Merge pull request #192 from tpapp/tp/update-test-framework

update test framework

Author: tpapp

test/Project.toml

@@ -19,4 +19,4 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TransformVariables = "84d833dd-6860-57f9-a1a7-6da5db126cff"
 
 [compat]
-LogDensityTestSuite = "0.6"
+LogDensityTestSuite = "0.7"

test/sample-correctness_tests.jl

@@ -99,12 +99,20 @@ end
 
 @testset "NUTS tests with heavier tails and skewness" begin
     K = 5
+    𝒩 = StandardMultivariateNormal(K)
 
     # somewhat nasty, relaxed requirements
-    ℓ = elongate(1.1, StandardMultivariateNormal(K))
-    NUTS_tests(RNG, ℓ, "elongate(1.1, 𝑁)", 10000; p_alert = 1e-5, EBFMI_alert = 0.2, R̂_fail = 1.2)
+    ℓ = elongate(1.1)(𝒩)
+    NUTS_tests(RNG, ℓ, "elongate(1.1, 𝑁)",
+               10000; p_alert = 0.05, EBFMI_alert = 0.2, R̂_fail = 1.05, τ_fail = 0.3)
 
     # this has very nasty tails so we relax requirements a bit
-    ℓ = elongate(1.1, shift(ones(K), StandardMultivariateNormal(K)))
-    NUTS_tests(RNG, ℓ, "skew elongate(1.1, 𝑁)", 10000; τ_alert = 0.1, EBFMI_alert = 0.2)
+    ℓ = (elongate(1.1) ∘ shift(ones(K)))(𝒩)
+    NUTS_tests(RNG, ℓ, "skew elongate(1.1, 𝑁)",
+               10000; τ_alert = 0.1, EBFMI_alert = 0.2, R̂_fail = 1.05, p_fail = 0.001)
+
+    # funnel, mixed with a normal
+    ℓ = mix(0.8, funnel()(𝒩), 𝒩)
+    NUTS_tests(RNG, ℓ, "funnel", 10000;
+               EBFMI_alert = 0.2, τ_alert = 0.1, p_fail = 5e-3, R̂_fail = 1.05)
 end

test/test_hamiltonian.jl

@@ -110,7 +110,7 @@ end
 
     @testset "invalid values" begin
         n = 3
-        ℓ = multivariate_normal(randn(RNG, n), I)
+        ℓ = multivariate_normal(randn(RNG, n), I(n))
         @test_throws DynamicHMCError evaluate_ℓ(ℓ, fill(NaN, n))
     end
 end

test/utilities.jl

@@ -61,10 +61,10 @@ thus decorrelates the density perfectly.
 find_stable_ϵ(κ::GaussianKineticEnergy, Σ) = √eigmin(κ.W'*Σ*κ.W)
 
 "Multivariate normal with `Σ = LL'`."
-multivariate_normal(μ, L) = shift(μ, linear(L, StandardMultivariateNormal(length(μ))))
+multivariate_normal(μ, L) = (shift(μ) ∘ linear(L))(StandardMultivariateNormal(length(μ)))
 
 "Multivariate normal with diagonal `Σ` (constant `v` variance)."
-multivariate_normal(μ, v::Real = 1) = multivariate_normal(μ, I * v)
+multivariate_normal(μ, v::Real = 1) = multivariate_normal(μ, I(length(μ)) * v)
 
 """
 $(SIGNATURES)