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Remove overly specialized bundle_samples #120

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Remove overly specialized bundle_samples #120

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1984557
remove an overly specialized default bundle_sample
torfjelde Mar 13, 2023
f91067c
bump minor version
torfjelde Mar 13, 2023
5a6fffc
removed no longer relevant test
torfjelde Mar 13, 2023
216488f
removed no longer relevant piece of code
torfjelde Mar 13, 2023
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2 changes: 1 addition & 1 deletion Project.toml
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Expand Up @@ -3,7 +3,7 @@ uuid = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
keywords = ["markov chain monte carlo", "probablistic programming"]
license = "MIT"
desc = "A lightweight interface for common MCMC methods."
version = "4.4.0"
version = "4.5.0"

[deps]
BangBang = "198e06fe-97b7-11e9-32a5-e1d131e6ad66"
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8 changes: 0 additions & 8 deletions src/interface.jl
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Expand Up @@ -35,14 +35,6 @@ function bundle_samples(
return samples
end

function bundle_samples(
samples::Vector, ::AbstractModel, ::AbstractSampler, ::Any, ::Type{Vector{T}}; kwargs...
) where {T}
return map(samples) do sample
convert(T, sample)
end
end

"""
step(rng, model, sampler[, state; kwargs...])

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15 changes: 0 additions & 15 deletions test/sample.jl
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Expand Up @@ -553,21 +553,6 @@
@test all(chain[i].b == ref_chain[i].b for i in 1:N)
end

@testset "Sample vector of `NamedTuple`s" begin
chain = sample(MyModel(), MySampler(), 1_000; chain_type=Vector{NamedTuple})
# Check output type
@test chain isa Vector{<:NamedTuple}
@test length(chain) == 1_000
@test all(keys(x) == (:a, :b) for x in chain)

# Check some statistical properties
@test ismissing(chain[1].a)
@test mean(x.a for x in view(chain, 2:1_000)) ≈ 0.5 atol = 6e-2
@test var(x.a for x in view(chain, 2:1_000)) ≈ 1 / 12 atol = 1e-2
@test mean(x.b for x in chain) ≈ 0 atol = 0.1
@test var(x.b for x in chain) ≈ 1 atol = 0.15
end

@testset "Testing callbacks" begin
function count_iterations(
rng, model, sampler, sample, state, i; iter_array, kwargs...
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3 changes: 0 additions & 3 deletions test/utils.jl
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Expand Up @@ -79,9 +79,6 @@ function AbstractMCMC.chainscat(
return vcat(chain, chains...)
end

# Conversion to NamedTuple
Base.convert(::Type{NamedTuple}, x::MySample) = (a=x.a, b=x.b)

# Gaussian log density (without additive constants)
# Without LogDensityProblems.jl interface
mylogdensity(x) = -sum(abs2, x) / 2
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