CompatHelper: bump compat for "AdvancedPS" to "0.2" (#1610)

* CompatHelper: bump compat for "AdvancedPS" to "0.2"

* CompatHelper: bump compat for "AdvancedPS" to "0.2" for package test (#1612)

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

* Add missing argument

* Not compatible with 0.1 anymore

* Fix `randcat`

* Not compatible with 0.1 anymore

* Fix resample

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

Project.toml

@@ -1,6 +1,6 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.15.22"
+version = "0.15.23"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
@@ -36,7 +36,7 @@ ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"
 AbstractMCMC = "3.2"
 AdvancedHMC = "0.2.24"
 AdvancedMH = "0.6"
-AdvancedPS = "0.1"
+AdvancedPS = "0.2"
 AdvancedVI = "0.1"
 BangBang = "0.3"
 Bijectors = "0.8, 0.9"

src/inference/AdvancedSMC.jl

@@ -22,7 +22,7 @@ end
 """
     SMC(space...)
     SMC([resampler = AdvancedPS.ResampleWithESSThreshold(), space = ()])
-    SMC([resampler = AdvancedPS.resample, ]threshold[, space = ()])
+    SMC([resampler = AdvancedPS.resample_systematic, ]threshold[, space = ()])
 
 Create a sequential Monte Carlo sampler of type [`SMC`](@ref) for the variables in `space`.
 
@@ -37,7 +37,7 @@ end
 function SMC(resampler, threshold::Real, space::Tuple = ())
     return SMC(AdvancedPS.ResampleWithESSThreshold(resampler, threshold), space)
 end
-SMC(threshold::Real, space::Tuple = ()) = SMC(AdvancedPS.resample, threshold, space)
+SMC(threshold::Real, space::Tuple = ()) = SMC(AdvancedPS.resample_systematic, threshold, space)
 
 # If only the space is defined
 SMC(space::Symbol...) = SMC(space)
@@ -100,7 +100,7 @@ function AbstractMCMC.sample(
 end
 
 function DynamicPPL.initialstep(
-    ::AbstractRNG,
+    rng::AbstractRNG,
     model::AbstractModel,
     spl::Sampler{<:SMC},
     vi::AbstractVarInfo;
@@ -119,7 +119,7 @@ function DynamicPPL.initialstep(
     )
 
     # Perform particle sweep.
-    logevidence = AdvancedPS.sweep!(particles, spl.alg.resampler)
+    logevidence = AdvancedPS.sweep!(rng, particles, spl.alg.resampler)
 
     # Extract the first particle and its weight.
     particle = particles.vals[1]
@@ -180,7 +180,7 @@ end
 """
     PG(n, space...)
     PG(n, [resampler = AdvancedPS.ResampleWithESSThreshold(), space = ()])
-    PG(n, [resampler = AdvancedPS.resample, ]threshold[, space = ()])
+    PG(n, [resampler = AdvancedPS.resample_systematic, ]threshold[, space = ()])
 
 Create a Particle Gibbs sampler of type [`PG`](@ref) with `n` particles for the variables
 in `space`.
@@ -201,7 +201,7 @@ function PG(nparticles::Int, resampler, threshold::Real, space::Tuple = ())
     return PG(nparticles, AdvancedPS.ResampleWithESSThreshold(resampler, threshold), space)
 end
 function PG(nparticles::Int, threshold::Real, space::Tuple = ())
-    return PG(nparticles, AdvancedPS.resample, threshold, space)
+    return PG(nparticles, AdvancedPS.resample_systematic, threshold, space)
 end
 
 # If only the number of particles and the space is defined
@@ -258,11 +258,11 @@ function DynamicPPL.initialstep(
     )
 
     # Perform a particle sweep.
-    logevidence = AdvancedPS.sweep!(particles, spl.alg.resampler)
+    logevidence = AdvancedPS.sweep!(rng, particles, spl.alg.resampler)
 
     # Pick a particle to be retained.
     Ws = AdvancedPS.getweights(particles)
-    indx = AdvancedPS.randcat(Ws)
+    indx = AdvancedPS.randcat(rng, Ws)
     reference = particles.vals[indx]
 
     # Compute the first transition.
@@ -273,7 +273,7 @@ function DynamicPPL.initialstep(
 end
 
 function AbstractMCMC.step(
-    ::AbstractRNG,
+    rng::AbstractRNG,
     model::AbstractModel,
     spl::Sampler{<:PG},
     vi::AbstractVarInfo;
@@ -301,11 +301,11 @@ function AbstractMCMC.step(
     particles = AdvancedPS.ParticleContainer(x)
 
     # Perform a particle sweep.
-    logevidence = AdvancedPS.sweep!(particles, spl.alg.resampler)
+    logevidence = AdvancedPS.sweep!(rng, particles, spl.alg.resampler)
 
     # Pick a particle to be retained.
     Ws = AdvancedPS.getweights(particles)
-    indx = AdvancedPS.randcat(Ws)
+    indx = AdvancedPS.randcat(rng, Ws)
     newreference = particles.vals[indx]
 
     # Compute the transition.

test/Project.toml

@@ -30,7 +30,7 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 [compat]
 AbstractMCMC = "3.2"
 AdvancedMH = "0.6"
-AdvancedPS = "0.1"
+AdvancedPS = "0.2"
 AdvancedVI = "0.1"
 Clustering = "0.14"
 CmdStan = "6.0.8"