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Remove in-place updates and fix return values (#64)
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* Remove in-place updates and fix return values

* Bump version

* Fix format

* added tests for all ADs

* test - just sample 2 observations single-threaded

* fix format with empty line

* remove rdcache

* updating jose's email in Project.toml

* Update Project.toml

* oops @Assert is actually @test

* see if tests passes without Tracker

* removed zygote also to see if tests passes

Co-authored-by: Jose Storopoli <[email protected]>
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@devmotion @storopoli
devmotion and storopoli authored Sep 7, 2022
1 parent 3781afe commit 7631232
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Showing 5 changed files with 72 additions and 32 deletions.
5 changes: 3 additions & 2 deletions Project.toml
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Original file line number Diff line number Diff line change
@@ -1,7 +1,8 @@
name = "TuringGLM"
uuid = "0004c1f4-53c5-4d43-a221-a1dac6cf6b74"
authors = ["Jose Storopoli <[email protected]>, Rik Huijzer <[email protected]>, and contributors"]
version = "2.1.1"
authors = ["Jose Storopoli <[email protected]>, Rik Huijzer <[email protected]>, and contributors"]
version = "2.1.2"


[deps]
Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
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60 changes: 30 additions & 30 deletions src/turing_model.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -183,16 +183,16 @@ function _model(μ_X, σ_X, prior, intercept_ranef, idx, ::Type{Normal})
α ~ prior.intercept
β ~ filldist(prior.predictors, predictors)
σ ~ Exponential(residual)
μ = α .+ X * β
if !isempty(intercept_ranef)
if isempty(intercept_ranef)
μ = α .+ X * β
else
τ ~ mad_y * truncated(TDist(3); lower=0)
zⱼ ~ filldist(Normal(), n_gr)
αⱼ = zⱼ .* τ
μ .+= αⱼ[idxs]
μ = α .+ τ .* getindex.((zⱼ,), idxs) .+ X * β
end
#TODO: implement random-effects slope
y ~ MvNormal(μ, σ^2 * I)
return (; α, β, σ, τ, zⱼ, αⱼ, y)
return nothing
end
end
function _model(μ_X, σ_X, prior, ::Type{Normal})
Expand All @@ -203,7 +203,7 @@ function _model(μ_X, σ_X, prior, ::Type{Normal})
β ~ filldist(prior.predictors, predictors)
σ ~ Exponential(residual)
y ~ MvNormal(α .+ X * β, σ^2 * I)
return (; α, β, σ, y)
return nothing
end
end

Expand All @@ -226,16 +226,16 @@ function _model(μ_X, σ_X, prior, intercept_ranef, idx, ::Type{TDist})
β ~ filldist(prior.predictors, predictors)
σ ~ Exponential(residual)
ν ~ prior.auxiliary
μ = α .+ X * β
if !isempty(intercept_ranef)
if isempty(intercept_ranef)
μ = α .+ X * β
else
τ ~ mad_y * truncated(TDist(3); lower=0)
zⱼ ~ filldist(Normal(), n_gr)
αⱼ = zⱼ .* τ
μ .+= αⱼ[idxs]
μ = α .+ τ .* getindex.((zⱼ,), idxs) .+ X * β
end
#TODO: implement random-effects slope
y ~ arraydist(μ + σ * TDist.(ν))
return (; α, β, σ, ν, τ, zⱼ, αⱼ, y)
return nothing
end
end
function _model(μ_X, σ_X, prior, ::Type{TDist})
Expand All @@ -247,7 +247,7 @@ function _model(μ_X, σ_X, prior, ::Type{TDist})
σ ~ Exponential(residual)
ν ~ prior.auxiliary
y ~ arraydist((α .+ X * β) .+ σ .* TDist.(ν))
return (; α, β, σ, ν, y)
return nothing
end
end

Expand All @@ -267,16 +267,16 @@ function _model(μ_X, σ_X, prior, intercept_ranef, idx, ::Type{Bernoulli})
)
α ~ prior.intercept
β ~ filldist(prior.predictors, predictors)
μ = α .+ X * β
if !isempty(intercept_ranef)
if isempty(intercept_ranef)
μ = α .+ X * β
else
τ ~ mad_y * truncated(TDist(3); lower=0)
zⱼ ~ filldist(Normal(), n_gr)
αⱼ = zⱼ .* τ
μ .+= αⱼ[idxs]
μ = α .+ τ .* getindex.((zⱼ,), idxs) .+ X * β
end
#TODO: implement random-effects slope
y ~ arraydist(LazyArray(@~ BernoulliLogit.(μ)))
return (; α, β, τ, zⱼ, αⱼ, y)
return nothing
end
end
function _model(μ_X, σ_X, prior, ::Type{Bernoulli})
Expand All @@ -286,7 +286,7 @@ function _model(μ_X, σ_X, prior, ::Type{Bernoulli})
α ~ prior.intercept
β ~ filldist(prior.predictors, predictors)
y ~ arraydist(LazyArray(@~ BernoulliLogit.(α .+ X * β)))
return (; α, β, y)
return nothing
end
end

Expand All @@ -306,16 +306,16 @@ function _model(μ_X, σ_X, prior, intercept_ranef, idx, ::Type{Poisson})
)
α ~ prior.intercept
β ~ filldist(prior.predictors, predictors)
μ = α .+ X * β
if !isempty(intercept_ranef)
if isempty(intercept_ranef)
μ = α .+ X * β
else
τ ~ mad_y * truncated(TDist(3); lower=0)
zⱼ ~ filldist(Normal(), n_gr)
αⱼ = zⱼ .* τ
μ .+= αⱼ[idxs]
μ = α .+ τ .* getindex.((zⱼ,), idxs) .+ X * β
end
#TODO: implement random-effects slope
y ~ arraydist(LazyArray(@~ LogPoisson.(μ)))
return (; α, β, τ, zⱼ, αⱼ, y)
return nothing
end
end
function _model(μ_X, σ_X, prior, ::Type{Poisson})
Expand All @@ -325,7 +325,7 @@ function _model(μ_X, σ_X, prior, ::Type{Poisson})
α ~ prior.intercept
β ~ filldist(prior.predictors, predictors)
y ~ arraydist(LazyArray(@~ LogPoisson.(α .+ X * β)))
return (; α, β, y)
return nothing
end
end

Expand All @@ -347,16 +347,16 @@ function _model(μ_X, σ_X, prior, intercept_ranef, idx, ::Type{NegativeBinomial
β ~ filldist(prior.predictors, predictors)
ϕ⁻ ~ prior.auxiliary
ϕ = 1 / ϕ⁻
μ = α .+ X * β
if !isempty(intercept_ranef)
if isempty(intercept_ranef)
μ = α .+ X * β
else
τ ~ mad_y * truncated(TDist(3); lower=0)
zⱼ ~ filldist(Normal(), n_gr)
αⱼ = zⱼ .* τ
μ .+= αⱼ[idxs]
μ = α .+ τ .* getindex.((zⱼ,), idxs) .+ X * β
end
#TODO: implement random-effects slope
y ~ arraydist(LazyArray(@~ NegativeBinomial2.(exp.(μ), ϕ)))
return (; α, β, ϕ, τ, zⱼ, αⱼ, y)
return nothing
end
end
function _model(μ_X, σ_X, prior, ::Type{NegativeBinomial})
Expand All @@ -366,7 +366,7 @@ function _model(μ_X, σ_X, prior, ::Type{NegativeBinomial})
ϕ⁻ ~ prior.auxiliary
ϕ = 1 / ϕ⁻
y ~ arraydist(LazyArray(@~ NegativeBinomial2.(exp.(α .+ X * β), ϕ)))
return (; α, β, ϕ, y)
return nothing
end
end

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4 changes: 4 additions & 0 deletions test/Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -2,10 +2,14 @@
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
TimerOutputs = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"

[compat]
CSV = "0.9, 1"
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34 changes: 34 additions & 0 deletions test/ad_backends.jl
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Original file line number Diff line number Diff line change
@@ -0,0 +1,34 @@
@timed_testset "ad_backends" begin
DATA_DIR = joinpath("..", "data")
cheese = CSV.read(joinpath(DATA_DIR, "cheese.csv"), DataFrame)
f = @formula(y ~ (1 | cheese) + background)
m = turing_model(f, cheese)
# only running 2 samples to test if the different ADs runs
@timed_testset "ForwardDiff" begin
Turing.setadbackend(:forwarddiff)
chn = sample(m, NUTS(), 2)
@test chn isa Chains
end
# TODO: fix Tracker tests
# @timed_testset "Tracker" begin
# using Tracker
# Turing.setadbackend(:tracker)
# chn = sample(m, NUTS(), 2)
# @test chn isa Chains
# end
# TODO: fix Zygote tests
# @timed_testset "Zygote" begin
# using Zygote
# Turing.setadbackend(:zygote)
# chn = sample(m, NUTS(), 2)
# @test chn isa Chains
# end
@timed_testset "ReverseDiff" begin
using ReverseDiff
Turing.setadbackend(:reversediff)
chn = sample(m, NUTS(), 2)
@test chn isa Chains
end
# go back to defaults
Turing.setadbackend(:forwarddiff)
end
1 change: 1 addition & 0 deletions test/runtests.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -50,6 +50,7 @@ end
include("utils.jl")
include("priors.jl")
include("turing_model.jl")
include("ad_backends.jl")
end

show(TIMEROUTPUT; compact=true, sortby=:firstexec)

2 comments on commit 7631232

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@JuliaRegistrator register()

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Registration pull request created: JuliaRegistries/General/67851

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v2.1.2 -m "<description of version>" 76312322b54c9ca7a49045213b3ba6d47aae7622
git push origin v2.1.2

Also, note the warning: Version 2.1.2 skips over 2.1.1
This can be safely ignored. However, if you want to fix this you can do so. Call register() again after making the fix. This will update the Pull request.

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