Merge 0db2083 into 6a2454f

Project.toml

@@ -1,11 +1,12 @@
 name = "DynamicPPL"
 uuid = "366bfd00-2699-11ea-058f-f148b4cae6d8"
-version = "0.24.9"
+version = "0.25.0"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
 AbstractPPL = "7a57a42e-76ec-4ea3-a279-07e840d6d9cf"
+Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
 BangBang = "198e06fe-97b7-11e9-32a5-e1d131e6ad66"
 Bijectors = "76274a88-744f-5084-9051-94815aaf08c4"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
@@ -20,15 +21,31 @@ MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
-Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"
 
+[weakdeps]
+ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+EnzymeCore = "f151be2c-9106-41f4-ab19-57ee4f262869"
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
+ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
+ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"
+
+[extensions]
+DynamicPPLChainRulesCoreExt = ["ChainRulesCore"]
+DynamicPPLEnzymeCoreExt = ["EnzymeCore"]
+DynamicPPLForwardDiffExt = ["ForwardDiff"]
+DynamicPPLMCMCChainsExt = ["MCMCChains"]
+DynamicPPLReverseDiffExt = ["ReverseDiff"]
+DynamicPPLZygoteRulesExt = ["ZygoteRules"]
+
 [compat]
 ADTypes = "0.2"
 AbstractMCMC = "5"
-AbstractPPL = "0.7"
-BangBang = "0.3"
+AbstractPPL = "0.8.2"
+Accessors = "0.1"
+BangBang = "0.4"
 Bijectors = "0.13"
 ChainRulesCore = "1"
 Compat = "4"
@@ -44,29 +61,12 @@ MacroTools = "0.5.6"
 OrderedCollections = "1"
 Random = "1.6"
 Requires = "1"
-Setfield = "1"
 Test = "1.6"
 ZygoteRules = "0.2"
 julia = "1.6"
 
-[extensions]
-DynamicPPLChainRulesCoreExt = ["ChainRulesCore"]
-DynamicPPLEnzymeCoreExt = ["EnzymeCore"]
-DynamicPPLForwardDiffExt = ["ForwardDiff"]
-DynamicPPLMCMCChainsExt = ["MCMCChains"]
-DynamicPPLReverseDiffExt = ["ReverseDiff"]
-DynamicPPLZygoteRulesExt = ["ZygoteRules"]
-
 [extras]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 EnzymeCore = "f151be2c-9106-41f4-ab19-57ee4f262869"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"
-
-[weakdeps]
-ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
-EnzymeCore = "f151be2c-9106-41f4-ab19-57ee4f262869"
-ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
-MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
-ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
-ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"

docs/Project.toml

@@ -1,21 +1,19 @@
 [deps]
+Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
-MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
-Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 
 [compat]
+Accessors = "0.1"
 DataStructures = "0.18"
 Distributions = "0.25"
 Documenter = "1"
 FillArrays = "0.13, 1"
 LogDensityProblems = "2"
 MCMCChains = "5, 6"
-MLUtils = "0.3, 0.4"
-Setfield = "0.7.1, 0.8, 1"
 StableRNGs = "1"

docs/src/tutorials/prob-interface.md

@@ -107,12 +107,28 @@ To give an example of the probability interface in use, we can use it to estimat
 In cross-validation, we split the dataset into several equal parts.
 Then, we choose one of these sets to serve as the validation set.
 Here, we measure fit using the cross entropy (Bayes loss).[^1]
+(For the sake of simplicity, in the following code, we enforce that `nfolds` must divide the number of data points. For a more competent implementation, see [MLUtils.jl](https://juliaml.github.io/MLUtils.jl/dev/api/#MLUtils.kfolds).)
 
 ```@example probinterface
-using MLUtils
+# Calculate the train/validation splits across `nfolds` partitions, assume `length(dataset)` divides `nfolds`
+function kfolds(dataset::Array{<:Real}, nfolds::Int)
+    fold_size, remaining = divrem(length(dataset), nfolds)
+    if remaining != 0
+        error("The number of folds must divide the number of data points.")
+    end
+    first_idx = firstindex(dataset)
+    last_idx = lastindex(dataset)
+    splits = map(0:(nfolds - 1)) do i
+        start_idx = first_idx + i * fold_size
+        end_idx = start_idx + fold_size
+        train_set_indices = [first_idx:(start_idx - 1); end_idx:last_idx]
+        return (view(dataset, train_set_indices), view(dataset, start_idx:(end_idx - 1)))
+    end
+    return splits
+end
 
 function cross_val(
-    dataset::AbstractVector{<:Real};
+    dataset::Vector{<:Real};
     nfolds::Int=5,
     nsamples::Int=1_000,
     rng::Random.AbstractRNG=Random.default_rng(),

src/DynamicPPL.jl

@@ -12,7 +12,7 @@ using ADTypes: ADTypes
 using BangBang: BangBang, push!!, empty!!, setindex!!
 using MacroTools: MacroTools
 using ConstructionBase: ConstructionBase
-using Setfield: Setfield
+using Accessors: Accessors
 using LogDensityProblems: LogDensityProblems
 using LogDensityProblemsAD: LogDensityProblemsAD
 

src/abstract_varinfo.jl

@@ -262,7 +262,7 @@ julia> values_as(SimpleVarInfo(data), NamedTuple)
 (x = 1.0, m = [2.0])
 
 julia> values_as(SimpleVarInfo(data), OrderedDict)
-OrderedDict{VarName{sym, Setfield.IdentityLens} where sym, Any} with 2 entries:
+OrderedDict{VarName{sym, typeof(identity)} where sym, Any} with 2 entries:
   x => 1.0
   m => [2.0]
 
@@ -312,7 +312,7 @@ julia> values_as(vi, NamedTuple)
 (s = 1.0, m = 2.0)
 
 julia> values_as(vi, OrderedDict)
-OrderedDict{VarName{sym, Setfield.IdentityLens} where sym, Float64} with 2 entries:
+OrderedDict{VarName{sym, typeof(identity)} where sym, Float64} with 2 entries:
   s => 1.0
   m => 2.0
 
@@ -338,7 +338,7 @@ julia> values_as(vi, NamedTuple)
 (s = 1.0, m = 2.0)
 
 julia> values_as(vi, OrderedDict)
-OrderedDict{VarName{sym, Setfield.IdentityLens} where sym, Float64} with 2 entries:
+OrderedDict{VarName{sym, typeof(identity)} where sym, Float64} with 2 entries:
   s => 1.0
   m => 2.0
 
@@ -426,7 +426,7 @@ julia> # Extract one with only `m`.
 
 
 julia> keys(varinfo_subset1)
-1-element Vector{VarName{:m, Setfield.IdentityLens}}:
+1-element Vector{VarName{:m, typeof(identity)}}:
  m
 
 julia> varinfo_subset1[@varname(m)]

src/compiler.jl

@@ -4,11 +4,11 @@ const INTERNALNAMES = (:__model__, :__context__, :__varinfo__)
     need_concretize(expr)
 
 Return `true` if `expr` needs to be concretized, i.e., if it contains a colon `:` or 
-requires a dynamic lens.
+requires a dynamic optic.
 
 # Examples
 
-```jldoctest; setup=:(using Setfield)
+```jldoctest; setup=:(using Accessors)
 julia> DynamicPPL.need_concretize(:(x[1, :]))
 true
 
@@ -19,7 +19,7 @@ julia> DynamicPPL.need_concretize(:(x[1, 1]))
 false
 """
 function need_concretize(expr)
-    return Setfield.need_dynamic_lens(expr) || begin
+    return Accessors.need_dynamic_optic(expr) || begin
         flag = false
         MacroTools.postwalk(expr) do ex
             # Concretise colon by default
@@ -202,13 +202,13 @@ variables.
 # Example
 ```jldoctest; setup=:(using Distributions, LinearAlgebra)
 julia> _, _, vns = DynamicPPL.unwrap_right_left_vns(MvNormal(ones(2), I), randn(2, 2), @varname(x)); vns[end]
-x[:,2]
+x[:, 2]
 
 julia> _, _, vns = DynamicPPL.unwrap_right_left_vns(Normal(), randn(1, 2), @varname(x)); vns[end]
-x[1,2]
+x[1, 2]
 
 julia> _, _, vns = DynamicPPL.unwrap_right_left_vns(Normal(), randn(1, 2), @varname(x[:])); vns[end]
-x[:][1,2]
+x[:][1, 2]
 
 julia> _, _, vns = DynamicPPL.unwrap_right_left_vns(Normal(), randn(3), @varname(x[1])); vns[end]
 x[1][3]
@@ -226,7 +226,7 @@ function unwrap_right_left_vns(
     # for `i = size(left, 2)`. Hence the symbol should be `x[:, i]`,
     # and we therefore add the `Colon()` below.
     vns = map(axes(left, 2)) do i
-        return AbstractPPL.concretize(vn ∘ Setfield.IndexLens((Colon(), i)), left)
+        return AbstractPPL.concretize(Accessors.IndexLens((Colon(), i)) ∘ vn, left)
     end
     return unwrap_right_left_vns(right, left, vns)
 end
@@ -236,7 +236,7 @@ function unwrap_right_left_vns(
     vn::VarName,
 )
     vns = map(CartesianIndices(left)) do i
-        return vn ∘ Setfield.IndexLens(Tuple(i))
+        return Accessors.IndexLens(Tuple(i)) ∘ vn
     end
     return unwrap_right_left_vns(right, left, vns)
 end
@@ -437,7 +437,7 @@ function generate_tilde_assume(left, right, vn)
     expr = :($left = $value)
     if left isa Expr
         expr = AbstractPPL.drop_escape(
-            Setfield.setmacro(BangBang.prefermutation, expr; overwrite=true)
+            Accessors.setmacro(BangBang.prefermutation, expr; overwrite=true)
         )
     end
 

src/contexts.jl

@@ -288,9 +288,9 @@ end
 
 function prefix(::PrefixContext{Prefix}, vn::VarName{Sym}) where {Prefix,Sym}
     if @generated
-        return :(VarName{$(QuoteNode(Symbol(Prefix, PREFIX_SEPARATOR, Sym)))}(getlens(vn)))
+        return :(VarName{$(QuoteNode(Symbol(Prefix, PREFIX_SEPARATOR, Sym)))}(getoptic(vn)))
     else
-        VarName{Symbol(Prefix, PREFIX_SEPARATOR, Sym)}(getlens(vn))
+        VarName{Symbol(Prefix, PREFIX_SEPARATOR, Sym)}(getoptic(vn))
     end
 end
 

src/model.jl

@@ -279,11 +279,11 @@ in their trace/`VarInfo`:
 
 ```jldoctest condition
 julia> keys(VarInfo(demo_outer()))
-1-element Vector{VarName{:m, Setfield.IdentityLens}}:
+1-element Vector{VarName{:m, typeof(identity)}}:
  m
 
 julia> keys(VarInfo(demo_outer_prefix()))
-1-element Vector{VarName{Symbol("inner.m"), Setfield.IdentityLens}}:
+1-element Vector{VarName{Symbol("inner.m"), typeof(identity)}}:
  inner.m
 ```
 
@@ -448,7 +448,7 @@ julia> conditioned(cm)
 julia> # Since we conditioned on `m`, not `a.m` as it will appear after prefixed,
        # `a.m` is treated as a random variable.
        keys(VarInfo(cm))
-1-element Vector{VarName{Symbol("a.m"), Setfield.IdentityLens}}:
+1-element Vector{VarName{Symbol("a.m"), typeof(identity)}}:
  a.m
 
 julia> # If we instead condition on `a.m`, `m` in the model will be considered an observation.
@@ -634,11 +634,11 @@ in their trace/`VarInfo`:
 
 ```jldoctest fix
 julia> keys(VarInfo(demo_outer()))
-1-element Vector{VarName{:m, Setfield.IdentityLens}}:
+1-element Vector{VarName{:m, typeof(identity)}}:
  m
 
 julia> keys(VarInfo(demo_outer_prefix()))
-1-element Vector{VarName{Symbol("inner.m"), Setfield.IdentityLens}}:
+1-element Vector{VarName{Symbol("inner.m"), typeof(identity)}}:
  inner.m
 ```
 
@@ -830,7 +830,7 @@ julia> fixed(cm)
 julia> # Since we fixed on `m`, not `a.m` as it will appear after prefixed,
        # `a.m` is treated as a random variable.
        keys(VarInfo(cm))
-1-element Vector{VarName{Symbol("a.m"), Setfield.IdentityLens}}:
+1-element Vector{VarName{Symbol("a.m"), typeof(identity)}}:
  a.m
 
 julia> # If we instead fix on `a.m`, `m` in the model will be considered an observation.

src/model_utils.jl

@@ -78,13 +78,13 @@ end
 function varname_in_chain!(
     x::AbstractArray, vn_parent::VarName{sym}, chain, chain_idx, iteration_idx, out
 ) where {sym}
-    # We use `VarName{sym}()` so that the resulting leaf `vn` only contains the tail of the lens.
-    # This way we can use `getlens(vn)` to extract the value from `x` and use `vn_parent ∘ getlens(vn)`
+    # We use `VarName{sym}()` so that the resulting leaf `vn` only contains the tail of the optic.
+    # This way we can use `getoptic(vn)` to extract the value from `x` and use `getoptic(vn) ∘ vn_parent`
     # to extract the value from the `chain`.
     for vn in varname_leaves(VarName{sym}(), x)
         # Update `out`, possibly in place, and return.
-        l = AbstractPPL.getlens(vn)
-        varname_in_chain!(x, vn_parent ∘ l, chain, chain_idx, iteration_idx, out)
+        l = AbstractPPL.getoptic(vn)
+        varname_in_chain!(x, l ∘ vn_parent, chain, chain_idx, iteration_idx, out)
     end
     return out
 end
@@ -103,17 +103,17 @@ end
 function values_from_chain(
     x::AbstractArray, vn_parent::VarName{sym}, chain, chain_idx, iteration_idx
 ) where {sym}
-    # We use `VarName{sym}()` so that the resulting leaf `vn` only contains the tail of the lens.
-    # This way we can use `getlens(vn)` to extract the value from `x` and use `vn_parent ∘ getlens(vn)`
+    # We use `VarName{sym}()` so that the resulting leaf `vn` only contains the tail of the optic.
+    # This way we can use `getoptic(vn)` to extract the value from `x` and use `getoptic(vn) ∘ vn_parent`
     # to extract the value from the `chain`.
     out = similar(x)
     for vn in varname_leaves(VarName{sym}(), x)
         # Update `out`, possibly in place, and return.
-        l = AbstractPPL.getlens(vn)
-        out = Setfield.set(
+        l = AbstractPPL.getoptic(vn)
+        out = Accessors.set(
             out,
             BangBang.prefermutation(l),
-            chain[iteration_idx, Symbol(vn_parent ∘ l), chain_idx],
+            chain[iteration_idx, Symbol(l ∘ vn_parent), chain_idx],
         )
     end