Fixing type stability issues for constructors (#65)

Fixes #64 by adding more type parameters to the constructors to make the `NamedTuples` concrete.

src/graphinfo.jl

@@ -20,11 +20,11 @@ adjacency matrix and topologically ordered vertex list and stored.
 GraphInfo is instantiated using the `Model` constctor. 
 """
 
-struct GraphInfo{T} <: AbstractModelTrace
-    input::NamedTuple{T}
-    value::NamedTuple{T}
-    eval::NamedTuple{T}
-    kind::NamedTuple{T}
+struct GraphInfo{Tnames, Tinput, Tvalue, Teval, Tkind} <: AbstractModelTrace
+    input::NamedTuple{Tnames, Tinput}
+    value::NamedTuple{Tnames, Tvalue}
+    eval::NamedTuple{Tnames, Teval}
+    kind::NamedTuple{Tnames, Tkind}
     A::SparseMatrixCSC
     sorted_vertices::Vector{Symbol}
 end
@@ -55,8 +55,8 @@ y = (value = 0.0, input = (:μ, :s2), eval = var"#7#10"(), kind = :Stochastic)
 ```
 """
 
-struct Model{T} <: AbstractProbabilisticProgram
-    g::GraphInfo{T}
+struct Model{Tnames, Tinput, Tvalue, Teval, Tkind} <: AbstractProbabilisticProgram
+    g::GraphInfo{Tnames, Tinput, Tvalue, Teval, Tkind}
 end
 
 function Model(;kwargs...)

test/graphinfo.jl

@@ -24,11 +24,11 @@ model = (
 m = Model(; zip(keys(model), values(model))...) # uses Model(; kwargs...) constructor
 
 # test the type of the model is correct
-@test typeof(m) <: Model
+@test m isa Model
 sorted_vertices = get_sorted_vertices(m)
-@test typeof(m) == Model{Tuple(sorted_vertices)}
-@test typeof(m.g) <: GraphInfo <: AbstractModelTrace
-@test typeof(m.g) == GraphInfo{Tuple(sorted_vertices)}
+@test m isa Model{Tuple(sorted_vertices)}
+@test m.g isa GraphInfo <: AbstractModelTrace
+@test m.g isa GraphInfo{Tuple(sorted_vertices)}
 
 # test the dag is correct
 A = sparse([0 0 0 0 0; 0 0 0 0 0; 0 0 0 0 0; 0 1 1 0 0; 1 0 0 1 0])
@@ -37,11 +37,18 @@ A = sparse([0 0 0 0 0; 0 0 0 0 0; 0 0 0 0 0; 0 1 1 0 0; 1 0 0 1 0])
 @test length(m) == 5
 @test eltype(m) == valtype(m)
 
+
 # check the values from the NamedTuple match the values in the fields of GraphInfo
 vals, evals, kinds = AbstractPPL.GraphPPL.getvals(NamedTuple{Tuple(sorted_vertices)}(model))
 inputs = (s2 = (), xmat = (), β = (), μ = (:xmat, :β), y = (:μ, :s2))
 
 for (i, vn) in enumerate(keys(m))
+    @inferred m[vn]
+    @inferred get_node_value(m, vn)
+    @inferred get_node_eval(m, vn)
+    @inferred get_nodekind(m, vn)
+    @inferred get_node_input(m, vn)
+
     @test vn isa VarName
     @test get_node_value(m, vn) == vals[i]
     @test get_node_eval(m, vn) == evals[i]
@@ -50,16 +57,16 @@ for (i, vn) in enumerate(keys(m))
 end
 
 for node in m 
-    @test typeof(node) <: NamedTuple{fieldnames(GraphInfo)[1:4]}
+    @test node isa NamedTuple{fieldnames(GraphInfo)[1:4]}
 end
 
 # test Model constructor for model with single parent node
 single_parent_m = Model(μ = (1.0, () -> 3, :Logical), y = (1.0, (μ) -> MvNormal(μ, sqrt(1)), :Stochastic))
-@test typeof(single_parent_m) == Model{(:μ, :y)}
-@test typeof(single_parent_m.g) == GraphInfo{(:μ, :y)}
+@test single_parent_m isa Model{(:μ, :y)}
+@test single_parent_m.g isa GraphInfo{(:μ, :y)}
 
-# test setindex
 
+# test setindex
 @test_throws AssertionError set_node_value!(m, @varname(s2), [0.0])
 @test_throws AssertionError set_node_value!(m, @varname(s2), (1.0,))
 set_node_value!(m, @varname(s2), 1.0)