From b342b3d62c049a7f89088c76309a11b9870b912d Mon Sep 17 00:00:00 2001
From: Hong Ge <3279477+yebai@users.noreply.github.com>
Date: Wed, 25 Oct 2023 20:59:46 +0100
Subject: [PATCH] Delete test/graphinfo directory (#86)

* Delete test/graphinfo directory

* Update runtests.jl
---
 test/graphinfo/graphinfo.jl | 122 ------------------------------------
 test/runtests.jl            |   1 -
 2 files changed, 123 deletions(-)
 delete mode 100644 test/graphinfo/graphinfo.jl

diff --git a/test/graphinfo/graphinfo.jl b/test/graphinfo/graphinfo.jl
deleted file mode 100644
index 2d8e417..0000000
--- a/test/graphinfo/graphinfo.jl
+++ /dev/null
@@ -1,122 +0,0 @@
-using AbstractPPL
-import AbstractPPL.GraphPPL:GraphInfo, Model, get_dag, set_node_value!, 
-                            get_node_value, get_sorted_vertices, get_node_eval,
-                            get_nodekind, get_node_input, get_model_values, 
-                            set_model_values!, rand, rand!, logdensityof
-using SparseArrays
-using LinearAlgebra
-using Distributions
-using Random; Random.seed!(1234)
-
-## Example taken from Mamba
-line = Dict{Symbol, Any}(
-  :x => [1, 2, 3, 4, 5],
-  :y => [1, 3, 3, 3, 5]
-)
-
-line[:xmat] = [ones(5) line[:x]]
-
-# just making it a NamedTuple so that the values can be tested later. Constructor should be used as Model(;kwargs...).
-model = (
-    β = (zeros(2), () -> MvNormal(2, sqrt(1000)), :Stochastic),
-    xmat = (line[:xmat], () -> line[:xmat], :Logical), 
-    s2 = (1.0, () -> InverseGamma(2.0,3.0), :Stochastic), 
-    μ = (zeros(5), (xmat, β) -> xmat * β, :Logical), 
-    y = (zeros(5), (μ, s2) -> MvNormal(μ, sqrt(s2)), :Stochastic)
-)
-
-# better handling of stochastic and logical nodes. 
-# Automatically set logistic nodes initial values to their deterministic values.
-
-# construct the model!
-m = Model(; zip(keys(model), values(model))...) # uses Model(; kwargs...) constructor
-
-# test the type of the model is correct
-@test m isa Model
-sorted_vertices = get_sorted_vertices(m)
-@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])
-@test get_dag(m) == A
-
-@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]
-    @test get_nodekind(m, vn) == kinds[i]
-    @test get_node_input(m, vn) == inputs[i]
-end
-
-for node in m 
-    @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) -> Normal(μ, sqrt(1)), :Stochastic))
-@test single_parent_m isa Model{(:μ, :y)}
-@test single_parent_m.g isa GraphInfo{(:μ, :y)}
-
-# 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), 2.0)
-@test get_node_value(m, @varname s2) == 2.0
-
-# test ErrorException for parent node not found
-@test_throws ErrorException Model( μ = (1.0, (β) -> 3, :Logical), y = (1.0, (μ) -> Normal(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) -> Normal(μ, sqrt(1)), :Stochastic))
-
-# testing random + logdensityof
-sample = rand(m)
-
-@test keys(sample) == Tuple(get_sorted_vertices(m))
-
-for vn in keys(m)
-    mv = get_node_value(m, vn)
-    sv = get(sample, vn)
-    @test size(mv) == size(sv)
-    @test sv isa typeof(mv)
-end
-
-model1 = Model(μ = (3.0, () -> 3.0, :Logical),
-               y = (0.0, (μ) -> Normal(μ, 1.0), :Stochastic))
-
-priorsamples = rand(model1, 1000)
-
-@test mean([s.μ for s in priorsamples]) == 3
-@test mean([s.y for s in priorsamples]) ≈ 3 atol=2
-
-@test logdensityof(model1, (μ = 3.0, y = 3.0)) == logdensityof(Normal(3.0,1.0), 3.0)
-
-model2 = Model(μ = (3.0, () -> 3.0, :Logical),
-               y1 = (0.0, (μ) -> Normal(μ, 1.0), :Stochastic),
-               y2 = (0.0, (μ) -> Normal(μ, 1.0), :Stochastic),
-               y3 = (0.0, (μ) -> Normal(μ, 1.0), :Stochastic))
-
-model3 = Model(μ = (ones(3) * 3, () -> ones(3) * 3, :Logical),
-               y = (zeros(3), (μ) -> MvNormal(μ, I), :Stochastic))
-
-ldmodel1 = logdensityof(model1, (μ = 3.0, y = 3.0))
-ldmodel2 = logdensityof(model2, (μ = 3.0, y3 = 3.0, y2 = 3.0, y1 = 3.0))
-ldmodel3 = logdensityof(model3, (μ = ones(3) * 3, y = ones(3) * 3))
-
-@test 3 * ldmodel1 == ldmodel2 == ldmodel3
\ No newline at end of file
diff --git a/test/runtests.jl b/test/runtests.jl
index 3707090..eb97c36 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -14,7 +14,6 @@ using Test
     include("deprecations.jl")
     include("varname.jl")
     include("abstractprobprog.jl")
-    include("graphinfo/graphinfo.jl")
     @testset "doctests" begin
         DocMeta.setdocmeta!(
             AbstractPPL,