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github-actions[bot]CompatHelper Juliastoropolipenelopeysm
authored
CompatHelper: bump compat for Turing to 0.40, (keep existing compat) (#173)
This pull request changes the compat entry for the `Turing` package from `0.20 - 0.39` to `0.20 - 0.39, 0.40`. This keeps the compat entries for earlier versions. Note: I have not tested your package with this new compat entry. It is your responsibility to make sure that your package tests pass before you merge this pull request. --------- Co-authored-by: CompatHelper Julia <[email protected]> Co-authored-by: Jose Storopoli <[email protected]> Co-authored-by: Penelope Yong <[email protected]>
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‎.github/workflows/CI.yml‎

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@@ -49,3 +49,14 @@ jobs:
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with:
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github-token: ${{ secrets.GITHUB_TOKEN }}
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path-to-lcov: lcov.info
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parallel: true
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finish:
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needs: test
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if: ${{ always() }}
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runs-on: ubuntu-latest
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steps:
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- name: Coveralls Finished
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uses: coverallsapp/github-action@v2
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with:
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parallel-finished: true

‎Project.toml‎

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@@ -29,5 +29,5 @@ StatsBase = "0.33 - 0.34"
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StatsModels = "0.6.28 - 0.7"
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TableOperations = "1.2"
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Tables = "1.6"
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Turing = "0.20 - 0.39"
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Turing = "0.20 - 0.40"
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julia = "1"

‎test/Project.toml‎

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@@ -6,6 +6,7 @@ ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
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Mooncake = "da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"
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Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
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ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
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StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
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Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
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TimerOutputs = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"

‎test/runtests.jl‎

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@@ -6,7 +6,6 @@ using CategoricalArrays: CategoricalValue
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using CategoricalArrays: categorical, levels
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using Statistics: mean, std
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using TimerOutputs: TimerOutputs, @timeit
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using Random: seed!
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const T = TuringGLM
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‎test/turing_model.jl‎

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@@ -1,3 +1,5 @@
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using StableRNGs: StableRNG
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@timed_testset "turing_model" begin
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DATA_DIR = joinpath("..", "data")
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kidiq = CSV.read(joinpath(DATA_DIR, "kidiq.csv"), DataFrame)
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f = @formula(kid_score ~ mom_iq * mom_hs)
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@testset "standardize=false" begin
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m = turing_model(f, kidiq)
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chn = sample(seed!(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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chn = sample(StableRNG(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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@test summarystats(chn)[:α, :mean] ≈ 31.80 atol = 2.0
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@test summarystats(chn)[Symbol("β[1]"), :mean] ≈ 0.507 atol = 0.2
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@test quantile(chn)[Symbol("β[2]"), Symbol("50.0%")] ≈ 0.22 atol = 0.2
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end
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@testset "standardize=true" begin
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m = turing_model(f, kidiq; standardize=true)
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chn = sample(seed!(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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chn = sample(StableRNG(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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@test summarystats(chn)[:α, :mean] ≈ 0.000 atol = 0.2
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@test summarystats(chn)[Symbol("β[1]"), :mean] ≈ 0.648 atol = 0.2
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@test quantile(chn)[Symbol("β[2]"), Symbol("50.0%")] ≈ 0.849 atol = 0.2
@@ -25,14 +27,14 @@
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@testset "custom_priors" begin
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priors = CustomPrior(Normal(), Normal(28, 5), nothing)
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m = turing_model(f, kidiq; priors)
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chn = sample(seed!(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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chn = sample(StableRNG(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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@test summarystats(chn)[:α, :mean] ≈ 28.758 atol = 2.0
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@test summarystats(chn)[Symbol("β[1]"), :mean] ≈ 0.539 atol = 0.2
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@test quantile(chn)[Symbol("β[2]"), Symbol("50.0%")] ≈ 0.3863 atol = 0.2
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end
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@testset "explicit calling Normal" begin
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m = turing_model(f, kidiq; model=Normal)
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chn = sample(seed!(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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chn = sample(StableRNG(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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@test summarystats(chn)[:α, :mean] ≈ 31.80 atol = 2.0
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@test summarystats(chn)[Symbol("β[1]"), :mean] ≈ 0.507 atol = 0.2
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@test quantile(chn)[Symbol("β[2]"), Symbol("50.0%")] ≈ 0.22 atol = 0.2
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f = @formula(kid_score ~ mom_iq * mom_hs)
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@testset "standardize=false" begin
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m = turing_model(f, kidiq; model=TDist)
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chn = sample(seed!(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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chn = sample(StableRNG(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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@test summarystats(chn)[:α, :mean] ≈ 33.31 atol = 2.0
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@test summarystats(chn)[Symbol("β[1]"), :mean] ≈ 0.519 atol = 0.2
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@test quantile(chn)[Symbol("β[2]"), Symbol("50.0%")] ≈ 0.340 atol = 0.2
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@testset "custom_priors" begin
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priors = CustomPrior(Normal(), Normal(28, 5), Exponential(2))
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m = turing_model(f, kidiq; model=TDist, priors)
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chn = sample(seed!(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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chn = sample(StableRNG(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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@test summarystats(chn)[:α, :mean] ≈ 28.565 atol = 2.0
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@test summarystats(chn)[Symbol("β[1]"), :mean] ≈ 0.551 atol = 0.2
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@test quantile(chn)[Symbol("β[2]"), Symbol("50.0%")] ≈ 0.255 atol = 0.2
@@ -63,7 +65,7 @@
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f = @formula(switch ~ arsenic + dist + assoc + educ)
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@testset "standardize=false" begin
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m = turing_model(f, wells; model=Bernoulli)
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chn = sample(seed!(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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chn = sample(StableRNG(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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@test summarystats(chn)[:α, :mean] ≈ -0.153 atol = 0.2
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@test summarystats(chn)[Symbol("β[1]"), :mean] ≈ 0.467 atol = 0.2
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@test quantile(chn)[Symbol("β[2]"), Symbol("50.0%")] ≈ -0.009 atol = 0.2
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@testset "custom_priors" begin
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priors = CustomPrior(Normal(), Normal(), nothing)
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m = turing_model(f, wells; model=Bernoulli, priors)
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chn = sample(seed!(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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chn = sample(StableRNG(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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@test summarystats(chn)[:α, :mean] ≈ -0.155 atol = 0.2
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@test summarystats(chn)[Symbol("β[1]"), :mean] ≈ 0.468 atol = 0.2
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@test quantile(chn)[Symbol("β[2]"), Symbol("50.0%")] ≈ -0.009 atol = 0.2
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f = @formula(y ~ roach1 + treatment + senior + exposure2)
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@testset "standardize=false" begin
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m = turing_model(f, roaches; model=Poisson)
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chn = sample(seed!(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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# seed of 123 gives bad results
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chn = sample(StableRNG(124), m, NUTS(), MCMCThreads(), 2_000, 2)
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@test summarystats(chn)[:α, :mean] ≈ 2.969 atol = 0.5
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@test summarystats(chn)[Symbol("β[1]"), :mean] ≈ 0.006 atol = 0.2
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@test quantile(chn)[Symbol("β[2]"), Symbol("50.0%")] ≈ -0.5145 atol = 0.2
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@testset "custom_priors" begin
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priors = CustomPrior(Normal(2, 5), Normal(), nothing)
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m = turing_model(f, roaches; model=Poisson, priors)
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chn = sample(seed!(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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chn = sample(StableRNG(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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@test summarystats(chn)[:α, :mean] ≈ 2.963 atol = 0.5
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@test summarystats(chn)[Symbol("β[1]"), :mean] ≈ 0.006 atol = 0.2
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@test quantile(chn)[Symbol("β[2]"), Symbol("50.0%")] ≈ -0.5145 atol = 0.2
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f = @formula(y ~ roach1 + treatment + senior + exposure2)
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@testset "standardize=false" begin
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m = turing_model(f, roaches; model=NegativeBinomial)
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chn = sample(seed!(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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chn = sample(StableRNG(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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@test summarystats(chn)[:α, :mean] ≈ 2.448 atol = 0.5
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@test summarystats(chn)[Symbol("β[1]"), :mean] ≈ 0.013 atol = 0.2
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@test quantile(chn)[Symbol("β[2]"), Symbol("50.0%")] ≈ -0.734 atol = 0.2
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@testset "custom_priors" begin
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priors = CustomPrior(Normal(), Normal(2, 5), Exponential(0.5))
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m = turing_model(f, roaches; model=NegativeBinomial, priors)
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chn = sample(seed!(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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chn = sample(StableRNG(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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@test summarystats(chn)[:α, :mean] ≈ 2.401 atol = 0.5
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@test summarystats(chn)[Symbol("β[1]"), :mean] ≈ 0.013 atol = 0.2
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@test quantile(chn)[Symbol("β[2]"), Symbol("50.0%")] ≈ -0.723 atol = 0.2
@@ -121,7 +124,7 @@
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@timed_testset "Hierarchical Model" begin
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f = @formula(y ~ (1 | cheese) + background)
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m = turing_model(f, cheese)
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chn = sample(seed!(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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chn = sample(StableRNG(123), m, NUTS(), MCMCThreads(), 2_000, 2)
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@test summarystats(chn)[:α, :mean] ≈ 68.07 atol = 2.0
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@test summarystats(chn)[Symbol("β[1]"), :mean] ≈ 6.60 atol = 0.2
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@test summarystats(chn)[Symbol("zⱼ[1]"), :mean] ≈ 0.348 atol = 0.2

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