Fix broken case when variables are multi-dimensional and add tests for AdvancedHMC extension and inference

src/BUGSExamples/BUGSExamples.jl

@@ -52,4 +52,12 @@
 
 const VOLUME_II = (birats=birats, eyes=eyes)
 
+function has_ground_truth(m::Symbol)
+    if m in union(keys(VOLUME_I), keys(VOLUME_II))
+        return haskey(getfield(BUGSExamples, m), :reference_results)
+    else
+        return false
+    end
 end
+
+end

src/BUGSExamples/Backgrounds/rats.md

@@ -4,9 +4,6 @@ This example is taken from section 6 of *Gelfand et al. (1990)*, and concerns 30
 weights were measured weekly for five weeks. Part of the data is shown below, where $Y_{ij}$ is the
 weight of the $i^{th}$ rat measured at age $x_j$.
 
-
-
-
 <center>
 
 $$\text{Weights } Y_{ij} \text{ of rat } i \text{ on day } x_j$$
@@ -44,5 +41,3 @@ be independent).
 
 $ a_c $, $ \tau_a $, $ b_c $, $ \tau_b $, $ \tau_c $ are given independent "noninformative" priors. Interest particularly focuses on
 the intercept at zero time (birth), denoted $ a_0 = a_c - b_c \cdot \bar{x} $.
-
-

src/BUGSExamples/Volume_I/Equiv.jl

@@ -46,4 +46,10 @@ equiv <- step(theta - 0.8) - step(theta - 1.2)
         sign=[1, -1],
     ),
     inits=[(mu=0, phi=0, pi=0, tau1=1, tau2=1), (mu=10, phi=10, pi=10, tau1=0.1, tau2=0.1)],
+    reference_results=(
+        equiv=(mean=0.998, std=0.04468),
+        mu=(mean=1.436, std=0.05751),
+        phi=(mean=-0.008613, std=0.05187),
+        sigma1=(mean=0.1102, std=0.03268),
+    ),
 )

src/BUGSExamples/Volume_I/Rats.jl

@@ -81,4 +81,9 @@ rats = (
             var"beta.tau"=0.1,
         ),
     ],
+    reference_results=(
+        alpha0=(mean=106.6, std=3.66),
+        var"beta.c"=(mean=6.186, std=0.1086),
+        sigma=(mean=6.093, std=0.4643),
+    ),
 )

src/BUGSExamples/Volume_I/Seeds.jl

@@ -38,4 +38,11 @@ sigma <- 1 / sqrt(tau)
             b=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         ),
     ],
-)
+    reference_results=(
+        alpha0=(mean=-0.5499, std=0.1965),
+        alpha1=(mean=0.08902, std=0.3124),
+        alpha12=(mean=-0.841, std=0.4372),
+        alpha2=(mean=1.356, std=0.2772),
+        sigma=(mean=0.2922, std=0.1467),
+    ),
+)

src/BUGSExamples/Volume_I/Stacks.jl

@@ -73,4 +73,7 @@ sigma <- sqrt(1 / tau) # normal errors
         (beta0=10, beta=[0, 0, 0], tau=0.1, phi=0.1),
         (beta0=1.0, beta=[1.0, 1.0, 1.0], tau=1.0, phi=1.0),
     ],
+    reference_results=(
+        b0=(mean=-39.64, std=12.63), var"outlier[21]"=(mean=0.3324, std=0.4711)
+    ),
 )

test/inference.jl

@@ -14,65 +14,65 @@ end
 
 # AdvancedHMC
 
-# test generation of parameter names
-model = compile(
-    (@bugs begin
-        x[1:2] ~ dmnorm(mu[:], sigma[:, :])
-        x[3] ~ dnorm(0, 1)
-        y = x[1] + x[3]
-    end), (mu=[0, 0], sigma=[1 0; 0 1]), NamedTuple()
-)
+@testset "AdvancedHMC" begin
+    @testset "Generation of parameter names" begin
+        model = compile(
+            (@bugs begin
+                x[1:2] ~ dmnorm(mu[:], sigma[:, :])
+                x[3] ~ dnorm(0, 1)
+                y = x[1] + x[3]
+            end),
+            (mu=[0, 0], sigma=[1 0; 0 1]),
+            NamedTuple(),
+        )
 
-ad_model = ADgradient(:ReverseDiff, model; compile=Val(true))
-n_samples, n_adapts = 10, 0
-D = LogDensityProblems.dimension(model);
-initial_θ = rand(D);
-samples_and_stats = AbstractMCMC.sample(
-    ad_model,
-    NUTS(0.8),
-    n_samples;
-    chain_type=Chains,
-    n_adapts=n_adapts,
-    init_params=initial_θ,
-    discard_initial=n_adapts,
-)
+        ad_model = ADgradient(:ReverseDiff, model; compile=Val(true))
+        n_samples, n_adapts = 10, 0
+        D = LogDensityProblems.dimension(model)
+        initial_θ = rand(D)
+        samples_and_stats = AbstractMCMC.sample(
+            ad_model,
+            NUTS(0.8),
+            n_samples;
+            chain_type=Chains,
+            n_adapts=n_adapts,
+            init_params=initial_θ,
+            discard_initial=n_adapts,
+        )
 
-@test samples_and_stats.name_map.parameters ==
-    [Symbol("x[3]"), Symbol("x[1:2][1]"), Symbol("x[1:2][2]"), :y]
+        @test samples_and_stats.name_map.parameters ==
+            [Symbol("x[3]"), Symbol("x[1:2][1]"), Symbol("x[1:2][2]"), :y]
+    end
 
-# test inference result with Seeds 
-data = JuliaBUGS.BUGSExamples.VOLUME_I[:seeds].data
-inits = JuliaBUGS.BUGSExamples.VOLUME_I[:seeds].inits[1]
-model = JuliaBUGS.compile(JuliaBUGS.BUGSExamples.VOLUME_I[:seeds].model_def, data, inits)
+    @testset "Inference results on examples: $m" for m in [:seeds, :rats, :equiv, :stacks]
+        data = JuliaBUGS.BUGSExamples.VOLUME_I[m].data
+        inits = JuliaBUGS.BUGSExamples.VOLUME_I[m].inits[1]
+        model = JuliaBUGS.compile(JuliaBUGS.BUGSExamples.VOLUME_I[m].model_def, data, inits)
 
-ad_model = ADgradient(:ReverseDiff, model; compile=Val(true))
+        ad_model = ADgradient(:ReverseDiff, model; compile=Val(true))
 
-n_samples, n_adapts = 2000, 1000
+        n_samples, n_adapts = 2000, 1000
 
-D = LogDensityProblems.dimension(model);
-initial_θ = rand(D);
+        D = LogDensityProblems.dimension(model)
+        initial_θ = rand(D)
 
-samples_and_stats = AbstractMCMC.sample(
-    ad_model,
-    NUTS(0.8),
-    n_samples;
-    chain_type=Chains,
-    n_adapts=n_adapts,
-    init_params=initial_θ,
-    discard_initial=n_adapts,
-)
+        samples_and_stats = AbstractMCMC.sample(
+            ad_model,
+            NUTS(0.8),
+            n_samples;
+            chain_type=Chains,
+            n_adapts=n_adapts,
+            init_params=initial_θ,
+            discard_initial=n_adapts,
+        )
 
-@test summarize(samples_and_stats)[:alpha0].nt.mean[1] ≈ -0.5499 rtol = 0.1
-@test summarize(samples_and_stats)[:alpha0].nt.std[1] ≈ 0.1965 rtol = 0.1
-
-@test summarize(samples_and_stats)[:alpha1].nt.mean[1] ≈ 0.08902 rtol = 0.1
-@test summarize(samples_and_stats)[:alpha1].nt.std[1] ≈ 0.3124 rtol = 0.1
-
-@test summarize(samples_and_stats)[:alpha12].nt.mean[1] ≈ -0.841 rtol = 0.1
-@test summarize(samples_and_stats)[:alpha12].nt.std[1] ≈ 0.4372 rtol = 0.1
-
-@test summarize(samples_and_stats)[:alpha2].nt.mean[1] ≈ 1.356 rtol = 0.1
-@test summarize(samples_and_stats)[:alpha2].nt.std[1] ≈ 0.2772 rtol = 0.1
-
-@test summarize(samples_and_stats)[:sigma].nt.mean[1] ≈ 0.2922 rtol = 0.1
-@test summarize(samples_and_stats)[:sigma].nt.std[1] ≈ 0.1467 rtol = 0.1
+        @assert JuliaBUGS.BUGSExamples.has_ground_truth(m) "No reference inference results for $m"
+        ref_inference_results = JuliaBUGS.BUGSExamples.VOLUME_I[m].reference_results
+        @testset "$m: $var" for var in keys(ref_inference_results)
+            @test summarize(samples_and_stats)[var].nt.mean[1] ≈
+                ref_inference_results[var].mean rtol = 0.2
+            @test summarize(samples_and_stats)[var].nt.std[1] ≈
+                ref_inference_results[var].std rtol = 0.2
+        end
+    end
+end

test/runtests.jl

@@ -32,11 +32,14 @@ using JuliaBUGS.BUGSPrimitives: mean
 using LogDensityProblems, LogDensityProblemsAD
 using MacroTools
 using MCMCChains
+using Random
 using ReverseDiff
 using Setfield
 using Test
 using UnPack
 
+Random.seed!(12345)
+
 @testset "Function Unit Tests" begin
     DocMeta.setdocmeta!(
         JuliaBUGS,