Adds values_as_in_model

docs/src/api.md

@@ -143,6 +143,12 @@ Sometimes it can be useful to extract the priors of a model. This is the possibl
 extract_priors
 ```
 
+Safe extraction of values from a given [`AbstractVarInfo`](@ref) as they are seen in the model can be done using [`values_as_in_model`](@ref).
+
+```@docs
+values_as_in_model
+```
+
 ```@docs
 NamedDist
 ```

src/DynamicPPL.jl

@@ -93,6 +93,7 @@ export AbstractVarInfo,
     getargnames,
     generated_quantities,
     extract_priors,
+    values_as_in_model,
     # Samplers
     Sampler,
     SampleFromPrior,
@@ -178,6 +179,7 @@ include("transforming.jl")
 include("logdensityfunction.jl")
 include("model_utils.jl")
 include("extract_priors.jl")
+include("values_as_in_model.jl")
 
 if !isdefined(Base, :get_extension)
     using Requires

src/values_as_in_model.jl

@@ -0,0 +1,181 @@
+
+"""
+    ValuesAsInModelContext
+
+A context that is used by [`values_as_in_model`](@ref) to obtain values
+of the model parameters as they are in the model.
+
+This is particularly useful when working in unconstrained space, but one
+wants to extract the realization of a model in a constrained space.
+
+# Fields
+$(TYPEDFIELDS)
+"""
+struct ValuesAsInModelContext{T,C<:AbstractContext} <: AbstractContext
+    "values that are extracted from the model"
+    values::T
+    "child context"
+    context::C
+end
+
+ValuesAsInModelContext(values) = ValuesAsInModelContext(values, DefaultContext())
+function ValuesAsInModelContext(context::AbstractContext)
+    return ValuesAsInModelContext(OrderedDict(), context)
+end
+
+NodeTrait(::ValuesAsInModelContext) = IsParent()
+childcontext(context::ValuesAsInModelContext) = context.context
+function setchildcontext(context::ValuesAsInModelContext, child)
+    return ValuesAsInModelContext(context.values, child)
+end
+
+function Base.push!(context::ValuesAsInModelContext, vn::VarName, value)
+    return setindex!(context.values, copy(value), vn)
+end
+
+function broadcast_push!(context::ValuesAsInModelContext, vns, values)
+    return push!.((context,), vns, values)
+end
+
+# This will be hit if we're broadcasting an `AbstractMatrix` over a `MultivariateDistribution`.
+function broadcast_push!(
+    context::ValuesAsInModelContext, vns::AbstractVector, values::AbstractMatrix
+)
+    for (vn, col) in zip(vns, eachcol(values))
+        push!(context, vn, col)
+    end
+end
+
+# `tilde_asssume`
+function tilde_assume(context::ValuesAsInModelContext, right, vn, vi)
+    value, logp, vi = tilde_assume(childcontext(context), right, vn, vi)
+    # Save the value.
+    push!(context, vn, value)
+    # Save the value.
+    # Pass on.
+    return value, logp, vi
+end
+function tilde_assume(
+    rng::Random.AbstractRNG, context::ValuesAsInModelContext, sampler, right, vn, vi
+)
+    value, logp, vi = tilde_assume(rng, childcontext(context), sampler, right, vn, vi)
+    # Save the value.
+    push!(context, vn, value)
+    # Pass on.
+    return value, logp, vi
+end
+
+# `dot_tilde_assume`
+function dot_tilde_assume(context::ValuesAsInModelContext, right, left, vn, vi)
+    value, logp, vi = dot_tilde_assume(childcontext(context), right, left, vn, vi)
+
+    # Save the value.
+    _right, _left, _vns = unwrap_right_left_vns(right, var, vn)
+    broadcast_push!(context, _vns, value)
+
+    return value, logp, vi
+end
+function dot_tilde_assume(
+    rng::Random.AbstractRNG, context::ValuesAsInModelContext, sampler, right, left, vn, vi
+)
+    value, logp, vi = dot_tilde_assume(
+        rng, childcontext(context), sampler, right, left, vn, vi
+    )
+    # Save the value.
+    _right, _left, _vns = unwrap_right_left_vns(right, left, vn)
+    broadcast_push!(context, _vns, value)
+
+    return value, logp, vi
+end
+
+"""
+    values_as_in_model(model::Model[, varinfo::AbstractVarInfo, context::AbstractContext])
+    values_as_in_model(rng::Random.AbstractRNG, model::Model[, varinfo::AbstractVarInfo, context::AbstractContext])
+
+Get the values of `varinfo` as they would be seen in the model.
+
+If no `varinfo` is provided, then this is effectively the same as
+[`Base.rand(rng::Random.AbstractRNG, model::Model)`](@ref).
+
+More specifically, this method attempts to extract the realization _as seen in the model_.
+For example, `x[1] ~ truncated(Normal(); lower=0)` will result in a realization compatible
+with `truncated(Normal(); lower=0)` regardless of whether `varinfo` is working in unconstrained
+space.
+
+Hence this method is a "safe" way of obtaining realizations in constrained space at the cost
+of additional model evaluations.
+
+# Arguments
+- `model::Model`: model to extract realizations from.
+- `varinfo::AbstractVarInfo`: variable information to use for the extraction.
+- `context::AbstractContext`: context to use for the extraction. If `rng` is specified, then `context`
+    will be wrapped in a [`SamplingContext`](@ref) with the provided `rng`.
+
+# Examples
+
+## When `VarInfo` fails
+
+The following demonstrates a common pitfall when working with [`VarInfo`](@ref) and constrained variables.
+
+```jldoctest
+julia> using Distributions, StableRNGs
+
+julia> rng = StableRNG(42);
+
+julia> @model function model_changing_support()
+           x ~ Bernoulli(0.5)
+           y ~ x == 1 ? Uniform(0, 1) : Uniform(11, 12)
+       end;
+
+julia> model = model_changing_support();
+
+julia> # Construct initial type-stable `VarInfo`.
+       varinfo = VarInfo(rng, model);
+
+julia> # Link it so it works in unconstrained space.
+       varinfo_linked = DynamicPPL.link(varinfo, model);
+
+julia> # Perform computations in unconstrained space, e.g. changing the values of `θ`.
+       # Flip `x` so we hit the other support of `y`.
+       θ = [!varinfo[@varname(x)], rand(rng)];
+
+julia> # Update the `VarInfo` with the new values.
+       varinfo_linked = DynamicPPL.unflatten(varinfo_linked, θ);
+
+julia> # Determine the expected support of `y`.
+       lb, ub = θ[1] == 1 ? (0, 1) : (11, 12)
+(0, 1)
+
+julia> # Approach 1: Convert back to constrained space using `invlink` and extract.
+       varinfo_invlinked = DynamicPPL.invlink(varinfo_linked, model);
+
+julia> # (×) Fails! Because `VarInfo` _saves_ the original distributions
+       # used in the very first model evaluation, hence the support of `y`
+       # is not updated even though `x` has changed.
+       lb ≤ varinfo_invlinked[@varname(y)] ≤ ub
+false
+
+julia> # Approach 2: Extract realizations using `values_as_in_model`.
+       # (✓) `values_as_in_model` will re-run the model and extract
+       # the correct realization of `y` given the new values of `x`.
+       lb ≤ values_as_in_model(model, varinfo_linked)[@varname(y)] ≤ ub
+true
+```
+"""
+function values_as_in_model(
+    model::Model,
+    varinfo::AbstractVarInfo=VarInfo(),
+    context::AbstractContext=DefaultContext(),
+)
+    context = ValuesAsInModelContext(context)
+    evaluate!!(model, varinfo, context)
+    return context.values
+end
+function values_as_in_model(
+    rng::Random.AbstractRNG,
+    model::Model,
+    varinfo::AbstractVarInfo=VarInfo(),
+    context::AbstractContext=DefaultContext(),
+)
+    return values_as_in_model(model, varinfo, SamplingContext(rng, context))
+end

test/model.jl

@@ -356,7 +356,7 @@ is_typed_varinfo(varinfo::DynamicPPL.SimpleVarInfo{<:NamedTuple}) = true
         ]
         @testset "$(model.f)" for model in models_to_test
             vns = DynamicPPL.TestUtils.varnames(model)
-            example_values = DynamicPPL.TestUtils.rand(model)
+            example_values = DynamicPPL.TestUtils.rand_prior_true(model)
             varinfos = filter(
                 is_typed_varinfo,
                 DynamicPPL.TestUtils.setup_varinfos(model, example_values, vns),
@@ -375,4 +375,22 @@ is_typed_varinfo(varinfo::DynamicPPL.SimpleVarInfo{<:NamedTuple}) = true
             end
         end
     end
+
+    @testset "values_as_in_model" begin
+        @testset "$(model.f)" for model in DynamicPPL.TestUtils.DEMO_MODELS
+            vns = DynamicPPL.TestUtils.varnames(model)
+            example_values = DynamicPPL.TestUtils.rand_prior_true(model)
+            varinfos = DynamicPPL.TestUtils.setup_varinfos(model, example_values, vns)
+            @testset "$(short_varinfo_name(varinfo))" for varinfo in varinfos
+                realizations = values_as_in_model(model, varinfo)
+                # Ensure that all variables are found.
+                vns_found = collect(keys(realizations))
+                @test vns ∩ vns_found == vns ∪ vns_found
+                # Ensure that the values are the same.
+                for vn in vns
+                    @test realizations[vn] == varinfo[vn]
+                end
+            end
+        end
+    end
 end