Rename init_params keyword argument to initial_params (#33)

* Rename `init_params` keyword argument to `initial_params`

* Apply suggestions from code review

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

---------

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Project.toml

@@ -1,7 +1,7 @@
 name = "EllipticalSliceSampling"
 uuid = "cad2338a-1db2-11e9-3401-43bc07c9ede2"
 authors = ["David Widmann <david.widmann@it.uu.se>"]
-version = "1.1.0"
+version = "2.0.0"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
@@ -11,7 +11,7 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
-AbstractMCMC = "3.2, 4"
+AbstractMCMC = "5"
 ArrayInterface = "7"
 Distributions = "0.22, 0.23, 0.24, 0.25"
 julia = "1.6"

docs/src/index.md

@@ -55,7 +55,7 @@ AbstractMCMC.steps(
 gives you access to an iterator from which you can generate an unlimited
 number of samples.
 
-You can define the starting point of your chain using the `init_params` keyword argument.
+You can define the starting point of your chain using the `initial_params` keyword argument.
 
 For more details regarding `sample` and `steps` please check the documentation of
 [AbstractMCMC.jl](https://github.com/TuringLang/AbstractMCMC.jl).

src/abstractmcmc.jl

@@ -22,11 +22,11 @@ function AbstractMCMC.step(
     rng::Random.AbstractRNG,
     model::AbstractMCMC.AbstractModel,
     ::ESS;
-    init_params=nothing,
+    initial_params=nothing,
     kwargs...,
 )
     # initial sample from the Gaussian prior
-    f = init_params === nothing ? initial_sample(rng, model) : init_params
+    f = initial_params === nothing ? initial_sample(rng, model) : initial_params
 
     # compute log-likelihood of the initial sample
     loglikelihood = Distributions.loglikelihood(model, f)

test/simple.jl

@@ -37,14 +37,16 @@
             # initial parameter
             init_x = randn(5)
             samples = sample(
-                ESSModel(prior, ℓ), ESS(), alg, 10, 5; progress=false, init_params=init_x
+                ESSModel(prior, ℓ), ESS(), alg, 10, 5; progress=false, initial_params=init_x
             )
             @test map(first, samples) == init_x
         end
 
         # initial parameter
         init_x = randn()
-        samples = sample(ESSModel(prior, ℓ), ESS(), 10; progress=false, init_params=init_x)
+        samples = sample(
+            ESSModel(prior, ℓ), ESS(), 10; progress=false, initial_params=init_x
+        )
         @test first(samples) == init_x
     end
 
@@ -77,14 +79,16 @@
             # initial parameter
             init_x = randn(5)
             samples = sample(
-                ESSModel(prior, ℓ), ESS(), alg, 10, 5; progress=false, init_params=init_x
+                ESSModel(prior, ℓ), ESS(), alg, 10, 5; progress=false, initial_params=init_x
             )
             @test map(first, samples) == init_x
         end
 
         # initial parameter
         init_x = randn()
-        samples = sample(ESSModel(prior, ℓ), ESS(), 10; progress=false, init_params=init_x)
+        samples = sample(
+            ESSModel(prior, ℓ), ESS(), 10; progress=false, initial_params=init_x
+        )
         @test first(samples) == init_x
     end
 
@@ -118,15 +122,21 @@
             # initial parameter
             init_x = [randn(1) for _ in 1:5]
             samples = sample(
-                ESSModel(prior, ℓvec), ESS(), alg, 10, 5; progress=false, init_params=init_x
+                ESSModel(prior, ℓvec),
+                ESS(),
+                alg,
+                10,
+                5;
+                progress=false,
+                initial_params=init_x,
             )
             @test map(first, samples) == init_x
         end
 
         # initial parameter
         init_x = randn(1)
         samples = sample(
-            ESSModel(prior, ℓvec), ESS(), 10; progress=false, init_params=init_x
+            ESSModel(prior, ℓvec), ESS(), 10; progress=false, initial_params=init_x
         )
         @test first(samples) == init_x
     end
@@ -161,15 +171,21 @@
             # initial parameter
             init_x = [randn(1) for _ in 1:5]
             samples = sample(
-                ESSModel(prior, ℓvec), ESS(), alg, 10, 5; progress=false, init_params=init_x
+                ESSModel(prior, ℓvec),
+                ESS(),
+                alg,
+                10,
+                5;
+                progress=false,
+                initial_params=init_x,
             )
             @test map(first, samples) == init_x
         end
 
         # initial parameter
         init_x = randn(1)
         samples = sample(
-            ESSModel(prior, ℓvec), ESS(), 10; progress=false, init_params=init_x
+            ESSModel(prior, ℓvec), ESS(), 10; progress=false, initial_params=init_x
         )
         @test first(samples) == init_x
     end