p0[i]) * 0.05