Merge branch 'main' into hmc_proposed_sample_handling_refactor

.github/workflows/ci.yml

@@ -66,7 +66,7 @@ jobs:
         with:
           fail_ci_if_error: true
           token: ${{ secrets.CODECOV_TOKEN }}
-          file: lcov.info
+          files: lcov.info
   docs:
     name: Documentation
     runs-on: ubuntu-latest

Project.toml

@@ -1,6 +1,6 @@
 name = "BAT"
 uuid = "c0cd4b16-88b7-57fa-983b-ab80aecada7e"
-version = "3.3.1"
+version = "4.0.0"
 
 [deps]
 Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
@@ -120,7 +120,7 @@ Folds = "0.2"
 ForwardDiff = "0.10"
 ForwardDiffPullbacks = "0.1.1, 0.2"
 FunctionChains = "0.1.4"
-Functors = "0.2, 0.3, 0.4"
+Functors = "0.2, 0.3, 0.4, 0.5"
 HDF5 = "0.15, 0.16, 0.17"
 HeterogeneousComputing = "0.2"
 HypothesisTests = "0.10, 0.11"

ext/BATOptimizationExt.jl

@@ -24,13 +24,13 @@ function test_bat_optimization_ext()
 end
 
 AbstractModeEstimator(optalg::Any) = OptimizationAlg(optalg)
-convert(::Type{AbstractModeEstimator}, alg::OptimizationAlg) = alg.optalg
+Base.convert(::Type{AbstractModeEstimator}, alg::OptimizationAlg) = alg.optalg
 
 BAT.ext_default(::BAT.PackageExtension{:Optimization}, ::Val{:DEFAULT_OPTALG}) = nothing #Optim.NelderMead()
 
 
 function build_optimizationfunction(f, adsel::AutoDiffOperators.ADSelector)
-    adm = convert_ad(ADTypes.AbstractADType, adsel)
+    adm = convert(ADTypes.AbstractADType, reverse_ad_selector(adsel))
     optimization_function = Optimization.OptimizationFunction(f, adm)
     return optimization_function
 end
@@ -59,7 +59,9 @@ function BAT.bat_findmode_impl(target::MeasureLike, algorithm::OptimizationAlg,
     optimization_problem = Optimization.OptimizationProblem(optimization_function, x_init)
 
     algopts = (maxiters = algorithm.maxiters, maxtime = algorithm.maxtime, abstol = algorithm.abstol, reltol = algorithm.reltol)
-    optimization_result = Optimization.solve(optimization_problem, algorithm.optalg; algopts..., algorithm.kwargs...) 
+    # Not all algorithms support abstol, just filter all NaN-valued opts out:
+    filtered_algopts = NamedTuple(filter(p -> !isnan(p[2]), pairs(algopts)))
+    optimization_result = Optimization.solve(optimization_problem, algorithm.optalg; filtered_algopts..., algorithm.kwargs...) 
 
     transformed_mode =  optimization_result.u
     result_mode = inv_trafo(transformed_mode)

src/BAT.jl

@@ -99,7 +99,7 @@ using DomainSets: UnitInterval, UnitCube, Rectangle, FullSpace, RealNumbers
 
 using ChainRulesCore: AbstractTangent, Tangent, NoTangent, ZeroTangent, AbstractThunk, unthunk
 
-using Functors: fmap, @functor
+using Functors: fmap
 
 # For Dual specializations:
 import ForwardDiff

test/optimization/test_mode_estimators.jl

@@ -1,6 +1,7 @@
 using BAT
 using Test
 
+using AutoDiffOperators
 using LinearAlgebra, Distributions, StatsBase, ValueShapes, Random123, DensityInterface
 using UnPack, InverseFunctions
 import ForwardDiff
@@ -101,6 +102,10 @@ using Optim, OptimizationOptimJL
         context = BATContext(rng = Philox4x((0, 0)))
         # result is not type-stable:
         test_findmode(posterior, OptimizationAlg(optalg = OptimizationOptimJL.NelderMead(), pretransform = DoNotTransform()), 0.01, context, inferred = false) 
+
+        context = BATContext(rng = Philox4x((0, 0)), ad = ADSelector(ForwardDiff))
+        # result is not type-stable:
+        test_findmode(posterior, OptimizationAlg(optalg = Optimization.LBFGS(), pretransform = DoNotTransform()), 0.01, context, inferred = false) 
     end
 
     @testset "Optimization.jl with custom options" begin # checks that options are correctly passed to Optimization.jl