renamed references for init_params to initial_params

docs/src/api.md

@@ -82,9 +82,9 @@ Common keyword arguments for regular and parallel sampling are:
     The common keyword arguments `progress`, `chain_type`, and `callback` are not supported by the iterator [`AbstractMCMC.steps`](@ref) and the transducer [`AbstractMCMC.Sample`](@ref).
 
 There is no "official" way for providing initial parameter values yet.
-However, multiple packages such as [EllipticalSliceSampling.jl](https://github.com/TuringLang/EllipticalSliceSampling.jl) and [AdvancedMH.jl](https://github.com/TuringLang/AdvancedMH.jl) support an `init_params` keyword argument for setting the initial values when sampling a single chain.
-To ensure that sampling multiple chains "just works" when sampling of a single chain is implemented, [we decided to support `init_params` in the default implementations of the ensemble methods](https://github.com/TuringLang/AbstractMCMC.jl/pull/94):
-- `init_params` (default: `nothing`): if `init_params isa AbstractArray`, then the `i`th element of `init_params` is used as initial parameters of the `i`th chain. If one wants to use the same initial parameters `x` for every chain, one can specify e.g. `init_params = FillArrays.Fill(x, N)`.
+However, multiple packages such as [EllipticalSliceSampling.jl](https://github.com/TuringLang/EllipticalSliceSampling.jl) and [AdvancedMH.jl](https://github.com/TuringLang/AdvancedMH.jl) support an `initial_params` keyword argument for setting the initial values when sampling a single chain.
+To ensure that sampling multiple chains "just works" when sampling of a single chain is implemented, [we decided to support `initial_params` in the default implementations of the ensemble methods](https://github.com/TuringLang/AbstractMCMC.jl/pull/94):
+- `initial_params` (default: `nothing`): if `initial_params isa AbstractArray`, then the `i`th element of `initial_params` is used as initial parameters of the `i`th chain. If one wants to use the same initial parameters `x` for every chain, one can specify e.g. `initial_params = FillArrays.Fill(x, N)`.
 
 Progress logging can be enabled and disabled globally with `AbstractMCMC.setprogress!(progress)`.
 

test/sample.jl

@@ -28,7 +28,7 @@
 
             # initial parameters
             chain = sample(
-                MyModel(), MySampler(), 3; progress=false, init_params=(b=3.2, a=-1.8)
+                MyModel(), MySampler(), 3; progress=false, initial_params=(b=3.2, a=-1.8)
             )
             @test chain[1].a == -1.8
             @test chain[1].b == 3.2
@@ -163,57 +163,57 @@
 
         # initial parameters
         nchains = 100
-        init_params = [(b=randn(), a=rand()) for _ in 1:nchains]
+        initial_params = [(b=randn(), a=rand()) for _ in 1:nchains]
         chains = sample(
             MyModel(),
             MySampler(),
             MCMCThreads(),
             3,
             nchains;
             progress=false,
-            init_params=init_params,
+            initial_params=initial_params,
         )
         @test length(chains) == nchains
         @test all(
             chain[1].a == params.a && chain[1].b == params.b for
-            (chain, params) in zip(chains, init_params)
+            (chain, params) in zip(chains, initial_params)
         )
 
-        init_params = (a=randn(), b=rand())
+        initial_params = (a=randn(), b=rand())
         chains = sample(
             MyModel(),
             MySampler(),
             MCMCThreads(),
             3,
             nchains;
             progress=false,
-            init_params=Iterators.repeated(init_params, nchains),
+            initial_params=Iterators.repeated(initial_params, nchains),
         )
         @test length(chains) == nchains
         @test all(
-            chain[1].a == init_params.a && chain[1].b == init_params.b for chain in chains
+            chain[1].a == initial_params.a && chain[1].b == initial_params.b for chain in chains
         )
 
-        # Too many `init_params`
+        # Too many `initial_params`
         @test_throws ArgumentError sample(
             MyModel(),
             MySampler(),
             MCMCThreads(),
             3,
             nchains;
             progress=false,
-            init_params=Iterators.repeated(init_params, nchains + 1),
+            initial_params=Iterators.repeated(initial_params, nchains + 1),
         )
 
-        # Too few `init_params`
+        # Too few `initial_params`
         @test_throws ArgumentError sample(
             MyModel(),
             MySampler(),
             MCMCThreads(),
             3,
             nchains;
             progress=false,
-            init_params=Iterators.repeated(init_params, nchains - 1),
+            initial_params=Iterators.repeated(initial_params, nchains - 1),
         )
     end
 
@@ -298,57 +298,57 @@
 
         # initial parameters
         nchains = 100
-        init_params = [(a=randn(), b=rand()) for _ in 1:nchains]
+        initial_params = [(a=randn(), b=rand()) for _ in 1:nchains]
         chains = sample(
             MyModel(),
             MySampler(),
             MCMCDistributed(),
             3,
             nchains;
             progress=false,
-            init_params=init_params,
+            initial_params=initial_params,
         )
         @test length(chains) == nchains
         @test all(
             chain[1].a == params.a && chain[1].b == params.b for
-            (chain, params) in zip(chains, init_params)
+            (chain, params) in zip(chains, initial_params)
         )
 
-        init_params = (b=randn(), a=rand())
+        initial_params = (b=randn(), a=rand())
         chains = sample(
             MyModel(),
             MySampler(),
             MCMCDistributed(),
             3,
             nchains;
             progress=false,
-            init_params=Iterators.repeated(init_params, nchains),
+            initial_params=Iterators.repeated(initial_params, nchains),
         )
         @test length(chains) == nchains
         @test all(
-            chain[1].a == init_params.a && chain[1].b == init_params.b for chain in chains
+            chain[1].a == initial_params.a && chain[1].b == initial_params.b for chain in chains
         )
 
-        # Too many `init_params`
+        # Too many `initial_params`
         @test_throws ArgumentError sample(
             MyModel(),
             MySampler(),
             MCMCDistributed(),
             3,
             nchains;
             progress=false,
-            init_params=Iterators.repeated(init_params, nchains + 1),
+            initial_params=Iterators.repeated(initial_params, nchains + 1),
         )
 
-        # Too few `init_params`
+        # Too few `initial_params`
         @test_throws ArgumentError sample(
             MyModel(),
             MySampler(),
             MCMCDistributed(),
             3,
             nchains;
             progress=false,
-            init_params=Iterators.repeated(init_params, nchains - 1),
+            initial_params=Iterators.repeated(initial_params, nchains - 1),
         )
 
         # Remove workers
@@ -407,57 +407,57 @@
 
         # initial parameters
         nchains = 100
-        init_params = [(a=rand(), b=randn()) for _ in 1:nchains]
+        initial_params = [(a=rand(), b=randn()) for _ in 1:nchains]
         chains = sample(
             MyModel(),
             MySampler(),
             MCMCSerial(),
             3,
             nchains;
             progress=false,
-            init_params=init_params,
+            initial_params=initial_params,
         )
         @test length(chains) == nchains
         @test all(
             chain[1].a == params.a && chain[1].b == params.b for
-            (chain, params) in zip(chains, init_params)
+            (chain, params) in zip(chains, initial_params)
         )
 
-        init_params = (b=rand(), a=randn())
+        initial_params = (b=rand(), a=randn())
         chains = sample(
             MyModel(),
             MySampler(),
             MCMCSerial(),
             3,
             nchains;
             progress=false,
-            init_params=Iterators.repeated(init_params, nchains),
+            initial_params=Iterators.repeated(initial_params, nchains),
         )
         @test length(chains) == nchains
         @test all(
-            chain[1].a == init_params.a && chain[1].b == init_params.b for chain in chains
+            chain[1].a == initial_params.a && chain[1].b == initial_params.b for chain in chains
         )
 
-        # Too many `init_params`
+        # Too many `initial_params`
         @test_throws ArgumentError sample(
             MyModel(),
             MySampler(),
             MCMCSerial(),
             3,
             nchains;
             progress=false,
-            init_params=Iterators.repeated(init_params, nchains + 1),
+            initial_params=Iterators.repeated(initial_params, nchains + 1),
         )
 
-        # Too few `init_params`
+        # Too few `initial_params`
         @test_throws ArgumentError sample(
             MyModel(),
             MySampler(),
             MCMCSerial(),
             3,
             nchains;
             progress=false,
-            init_params=Iterators.repeated(init_params, nchains - 1),
+            initial_params=Iterators.repeated(initial_params, nchains - 1),
         )
     end
 

test/utils.jl

@@ -22,12 +22,12 @@ function AbstractMCMC.step(
     sampler::MySampler,
     state::Union{Nothing,Integer}=nothing;
     loggers=false,
-    init_params=nothing,
+    initial_params=nothing,
     kwargs...,
 )
     # sample `a` is missing in the first step if not provided
-    a, b = if state === nothing && init_params !== nothing
-        init_params.a, init_params.b
+    a, b = if state === nothing && initial_params !== nothing
+        initial_params.a, initial_params.b
     else
         (state === nothing ? missing : rand(rng)), randn(rng)
     end