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Summarizing ZigZag results
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using Soss | |
using ZigZagBoomerang | |
using MeasureTheory | |
using Soss: logdensity, xform, ConditionalModel | |
using ZigZagBoomerang | |
using ForwardDiff | |
using ForwardDiff: gradient! | |
using LinearAlgebra | |
using SparseArrays | |
using StructArrays | |
using TransformVariables | |
# Soss.xform(m::SpikeMixture) = Soss.xform(m.m) | |
# Adapted from https://github.com/mschauer/ZigZagBoomerang.jl/blob/master/Soss/sparsezigzag.jl | |
function zigzag(m::ConditionalModel, T = 100000.0; c=10.0, adapt=false) | |
ℓ(pars) = logdensity(m, pars) | |
t = xform(m) | |
function f(x) | |
(θ, logjac) = TransformVariables.transform_and_logjac(t, x) | |
-ℓ(θ) - logjac | |
end | |
d = t.dimension | |
function partiali() | |
ith = zeros(d) | |
function (x,i) | |
ith[i] = 1 | |
sa = StructArray{ForwardDiff.Dual{}}((x, ith)) | |
δ = f(sa).partials[] | |
ith[i] = 0 | |
return δ | |
end | |
end | |
∇ϕi = partiali() | |
# Draw a random starting points and velocity | |
tkeys = keys(transform(t, zeros(d))) | |
vars = Soss.select(rand(m), tkeys) | |
t0 = 0.0 | |
x0 = inverse(t, vars) | |
θ0 = randn(d) | |
pdmp(∇ϕi, t0, x0, θ0, T, c*ones(d), ZigZag(sparse(I(d)), 0*x0); adapt=adapt) | |
end | |
m = @model x begin | |
α ~ Uniform() | |
β ~ Normal() | |
yhat = α .+ β .* x | |
y ~ For(eachindex(x)) do j | |
Normal(yhat[j], 2.0) | |
end | |
end | |
x = randn(20); | |
obs = -0.1 .+ 2x + 1randn(20); | |
posterior = m(x=x) | (y=obs,) | |
using ParameterHandling | |
using OnlineStats | |
T = 10000.0 | |
trace, final, (num, acc) = @time zigzag(posterior, T) | |
# trace is a continous object, discretize to obtain samples | |
ts, xs = ZigZagBoomerang.sep(discretize(trace, 0.1)) | |
using Measurements | |
function summarize(trace, tform; dt=0.1, ε=0.0001, maxiter=20000) | |
disc = discretize(trace, dt) | |
# iterate(disc) has the form (0.0 => vector_we_want, state) | |
nt = transform(tform, last(first(iterate(disc)))) | |
v, unflatten = flatten(nt) | |
os = [FitNormal() for _ in v] | |
function close_enough(n) | |
o -> √(var(o) / n) / mean(o) < ε | |
end | |
n = 0 | |
for xs in disc | |
n += 1 | |
nt = transform(tform, last(xs)) | |
fit!.(os, first(flatten(nt))) | |
if all(close_enough(n), os) | |
@info "Summarization used $n points" | |
break | |
end | |
if n > maxiter | |
@info "Reached iteration limit $maxiter" | |
break | |
end | |
end | |
means = mean.(os) | |
stds = std.(os) | |
return transform(tform, means .± stds) | |
end | |
# julia> summarize(trace, xform(posterior); maxiter=2000) | |
# [ Info: Reached iteration limit 2000 | |
# (β = 1.45±0.51, α = 0.572±0.052) | |
# julia> summarize(trace, xform(posterior); ε=0.01) | |
# [ Info: Summarization used 5746 points | |
# (β = 1.48±0.42, α = 0.583±0.062) | |
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