Soss: Lightweight Probabilistic Programming in Julia

Chad Scherrer

Senior Data Scientist, Metis

https://bit.ly/2SNDr1N

Follow Along Live

About Me

Served as Technical Lead for language evaluation for the DARPA program
Probabilistic Programming for Advancing Machine Learning (PPAML)

PPL Publications

Making Rational Decisions

Managed Uncertainty

Rational Decisions

Bayesian Analysis

Probabilistic Programming

  • Physical systems
  • Hypothesis testing
  • Modeling as simulation
  • Medicine
  • Finance
  • Insurance

Risk

Custom models

Business Applications

https://cscherrer.github.io/post/max-profit/

Missing Data

The Two-Language Problem

A disconnect between the "user language" and "developer language"

X

3

Python

C

Deep Learning Framework

  • Harder for beginner users
  • Barrier to entry for developers
  • Limits extensibility

?

A New Approach in Julia

  • Models begin with "Tilde code" based on math notation


     
  • A Model is a "lossless" data structure

     

  • Combinators transform code before compilation

Model

Tilde code
\begin{aligned} \mu &\sim \text{Normal}(0,5)\\ \sigma &\sim \text{Cauchy}_+(0,3) \\ x_j &\sim \text{Normal}(\mu,\sigma) \end{aligned}
μ∼Normal(0,5)σ∼Cauchy+(0,3)xj∼Normal(μ,σ)\begin{aligned} \mu &\sim \text{Normal}(0,5)\\ \sigma &\sim \text{Cauchy}_+(0,3) \\ x_j &\sim \text{Normal}(\mu,\sigma) \end{aligned}

Advantages

  • Transparent
  • Composable
  • Backend-agnostic
  • No intrinsic overhead

A Simple Example
P(\mu,\sigma|x)\propto P(\mu,\sigma)P(x|\mu,\sigma)
P(μ,σ∣x)∝P(μ,σ)P(x∣μ,σ)P(\mu,\sigma|x)\propto P(\mu,\sigma)P(x|\mu,\sigma)
\begin{aligned} \mu &\sim \text{Normal}(0,5)\\ \sigma &\sim \text{Cauchy}_+(0,3) \\ x_j &\sim \text{Normal}(\mu,\sigma) \end{aligned}
μ∼Normal(0,5)σ∼Cauchy+(0,3)xj∼Normal(μ,σ)\begin{aligned} \mu &\sim \text{Normal}(0,5)\\ \sigma &\sim \text{Cauchy}_+(0,3) \\ x_j &\sim \text{Normal}(\mu,\sigma) \end{aligned}
julia> nuts(m(:x), data=data, numSamples=10).samples
 (μ = 0.524, σ = 3.549) 
 (μ = 3.540, σ = 2.089)  
 (μ = 3.727, σ = 2.483) 
 (μ = 3.463, σ = 2.582) 
 (μ = 5.052, σ = 2.919) 
 (μ = 4.813, σ = 1.818) 
 (μ = 4.015, σ = 1.587)  
 (μ = 5.244, σ = 3.949)  
 (μ = 1.456, σ = 5.624)
 (μ = 4.045, σ = 2.010) 

m = @model begin
    μ ~ Normal(0, 5)
    σ ~ HalfCauchy(3)
    x ~ Normal(μ, σ) |> iid
end

Theory

Soss.jl

Some Simple Model Combinators

m = @model begin
    μ ~ Normal(0, 5)
    σ ~ HalfCauchy(3)
    x ~ Normal(μ, σ) |> iid
end
julia> m(:x)
m = @model x begin
    μ ~ Normal(0, 5)
    σ ~ HalfCauchy(3)
    x ~ Normal(μ, σ) |> iid
end
julia> m(x = [1.0, 1.1, 1.5])
@model begin
    x = [1.0, 1.1, 1.5]
    μ ~ Normal(0, 5)
    σ ~ HalfCauchy(3)
    x ~ Normal(μ, σ) |> iid
end

Observe values statically

or dynamically

Composability: "First Class" Models

m1 = @model begin
    ...
end
mix = @model x,components begin
    α = ones(length(components))
    weights ~ Dirichlet(α)
    x ~ MixtureModel(components,weights) |> iid
end
julia> mix(components=[m1, m2])
@model x begin
    components = [m1, m2]
    K = length(components)
    weights ~ Dirichlet(ones(K))
    x ~ MixtureModel(components, weights) |> iid
end

m2 = @model begin
    ...
end

Metaprogramming: Log-Density Calculation

function logdensity(model)
    body = postwalk(model.body) do x
        if @capture(x, v_ ~ dist_)
            assignment = ifelse(
                v ∈ parameters(model),
                :($v = par.$v),
                :($v = data.$v)
            )
            quote
                $assignment
                ℓ += logpdf($dist, $v)
            end
        else x
        end
    end

    return quote 
        function(par, data)
            ℓ = 0.0
            $body
            return ℓ
        end
    end

end
julia> m(:x)
@model x begin
    μ ~ Normal(0, 5)
    σ ~ HalfCauchy(3)
    x ~ Normal(μ, σ) |> iid
end
julia> logdensity(m(:x))
:(function (par, data)
      ℓ = 0.0
      μ = par.μ
      ℓ += logpdf(Normal(0, 5), μ)
      σ = par.σ
      ℓ += logpdf(HalfCauchy(3), σ)
      x = data.x
      ℓ += logpdf(Normal(μ, σ) |> iid, x)
      return ℓ
  end)

Static Dependency Analysis

julia> lda
@model (α, η, K, V, N) begin
    M = length(N)
    β ~ Dirichlet(repeat([η], V)) |> iid(K)
    θ ~ Dirichlet(repeat([α], K)) |> iid(M)
    z ~ For(1:M) do m
            Categorical(θ[m]) |> iid(N[m])
        end
    w ~ For(1:M) do m
            For(1:N[m]) do n
                Categorical(β[(z[m])[n]])
            end
        end
end
julia> dependencies(lda)
10-element Array{Pair{Array{Symbol,1},Symbol},1}:
               [] => :α
               [] => :η
               [] => :K
               [] => :V
               [] => :N
             [:N] => :M
     [:η, :V, :K] => :β
     [:α, :K, :M] => :θ
     [:M, :θ, :N] => :z
 [:M, :N, :β, :z] => :w

Blei, Ng, & Jordan (2003). Latent Dirichlet Allocation. JMLR, 3(4–5), 993–1022.

Future Work

  • More  model types
    • Online algorithms (Kalman filtering, etc)
    • Model type hierarchy (StanLike <: Universal, etc)
  • More back-ends
    • Existing PPLs
    • Variational inference (ADVI, Pyro-style, etc)
  • More model combinators
    • Non-centered parameterization
    • Distributional approximation
    • "Pre-Gibbs" decompositions

logo by James Fairbanks, on Julia  Discourse site

JuliaCon 2019

July 21-27

Baltimore, MD

Thank You!

https://cscherrer.github.io/

https://github.com/cscherrer/Soss.jl

2019-01-11 PyData Miami

By Chad Scherrer

2019-01-11 PyData Miami

  • 3,833

More from Chad Scherrer