Fix tests

Author: penelopeysm

test/lkj.jl

@@ -21,7 +21,7 @@ _lkj_atol = 0.05
 
 @testset "Sample from x ~ LKJ(2, 1)" begin
     model = lkj_prior_demo()
-    for init_strategy in [PriorInit(), UniformInit()]
+    for init_strategy in [InitFromPrior(), InitFromUniform()]
         samples = [
             last(DynamicPPL.init!!(model, VarInfo(), init_strategy)) for _ in 1:n_samples
         ]
@@ -32,8 +32,7 @@ end
 
 @testset "Sample from x ~ LKJCholesky(2, 1, $(uplo))" for uplo in ['U', 'L']
     model = lkj_chol_prior_demo(uplo)
-    # `SampleFromPrior` will sample in unconstrained space.
-    for init_strategy in [PriorInit(), UniformInit()]
+    for init_strategy in [InitFromPrior(), InitFromUniform()]
         samples = [
             last(DynamicPPL.init!!(model, VarInfo(), init_strategy)) for _ in 1:n_samples
         ]

test/sampler.jl

@@ -113,60 +113,6 @@
         end
     end
 
-    @testset "SampleFromPrior and SampleUniform" begin
-        @model function gdemo(x, y)
-            s ~ InverseGamma(2, 3)
-            m ~ Normal(2.0, sqrt(s))
-            x ~ Normal(m, sqrt(s))
-            return y ~ Normal(m, sqrt(s))
-        end
-
-        model = gdemo(1.0, 2.0)
-        N = 1_000
-
-        chains = sample(model, SampleFromPrior(), N; progress=false)
-        @test chains isa Vector{<:VarInfo}
-        @test length(chains) == N
-
-        # Expected value of ``X`` where ``X ~ N(2, ...)`` is 2.
-        @test mean(vi[@varname(m)] for vi in chains) ≈ 2 atol = 0.15
-
-        # Expected value of ``X`` where ``X ~ IG(2, 3)`` is 3.
-        @test mean(vi[@varname(s)] for vi in chains) ≈ 3 atol = 0.2
-
-        chains = sample(model, SampleFromUniform(), N; progress=false)
-        @test chains isa Vector{<:VarInfo}
-        @test length(chains) == N
-
-        # `m` is Gaussian, i.e. no transformation is used, so it
-        # will be drawn from U[-2, 2] and its mean should be 0.
-        @test mean(vi[@varname(m)] for vi in chains) ≈ 0.0 atol = 0.1
-
-        # Expected value of ``exp(X)`` where ``X ~ U[-2, 2]`` is ≈ 1.8.
-        @test mean(vi[@varname(s)] for vi in chains) ≈ 1.8 atol = 0.1
-    end
-
-    @testset "init" begin
-        @testset "$(model.f)" for model in DynamicPPL.TestUtils.DEMO_MODELS
-            N = 1000
-            chain_init = sample(model, SampleFromUniform(), N; progress=false)
-
-            for vn in keys(first(chain_init))
-                if AbstractPPL.subsumes(@varname(s), vn)
-                    # `s ~ InverseGamma(2, 3)` and its unconstrained value will be sampled from Unif[-2,2].
-                    dist = InverseGamma(2, 3)
-                    b = DynamicPPL.link_transform(dist)
-                    @test mean(mean(b(vi[vn])) for vi in chain_init) ≈ 0 atol = 0.11
-                elseif AbstractPPL.subsumes(@varname(m), vn)
-                    # `m ~ Normal(0, sqrt(s))` and its constrained value is the same.
-                    @test mean(mean(vi[vn]) for vi in chain_init) ≈ 0 atol = 0.11
-                else
-                    error("Unknown variable name: $vn")
-                end
-            end
-        end
-    end
-
     @testset "Initial parameters" begin
         # dummy algorithm that just returns initial value and does not perform any sampling
         abstract type OnlyInitAlg end