Use BangBang to widen containers if needed

.travis.yml

@@ -6,10 +6,9 @@ branches:
 os:
   - linux
   - osx
-julia:
-  - 1.0
 matrix:
   include:
+    - julia: 1.0
     - julia: 1
       env: JULIA_NUM_THREADS=1
     - julia: 1

Project.toml

@@ -6,6 +6,7 @@ desc = "A lightweight interface for common MCMC methods."
 version = "1.0.1"
 
 [deps]
+BangBang = "198e06fe-97b7-11e9-32a5-e1d131e6ad66"
 ConsoleProgressMonitor = "88cd18e8-d9cc-4ea6-8889-5259c0d15c8b"
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
@@ -16,6 +17,7 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 TerminalLoggers = "5d786b92-1e48-4d6f-9151-6b4477ca9bed"
 
 [compat]
+BangBang = "0.3.19"
 ConsoleProgressMonitor = "0.1"
 LoggingExtras = "0.4"
 ProgressLogging = "0.1"

src/AbstractMCMC.jl

@@ -1,5 +1,6 @@
 module AbstractMCMC
 
+import BangBang
 import ConsoleProgressMonitor
 import LoggingExtras
 import ProgressLogging

src/interface.jl

@@ -88,13 +88,15 @@ function step!(
 end
 
 """
-    transitions_init(transition, model, sampler, N[; kwargs...])
-    transitions_init(transition, model, sampler[; kwargs...])
+    transitions(transition, model, sampler, N[; kwargs...])
+    transitions(transition, model, sampler[; kwargs...])
 
 Generate a container for the `N` transitions of the MCMC `sampler` for the provided
-`model`, whose first transition is `transition`. Can be called with and without a predefined size `N`.
+`model`, whose first transition is `transition`.
+
+The method can be called with and without a predefined size `N`.
 """
-function transitions_init(
+function transitions(
     transition,
     ::AbstractModel,
     ::AbstractSampler,
@@ -104,43 +106,52 @@ function transitions_init(
     return Vector{typeof(transition)}(undef, N)
 end
 
-function transitions_init(
+function transitions(
     transition,
     ::AbstractModel,
     ::AbstractSampler;
     kwargs...
 )
-    return [transition]
+    return Vector{typeof(transition)}(undef, 1)
 end
 
 """
-    transitions_save!(transitions, iteration, transition, model, sampler, N[; kwargs...])
-    transitions_save!(transitions, iteration, transition, model, sampler[; kwargs...])
+    save!!(transitions, transition, iteration, model, sampler, N[; kwargs...])
+    save!!(transitions, transition, iteration, model, sampler[; kwargs...])
 
 Save the `transition` of the MCMC `sampler` at the current `iteration` in the container of
-`transitions`. Can be called with and without a predefined size `N`.
+`transitions`.
+
+The function can be called with and without a predefined size `N`. By default, AbstractMCMC
+uses ``setindex!`` and ``push!!`` from the Julia package
+[BangBang](https://github.com/tkf/BangBang.jl) to write to and append to the container,
+and widen the container type if needed.
 """
-function transitions_save!(
-    transitions::AbstractVector,
-    iteration::Integer,
+function save!!(
+    transitions,
     transition,
+    iteration::Integer,
     ::AbstractModel,
     ::AbstractSampler,
     ::Integer;
     kwargs...
 )
-    transitions[iteration] = transition
-    return
+    return BangBang.setindex!!(transitions, transition, iteration)
 end
 
-function transitions_save!(
-    transitions::AbstractVector,
-    iteration::Integer,
+function save!!(
+    transitions,
     transition,
+    iteration::Integer,
     ::AbstractModel,
     ::AbstractSampler;
     kwargs...
 )
-    push!(transitions, transition)
-    return
+    return BangBang.push!!(transitions, transition)
 end
+
+Base.@deprecate transitions_init(transition, model::AbstractModel, sampler::AbstractSampler, N::Integer; kwargs...) transitions(transition, model, sampler, N; kwargs...) false
+Base.@deprecate transitions_init(transition, model::AbstractModel, sampler::AbstractSampler; kwargs...) transitions(transition, model, sampler; kwargs...) false
+Base.@deprecate transitions_save!(transitions, iteration::Integer, transition, model::AbstractModel, sampler::AbstractSampler; kwargs...) save!!(transitions, transition, iteration, model, sampler; kwargs...) false
+Base.@deprecate transitions_save!(transitions, iteration::Integer, transition, model::AbstractModel, sampler::AbstractSampler, N::Integer; kwargs...) save!!(transitions, transition, iteration, model, sampler, N; kwargs...) false
+

src/sample.jl

@@ -81,8 +81,8 @@ function mcmcsample(
         callback(rng, model, sampler, transition, 1)
 
         # Save the transition.
-        transitions = transitions_init(transition, model, sampler, N; kwargs...)
-        transitions_save!(transitions, 1, transition, model, sampler, N; kwargs...)
+        transitions = AbstractMCMC.transitions(transition, model, sampler, N; kwargs...)
+        transitions = save!!(transitions, transition, 1, model, sampler, N; kwargs...)
 
         # Update the progress bar.
         progress && ProgressLogging.@logprogress 1/N
@@ -96,7 +96,7 @@ function mcmcsample(
             callback(rng, model, sampler, transition, i)
 
             # Save the transition.
-            transitions_save!(transitions, i, transition, model, sampler, N; kwargs...)
+            transitions = save!!(transitions, transition, i, model, sampler, N; kwargs...)
 
             # Update the progress bar.
             progress && ProgressLogging.@logprogress i/N
@@ -148,7 +148,8 @@ function mcmcsample(
         callback(rng, model, sampler, transition, 1)
 
         # Save the transition.
-        transitions = transitions_init(transition, model, sampler; kwargs...)
+        transitions = AbstractMCMC.transitions(transition, model, sampler; kwargs...)
+        transitions = save!!(transitions, transition, 1, model, sampler; kwargs...)
 
         # Step through the sampler until stopping.
         i = 2
@@ -161,7 +162,7 @@ function mcmcsample(
             callback(rng, model, sampler, transition, i)
 
             # Save the transition.
-            transitions_save!(transitions, i, transition, model, sampler; kwargs...)
+            transitions = save!!(transitions, transition, i, model, sampler; kwargs...)
 
             # Increment iteration counter.
             i += 1

test/interface.jl

@@ -1,16 +1,16 @@
 struct MyModel <: AbstractMCMC.AbstractModel end
 
-struct MyTransition
-    a::Float64
-    b::Float64
+struct MyTransition{A,B}
+    a::A
+    b::B
 end
 
 struct MySampler <: AbstractMCMC.AbstractSampler end
 struct AnotherSampler <: AbstractMCMC.AbstractSampler end
 
-struct MyChain <: AbstractMCMC.AbstractChains
-    as::Vector{Float64}
-    bs::Vector{Float64}
+struct MyChain{A,B} <: AbstractMCMC.AbstractChains
+    as::Vector{A}
+    bs::Vector{B}
 end
 
 function AbstractMCMC.step!(
@@ -23,7 +23,8 @@ function AbstractMCMC.step!(
     loggers = false,
     kwargs...
 )
-    a = rand(rng)
+    # sample `a` is missing in the first step
+    a = transition === nothing ? missing : rand(rng)
     b = randn(rng)
 
     loggers && push!(LOGGERS, Logging.current_logger())
@@ -37,18 +38,12 @@ function AbstractMCMC.bundle_samples(
     model::MyModel,
     sampler::MySampler,
     N::Integer,
-    transitions::Vector{MyTransition},
+    transitions::Vector{<:MyTransition},
     chain_type::Type{MyChain};
     kwargs...
 )
-    n = length(transitions)
-    as = Vector{Float64}(undef, n)
-    bs = Vector{Float64}(undef, n)
-    for i in 1:n
-        transition = transitions[i]
-        as[i] = transition.a
-        bs[i] = transition.b
-    end
+    as = [t.a for t in transitions]
+    bs = [t.b for t in transitions]
 
     return MyChain(as, bs)
 end

test/runtests.jl

@@ -35,14 +35,15 @@ include("interface.jl")
             @test Logging.current_logger() === CURRENT_LOGGER
 
             # test output type and size
-            @test chain isa Vector{MyTransition}
+            @test chain isa Vector{<:MyTransition}
             @test length(chain) == N
 
             # test some statistical properties
-            @test mean(x.a for x in chain) ≈ 0.5 atol=6e-2
-            @test var(x.a for x in chain) ≈ 1 / 12 atol=5e-3
-            @test mean(x.b for x in chain) ≈ 0.0 atol=5e-2
-            @test var(x.b for x in chain) ≈ 1 atol=6e-2
+            tail_chain = @view chain[2:end]
+            @test mean(x.a for x in tail_chain) ≈ 0.5 atol=6e-2
+            @test var(x.a for x in tail_chain) ≈ 1 / 12 atol=5e-3
+            @test mean(x.b for x in tail_chain) ≈ 0.0 atol=5e-2
+            @test var(x.b for x in tail_chain) ≈ 1 atol=6e-2
         end
 
         @testset "Juno" begin
@@ -118,26 +119,28 @@ include("interface.jl")
             end
 
             Random.seed!(1234)
-            chains = sample(MyModel(), MySampler(), MCMCThreads(), 10_000, 1000;
+            N = 10_000
+            chains = sample(MyModel(), MySampler(), MCMCThreads(), N, 1000;
                             chain_type = MyChain)
 
             # test output type and size
-            @test chains isa Vector{MyChain}
+            @test chains isa Vector{<:MyChain}
             @test length(chains) == 1000
-            @test all(x -> length(x.as) == length(x.bs) == 10_000, chains)
+            @test all(x -> length(x.as) == length(x.bs) == N, chains)
 
             # test some statistical properties
-            @test all(x -> isapprox(mean(x.as), 0.5; atol=1e-2), chains)
-            @test all(x -> isapprox(var(x.as), 1 / 12; atol=5e-3), chains)
-            @test all(x -> isapprox(mean(x.bs), 0; atol=5e-2), chains)
-            @test all(x -> isapprox(var(x.bs), 1; atol=5e-2), chains)
+            @test all(x -> isapprox(mean(@view x.as[2:end]), 0.5; atol=1e-2), chains)
+            @test all(x -> isapprox(var(@view x.as[2:end]), 1 / 12; atol=5e-3), chains)
+            @test all(x -> isapprox(mean(@view x.bs[2:end]), 0; atol=5e-2), chains)
+            @test all(x -> isapprox(var(@view x.bs[2:end]), 1; atol=5e-2), chains)
 
             # test reproducibility
             Random.seed!(1234)
-            chains2 = sample(MyModel(), MySampler(), MCMCThreads(), 10_000, 1000;
+            chains2 = sample(MyModel(), MySampler(), MCMCThreads(), N, 1000;
                              chain_type = MyChain)
 
-            @test all(((x, y),) -> x.as == y.as && x.bs == y.bs, zip(chains, chains2))
+            @test all(c1.as[i] === c2.as[i] for (c1, c2) in zip(chains, chains2), i in 1:N)
+            @test all(c1.bs[i] === c2.bs[i] for (c1, c2) in zip(chains, chains2), i in 1:N)
 
             # Suppress output.
             logs, _ = collect_test_logs(; min_level=Logging.LogLevel(-1)) do
@@ -167,27 +170,30 @@ include("interface.jl")
             include("interface.jl")
         end
 
+        N = 10_000
         Random.seed!(1234)
-        chains = sample(MyModel(), MySampler(), MCMCDistributed(), 10_000, 1000;
+        chains = sample(MyModel(), MySampler(), MCMCDistributed(), N, 1000;
                         chain_type = MyChain)
 
         # Test output type and size.
-        @test chains isa Vector{MyChain}
+        @test chains isa Vector{<:MyChain}
+        @test all(c.as[1] === missing for c in chains)
         @test length(chains) == 1000
-        @test all(x -> length(x.as) == length(x.bs) == 10_000, chains)
+        @test all(x -> length(x.as) == length(x.bs) == N, chains)
 
         # Test some statistical properties.
-        @test all(x -> isapprox(mean(x.as), 0.5; atol=1e-2), chains)
-        @test all(x -> isapprox(var(x.as), 1 / 12; atol=5e-3), chains)
-        @test all(x -> isapprox(mean(x.bs), 0; atol=5e-2), chains)
-        @test all(x -> isapprox(var(x.bs), 1; atol=5e-2), chains)
+        @test all(x -> isapprox(mean(@view x.as[2:end]), 0.5; atol=1e-2), chains)
+        @test all(x -> isapprox(var(@view x.as[2:end]), 1 / 12; atol=5e-3), chains)
+        @test all(x -> isapprox(mean(@view x.bs[2:end]), 0; atol=5e-2), chains)
+        @test all(x -> isapprox(var(@view x.bs[2:end]), 1; atol=5e-2), chains)
 
         # Test reproducibility.
         Random.seed!(1234)
-        chains2 = sample(MyModel(), MySampler(), MCMCDistributed(), 10_000, 1000;
+        chains2 = sample(MyModel(), MySampler(), MCMCDistributed(), N, 1000;
                          chain_type = MyChain)
 
-        @test all(((x, y),) -> x.as == y.as && x.bs == y.bs, zip(chains, chains2))
+        @test all(c1.as[i] === c2.as[i] for (c1, c2) in zip(chains, chains2), i in 1:N)
+        @test all(c1.bs[i] === c2.bs[i] for (c1, c2) in zip(chains, chains2), i in 1:N)
 
         # Suppress output.
         logs, _ = collect_test_logs(; min_level=Logging.LogLevel(-1)) do
@@ -201,7 +207,7 @@ include("interface.jl")
         chain1 = sample(MyModel(), MySampler(), 100; sleepy = true)
         chain2 = sample(MyModel(), MySampler(), 100; sleepy = true, chain_type = MyChain)
 
-        @test chain1 isa Vector{MyTransition}
+        @test chain1 isa Vector{<:MyTransition}
         @test chain2 isa MyChain
     end
 
@@ -217,10 +223,15 @@ include("interface.jl")
                 break
             end
 
+            # don't save missing values
+            t.a === missing && continue
+
             push!(as, t.a)
             push!(bs, t.b)
         end
 
+        @test length(as) == length(bs) == 998
+
         @test mean(as) ≈ 0.5 atol=1e-2
         @test var(as) ≈ 1 / 12 atol=5e-3
         @test mean(bs) ≈ 0.0 atol=5e-2