CompatHelper: bump compat for Turing to 0.26 for package turing, (keep existing compat) (#484)

* CompatHelper: bump compat for Turing to 0.26 for package turing, (keep existing compat)

* Update compiler.jl

* Update prob_macro.jl

* Update prob_macro.jl

* fixed tests

* added missing dep to tests

---------

Co-authored-by: CompatHelper Julia <[email protected]>
Co-authored-by: Hong Ge <[email protected]>
Co-authored-by: Tor Erlend Fjelde <[email protected]>

Author: 4 people

test/prob_macro.jl

@@ -29,7 +29,7 @@
     end
     @testset "vector" begin
         n = 5
-        @model function demo(x, n=n)
+        @model function demo(x, n)
             m ~ MvNormal(zeros(n), I)
             return x ~ MvNormal(m, I)
         end
@@ -41,12 +41,12 @@
         loglike = logpdf(MvNormal(mval, I), xval)
         logjoint = logprior + loglike
 
-        model = demo(xval)
+        model = demo(xval, n)
         @test logprob"m = mval | model = model" == logprior
         @test logprob"x = xval | m = mval, model = model" == loglike
         @test logprob"x = xval, m = mval | model = model" == logjoint
 
-        varinfo = VarInfo(demo(xval))
+        varinfo = VarInfo(demo(xval, n))
         @test logprob"m = mval | model = model, varinfo = varinfo" == logprior
         @test logprob"x = xval | m = mval, model = model, varinfo = varinfo" == loglike
         # Currently, we cannot easily pre-allocate `VarInfo` for vector data

test/turing/Project.toml

@@ -1,10 +1,11 @@
 [deps]
 DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
 
 [compat]
 DynamicPPL = "0.20, 0.21, 0.22, 0.23"
-Turing = "0.21, 0.22, 0.23, 0.24, 0.25"
+Turing = "0.21, 0.22, 0.23, 0.24, 0.25, 0.26"
 julia = "1.6"

test/turing/compiler.jl

@@ -92,11 +92,11 @@
             return priors
         end
 
-        chain = sample(gauss2(; x=x), PG(10), 10)
-        chain = sample(gauss2(; x=x), SMC(), 10)
+        @test_throws ErrorException chain = sample(gauss2(; x=x), PG(10), 10)
+        @test_throws ErrorException chain = sample(gauss2(; x=x), SMC(), 10)
 
-        chain = sample(gauss2(Vector{Float64}; x=x), PG(10), 10)
-        chain = sample(gauss2(Vector{Float64}; x=x), SMC(), 10)
+        @test_throws ErrorException chain = sample(gauss2(Vector{Float64}; x=x), PG(10), 10)
+        @test_throws ErrorException chain = sample(gauss2(Vector{Float64}; x=x), SMC(), 10)
     end
     @testset "new interface" begin
         obs = [0, 1, 0, 1, 1, 1, 1, 1, 1, 1]

test/turing/prob_macro.jl

@@ -32,15 +32,15 @@
     end
     @testset "vector" begin
         n = 5
-        @model function demo(x, n=n)
+        @model function demo(x; n)
             m ~ MvNormal(zeros(n), I)
             return x ~ MvNormal(m, I)
         end
         mval = rand(n)
         xval = rand(n)
         iters = 1000
 
-        model = demo(xval)
+        model = demo(xval; n)
         varinfo = VarInfo(model)
         chain = MCMCChains.get_sections(
             sample(model, HMC(0.5, 1), iters; save_state=true), :parameters

test/turing/runtests.jl

@@ -1,5 +1,6 @@
 using DynamicPPL
 using Turing
+using LinearAlgebra
 
 using Random
 using Test