Merge pull request #173 from SciML/noncrete_solve

end of concrete_solve
SciML · May 30, 2020 · acb0219 · acb0219
2 parents 500a6e0 + 140cc2e
commit acb0219
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Showing 4 changed files with 11 additions and 11 deletions.
diff --git a/Project.toml b/Project.toml
@@ -32,7 +32,7 @@ Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
 
 [compat]
 ApproxBayes = "0.3"
-DiffEqBase = "6.5"
+DiffEqBase = "6.36"
 DiffResults = "0.0.4, 1.0"
 Distances = "0.8, 0.9"
 Distributions = "0.21, 0.22, 0.23"

diff --git a/src/abc_inference.jl b/src/abc_inference.jl
@@ -11,7 +11,7 @@ function createabcfunction(prob, t, distancefunction, alg; save_idxs = nothing,
         else
             u0 = prob.u0
         end
-        sol = concrete_solve(STANDARD_PROB_GENERATOR(prob, params), alg, u0; saveat = t, save_idxs = save_idxs, kwargs...)
+        sol = solve(prob, alg, u0=u0, p=params, saveat = t, save_idxs = save_idxs, kwargs...)
         if size(sol, 2) < length(t)
             return Inf,nothing
         else

diff --git a/src/dynamichmc_inference.jl b/src/dynamichmc_inference.jl
@@ -40,13 +40,13 @@ function (P::DynamicHMCPosterior)(θ)
     u0 = convert.(T, sample_u0 ? parameters[1:nu] : problem.u0)
     p = convert.(T, sample_u0 ? parameters[(nu + 1):end] : parameters)
     if length(u0) < length(problem.u0)
-      # assumes u is ordered such that the observed variables are in the begining, consistent with ordered theta 
+      # assumes u is ordered such that the observed variables are in the begining, consistent with ordered theta
       for i in length(u0):length(problem.u0)
           push!(u0, convert(T,problem.u0[i]))
       end
     end
     _saveat = t === nothing ? Float64[] : t
-    sol = concrete_solve(problem, algorithm, u0, p; saveat = _saveat, save_idxs = save_idxs, solve_kwargs...)
+    sol = solve(problem, algorithm; u0=u0, p=p, saveat = _saveat, save_idxs = save_idxs, solve_kwargs...)
     failure = size(sol, 2) < length(_saveat)
     failure && return T(0) * sum(σ) + T(-Inf)
     log_likelihood = sum(sum(map(logpdf, Normal.(0.0, σ), sol[:, i] .- data[:, i])) for (i, t) in enumerate(t))
@@ -93,15 +93,15 @@ posterior values (transformed from `ℝⁿ`).
 function dynamichmc_inference(problem::DiffEqBase.DEProblem, algorithm, t, data,
                               parameter_priors, parameter_transformations=as(Vector, asℝ₊, length(parameter_priors));
                               σ_priors = fill(Normal(0, 5), size(data, 1)),sample_u0 = false, rng = Random.GLOBAL_RNG,
-                              num_samples = 1000, AD_gradient_kind = Val(:ForwardDiff), save_idxs = nothing,solve_kwargs = (), 
+                              num_samples = 1000, AD_gradient_kind = Val(:ForwardDiff), save_idxs = nothing,solve_kwargs = (),
                               mcmc_kwargs = (initialization = (q = zeros(length(parameter_priors) + (save_idxs === nothing ? length(data[:,1]) : length(save_idxs))),),))
     P = DynamicHMCPosterior(; algorithm = algorithm, problem = problem, t = t, data = data,
-                            parameter_priors = parameter_priors, σ_priors = σ_priors, 
+                            parameter_priors = parameter_priors, σ_priors = σ_priors,
                             solve_kwargs = solve_kwargs, sample_u0 = sample_u0, save_idxs = save_idxs)
     trans = as((parameters = parameter_transformations,
                 σ = as(Vector, asℝ₊, length(σ_priors))))
     ℓ = TransformedLogDensity(trans, P)
     ∇ℓ = LogDensityProblems.ADgradient(AD_gradient_kind, ℓ)
     results = mcmc_with_warmup(rng, ∇ℓ, num_samples; mcmc_kwargs...)
     merge((posterior = transform.(Ref(trans), results.chain), ), results)
-end
+end
diff --git a/src/turing_inference.jl b/src/turing_inference.jl
@@ -11,8 +11,8 @@ function turing_inference(
     num_samples=1000, sampler = Turing.NUTS(0.65),
     syms = [Turing.@varname(theta[i]) for i in 1:length(priors)],
     sample_u0 = false,
-    save_idxs = nothing, 
-    progress = false, 
+    save_idxs = nothing,
+    progress = false,
     kwargs...,
 )
     N = length(priors)
@@ -29,13 +29,13 @@ function turing_inference(
         u0 = convert.(T, sample_u0 ? theta[1:nu] : prob.u0)
         p = convert.(T, sample_u0 ? theta[(nu + 1):end] : theta)
         if length(u0) < length(prob.u0)
-            # assumes u is ordered such that the observed variables are in the begining, consistent with ordered theta 
+            # assumes u is ordered such that the observed variables are in the begining, consistent with ordered theta
             for i in length(u0):length(prob.u0)
                 push!(u0, convert(T,prob.u0[i]))
             end
         end
         _saveat = t === nothing ? Float64[] : t
-        sol = concrete_solve(prob, alg, u0, p; saveat = _saveat, progress = progress, save_idxs = save_idxs, kwargs...)
+        sol = solve(prob, alg; u0=u0, p=p, saveat = _saveat, progress = progress, save_idxs = save_idxs, kwargs...)
         failure = size(sol, 2) < length(_saveat)
 
         if failure