Rename test files and organize testsets

Author: mroavi

test/cuda.jl

@@ -4,7 +4,6 @@ using TensorInference, CUDA
 CUDA.allowscalar(false)
 
 @testset "gradient-based tensor network solvers" begin
-    ################# Load problem ####################
     model_filepath, evidence_filepath, solution_filepath = get_instance_filepaths("Promedus_14", "MAR")
     instance = read_instance(model_filepath; evidence_filepath, solution_filepath)
 
@@ -22,7 +21,6 @@ CUDA.allowscalar(false)
 end
 
 @testset "map" begin
-    ################# Load problem ####################
     model_filepath, evidence_filepath, solution_filepath = get_instance_filepaths("Promedus_14", "MAR")
     instance = read_instance(model_filepath; evidence_filepath, solution_filepath)
 
@@ -38,7 +36,6 @@ end
 end
 
 @testset "mmap" begin
-    ################# Load problem ####################
     model_filepath, evidence_filepath, solution_filepath = get_instance_filepaths("Promedus_14", "MAR")
     instance = read_instance(model_filepath; evidence_filepath, solution_filepath)
 

test/inference.jl renamed to test/mar.jl

@@ -7,8 +7,7 @@ using TensorInference
     @test TensorInference.connected_clusters(ixs, [2, 3, 6]) == [[2, 3] => [1, 2], [6] => [3]]
 end
 
-@testset "mmap" begin
-    ################# Load problem ####################
+@testset "gradient-based tensor network solvers" begin
     model_filepath, evidence_filepath, solution_filepath = get_instance_filepaths("Promedus_14", "MAR")
     instance = read_instance(model_filepath; evidence_filepath, solution_filepath)
 
@@ -30,3 +29,57 @@ end
     logp, config = most_probable_config(mmap3)
     @test log_probability(mmap3, config) ≈ logp
 end
+
+@testset "sampling" begin
+    instance = TensorInference.read_instance_from_string("""MARKOV
+8
+ 2 2 2 2 2 2 2 2
+8
+ 1 0
+ 2 1 0
+ 1 2
+ 2 3 2
+ 2 4 2
+ 3 5 3 1
+ 2 6 5
+ 3 7 5 4
+
+2
+ 0.01
+ 0.99
+
+4
+ 0.05 0.01
+ 0.95 0.99
+
+2
+ 0.5
+ 0.5
+
+4
+ 0.1 0.01
+ 0.9 0.99
+
+4
+ 0.6 0.3
+ 0.4 0.7 
+
+8
+ 1 1 1 0
+ 0 0 0 1
+
+4
+ 0.98 0.05
+ 0.02 0.95
+
+8
+ 0.9 0.7 0.8 0.1
+ 0.1 0.3 0.2 0.9
+""")
+    n = 10000
+    tnet = TensorNetworkModel(instance)
+    samples = sample(tnet, n)
+    mars = getindex.(marginals(tnet), 2)
+    mars_sample = [count(s->s[k]==(1), samples) for k=1:8] ./ n
+    @test isapprox(mars, mars_sample, atol=0.05)
+end

test/mmap.jl

@@ -2,8 +2,7 @@ using Test
 using OMEinsum
 using TensorInference
 
-@testset "map" begin
-    ################# Load problem ####################
+@testset "gradient-based tensor network solvers" begin
     model_filepath, evidence_filepath, solution_filepath = get_instance_filepaths("Promedus_14", "MAR")
     instance = read_instance(model_filepath; evidence_filepath, solution_filepath)
 

test/maxprob.jl renamed to test/mpe.jl

@@ -1,29 +1,22 @@
-using Test, TensorInference, Documenter, Pkg
+using Test, TensorInference, Documenter, Pkg, Artifacts
 
-include("utils.jl")
+import Pkg;
+Pkg.ensure_artifact_installed("uai2014", "Artifacts.toml");
 
-artifacts_toml = "Artifacts.toml"
-artifacts = Pkg.Artifacts.select_downloadable_artifacts(artifacts_toml)
-for name in keys(artifacts)
-    Pkg.Artifacts.ensure_artifact_installed(name, artifacts[name], artifacts_toml)
-end
+include("utils.jl")
 
-@testset "inference" begin
-    include("inference.jl")
+@testset "MAR" begin
+    include("mar.jl")
 end
 
-@testset "MAP" begin
-    include("maxprob.jl")
+@testset "MPE" begin
+    include("mpe.jl")
 end
 
 @testset "MMAP" begin
     include("mmap.jl")
 end
 
-@testset "MMAP" begin
-    include("sampling.jl")
-end
-
 using CUDA
 if CUDA.functional()
     include("cuda.jl")

test/runtests.jl

@@ -1,5 +1,3 @@
-using Artifacts
-
 """
 Helper function to obtain the filepaths for an instance's model, evidence, and
 solution files within the uai2014 artifact, corresponding to the provided
@@ -10,8 +8,8 @@ task.
 - `task::String`: The task type. Supported tasks are "MAR", "MPE", "MMAP", and "PR".
 """
 function get_instance_filepaths(problem_name::AbstractString, task::AbstractString)
-  model_filepath = joinpath(artifact"uai2014", task, problem_name * ".uai")
-  evidence_filepath = joinpath(artifact"uai2014", task, problem_name * ".uai.evid")
-  solution_filepath = joinpath(artifact"uai2014", task, problem_name * ".uai." * task)
-  return model_filepath, evidence_filepath, solution_filepath
+    model_filepath = joinpath(artifact"uai2014", task, problem_name * ".uai")
+    evidence_filepath = joinpath(artifact"uai2014", task, problem_name * ".uai.evid")
+    solution_filepath = joinpath(artifact"uai2014", task, problem_name * ".uai." * task)
+    return model_filepath, evidence_filepath, solution_filepath
 end