Merge pull request #70 from TuringLang/torfjelde/logdensitymodel

Added support for models using the LogDensityProblems.jl interface

Project.toml

@@ -1,15 +1,16 @@
 name = "AdvancedMH"
 uuid = "5b7e9947-ddc0-4b3f-9b55-0d8042f74170"
-version = "0.6.8"
+version = "0.7.0"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 
 [compat]
-AbstractMCMC = "2, 3.0, 4"
+AbstractMCMC = "4"
 Distributions = "0.20, 0.21, 0.22, 0.23, 0.24, 0.25"
 Requires = "1"
 julia = "1"
@@ -18,9 +19,11 @@ julia = "1"
 DiffResults = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
+LogDensityProblemsAD = "996a588d-648d-4e1f-a8f0-a84b347e47b1"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 StructArrays = "09ab397b-f2b6-538f-b94a-2f83cf4a842a"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["DiffResults", "ForwardDiff", "LinearAlgebra", "MCMCChains", "StructArrays", "Test"]
+test = ["DiffResults", "ForwardDiff", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "MCMCChains", "StructArrays", "Test"]

README.md

@@ -68,6 +68,24 @@ Quantiles
 
 ```
 
+### Usage with [`LogDensityProblems.jl`](https://github.com/tpapp/LogDensityProblems.jl)
+
+It can also be used with models defining the [`LogDensityProblems.jl`](https://github.com/tpapp/LogDensityProblems.jl) interface by wrapping it in `AbstractMCMC.LogDensityModel` before passing it to `sample`:
+
+``` julia
+using AbstractMCMC: LogDensityModel
+using LogDensityProblems
+
+# Use a struct instead of `typeof(density)` for sake of readability.
+struct LogTargetDensity end
+
+LogDensityProblems.logdensity(p::LogTargetDensity, θ) = density(θ)  # standard multivariate normal
+LogDensityProblems.dimension(p::LogTargetDensity) = 2
+LogDensityProblems.capabilities(::LogTargetDensity) = LogDensityProblems.LogDensityOrder{0}()
+
+sample(LogDensityModel(LogTargetDensity()), spl, 100000; param_names=["μ", "σ"], chain_type=Chains)
+```
+
 ## Proposals
 
 AdvancedMH offers various methods of defining your inference problem. Behind the scenes, a `MetropolisHastings` sampler simply holds
@@ -162,3 +180,13 @@ spl = MALA(x -> MvNormal((σ² / 2) .* x, σ² * I))
 # Sample from the posterior.
 chain = sample(model, spl, 100000; init_params=ones(2), chain_type=StructArray, param_names=["μ", "σ"])
 ```
+
+### Usage with [`LogDensityProblemsAD.jl`](https://github.com/tpapp/LogDensityProblemsAD.jl)
+
+Using our implementation of the `LogDensityProblems.jl` interface from earlier, we can use [`LogDensityProblemsAD.jl`](https://github.com/tpapp/LogDensityProblemsAD.jl) to provide us with the gradient computation used in MALA:
+
+```julia
+using LogDensityProblemsAD
+model_with_ad = LogDensityProblemsAD.ADgradient(Val(:ForwardDiff), LogTargetDensity())
+sample(LogDensityModel(model_with_ad), spl, 100000; init_params=ones(2), chain_type=StructArray, param_names=["μ", "σ"])
+```

src/AdvancedMH.jl

@@ -5,6 +5,8 @@ using AbstractMCMC
 using Distributions
 using Requires
 
+using LogDensityProblems: LogDensityProblems
+
 import Random
 
 # Exports
@@ -48,6 +50,8 @@ struct DensityModel{F} <: AbstractMCMC.AbstractModel
     logdensity :: F
 end
 
+const DensityModelOrLogDensityModel = Union{<:DensityModel,<:AbstractMCMC.LogDensityModel}
+
 # Create a very basic Transition type, only stores the
 # parameter draws and the log probability of the draw.
 struct Transition{T,L<:Real} <: AbstractTransition
@@ -56,16 +60,22 @@ struct Transition{T,L<:Real} <: AbstractTransition
 end
 
 # Store the new draw and its log density.
-Transition(model::DensityModel, params) = Transition(params, logdensity(model, params))
+Transition(model::DensityModelOrLogDensityModel, params) = Transition(params, logdensity(model, params))
+function Transition(model::AbstractMCMC.LogDensityModel, params)
+    return Transition(params, LogDensityProblems.logdensity(model.logdensity, params))
+end
 
 # Calculate the density of the model given some parameterization.
-logdensity(model::DensityModel, params) = model.logdensity(params)
-logdensity(model::DensityModel, t::Transition) = t.lp
+logdensity(model::DensityModelOrLogDensityModel, params) = model.logdensity(params)
+logdensity(model::DensityModelOrLogDensityModel, t::Transition) = t.lp
+
+logdensity(model::AbstractMCMC.LogDensityModel, params) = LogDensityProblems.logdensity(model.logdensity, params)
+logdensity(model::AbstractMCMC.LogDensityModel, t::Transition) = t.lp
 
 # A basic chains constructor that works with the Transition struct we defined.
 function AbstractMCMC.bundle_samples(
     ts::Vector{<:AbstractTransition},
-    model::DensityModel,
+    model::Union{<:DensityModelOrLogDensityModel,<:AbstractMCMC.LogDensityModel},
     sampler::MHSampler,
     state,
     chain_type::Type{Vector{NamedTuple}};
@@ -91,7 +101,7 @@ end
 
 function AbstractMCMC.bundle_samples(
     ts::Vector{<:Transition{<:NamedTuple}},
-    model::DensityModel,
+    model::Union{<:DensityModelOrLogDensityModel,<:AbstractMCMC.LogDensityModel},
     sampler::MHSampler,
     state,
     chain_type::Type{Vector{NamedTuple}};

src/MALA.jl

@@ -19,20 +19,34 @@ struct GradientTransition{T<:Union{Vector, Real, NamedTuple}, L<:Real, G<:Union{
     gradient::G
 end
 
-logdensity(model::DensityModel, t::GradientTransition) = t.lp
+logdensity(model::DensityModelOrLogDensityModel, t::GradientTransition) = t.lp
 
 propose(rng::Random.AbstractRNG, ::MALA, model) = error("please specify initial parameters")
-function transition(sampler::MALA, model::DensityModel, params)
+function transition(sampler::MALA, model::DensityModelOrLogDensityModel, params)
     return GradientTransition(params, logdensity_and_gradient(model, params)...)
 end
 
+check_capabilities(model::DensityModelOrLogDensityModel) = nothing
+function check_capabilities(model::AbstractMCMC.LogDensityModel)
+    cap = LogDensityProblems.capabilities(model.logdensity)
+    if cap === nothing
+        throw(ArgumentError("The log density function does not support the LogDensityProblems.jl interface"))
+    end
+
+    if cap === LogDensityProblems.LogDensityOrder{0}()
+        throw(ArgumentError("The gradient of the log density function is not defined: Implement `LogDensityProblems.logdensity_and_gradient` or use automatic differentiation provided by LogDensityProblemsAD.jl"))
+    end
+end
+
 function AbstractMCMC.step(
     rng::Random.AbstractRNG,
-    model::DensityModel,
+    model::DensityModelOrLogDensityModel,
     sampler::MALA,
     transition_prev::GradientTransition;
     kwargs...
 )
+    check_capabilities(model)
+
     # Extract value and gradient of the log density of the current state.
     state = transition_prev.params
     logdensity_state = transition_prev.lp
@@ -76,3 +90,13 @@ function logdensity_and_gradient(model::DensityModel, params)
     return value(res), gradient(res)
 end
 
+"""
+    logdensity_and_gradient(model::AbstractMCMC.LogDensityModel, params)
+
+Return the value and gradient of the log density of the parameters `params` for the `model`.
+"""
+function logdensity_and_gradient(model::AbstractMCMC.LogDensityModel, params)
+     return LogDensityProblems.logdensity_and_gradient(model.logdensity, params)
+ end
+
+

src/emcee.jl

@@ -3,7 +3,7 @@ struct Ensemble{D} <: MHSampler
     proposal::D
 end
 
-function transition(sampler::Ensemble, model::DensityModel, params)
+function transition(sampler::Ensemble, model::DensityModelOrLogDensityModel, params)
     return [Transition(model, x) for x in params]
 end
 
@@ -13,7 +13,7 @@ end
 # (if accepted) or the previous proposal (if not accepted).
 function AbstractMCMC.step(
     rng::Random.AbstractRNG,
-    model::DensityModel,
+    model::DensityModelOrLogDensityModel,
     spl::Ensemble,
     params_prev::Vector{<:Transition};
     kwargs...,
@@ -26,7 +26,7 @@ end
 #
 # Initial proposal
 #
-function propose(rng::Random.AbstractRNG, spl::Ensemble, model::DensityModel)
+function propose(rng::Random.AbstractRNG, spl::Ensemble, model::DensityModelOrLogDensityModel)
     # Make the first proposal with a static draw from the prior.
     static_prop = StaticProposal(spl.proposal.proposal)
     mh_spl = MetropolisHastings(static_prop)
@@ -39,7 +39,7 @@ end
 function propose(
     rng::Random.AbstractRNG,
     spl::Ensemble,
-    model::DensityModel,
+    model::DensityModelOrLogDensityModel,
     walkers::Vector{<:Transition},
 )
     new_walkers = similar(walkers)
@@ -68,7 +68,7 @@ StretchProposal(p) = StretchProposal(p, 2.0)
 function move(
     rng::Random.AbstractRNG, 
     spl::Ensemble{<:StretchProposal},
-    model::DensityModel,
+    model::DensityModelOrLogDensityModel,
     walker::Transition,
     other_walker::Transition,
 )

src/mcmcchains-connect.jl

@@ -3,7 +3,7 @@ import .MCMCChains: Chains
 # A basic chains constructor that works with the Transition struct we defined.
 function AbstractMCMC.bundle_samples(
     ts::Vector{<:AbstractTransition},
-    model::DensityModel,
+    model::DensityModelOrLogDensityModel,
     sampler::MHSampler,
     state,
     chain_type::Type{Chains};
@@ -32,7 +32,7 @@ end
 
 function AbstractMCMC.bundle_samples(
     ts::Vector{<:Transition{<:NamedTuple}},
-    model::DensityModel,
+    model::DensityModelOrLogDensityModel,
     sampler::MHSampler,
     state,
     chain_type::Type{Chains};
@@ -71,7 +71,7 @@ end
 
 function AbstractMCMC.bundle_samples(
     ts::Vector{<:Vector{<:AbstractTransition}},
-    model::DensityModel,
+    model::DensityModelOrLogDensityModel,
     sampler::Ensemble,
     state,
     chain_type::Type{Chains};

src/mh-core.jl

@@ -48,23 +48,23 @@ end
 StaticMH(d) = MetropolisHastings(StaticProposal(d))
 RWMH(d) = MetropolisHastings(RandomWalkProposal(d))
 
-function propose(rng::Random.AbstractRNG, sampler::MHSampler, model::DensityModel)
+function propose(rng::Random.AbstractRNG, sampler::MHSampler, model::DensityModelOrLogDensityModel)
     return propose(rng, sampler.proposal, model)
 end
 function propose(
     rng::Random.AbstractRNG,
     sampler::MHSampler,
-    model::DensityModel,
+    model::DensityModelOrLogDensityModel,
     transition_prev::Transition,
 )
     return propose(rng, sampler.proposal, model, transition_prev.params)
 end
 
-function transition(sampler::MHSampler, model::DensityModel, params)
+function transition(sampler::MHSampler, model::DensityModelOrLogDensityModel, params)
     logdensity = AdvancedMH.logdensity(model, params)
     return transition(sampler, model, params, logdensity)
 end
-function transition(sampler::MHSampler, model::DensityModel, params, logdensity::Real)
+function transition(sampler::MHSampler, model::DensityModelOrLogDensityModel, params, logdensity::Real)
     return Transition(params, logdensity)
 end
 
@@ -73,7 +73,7 @@ end
 # In this case they are identical.
 function AbstractMCMC.step(
     rng::Random.AbstractRNG,
-    model::DensityModel,
+    model::DensityModelOrLogDensityModel,
     sampler::MHSampler;
     init_params=nothing,
     kwargs...
@@ -89,7 +89,7 @@ end
 # or the previous proposal (if not accepted).
 function AbstractMCMC.step(
     rng::Random.AbstractRNG,
-    model::DensityModel,
+    model::DensityModelOrLogDensityModel,
     sampler::MHSampler,
     transition_prev::AbstractTransition;
     kwargs...

src/proposal.jl

@@ -41,15 +41,15 @@ end
 function propose(
     rng::Random.AbstractRNG,
     proposal::RandomWalkProposal{issymmetric,<:Union{Distribution,AbstractArray}},
-    ::DensityModel
+    ::DensityModelOrLogDensityModel
 ) where {issymmetric}
     return rand(rng, proposal)
 end
 
 function propose(
     rng::Random.AbstractRNG,
     proposal::RandomWalkProposal{issymmetric,<:Union{Distribution,AbstractArray}},
-    model::DensityModel,
+    model::DensityModelOrLogDensityModel,
     t
 ) where {issymmetric}
     return t + rand(rng, proposal)
@@ -70,7 +70,7 @@ end
 function propose(
     rng::Random.AbstractRNG,
     proposal::StaticProposal{issymmetric,<:Union{Distribution,AbstractArray}},
-    model::DensityModel,
+    model::DensityModelOrLogDensityModel,
     t=nothing
 ) where {issymmetric}
     return rand(rng, proposal)
@@ -103,15 +103,15 @@ end
 function propose(
     rng::Random.AbstractRNG,
     proposal::Proposal{<:Function},
-    model::DensityModel
+    model::DensityModelOrLogDensityModel
 )
     return propose(rng, proposal(), model)
 end
 
 function propose(
     rng::Random.AbstractRNG,
     proposal::Proposal{<:Function}, 
-    model::DensityModel,
+    model::DensityModelOrLogDensityModel,
     t
 )
     return propose(rng, proposal(t), model)
@@ -132,7 +132,7 @@ end
 function propose(
     rng::Random.AbstractRNG,
     proposals::AbstractArray{<:Proposal},
-    model::DensityModel,
+    model::DensityModelOrLogDensityModel,
 )
     return map(proposals) do proposal
         return propose(rng, proposal, model)
@@ -141,7 +141,7 @@ end
 function propose(
     rng::Random.AbstractRNG,
     proposals::AbstractArray{<:Proposal},
-    model::DensityModel,
+    model::DensityModelOrLogDensityModel,
     ts,
 )
     return map(proposals, ts) do proposal, t
@@ -152,7 +152,7 @@ end
 @generated function propose(
     rng::Random.AbstractRNG,
     proposals::NamedTuple{names},
-    model::DensityModel,
+    model::DensityModelOrLogDensityModel,
 ) where {names}
     isempty(names) && return :(NamedTuple())
     expr = Expr(:tuple)
@@ -163,7 +163,7 @@ end
 @generated function propose(
     rng::Random.AbstractRNG,
     proposals::NamedTuple{names},
-    model::DensityModel,
+    model::DensityModelOrLogDensityModel,
     ts,
 ) where {names}
     isempty(names) && return :(NamedTuple())

src/structarray-connect.jl

@@ -3,7 +3,7 @@ import .StructArrays: StructArray
 # A basic chains constructor that works with the Transition struct we defined.
 function AbstractMCMC.bundle_samples(
     ts::Vector{<:AbstractTransition},
-    model::DensityModel,
+    model::DensityModelOrLogDensityModel,
     sampler::MHSampler,
     state,
     chain_type::Type{StructArray};