Dev add re test items

Project.toml

@@ -45,6 +45,7 @@ Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 BenchmarkCI = "20533458-34a3-403d-a444-e18f38190b5b"
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 Coverage = "a2441757-f6aa-5fb2-8edb-039e3f45d037"
+CpuId = "adafc99b-e345-5852-983c-f28acb93d879"
 Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
@@ -54,10 +55,11 @@ Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 PkgBenchmark = "32113eaa-f34f-5b0d-bd6c-c81e245fc73d"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+ReTestItems = "817f1d60-ba6b-4fd5-9520-3cf149f6a823"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TestSetExtensions = "98d24dd4-01ad-11ea-1b02-c9a08f80db04"
 
 [targets]
-test = ["Test", "Pkg", "Logging", "InteractiveUtils", "TestSetExtensions", "Coverage", "Dates", "Distributed", "Documenter", "Plots", "BenchmarkCI", "BenchmarkTools", "PkgBenchmark", "Aqua", "StableRNGs", "StatsFuns", "Optimisers"]
+test = ["Test", "Pkg", "Logging", "InteractiveUtils", "TestSetExtensions", "Coverage", "CpuId", "Dates", "Distributed", "Documenter", "Plots", "BenchmarkCI", "BenchmarkTools", "PkgBenchmark", "Aqua", "StableRNGs", "StatsFuns", "Optimisers", "ReTestItems"]

test/constraints/test_factorisation_constraints.jl → test/constraints/factorisation_constraints_tests.jl

@@ -1,12 +1,7 @@
-module RxInferFactorisationConstraintsTest
-
-using Test, Logging
-using RxInfer
-
-import ReactiveMP: resolve_factorisation, setanonymous!
-import ReactiveMP: activate!
-
-@testset "Factorisation constraints resolution with @constraints" begin
+@testitem "Factorisation constraints resolution with @constraints" begin
+    using Logging
+    import ReactiveMP: resolve_factorisation, setanonymous!
+    import ReactiveMP: activate!
 
     # Factorisation constrains resolution function accepts a `fform` symbol as an input for error printing
     # We don't care about actual symbol in tests
@@ -635,5 +630,3 @@ import ReactiveMP: activate!
         @test_throws ErrorException ReactiveMP.resolve_factorisation(cs, getvariables(model), fform, (x, y))
     end
 end
-
-end

test/constraints/form/test_form_point_mass.jl → test/constraints/form/form_point_mass_tests.jl

@@ -1,29 +1,24 @@
-module RxInferPointMassFormConstraintTest
+@testitem "PointMassFormConstraint" begin
+    using LinearAlgebra
+    using Random, StableRNGs, DomainSets, Distributions
+    import RxInfer: PointMassFormConstraint, is_point_mass_form_constraint, call_boundaries, call_starting_point, call_optimizer
 
-using Test
-using RxInfer, LinearAlgebra
-using Random, StableRNGs, DomainSets, Distributions
-
-import ReactiveMP: constrain_form
-import RxInfer: PointMassFormConstraint, is_point_mass_form_constraint, call_boundaries, call_starting_point, call_optimizer
-
-struct MyDistributionWithMode <: ContinuousUnivariateDistribution
-    mode::Float64
-end
+    struct MyDistributionWithMode <: ContinuousUnivariateDistribution
+        mode::Float64
+    end
 
-# We are testing specifically that the point mass optimizer does not call `logpdf` and 
-# chooses a fast path with `mode` for `<: Distribution` objects
-Distributions.logpdf(::MyDistributionWithMode, _) = error("This should not be called")
-Distributions.mode(d::MyDistributionWithMode)     = d.mode
-Distributions.support(::MyDistributionWithMode)   = RealInterval(-Inf, Inf)
+    # We are testing specifically that the point mass optimizer does not call `logpdf` and 
+    # chooses a fast path with `mode` for `<: Distribution` objects
+    Distributions.logpdf(::MyDistributionWithMode, _) = error("This should not be called")
+    Distributions.mode(d::MyDistributionWithMode)     = d.mode
+    Distributions.support(::MyDistributionWithMode)   = RealInterval(-Inf, Inf)
 
-const arbitrary_dist_1 = ContinuousUnivariateLogPdf(RealLine(), (x) -> logpdf(NormalMeanVariance(0, 1), x))
-const arbitrary_dist_2 = ContinuousUnivariateLogPdf(HalfLine(), (x) -> logpdf(Gamma(1, 1), x))
-const arbitrary_dist_3 = ContinuousUnivariateLogPdf(RealLine(), (x) -> logpdf(NormalMeanVariance(-10, 10), x))
-const arbitrary_dist_4 = ContinuousUnivariateLogPdf(HalfLine(), (x) -> logpdf(GammaShapeRate(100, 10), x))
-const arbitrary_dist_5 = ContinuousUnivariateLogPdf(HalfLine(), (x) -> logpdf(GammaShapeRate(100, 100), x))
+    const arbitrary_dist_1 = ContinuousUnivariateLogPdf(RealLine(), (x) -> logpdf(NormalMeanVariance(0, 1), x))
+    const arbitrary_dist_2 = ContinuousUnivariateLogPdf(HalfLine(), (x) -> logpdf(Gamma(1, 1), x))
+    const arbitrary_dist_3 = ContinuousUnivariateLogPdf(RealLine(), (x) -> logpdf(NormalMeanVariance(-10, 10), x))
+    const arbitrary_dist_4 = ContinuousUnivariateLogPdf(HalfLine(), (x) -> logpdf(GammaShapeRate(100, 10), x))
+    const arbitrary_dist_5 = ContinuousUnivariateLogPdf(HalfLine(), (x) -> logpdf(GammaShapeRate(100, 100), x))
 
-@testset "PointMassFormConstraint" begin
     @testset "is_point_mass_form_constraint" begin
         @test is_point_mass_form_constraint(PointMassFormConstraint())
     end
@@ -112,5 +107,3 @@ const arbitrary_dist_5 = ContinuousUnivariateLogPdf(HalfLine(), (x) -> logpdf(Ga
         end
     end
 end
-
-end

test/constraints/form/test_form_sample_list.jl → test/constraints/form/form_sample_list_tests.jl

@@ -1,13 +1,6 @@
-
-module RxInferSampleListFormConstraintTest
-
-using Test
-using RxInfer, LinearAlgebra
-using Random, StableRNGs, DomainSets
-
-import RxInfer: SampleListFormConstraint, is_point_mass_form_constraint, constrain_form
-
-@testset "PointMassFormConstraint" begin
+@testitem "PointMassFormConstraint" begin
+    using DomainSets, StableRNGs, DomainSets, Distributions, Random, LinearAlgebra
+    import RxInfer: SampleListFormConstraint, is_point_mass_form_constraint, constrain_form
     @testset "is_point_mass_form_constraint" begin
         @test !is_point_mass_form_constraint(SampleListFormConstraint(100))
     end
@@ -58,5 +51,3 @@ import RxInfer: SampleListFormConstraint, is_point_mass_form_constraint, constra
         end
     end
 end
-
-end

test/constraints/test_form_constraints.jl → test/constraints/form_constraints_tests.jl

@@ -1,14 +1,9 @@
-module RxInferFormConstraintsSpecificationTest
-
-using Test, Logging
-using RxInfer
-
-import RxInfer: PointMassFormConstraint, SampleListFormConstraint, FixedMarginalFormConstraint
-import ReactiveMP: CompositeFormConstraint
-import ReactiveMP: resolve_marginal_form_prod, resolve_messages_form_prod
-import ReactiveMP: activate!
-
-@testset "Form constraints specification with @constraints macro" begin
+@testitem "Form constraints specification with @constraints macro" begin
+    using Logging
+    import RxInfer: PointMassFormConstraint, SampleListFormConstraint, FixedMarginalFormConstraint
+    import ReactiveMP: CompositeFormConstraint
+    import ReactiveMP: resolve_marginal_form_prod, resolve_messages_form_prod
+    import ReactiveMP: activate!
     @testset "Use case #1" begin
         cs = @constraints begin
             q(x)::PointMass
@@ -410,5 +405,3 @@ import ReactiveMP: activate!
         end
     end
 end
-
-end

test/constraints/test_meta_constraints.jl → test/constraints/meta_constraints_tests.jl

@@ -1,12 +1,7 @@
-module ReactiveMPMetaSpecificationHelpers
-
-using Test
-using RxInfer
-using Distributions
-using Logging
-
-@testset "Meta specification with @meta macro" begin
+@testitem "Meta specification with @meta macro" begin
     import ReactiveMP: resolve_meta, make_node, activate!
+    using Distributions
+    using Logging
 
     struct SomeNode end
     struct SomeOtherNode end
@@ -231,5 +226,3 @@ using Logging
         @test resolve_meta(meta, SomeNode, (z,)) === nothing
     end
 end
-
-end

test/test_helpers.jl → test/helpers_tests.jl

@@ -1,9 +1,4 @@
-module RxInferHelpersTest
-
-using Test
-using RxInfer
-
-@testset "NamedTuple helpers" begin
+@testitem "NamedTuple helpers" begin
     import RxInfer: fields, nthasfield
 
     @test fields((x = 1, y = 2)) === (:x, :y)
@@ -17,7 +12,7 @@ using RxInfer
     @test nthasfield(:c, typeof((x = 1, y = 2))) === false
 end
 
-@testset "Tuple helpers" begin
+@testitem "Tuple helpers" begin
     import RxInfer: as_tuple
 
     @test as_tuple(1) === (1,)
@@ -27,7 +22,7 @@ end
     @test as_tuple(("string",)) === ("string",)
 end
 
-@testset "Val helpers" begin
+@testitem "Val helpers" begin
     import RxInfer: unval
 
     @test unval(Val(1)) === 1
@@ -38,5 +33,3 @@ end
     @test_throws ErrorException unval(())
     @test_throws ErrorException unval(nothing)
 end
-
-end

test/test_inference.jl → test/inference_test.jl

@@ -1,10 +1,4 @@
-module RxInferInferenceTest
-
-using Test
-using RxInfer
-using Random
-
-@testset "__inference_check_itertype" begin
+@testitem "__inference_check_itertype" begin
     import RxInfer: __inference_check_itertype
 
     @test __inference_check_itertype(:something, nothing) === nothing
@@ -18,7 +12,7 @@ using Random
     @test_throws ErrorException __inference_check_itertype(:something, missing)
 end
 
-@testset "__inference_check_dicttype" begin
+@testitem "__inference_check_dicttype" begin
     import RxInfer: __inference_check_dicttype
 
     @test __inference_check_dicttype(:something, nothing) === nothing
@@ -33,7 +27,7 @@ end
     @test_throws ErrorException __inference_check_dicttype(:something, (missing))
 end
 
-@testset "`@autoupdates` macro" begin
+@testitem "`@autoupdates` macro" begin
     function somefunction(something)
         return nothing
     end
@@ -143,7 +137,7 @@ end
     end
 end
 
-@testset "Static inference with `inference`" begin
+@testitem "Static inference with `inference`" begin
 
     # A simple model for testing that resembles a simple kalman filter with
     # random walk state transition and unknown observational noise
@@ -311,7 +305,7 @@ end
     end
 end
 
-@testset "Test warn argument in `inference()`" begin
+@testitem "Test warn argument in `inference()`" begin
     @testset "Test warning for addons" begin
 
         #Add a new case for testing warning of addons
@@ -406,7 +400,7 @@ end
     end
 end
 
-@testset "Reactive inference with `rxinference` for test model #1" begin
+@testitem "Reactive inference with `rxinference` for test model #1" begin
 
     # A simple model for testing that resembles a simple kalman filter with
     # random walk state transition and unknown observational noise
@@ -815,7 +809,7 @@ end
     end
 end
 
-@testset "Predictions functionality" begin
+@testitem "Predictions functionality" begin
 
     # test #1 (array with missing + predictvars)
     data = (y = [1.0, -500.0, missing, 100.0],)
@@ -1032,5 +1026,3 @@ end
 
     @test all(result.predictions[:y] .== Bernoulli(mean(Beta(1.0, 1.0))))
 end
-
-end

test/test_model.jl → test/model_tests.jl

@@ -1,10 +1,4 @@
-module RxInferModelTest
-
-using Test
-using RxInfer
-using Random
-
-@testset "@model macro tests" begin
+@testitem "@model macro tests" begin
     @testset "Tuple based variables usage #1" begin
         @model function mixture_model()
             mean1 ~ Normal(mean = 10, variance = 10000)
@@ -97,6 +91,7 @@ using Random
     end
 
     @testset "Priors in arguments" begin
+        import Random: MersenneTwister
         @model function coin_model_priors1(n, prior)
             y = datavar(Float64, n)
             θ ~ prior
@@ -187,5 +182,3 @@ using Random
         @test_throws ErrorException ReactiveMP.make_node(FactorGraphModel(), FactorNodeCreationOptions(), DummyDistributionTestModelError3, randomvar(:θ))
     end
 end
-
-end

test/models/aliases/aliases_binary_tests.jl

@@ -0,0 +1,21 @@
+@testitem "aliases for binary operations" begin
+    @model function binary_aliases()
+        x1 ~ Bernoulli(0.5)
+        x2 ~ Bernoulli(0.5)
+        x3 ~ Bernoulli(0.5)
+        x4 ~ Bernoulli(0.5)
+
+        x ~ x1 -> x2 && x3 || ¬x4
+
+        y = datavar(Float64)
+        x ~ Bernoulli(y)
+    end
+
+    function binary_aliases_inference()
+        return inference(model = binary_aliases(), data = (y = 0.5,), free_energy = true)
+    end
+    results = binary_aliases_inference()
+    # Here we simply test that it ran and gave some output 
+    @test mean(results.posteriors[:x1]) ≈ 0.5
+    @test first(results.free_energy) ≈ 0.6931471805599454
+end

test/models/aliases/aliases_normal_tests.jl

@@ -0,0 +1,43 @@
+@testitem "aliases for `Normal` family of distributions" begin
+    @model function normal_aliases()
+        x1 ~ MvNormal(μ = zeros(2), Σ⁻¹ = diageye(2))
+        x2 ~ MvNormal(μ = zeros(2), Λ = diageye(2))
+        x3 ~ MvNormal(mean = zeros(2), W = diageye(2))
+        x4 ~ MvNormal(μ = zeros(2), prec = diageye(2))
+        x5 ~ MvNormal(m = zeros(2), precision = diageye(2))
+
+        y1 ~ MvNormal(mean = zeros(2), Σ = diageye(2))
+        y2 ~ MvNormal(m = zeros(2), Λ⁻¹ = diageye(2))
+        y3 ~ MvNormal(μ = zeros(2), V = diageye(2))
+        y4 ~ MvNormal(mean = zeros(2), cov = diageye(2))
+        y5 ~ MvNormal(mean = zeros(2), covariance = diageye(2))
+
+        x ~ x1 + x2 + x3 + x4 + x5
+        y ~ y1 + y2 + y3 + y4 + y5
+
+        r1 ~ Normal(μ = dot(x + y, ones(2)), τ = 1.0)
+        r2 ~ Normal(m = r1, γ = 1.0)
+        r3 ~ Normal(mean = r2, σ⁻² = 1.0)
+        r4 ~ Normal(mean = r3, w = 1.0)
+        r5 ~ Normal(mean = r4, p = 1.0)
+        r6 ~ Normal(mean = r5, prec = 1.0)
+        r7 ~ Normal(mean = r6, precision = 1.0)
+
+        s1 ~ Normal(μ = r7, σ² = 1.0)
+        s2 ~ Normal(m = s1, τ⁻¹ = 1.0)
+        s3 ~ Normal(mean = s2, v = 1.0)
+        s4 ~ Normal(mean = s3, var = 1.0)
+        s5 ~ Normal(mean = s4, variance = 1.0)
+
+        d = datavar(Float64)
+        d ~ Normal(μ = s5, variance = 1.0)
+    end
+
+    function normal_aliases_inference()
+        return inference(model = normal_aliases(), data = (d = 1.0,), returnvars = (x1 = KeepLast(),), free_energy = true)
+    end
+    result = normal_aliases_inference()
+    # Here we simply test that it ran and gave some output 
+    @test first(mean(result.posteriors[:x1])) ≈ 0.04182509505703423
+    @test first(result.free_energy) ≈ 2.319611135721246
+end