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InitContext
, part 3 - Introduce InitContext
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54446c1
Implement InitContext
penelopeysm 1ef1a92
Fix loading order of modules; move `prefix(::Model)` to model.jl
penelopeysm a90d95e
Add tests for InitContext behaviour
penelopeysm 001a05a
inline `rand(::Distributions.Uniform)`
penelopeysm b55c1e1
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Original file line number | Diff line number | Diff line change |
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""" | ||
AbstractInitStrategy | ||
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||
Abstract type representing the possible ways of initialising new values for | ||
the random variables in a model (e.g., when creating a new VarInfo). | ||
""" | ||
abstract type AbstractInitStrategy end | ||
|
||
""" | ||
init(rng::Random.AbstractRNG, vn::VarName, dist::Distribution, strategy::AbstractInitStrategy) | ||
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Generate a new value for a random variable with the given distribution. | ||
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!!! warning "Values must be unlinked" | ||
The values returned by `init` are always in the untransformed space, i.e., | ||
they must be within the support of the original distribution. That means that, | ||
for example, `init(rng, dist, u::UniformInit)` will in general return values that | ||
are outside the range [u.lower, u.upper]. | ||
""" | ||
function init end | ||
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""" | ||
PriorInit() | ||
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Obtain new values by sampling from the prior distribution. | ||
""" | ||
struct PriorInit <: AbstractInitStrategy end | ||
init(rng::Random.AbstractRNG, ::VarName, dist::Distribution, ::PriorInit) = rand(rng, dist) | ||
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""" | ||
UniformInit() | ||
UniformInit(lower, upper) | ||
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Obtain new values by first transforming the distribution of the random variable | ||
to unconstrained space, then sampling a value uniformly between `lower` and | ||
`upper`, and transforming that value back to the original space. | ||
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If `lower` and `upper` are unspecified, they default to `(-2, 2)`, which mimics | ||
Stan's default initialisation strategy. | ||
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Requires that `lower <= upper`. | ||
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# References | ||
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[Stan reference manual page on initialization](https://mc-stan.org/docs/reference-manual/execution.html#initialization) | ||
""" | ||
struct UniformInit{T<:AbstractFloat} <: AbstractInitStrategy | ||
lower::T | ||
upper::T | ||
function UniformInit(lower::T, upper::T) where {T<:AbstractFloat} | ||
lower > upper && | ||
throw(ArgumentError("`lower` must be less than or equal to `upper`")) | ||
return new{T}(lower, upper) | ||
end | ||
UniformInit() = UniformInit(-2.0, 2.0) | ||
end | ||
function init(rng::Random.AbstractRNG, ::VarName, dist::Distribution, u::UniformInit) | ||
b = Bijectors.bijector(dist) | ||
sz = Bijectors.output_size(b, size(dist)) | ||
y = u.lower .+ ((u.upper - u.lower) .* rand(rng, sz...)) | ||
b_inv = Bijectors.inverse(b) | ||
x = b_inv(y) | ||
# 0-dim arrays: https://github.com/TuringLang/Bijectors.jl/issues/398 | ||
if x isa Array{<:Any,0} | ||
x = x[] | ||
end | ||
return x | ||
end | ||
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""" | ||
ParamsInit(params::AbstractDict{<:VarName}, default::AbstractInitStrategy=PriorInit()) | ||
ParamsInit(params::NamedTuple, default::AbstractInitStrategy=PriorInit()) | ||
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Obtain new values by extracting them from the given dictionary or NamedTuple. | ||
The parameter `default` specifies how new values are to be obtained if they | ||
cannot be found in `params`, or they are specified as `missing`. The default | ||
for `default` is `PriorInit()`. | ||
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!!! note | ||
These values must be provided in the space of the untransformed distribution. | ||
""" | ||
struct ParamsInit{P,S<:AbstractInitStrategy} <: AbstractInitStrategy | ||
params::P | ||
default::S | ||
function ParamsInit(params::AbstractDict{<:VarName}, default::AbstractInitStrategy) | ||
return new{typeof(params),typeof(default)}(params, default) | ||
end | ||
ParamsInit(params::AbstractDict{<:VarName}) = ParamsInit(params, PriorInit()) | ||
function ParamsInit(params::NamedTuple, default::AbstractInitStrategy=PriorInit()) | ||
return ParamsInit(to_varname_dict(params), default) | ||
end | ||
end | ||
function init(rng::Random.AbstractRNG, vn::VarName, dist::Distribution, p::ParamsInit) | ||
# TODO(penelopeysm): It would be nice to do a check to make sure that all | ||
# of the parameters in `p.params` were actually used, and either warn or | ||
# error if they aren't. This is actually quite non-trivial though because | ||
# the structure of Dicts in particular can have arbitrary nesting. | ||
return if hasvalue(p.params, vn, dist) | ||
x = getvalue(p.params, vn, dist) | ||
if x === missing | ||
init(rng, vn, dist, p.default) | ||
else | ||
# TODO(penelopeysm): Since x is user-supplied, maybe we could also | ||
# check here that the type / size of x matches the dist? | ||
x | ||
end | ||
else | ||
init(rng, vn, dist, p.default) | ||
end | ||
end | ||
|
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""" | ||
InitContext( | ||
[rng::Random.AbstractRNG=Random.default_rng()], | ||
[strategy::AbstractInitStrategy=PriorInit()], | ||
) | ||
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A leaf context that indicates that new values for random variables are | ||
currently being obtained through sampling. Used e.g. when initialising a fresh | ||
VarInfo. Note that, if `leafcontext(model.context) isa InitContext`, then | ||
`evaluate!!(model, varinfo)` will override all values in the VarInfo. | ||
""" | ||
struct InitContext{R<:Random.AbstractRNG,S<:AbstractInitStrategy} <: AbstractContext | ||
rng::R | ||
strategy::S | ||
function InitContext( | ||
rng::Random.AbstractRNG, strategy::AbstractInitStrategy=PriorInit() | ||
) | ||
return new{typeof(rng),typeof(strategy)}(rng, strategy) | ||
end | ||
function InitContext(strategy::AbstractInitStrategy=PriorInit()) | ||
return InitContext(Random.default_rng(), strategy) | ||
end | ||
end | ||
NodeTrait(::InitContext) = IsLeaf() | ||
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||
function tilde_assume( | ||
ctx::InitContext, dist::Distribution, vn::VarName, vi::AbstractVarInfo | ||
) | ||
in_varinfo = haskey(vi, vn) | ||
# `init()` always returns values in original space, i.e. possibly | ||
# constrained | ||
x = init(ctx.rng, vn, dist, ctx.strategy) | ||
# Determine whether to insert a transformed value into the VarInfo. | ||
# If the VarInfo alrady had a value for this variable, we will | ||
# keep the same linked status as in the original VarInfo. If not, we | ||
# check the rest of the VarInfo to see if other variables are linked. | ||
# istrans(vi) returns true if vi is nonempty and all variables in vi | ||
# are linked. | ||
insert_transformed_value = in_varinfo ? istrans(vi, vn) : istrans(vi) | ||
f = if insert_transformed_value | ||
link_transform(dist) | ||
else | ||
identity | ||
end | ||
y, logjac = with_logabsdet_jacobian(f, x) | ||
# Add the new value to the VarInfo. `push!!` errors if the value already | ||
# exists, hence the need for setindex!!. | ||
if in_varinfo | ||
vi = setindex!!(vi, y, vn) | ||
else | ||
vi = push!!(vi, vn, y, dist) | ||
end | ||
# Neither of these set the `trans` flag so we have to do it manually if | ||
# necessary. | ||
insert_transformed_value && settrans!!(vi, true, vn) | ||
# `accumulate_assume!!` wants untransformed values as the second argument. | ||
vi = accumulate_assume!!(vi, x, -logjac, vn, dist) | ||
# We always return the untransformed value here, as that will determine | ||
# what the lhs of the tilde-statement is set to. | ||
return x, vi | ||
end | ||
|
||
function tilde_observe!!(::InitContext, right, left, vn, vi) | ||
return tilde_observe!!(DefaultContext(), right, left, vn, vi) | ||
end |
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This code was shifted verbatim to
src/model.jl
to avoid circular dependencies between files.