remove exports; adding checks for input to Models are correct

Author: PavanChaggar

src/AbstractPPL.jl

@@ -19,7 +19,6 @@ include("deprecations.jl")
 # GraphInfo
 module GraphPPL
     include("graphinfo.jl")    
-    export GraphInfo, Model, dag, nodes
 end
 
 end # module

src/graphinfo.jl

@@ -21,8 +21,8 @@ GraphInfo is instantiated using the `Model` constctor.
 """
 
 struct GraphInfo{T} <: AbstractModelTrace
-    value::NamedTuple{T}
     input::NamedTuple{T}
+    value::NamedTuple{T}
     eval::NamedTuple{T}
     kind::NamedTuple{T}
     A::SparseMatrixCSC
@@ -55,29 +55,30 @@ y = (value = 0.0, input = (:μ, :s2), eval = var"#7#10"(), kind = :Stochastic)
 ```
 """
 
-struct Model{T}
+struct Model{T} <: AbstractProbabilisticProgram
     g::GraphInfo{T}
 end
 
 function Model(;kwargs...)
+    for (i, node) in enumerate(values(kwargs))
+        @assert typeof(node) <: Tuple{Union{Array{Float64}, Float64}, Function, Symbol} "Check input order for node $(i) matches Tuple(value, function, kind)"
+    end
     vals = getvals(NamedTuple(kwargs))
     args = [argnames(f) for f in vals[2]]
-    A, sorted_vertices = DAG(NamedTuple{keys(kwargs)}(args))    
-    modelinputs = NamedTuple{Tuple(sorted_vertices)}.([vals[1], Tuple.(args), vals[2], vals[3]])
+    A, sorted_vertices = dag(NamedTuple{keys(kwargs)}(args))    
+    modelinputs = NamedTuple{Tuple(sorted_vertices)}.([Tuple.(args), vals...])
     Model(GraphInfo(modelinputs..., A, sorted_vertices))
 end
 
-
 """
-    DAG(inputs)
+    dag(inputs)
 
 Function taking in a NamedTuple containing the inputs to each node 
 and returns the implied adjacency matrix and topologically ordered 
 vertex list.
 """
-function DAG(inputs)
+function dag(inputs)
     input_names = Symbol[keys(inputs)...]
-    println(inputs)
     A = adjacency_matrix(inputs) 
     sorted_vertices = topological_sort_by_dfs(A)
     sorted_A = permute(A, collect(1:length(inputs)), sorted_vertices)
@@ -94,7 +95,7 @@ input, eval and kind, as required by the GraphInfo type.
 @generated function getvals(nt::NamedTuple{T}) where T
     values = [:(nt[$i][$j]) for i in 1:length(T), j in 1:3]
     m = [:($(values[:,i]...), ) for i in 1:3]
-    return :($(m...),)
+    return Expr(:tuple, m...) # :($(m...),)
 end
 
 """
@@ -243,7 +244,7 @@ Base.valtype(m::Model) = eltype(m)
 
 Returns the adjacency matrix of the model as a SparseArray.
 """
-dag(m::Model) = m.g.A
+get_dag(m::Model) = m.g.A
 
 """
     nodes(m::Model)

test/graphinfo.jl

@@ -1,6 +1,7 @@
-using AbstractPPL.GraphPPL
+using AbstractPPL
+import AbstractPPL.GraphPPL: GraphInfo, Model, get_dag
 using SparseArrays
-
+using Test
 ## Example taken from Mamba
 line = Dict{Symbol, Any}(
   :x => [1, 2, 3, 4, 5],
@@ -19,7 +20,7 @@ model = (
 
 # construct the model!
 m = Model(; zip(keys(model), values(model))...) # uses Model(; kwargs...) constructor
-typeof(m)
+
 # test the type of the model is correct
 @test typeof(m) <: Model
 @test typeof(m) == Model{(:s2, :xmat, :β, :μ, :y)}
@@ -28,7 +29,7 @@ typeof(m)
 
 # 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])
-@test dag(m) == A
+@test get_dag(m) == A
 
 @test length(m) == 5
 @test eltype(m) == valtype(m)
@@ -57,4 +58,7 @@ single_parent_m = Model(μ = (1.0, () -> 3, :Logical), y = (1.0, (μ) -> MvNorma
 @test typeof(single_parent_m.g) == GraphInfo{(:μ, :y)}
 
 # test ErrorException for parent node not found
-@test_throws ErrorException Model( μ = (1.0, (β) -> 3, :Logical), y = (1.0, (μ) -> MvNormal(μ, sqrt(1)), :Stochastic))
+@test_throws ErrorException Model( μ = (1.0, (β) -> 3, :Logical), y = (1.0, (μ) -> MvNormal(μ, sqrt(1)), :Stochastic))
+
+# test AssertionError thrown for kwargs with the wrong order of inputs
+@test_throws AssertionError Model( μ = ((β) -> 3, 1.0, :Logical), y = (1.0, (μ) -> MvNormal(μ, sqrt(1)), :Stochastic))