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simulation.jl
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simulation.jl
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#################### Model Simulation ####################
"""
gettune(m::Model, block::Integer)
"""
function gettune(m::Model, block::Integer)
block == 0 && return gettune(m)
m.samplers[block].tune
end
"""
gettune(m::Model)
Get block-sampler tuning parameters.
Returns a `Vector{Any}` of all block-specific tuning parameters without `block` input, and turning parameters for the specified `block` otherwise.
"""
function gettune(m::Model)
Any[gettune(m, i) for i = 1:length(m.samplers)]
end
"""
settune!(m::Model, tune, block::Integer)
"""
function settune!(m::Model, tune, block::Integer)
block == 0 && return settune!(m, tune)
m.samplers[block].tune = tune
end
"""
settune!(m::Model, tune::Vector{Any})
Set tuning parameters for one or all blocks.
Assigns desired tune values to model.
* `m` : model containing the nodes of interest.
* `tune` : tune values to be assigned to models; if no `block` value is input, `tune` must be a Vector with length equal to `m.samplers`.
* `block` : Integer denoting which block's tune value is to be reassigned.
"""
function settune!(m::Model, tune::Vector{Any})
nsamplers = length(m.samplers)
ntune = length(tune)
nsamplers == ntune || throw(
DimensionMismatch("tried to assign $ntune tune elements to $nsamplers samplers"),
)
for i = 1:nsamplers
settune!(m, tune[i], i)
end
end
"""
gradlogpdf(m::Model, block::Integer=0, transform::Bool=false;
dtype::Symbol=:forward)
"""
function gradlogpdf(
m::Model,
block::Integer = 0,
transform::Bool = false;
dtype::Symbol = :forward,
)
x0 = unlist(m, block, transform)
value = gradlogpdf!(m, x0, block, transform, dtype = dtype)
relist!(m, x0, block, transform)
value
end
"""
gradlogpdf!(m::Model, x::AbstractVector{T}, block::Integer=0,
transform::Bool=false; dtype::Symbol=:forward) where {T<:Real}
"""
function gradlogpdf!(
m::Model,
x::AbstractVector{T},
block::Integer = 0,
transform::Bool = false;
dtype::Symbol = :forward,
) where {T<:Real}
f = y -> logpdf!(m, y, block, transform)
if dtype == :Zygote
mylogpdf!(m, x, block, transform)
#Zygote.gradient(f, x)
else
FiniteDiff.finite_difference_gradient(f, x)
end
end
"""
logpdf(m::Model, block::Integer=0, transform::Bool=false)
"""
function logpdf(m::Model, block::Integer = 0, transform::Bool = false)
params = keys(m, :block, block)
targets = keys(m, :target, block)
logpdf(m, params, transform) + logpdf(m, setdiff(targets, params))
end
"""
logpdf(m::Model, nodekeys::Vector{Symbol}, transform::Bool=false)
Compute the sum of log-densities for stochastic nodes.
Returns the resulting numeric value of summed log-densities.
* `m`: model containing the stochastic nodes for which to evaluate log-densities.
* `block` : sampling block of stochastic nodes over which to sum densities (default: all stochastic nodes).
* `nodekeys` : nodes over which to sum densities.
* `x` : value (possibly different than the current one) at which to evaluate densities.
* `transform` : whether to evaluate evaluate log-densities of block parameters on the link–transformed scale.
"""
function logpdf(m::Model, nodekeys::Vector{Symbol}, transform::Bool = false)
lp = 0.0
for key in nodekeys
lp += logpdf(m[key], transform)
isfinite(lp) || break
end
lp
end
function pseudologpdf(m::Model, nodekeys::Vector{Symbol}, y, transform::Bool = false)
lp = 0.0
for key in nodekeys
lp += pseudologpdf(m[key], y, transform)
isfinite(lp) || break
end
lp
end
function conditional_likelihood(m::Model, nodekeys::Vector{Symbol}, args...)
conditional_likelihood(m[nodekeys[1]], args...)
end
function rand(m::Model, nodekeys::Vector{Symbol}, x::Int64)
rand(m[nodekeys[1]], x)
end
"""
logpdf(m::Model, x::AbstractArray{T}, block::Integer=0,
transform::Bool=false) where {T<:Real}
"""
function logpdf(
m::Model,
x::AbstractArray{T},
block::Integer = 0,
transform::Bool = false,
) where {T}
x0 = unlist(m, block)
lp = logpdf!(m, x, block, transform)
relist!(m, x0, block)
lp
end
"""
logpdf!(m::Model, x::N, block::Integer=0,
transform::Bool=false) where N<:GeneralNode
"""
function logpdf!(
m::Model,
x::N,
block::Integer = 0,
transform::Bool = false,
) where {N<:GeneralNode}
params = keys(m, :block, block)
targets = keys(m, :target, block)
m[params] = relist(m, x, params, transform)
lp = logpdf(m, setdiff(params, targets), transform)
for key in targets
isfinite(lp) || break
#node = m[key]
m[key] = update!(m[key], m)
lp += key in params ? logpdf(m[key], transform) : logpdf(m[key])
end
lp
end
"""
gradlogpdf!(m::Model, x::AbstractArray{T}, block::Integer=0,transform::Bool=false)
where T<:GeneralNode
"""
function gradlogpdf!(
m::Model,
x::AbstractArray{T},
block::Integer = 0,
transform::Bool = false,
) where {T<:GeneralNode}
gradlogpdf!(m, x, block, transform)
end
"""
gradlogpdf(m::Model, targets::Array{Symbol, 1})::Tuple{Float64, Array{Float64}}
Compute the gradient of log-densities for stochastic nodes.
Returns the resulting gradient vector. Method `gradlogpdf!()` additionally updates model `m` with supplied values `x`.
* `m` : model containing the stochastic nodes for which to compute the gradient.
* `block` : sampling block of stochastic nodes for which to compute the gradient (default: all stochastic nodes).
* `x`: value (possibly different than the current one) at which to compute the gradient.
* `transform`: whether to compute the gradient of block parameters on the link–transformed scale.
* `dtype` : type of differentiation for gradient calculations. Options are
* `:central` : central differencing.
* `:forward` : forward differencing.
"""
function gradlogpdf(m::Model, targets::Vector{Symbol})::Tuple{Float64,Array{Float64}}
vp = 0.0
gradp = Array[]
for key in targets
m[key] = update!(m[key], m)
v, grad = gradlogpdf(m[key])
vp += v
push!(gradp, grad)
end
gr = .+(gradp...)
vp, gr
end
"""
gradlogpdf!(m::Model, x::N, block::Integer=0,transform::Bool=false)::Tuple{Float64, Vector{Float64}}
where N<:GeneralNode
Returns the resulting gradient vector. Method `gradlogpdf!()` additionally updates model `m` with supplied values `x`.
"""
function gradlogpdf!(
m::Model,
x::N,
block::Integer = 0,
transform::Bool = false,
)::Tuple{Float64,Vector{Float64}} where {N<:GeneralNode}
params = keys(m, :block, block)
targets = keys(m, :target, block)
# likelihood
v, grad = gradlogpdf(m, targets)
# prior
vp, gradp = gradlogpdf(m[params[1]], x)
gr = gradp .+ grad
gr .= any(isnan.(gr)) ? -Inf : gr[:]
vp + v, gr
end
function logpdf!(
m::Model,
x::AbstractArray{T},
params::Array{Symbol},
targets::Array{Symbol},
transform::Bool = false,
) where {T}
m[params] = relist(m, x, params, transform)
df = [i for i in params if i ∉ targets]
lp = logpdf(m, df, transform)
isnan(lp) && return -Inf
for key in targets
isfinite(lp) || break
isnan(lp) && return -Inf
#node = m[key]
m[key] = update!(m[key], m)
lp += key in params ? logpdf(m[key], transform) : logpdf(m[key])
end
lp
end
function mylogpdf!(
m::Model,
x::AbstractArray{T},
block::Integer = 0,
transform::Bool = false,
) where {T<:Real}
params = keys(m, :block, block)
targets = keys(m, :target, block)
diff = setdiff(params, targets)
myfun(y) = begin
m[params] = relist(m, y, params, transform)
lp = logpdf(m, diff, transform)
for key in targets
isfinite(lp) || break
#node = m[key]
m[key] = update!(m[key], m)
lp += key in params ? logpdf(m[key], transform) : logpdf(m[key])
end
lp
end
lp = myfun(x)
p = pullback(myfun, x)
d1 = p[2](1.0)
lp
end
function rand!(m::Model, x::Int64, block::Integer = 0)
params = keys(m, :block, block)
res = rand(m, params, x)
res
end
"""
sample!(m::Model, block::Integer=0)
Generate one MCMC sample of values for a specified model.
Returns the model updated with the MCMC sample and, in the case of `block=0`, the `iter` field incremented by 1.
* `m` : model specification.
* `block` : block for which to sample values (default: all blocks).
"""
function sample!(m::Model)
m.iter += 1
sams::Vector{Sampler} = m.samplers
for sampler in sams
res = sample!(sampler, m)
if res !== nothing
m[sampler.params] = res
end
end
#m.likelihood = final_likelihood(m)
m
end
function sample!(s::Sampler{T,R}, m::Model) where {T<:SamplerTune,R}
s.value = unlist(m, s.params, transform = s.transform)
lpdf(x) =
let m = m, s = s
s.tune.logf(m, x, s)
end
grlpdf(x) =
let m = m, s = s
s.tune.logfgrad(m, x, s)
end
sample!(s, lpdf, grlpdf = grlpdf, adapt = m.iter < m.burnin, gen = m.iter, model = m)
relist(m, s.value, s.params, s.transform)
end
function pseudologpdf!(
m::Model,
x::AbstractArray{T},
y::AbstractArray,
params::Vector{Symbol},
targets::Vector{Symbol},
transform::Bool = false,
) where {T<:Real}
m[params] = relist(m, x, params, transform)
lp = 0.0
for key in targets
isfinite(lp) || break
#node = m[key]
m[key] = update!(m[key], m)
lp += key in params ? pseudologpdf(m[key], y, transform) : pseudologpdf(m[key], y)
end
lp
end
function conditional_likelihood!(
m::Model,
x::AbstractArray{T},
params::Vector{Symbol},
targets::Vector{Symbol},
args...,
) where {T<:Real}
m[targets] = relist(m, vec(x), targets)
conditional_likelihood(m, targets, args...)
end
function final_likelihood(model::Model)::Float64
targets = keys(model, :dependent)
for key in targets
model[key] = update!(model[key], model)
end
logpdf(model, keys_output(model))
end
"""
unlist(m::Model, block::Integer=0, transform::Bool=false)
"""
function unlist(m::Model, block::Integer = 0; transform::Bool = false)
unlist(m, keys(m, :block, block), transform = transform)
end
"""
unlist(m::Model, monitoronly::Bool)
"""
function unlist(m::Model, monitoronly::Bool)
f = let m = m, monitoronly = monitoronly
key -> begin
node = m[key]
lvalue = isa(node, TreeVariate) ? unlist_tree(node) : unlist(node)
monitoronly ? lvalue[node.monitor] : lvalue
end
end
r = vcat(vmap(f, keys(m, :dependent))...)
r = [isa(i, ForwardDiff.Dual) ? ForwardDiff.value(i) : i for i in r]
r
end
"""
unlist(m::Model, nodekeys::Vector{Symbol}, transform::Bool=false)
Convert (unlist) sets of logical and/or stochastic node values to vectors.
Returns vectors of concatenated node values.
* `m` : model containing nodes to be unlisted or relisted.
* `block` : sampling block of nodes to be listed (default: all blocks).
* `nodekeys` : node(s) to be listed.
* `transform` : whether to apply a link transformation in the conversion.
"""
function unlist(m::Model, nodekeys::Vector{Symbol}; transform::Bool = false)
f = let m = m, transform = transform
key -> unlist(m[key], transform)
end
vcat(vmap(f, nodekeys)...)
end
"""
relist(m::Model, x::AbstractArray{T}, block::Integer=0,
transform::Bool=false) where {T<:Real}
"""
function relist(
m::Model,
x::T,
block::Integer = 0,
transform::Bool = false,
) where {T<:AbstractVector{<:Real}}
relist(m, x, keys(m, :block, block), transform)
end
"""
relist(m::Model, x::AbstractArray{T}, block::Integer=0,
transform::Bool=false) where {T<:GeneralNode}
"""
function relist(
m::Model,
x::T,
block::Integer = 0,
transform::Bool = false,
) where {T<:AbstractVector{<:GeneralNode}}
relist(m, x, keys(m, :block, block), transform)
end
"""
relist(m::Model, x::AbstractArray{T},
nodekeys::Vector{Symbol}, transform::Bool=false) where {T<:Any}
"""
function relist(
m::Model,
x::T,
nodekeys::Vector{Symbol},
transform::Bool = false,
) where {T<:AbstractVector}
values = Dict{Symbol,Union{Any,Real}}()
N = length(x)
offset = 0
@inbounds for key in nodekeys
value, n = relistlength(m[key], view(x, (offset+1):N), transform)
values[key] = value
offset += n
end
offset == length(x) ||
throw(ArgumentError("incompatible number of values to put in nodes"))
values
end
"""
relist(m::Model, x::N, nodekeys::Vector{Symbol}, transform::Bool=false) where N<:GeneralNode
Reverse of unlist; ie. Converts vectors to sets of logical and/or stochastic node values. Same inputs and return values as unlist.
"""
function relist(
m::Model,
x::N,
nodekeys::Vector{Symbol},
transform::Bool = false,
) where {N<:GeneralNode}
values = Dict{Symbol,Any}()
offset = 0
@inbounds for key in nodekeys
value, n = relistlength(m[key], x, transform)
values[key] = value
offset += n
end
values
end
"""
relist!(m::Model, x::AbstractArray{T}, block::Integer=0,
transform::Bool=false) where {T<:Any}
"""
function relist!(
m::Model,
x::T,
block::Integer = 0,
transform::Bool = false,
) where {T<:AbstractVector}
nodekeys = keys(m, :block, block)
values = relist(m, x, nodekeys, transform)
@inbounds for key in nodekeys
assign!(m, key, values[key])
end
update!(m, block)
end
function assign!(m::Model, key::Symbol, value::T) where {T<:Real}
m[key].value = value
end
function assign!(m::Model, key::Symbol, value::T) where {T<:GeneralNode}
m[key].value = value
end
function assign!(m::Model, key::Symbol, value::T) where {T<:Array}
if isa(m[key], TreeVariate)
@assert (2,) == size(value)
d = Array{Float64,1}(undef, 2)
else
@assert size(m[key].value) == size(value)
d = similar(m[key].value)
end
@inbounds for ind in eachindex(value)
d[ind] = value[ind]
end
m[key].value = d
end
"""
relist!(m::Model, x::AbstractArray{T}, nodekey::Symbol,
transform::Bool=false) where {T<:Real}
Reverse of unlist; ie. Converts vectors to sets of logical and/or stochastic node values. Same inputs as unlist.
Returns `m`, with values copied to the nodes.
"""
function relist!(
m::Model,
x::T,
nodekey::Symbol,
transform::Bool = false,
) where {T<:AbstractArray{<:Real}}
node = m[nodekey]
m[nodekey] = relist(node, x, transform)
update!(m, node.targets)
end
"""
update!(m::Model, block::Integer=0)
"""
function update!(m::Model, block::Integer = 0)
nodekeys = block == 0 ? keys(m, :dependent) : m.samplers[block].targets
update!(m, nodekeys)
end
"""
update!(m::Model, nodekeys::Vector{Symbol})
Update values of logical and stochastic model node according to their relationship with others in a model.
Returns the model with updated nodes.
* `m` : mode with nodes to be updated.
* `block` : sampling block of nodes to be updated (default: all blocks).
* `nodekeys` : nodes to be updated in the given order.
"""
function update!(m::Model, nodekeys::Vector{Symbol})
@inbounds for key_ind in eachindex(nodekeys)
update!(m[nodekeys[key_ind]], m)
end
m
end