allow θ transform in CMBLensingMuseProblem

Author: marius311

src/muse.jl

@@ -1,52 +1,48 @@
 
 # interface with MuseInference.jl
 
-using .MuseInference: AbstractMuseProblem, MuseResult
+using .MuseInference: AbstractMuseProblem, MuseResult, Transformedθ, UnTransformedθ
 using .MuseInference.AbstractDifferentiation
-import .MuseInference: logLike, ∇θ_logLike, sample_x_z, ẑ_at_θ, muse!, standardizeθ
 
 export CMBLensingMuseProblem
 
-struct CMBLensingMuseProblem{DS<:DataSet,DS_SIM<:DataSet} <: AbstractMuseProblem
+@kwdef struct CMBLensingMuseProblem{DS<:DataSet,DS_SIM<:DataSet} <: AbstractMuseProblem
     ds :: DS
-    ds_for_sims :: DS_SIM
-    parameterization
-    MAP_joint_kwargs
-    θ_fixed
-    x
-    latent_vars
-    autodiff
+    ds_for_sims :: DS_SIM = ds
+    parameterization = 0
+    MAP_joint_kwargs = (;)
+    θ_fixed = (;)
+    x = ds.d
+    latent_vars = nothing
+    autodiff = AD.HigherOrderBackend((AD.ForwardDiffBackend(tag=false), AD.ZygoteBackend()))
+    transform_θ = identity
+    inv_transform_θ = identity
 end
+CMBLensingMuseProblem(ds, ds_for_sims=ds; kwargs...) = CMBLensingMuseProblem(;ds, ds_for_sims, kwargs...)
 
-function CMBLensingMuseProblem(
-    ds, 
-    ds_for_sims = ds; 
-    parameterization = 0, 
-    MAP_joint_kwargs = (;), 
-    θ_fixed = (;), 
-    latent_vars = nothing,
-    autodiff = AD.HigherOrderBackend((AD.ForwardDiffBackend(tag=false), AD.ZygoteBackend())),
-)
-    parameterization == 0 || error("only parameterization=0 (unlensed parameterization) currently implemented")
-    CMBLensingMuseProblem(ds, ds_for_sims, parameterization, MAP_joint_kwargs, θ_fixed, ds.d, latent_vars, autodiff)
-end
 
 mergeθ(prob::CMBLensingMuseProblem, θ) = isempty(prob.θ_fixed) ? θ : (;prob.θ_fixed..., θ...)
 
-function standardizeθ(prob::CMBLensingMuseProblem, θ)
+function MuseInference.standardizeθ(prob::CMBLensingMuseProblem, θ)
     θ isa Union{NamedTuple,ComponentVector} || error("θ should be a NamedTuple or ComponentVector")
     1f0 * ComponentVector(θ) # ensure component vector and float
 end
 
-function MuseInference.logLike(prob::CMBLensingMuseProblem, d, z, θ) 
+MuseInference.transform_θ(prob::CMBLensingMuseProblem, θ) = prob.transform_θ(θ)
+MuseInference.inv_transform_θ(prob::CMBLensingMuseProblem, θ) = prob.inv_transform_θ(θ)
+
+function MuseInference.logLike(prob::CMBLensingMuseProblem, d, z, θ, ::UnTransformedθ) 
     logpdf(prob.ds; z..., θ = mergeθ(prob, θ), d)
 end
+function MuseInference.logLike(prob::CMBLensingMuseProblem, d, z, θ, ::Transformedθ) 
+    MuseInference.logLike(prob, d, z, MuseInference.inv_transform_θ(prob, θ), UnTransformedθ())
+end
 
-function ∇θ_logLike(prob::CMBLensingMuseProblem, d, z, θ) 
-    AD.gradient(prob.autodiff, θ -> logLike(prob, d, z, θ), θ)[1]
+function MuseInference.∇θ_logLike(prob::CMBLensingMuseProblem, d, z, θ, θ_space)
+    AD.gradient(prob.autodiff, θ -> MuseInference.logLike(prob, d, z, θ, θ_space), θ)[1]
 end
 
-function sample_x_z(prob::CMBLensingMuseProblem, rng::AbstractRNG, θ) 
+function MuseInference.sample_x_z(prob::CMBLensingMuseProblem, rng::AbstractRNG, θ) 
     sim = simulate(rng, prob.ds_for_sims, θ = mergeθ(prob, θ))
     if prob.latent_vars == nothing
         # this is a guess which might not work for everything necessarily
@@ -58,18 +54,18 @@ function sample_x_z(prob::CMBLensingMuseProblem, rng::AbstractRNG, θ)
     (;x, z)
 end
 
-function ẑ_at_θ(prob::CMBLensingMuseProblem, d, zguess, θ; ∇z_logLike_atol=nothing)
+function MuseInference.ẑ_at_θ(prob::CMBLensingMuseProblem, d, zguess, θ; ∇z_logLike_atol=nothing)
     @unpack ds = prob
     Ωstart = delete(NamedTuple(zguess), :f)
     MAP = MAP_joint(mergeθ(prob, θ), @set(ds.d=d), Ωstart; fstart=zguess.f, prob.MAP_joint_kwargs...)
     LenseBasis(FieldTuple(;delete(MAP, :history)...)), MAP.history
 end
 
-function ẑ_at_θ(prob::CMBLensingMuseProblem{<:NoLensingDataSet}, d, (f₀,), θ; ∇z_logLike_atol=nothing)
+function MuseInference.ẑ_at_θ(prob::CMBLensingMuseProblem{<:NoLensingDataSet}, d, (f₀,), θ; ∇z_logLike_atol=nothing)
     @unpack ds = prob
     LenseBasis(FieldTuple(f=argmaxf_logpdf(I, mergeθ(prob, θ), @set(ds.d=d); fstart=f₀, prob.MAP_joint_kwargs...))), nothing
 end
 
-function muse!(result::MuseResult, ds::DataSet, θ₀=nothing; parameterization=0, MAP_joint_kwargs=(;), kwargs...)
+function MuseInference.muse!(result::MuseResult, ds::DataSet, θ₀=nothing; parameterization=0, MAP_joint_kwargs=(;), kwargs...)
     muse!(result, CMBLensingMuseProblem(ds; parameterization, MAP_joint_kwargs), θ₀; kwargs...)
 end