SSMProblems integration

Project.toml

@@ -1,14 +1,15 @@
 name = "AdvancedPS"
 uuid = "576499cb-2369-40b2-a588-c64705576edc"
 authors = ["TuringLang"]
-version = "0.5.4"
+version = "0.6"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Random123 = "74087812-796a-5b5d-8853-05524746bad3"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
+SSMProblems = "26aad666-b158-4e64-9d35-0e672562fa48"
 StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
 
 [weakdeps]
@@ -21,10 +22,10 @@ AdvancedPSLibtaskExt = "Libtask"
 AbstractMCMC = "2, 3, 4, 5"
 Distributions = "0.23, 0.24, 0.25"
 Libtask = "0.8"
+Random = "1.6"
 Random123 = "1.3"
 Requires = "1.0"
 StatsFuns = "0.9, 1"
-Random = "1.6"
 julia = "1.6"
 
 [extras]

examples/gaussian-process/Project.toml

@@ -5,3 +5,4 @@ Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Libtask = "6f1fad26-d15e-5dc8-ae53-837a1d7b8c9f"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+SSMProblems = "26aad666-b158-4e64-9d35-0e672562fa48"

examples/gaussian-process/script.jl

@@ -6,18 +6,21 @@ using AbstractGPs
 using Plots
 using Distributions
 using Libtask
+using SSMProblems
 
 Parameters = @NamedTuple begin
     a::Float64
     q::Float64
     kernel
 end
 
-mutable struct GPSSM <: AdvancedPS.AbstractStateSpaceModel
+mutable struct GPSSM <: SSMProblems.AbstractStateSpaceModel
     X::Vector{Float64}
+    observations::Vector{Float64}
     θ::Parameters
 
     GPSSM(params::Parameters) = new(Vector{Float64}(), params)
+    GPSSM(y::Vector{Float64}, params::Parameters) = new(Vector{Float64}(), y, params)
 end
 
 seed = 1
@@ -29,21 +32,20 @@ q = 0.5
 
 params = Parameters((a, q, SqExponentialKernel()))
 
-f(model::GPSSM, x, t) = Normal(model.θ.a * x, model.θ.q)
-h(model::GPSSM) = Normal(0, model.θ.q)
-g(model::GPSSM, x, t) = Normal(0, exp(0.5 * x)^2)
+f(θ::Parameters, x, t) = Normal(θ.a * x, θ.q)
+h(θ::Parameters) = Normal(0, θ.q)
+g(θ::Parameters, x, t) = Normal(0, exp(0.5 * x)^2)
 
 rng = Random.MersenneTwister(seed)
-ref_model = GPSSM(params)
 
 x = zeros(T)
 y = similar(x)
-x[1] = rand(rng, h(ref_model))
+x[1] = rand(rng, h(params))
 for t in 1:T
     if t < T
-        x[t + 1] = rand(rng, f(ref_model, x[t], t))
+        x[t + 1] = rand(rng, f(params, x[t], t))
     end
-    y[t] = rand(rng, g(ref_model, x[t], t))
+    y[t] = rand(rng, g(params, x[t], t))
 end
 
 function gp_update(model::GPSSM, state, step)
@@ -54,12 +56,21 @@ function gp_update(model::GPSSM, state, step)
     return Normal(μ[1], σ[1])
 end
 
-AdvancedPS.initialization(::GPSSM) = h(model)
-AdvancedPS.transition(model::GPSSM, state, step) = gp_update(model, state, step)
-AdvancedPS.observation(model::GPSSM, state, step) = logpdf(g(model, state, step), y[step])
+SSMProblems.transition!!(rng::AbstractRNG, model::GPSSM) = rand(rng, h(model.θ))
+function SSMProblems.transition!!(rng::AbstractRNG, model::GPSSM, state, step)
+    return rand(rng, gp_update(model, state, step))
+end
+
+function SSMProblems.emission_logdensity(model::GPSSM, state, step)
+    return logpdf(g(model.θ, state, step), model.observations[step])
+end
+function SSMProblems.transition_logdensity(model::GPSSM, prev_state, current_state, step)
+    return logpdf(gp_update(model, prev_state, step), current_state)
+end
+
 AdvancedPS.isdone(::GPSSM, step) = step > T
 
-model = GPSSM(params)
+model = GPSSM(y, params)
 pg = AdvancedPS.PGAS(Nₚ)
 chains = sample(rng, model, pg, Nₛ)
 

examples/gaussian-ssm/Project.toml

@@ -5,4 +5,5 @@ Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Libtask = "6f1fad26-d15e-5dc8-ae53-837a1d7b8c9f"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+SSMProblems = "26aad666-b158-4e64-9d35-0e672562fa48"
 StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"

examples/gaussian-ssm/script.jl

@@ -3,6 +3,7 @@ using AdvancedPS
 using Random
 using Distributions
 using Plots
+using SSMProblems
 
 # We consider the following linear state-space model with Gaussian innovations. The latent state is a simple gaussian random walk
 # and the observation is linear in the latent states, namely:
@@ -33,32 +34,45 @@ Parameters = @NamedTuple begin
     r::Float64
 end
 
-mutable struct LinearSSM <: AdvancedPS.AbstractStateSpaceModel
+mutable struct LinearSSM <: SSMProblems.AbstractStateSpaceModel
     X::Vector{Float64}
+    observations::Vector{Float64}
     θ::Parameters
     LinearSSM(θ::Parameters) = new(Vector{Float64}(), θ)
+    LinearSSM(y::Vector, θ::Parameters) = new(Vector{Float64}(), y, θ)
 end
 
 # and the densities defined above.
-f(m::LinearSSM, state, t) = Normal(m.θ.a * state, m.θ.q) # Transition density
-g(m::LinearSSM, state, t) = Normal(state, m.θ.r)         # Observation density
-f₀(m::LinearSSM) = Normal(0, m.θ.q^2 / (1 - m.θ.a^2))    # Initial state density
+f(θ::Parameters, state, t) = Normal(θ.a * state, θ.q) # Transition density
+g(θ::Parameters, state, t) = Normal(state, θ.r)         # Observation density
+f₀(θ::Parameters) = Normal(0, θ.q^2 / (1 - θ.a^2))    # Initial state density
 #md nothing #hide
 
 # We also need to specify the dynamics of the system through the transition equations:
 # - `AdvancedPS.initialization`: the initial state density
 # - `AdvancedPS.transition`: the state transition density
 # - `AdvancedPS.observation`: the observation score given the observed data
 # - `AdvancedPS.isdone`: signals the end of the execution for the model
-AdvancedPS.initialization(model::LinearSSM) = f₀(model)
-AdvancedPS.transition(model::LinearSSM, state, step) = f(model, state, step)
-function AdvancedPS.observation(model::LinearSSM, state, step)
-    return logpdf(g(model, state, step), y[step])
+SSMProblems.transition!!(rng::AbstractRNG, model::LinearSSM) = rand(rng, f₀(model.θ))
+function SSMProblems.transition!!(
+    rng::AbstractRNG, model::LinearSSM, state::Float64, step::Int
+)
+    return rand(rng, f(model.θ, state, step))
+end
+
+function SSMProblems.emission_logdensity(modeL::LinearSSM, state::Float64, step::Int)
+    return logpdf(g(model.θ, state, step), model.observations[step])
+end
+function SSMProblems.transition_logdensity(
+    model::LinearSSM, prev_state, current_state, step
+)
+    return logpdf(f(model.θ, prev_state, step), current_state)
 end
+
+# We need to think seriously about how the data is handled
 AdvancedPS.isdone(::LinearSSM, step) = step > Tₘ
 
 # Everything is now ready to simulate some data. 
-
 a = 0.9   # Scale
 q = 0.32  # State variance
 r = 1     # Observation variance
@@ -72,14 +86,12 @@ rng = Random.MersenneTwister(seed)
 
 x = zeros(Tₘ)
 y = zeros(Tₘ)
-
-reference = LinearSSM(θ₀)
-x[1] = rand(rng, f₀(reference))
+x[1] = rand(rng, f₀(θ₀))
 for t in 1:Tₘ
     if t < Tₘ
-        x[t + 1] = rand(rng, f(reference, x[t], t))
+        x[t + 1] = rand(rng, f(θ₀, x[t], t))
     end
-    y[t] = rand(rng, g(reference, x[t], t))
+    y[t] = rand(rng, g(θ₀, x[t], t))
 end
 
 # Here are the latent and obseravation timeseries
@@ -88,7 +100,7 @@ plot!(y; seriestype=:scatter, label="y", xlabel="t", mc=:red, ms=2, ma=0.5)
 
 # `AdvancedPS` subscribes to the `AbstractMCMC` API. To sample we just need to define a Particle Gibbs kernel
 # and a model interface. 
-model = LinearSSM(θ₀)
+model = LinearSSM(y, θ₀)
 pgas = AdvancedPS.PGAS(Nₚ)
 chains = sample(rng, model, pgas, Nₛ; progress=false);
 #md nothing #hide

examples/levy-ssm/Project.toml

@@ -0,0 +1,7 @@
+[deps]
+AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
+AdvancedPS = "576499cb-2369-40b2-a588-c64705576edc"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+SSMProblems = "26aad666-b158-4e64-9d35-0e672562fa48"