Conversion of VarName to/from string (#100)

* Fix typo in comment

* Conversion of VarName to/from string

* Add one more test

* More thorough serialisation

* Add doctests

* Add API docs

* Add warning to docstring

Co-authored-by: Tor Erlend Fjelde <[email protected]>

* Add alternate implementation with StructTypes

* Reduce calls to Meta.parse()

It's only called for ConcretizedSlice now, which could potentially be
removed too.

* Restrict allowed ranges for ConcretizedSlice

* Fix name of wrapper type

* More tests

* Remove unneeded extra method for ConcretizedSlice

* Add StepRange support

* Support arrays of integers as indices

* Simplify implementation even more

* Bump to 0.9.0

* Clean up old code, add docs

* Allow de/serialisation methods to be extended

* Update docs

* Name functions more consistently

---------

Co-authored-by: Tor Erlend Fjelde <[email protected]>

Project.toml

@@ -3,12 +3,13 @@ uuid = "7a57a42e-76ec-4ea3-a279-07e840d6d9cf"
 keywords = ["probablistic programming"]
 license = "MIT"
 desc = "Common interfaces for probabilistic programming"
-version = "0.8.4"
+version = "0.9.0"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
 Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
 DensityInterface = "b429d917-457f-4dbc-8f4c-0cc954292b1d"
+JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 
 [compat]

docs/src/api.md

@@ -14,6 +14,15 @@ vsym
 @vsym
 ```
 
+## VarName serialisation
+
+```@docs
+index_to_dict
+dict_to_index
+varname_to_string
+string_to_varname
+```
+
 ## Abstract model functions
 
 ```@docs

src/AbstractPPL.jl

@@ -10,7 +10,11 @@ export VarName,
     varname,
     vsym,
     @varname,
-    @vsym
+    @vsym,
+    index_to_dict,
+    dict_to_index,
+    varname_to_string,
+    string_to_varname
 
 
 # Abstract model functions

src/varname.jl

@@ -1,5 +1,6 @@
 using Accessors
 using Accessors: ComposedOptic, PropertyLens, IndexLens, DynamicIndexLens
+using JSON: JSON
 
 const ALLOWED_OPTICS = Union{typeof(identity),PropertyLens,IndexLens,ComposedOptic}
 
@@ -302,7 +303,7 @@ subsumes(t::ComposedOptic, u::ComposedOptic) =
 # If `t` is still a composed lens, then there is no way it can subsume `u` since `u` is a
 # leaf of the "lens-tree".
 subsumes(t::ComposedOptic, u::PropertyLens) = false
-# Here we need to check if `u.outer` (i.e. the next lens to be applied from `u`) is
+# Here we need to check if `u.inner` (i.e. the next lens to be applied from `u`) is
 # subsumed by `t`, since this would mean that the rest of the composition is also subsumed
 # by `t`.
 subsumes(t::PropertyLens, u::ComposedOptic) = subsumes(t, u.inner)
@@ -752,3 +753,147 @@ function vsym(expr::Expr)
         error("Malformed variable name `$(expr)`!")
     end
 end
+
+# String constants for each index type that we support serialisation /
+# deserialisation of
+const _BASE_INTEGER_TYPE = "Base.Integer"
+const _BASE_VECTOR_TYPE = "Base.Vector"
+const _BASE_UNITRANGE_TYPE = "Base.UnitRange"
+const _BASE_STEPRANGE_TYPE = "Base.StepRange"
+const _BASE_ONETO_TYPE = "Base.OneTo"
+const _BASE_COLON_TYPE = "Base.Colon"
+const _CONCRETIZED_SLICE_TYPE = "AbstractPPL.ConcretizedSlice"
+const _BASE_TUPLE_TYPE = "Base.Tuple"
+
+"""
+    index_to_dict(::Integer)
+    index_to_dict(::AbstractVector{Int})
+    index_to_dict(::UnitRange)
+    index_to_dict(::StepRange)
+    index_to_dict(::Colon)
+    index_to_dict(::ConcretizedSlice{T, Base.OneTo{I}}) where {T, I}
+    index_to_dict(::Tuple)
+
+Convert an index `i` to a dictionary representation.
+"""
+index_to_dict(i::Integer) = Dict("type" => _BASE_INTEGER_TYPE, "value" => i)
+index_to_dict(v::Vector{Int}) = Dict("type" => _BASE_VECTOR_TYPE, "values" => v)
+index_to_dict(r::UnitRange) = Dict("type" => _BASE_UNITRANGE_TYPE, "start" => r.start, "stop" => r.stop)
+index_to_dict(r::StepRange) = Dict("type" => _BASE_STEPRANGE_TYPE, "start" => r.start, "stop" => r.stop, "step" => r.step)
+index_to_dict(r::Base.OneTo{I}) where {I} = Dict("type" => _BASE_ONETO_TYPE, "stop" => r.stop)
+index_to_dict(::Colon) = Dict("type" => _BASE_COLON_TYPE)
+index_to_dict(s::ConcretizedSlice{T,R}) where {T,R} = Dict("type" => _CONCRETIZED_SLICE_TYPE, "range" => index_to_dict(s.range))
+index_to_dict(t::Tuple) = Dict("type" => _BASE_TUPLE_TYPE, "values" => map(index_to_dict, t))
+
+"""
+    dict_to_index(dict)
+    dict_to_index(symbol_val, dict)
+
+Convert a dictionary representation of an index `dict` to an index.
+
+Users can extend the functionality of `dict_to_index` (and hence `VarName`
+de/serialisation) by extending this method along with [`index_to_dict`](@ref).
+Specifically, suppose you have a custom index type `MyIndexType` and you want
+to be able to de/serialise a `VarName` containing this index type. You should
+then implement the following two methods:
+
+1. `AbstractPPL.index_to_dict(i::MyModule.MyIndexType)` should return a
+   dictionary representation of the index `i`. This dictionary must contain the
+   key `"type"`, and the corresponding value must be a string that uniquely
+   identifies the index type. Generally, it makes sense to use the name of the
+   type (perhaps prefixed with module qualifiers) as this value to avoid
+   clashes. The remainder of the dictionary can have any structure you like.
+
+2. Suppose the value of `index_to_dict(i)["type"]` is `"MyModule.MyIndexType"`.
+   You should then implement the corresponding method
+   `AbstractPPL.dict_to_index(::Val{Symbol("MyModule.MyIndexType")}, dict)`,
+   which should take the dictionary representation as the second argument and
+   return the original `MyIndexType` object.
+
+To see an example of this in action, you can look in the the AbstractPPL test
+suite, which contains a test for serialising OffsetArrays.
+"""
+function dict_to_index(dict)
+    t = dict["type"]
+    if t == _BASE_INTEGER_TYPE
+        return dict["value"]
+    elseif t == _BASE_VECTOR_TYPE
+        return collect(Int, dict["values"])
+    elseif t == _BASE_UNITRANGE_TYPE
+        return dict["start"]:dict["stop"]
+    elseif t == _BASE_STEPRANGE_TYPE
+        return dict["start"]:dict["step"]:dict["stop"]
+    elseif t == _BASE_ONETO_TYPE
+        return Base.OneTo(dict["stop"])
+    elseif t == _BASE_COLON_TYPE
+        return Colon()
+    elseif t == _CONCRETIZED_SLICE_TYPE
+        return ConcretizedSlice(Base.Slice(dict_to_index(dict["range"])))
+    elseif t == _BASE_TUPLE_TYPE
+        return tuple(map(dict_to_index, dict["values"])...)
+    else
+        # Will error if the method is not defined, but this hook allows users
+        # to extend this function
+        return dict_to_index(Val(Symbol(t)), dict)
+    end
+end
+
+optic_to_dict(::typeof(identity)) = Dict("type" => "identity")
+optic_to_dict(::PropertyLens{sym}) where {sym} = Dict("type" => "property", "field" => String(sym))
+optic_to_dict(i::IndexLens) = Dict("type" => "index", "indices" => index_to_dict(i.indices))
+optic_to_dict(c::ComposedOptic) = Dict("type" => "composed", "outer" => optic_to_dict(c.outer), "inner" => optic_to_dict(c.inner))
+
+function dict_to_optic(dict)
+    if dict["type"] == "identity"
+        return identity
+    elseif dict["type"] == "index"
+        return IndexLens(dict_to_index(dict["indices"]))
+    elseif dict["type"] == "property"
+        return PropertyLens{Symbol(dict["field"])}()
+    elseif dict["type"] == "composed"
+        return dict_to_optic(dict["outer"]) ∘ dict_to_optic(dict["inner"])
+    else
+        error("Unknown optic type: $(dict["type"])")
+    end
+end
+
+varname_to_dict(vn::VarName) = Dict("sym" => getsym(vn), "optic" => optic_to_dict(getoptic(vn)))
+
+dict_to_varname(dict::Dict{<:AbstractString, Any}) = VarName{Symbol(dict["sym"])}(dict_to_optic(dict["optic"]))
+
+"""
+    varname_to_string(vn::VarName)
+
+Convert a `VarName` as a string, via an intermediate dictionary. This differs
+from `string(vn)` in that concretised slices are faithfully represented (rather
+than being pretty-printed as colons).
+
+For `VarName`s which index into an array, this function will only work if the
+indices can be serialised. This is true for all standard Julia index types, but
+if you are using custom index types, you will need to implement the
+`index_to_dict` and `dict_to_index` methods for those types. See the
+documentation of [`dict_to_index`](@ref) for instructions on how to do this.
+
+```jldoctest
+julia> varname_to_string(@varname(x))
+"{\\"optic\\":{\\"type\\":\\"identity\\"},\\"sym\\":\\"x\\"}"
+
+julia> varname_to_string(@varname(x.a))
+"{\\"optic\\":{\\"field\\":\\"a\\",\\"type\\":\\"property\\"},\\"sym\\":\\"x\\"}"
+
+julia> y = ones(2); varname_to_string(@varname(y[:]))
+"{\\"optic\\":{\\"indices\\":{\\"values\\":[{\\"type\\":\\"Base.Colon\\"}],\\"type\\":\\"Base.Tuple\\"},\\"type\\":\\"index\\"},\\"sym\\":\\"y\\"}"
+
+julia> y = ones(2); varname_to_string(@varname(y[:], true))
+"{\\"optic\\":{\\"indices\\":{\\"values\\":[{\\"range\\":{\\"stop\\":2,\\"type\\":\\"Base.OneTo\\"},\\"type\\":\\"AbstractPPL.ConcretizedSlice\\"}],\\"type\\":\\"Base.Tuple\\"},\\"type\\":\\"index\\"},\\"sym\\":\\"y\\"}"
+```
+"""
+varname_to_string(vn::VarName) = JSON.json(varname_to_dict(vn))
+
+"""
+    string_to_varname(str::AbstractString)
+
+Convert a string representation of a `VarName` back to a `VarName`. The string
+should have been generated by `varname_to_string`.
+"""
+string_to_varname(str::AbstractString) = dict_to_varname(JSON.parse(str))

test/varname.jl

@@ -137,4 +137,67 @@ end
         @inferred get(c, @varname(b.a[1]))
         @inferred Accessors.set(c, @varname(b.a[1]), 10)
     end
+
+    @testset "de/serialisation of VarNames" begin
+        y = ones(10)
+        z = ones(5, 2)
+        vns = [
+            @varname(x),
+            @varname(ä),
+            @varname(x.a),
+            @varname(x.a.b),
+            @varname(var"x.a"),
+            @varname(x[1]),
+            @varname(var"x[1]"),
+            @varname(x[1:10]),
+            @varname(x[1:3:10]),
+            @varname(x[1, 2]),
+            @varname(x[1, 2:5]),
+            @varname(x[:]),
+            @varname(x.a[1]),
+            @varname(x.a[1:10]),
+            @varname(x[1].a),
+            @varname(y[:]),
+            @varname(y[begin:end]),
+            @varname(y[end]),
+            @varname(y[:], false),
+            @varname(y[:], true),
+            @varname(z[:], false),
+            @varname(z[:], true),
+            @varname(z[:][:], false),
+            @varname(z[:][:], true),
+            @varname(z[:,:], false),
+            @varname(z[:,:], true),
+            @varname(z[2:5,:], false),
+            @varname(z[2:5,:], true),
+        ]
+        for vn in vns
+            @test string_to_varname(varname_to_string(vn)) == vn
+        end
+
+        # For this VarName, the {de,}serialisation works correctly but we must
+        # test in a different way because equality comparison of structs with
+        # vector fields (such as Accessors.IndexLens) compares the memory
+        # addresses rather than the contents (thus vn_vec == vn_vec2 returns
+        # false).
+        vn_vec = @varname(x[[1, 2, 5, 6]])
+        vn_vec2 = string_to_varname(varname_to_string(vn_vec))
+        @test hash(vn_vec) == hash(vn_vec2)
+    end
+
+    @testset "de/serialisation of VarNames with custom index types" begin
+        using OffsetArrays: OffsetArrays, Origin
+        weird = Origin(4)(ones(10))
+        vn = @varname(weird[:], true)
+
+        # This won't work as we don't yet know how to handle OffsetArray
+        @test_throws MethodError varname_to_string(vn)
+
+        # Now define the relevant methods
+        AbstractPPL.index_to_dict(o::OffsetArrays.IdOffsetRange{I, R}) where {I,R} = Dict("type" => "OffsetArrays.OffsetArray", "parent" => AbstractPPL.index_to_dict(o.parent), "offset" => o.offset)
+        AbstractPPL.dict_to_index(::Val{Symbol("OffsetArrays.OffsetArray")}, d) = OffsetArrays.IdOffsetRange(AbstractPPL.dict_to_index(d["parent"]), d["offset"])
+
+        # Serialisation should now work
+        @test string_to_varname(varname_to_string(vn)) == vn
+    end
 end