From 90ff7d72dc6bddf378e7cf29d9d867e2580e97ef Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Fri, 20 Oct 2023 10:17:45 +0000 Subject: [PATCH] build based on 89afc09 --- previews/PR546/.documenter-siteinfo.json | 1 + previews/PR546/api/index.html | 1342 +++++++++++++++++ previews/PR546/assets/documenter.js | 889 +++++++++++ previews/PR546/assets/logo.svg | 140 ++ .../PR546/assets/themes/documenter-dark.css | 7 + .../PR546/assets/themes/documenter-light.css | 9 + previews/PR546/assets/themeswap.js | 84 ++ previews/PR546/assets/warner.js | 52 + previews/PR546/index.html | 2 + previews/PR546/search_index.js | 3 + previews/PR546/siteinfo.js | 1 + .../PR546/tutorials/prob-interface/index.html | 52 + 12 files changed, 2582 insertions(+) create mode 100644 previews/PR546/.documenter-siteinfo.json create mode 100644 previews/PR546/api/index.html create mode 100644 previews/PR546/assets/documenter.js create mode 100644 previews/PR546/assets/logo.svg create mode 100644 previews/PR546/assets/themes/documenter-dark.css create mode 100644 previews/PR546/assets/themes/documenter-light.css create mode 100644 previews/PR546/assets/themeswap.js create mode 100644 previews/PR546/assets/warner.js create mode 100644 previews/PR546/index.html create mode 100644 previews/PR546/search_index.js create mode 100644 previews/PR546/siteinfo.js create mode 100644 previews/PR546/tutorials/prob-interface/index.html diff --git a/previews/PR546/.documenter-siteinfo.json b/previews/PR546/.documenter-siteinfo.json new file mode 100644 index 000000000..2a9fe2f14 --- /dev/null +++ b/previews/PR546/.documenter-siteinfo.json @@ -0,0 +1 @@ +{"documenter":{"julia_version":"1.9.3","generation_timestamp":"2023-10-20T10:17:38","documenter_version":"1.1.1"}} \ No newline at end of file diff --git a/previews/PR546/api/index.html b/previews/PR546/api/index.html new file mode 100644 index 000000000..c407af28e --- /dev/null +++ b/previews/PR546/api/index.html @@ -0,0 +1,1342 @@ + +API · DynamicPPL
GitHub

API

Part of the API of DynamicPPL is defined in the more lightweight interface package AbstractPPL.jl and reexported here.

Model

Macros

A core component of DynamicPPL is the @model macro. It can be used to define probabilistic models in an intuitive way by specifying random variables and their distributions with ~ statements. These statements are rewritten by @model as calls of internal functions for sampling the variables and computing their log densities.

DynamicPPL.@model — Macro
@model(expr[, warn = false])

Macro to specify a probabilistic model.

If warn is true, a warning is displayed if internal variable names are used in the model definition.

Examples

Model definition:

@model function model(x, y = 42)
+    ...
+end

To generate a Model, call model(xvalue) or model(xvalue, yvalue).

source

One can nest models and call another model inside the model function with @submodel.

DynamicPPL.@submodel — Macro
@submodel model
+@submodel ... = model

Run a Turing model nested inside of a Turing model.

Examples

julia> @model function demo1(x)
+           x ~ Normal()
+           return 1 + abs(x)
+       end;
+
+julia> @model function demo2(x, y)
+            @submodel a = demo1(x)
+            return y ~ Uniform(0, a)
+       end;

When we sample from the model demo2(missing, 0.4) random variable x will be sampled:

julia> vi = VarInfo(demo2(missing, 0.4));
+
+julia> @varname(x) in keys(vi)
+true

Variable a is not tracked since it can be computed from the random variable x that was tracked when running demo1:

julia> @varname(a) in keys(vi)
+false

We can check that the log joint probability of the model accumulated in vi is correct:

julia> x = vi[@varname(x)];
+
+julia> getlogp(vi) ≈ logpdf(Normal(), x) + logpdf(Uniform(0, 1 + abs(x)), 0.4)
+true
source
@submodel prefix=... model
+@submodel prefix=... ... = model

Run a Turing model nested inside of a Turing model and add "prefix." as a prefix to all random variables inside of the model.

Valid expressions for prefix=... are:

  • prefix=false: no prefix is used.
  • prefix=true: attempt to automatically determine the prefix from the left-hand side ... = model by first converting into a VarName, and then calling Symbol on this.
  • prefix=expression: results in the prefix Symbol(expression).

The prefix makes it possible to run the same Turing model multiple times while keeping track of all random variables correctly.

Examples

Example models

julia> @model function demo1(x)
+           x ~ Normal()
+           return 1 + abs(x)
+       end;
+
+julia> @model function demo2(x, y, z)
+            @submodel prefix="sub1" a = demo1(x)
+            @submodel prefix="sub2" b = demo1(y)
+            return z ~ Uniform(-a, b)
+       end;

When we sample from the model demo2(missing, missing, 0.4) random variables sub1.x and sub2.x will be sampled:

julia> vi = VarInfo(demo2(missing, missing, 0.4));
+
+julia> @varname(var"sub1.x") in keys(vi)
+true
+
+julia> @varname(var"sub2.x") in keys(vi)
+true

Variables a and b are not tracked since they can be computed from the random variables sub1.x and sub2.x that were tracked when running demo1:

julia> @varname(a) in keys(vi)
+false
+
+julia> @varname(b) in keys(vi)
+false

We can check that the log joint probability of the model accumulated in vi is correct:

julia> sub1_x = vi[@varname(var"sub1.x")];
+
+julia> sub2_x = vi[@varname(var"sub2.x")];
+
+julia> logprior = logpdf(Normal(), sub1_x) + logpdf(Normal(), sub2_x);
+
+julia> loglikelihood = logpdf(Uniform(-1 - abs(sub1_x), 1 + abs(sub2_x)), 0.4);
+
+julia> getlogp(vi) ≈ logprior + loglikelihood
+true

Different ways of setting the prefix

julia> @model inner() = x ~ Normal()
+inner (generic function with 2 methods)
+
+julia> # When `prefix` is unspecified, no prefix is used.
+       @model outer() = @submodel a = inner()
+outer (generic function with 2 methods)
+
+julia> @varname(x) in keys(VarInfo(outer()))
+true
+
+julia> # Explicitely don't use any prefix.
+       @model outer() = @submodel prefix=false a = inner()
+outer (generic function with 2 methods)
+
+julia> @varname(x) in keys(VarInfo(outer()))
+true
+
+julia> # Automatically determined from `a`.
+       @model outer() = @submodel prefix=true a = inner()
+outer (generic function with 2 methods)
+
+julia> @varname(var"a.x") in keys(VarInfo(outer()))
+true
+
+julia> # Using a static string.
+       @model outer() = @submodel prefix="my prefix" a = inner()
+outer (generic function with 2 methods)
+
+julia> @varname(var"my prefix.x") in keys(VarInfo(outer()))
+true
+
+julia> # Using string interpolation.
+       @model outer() = @submodel prefix="$(nameof(inner()))" a = inner()
+outer (generic function with 2 methods)
+
+julia> @varname(var"inner.x") in keys(VarInfo(outer()))
+true
+
+julia> # Or using some arbitrary expression.
+       @model outer() = @submodel prefix=1 + 2 a = inner()
+outer (generic function with 2 methods)
+
+julia> @varname(var"3.x") in keys(VarInfo(outer()))
+true
+
+julia> # (×) Automatic prefixing without a left-hand side expression does not work!
+       @model outer() = @submodel prefix=true inner()
+ERROR: LoadError: cannot automatically prefix with no left-hand side
+[...]

Notes

  • The choice prefix=expression means that the prefixing will incur a runtime cost. This is also the case for prefix=true, depending on whether the expression on the the right-hand side of ... = model requires runtime-information or not, e.g. x = model will result in the static prefix x, while x[i] = model will be resolved at runtime.
source

Type

A Model can be created by calling the model function, as defined by @model.

DynamicPPL.Model — Type
struct Model{F,argnames,defaultnames,missings,Targs,Tdefaults}
+    f::F
+    args::NamedTuple{argnames,Targs}
+    defaults::NamedTuple{defaultnames,Tdefaults}
+end

A Model struct with model evaluation function of type F, arguments of names argnames types Targs, default arguments of names defaultnames with types Tdefaults, and missing arguments missings.

Here argnames, defaultargnames, and missings are tuples of symbols, e.g. (:a, :b).

An argument with a type of Missing will be in missings by default. However, in non-traditional use-cases missings can be defined differently. All variables in missings are treated as random variables rather than observations.

The default arguments are used internally when constructing instances of the same model with different arguments.

Examples

julia> Model(f, (x = 1.0, y = 2.0))
+Model{typeof(f),(:x, :y),(),(),Tuple{Float64,Float64},Tuple{}}(f, (x = 1.0, y = 2.0), NamedTuple())
+
+julia> Model(f, (x = 1.0, y = 2.0), (x = 42,))
+Model{typeof(f),(:x, :y),(:x,),(),Tuple{Float64,Float64},Tuple{Int64}}(f, (x = 1.0, y = 2.0), (x = 42,))
+
+julia> Model{(:y,)}(f, (x = 1.0, y = 2.0), (x = 42,)) # with special definition of missings
+Model{typeof(f),(:x, :y),(:x,),(:y,),Tuple{Float64,Float64},Tuple{Int64}}(f, (x = 1.0, y = 2.0), (x = 42,))
source

Models are callable structs.

DynamicPPL.Model — Method
(model::Model)([rng, varinfo, sampler, context])

Sample from the model using the sampler with random number generator rng and the context, and store the sample and log joint probability in varinfo.

The method resets the log joint probability of varinfo and increases the evaluation number of sampler.

source

Basic properties of a model can be accessed with getargnames, getmissings, and nameof.

Evaluation

With rand one can draw samples from the prior distribution of a Model.

Base.rand — Function
rand([rng=Random.default_rng()], [T=NamedTuple], model::Model)

Generate a sample of type T from the prior distribution of the model.

source

One can also evaluate the log prior, log likelihood, and log joint probability.

DynamicPPL.logprior — Function
logprior(model::Model, varinfo::AbstractVarInfo)

Return the log prior probability of variables varinfo for the probabilistic model.

See also logjoint and loglikelihood.

source
logprior(model::Model, chain::AbstractMCMC.AbstractChains)

Return an array of log prior probabilities evaluated at each sample in an MCMC chain.

Examples

julia> using MCMCChains, Distributions
+
+julia> @model function demo_model(x)
+           s ~ InverseGamma(2, 3)
+           m ~ Normal(0, sqrt(s))
+           for i in eachindex(x)
+               x[i] ~ Normal(m, sqrt(s))
+           end
+       end;
+
+julia> # construct a chain of samples using MCMCChains
+       chain = Chains(rand(10, 2, 3), [:s, :m]);
+
+julia> logprior(demo_model([1., 2.]), chain);
source
logprior(model::Model, θ)

Return the log prior probability of variables θ for the probabilistic model.

See also logjoint and loglikelihood.

Examples

julia> @model function demo(x)
+           m ~ Normal()
+           for i in eachindex(x)
+               x[i] ~ Normal(m, 1.0)
+           end
+       end
+demo (generic function with 2 methods)
+
+julia> # Using a `NamedTuple`.
+       logprior(demo([1.0]), (m = 100.0, ))
+-5000.918938533205
+
+julia> # Using a `OrderedDict`.
+       logprior(demo([1.0]), OrderedDict(@varname(m) => 100.0))
+-5000.918938533205
+
+julia> # Truth.
+       logpdf(Normal(), 100.0)
+-5000.918938533205
source
StatsAPI.loglikelihood — Function
loglikelihood(model::Model, varinfo::AbstractVarInfo)

Return the log likelihood of variables varinfo for the probabilistic model.

See also logjoint and logprior.

source
loglikelihood(model::Model, chain::AbstractMCMC.AbstractChains)

Return an array of log likelihoods evaluated at each sample in an MCMC chain.

Examples

julia> using MCMCChains, Distributions
+
+julia> @model function demo_model(x)
+           s ~ InverseGamma(2, 3)
+           m ~ Normal(0, sqrt(s))
+           for i in eachindex(x)
+               x[i] ~ Normal(m, sqrt(s))
+           end
+       end;
+
+julia> # construct a chain of samples using MCMCChains
+       chain = Chains(rand(10, 2, 3), [:s, :m]);
+
+julia> loglikelihood(demo_model([1., 2.]), chain);
source
loglikelihood(model::Model, θ)

Return the log likelihood of variables θ for the probabilistic model.

See also logjoint and logprior.

Examples

julia> @model function demo(x)
+           m ~ Normal()
+           for i in eachindex(x)
+               x[i] ~ Normal(m, 1.0)
+           end
+       end
+demo (generic function with 2 methods)
+
+julia> # Using a `NamedTuple`.
+       loglikelihood(demo([1.0]), (m = 100.0, ))
+-4901.418938533205
+
+julia> # Using a `OrderedDict`.
+       loglikelihood(demo([1.0]), OrderedDict(@varname(m) => 100.0))
+-4901.418938533205
+
+julia> # Truth.
+       logpdf(Normal(100.0, 1.0), 1.0)
+-4901.418938533205
source
DynamicPPL.logjoint — Function
logjoint(model::Model, varinfo::AbstractVarInfo)

Return the log joint probability of variables varinfo for the probabilistic model.

See logjoint and loglikelihood.

source
logjoint(model::Model, chain::AbstractMCMC.AbstractChains)

Return an array of log joint probabilities evaluated at each sample in an MCMC chain.

Examples

julia> using MCMCChains, Distributions
+
+julia> @model function demo_model(x)
+           s ~ InverseGamma(2, 3)
+           m ~ Normal(0, sqrt(s))
+           for i in eachindex(x)
+               x[i] ~ Normal(m, sqrt(s))
+           end
+       end;
+
+julia> # construct a chain of samples using MCMCChains
+       chain = Chains(rand(10, 2, 3), [:s, :m]);
+
+julia> logjoint(demo_model([1., 2.]), chain);
source
logjoint(model::Model, θ)

Return the log joint probability of variables θ for the probabilistic model.

See logjoint and loglikelihood.

Examples

julia> @model function demo(x)
+           m ~ Normal()
+           for i in eachindex(x)
+               x[i] ~ Normal(m, 1.0)
+           end
+       end
+demo (generic function with 2 methods)
+
+julia> # Using a `NamedTuple`.
+       logjoint(demo([1.0]), (m = 100.0, ))
+-9902.33787706641
+
+julia> # Using a `OrderedDict`.
+       logjoint(demo([1.0]), OrderedDict(@varname(m) => 100.0))
+-9902.33787706641
+
+julia> # Truth.
+       logpdf(Normal(100.0, 1.0), 1.0) + logpdf(Normal(), 100.0)
+-9902.33787706641
source

LogDensityProblems.jl interface

The LogDensityProblems.jl interface is also supported by simply wrapping a Model in a DynamicPPL.LogDensityFunction:

DynamicPPL.LogDensityFunction — Type
LogDensityFunction

A callable representing a log density function of a model.

Fields

  • varinfo: varinfo used for evaluation

  • model: model used for evaluation

  • context: context used for evaluation

Examples

julia> using Distributions
+
+julia> using DynamicPPL: LogDensityFunction, contextualize
+
+julia> @model function demo(x)
+           m ~ Normal()
+           x ~ Normal(m, 1)
+       end
+demo (generic function with 2 methods)
+
+julia> model = demo(1.0);
+
+julia> f = LogDensityFunction(model);
+
+julia> # It implements the interface of LogDensityProblems.jl.
+       using LogDensityProblems
+
+julia> LogDensityProblems.logdensity(f, [0.0])
+-2.3378770664093453
+
+julia> LogDensityProblems.dimension(f)
+1
+
+julia> # By default it uses `VarInfo` under the hood, but this is not necessary.
+       f = LogDensityFunction(model, SimpleVarInfo(model));
+
+julia> LogDensityProblems.logdensity(f, [0.0])
+-2.3378770664093453
+
+julia> # This also respects the context in `model`.
+       f_prior = LogDensityFunction(contextualize(model, DynamicPPL.PriorContext()), VarInfo(model));
+
+julia> LogDensityProblems.logdensity(f_prior, [0.0]) == logpdf(Normal(), 0.0)
+true
source

Condition and decondition

A Model can be conditioned on a set of observations with AbstractPPL.condition or its alias |.

Base.:| — Method
model | (x = 1.0, ...)

Return a Model which now treats variables on the right-hand side as observations.

See condition for more information and examples.

source
AbstractPPL.condition — Function
condition(model::Model; values...)
+condition(model::Model, values::NamedTuple)

Return a Model which now treats the variables in values as observations.

See also: decondition, conditioned

Limitations

This does currently not work with variables that are provided to the model as arguments, e.g. @model function demo(x) ... end means that condition will not affect the variable x.

Therefore if one wants to make use of condition and decondition one should not be specifying any random variables as arguments.

This is done for the sake of backwards compatibility.

Examples

Simple univariate model

julia> using Distributions
+
+julia> @model function demo()
+           m ~ Normal()
+           x ~ Normal(m, 1)
+           return (; m=m, x=x)
+       end
+demo (generic function with 2 methods)
+
+julia> model = demo();
+
+julia> m, x = model(); (m ≠ 1.0 && x ≠ 100.0)
+true
+
+julia> # Create a new instance which treats `x` as observed
+       # with value `100.0`, and similarly for `m=1.0`.
+       conditioned_model = condition(model, x=100.0, m=1.0);
+
+julia> m, x = conditioned_model(); (m == 1.0 && x == 100.0)
+true
+
+julia> # Let's only condition on `x = 100.0`.
+       conditioned_model = condition(model, x = 100.0);
+
+julia> m, x =conditioned_model(); (m ≠ 1.0 && x == 100.0)
+true
+
+julia> # We can also use the nicer `|` syntax.
+       conditioned_model = model | (x = 100.0, );
+
+julia> m, x = conditioned_model(); (m ≠ 1.0 && x == 100.0)
+true

The above uses a NamedTuple to hold the conditioning variables, which allows us to perform some additional optimizations; in many cases, the above has zero runtime-overhead.

But we can also use a Dict, which offers more flexibility in the conditioning (see examples further below) but generally has worse performance than the NamedTuple approach:

julia> conditioned_model_dict = condition(model, Dict(@varname(x) => 100.0));
+
+julia> m, x = conditioned_model_dict(); (m ≠ 1.0 && x == 100.0)
+true
+
+julia> # There's also an option using `|` by letting the right-hand side be a tuple
+       # with elements of type `Pair{<:VarName}`, i.e. `vn => value` with `vn isa VarName`.
+       conditioned_model_dict = model | (@varname(x) => 100.0, );
+
+julia> m, x = conditioned_model_dict(); (m ≠ 1.0 && x == 100.0)
+true

Condition only a part of a multivariate variable

Not only can be condition on multivariate random variables, but we can also use the standard mechanism of setting something to missing in the call to condition to only condition on a part of the variable.

julia> @model function demo_mv(::Type{TV}=Float64) where {TV}
+           m = Vector{TV}(undef, 2)
+           m[1] ~ Normal()
+           m[2] ~ Normal()
+           return m
+       end
+demo_mv (generic function with 4 methods)
+
+julia> model = demo_mv();
+
+julia> conditioned_model = condition(model, m = [missing, 1.0]);
+
+julia> # (✓) `m[1]` sampled while `m[2]` is fixed
+       m = conditioned_model(); (m[1] ≠ 1.0 && m[2] == 1.0)
+true

Intuitively one might also expect to be able to write model | (m[1] = 1.0, ). Unfortunately this is not supported as it has the potential of increasing compilation times but without offering any benefit with respect to runtime:

julia> # (×) `m[2]` is not set to 1.0.
+       m = condition(model, var"m[2]" = 1.0)(); m[2] == 1.0
+false

But you can do this if you use a Dict as the underlying storage instead:

julia> # Alternatives:
+       # - `model | (@varname(m[2]) => 1.0,)`
+       # - `condition(model, Dict(@varname(m[2] => 1.0)))`
+       # (✓) `m[2]` is set to 1.0.
+       m = condition(model, @varname(m[2]) => 1.0)(); (m[1] ≠ 1.0 && m[2] == 1.0)
+true

Nested models

condition of course also supports the use of nested models through the use of @submodel.

julia> @model demo_inner() = m ~ Normal()
+demo_inner (generic function with 2 methods)
+
+julia> @model function demo_outer()
+           @submodel m = demo_inner()
+           return m
+       end
+demo_outer (generic function with 2 methods)
+
+julia> model = demo_outer();
+
+julia> model() ≠ 1.0
+true
+
+julia> conditioned_model = model | (m = 1.0, );
+
+julia> conditioned_model()
+1.0

But one needs to be careful when prefixing variables in the nested models:

julia> @model function demo_outer_prefix()
+           @submodel prefix="inner" m = demo_inner()
+           return m
+       end
+demo_outer_prefix (generic function with 2 methods)
+
+julia> # (×) This doesn't work now!
+       conditioned_model = demo_outer_prefix() | (m = 1.0, );
+
+julia> conditioned_model() == 1.0
+false
+
+julia> # (✓) `m` in `demo_inner` is referred to as `inner.m` internally, so we do:
+       conditioned_model = demo_outer_prefix() | (var"inner.m" = 1.0, );
+
+julia> conditioned_model()
+1.0
+
+julia> # Note that the above `var"..."` is just standard Julia syntax:
+       keys((var"inner.m" = 1.0, ))
+(Symbol("inner.m"),)

And similarly when using Dict:

julia> conditioned_model_dict = demo_outer_prefix() | (@varname(var"inner.m") => 1.0);
+
+julia> conditioned_model_dict()
+1.0

The difference is maybe more obvious once we look at how these different in their trace/VarInfo:

julia> keys(VarInfo(demo_outer()))
+1-element Vector{VarName{:m, Setfield.IdentityLens}}:
+ m
+
+julia> keys(VarInfo(demo_outer_prefix()))
+1-element Vector{VarName{Symbol("inner.m"), Setfield.IdentityLens}}:
+ inner.m

From this we can tell what the correct way to condition m within demo_inner is in the two different models.

source
condition([context::AbstractContext,] values::NamedTuple)
+condition([context::AbstractContext]; values...)

Return ConditionContext with values and context if values is non-empty, otherwise return context which is DefaultContext by default.

See also: decondition

source
DynamicPPL.conditioned — Function
conditioned(model::Model)

Return the conditioned values in model.

Examples

julia> using Distributions
+
+julia> using DynamicPPL: conditioned, contextualize
+
+julia> @model function demo()
+           m ~ Normal()
+           x ~ Normal(m, 1)
+       end
+demo (generic function with 2 methods)
+
+julia> m = demo();
+
+julia> # Returns all the variables we have conditioned on + their values.
+       conditioned(condition(m, x=100.0, m=1.0))
+(x = 100.0, m = 1.0)
+
+julia> # Nested ones also work (note that `PrefixContext` does nothing to the result).
+       cm = condition(contextualize(m, PrefixContext{:a}(condition(m=1.0))), x=100.0);
+
+julia> conditioned(cm)
+(x = 100.0, m = 1.0)
+
+julia> # Since we conditioned on `m`, not `a.m` as it will appear after prefixed,
+       # `a.m` is treated as a random variable.
+       keys(VarInfo(cm))
+1-element Vector{VarName{Symbol("a.m"), Setfield.IdentityLens}}:
+ a.m
+
+julia> # If we instead condition on `a.m`, `m` in the model will be considered an observation.
+       cm = condition(contextualize(m, PrefixContext{:a}(condition(var"a.m"=1.0))), x=100.0);
+
+julia> conditioned(cm).x
+100.0
+
+julia> conditioned(cm).var"a.m"
+1.0
+
+julia> keys(VarInfo(cm)) # <= no variables are sampled
+VarName[]
source
conditioned(context::AbstractContext)

Return NamedTuple of values that are conditioned on under context`.

Note that this will recursively traverse the context stack and return a merged version of the condition values.

source

Similarly, one can specify with AbstractPPL.decondition that certain, or all, random variables are not observed.

AbstractPPL.decondition — Function
decondition(model::Model)
+decondition(model::Model, variables...)

Return a Model for which variables... are not considered observations. If no variables are provided, then all variables currently considered observations will no longer be.

This is essentially the inverse of condition. This also means that it suffers from the same limitiations.

Note that currently we only support variables to take on explicit values provided to condition.

Examples

julia> using Distributions
+
+julia> @model function demo()
+           m ~ Normal()
+           x ~ Normal(m, 1)
+           return (; m=m, x=x)
+       end
+demo (generic function with 2 methods)
+
+julia> conditioned_model = condition(demo(), m = 1.0, x = 10.0);
+
+julia> conditioned_model()
+(m = 1.0, x = 10.0)
+
+julia> # By specifying the `VarName` to `decondition`.
+       model = decondition(conditioned_model, @varname(m));
+
+julia> (m, x) = model(); (m ≠ 1.0 && x == 10.0)
+true
+
+julia> # When `NamedTuple` is used as the underlying, you can also provide
+       # the symbol directly (though the `@varname` approach is preferable if
+       # if the variable is known at compile-time).
+       model = decondition(conditioned_model, :m);
+
+julia> (m, x) = model(); (m ≠ 1.0 && x == 10.0)
+true
+
+julia> # `decondition` multiple at once:
+       (m, x) = decondition(model, :m, :x)(); (m ≠ 1.0 && x ≠ 10.0)
+true
+
+julia> # `decondition` without any symbols will `decondition` all variables.
+       (m, x) = decondition(model)(); (m ≠ 1.0 && x ≠ 10.0)
+true
+
+julia> # Usage of `Val` to perform `decondition` at compile-time if possible
+       # is also supported.
+       model = decondition(conditioned_model, Val{:m}());
+
+julia> (m, x) = model(); (m ≠ 1.0 && x == 10.0)
+true

Similarly when using a Dict:

julia> conditioned_model_dict = condition(demo(), @varname(m) => 1.0, @varname(x) => 10.0);
+
+julia> conditioned_model_dict()
+(m = 1.0, x = 10.0)
+
+julia> deconditioned_model_dict = decondition(conditioned_model_dict, @varname(m));
+
+julia> (m, x) = deconditioned_model_dict(); m ≠ 1.0 && x == 10.0
+true

But, as mentioned, decondition is only supported for variables explicitly provided to condition earlier;

julia> @model function demo_mv(::Type{TV}=Float64) where {TV}
+           m = Vector{TV}(undef, 2)
+           m[1] ~ Normal()
+           m[2] ~ Normal()
+           return m
+       end
+demo_mv (generic function with 4 methods)
+
+julia> model = demo_mv();
+
+julia> conditioned_model = condition(model, @varname(m) => [1.0, 2.0]);
+
+julia> conditioned_model()
+2-element Vector{Float64}:
+ 1.0
+ 2.0
+
+julia> deconditioned_model = decondition(conditioned_model, @varname(m[1]));
+
+julia> deconditioned_model()  # (×) `m[1]` is still conditioned
+2-element Vector{Float64}:
+ 1.0
+ 2.0
+
+julia> # (✓) this works though
+       deconditioned_model_2 = deconditioned_model | (@varname(m[1]) => missing);
+
+julia> m = deconditioned_model_2(); (m[1] ≠ 1.0 && m[2] == 2.0)
+true
source
decondition(context::AbstractContext, syms...)

Return context but with syms no longer conditioned on.

Note that this recursively traverses contexts, deconditioning all along the way.

See also: condition

source

Fixing and unfixing

We can also fix a collection of variables in a Model to certain using fix.

This might seem quite similar to the aforementioned condition and its siblings, but they are indeed different operations:

  • conditioned variables are considered to be observations, and are thus included in the computation logjoint and loglikelihood, but not in logprior.
  • fixed variables are considered to be constant, and are thus not included in any log-probability computations.

The differences are more clearly spelled out in the docstring of fix below.

DynamicPPL.fix — Function
fix(model::Model; values...)
+fix(model::Model, values::NamedTuple)

Return a Model which now treats the variables in values as fixed.

See also: unfix, fixed

Examples

Simple univariate model

julia> using Distributions
+
+julia> @model function demo()
+           m ~ Normal()
+           x ~ Normal(m, 1)
+           return (; m=m, x=x)
+       end
+demo (generic function with 2 methods)
+
+julia> model = demo();
+
+julia> m, x = model(); (m ≠ 1.0 && x ≠ 100.0)
+true
+
+julia> # Create a new instance which treats `x` as observed
+       # with value `100.0`, and similarly for `m=1.0`.
+       fixed_model = fix(model, x=100.0, m=1.0);
+
+julia> m, x = fixed_model(); (m == 1.0 && x == 100.0)
+true
+
+julia> # Let's only fix on `x = 100.0`.
+       fixed_model = fix(model, x = 100.0);
+
+julia> m, x = fixed_model(); (m ≠ 1.0 && x == 100.0)
+true

The above uses a NamedTuple to hold the fixed variables, which allows us to perform some additional optimizations; in many cases, the above has zero runtime-overhead.

But we can also use a Dict, which offers more flexibility in the fixing (see examples further below) but generally has worse performance than the NamedTuple approach:

julia> fixed_model_dict = fix(model, Dict(@varname(x) => 100.0));
+
+julia> m, x = fixed_model_dict(); (m ≠ 1.0 && x == 100.0)
+true
+
+julia> # Alternative: pass `Pair{<:VarName}` as positional argument.
+       fixed_model_dict = fix(model, @varname(x) => 100.0, );
+
+julia> m, x = fixed_model_dict(); (m ≠ 1.0 && x == 100.0)
+true

Fix only a part of a multivariate variable

We can not only fix multivariate random variables, but we can also use the standard mechanism of setting something to missing in the call to fix to only fix a part of the variable.

julia> @model function demo_mv(::Type{TV}=Float64) where {TV}
+           m = Vector{TV}(undef, 2)
+           m[1] ~ Normal()
+           m[2] ~ Normal()
+           return m
+       end
+demo_mv (generic function with 4 methods)
+
+julia> model = demo_mv();
+
+julia> fixed_model = fix(model, m = [missing, 1.0]);
+
+julia> # (✓) `m[1]` sampled while `m[2]` is fixed
+       m = fixed_model(); (m[1] ≠ 1.0 && m[2] == 1.0)
+true

Intuitively one might also expect to be able to write something like fix(model, var"m[1]" = 1.0, ). Unfortunately this is not supported as it has the potential of increasing compilation times but without offering any benefit with respect to runtime:

julia> # (×) `m[2]` is not set to 1.0.
+       m = fix(model, var"m[2]" = 1.0)(); m[2] == 1.0
+false

But you can do this if you use a Dict as the underlying storage instead:

julia> # Alternative: `fix(model, Dict(@varname(m[2] => 1.0)))`
+       # (✓) `m[2]` is set to 1.0.
+       m = fix(model, @varname(m[2]) => 1.0)(); (m[1] ≠ 1.0 && m[2] == 1.0)
+true

Nested models

fix of course also supports the use of nested models through the use of @submodel.

julia> @model demo_inner() = m ~ Normal()
+demo_inner (generic function with 2 methods)
+
+julia> @model function demo_outer()
+           @submodel m = demo_inner()
+           return m
+       end
+demo_outer (generic function with 2 methods)
+
+julia> model = demo_outer();
+
+julia> model() ≠ 1.0
+true
+
+julia> fixed_model = model | (m = 1.0, );
+
+julia> fixed_model()
+1.0

But one needs to be careful when prefixing variables in the nested models:

julia> @model function demo_outer_prefix()
+           @submodel prefix="inner" m = demo_inner()
+           return m
+       end
+demo_outer_prefix (generic function with 2 methods)
+
+julia> # (×) This doesn't work now!
+       fixed_model = demo_outer_prefix() | (m = 1.0, );
+
+julia> fixed_model() == 1.0
+false
+
+julia> # (✓) `m` in `demo_inner` is referred to as `inner.m` internally, so we do:
+       fixed_model = demo_outer_prefix() | (var"inner.m" = 1.0, );
+
+julia> fixed_model()
+1.0
+
+julia> # Note that the above `var"..."` is just standard Julia syntax:
+       keys((var"inner.m" = 1.0, ))
+(Symbol("inner.m"),)

And similarly when using Dict:

julia> fixed_model_dict = demo_outer_prefix() | (@varname(var"inner.m") => 1.0);
+
+julia> fixed_model_dict()
+1.0

The difference is maybe more obvious once we look at how these different in their trace/VarInfo:

julia> keys(VarInfo(demo_outer()))
+1-element Vector{VarName{:m, Setfield.IdentityLens}}:
+ m
+
+julia> keys(VarInfo(demo_outer_prefix()))
+1-element Vector{VarName{Symbol("inner.m"), Setfield.IdentityLens}}:
+ inner.m

From this we can tell what the correct way to fix m within demo_inner is in the two different models.

Difference from condition

A very similar functionality is also provided by condition which, not surprisingly, conditions variables instead of fixing them. The only difference between fixing and conditioning is as follows:

  • conditioned variables are considered to be observations, and are thus included in the computation logjoint and loglikelihood, but not in logprior.
  • fixed variables are considered to be constant, and are thus not included in any log-probability computations.
julia> @model function demo()
+           m ~ Normal()
+           x ~ Normal(m, 1)
+           return (; m=m, x=x)
+       end
+demo (generic function with 2 methods)
+
+julia> model = demo();
+
+julia> model_fixed = fix(model, m = 1.0);
+
+julia> model_conditioned = condition(model, m = 1.0);
+
+julia> logjoint(model_fixed, (x=1.0,))
+-0.9189385332046728
+
+julia> # Different!
+       logjoint(model_conditioned, (x=1.0,))
+-2.3378770664093453
+
+julia> # And the difference is the missing log-probability of `m`:
+       logjoint(model_fixed, (x=1.0,)) + logpdf(Normal(), 1.0) == logjoint(model_conditioned, (x=1.0,))
+true
source
fix([context::AbstractContext,] values::NamedTuple)
+fix([context::AbstractContext]; values...)

Return FixedContext with values and context if values is non-empty, otherwise return context which is DefaultContext by default.

See also: unfix

source
DynamicPPL.fixed — Function
fixed(model::Model)

Return the fixed values in model.

Examples

julia> using Distributions
+
+julia> using DynamicPPL: fixed, contextualize
+
+julia> @model function demo()
+           m ~ Normal()
+           x ~ Normal(m, 1)
+       end
+demo (generic function with 2 methods)
+
+julia> m = demo();
+
+julia> # Returns all the variables we have fixed on + their values.
+       fixed(fix(m, x=100.0, m=1.0))
+(x = 100.0, m = 1.0)
+
+julia> # Nested ones also work (note that `PrefixContext` does nothing to the result).
+       cm = fix(contextualize(m, PrefixContext{:a}(fix(m=1.0))), x=100.0);
+
+julia> fixed(cm)
+(x = 100.0, m = 1.0)
+
+julia> # Since we fixed on `m`, not `a.m` as it will appear after prefixed,
+       # `a.m` is treated as a random variable.
+       keys(VarInfo(cm))
+1-element Vector{VarName{Symbol("a.m"), Setfield.IdentityLens}}:
+ a.m
+
+julia> # If we instead fix on `a.m`, `m` in the model will be considered an observation.
+       cm = fix(contextualize(m, PrefixContext{:a}(fix(var"a.m"=1.0))), x=100.0);
+
+julia> fixed(cm).x
+100.0
+
+julia> fixed(cm).var"a.m"
+1.0
+
+julia> keys(VarInfo(cm)) # <= no variables are sampled
+VarName[]
source
fixed(context::AbstractContext)

Return the values that are fixed under context.

Note that this will recursively traverse the context stack and return a merged version of the fix values.

source

The difference between fix and condition is described in the docstring of fix above.

Similarly, we can unfix variables, i.e. return them to their original meaning:

DynamicPPL.unfix — Function
unfix(model::Model)
+unfix(model::Model, variables...)

Return a Model for which variables... are not considered fixed. If no variables are provided, then all variables currently considered fixed will no longer be.

This is essentially the inverse of fix. This also means that it suffers from the same limitiations.

Note that currently we only support variables to take on explicit values provided to fix.

Examples

julia> using Distributions
+
+julia> @model function demo()
+           m ~ Normal()
+           x ~ Normal(m, 1)
+           return (; m=m, x=x)
+       end
+demo (generic function with 2 methods)
+
+julia> fixed_model = fix(demo(), m = 1.0, x = 10.0);
+
+julia> fixed_model()
+(m = 1.0, x = 10.0)
+
+julia> # By specifying the `VarName` to `unfix`.
+       model = unfix(fixed_model, @varname(m));
+
+julia> (m, x) = model(); (m ≠ 1.0 && x == 10.0)
+true
+
+julia> # When `NamedTuple` is used as the underlying, you can also provide
+       # the symbol directly (though the `@varname` approach is preferable if
+       # if the variable is known at compile-time).
+       model = unfix(fixed_model, :m);
+
+julia> (m, x) = model(); (m ≠ 1.0 && x == 10.0)
+true
+
+julia> # `unfix` multiple at once:
+       (m, x) = unfix(model, :m, :x)(); (m ≠ 1.0 && x ≠ 10.0)
+true
+
+julia> # `unfix` without any symbols will `unfix` all variables.
+       (m, x) = unfix(model)(); (m ≠ 1.0 && x ≠ 10.0)
+true
+
+julia> # Usage of `Val` to perform `unfix` at compile-time if possible
+       # is also supported.
+       model = unfix(fixed_model, Val{:m}());
+
+julia> (m, x) = model(); (m ≠ 1.0 && x == 10.0)
+true

Similarly when using a Dict:

julia> fixed_model_dict = fix(demo(), @varname(m) => 1.0, @varname(x) => 10.0);
+
+julia> fixed_model_dict()
+(m = 1.0, x = 10.0)
+
+julia> unfixed_model_dict = unfix(fixed_model_dict, @varname(m));
+
+julia> (m, x) = unfixed_model_dict(); m ≠ 1.0 && x == 10.0
+true

But, as mentioned, unfix is only supported for variables explicitly provided to fix earlier:

julia> @model function demo_mv(::Type{TV}=Float64) where {TV}
+           m = Vector{TV}(undef, 2)
+           m[1] ~ Normal()
+           m[2] ~ Normal()
+           return m
+       end
+demo_mv (generic function with 4 methods)
+
+julia> model = demo_mv();
+
+julia> fixed_model = fix(model, @varname(m) => [1.0, 2.0]);
+
+julia> fixed_model()
+2-element Vector{Float64}:
+ 1.0
+ 2.0
+
+julia> unfixed_model = unfix(fixed_model, @varname(m[1]));
+
+julia> unfixed_model()  # (×) `m[1]` is still fixed
+2-element Vector{Float64}:
+ 1.0
+ 2.0
+
+julia> # (✓) this works though
+       unfixed_model_2 = fix(unfixed_model, @varname(m[1]) => missing);
+
+julia> m = unfixed_model_2(); (m[1] ≠ 1.0 && m[2] == 2.0)
+true
source
unfix(context::AbstractContext, syms...)

Return context but with syms no longer fixed.

Note that this recursively traverses contexts, unfixing all along the way.

See also: fix

source

Utilities

It is possible to manually increase (or decrease) the accumulated log density from within a model function.

DynamicPPL.@addlogprob! — Macro
@addlogprob!(ex)

Add the result of the evaluation of ex to the joint log probability.

Examples

This macro allows you to include arbitrary terms in the likelihood

julia> myloglikelihood(x, μ) = loglikelihood(Normal(μ, 1), x);
+
+julia> @model function demo(x)
+           μ ~ Normal()
+           @addlogprob! myloglikelihood(x, μ)
+       end;
+
+julia> x = [1.3, -2.1];
+
+julia> loglikelihood(demo(x), (μ=0.2,)) ≈ myloglikelihood(x, 0.2)
+true

and to reject samples:

julia> @model function demo(x)
+           m ~ MvNormal(zero(x), I)
+           if dot(m, x) < 0
+               @addlogprob! -Inf
+               # Exit the model evaluation early
+               return
+           end
+           x ~ MvNormal(m, I)
+           return
+       end;
+
+julia> logjoint(demo([-2.1]), (m=[0.2],)) == -Inf
+true
Note

The @addlogprob! macro increases the accumulated log probability regardless of the evaluation context, i.e., regardless of whether you evaluate the log prior, the log likelihood or the log joint density. If you would like to avoid this behaviour you should check the evaluation context. It can be accessed with the internal variable __context__. For instance, in the following example the log density is not accumulated when only the log prior is computed: 

julia> myloglikelihood(x, μ) = loglikelihood(Normal(μ, 1), x);
+
+julia> @model function demo(x)
+           μ ~ Normal()
+           if DynamicPPL.leafcontext(__context__) !== PriorContext()
+               @addlogprob! myloglikelihood(x, μ)
+           end
+       end;
+
+julia> x = [1.3, -2.1];
+
+julia> logprior(demo(x), (μ=0.2,)) ≈ logpdf(Normal(), 0.2)
+true
+
+julia> loglikelihood(demo(x), (μ=0.2,)) ≈ myloglikelihood(x, 0.2)
+true
source

Return values of the model function for a collection of samples can be obtained with generated_quantities.

DynamicPPL.generated_quantities — Function
generated_quantities(model::Model, chain::AbstractChains)

Execute model for each of the samples in chain and return an array of the values returned by the model for each sample.

Examples

General

Often you might have additional quantities computed inside the model that you want to inspect, e.g.

@model function demo(x)
+    # sample and observe
+    θ ~ Prior()
+    x ~ Likelihood()
+    return interesting_quantity(θ, x)
+end
+m = demo(data)
+chain = sample(m, alg, n)
+# To inspect the `interesting_quantity(θ, x)` where `θ` is replaced by samples
+# from the posterior/`chain`:
+generated_quantities(m, chain) # <= results in a `Vector` of returned values
+                               #    from `interesting_quantity(θ, x)`

Concrete (and simple)

julia> using DynamicPPL, Turing
+
+julia> @model function demo(xs)
+           s ~ InverseGamma(2, 3)
+           m_shifted ~ Normal(10, √s)
+           m = m_shifted - 10
+
+           for i in eachindex(xs)
+               xs[i] ~ Normal(m, √s)
+           end
+
+           return (m, )
+       end
+demo (generic function with 1 method)
+
+julia> model = demo(randn(10));
+
+julia> chain = sample(model, MH(), 10);
+
+julia> generated_quantities(model, chain)
+10×1 Array{Tuple{Float64},2}:
+ (2.1964758025119338,)
+ (2.1964758025119338,)
+ (0.09270081916291417,)
+ (0.09270081916291417,)
+ (0.09270081916291417,)
+ (0.09270081916291417,)
+ (0.09270081916291417,)
+ (0.043088571494005024,)
+ (-0.16489786710222099,)
+ (-0.16489786710222099,)
source
generated_quantities(model::Model, parameters::NamedTuple)
+generated_quantities(model::Model, values, keys)
+generated_quantities(model::Model, values, keys)

Execute model with variables keys set to values and return the values returned by the model.

If a NamedTuple is given, keys=keys(parameters) and values=values(parameters).

Example

julia> using DynamicPPL, Distributions
+
+julia> @model function demo(xs)
+           s ~ InverseGamma(2, 3)
+           m_shifted ~ Normal(10, √s)
+           m = m_shifted - 10
+           for i in eachindex(xs)
+               xs[i] ~ Normal(m, √s)
+           end
+           return (m, )
+       end
+demo (generic function with 2 methods)
+
+julia> model = demo(randn(10));
+
+julia> parameters = (; s = 1.0, m_shifted=10);
+
+julia> generated_quantities(model, parameters)
+(0.0,)
+
+julia> generated_quantities(model, values(parameters), keys(parameters))
+(0.0,)
source

For a chain of samples, one can compute the pointwise log-likelihoods of each observed random variable with pointwise_loglikelihoods.

DynamicPPL.pointwise_loglikelihoods — Function
pointwise_loglikelihoods(model::Model, chain::Chains, keytype = String)

Runs model on each sample in chain returning a OrderedDict{String, Matrix{Float64}} with keys corresponding to symbols of the observations, and values being matrices of shape (num_chains, num_samples).

keytype specifies what the type of the keys used in the returned OrderedDict are. Currently, only String and VarName are supported.

Notes

Say y is a Vector of n i.i.d. Normal(μ, σ) variables, with μ and σ both being <:Real. Then the observe (i.e. when the left-hand side is an observation) statements can be implemented in three ways:

  1. using a for loop:
for i in eachindex(y)
+    y[i] ~ Normal(μ, σ)
+end
  1. using .~:
y .~ Normal(μ, σ)
  1. using MvNormal:
y ~ MvNormal(fill(μ, n), σ^2 * I)

In (1) and (2), y will be treated as a collection of n i.i.d. 1-dimensional variables, while in (3) y will be treated as a single n-dimensional observation.

This is important to keep in mind, in particular if the computation is used for downstream computations.

Examples

From chain

julia> using DynamicPPL, Turing
+
+julia> @model function demo(xs, y)
+           s ~ InverseGamma(2, 3)
+           m ~ Normal(0, √s)
+           for i in eachindex(xs)
+               xs[i] ~ Normal(m, √s)
+           end
+
+           y ~ Normal(m, √s)
+       end
+demo (generic function with 1 method)
+
+julia> model = demo(randn(3), randn());
+
+julia> chain = sample(model, MH(), 10);
+
+julia> pointwise_loglikelihoods(model, chain)
+OrderedDict{String,Array{Float64,2}} with 4 entries:
+  "xs[1]" => [-1.42932; -2.68123; … ; -1.66333; -1.66333]
+  "xs[2]" => [-1.6724; -0.861339; … ; -1.62359; -1.62359]
+  "xs[3]" => [-1.42862; -2.67573; … ; -1.66251; -1.66251]
+  "y"     => [-1.51265; -0.914129; … ; -1.5499; -1.5499]
+
+julia> pointwise_loglikelihoods(model, chain, String)
+OrderedDict{String,Array{Float64,2}} with 4 entries:
+  "xs[1]" => [-1.42932; -2.68123; … ; -1.66333; -1.66333]
+  "xs[2]" => [-1.6724; -0.861339; … ; -1.62359; -1.62359]
+  "xs[3]" => [-1.42862; -2.67573; … ; -1.66251; -1.66251]
+  "y"     => [-1.51265; -0.914129; … ; -1.5499; -1.5499]
+
+julia> pointwise_loglikelihoods(model, chain, VarName)
+OrderedDict{VarName,Array{Float64,2}} with 4 entries:
+  xs[1] => [-1.42932; -2.68123; … ; -1.66333; -1.66333]
+  xs[2] => [-1.6724; -0.861339; … ; -1.62359; -1.62359]
+  xs[3] => [-1.42862; -2.67573; … ; -1.66251; -1.66251]
+  y     => [-1.51265; -0.914129; … ; -1.5499; -1.5499]

Broadcasting

Note that x .~ Dist() will treat x as a collection of independent observations rather than as a single observation.

julia> @model function demo(x)
+           x .~ Normal()
+       end;
+
+julia> m = demo([1.0, ]);
+
+julia> ℓ = pointwise_loglikelihoods(m, VarInfo(m)); first(ℓ[@varname(x[1])])
+-1.4189385332046727
+
+julia> m = demo([1.0; 1.0]);
+
+julia> ℓ = pointwise_loglikelihoods(m, VarInfo(m)); first.((ℓ[@varname(x[1])], ℓ[@varname(x[2])]))
+(-1.4189385332046727, -1.4189385332046727)
source

For converting a chain into a format that can more easily be fed into a Model again, for example using condition, you can use value_iterator_from_chain.

DynamicPPL.value_iterator_from_chain — Function
value_iterator_from_chain(model::Model, chain)
+value_iterator_from_chain(varinfo::AbstractVarInfo, chain)

Return an iterator over the values in chain for each variable in model/varinfo.

Example

julia> using MCMCChains, DynamicPPL, Distributions, StableRNGs
+
+julia> rng = StableRNG(42);
+
+julia> @model function demo_model(x)
+           s ~ InverseGamma(2, 3)
+           m ~ Normal(0, sqrt(s))
+           for i in eachindex(x)
+               x[i] ~ Normal(m, sqrt(s))
+           end
+
+           return s, m
+       end
+demo_model (generic function with 2 methods)
+
+julia> model = demo_model([1.0, 2.0]);
+
+julia> chain = Chains(rand(rng, 10, 2, 3), [:s, :m]);
+
+julia> iter = value_iterator_from_chain(model, chain);
+
+julia> first(iter)
+OrderedDict{VarName, Any} with 2 entries:
+  s => 0.580515
+  m => 0.739328
+
+julia> collect(iter)
+10×3 Matrix{OrderedDict{VarName, Any}}:
+ OrderedDict(s=>0.580515, m=>0.739328)  …  OrderedDict(s=>0.186047, m=>0.402423)
+ OrderedDict(s=>0.191241, m=>0.627342)     OrderedDict(s=>0.776277, m=>0.166342)
+ OrderedDict(s=>0.971133, m=>0.637584)     OrderedDict(s=>0.651655, m=>0.712044)
+ OrderedDict(s=>0.74345, m=>0.110359)      OrderedDict(s=>0.469214, m=>0.104502)
+ OrderedDict(s=>0.170969, m=>0.598514)     OrderedDict(s=>0.853546, m=>0.185399)
+ OrderedDict(s=>0.704776, m=>0.322111)  …  OrderedDict(s=>0.638301, m=>0.853802)
+ OrderedDict(s=>0.441044, m=>0.162285)     OrderedDict(s=>0.852959, m=>0.0956922)
+ OrderedDict(s=>0.803972, m=>0.643369)     OrderedDict(s=>0.245049, m=>0.871985)
+ OrderedDict(s=>0.772384, m=>0.646323)     OrderedDict(s=>0.906603, m=>0.385502)
+ OrderedDict(s=>0.70882, m=>0.253105)      OrderedDict(s=>0.413222, m=>0.953288)
+
+julia> # This can be used to `condition` a `Model`.
+       conditioned_model = model | first(iter);
+
+julia> conditioned_model()  # <= results in same values as the `first(iter)` above
+(0.5805148626851955, 0.7393275279160691)
source

Sometimes it can be useful to extract the priors of a model. This is the possible using extract_priors.

DynamicPPL.extract_priors — Function
extract_priors([rng::Random.AbstractRNG, ]model::Model)

Extract the priors from a model.

This is done by sampling from the model and recording the distributions that are used to generate the samples.

Warning

Because the extraction is done by execution of the model, there are several caveats:

  1. If one variable, say, y ~ Normal(0, x), where x ~ Normal() is also a random variable, then the extracted prior will have different parameters in every extraction!
  2. If the model does not have static support, say, n ~ Categorical(1:10); x ~ MvNormmal(zeros(n), I), then the extracted priors themselves will be different between extractions, not just their parameters.

Both of these caveats are demonstrated below.

Examples

Changing parameters

julia> using Distributions, StableRNGs
+
+julia> rng = StableRNG(42);
+
+julia> @model function model_dynamic_parameters()
+           x ~ Normal(0, 1)
+           y ~ Normal(x, 1)
+       end;
+
+julia> model = model_dynamic_parameters();
+
+julia> extract_priors(rng, model)[@varname(y)]
+Normal{Float64}(μ=-0.6702516921145671, σ=1.0)
+
+julia> extract_priors(rng, model)[@varname(y)]
+Normal{Float64}(μ=1.3736306979834252, σ=1.0)

Changing support

julia> using LinearAlgebra, Distributions, StableRNGs
+
+julia> rng = StableRNG(42);
+
+julia> @model function model_dynamic_support()
+           n ~ Categorical(ones(10) ./ 10)
+           x ~ MvNormal(zeros(n), I)
+       end;
+
+julia> model = model_dynamic_support();
+
+julia> length(extract_priors(rng, model)[@varname(x)])
+6
+
+julia> length(extract_priors(rng, model)[@varname(x)])
+9
source

Testing Utilities

DynamicPPL provides several demo models and helpers for testing samplers in the DynamicPPL.TestUtils submodule.

DynamicPPL.TestUtils.test_sampler — Function
test_sampler(models, sampler, args...; kwargs...)

Test that sampler produces correct marginal posterior means on each model in models.

In short, this method iterates through models, calls AbstractMCMC.sample on the model and sampler to produce a chain, and then checks marginal_mean_of_samples(chain, vn) for every (leaf) varname vn against the corresponding value returned by posterior_mean for each model.

To change how comparison is done for a particular chain type, one can overload marginal_mean_of_samples for the corresponding type.

Arguments

  • models: A collection of instaces of DynamicPPL.Model to test on.
  • sampler: The AbstractMCMC.AbstractSampler to test.
  • args...: Arguments forwarded to sample.

Keyword arguments

  • varnames_filter: A filter to apply to varnames(model), allowing comparison for only a subset of the varnames.
  • atol=1e-1: Absolute tolerance used in @test.
  • rtol=1e-3: Relative tolerance used in @test.
  • kwargs...: Keyword arguments forwarded to sample.
source
DynamicPPL.TestUtils.DEMO_MODELS — Constant

A collection of models corresponding to the posterior distribution defined by the generative process

s ~ InverseGamma(2, 3)
+m ~ Normal(0, √s)
+1.5 ~ Normal(m, √s)
+2.0 ~ Normal(m, √s)

or by

s[1] ~ InverseGamma(2, 3)
+s[2] ~ InverseGamma(2, 3)
+m[1] ~ Normal(0, √s)
+m[2] ~ Normal(0, √s)
+1.5 ~ Normal(m[1], √s[1])
+2.0 ~ Normal(m[2], √s[2])

These are examples of a Normal-InverseGamma conjugate prior with Normal likelihood, for which the posterior is known in closed form.

In particular, for the univariate model (the former one):

mean(s) == 49 / 24
+mean(m) == 7 / 6

And for the multivariate one (the latter one):

mean(s[1]) == 19 / 8
+mean(m[1]) == 3 / 4
+mean(s[2]) == 8 / 3
+mean(m[2]) == 1
source

For every demo model, one can define the true log prior, log likelihood, and log joint probabilities.

DynamicPPL.TestUtils.logjoint_true — Function
logjoint_true(model, args...)

Return the logjoint of model for args.

Defaults to logprior_true(model, args...) + loglikelihood_true(model, args..).

This should generally be implemented by hand for every specific model so that the returned value can be used as a ground-truth for testing things like:

  1. Validity of evaluation of model using a particular implementation of AbstractVarInfo.
  2. Validity of a sampler when combined with DynamicPPL by running the sampler twice: once targeting ground-truth functions, e.g. logjoint_true, and once targeting model.

And more.

See also: logprior_true, loglikelihood_true.

source

And in the case where the model includes constrained variables, it can also be useful to define

DynamicPPL.TestUtils.logprior_true_with_logabsdet_jacobian — Function
logprior_true_with_logabsdet_jacobian(model::Model, args...)

Return a tuple (args_unconstrained, logprior_unconstrained) of model for args....

Unlike logprior_true, the returned logprior computation includes the log-absdet-jacobian adjustment, thus computing logprior for the unconstrained variables.

Note that args are assumed be in the support of model, while args_unconstrained will be unconstrained.

See also: logprior_true.

source
DynamicPPL.TestUtils.logjoint_true_with_logabsdet_jacobian — Function
logjoint_true_with_logabsdet_jacobian(model::Model, args...)

Return a tuple (args_unconstrained, logjoint) of model for args.

Unlike logjoint_true, the returned logjoint computation includes the log-absdet-jacobian adjustment, thus computing logjoint for the unconstrained variables.

Note that args are assumed be in the support of model, while args_unconstrained will be unconstrained.

This should generally not be implemented directly, instead one should implement logprior_true_with_logabsdet_jacobian for a given model.

See also: logjoint_true, logprior_true_with_logabsdet_jacobian.

source

Finally, the following methods can also be of use:

DynamicPPL.TestUtils.varnames — Function
varnames(model::Model)

Return a collection of VarName as they are expected to appear in the model.

Even though it is recommended to implement this by hand for a particular Model, a default implementation using SimpleVarInfo{<:Dict} is provided.

source
DynamicPPL.TestUtils.posterior_mean — Function
posterior_mean(model::Model)

Return a NamedTuple compatible with varnames(model) where the values represent the posterior mean under model.

"Compatible" means that a varname from varnames(model) can be used to extract the corresponding value using get, e.g. get(posterior_mean(model), varname).

source
DynamicPPL.TestUtils.setup_varinfos — Function
setup_varinfos(model::Model, example_values::NamedTuple, varnames; include_threadsafe::Bool=false)

Return a tuple of instances for different implementations of AbstractVarInfo with each vi, supposedly, satisfying vi[vn] == get(example_values, vn) for vn in varnames.

If include_threadsafe is true, then the returned tuple will also include thread-safe versions of the varinfo instances.

source

Advanced

Variable names

Names and possibly nested indices of variables are described with AbstractPPL.VarName. They can be defined with AbstractPPL.@varname. Please see the documentation of AbstractPPL.jl for further information.

Data Structures of Variables

DynamicPPL provides different data structures for samples from the model and their log density. All of them are subtypes of AbstractVarInfo.

DynamicPPL.AbstractVarInfo — Type
AbstractVarInfo

Abstract supertype for data structures that capture random variables when executing a probabilistic model and accumulate log densities such as the log likelihood or the log joint probability of the model.

See also: VarInfo, SimpleVarInfo.

source

Common API

Accumulation of log-probabilities

DynamicPPL.getlogp — Function
getlogp(vi::AbstractVarInfo)

Return the log of the joint probability of the observed data and parameters sampled in vi.

source
DynamicPPL.setlogp!! — Function
setlogp!!(vi::AbstractVarInfo, logp)

Set the log of the joint probability of the observed data and parameters sampled in vi to logp, mutating if it makes sense.

source
DynamicPPL.acclogp!! — Function
acclogp!!(vi::AbstractVarInfo, logp)

Add logp to the value of the log of the joint probability of the observed data and parameters sampled in vi, mutating if it makes sense.

source
DynamicPPL.resetlogp!! — Function
resetlogp!!(vi::AbstractVarInfo)

Reset the value of the log of the joint probability of the observed data and parameters sampled in vi to 0, mutating if it makes sense.

source

Variables and their realizations

Base.keys — Function
keys(vi::AbstractVarInfo)

Return an iterator over all vns in vi.

source
DynamicPPL.getindex_raw — Function
getindex_raw(vi::AbstractVarInfo, vn::VarName[, dist::Distribution])
+getindex_raw(vi::AbstractVarInfo, vns::Vector{<:VarName}[, dist::Distribution])

Return the current value(s) of vn (vns) in vi.

If dist is specified, the value(s) will be reshaped accordingly.

See also: getindex(vi::AbstractVarInfo, vn::VarName, dist::Distribution)

Note

The difference between getindex(vi, vn, dist) and getindex_raw is that getindex will also transform the value(s) to the support of the distribution(s). This is not the case for getindex_raw.

source
BangBang.push!! — Function
push!!(vi::AbstractVarInfo, vn::VarName, r, dist::Distribution)

Push a new random variable vn with a sampled value r from a distribution dist to the VarInfo vi, mutating if it makes sense.

source
push!!(vi::AbstractVarInfo, vn::VarName, r, dist::Distribution, spl::AbstractSampler)

Push a new random variable vn with a sampled value r sampled with a sampler spl from a distribution dist to VarInfo vi, if it makes sense.

The sampler is passed here to invalidate its cache where defined.

Warning

This method is considered legacy, and is likely to be deprecated in the future.

source
push!!(vi::AbstractVarInfo, vn::VarName, r, dist::Distribution, gid::Selector)

Push a new random variable vn with a sampled value r sampled with a sampler of selector gid from a distribution dist to VarInfo vi.

Warning

This method is considered legacy, and is likely to be deprecated in the future.

source
BangBang.empty!! — Function
empty!!(vi::AbstractVarInfo)

Empty the fields of vi.metadata and reset vi.logp[] and vi.num_produce[] to zeros.

This is useful when using a sampling algorithm that assumes an empty vi, e.g. SMC.

source
Base.isempty — Function
isempty(vi::AbstractVarInfo)

Return true if vi is empty and false otherwise.

source
DynamicPPL.values_as — Function
values_as(varinfo[, Type])

Return the values/realizations in varinfo as Type, if implemented.

If no Type is provided, return values as stored in varinfo.

Examples

SimpleVarInfo with NamedTuple:

julia> data = (x = 1.0, m = [2.0]);
+
+julia> values_as(SimpleVarInfo(data))
+(x = 1.0, m = [2.0])
+
+julia> values_as(SimpleVarInfo(data), NamedTuple)
+(x = 1.0, m = [2.0])
+
+julia> values_as(SimpleVarInfo(data), OrderedDict)
+OrderedDict{VarName{sym, Setfield.IdentityLens} where sym, Any} with 2 entries:
+  x => 1.0
+  m => [2.0]
+
+julia> values_as(SimpleVarInfo(data), Vector)
+2-element Vector{Float64}:
+ 1.0
+ 2.0

SimpleVarInfo with OrderedDict:

julia> data = OrderedDict{Any,Any}(@varname(x) => 1.0, @varname(m) => [2.0]);
+
+julia> values_as(SimpleVarInfo(data))
+OrderedDict{Any, Any} with 2 entries:
+  x => 1.0
+  m => [2.0]
+
+julia> values_as(SimpleVarInfo(data), NamedTuple)
+(x = 1.0, m = [2.0])
+
+julia> values_as(SimpleVarInfo(data), OrderedDict)
+OrderedDict{Any, Any} with 2 entries:
+  x => 1.0
+  m => [2.0]
+
+julia> values_as(SimpleVarInfo(data), Vector)
+2-element Vector{Float64}:
+ 1.0
+ 2.0

TypedVarInfo:

julia> # Just use an example model to construct the `VarInfo` because we're lazy.
+       vi = VarInfo(DynamicPPL.TestUtils.demo_assume_dot_observe());
+
+julia> vi[@varname(s)] = 1.0; vi[@varname(m)] = 2.0;
+
+julia> # For the sake of brevity, let's just check the type.
+       md = values_as(vi); md.s isa DynamicPPL.Metadata
+true
+
+julia> values_as(vi, NamedTuple)
+(s = 1.0, m = 2.0)
+
+julia> values_as(vi, OrderedDict)
+OrderedDict{VarName{sym, Setfield.IdentityLens} where sym, Float64} with 2 entries:
+  s => 1.0
+  m => 2.0
+
+julia> values_as(vi, Vector)
+2-element Vector{Float64}:
+ 1.0
+ 2.0

UntypedVarInfo:

julia> # Just use an example model to construct the `VarInfo` because we're lazy.
+       vi = VarInfo(); DynamicPPL.TestUtils.demo_assume_dot_observe()(vi);
+
+julia> vi[@varname(s)] = 1.0; vi[@varname(m)] = 2.0;
+
+julia> # For the sake of brevity, let's just check the type.
+       values_as(vi) isa DynamicPPL.Metadata
+true
+
+julia> values_as(vi, NamedTuple)
+(s = 1.0, m = 2.0)
+
+julia> values_as(vi, OrderedDict)
+OrderedDict{VarName{sym, Setfield.IdentityLens} where sym, Float64} with 2 entries:
+  s => 1.0
+  m => 2.0
+
+julia> values_as(vi, Vector)
+2-element Vector{Real}:
+ 1.0
+ 2.0
source

Transformations

DynamicPPL.AbstractTransformation — Type
abstract type AbstractTransformation

Represents a transformation to be used in link!! and invlink!!, amongst others.

A concrete implementation of this should implement the following methods:

And potentially:

See also: link!!, invlink!!, maybe_invlink_before_eval!!.

source
DynamicPPL.DynamicTransformation — Type
struct DynamicTransformation <: DynamicPPL.AbstractTransformation

Transformation which transforms the variables on a per-need-basis in the execution of a given Model.

This is in constrast to StaticTransformation which transforms all variables before the execution of a given Model.

See also: StaticTransformation.

source
DynamicPPL.StaticTransformation — Type
struct StaticTransformation{F} <: DynamicPPL.AbstractTransformation

Transformation which transforms all variables before the execution of a given Model.

This is done through the maybe_invlink_before_eval!! method.

See also: DynamicTransformation, maybe_invlink_before_eval!!.

Fields

  • bijector::Any: The function, assumed to implement the Bijectors interface, to be applied to the variables
source
DynamicPPL.istrans — Function
istrans(vi::AbstractVarInfo[, vns::Union{VarName, AbstractVector{<:Varname}}])

Return true if vi is working in unconstrained space, and false if vi is assuming realizations to be in support of the corresponding distributions.

If vns is provided, then only check if this/these varname(s) are transformed.

Warning

Not all implementations of AbstractVarInfo support transforming only a subset of the variables.

source
DynamicPPL.settrans!! — Function
settrans!!(vi::AbstractVarInfo, trans::Bool[, vn::VarName])

Return vi with istrans(vi, vn) evaluating to true.

If vn is not specified, then istrans(vi) evaluates to true for all variables.

source
Bijectors.link — Function
link([t::AbstractTransformation, ]vi::AbstractVarInfo, model::Model)
+link([t::AbstractTransformation, ]vi::AbstractVarInfo, spl::AbstractSampler, model::Model)

Transform the variables in vi to their linked space without mutating vi, using the transformation t.

If t is not provided, default_transformation(model, vi) will be used.

See also: default_transformation, invlink.

source
Bijectors.invlink — Function
invlink([t::AbstractTransformation, ]vi::AbstractVarInfo, model::Model)
+invlink([t::AbstractTransformation, ]vi::AbstractVarInfo, spl::AbstractSampler, model::Model)

Transform the variables in vi to their constrained space without mutating vi, using the (inverse of) transformation t.

If t is not provided, default_transformation(model, vi) will be used.

See also: default_transformation, link.

source
DynamicPPL.link!! — Function
link!!([t::AbstractTransformation, ]vi::AbstractVarInfo, model::Model)
+link!!([t::AbstractTransformation, ]vi::AbstractVarInfo, spl::AbstractSampler, model::Model)

Transform the variables in vi to their linked space, using the transformation t, mutating vi if possible.

If t is not provided, default_transformation(model, vi) will be used.

See also: default_transformation, invlink!!.

source
DynamicPPL.invlink!! — Function
invlink!!([t::AbstractTransformation, ]vi::AbstractVarInfo, model::Model)
+invlink!!([t::AbstractTransformation, ]vi::AbstractVarInfo, spl::AbstractSampler, model::Model)

Transform the variables in vi to their constrained space, using the (inverse of) transformation t, mutating vi if possible.

If t is not provided, default_transformation(model, vi) will be used.

See also: default_transformation, link!!.

source
DynamicPPL.maybe_invlink_before_eval!! — Function
maybe_invlink_before_eval!!([t::Transformation,] vi, context, model)

Return a possibly invlinked version of vi.

This will be called prior to model evaluation, allowing one to perform a single invlink!! before evaluation rather than lazyily evaluating the transforms on as-we-need basis as is done with DynamicTransformation.

See also: StaticTransformation, DynamicTransformation.

Examples

julia> using DynamicPPL, Distributions, Bijectors
+
+julia> @model demo() = x ~ Normal()
+demo (generic function with 2 methods)
+
+julia> # By subtyping `Transform`, we inherit the `(inv)link!!`.
+       struct MyBijector <: Bijectors.Transform end
+
+julia> # Define some dummy `inverse` which will be used in the `link!!` call.
+       Bijectors.inverse(f::MyBijector) = identity
+
+julia> # We need to define `with_logabsdet_jacobian` for `MyBijector`
+       # (`identity` already has `with_logabsdet_jacobian` defined)
+       function Bijectors.with_logabsdet_jacobian(::MyBijector, x)
+           # Just using a large number of the logabsdet-jacobian term
+           # for demonstration purposes.
+           return (x, 1000)
+       end
+
+julia> # Change the `default_transformation` for our model to be a
+       # `StaticTransformation` using `MyBijector`.
+       function DynamicPPL.default_transformation(::Model{typeof(demo)})
+           return DynamicPPL.StaticTransformation(MyBijector())
+       end
+
+julia> model = demo();
+
+julia> vi = SimpleVarInfo(x=1.0)
+SimpleVarInfo((x = 1.0,), 0.0)
+
+julia> # Uses the `inverse` of `MyBijector`, which we have defined as `identity`
+       vi_linked = link!!(vi, model)
+Transformed SimpleVarInfo((x = 1.0,), 0.0)
+
+julia> # Now performs a single `invlink!!` before model evaluation.
+       logjoint(model, vi_linked)
+-1001.4189385332047
source
DynamicPPL.reconstruct — Function
reconstruct([f, ]dist, val)

Reconstruct val so that it's compatible with dist.

If f is also provided, the reconstruct value will be such that f(reconstruct_val) is compatible with dist.

source

Utils

DynamicPPL.subset — Function
subset(varinfo::AbstractVarInfo, vns::AbstractVector{<:VarName})

Subset a varinfo to only contain the variables vns.

Warning

The ordering of the variables in the resulting varinfo is not guaranteed to follow the ordering of the variables in varinfo. Hence care must be taken, in particular when used in conjunction with other methods which uses the vector-representation of the varinfo, e.g. getindex(varinfo, sampler).

Examples

julia> @model function demo()
+           s ~ InverseGamma(2, 3)
+           m ~ Normal(0, sqrt(s))
+           x = Vector{Float64}(undef, 2)
+           x[1] ~ Normal(m, sqrt(s))
+           x[2] ~ Normal(m, sqrt(s))
+       end
+demo (generic function with 2 methods)
+
+julia> model = demo();
+
+julia> varinfo = VarInfo(model);
+
+julia> keys(varinfo)
+4-element Vector{VarName}:
+ s
+ m
+ x[1]
+ x[2]
+
+julia> for (i, vn) in enumerate(keys(varinfo))
+           varinfo[vn] = i
+       end
+
+julia> varinfo[[@varname(s), @varname(m), @varname(x[1]), @varname(x[2])]]
+4-element Vector{Float64}:
+ 1.0
+ 2.0
+ 3.0
+ 4.0
+
+julia> # Extract one with only `m`.
+       varinfo_subset1 = subset(varinfo, [@varname(m),]);
+
+
+julia> keys(varinfo_subset1)
+1-element Vector{VarName{:m, Setfield.IdentityLens}}:
+ m
+
+julia> varinfo_subset1[@varname(m)]
+2.0
+
+julia> # Extract one with both `s` and `x[2]`.
+       varinfo_subset2 = subset(varinfo, [@varname(s), @varname(x[2])]);
+
+julia> keys(varinfo_subset2)
+2-element Vector{VarName}:
+ s
+ x[2]
+
+julia> varinfo_subset2[[@varname(s), @varname(x[2])]]
+2-element Vector{Float64}:
+ 1.0
+ 4.0

subset is particularly useful when combined with merge(varinfo::AbstractVarInfo)

julia> # Merge the two.
+       varinfo_subset_merged = merge(varinfo_subset1, varinfo_subset2);
+
+julia> keys(varinfo_subset_merged)
+3-element Vector{VarName}:
+ m
+ s
+ x[2]
+
+julia> varinfo_subset_merged[[@varname(s), @varname(m), @varname(x[2])]]
+3-element Vector{Float64}:
+ 1.0
+ 2.0
+ 4.0
+
+julia> # Merge the two with the original.
+       varinfo_merged = merge(varinfo, varinfo_subset_merged);
+
+julia> keys(varinfo_merged)
+4-element Vector{VarName}:
+ s
+ m
+ x[1]
+ x[2]
+
+julia> varinfo_merged[[@varname(s), @varname(m), @varname(x[1]), @varname(x[2])]]
+4-element Vector{Float64}:
+ 1.0
+ 2.0
+ 3.0
+ 4.0

Notes

Type-stability

Warning

This function is only type-stable when vns contains only varnames with the same symbol. For exmaple, [@varname(m[1]), @varname(m[2])] will be type-stable, but [@varname(m[1]), @varname(x)] will not be.

source
DynamicPPL.unflatten — Function
unflatten(original, x::AbstractVector)

Return instance of original constructed from x.

source
unflatten(vi::AbstractVarInfo[, context::AbstractContext], x::AbstractVector)

Return a new instance of vi with the values of x assigned to the variables.

If context is provided, x is assumed to be realizations only for variables not filtered out by context.

source
DynamicPPL.tonamedtuple — Function
tonamedtuple(vi::AbstractVarInfo)

Convert a vi into a NamedTuple where each variable symbol maps to the values and indexing string of the variable.

For example, a model that had a vector of vector-valued variables x would return

(x = ([1.5, 2.0], [3.0, 1.0], ["x[1]", "x[2]"]), )
source
DynamicPPL.varname_leaves — Function
varname_leaves(vn::VarName, val)

Return an iterator over all varnames that are represented by vn on val.

Examples

julia> using DynamicPPL: varname_leaves
+
+julia> foreach(println, varname_leaves(@varname(x), rand(2)))
+x[1]
+x[2]
+
+julia> foreach(println, varname_leaves(@varname(x[1:2]), rand(2)))
+x[1:2][1]
+x[1:2][2]
+
+julia> x = (y = 1, z = [[2.0], [3.0]]);
+
+julia> foreach(println, varname_leaves(@varname(x), x))
+x.y
+x.z[1][1]
+x.z[2][1]
source
DynamicPPL.varname_and_value_leaves — Function
varname_and_value_leaves(vn::VarName, val)

Return an iterator over all varname-value pairs that are represented by vn on val.

Examples

julia> using DynamicPPL: varname_and_value_leaves
+
+julia> foreach(println, varname_and_value_leaves(@varname(x), 1:2))
+(x[1], 1)
+(x[2], 2)
+
+julia> foreach(println, varname_and_value_leaves(@varname(x[1:2]), 1:2))
+(x[1:2][1], 1)
+(x[1:2][2], 2)
+
+julia> x = (y = 1, z = [[2.0], [3.0]]);
+
+julia> foreach(println, varname_and_value_leaves(@varname(x), x))
+(x.y, 1)
+(x.z[1][1], 2.0)
+(x.z[2][1], 3.0)

There are also some special handling for certain types:

julia> using LinearAlgebra
+
+julia> x = reshape(1:4, 2, 2);
+
+julia> # `LowerTriangular`
+       foreach(println, varname_and_value_leaves(@varname(x), LowerTriangular(x)))
+(x[1,1], 1)
+(x[2,1], 2)
+(x[2,2], 4)
+
+julia> # `UpperTriangular`
+       foreach(println, varname_and_value_leaves(@varname(x), UpperTriangular(x)))
+(x[1,1], 1)
+(x[1,2], 3)
+(x[2,2], 4)
+
+julia> # `Cholesky` with lower-triangular
+       foreach(println, varname_and_value_leaves(@varname(x), Cholesky([1.0 0.0; 0.0 1.0], 'L', 0)))
+(x.L[1,1], 1.0)
+(x.L[2,1], 0.0)
+(x.L[2,2], 1.0)
+
+julia> # `Cholesky` with upper-triangular
+       foreach(println, varname_and_value_leaves(@varname(x), Cholesky([1.0 0.0; 0.0 1.0], 'U', 0)))
+(x.U[1,1], 1.0)
+(x.U[1,2], 0.0)
+(x.U[2,2], 1.0)
source

SimpleVarInfo

DynamicPPL.SimpleVarInfo — Type
struct SimpleVarInfo{NT, T, C<:DynamicPPL.AbstractTransformation} <: AbstractVarInfo

A simple wrapper of the parameters with a logp field for accumulation of the logdensity.

Currently only implemented for NT<:NamedTuple and NT<:AbstractDict.

Fields

  • values: underlying representation of the realization represented

  • logp: holds the accumulated log-probability

  • transformation: represents whether it assumes variables to be transformed

Notes

The major differences between this and TypedVarInfo are:

  1. SimpleVarInfo does not require linearization.
  2. SimpleVarInfo can use more efficient bijectors.
  3. SimpleVarInfo is only type-stable if NT<:NamedTuple and either a) no indexing is used in tilde-statements, or b) the values have been specified with the correct shapes.

Examples

General usage

julia> using StableRNGs
+
+julia> @model function demo()
+           m ~ Normal()
+           x = Vector{Float64}(undef, 2)
+           for i in eachindex(x)
+               x[i] ~ Normal()
+           end
+           return x
+       end
+demo (generic function with 2 methods)
+
+julia> m = demo();
+
+julia> rng = StableRNG(42);
+
+julia> ### Sampling ###
+       ctx = SamplingContext(rng, SampleFromPrior(), DefaultContext());
+
+julia> # In the `NamedTuple` version we need to provide the place-holder values for
+       # the variables which are using "containers", e.g. `Array`.
+       # In this case, this means that we need to specify `x` but not `m`.
+       _, vi = DynamicPPL.evaluate!!(m, SimpleVarInfo((x = ones(2), )), ctx);
+
+julia> # (✓) Vroom, vroom! FAST!!!
+       vi[@varname(x[1])]
+0.4471218424633827
+
+julia> # We can also access arbitrary varnames pointing to `x`, e.g.
+       vi[@varname(x)]
+2-element Vector{Float64}:
+ 0.4471218424633827
+ 1.3736306979834252
+
+julia> vi[@varname(x[1:2])]
+2-element Vector{Float64}:
+ 0.4471218424633827
+ 1.3736306979834252
+
+julia> # (×) If we don't provide the container...
+       _, vi = DynamicPPL.evaluate!!(m, SimpleVarInfo(), ctx); vi
+ERROR: type NamedTuple has no field x
+[...]
+
+julia> # If one does not know the varnames, we can use a `OrderedDict` instead.
+       _, vi = DynamicPPL.evaluate!!(m, SimpleVarInfo{Float64}(OrderedDict()), ctx);
+
+julia> # (✓) Sort of fast, but only possible at runtime.
+       vi[@varname(x[1])]
+-1.019202452456547
+
+julia> # In addtion, we can only access varnames as they appear in the model!
+       vi[@varname(x)]
+ERROR: KeyError: key x not found
+[...]
+
+julia> vi[@varname(x[1:2])]
+ERROR: KeyError: key x[1:2] not found
+[...]

Technically, it's possible to use any implementation of AbstractDict in place of OrderedDict, but OrderedDict ensures that certain operations, e.g. linearization/flattening of the values in the varinfo, are consistent between evaluations. Hence OrderedDict is the preferred implementation of AbstractDict to use here.

You can also sample in transformed space:

julia> @model demo_constrained() = x ~ Exponential()
+demo_constrained (generic function with 2 methods)
+
+julia> m = demo_constrained();
+
+julia> _, vi = DynamicPPL.evaluate!!(m, SimpleVarInfo(), ctx);
+
+julia> vi[@varname(x)] # (✓) 0 ≤ x < ∞
+1.8632965762164932
+
+julia> _, vi = DynamicPPL.evaluate!!(m, DynamicPPL.settrans!!(SimpleVarInfo(), true), ctx);
+
+julia> vi[@varname(x)] # (✓) -∞ < x < ∞
+-0.21080155351918753
+
+julia> xs = [last(DynamicPPL.evaluate!!(m, DynamicPPL.settrans!!(SimpleVarInfo(), true), ctx))[@varname(x)] for i = 1:10];
+
+julia> any(xs .< 0)  # (✓) Positive probability mass on negative numbers!
+true
+
+julia> # And with `OrderedDict` of course!
+       _, vi = DynamicPPL.evaluate!!(m, DynamicPPL.settrans!!(SimpleVarInfo(OrderedDict()), true), ctx);
+
+julia> vi[@varname(x)] # (✓) -∞ < x < ∞
+0.6225185067787314
+
+julia> xs = [last(DynamicPPL.evaluate!!(m, DynamicPPL.settrans!!(SimpleVarInfo(), true), ctx))[@varname(x)] for i = 1:10];
+
+julia> any(xs .< 0) # (✓) Positive probability mass on negative numbers!
+true

Evaluation in transformed space of course also works:

julia> vi = DynamicPPL.settrans!!(SimpleVarInfo((x = -1.0,)), true)
+Transformed SimpleVarInfo((x = -1.0,), 0.0)
+
+julia> # (✓) Positive probability mass on negative numbers!
+       getlogp(last(DynamicPPL.evaluate!!(m, vi, DynamicPPL.DefaultContext())))
+-1.3678794411714423
+
+julia> # While if we forget to indicate that it's transformed:
+       vi = DynamicPPL.settrans!!(SimpleVarInfo((x = -1.0,)), false)
+SimpleVarInfo((x = -1.0,), 0.0)
+
+julia> # (✓) No probability mass on negative numbers!
+       getlogp(last(DynamicPPL.evaluate!!(m, vi, DynamicPPL.DefaultContext())))
+-Inf

Indexing

Using NamedTuple as underlying storage.

julia> svi_nt = SimpleVarInfo((m = (a = [1.0], ), ));
+
+julia> svi_nt[@varname(m)]
+(a = [1.0],)
+
+julia> svi_nt[@varname(m.a)]
+1-element Vector{Float64}:
+ 1.0
+
+julia> svi_nt[@varname(m.a[1])]
+1.0
+
+julia> svi_nt[@varname(m.a[2])]
+ERROR: BoundsError: attempt to access 1-element Vector{Float64} at index [2]
+[...]
+
+julia> svi_nt[@varname(m.b)]
+ERROR: type NamedTuple has no field b
+[...]

Using OrderedDict as underlying storage.

julia> svi_dict = SimpleVarInfo(OrderedDict(@varname(m) => (a = [1.0], )));
+
+julia> svi_dict[@varname(m)]
+(a = [1.0],)
+
+julia> svi_dict[@varname(m.a)]
+1-element Vector{Float64}:
+ 1.0
+
+julia> svi_dict[@varname(m.a[1])]
+1.0
+
+julia> svi_dict[@varname(m.a[2])]
+ERROR: BoundsError: attempt to access 1-element Vector{Float64} at index [2]
+[...]
+
+julia> svi_dict[@varname(m.b)]
+ERROR: type NamedTuple has no field b
+[...]
source

VarInfo

Another data structure is VarInfo.

DynamicPPL.VarInfo — Type
struct VarInfo{Tmeta, Tlogp} <: AbstractVarInfo
+    metadata::Tmeta
+    logp::Base.RefValue{Tlogp}
+    num_produce::Base.RefValue{Int}
+end

A light wrapper over one or more instances of Metadata. Let vi be an instance of VarInfo. If vi isa VarInfo{<:Metadata}, then only one Metadata instance is used for all the sybmols. VarInfo{<:Metadata} is aliased UntypedVarInfo. If vi isa VarInfo{<:NamedTuple}, then vi.metadata is a NamedTuple that maps each symbol used on the LHS of ~ in the model to its Metadata instance. The latter allows for the type specialization of vi after the first sampling iteration when all the symbols have been observed. VarInfo{<:NamedTuple} is aliased TypedVarInfo.

Note: It is the user's responsibility to ensure that each "symbol" is visited at least once whenever the model is called, regardless of any stochastic branching. Each symbol refers to a Julia variable and can be a hierarchical array of many random variables, e.g. x[1] ~ ... and x[2] ~ ... both have the same symbol x.

source
DynamicPPL.TypedVarInfo — Type
TypedVarInfo(vi::UntypedVarInfo)

This function finds all the unique syms from the instances of VarName{sym} found in vi.metadata.vns. It then extracts the metadata associated with each symbol from the global vi.metadata field. Finally, a new VarInfo is created with a new metadata as a NamedTuple mapping from symbols to type-stable Metadata instances, one for each symbol.

source

One main characteristic of VarInfo is that samples are stored in a linearized form.

DynamicPPL.link! — Function
link!(vi::VarInfo, spl::Sampler)

Transform the values of the random variables sampled by spl in vi from the support of their distributions to the Euclidean space and set their corresponding "trans" flag values to true.

source
DynamicPPL.invlink! — Function
invlink!(vi::VarInfo, spl::AbstractSampler)

Transform the values of the random variables sampled by spl in vi from the Euclidean space back to the support of their distributions and sets their corresponding "trans" flag values to false.

source
DynamicPPL.is_flagged — Function
is_flagged(vi::VarInfo, vn::VarName, flag::String)

Check whether vn has a true value for flag in vi.

source

For Gibbs sampling the following functions were added.

DynamicPPL.setgid! — Function
setgid!(vi::VarInfo, gid::Selector, vn::VarName)

Add gid to the set of sampler selectors associated with vn in vi.

source
DynamicPPL.updategid! — Function
updategid!(vi::VarInfo, vn::VarName, spl::Sampler)

Set vn's gid to Set([spl.selector]), if vn does not have a sampler selector linked and vn's symbol is in the space of spl.

source

The following functions were used for sequential Monte Carlo methods.

DynamicPPL.reset_num_produce! — Function
reset_num_produce!(vi::VarInfo)

Reset the value of num_produce the log of the joint probability of the observed data and parameters sampled in vi to 0.

source
DynamicPPL.setorder! — Function
setorder!(vi::VarInfo, vn::VarName, index::Int)

Set the order of vn in vi to index, where order is the number of observe statements run before samplingvn`.

source
Base.empty! — Function
empty!(meta::Metadata)

Empty the fields of meta.

This is useful when using a sampling algorithm that assumes an empty meta, e.g. SMC.

source

Evaluation Contexts

Internally, both sampling and evaluation of log densities are performed with AbstractPPL.evaluate!!.

AbstractPPL.evaluate!! — Function
evaluate!!(model::Model[, rng, varinfo, sampler, context])

Sample from the model using the sampler with random number generator rng and the context, and store the sample and log joint probability in varinfo.

Returns both the return-value of the original model, and the resulting varinfo.

The method resets the log joint probability of varinfo and increases the evaluation number of sampler.

source

The behaviour of a model execution can be changed with evaluation contexts that are passed as additional argument to the model function. Contexts are subtypes of AbstractPPL.AbstractContext.

DynamicPPL.SamplingContext — Type
SamplingContext(
+        [rng::Random.AbstractRNG=Random.default_rng()],
+        [sampler::AbstractSampler=SampleFromPrior()],
+        [context::AbstractContext=DefaultContext()],
+)

Create a context that allows you to sample parameters with the sampler when running the model. The context determines how the returned log density is computed when running the model.

See also: DefaultContext, LikelihoodContext, PriorContext

source
DynamicPPL.DefaultContext — Type
struct DefaultContext <: AbstractContext end

The DefaultContext is used by default to compute log the joint probability of the data and parameters when running the model.

source
DynamicPPL.LikelihoodContext — Type
struct LikelihoodContext{Tvars} <: AbstractContext
+    vars::Tvars
+end

The LikelihoodContext enables the computation of the log likelihood of the parameters when running the model. vars can be used to evaluate the log likelihood for specific values of the model's parameters. If vars is nothing, the parameter values inside the VarInfo will be used by default.

source
DynamicPPL.PriorContext — Type
struct PriorContext{Tvars} <: AbstractContext
+    vars::Tvars
+end

The PriorContext enables the computation of the log prior of the parameters vars when running the model.

source
DynamicPPL.MiniBatchContext — Type
struct MiniBatchContext{Tctx, T} <: AbstractContext
+    context::Tctx
+    loglike_scalar::T
+end

The MiniBatchContext enables the computation of log(prior) + s * log(likelihood of a batch) when running the model, where s is the loglike_scalar field, typically equal to the number of data points / batch size. This is useful in batch-based stochastic gradient descent algorithms to be optimizing log(prior) + log(likelihood of all the data points) in the expectation.

source
DynamicPPL.PrefixContext — Type
PrefixContext{Prefix}(context)

Create a context that allows you to use the wrapped context when running the model and adds the Prefix to all parameters.

This context is useful in nested models to ensure that the names of the parameters are unique.

See also: @submodel

source

Samplers

In DynamicPPL two samplers are defined that are used to initialize unobserved random variables: SampleFromPrior which samples from the prior distribution, and SampleFromUniform which samples from a uniform distribution.

Additionally, a generic sampler for inference is implemented.

DynamicPPL.Sampler — Type
Sampler{T}

Generic sampler type for inference algorithms of type T in DynamicPPL.

Sampler should implement the AbstractMCMC interface, and in particular AbstractMCMC.step. A default implementation of the initial sampling step is provided that supports resuming sampling from a previous state and setting initial parameter values. It requires to overload loadstate and initialstep for loading previous states and actually performing the initial sampling step, respectively. Additionally, sometimes one might want to implement initialsampler that specifies how the initial parameter values are sampled if they are not provided. By default, values are sampled from the prior.

source

The default implementation of Sampler uses the following unexported functions.

DynamicPPL.initialstep — Function
initialstep(rng, model, sampler, varinfo; kwargs...)

Perform the initial sampling step of the sampler for the model.

The varinfo contains the initial samples, which can be provided by the user or sampled randomly.

source

Model-Internal Functions

DynamicPPL.tilde_assume — Function
tilde_assume(context::SamplingContext, right, vn, vi)

Handle assumed variables, e.g., x ~ Normal() (where x does occur in the model inputs), accumulate the log probability, and return the sampled value with a context associated with a sampler.

Falls back to

tilde_assume(context.rng, context.context, context.sampler, right, vn, vi)
source
DynamicPPL.dot_tilde_assume — Function
dot_tilde_assume(context::SamplingContext, right, left, vn, vi)

Handle broadcasted assumed variables, e.g., x .~ MvNormal() (where x does not occur in the model inputs), accumulate the log probability, and return the sampled value for a context associated with a sampler.

Falls back to

dot_tilde_assume(context.rng, context.context, context.sampler, right, left, vn, vi)
source
DynamicPPL.tilde_observe — Function
tilde_observe(context::SamplingContext, right, left, vi)

Handle observed constants with a context associated with a sampler.

Falls back to tilde_observe(context.context, context.sampler, right, left, vi).

source
DynamicPPL.dot_tilde_observe — Function
dot_tilde_observe(context::SamplingContext, right, left, vi)

Handle broadcasted observed constants, e.g., [1.0] .~ MvNormal(), accumulate the log probability, and return the observed value for a context associated with a sampler.

Falls back to dot_tilde_observe(context.context, context.sampler, right, left, vi).

source
diff --git a/previews/PR546/assets/documenter.js b/previews/PR546/assets/documenter.js new file mode 100644 index 000000000..f5311607b --- /dev/null +++ b/previews/PR546/assets/documenter.js @@ -0,0 +1,889 @@ +// Generated by Documenter.jl +requirejs.config({ + paths: { + 'highlight-julia': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia.min', + 'headroom': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/headroom.min', + 'jqueryui': 'https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.13.2/jquery-ui.min', + 'minisearch': 'https://cdn.jsdelivr.net/npm/minisearch@6.1.0/dist/umd/index.min', + 'katex-auto-render': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/contrib/auto-render.min', + 'jquery': 'https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.0/jquery.min', + 'headroom-jquery': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/jQuery.headroom.min', + 'katex': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/katex.min', + 'highlight': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/highlight.min', + 'highlight-julia-repl': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia-repl.min', + }, + shim: { + "highlight-julia": { + "deps": [ + "highlight" + ] + }, + "katex-auto-render": { + "deps": [ + "katex" + ] + }, + "headroom-jquery": { + "deps": [ + "jquery", + "headroom" + ] + }, + "highlight-julia-repl": { + "deps": [ + "highlight" + ] + } +} +}); +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'katex', 'katex-auto-render'], function($, katex, renderMathInElement) { +$(document).ready(function() { + renderMathInElement( + document.body, + { + "delimiters": [ + { + "left": "$", + "right": "$", + "display": false + }, + { + "left": "$$", + "right": "$$", + "display": true + }, + { + "left": "\\[", + "right": "\\]", + "display": true + } + ] +} + + ); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'highlight', 'highlight-julia', 'highlight-julia-repl'], function($) { +$(document).ready(function() { + hljs.highlightAll(); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +let timer = 0; +var isExpanded = true; + +$(document).on("click", ".docstring header", function () { + let articleToggleTitle = "Expand docstring"; + + debounce(() => { + if ($(this).siblings("section").is(":visible")) { + $(this) + .find(".docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + } else { + $(this) + .find(".docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + articleToggleTitle = "Collapse docstring"; + } + + $(this) + .find(".docstring-article-toggle-button") + .prop("title", articleToggleTitle); + $(this).siblings("section").slideToggle(); + }); +}); + +$(document).on("click", ".docs-article-toggle-button", function () { + let articleToggleTitle = "Expand docstring"; + let navArticleToggleTitle = "Expand all docstrings"; + + debounce(() => { + if (isExpanded) { + $(this).removeClass("fa-chevron-up").addClass("fa-chevron-down"); + $(".docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + + isExpanded = false; + + $(".docstring section").slideUp(); + } else { + $(this).removeClass("fa-chevron-down").addClass("fa-chevron-up"); + $(".docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + isExpanded = true; + articleToggleTitle = "Collapse docstring"; + navArticleToggleTitle = "Collapse all docstrings"; + + $(".docstring section").slideDown(); + } + + $(this).prop("title", navArticleToggleTitle); + $(".docstring-article-toggle-button").prop("title", articleToggleTitle); + }); +}); + +function debounce(callback, timeout = 300) { + if (Date.now() - timer > timeout) { + callback(); + } + + clearTimeout(timer); + + timer = Date.now(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require([], function() { +function addCopyButtonCallbacks() { + for (const el of document.getElementsByTagName("pre")) { + const button = document.createElement("button"); + button.classList.add("copy-button", "fa-solid", "fa-copy"); + button.setAttribute("aria-label", "Copy this code block"); + button.setAttribute("title", "Copy"); + + el.appendChild(button); + + const success = function () { + button.classList.add("success", "fa-check"); + button.classList.remove("fa-copy"); + }; + + const failure = function () { + button.classList.add("error", "fa-xmark"); + button.classList.remove("fa-copy"); + }; + + button.addEventListener("click", function () { + copyToClipboard(el.innerText).then(success, failure); + + setTimeout(function () { + button.classList.add("fa-copy"); + button.classList.remove("success", "fa-check", "fa-xmark"); + }, 5000); + }); + } +} + +function copyToClipboard(text) { + // clipboard API is only available in secure contexts + if (window.navigator && window.navigator.clipboard) { + return window.navigator.clipboard.writeText(text); + } else { + return new Promise(function (resolve, reject) { + try { + const el = document.createElement("textarea"); + el.textContent = text; + el.style.position = "fixed"; + el.style.opacity = 0; + document.body.appendChild(el); + el.select(); + document.execCommand("copy"); + + resolve(); + } catch (err) { + reject(err); + } finally { + document.body.removeChild(el); + } + }); + } +} + +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", addCopyButtonCallbacks); +} else { + addCopyButtonCallbacks(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'headroom', 'headroom-jquery'], function($, Headroom) { + +// Manages the top navigation bar (hides it when the user starts scrolling down on the +// mobile). +window.Headroom = Headroom; // work around buggy module loading? +$(document).ready(function () { + $("#documenter .docs-navbar").headroom({ + tolerance: { up: 10, down: 10 }, + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'minisearch'], function($, minisearch) { + +// In general, most search related things will have "search" as a prefix. +// To get an in-depth about the thought process you can refer: https://hetarth02.hashnode.dev/series/gsoc + +let results = []; +let timer = undefined; + +let data = documenterSearchIndex["docs"].map((x, key) => { + x["id"] = key; // minisearch requires a unique for each object + return x; +}); + +// list below is the lunr 2.1.3 list minus the intersect with names(Base) +// (all, any, get, in, is, only, which) and (do, else, for, let, where, while, with) +// ideally we'd just filter the original list but it's not available as a variable +const stopWords = new Set([ + "a", + "able", + "about", + "across", + "after", + "almost", + "also", + "am", + "among", + "an", + "and", + "are", + "as", + "at", + "be", + "because", + "been", + "but", + "by", + "can", + "cannot", + "could", + "dear", + "did", + "does", + "either", + "ever", + "every", + "from", + "got", + "had", + "has", + "have", + "he", + "her", + "hers", + "him", + "his", + "how", + "however", + "i", + "if", + "into", + "it", + "its", + "just", + "least", + "like", + "likely", + "may", + "me", + "might", + "most", + "must", + "my", + "neither", + "no", + "nor", + "not", + "of", + "off", + "often", + "on", + "or", + "other", + "our", + "own", + "rather", + "said", + "say", + "says", + "she", + "should", + "since", + "so", + "some", + "than", + "that", + "the", + "their", + "them", + "then", + "there", + "these", + "they", + "this", + "tis", + "to", + "too", + "twas", + "us", + "wants", + "was", + "we", + "were", + "what", + "when", + "who", + "whom", + "why", + "will", + "would", + "yet", + "you", + "your", +]); + +let index = new minisearch({ + fields: ["title", "text"], // fields to index for full-text search + storeFields: ["location", "title", "text", "category", "page"], // fields to return with search results + processTerm: (term) => { + let word = stopWords.has(term) ? null : term; + if (word) { + // custom trimmer that doesn't strip @ and !, which are used in julia macro and function names + word = word + .replace(/^[^a-zA-Z0-9@!]+/, "") + .replace(/[^a-zA-Z0-9@!]+$/, ""); + } + + return word ?? null; + }, + // add . as a separator, because otherwise "title": "Documenter.Anchors.add!", would not find anything if searching for "add!", only for the entire qualification + tokenize: (string) => string.split(/[\s\-\.]+/), + // options which will be applied during the search + searchOptions: { + boost: { title: 100 }, + fuzzy: 2, + processTerm: (term) => { + let word = stopWords.has(term) ? null : term; + if (word) { + word = word + .replace(/^[^a-zA-Z0-9@!]+/, "") + .replace(/[^a-zA-Z0-9@!]+$/, ""); + } + + return word ?? null; + }, + tokenize: (string) => string.split(/[\s\-\.]+/), + }, +}); + +index.addAll(data); + +let filters = [...new Set(data.map((x) => x.category))]; +var modal_filters = make_modal_body_filters(filters); +var filter_results = []; + +$(document).on("keyup", ".documenter-search-input", function (event) { + // Adding a debounce to prevent disruptions from super-speed typing! + debounce(() => update_search(filter_results), 300); +}); + +$(document).on("click", ".search-filter", function () { + if ($(this).hasClass("search-filter-selected")) { + $(this).removeClass("search-filter-selected"); + } else { + $(this).addClass("search-filter-selected"); + } + + // Adding a debounce to prevent disruptions from crazy clicking! + debounce(() => get_filters(), 300); +}); + +/** + * A debounce function, takes a function and an optional timeout in milliseconds + * + * @function callback + * @param {number} timeout + */ +function debounce(callback, timeout = 300) { + clearTimeout(timer); + timer = setTimeout(callback, timeout); +} + +/** + * Make/Update the search component + * + * @param {string[]} selected_filters + */ +function update_search(selected_filters = []) { + let initial_search_body = ` +
Type something to get started!
+ `; + + let querystring = $(".documenter-search-input").val(); + + if (querystring.trim()) { + results = index.search(querystring, { + filter: (result) => { + // Filtering results + if (selected_filters.length === 0) { + return result.score >= 1; + } else { + return ( + result.score >= 1 && selected_filters.includes(result.category) + ); + } + }, + }); + + let search_result_container = ``; + let search_divider = `
`; + + if (results.length) { + let links = []; + let count = 0; + let search_results = ""; + + results.forEach(function (result) { + if (result.location) { + // Checking for duplication of results for the same page + if (!links.includes(result.location)) { + search_results += make_search_result(result, querystring); + count++; + } + + links.push(result.location); + } + }); + + let result_count = `
${count} result(s)
`; + + search_result_container = ` +
+ ${modal_filters} + ${search_divider} + ${result_count} +
+ ${search_results} +
+
+ `; + } else { + search_result_container = ` +
+ ${modal_filters} + ${search_divider} +
0 result(s)
+
+
No result found!
+ `; + } + + if ($(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").removeClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(search_result_container); + } else { + filter_results = []; + modal_filters = make_modal_body_filters(filters, filter_results); + + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(initial_search_body); + } +} + +/** + * Make the modal filter html + * + * @param {string[]} filters + * @param {string[]} selected_filters + * @returns string + */ +function make_modal_body_filters(filters, selected_filters = []) { + let str = ``; + + filters.forEach((val) => { + if (selected_filters.includes(val)) { + str += `${val}`; + } else { + str += `${val}`; + } + }); + + let filter_html = ` +
+ Filters: + ${str} +
+ `; + + return filter_html; +} + +/** + * Make the result component given a minisearch result data object and the value of the search input as queryString. + * To view the result object structure, refer: https://lucaong.github.io/minisearch/modules/_minisearch_.html#searchresult + * + * @param {object} result + * @param {string} querystring + * @returns string + */ +function make_search_result(result, querystring) { + let search_divider = `
`; + let display_link = + result.location.slice(Math.max(0), Math.min(50, result.location.length)) + + (result.location.length > 30 ? "..." : ""); // To cut-off the link because it messes with the overflow of the whole div + + if (result.page !== "") { + display_link += ` (${result.page})`; + } + + let textindex = new RegExp(`\\b${querystring}\\b`, "i").exec(result.text); + let text = + textindex !== null + ? result.text.slice( + Math.max(textindex.index - 100, 0), + Math.min( + textindex.index + querystring.length + 100, + result.text.length + ) + ) + : ""; // cut-off text before and after from the match + + let display_result = text.length + ? "..." + + text.replace( + new RegExp(`\\b${querystring}\\b`, "i"), // For first occurrence + '$&' + ) + + "..." + : ""; // highlights the match + + let in_code = false; + if (!["page", "section"].includes(result.category.toLowerCase())) { + in_code = true; + } + + // We encode the full url to escape some special characters which can lead to broken links + let result_div = ` + +
+
${result.title}
+
${result.category}
+
+

+ ${display_result} +

+
+ ${display_link} +
+ + ${search_divider} + `; + + return result_div; +} + +/** + * Get selected filters, remake the filter html and lastly update the search modal + */ +function get_filters() { + let ele = $(".search-filters .search-filter-selected").get(); + filter_results = ele.map((x) => $(x).text().toLowerCase()); + modal_filters = make_modal_body_filters(filters, filter_results); + update_search(filter_results); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Modal settings dialog +$(document).ready(function () { + var settings = $("#documenter-settings"); + $("#documenter-settings-button").click(function () { + settings.toggleClass("is-active"); + }); + // Close the dialog if X is clicked + $("#documenter-settings button.delete").click(function () { + settings.removeClass("is-active"); + }); + // Close dialog if ESC is pressed + $(document).keyup(function (e) { + if (e.keyCode == 27) settings.removeClass("is-active"); + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +let search_modal_header = ` +
+
+

+ + + + +

+
+
+ +
+
+`; + +let initial_search_body = ` +
Type something to get started!
+`; + +let search_modal_footer = ` + +`; + +$(document.body).append( + ` + + ` +); + +document.querySelector(".docs-search-query").addEventListener("click", () => { + openModal(); +}); + +document.querySelector(".close-search-modal").addEventListener("click", () => { + closeModal(); +}); + +$(document).on("click", ".search-result-link", function () { + closeModal(); +}); + +document.addEventListener("keydown", (event) => { + if ((event.ctrlKey || event.metaKey) && event.key === "/") { + openModal(); + } else if (event.key === "Escape") { + closeModal(); + } + + return false; +}); + +// Functions to open and close a modal +function openModal() { + let searchModal = document.querySelector("#search-modal"); + + searchModal.classList.add("is-active"); + document.querySelector(".documenter-search-input").focus(); +} + +function closeModal() { + let searchModal = document.querySelector("#search-modal"); + let initial_search_body = ` +
Type something to get started!
+ `; + + searchModal.classList.remove("is-active"); + document.querySelector(".documenter-search-input").blur(); + + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".documenter-search-input").val(""); + $(".search-modal-card-body").html(initial_search_body); +} + +document + .querySelector("#search-modal .modal-background") + .addEventListener("click", () => { + closeModal(); + }); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Manages the showing and hiding of the sidebar. +$(document).ready(function () { + var sidebar = $("#documenter > .docs-sidebar"); + var sidebar_button = $("#documenter-sidebar-button"); + sidebar_button.click(function (ev) { + ev.preventDefault(); + sidebar.toggleClass("visible"); + if (sidebar.hasClass("visible")) { + // Makes sure that the current menu item is visible in the sidebar. + $("#documenter .docs-menu a.is-active").focus(); + } + }); + $("#documenter > .docs-main").bind("click", function (ev) { + if ($(ev.target).is(sidebar_button)) { + return; + } + if (sidebar.hasClass("visible")) { + sidebar.removeClass("visible"); + } + }); +}); + +// Resizes the package name / sitename in the sidebar if it is too wide. +// Inspired by: https://github.com/davatron5000/FitText.js +$(document).ready(function () { + e = $("#documenter .docs-autofit"); + function resize() { + var L = parseInt(e.css("max-width"), 10); + var L0 = e.width(); + if (L0 > L) { + var h0 = parseInt(e.css("font-size"), 10); + e.css("font-size", (L * h0) / L0); + // TODO: make sure it survives resizes? + } + } + // call once and then register events + resize(); + $(window).resize(resize); + $(window).on("orientationchange", resize); +}); + +// Scroll the navigation bar to the currently selected menu item +$(document).ready(function () { + var sidebar = $("#documenter .docs-menu").get(0); + var active = $("#documenter .docs-menu .is-active").get(0); + if (typeof active !== "undefined") { + sidebar.scrollTop = active.offsetTop - sidebar.offsetTop - 15; + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Theme picker setup +$(document).ready(function () { + // onchange callback + $("#documenter-themepicker").change(function themepick_callback(ev) { + var themename = $("#documenter-themepicker option:selected").attr("value"); + if (themename === "auto") { + // set_theme(window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'); + window.localStorage.removeItem("documenter-theme"); + } else { + // set_theme(themename); + window.localStorage.setItem("documenter-theme", themename); + } + // We re-use the global function from themeswap.js to actually do the swapping. + set_theme_from_local_storage(); + }); + + // Make sure that the themepicker displays the correct theme when the theme is retrieved + // from localStorage + if (typeof window.localStorage !== "undefined") { + var theme = window.localStorage.getItem("documenter-theme"); + if (theme !== null) { + $("#documenter-themepicker option").each(function (i, e) { + e.selected = e.value === theme; + }); + } + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// update the version selector with info from the siteinfo.js and ../versions.js files +$(document).ready(function () { + // If the version selector is disabled with DOCUMENTER_VERSION_SELECTOR_DISABLED in the + // siteinfo.js file, we just return immediately and not display the version selector. + if ( + typeof DOCUMENTER_VERSION_SELECTOR_DISABLED === "boolean" && + DOCUMENTER_VERSION_SELECTOR_DISABLED + ) { + return; + } + + var version_selector = $("#documenter .docs-version-selector"); + var version_selector_select = $("#documenter .docs-version-selector select"); + + version_selector_select.change(function (x) { + target_href = version_selector_select + .children("option:selected") + .get(0).value; + window.location.href = target_href; + }); + + // add the current version to the selector based on siteinfo.js, but only if the selector is empty + if ( + typeof DOCUMENTER_CURRENT_VERSION !== "undefined" && + $("#version-selector > option").length == 0 + ) { + var option = $( + "" + ); + version_selector_select.append(option); + } + + if (typeof DOC_VERSIONS !== "undefined") { + var existing_versions = version_selector_select.children("option"); + var existing_versions_texts = existing_versions.map(function (i, x) { + return x.text; + }); + DOC_VERSIONS.forEach(function (each) { + var version_url = documenterBaseURL + "/../" + each + "/"; + var existing_id = $.inArray(each, existing_versions_texts); + // if not already in the version selector, add it as a new option, + // otherwise update the old option with the URL and enable it + if (existing_id == -1) { + var option = $( + "" + ); + version_selector_select.append(option); + } else { + var option = existing_versions[existing_id]; + option.value = version_url; + option.disabled = false; + } + }); + } + + // only show the version selector if the selector has been populated + if (version_selector_select.children("option").length > 0) { + version_selector.toggleClass("visible"); + } +}); + +}) diff --git a/previews/PR546/assets/logo.svg b/previews/PR546/assets/logo.svg new file mode 100644 index 000000000..d7f296a9f --- /dev/null +++ b/previews/PR546/assets/logo.svg @@ -0,0 +1,140 @@ + + + + + + + + + + + + + + + + + + ~ + ~ + ~ + + + diff --git a/previews/PR546/assets/themes/documenter-dark.css b/previews/PR546/assets/themes/documenter-dark.css new file mode 100644 index 000000000..ec054ecc1 --- /dev/null +++ b/previews/PR546/assets/themes/documenter-dark.css @@ -0,0 +1,7 @@ +html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .pagination-next,html.theme--documenter-dark .pagination-link,html.theme--documenter-dark .pagination-ellipsis,html.theme--documenter-dark .file-cta,html.theme--documenter-dark .file-name,html.theme--documenter-dark .select select,html.theme--documenter-dark .textarea,html.theme--documenter-dark .input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark .button{-moz-appearance:none;-webkit-appearance:none;align-items:center;border:1px solid transparent;border-radius:.4em;box-shadow:none;display:inline-flex;font-size:1rem;height:2.5em;justify-content:flex-start;line-height:1.5;padding-bottom:calc(0.5em - 1px);padding-left:calc(0.75em - 1px);padding-right:calc(0.75em - 1px);padding-top:calc(0.5em - 1px);position:relative;vertical-align:top}html.theme--documenter-dark .pagination-previous:focus,html.theme--documenter-dark .pagination-next:focus,html.theme--documenter-dark .pagination-link:focus,html.theme--documenter-dark .pagination-ellipsis:focus,html.theme--documenter-dark .file-cta:focus,html.theme--documenter-dark .file-name:focus,html.theme--documenter-dark .select select:focus,html.theme--documenter-dark .textarea:focus,html.theme--documenter-dark .input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:focus,html.theme--documenter-dark .button:focus,html.theme--documenter-dark .is-focused.pagination-previous,html.theme--documenter-dark .is-focused.pagination-next,html.theme--documenter-dark .is-focused.pagination-link,html.theme--documenter-dark .is-focused.pagination-ellipsis,html.theme--documenter-dark .is-focused.file-cta,html.theme--documenter-dark .is-focused.file-name,html.theme--documenter-dark .select select.is-focused,html.theme--documenter-dark .is-focused.textarea,html.theme--documenter-dark .is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-focused.button,html.theme--documenter-dark .pagination-previous:active,html.theme--documenter-dark .pagination-next:active,html.theme--documenter-dark .pagination-link:active,html.theme--documenter-dark .pagination-ellipsis:active,html.theme--documenter-dark .file-cta:active,html.theme--documenter-dark .file-name:active,html.theme--documenter-dark .select select:active,html.theme--documenter-dark .textarea:active,html.theme--documenter-dark .input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:active,html.theme--documenter-dark .button:active,html.theme--documenter-dark .is-active.pagination-previous,html.theme--documenter-dark .is-active.pagination-next,html.theme--documenter-dark .is-active.pagination-link,html.theme--documenter-dark .is-active.pagination-ellipsis,html.theme--documenter-dark .is-active.file-cta,html.theme--documenter-dark .is-active.file-name,html.theme--documenter-dark .select select.is-active,html.theme--documenter-dark .is-active.textarea,html.theme--documenter-dark .is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active,html.theme--documenter-dark .is-active.button{outline:none}html.theme--documenter-dark .pagination-previous[disabled],html.theme--documenter-dark .pagination-next[disabled],html.theme--documenter-dark .pagination-link[disabled],html.theme--documenter-dark .pagination-ellipsis[disabled],html.theme--documenter-dark .file-cta[disabled],html.theme--documenter-dark .file-name[disabled],html.theme--documenter-dark .select select[disabled],html.theme--documenter-dark .textarea[disabled],html.theme--documenter-dark .input[disabled],html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input[disabled],html.theme--documenter-dark .button[disabled],fieldset[disabled] html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark fieldset[disabled] .pagination-previous,fieldset[disabled] html.theme--documenter-dark .pagination-next,html.theme--documenter-dark fieldset[disabled] .pagination-next,fieldset[disabled] html.theme--documenter-dark .pagination-link,html.theme--documenter-dark fieldset[disabled] .pagination-link,fieldset[disabled] html.theme--documenter-dark .pagination-ellipsis,html.theme--documenter-dark fieldset[disabled] .pagination-ellipsis,fieldset[disabled] html.theme--documenter-dark .file-cta,html.theme--documenter-dark fieldset[disabled] .file-cta,fieldset[disabled] html.theme--documenter-dark .file-name,html.theme--documenter-dark fieldset[disabled] .file-name,fieldset[disabled] html.theme--documenter-dark .select select,fieldset[disabled] html.theme--documenter-dark .textarea,fieldset[disabled] html.theme--documenter-dark .input,fieldset[disabled] html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark fieldset[disabled] .select select,html.theme--documenter-dark .select fieldset[disabled] select,html.theme--documenter-dark fieldset[disabled] .textarea,html.theme--documenter-dark fieldset[disabled] .input,html.theme--documenter-dark fieldset[disabled] #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark #documenter .docs-sidebar fieldset[disabled] form.docs-search>input,fieldset[disabled] html.theme--documenter-dark .button,html.theme--documenter-dark fieldset[disabled] .button{cursor:not-allowed}html.theme--documenter-dark .tabs,html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .pagination-next,html.theme--documenter-dark .pagination-link,html.theme--documenter-dark .pagination-ellipsis,html.theme--documenter-dark .breadcrumb,html.theme--documenter-dark .file,html.theme--documenter-dark .button,.is-unselectable{-webkit-touch-callout:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}html.theme--documenter-dark .navbar-link:not(.is-arrowless)::after,html.theme--documenter-dark .select:not(.is-multiple):not(.is-loading)::after{border:3px solid rgba(0,0,0,0);border-radius:2px;border-right:0;border-top:0;content:" ";display:block;height:0.625em;margin-top:-0.4375em;pointer-events:none;position:absolute;top:50%;transform:rotate(-45deg);transform-origin:center;width:0.625em}html.theme--documenter-dark .admonition:not(:last-child),html.theme--documenter-dark .tabs:not(:last-child),html.theme--documenter-dark .pagination:not(:last-child),html.theme--documenter-dark .message:not(:last-child),html.theme--documenter-dark .level:not(:last-child),html.theme--documenter-dark .breadcrumb:not(:last-child),html.theme--documenter-dark .block:not(:last-child),html.theme--documenter-dark .title:not(:last-child),html.theme--documenter-dark .subtitle:not(:last-child),html.theme--documenter-dark .table-container:not(:last-child),html.theme--documenter-dark .table:not(:last-child),html.theme--documenter-dark .progress:not(:last-child),html.theme--documenter-dark .notification:not(:last-child),html.theme--documenter-dark .content:not(:last-child),html.theme--documenter-dark .box:not(:last-child){margin-bottom:1.5rem}html.theme--documenter-dark .modal-close,html.theme--documenter-dark 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html.theme--documenter-dark .button.is-white.is-inverted.is-outlined{background-color:transparent;border-color:#0a0a0a;box-shadow:none;color:#0a0a0a}html.theme--documenter-dark .button.is-black{background-color:#0a0a0a;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-black:hover,html.theme--documenter-dark .button.is-black.is-hovered{background-color:#040404;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-black:focus,html.theme--documenter-dark .button.is-black.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-black:focus:not(:active),html.theme--documenter-dark .button.is-black.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(10,10,10,0.25)}html.theme--documenter-dark .button.is-black:active,html.theme--documenter-dark .button.is-black.is-active{background-color:#000;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-black[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black{background-color:#0a0a0a;border-color:#0a0a0a;box-shadow:none}html.theme--documenter-dark .button.is-black.is-inverted{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-inverted:hover,html.theme--documenter-dark .button.is-black.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-black.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-outlined:hover,html.theme--documenter-dark .button.is-black.is-outlined.is-hovered,html.theme--documenter-dark 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html.theme--documenter-dark .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-light{background-color:#ecf0f1;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:hover,html.theme--documenter-dark .button.is-light.is-hovered{background-color:#e5eaec;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:focus,html.theme--documenter-dark .button.is-light.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:focus:not(:active),html.theme--documenter-dark .button.is-light.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(236,240,241,0.25)}html.theme--documenter-dark .button.is-light:active,html.theme--documenter-dark .button.is-light.is-active{background-color:#dde4e6;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light{background-color:#ecf0f1;border-color:#ecf0f1;box-shadow:none}html.theme--documenter-dark .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted:hover,html.theme--documenter-dark .button.is-light.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--documenter-dark .button.is-light.is-outlined{background-color:transparent;border-color:#ecf0f1;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-outlined:hover,html.theme--documenter-dark .button.is-light.is-outlined.is-hovered,html.theme--documenter-dark .button.is-light.is-outlined:focus,html.theme--documenter-dark .button.is-light.is-outlined.is-focused{background-color:#ecf0f1;border-color:#ecf0f1;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-outlined.is-loading::after{border-color:transparent transparent #ecf0f1 #ecf0f1 !important}html.theme--documenter-dark .button.is-light.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--documenter-dark .button.is-light.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark 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.button.is-light.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ecf0f1 #ecf0f1 !important}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-dark,html.theme--documenter-dark .content kbd.button{background-color:#282f2f;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:hover,html.theme--documenter-dark .content kbd.button:hover,html.theme--documenter-dark .button.is-dark.is-hovered,html.theme--documenter-dark .content kbd.button.is-hovered{background-color:#232829;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:focus,html.theme--documenter-dark .content kbd.button:focus,html.theme--documenter-dark .button.is-dark.is-focused,html.theme--documenter-dark .content kbd.button.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:focus:not(:active),html.theme--documenter-dark .content kbd.button:focus:not(:active),html.theme--documenter-dark .button.is-dark.is-focused:not(:active),html.theme--documenter-dark .content kbd.button.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(40,47,47,0.25)}html.theme--documenter-dark .button.is-dark:active,html.theme--documenter-dark .content kbd.button:active,html.theme--documenter-dark .button.is-dark.is-active,html.theme--documenter-dark .content kbd.button.is-active{background-color:#1d2122;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark[disabled],html.theme--documenter-dark .content kbd.button[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark,fieldset[disabled] html.theme--documenter-dark .content kbd.button{background-color:#282f2f;border-color:#282f2f;box-shadow:none}html.theme--documenter-dark .button.is-dark.is-inverted,html.theme--documenter-dark .content kbd.button.is-inverted{background-color:#fff;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted:hover,html.theme--documenter-dark .content kbd.button.is-inverted:hover,html.theme--documenter-dark .button.is-dark.is-inverted.is-hovered,html.theme--documenter-dark .content kbd.button.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-dark.is-inverted[disabled],html.theme--documenter-dark .content kbd.button.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-inverted,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-loading::after,html.theme--documenter-dark .content kbd.button.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-dark.is-outlined,html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-outlined:hover,html.theme--documenter-dark .button.is-dark.is-outlined.is-hovered,html.theme--documenter-dark .content kbd.button.is-outlined.is-hovered,html.theme--documenter-dark .button.is-dark.is-outlined:focus,html.theme--documenter-dark .content kbd.button.is-outlined:focus,html.theme--documenter-dark .button.is-dark.is-outlined.is-focused,html.theme--documenter-dark .content kbd.button.is-outlined.is-focused{background-color:#282f2f;border-color:#282f2f;color:#fff}html.theme--documenter-dark .button.is-dark.is-outlined.is-loading::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:hover::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-dark.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:focus,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-focused,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-focused{background-color:#fff;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-primary,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink{background-color:#375a7f;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:hover,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:hover,html.theme--documenter-dark .button.is-primary.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-hovered.docs-sourcelink{background-color:#335476;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:focus,html.theme--documenter-dark 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.docstring>section>a.button.is-inverted.docs-sourcelink[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-primary.is-inverted,fieldset[disabled] html.theme--documenter-dark .docstring>section>a.button.is-inverted.docs-sourcelink{background-color:#fff;border-color:transparent;box-shadow:none;color:#375a7f}html.theme--documenter-dark .button.is-primary.is-loading::after,html.theme--documenter-dark .docstring>section>a.button.is-loading.docs-sourcelink::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-primary.is-outlined,html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink{background-color:transparent;border-color:#375a7f;color:#375a7f}html.theme--documenter-dark .button.is-primary.is-outlined:hover,html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink:hover,html.theme--documenter-dark .button.is-primary.is-outlined.is-hovered,html.theme--documenter-dark 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.button.is-danger:hover,html.theme--documenter-dark .button.is-danger.is-hovered{background-color:#92190c;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger:focus,html.theme--documenter-dark .button.is-danger.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger:focus:not(:active),html.theme--documenter-dark .button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(158,27,13,0.25)}html.theme--documenter-dark .button.is-danger:active,html.theme--documenter-dark .button.is-danger.is-active{background-color:#86170b;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger{background-color:#9e1b0d;border-color:#9e1b0d;box-shadow:none}html.theme--documenter-dark .button.is-danger.is-inverted{background-color:#fff;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-inverted:hover,html.theme--documenter-dark .button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-danger.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-danger.is-outlined{background-color:transparent;border-color:#9e1b0d;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-outlined:hover,html.theme--documenter-dark .button.is-danger.is-outlined.is-hovered,html.theme--documenter-dark .button.is-danger.is-outlined:focus,html.theme--documenter-dark .button.is-danger.is-outlined.is-focused{background-color:#9e1b0d;border-color:#9e1b0d;color:#fff}html.theme--documenter-dark .button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #9e1b0d #9e1b0d !important}html.theme--documenter-dark .button.is-danger.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-danger.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-outlined{background-color:transparent;border-color:#9e1b0d;box-shadow:none;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #9e1b0d #9e1b0d !important}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-danger.is-light{background-color:#fdeeec;color:#ec311d}html.theme--documenter-dark .button.is-danger.is-light:hover,html.theme--documenter-dark .button.is-danger.is-light.is-hovered{background-color:#fce3e0;border-color:transparent;color:#ec311d}html.theme--documenter-dark .button.is-danger.is-light:active,html.theme--documenter-dark .button.is-danger.is-light.is-active{background-color:#fcd8d5;border-color:transparent;color:#ec311d}html.theme--documenter-dark .button.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--documenter-dark .button.is-small:not(.is-rounded),html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--documenter-dark .button.is-normal{font-size:1rem}html.theme--documenter-dark .button.is-medium{font-size:1.25rem}html.theme--documenter-dark .button.is-large{font-size:1.5rem}html.theme--documenter-dark .button[disabled],fieldset[disabled] html.theme--documenter-dark .button{background-color:#8c9b9d;border-color:#5e6d6f;box-shadow:none;opacity:.5}html.theme--documenter-dark .button.is-fullwidth{display:flex;width:100%}html.theme--documenter-dark .button.is-loading{color:transparent !important;pointer-events:none}html.theme--documenter-dark .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--documenter-dark .button.is-static{background-color:#282f2f;border-color:#5e6d6f;color:#dbdee0;box-shadow:none;pointer-events:none}html.theme--documenter-dark .button.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--documenter-dark .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--documenter-dark .buttons .button{margin-bottom:0.5rem}html.theme--documenter-dark .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--documenter-dark .buttons:last-child{margin-bottom:-0.5rem}html.theme--documenter-dark .buttons:not(:last-child){margin-bottom:1rem}html.theme--documenter-dark .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--documenter-dark .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large):not(.is-rounded){border-radius:3px}html.theme--documenter-dark .buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}html.theme--documenter-dark .buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}html.theme--documenter-dark .buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--documenter-dark .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--documenter-dark .buttons.has-addons .button:last-child{margin-right:0}html.theme--documenter-dark .buttons.has-addons .button:hover,html.theme--documenter-dark .buttons.has-addons .button.is-hovered{z-index:2}html.theme--documenter-dark .buttons.has-addons .button:focus,html.theme--documenter-dark .buttons.has-addons .button.is-focused,html.theme--documenter-dark .buttons.has-addons .button:active,html.theme--documenter-dark .buttons.has-addons .button.is-active,html.theme--documenter-dark .buttons.has-addons .button.is-selected{z-index:3}html.theme--documenter-dark .buttons.has-addons .button:focus:hover,html.theme--documenter-dark .buttons.has-addons .button.is-focused:hover,html.theme--documenter-dark .buttons.has-addons .button:active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--documenter-dark .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .buttons.is-centered{justify-content:center}html.theme--documenter-dark .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--documenter-dark .buttons.is-right{justify-content:flex-end}html.theme--documenter-dark .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:1rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1.25rem}}html.theme--documenter-dark .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--documenter-dark .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--documenter-dark .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--documenter-dark .container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){html.theme--documenter-dark .container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){html.theme--documenter-dark .container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--documenter-dark .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--documenter-dark .content li+li{margin-top:0.25em}html.theme--documenter-dark .content p:not(:last-child),html.theme--documenter-dark .content dl:not(:last-child),html.theme--documenter-dark .content ol:not(:last-child),html.theme--documenter-dark .content ul:not(:last-child),html.theme--documenter-dark .content blockquote:not(:last-child),html.theme--documenter-dark .content pre:not(:last-child),html.theme--documenter-dark .content table:not(:last-child){margin-bottom:1em}html.theme--documenter-dark .content h1,html.theme--documenter-dark .content h2,html.theme--documenter-dark .content h3,html.theme--documenter-dark .content h4,html.theme--documenter-dark .content h5,html.theme--documenter-dark .content h6{color:#f2f2f2;font-weight:600;line-height:1.125}html.theme--documenter-dark .content h1{font-size:2em;margin-bottom:0.5em}html.theme--documenter-dark .content h1:not(:first-child){margin-top:1em}html.theme--documenter-dark .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--documenter-dark .content h2:not(:first-child){margin-top:1.1428em}html.theme--documenter-dark .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--documenter-dark .content h3:not(:first-child){margin-top:1.3333em}html.theme--documenter-dark .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--documenter-dark .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--documenter-dark .content h6{font-size:1em;margin-bottom:1em}html.theme--documenter-dark .content blockquote{background-color:#282f2f;border-left:5px solid #5e6d6f;padding:1.25em 1.5em}html.theme--documenter-dark .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ol:not([type]){list-style-type:decimal}html.theme--documenter-dark .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--documenter-dark .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--documenter-dark .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--documenter-dark .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--documenter-dark .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--documenter-dark .content ul ul ul{list-style-type:square}html.theme--documenter-dark .content dd{margin-left:2em}html.theme--documenter-dark .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--documenter-dark .content figure:not(:first-child){margin-top:2em}html.theme--documenter-dark .content figure:not(:last-child){margin-bottom:2em}html.theme--documenter-dark .content figure img{display:inline-block}html.theme--documenter-dark .content figure figcaption{font-style:italic}html.theme--documenter-dark .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--documenter-dark .content sup,html.theme--documenter-dark .content sub{font-size:75%}html.theme--documenter-dark .content table{width:100%}html.theme--documenter-dark .content table td,html.theme--documenter-dark .content table th{border:1px solid #5e6d6f;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--documenter-dark .content table th{color:#f2f2f2}html.theme--documenter-dark .content table th:not([align]){text-align:inherit}html.theme--documenter-dark .content table thead td,html.theme--documenter-dark .content table thead th{border-width:0 0 2px;color:#f2f2f2}html.theme--documenter-dark .content table tfoot td,html.theme--documenter-dark .content table tfoot th{border-width:2px 0 0;color:#f2f2f2}html.theme--documenter-dark .content table tbody tr:last-child td,html.theme--documenter-dark .content table tbody tr:last-child th{border-bottom-width:0}html.theme--documenter-dark .content .tabs li+li{margin-top:0}html.theme--documenter-dark .content.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.content{font-size:.75rem}html.theme--documenter-dark .content.is-normal{font-size:1rem}html.theme--documenter-dark .content.is-medium{font-size:1.25rem}html.theme--documenter-dark .content.is-large{font-size:1.5rem}html.theme--documenter-dark .icon{align-items:center;display:inline-flex;justify-content:center;height:1.5rem;width:1.5rem}html.theme--documenter-dark .icon.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.icon{height:1rem;width:1rem}html.theme--documenter-dark .icon.is-medium{height:2rem;width:2rem}html.theme--documenter-dark .icon.is-large{height:3rem;width:3rem}html.theme--documenter-dark .icon-text{align-items:flex-start;color:inherit;display:inline-flex;flex-wrap:wrap;line-height:1.5rem;vertical-align:top}html.theme--documenter-dark .icon-text .icon{flex-grow:0;flex-shrink:0}html.theme--documenter-dark .icon-text .icon:not(:last-child){margin-right:.25em}html.theme--documenter-dark .icon-text .icon:not(:first-child){margin-left:.25em}html.theme--documenter-dark div.icon-text{display:flex}html.theme--documenter-dark .image,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img{display:block;position:relative}html.theme--documenter-dark .image img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img{display:block;height:auto;width:100%}html.theme--documenter-dark .image img.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img.is-rounded{border-radius:9999px}html.theme--documenter-dark .image.is-fullwidth,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-fullwidth{width:100%}html.theme--documenter-dark .image.is-square img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square img,html.theme--documenter-dark .image.is-square .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,html.theme--documenter-dark .image.is-1by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 img,html.theme--documenter-dark .image.is-1by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 .has-ratio,html.theme--documenter-dark .image.is-5by4 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 img,html.theme--documenter-dark .image.is-5by4 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 .has-ratio,html.theme--documenter-dark .image.is-4by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 img,html.theme--documenter-dark .image.is-4by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 .has-ratio,html.theme--documenter-dark .image.is-3by2 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 img,html.theme--documenter-dark .image.is-3by2 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 .has-ratio,html.theme--documenter-dark .image.is-5by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 img,html.theme--documenter-dark .image.is-5by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 .has-ratio,html.theme--documenter-dark .image.is-16by9 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9 img,html.theme--documenter-dark .image.is-16by9 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9 .has-ratio,html.theme--documenter-dark .image.is-2by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by1 img,html.theme--documenter-dark .image.is-2by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by1 .has-ratio,html.theme--documenter-dark .image.is-3by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by1 img,html.theme--documenter-dark .image.is-3by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by1 .has-ratio,html.theme--documenter-dark .image.is-4by5 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by5 img,html.theme--documenter-dark .image.is-4by5 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by5 .has-ratio,html.theme--documenter-dark .image.is-3by4 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DynamicPPL.jl

A domain-specific language and backend for probabilistic programming languages, used by Turing.jl.

diff --git a/previews/PR546/search_index.js b/previews/PR546/search_index.js new file mode 100644 index 000000000..ee7379694 --- /dev/null +++ b/previews/PR546/search_index.js @@ -0,0 +1,3 @@ +var documenterSearchIndex = {"docs": +[{"location":"api/#API","page":"API","title":"API","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"Part of the API of DynamicPPL is defined in the more lightweight interface package AbstractPPL.jl and reexported here.","category":"page"},{"location":"api/#Model","page":"API","title":"Model","text":"","category":"section"},{"location":"api/#Macros","page":"API","title":"Macros","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"A core component of DynamicPPL is the @model macro. It can be used to define probabilistic models in an intuitive way by specifying random variables and their distributions with ~ statements. These statements are rewritten by @model as calls of internal functions for sampling the variables and computing their log densities.","category":"page"},{"location":"api/","page":"API","title":"API","text":"@model","category":"page"},{"location":"api/#DynamicPPL.@model","page":"API","title":"DynamicPPL.@model","text":"@model(expr[, warn = false])\n\nMacro to specify a probabilistic model.\n\nIf warn is true, a warning is displayed if internal variable names are used in the model definition.\n\nExamples\n\nModel definition:\n\n@model function model(x, y = 42)\n ...\nend\n\nTo generate a Model, call model(xvalue) or model(xvalue, yvalue).\n\n\n\n\n\n","category":"macro"},{"location":"api/","page":"API","title":"API","text":"One can nest models and call another model inside the model function with @submodel.","category":"page"},{"location":"api/","page":"API","title":"API","text":"@submodel","category":"page"},{"location":"api/#DynamicPPL.@submodel","page":"API","title":"DynamicPPL.@submodel","text":"@submodel model\n@submodel ... = model\n\nRun a Turing model nested inside of a Turing model.\n\nExamples\n\njulia> @model function demo1(x)\n x ~ Normal()\n return 1 + abs(x)\n end;\n\njulia> @model function demo2(x, y)\n @submodel a = demo1(x)\n return y ~ Uniform(0, a)\n end;\n\nWhen we sample from the model demo2(missing, 0.4) random variable x will be sampled:\n\njulia> vi = VarInfo(demo2(missing, 0.4));\n\njulia> @varname(x) in keys(vi)\ntrue\n\nVariable a is not tracked since it can be computed from the random variable x that was tracked when running demo1:\n\njulia> @varname(a) in keys(vi)\nfalse\n\nWe can check that the log joint probability of the model accumulated in vi is correct:\n\njulia> x = vi[@varname(x)];\n\njulia> getlogp(vi) ≈ logpdf(Normal(), x) + logpdf(Uniform(0, 1 + abs(x)), 0.4)\ntrue\n\n\n\n\n\n@submodel prefix=... model\n@submodel prefix=... ... = model\n\nRun a Turing model nested inside of a Turing model and add \"prefix.\" as a prefix to all random variables inside of the model.\n\nValid expressions for prefix=... are:\n\nprefix=false: no prefix is used.\nprefix=true: attempt to automatically determine the prefix from the left-hand side ... = model by first converting into a VarName, and then calling Symbol on this.\nprefix=expression: results in the prefix Symbol(expression).\n\nThe prefix makes it possible to run the same Turing model multiple times while keeping track of all random variables correctly.\n\nExamples\n\nExample models\n\njulia> @model function demo1(x)\n x ~ Normal()\n return 1 + abs(x)\n end;\n\njulia> @model function demo2(x, y, z)\n @submodel prefix=\"sub1\" a = demo1(x)\n @submodel prefix=\"sub2\" b = demo1(y)\n return z ~ Uniform(-a, b)\n end;\n\nWhen we sample from the model demo2(missing, missing, 0.4) random variables sub1.x and sub2.x will be sampled:\n\njulia> vi = VarInfo(demo2(missing, missing, 0.4));\n\njulia> @varname(var\"sub1.x\") in keys(vi)\ntrue\n\njulia> @varname(var\"sub2.x\") in keys(vi)\ntrue\n\nVariables a and b are not tracked since they can be computed from the random variables sub1.x and sub2.x that were tracked when running demo1:\n\njulia> @varname(a) in keys(vi)\nfalse\n\njulia> @varname(b) in keys(vi)\nfalse\n\nWe can check that the log joint probability of the model accumulated in vi is correct:\n\njulia> sub1_x = vi[@varname(var\"sub1.x\")];\n\njulia> sub2_x = vi[@varname(var\"sub2.x\")];\n\njulia> logprior = logpdf(Normal(), sub1_x) + logpdf(Normal(), sub2_x);\n\njulia> loglikelihood = logpdf(Uniform(-1 - abs(sub1_x), 1 + abs(sub2_x)), 0.4);\n\njulia> getlogp(vi) ≈ logprior + loglikelihood\ntrue\n\nDifferent ways of setting the prefix\n\njulia> @model inner() = x ~ Normal()\ninner (generic function with 2 methods)\n\njulia> # When `prefix` is unspecified, no prefix is used.\n @model outer() = @submodel a = inner()\nouter (generic function with 2 methods)\n\njulia> @varname(x) in keys(VarInfo(outer()))\ntrue\n\njulia> # Explicitely don't use any prefix.\n @model outer() = @submodel prefix=false a = inner()\nouter (generic function with 2 methods)\n\njulia> @varname(x) in keys(VarInfo(outer()))\ntrue\n\njulia> # Automatically determined from `a`.\n @model outer() = @submodel prefix=true a = inner()\nouter (generic function with 2 methods)\n\njulia> @varname(var\"a.x\") in keys(VarInfo(outer()))\ntrue\n\njulia> # Using a static string.\n @model outer() = @submodel prefix=\"my prefix\" a = inner()\nouter (generic function with 2 methods)\n\njulia> @varname(var\"my prefix.x\") in keys(VarInfo(outer()))\ntrue\n\njulia> # Using string interpolation.\n @model outer() = @submodel prefix=\"$(nameof(inner()))\" a = inner()\nouter (generic function with 2 methods)\n\njulia> @varname(var\"inner.x\") in keys(VarInfo(outer()))\ntrue\n\njulia> # Or using some arbitrary expression.\n @model outer() = @submodel prefix=1 + 2 a = inner()\nouter (generic function with 2 methods)\n\njulia> @varname(var\"3.x\") in keys(VarInfo(outer()))\ntrue\n\njulia> # (×) Automatic prefixing without a left-hand side expression does not work!\n @model outer() = @submodel prefix=true inner()\nERROR: LoadError: cannot automatically prefix with no left-hand side\n[...]\n\nNotes\n\nThe choice prefix=expression means that the prefixing will incur a runtime cost. This is also the case for prefix=true, depending on whether the expression on the the right-hand side of ... = model requires runtime-information or not, e.g. x = model will result in the static prefix x, while x[i] = model will be resolved at runtime.\n\n\n\n\n\n","category":"macro"},{"location":"api/#Type","page":"API","title":"Type","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"A Model can be created by calling the model function, as defined by @model.","category":"page"},{"location":"api/","page":"API","title":"API","text":"Model","category":"page"},{"location":"api/#DynamicPPL.Model","page":"API","title":"DynamicPPL.Model","text":"struct Model{F,argnames,defaultnames,missings,Targs,Tdefaults}\n f::F\n args::NamedTuple{argnames,Targs}\n defaults::NamedTuple{defaultnames,Tdefaults}\nend\n\nA Model struct with model evaluation function of type F, arguments of names argnames types Targs, default arguments of names defaultnames with types Tdefaults, and missing arguments missings.\n\nHere argnames, defaultargnames, and missings are tuples of symbols, e.g. (:a, :b).\n\nAn argument with a type of Missing will be in missings by default. However, in non-traditional use-cases missings can be defined differently. All variables in missings are treated as random variables rather than observations.\n\nThe default arguments are used internally when constructing instances of the same model with different arguments.\n\nExamples\n\njulia> Model(f, (x = 1.0, y = 2.0))\nModel{typeof(f),(:x, :y),(),(),Tuple{Float64,Float64},Tuple{}}(f, (x = 1.0, y = 2.0), NamedTuple())\n\njulia> Model(f, (x = 1.0, y = 2.0), (x = 42,))\nModel{typeof(f),(:x, :y),(:x,),(),Tuple{Float64,Float64},Tuple{Int64}}(f, (x = 1.0, y = 2.0), (x = 42,))\n\njulia> Model{(:y,)}(f, (x = 1.0, y = 2.0), (x = 42,)) # with special definition of missings\nModel{typeof(f),(:x, :y),(:x,),(:y,),Tuple{Float64,Float64},Tuple{Int64}}(f, (x = 1.0, y = 2.0), (x = 42,))\n\n\n\n\n\n","category":"type"},{"location":"api/","page":"API","title":"API","text":"Models are callable structs.","category":"page"},{"location":"api/","page":"API","title":"API","text":"Model()","category":"page"},{"location":"api/#DynamicPPL.Model-Tuple{}","page":"API","title":"DynamicPPL.Model","text":"(model::Model)([rng, varinfo, sampler, context])\n\nSample from the model using the sampler with random number generator rng and the context, and store the sample and log joint probability in varinfo.\n\nThe method resets the log joint probability of varinfo and increases the evaluation number of sampler.\n\n\n\n\n\n","category":"method"},{"location":"api/","page":"API","title":"API","text":"Basic properties of a model can be accessed with getargnames, getmissings, and nameof.","category":"page"},{"location":"api/","page":"API","title":"API","text":"nameof(::Model)\ngetargnames\ngetmissings","category":"page"},{"location":"api/#Base.nameof-Tuple{Model}","page":"API","title":"Base.nameof","text":"nameof(model::Model)\n\nGet the name of the model as Symbol.\n\n\n\n\n\n","category":"method"},{"location":"api/#DynamicPPL.getargnames","page":"API","title":"DynamicPPL.getargnames","text":"getargnames(model::Model)\n\nGet a tuple of the argument names of the model.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.getmissings","page":"API","title":"DynamicPPL.getmissings","text":"getmissings(model::Model)\n\nGet a tuple of the names of the missing arguments of the model.\n\n\n\n\n\n","category":"function"},{"location":"api/#Evaluation","page":"API","title":"Evaluation","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"With rand one can draw samples from the prior distribution of a Model.","category":"page"},{"location":"api/","page":"API","title":"API","text":"rand","category":"page"},{"location":"api/#Base.rand","page":"API","title":"Base.rand","text":"rand([rng=Random.default_rng()], [T=NamedTuple], model::Model)\n\nGenerate a sample of type T from the prior distribution of the model.\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"One can also evaluate the log prior, log likelihood, and log joint probability.","category":"page"},{"location":"api/","page":"API","title":"API","text":"logprior\nloglikelihood\nlogjoint","category":"page"},{"location":"api/#DynamicPPL.logprior","page":"API","title":"DynamicPPL.logprior","text":"logprior(model::Model, varinfo::AbstractVarInfo)\n\nReturn the log prior probability of variables varinfo for the probabilistic model.\n\nSee also logjoint and loglikelihood.\n\n\n\n\n\nlogprior(model::Model, chain::AbstractMCMC.AbstractChains)\n\nReturn an array of log prior probabilities evaluated at each sample in an MCMC chain.\n\nExamples\n\njulia> using MCMCChains, Distributions\n\njulia> @model function demo_model(x)\n s ~ InverseGamma(2, 3)\n m ~ Normal(0, sqrt(s))\n for i in eachindex(x)\n x[i] ~ Normal(m, sqrt(s))\n end\n end;\n\njulia> # construct a chain of samples using MCMCChains\n chain = Chains(rand(10, 2, 3), [:s, :m]);\n\njulia> logprior(demo_model([1., 2.]), chain);\n\n\n\n\n\nlogprior(model::Model, θ)\n\nReturn the log prior probability of variables θ for the probabilistic model.\n\nSee also logjoint and loglikelihood.\n\nExamples\n\njulia> @model function demo(x)\n m ~ Normal()\n for i in eachindex(x)\n x[i] ~ Normal(m, 1.0)\n end\n end\ndemo (generic function with 2 methods)\n\njulia> # Using a `NamedTuple`.\n logprior(demo([1.0]), (m = 100.0, ))\n-5000.918938533205\n\njulia> # Using a `OrderedDict`.\n logprior(demo([1.0]), OrderedDict(@varname(m) => 100.0))\n-5000.918938533205\n\njulia> # Truth.\n logpdf(Normal(), 100.0)\n-5000.918938533205\n\n\n\n\n\n","category":"function"},{"location":"api/#StatsAPI.loglikelihood","page":"API","title":"StatsAPI.loglikelihood","text":"loglikelihood(model::Model, varinfo::AbstractVarInfo)\n\nReturn the log likelihood of variables varinfo for the probabilistic model.\n\nSee also logjoint and logprior.\n\n\n\n\n\nloglikelihood(model::Model, chain::AbstractMCMC.AbstractChains)\n\nReturn an array of log likelihoods evaluated at each sample in an MCMC chain.\n\nExamples\n\njulia> using MCMCChains, Distributions\n\njulia> @model function demo_model(x)\n s ~ InverseGamma(2, 3)\n m ~ Normal(0, sqrt(s))\n for i in eachindex(x)\n x[i] ~ Normal(m, sqrt(s))\n end\n end;\n\njulia> # construct a chain of samples using MCMCChains\n chain = Chains(rand(10, 2, 3), [:s, :m]);\n\njulia> loglikelihood(demo_model([1., 2.]), chain);\n\n\n\n\n\nloglikelihood(model::Model, θ)\n\nReturn the log likelihood of variables θ for the probabilistic model.\n\nSee also logjoint and logprior.\n\nExamples\n\njulia> @model function demo(x)\n m ~ Normal()\n for i in eachindex(x)\n x[i] ~ Normal(m, 1.0)\n end\n end\ndemo (generic function with 2 methods)\n\njulia> # Using a `NamedTuple`.\n loglikelihood(demo([1.0]), (m = 100.0, ))\n-4901.418938533205\n\njulia> # Using a `OrderedDict`.\n loglikelihood(demo([1.0]), OrderedDict(@varname(m) => 100.0))\n-4901.418938533205\n\njulia> # Truth.\n logpdf(Normal(100.0, 1.0), 1.0)\n-4901.418938533205\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.logjoint","page":"API","title":"DynamicPPL.logjoint","text":"logjoint(model::Model, varinfo::AbstractVarInfo)\n\nReturn the log joint probability of variables varinfo for the probabilistic model.\n\nSee logjoint and loglikelihood.\n\n\n\n\n\nlogjoint(model::Model, chain::AbstractMCMC.AbstractChains)\n\nReturn an array of log joint probabilities evaluated at each sample in an MCMC chain.\n\nExamples\n\njulia> using MCMCChains, Distributions\n\njulia> @model function demo_model(x)\n s ~ InverseGamma(2, 3)\n m ~ Normal(0, sqrt(s))\n for i in eachindex(x)\n x[i] ~ Normal(m, sqrt(s))\n end\n end;\n\njulia> # construct a chain of samples using MCMCChains\n chain = Chains(rand(10, 2, 3), [:s, :m]);\n\njulia> logjoint(demo_model([1., 2.]), chain);\n\n\n\n\n\nlogjoint(model::Model, θ)\n\nReturn the log joint probability of variables θ for the probabilistic model.\n\nSee logjoint and loglikelihood.\n\nExamples\n\njulia> @model function demo(x)\n m ~ Normal()\n for i in eachindex(x)\n x[i] ~ Normal(m, 1.0)\n end\n end\ndemo (generic function with 2 methods)\n\njulia> # Using a `NamedTuple`.\n logjoint(demo([1.0]), (m = 100.0, ))\n-9902.33787706641\n\njulia> # Using a `OrderedDict`.\n logjoint(demo([1.0]), OrderedDict(@varname(m) => 100.0))\n-9902.33787706641\n\njulia> # Truth.\n logpdf(Normal(100.0, 1.0), 1.0) + logpdf(Normal(), 100.0)\n-9902.33787706641\n\n\n\n\n\n","category":"function"},{"location":"api/#LogDensityProblems.jl-interface","page":"API","title":"LogDensityProblems.jl interface","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"The LogDensityProblems.jl interface is also supported by simply wrapping a Model in a DynamicPPL.LogDensityFunction:","category":"page"},{"location":"api/","page":"API","title":"API","text":"DynamicPPL.LogDensityFunction","category":"page"},{"location":"api/#DynamicPPL.LogDensityFunction","page":"API","title":"DynamicPPL.LogDensityFunction","text":"LogDensityFunction\n\nA callable representing a log density function of a model.\n\nFields\n\nvarinfo: varinfo used for evaluation\nmodel: model used for evaluation\ncontext: context used for evaluation\n\nExamples\n\njulia> using Distributions\n\njulia> using DynamicPPL: LogDensityFunction, contextualize\n\njulia> @model function demo(x)\n m ~ Normal()\n x ~ Normal(m, 1)\n end\ndemo (generic function with 2 methods)\n\njulia> model = demo(1.0);\n\njulia> f = LogDensityFunction(model);\n\njulia> # It implements the interface of LogDensityProblems.jl.\n using LogDensityProblems\n\njulia> LogDensityProblems.logdensity(f, [0.0])\n-2.3378770664093453\n\njulia> LogDensityProblems.dimension(f)\n1\n\njulia> # By default it uses `VarInfo` under the hood, but this is not necessary.\n f = LogDensityFunction(model, SimpleVarInfo(model));\n\njulia> LogDensityProblems.logdensity(f, [0.0])\n-2.3378770664093453\n\njulia> # This also respects the context in `model`.\n f_prior = LogDensityFunction(contextualize(model, DynamicPPL.PriorContext()), VarInfo(model));\n\njulia> LogDensityProblems.logdensity(f_prior, [0.0]) == logpdf(Normal(), 0.0)\ntrue\n\n\n\n\n\n","category":"type"},{"location":"api/#Condition-and-decondition","page":"API","title":"Condition and decondition","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"A Model can be conditioned on a set of observations with AbstractPPL.condition or its alias |.","category":"page"},{"location":"api/","page":"API","title":"API","text":"|(::Model, ::Any)\ncondition\nDynamicPPL.conditioned","category":"page"},{"location":"api/#Base.:|-Tuple{Model, Any}","page":"API","title":"Base.:|","text":"model | (x = 1.0, ...)\n\nReturn a Model which now treats variables on the right-hand side as observations.\n\nSee condition for more information and examples.\n\n\n\n\n\n","category":"method"},{"location":"api/#AbstractPPL.condition","page":"API","title":"AbstractPPL.condition","text":"condition(model::Model; values...)\ncondition(model::Model, values::NamedTuple)\n\nReturn a Model which now treats the variables in values as observations.\n\nSee also: decondition, conditioned\n\nLimitations\n\nThis does currently not work with variables that are provided to the model as arguments, e.g. @model function demo(x) ... end means that condition will not affect the variable x.\n\nTherefore if one wants to make use of condition and decondition one should not be specifying any random variables as arguments.\n\nThis is done for the sake of backwards compatibility.\n\nExamples\n\nSimple univariate model\n\njulia> using Distributions\n\njulia> @model function demo()\n m ~ Normal()\n x ~ Normal(m, 1)\n return (; m=m, x=x)\n end\ndemo (generic function with 2 methods)\n\njulia> model = demo();\n\njulia> m, x = model(); (m ≠ 1.0 && x ≠ 100.0)\ntrue\n\njulia> # Create a new instance which treats `x` as observed\n # with value `100.0`, and similarly for `m=1.0`.\n conditioned_model = condition(model, x=100.0, m=1.0);\n\njulia> m, x = conditioned_model(); (m == 1.0 && x == 100.0)\ntrue\n\njulia> # Let's only condition on `x = 100.0`.\n conditioned_model = condition(model, x = 100.0);\n\njulia> m, x =conditioned_model(); (m ≠ 1.0 && x == 100.0)\ntrue\n\njulia> # We can also use the nicer `|` syntax.\n conditioned_model = model | (x = 100.0, );\n\njulia> m, x = conditioned_model(); (m ≠ 1.0 && x == 100.0)\ntrue\n\nThe above uses a NamedTuple to hold the conditioning variables, which allows us to perform some additional optimizations; in many cases, the above has zero runtime-overhead.\n\nBut we can also use a Dict, which offers more flexibility in the conditioning (see examples further below) but generally has worse performance than the NamedTuple approach:\n\njulia> conditioned_model_dict = condition(model, Dict(@varname(x) => 100.0));\n\njulia> m, x = conditioned_model_dict(); (m ≠ 1.0 && x == 100.0)\ntrue\n\njulia> # There's also an option using `|` by letting the right-hand side be a tuple\n # with elements of type `Pair{<:VarName}`, i.e. `vn => value` with `vn isa VarName`.\n conditioned_model_dict = model | (@varname(x) => 100.0, );\n\njulia> m, x = conditioned_model_dict(); (m ≠ 1.0 && x == 100.0)\ntrue\n\nCondition only a part of a multivariate variable\n\nNot only can be condition on multivariate random variables, but we can also use the standard mechanism of setting something to missing in the call to condition to only condition on a part of the variable.\n\njulia> @model function demo_mv(::Type{TV}=Float64) where {TV}\n m = Vector{TV}(undef, 2)\n m[1] ~ Normal()\n m[2] ~ Normal()\n return m\n end\ndemo_mv (generic function with 4 methods)\n\njulia> model = demo_mv();\n\njulia> conditioned_model = condition(model, m = [missing, 1.0]);\n\njulia> # (✓) `m[1]` sampled while `m[2]` is fixed\n m = conditioned_model(); (m[1] ≠ 1.0 && m[2] == 1.0)\ntrue\n\nIntuitively one might also expect to be able to write model | (m[1] = 1.0, ). Unfortunately this is not supported as it has the potential of increasing compilation times but without offering any benefit with respect to runtime:\n\njulia> # (×) `m[2]` is not set to 1.0.\n m = condition(model, var\"m[2]\" = 1.0)(); m[2] == 1.0\nfalse\n\nBut you can do this if you use a Dict as the underlying storage instead:\n\njulia> # Alternatives:\n # - `model | (@varname(m[2]) => 1.0,)`\n # - `condition(model, Dict(@varname(m[2] => 1.0)))`\n # (✓) `m[2]` is set to 1.0.\n m = condition(model, @varname(m[2]) => 1.0)(); (m[1] ≠ 1.0 && m[2] == 1.0)\ntrue\n\nNested models\n\ncondition of course also supports the use of nested models through the use of @submodel.\n\njulia> @model demo_inner() = m ~ Normal()\ndemo_inner (generic function with 2 methods)\n\njulia> @model function demo_outer()\n @submodel m = demo_inner()\n return m\n end\ndemo_outer (generic function with 2 methods)\n\njulia> model = demo_outer();\n\njulia> model() ≠ 1.0\ntrue\n\njulia> conditioned_model = model | (m = 1.0, );\n\njulia> conditioned_model()\n1.0\n\nBut one needs to be careful when prefixing variables in the nested models:\n\njulia> @model function demo_outer_prefix()\n @submodel prefix=\"inner\" m = demo_inner()\n return m\n end\ndemo_outer_prefix (generic function with 2 methods)\n\njulia> # (×) This doesn't work now!\n conditioned_model = demo_outer_prefix() | (m = 1.0, );\n\njulia> conditioned_model() == 1.0\nfalse\n\njulia> # (✓) `m` in `demo_inner` is referred to as `inner.m` internally, so we do:\n conditioned_model = demo_outer_prefix() | (var\"inner.m\" = 1.0, );\n\njulia> conditioned_model()\n1.0\n\njulia> # Note that the above `var\"...\"` is just standard Julia syntax:\n keys((var\"inner.m\" = 1.0, ))\n(Symbol(\"inner.m\"),)\n\nAnd similarly when using Dict:\n\njulia> conditioned_model_dict = demo_outer_prefix() | (@varname(var\"inner.m\") => 1.0);\n\njulia> conditioned_model_dict()\n1.0\n\nThe difference is maybe more obvious once we look at how these different in their trace/VarInfo:\n\njulia> keys(VarInfo(demo_outer()))\n1-element Vector{VarName{:m, Setfield.IdentityLens}}:\n m\n\njulia> keys(VarInfo(demo_outer_prefix()))\n1-element Vector{VarName{Symbol(\"inner.m\"), Setfield.IdentityLens}}:\n inner.m\n\nFrom this we can tell what the correct way to condition m within demo_inner is in the two different models.\n\n\n\n\n\ncondition([context::AbstractContext,] values::NamedTuple)\ncondition([context::AbstractContext]; values...)\n\nReturn ConditionContext with values and context if values is non-empty, otherwise return context which is DefaultContext by default.\n\nSee also: decondition\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.conditioned","page":"API","title":"DynamicPPL.conditioned","text":"conditioned(model::Model)\n\nReturn the conditioned values in model.\n\nExamples\n\njulia> using Distributions\n\njulia> using DynamicPPL: conditioned, contextualize\n\njulia> @model function demo()\n m ~ Normal()\n x ~ Normal(m, 1)\n end\ndemo (generic function with 2 methods)\n\njulia> m = demo();\n\njulia> # Returns all the variables we have conditioned on + their values.\n conditioned(condition(m, x=100.0, m=1.0))\n(x = 100.0, m = 1.0)\n\njulia> # Nested ones also work (note that `PrefixContext` does nothing to the result).\n cm = condition(contextualize(m, PrefixContext{:a}(condition(m=1.0))), x=100.0);\n\njulia> conditioned(cm)\n(x = 100.0, m = 1.0)\n\njulia> # Since we conditioned on `m`, not `a.m` as it will appear after prefixed,\n # `a.m` is treated as a random variable.\n keys(VarInfo(cm))\n1-element Vector{VarName{Symbol(\"a.m\"), Setfield.IdentityLens}}:\n a.m\n\njulia> # If we instead condition on `a.m`, `m` in the model will be considered an observation.\n cm = condition(contextualize(m, PrefixContext{:a}(condition(var\"a.m\"=1.0))), x=100.0);\n\njulia> conditioned(cm).x\n100.0\n\njulia> conditioned(cm).var\"a.m\"\n1.0\n\njulia> keys(VarInfo(cm)) # <= no variables are sampled\nVarName[]\n\n\n\n\n\nconditioned(context::AbstractContext)\n\nReturn NamedTuple of values that are conditioned on under context`.\n\nNote that this will recursively traverse the context stack and return a merged version of the condition values.\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"Similarly, one can specify with AbstractPPL.decondition that certain, or all, random variables are not observed.","category":"page"},{"location":"api/","page":"API","title":"API","text":"decondition","category":"page"},{"location":"api/#AbstractPPL.decondition","page":"API","title":"AbstractPPL.decondition","text":"decondition(model::Model)\ndecondition(model::Model, variables...)\n\nReturn a Model for which variables... are not considered observations. If no variables are provided, then all variables currently considered observations will no longer be.\n\nThis is essentially the inverse of condition. This also means that it suffers from the same limitiations.\n\nNote that currently we only support variables to take on explicit values provided to condition.\n\nExamples\n\njulia> using Distributions\n\njulia> @model function demo()\n m ~ Normal()\n x ~ Normal(m, 1)\n return (; m=m, x=x)\n end\ndemo (generic function with 2 methods)\n\njulia> conditioned_model = condition(demo(), m = 1.0, x = 10.0);\n\njulia> conditioned_model()\n(m = 1.0, x = 10.0)\n\njulia> # By specifying the `VarName` to `decondition`.\n model = decondition(conditioned_model, @varname(m));\n\njulia> (m, x) = model(); (m ≠ 1.0 && x == 10.0)\ntrue\n\njulia> # When `NamedTuple` is used as the underlying, you can also provide\n # the symbol directly (though the `@varname` approach is preferable if\n # if the variable is known at compile-time).\n model = decondition(conditioned_model, :m);\n\njulia> (m, x) = model(); (m ≠ 1.0 && x == 10.0)\ntrue\n\njulia> # `decondition` multiple at once:\n (m, x) = decondition(model, :m, :x)(); (m ≠ 1.0 && x ≠ 10.0)\ntrue\n\njulia> # `decondition` without any symbols will `decondition` all variables.\n (m, x) = decondition(model)(); (m ≠ 1.0 && x ≠ 10.0)\ntrue\n\njulia> # Usage of `Val` to perform `decondition` at compile-time if possible\n # is also supported.\n model = decondition(conditioned_model, Val{:m}());\n\njulia> (m, x) = model(); (m ≠ 1.0 && x == 10.0)\ntrue\n\nSimilarly when using a Dict:\n\njulia> conditioned_model_dict = condition(demo(), @varname(m) => 1.0, @varname(x) => 10.0);\n\njulia> conditioned_model_dict()\n(m = 1.0, x = 10.0)\n\njulia> deconditioned_model_dict = decondition(conditioned_model_dict, @varname(m));\n\njulia> (m, x) = deconditioned_model_dict(); m ≠ 1.0 && x == 10.0\ntrue\n\nBut, as mentioned, decondition is only supported for variables explicitly provided to condition earlier;\n\njulia> @model function demo_mv(::Type{TV}=Float64) where {TV}\n m = Vector{TV}(undef, 2)\n m[1] ~ Normal()\n m[2] ~ Normal()\n return m\n end\ndemo_mv (generic function with 4 methods)\n\njulia> model = demo_mv();\n\njulia> conditioned_model = condition(model, @varname(m) => [1.0, 2.0]);\n\njulia> conditioned_model()\n2-element Vector{Float64}:\n 1.0\n 2.0\n\njulia> deconditioned_model = decondition(conditioned_model, @varname(m[1]));\n\njulia> deconditioned_model() # (×) `m[1]` is still conditioned\n2-element Vector{Float64}:\n 1.0\n 2.0\n\njulia> # (✓) this works though\n deconditioned_model_2 = deconditioned_model | (@varname(m[1]) => missing);\n\njulia> m = deconditioned_model_2(); (m[1] ≠ 1.0 && m[2] == 2.0)\ntrue\n\n\n\n\n\ndecondition(context::AbstractContext, syms...)\n\nReturn context but with syms no longer conditioned on.\n\nNote that this recursively traverses contexts, deconditioning all along the way.\n\nSee also: condition\n\n\n\n\n\n","category":"function"},{"location":"api/#Fixing-and-unfixing","page":"API","title":"Fixing and unfixing","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"We can also fix a collection of variables in a Model to certain using fix.","category":"page"},{"location":"api/","page":"API","title":"API","text":"This might seem quite similar to the aforementioned condition and its siblings, but they are indeed different operations:","category":"page"},{"location":"api/","page":"API","title":"API","text":"conditioned variables are considered to be observations, and are thus included in the computation logjoint and loglikelihood, but not in logprior.\nfixed variables are considered to be constant, and are thus not included in any log-probability computations.","category":"page"},{"location":"api/","page":"API","title":"API","text":"The differences are more clearly spelled out in the docstring of fix below.","category":"page"},{"location":"api/","page":"API","title":"API","text":"fix\nDynamicPPL.fixed","category":"page"},{"location":"api/#DynamicPPL.fix","page":"API","title":"DynamicPPL.fix","text":"fix(model::Model; values...)\nfix(model::Model, values::NamedTuple)\n\nReturn a Model which now treats the variables in values as fixed.\n\nSee also: unfix, fixed\n\nExamples\n\nSimple univariate model\n\njulia> using Distributions\n\njulia> @model function demo()\n m ~ Normal()\n x ~ Normal(m, 1)\n return (; m=m, x=x)\n end\ndemo (generic function with 2 methods)\n\njulia> model = demo();\n\njulia> m, x = model(); (m ≠ 1.0 && x ≠ 100.0)\ntrue\n\njulia> # Create a new instance which treats `x` as observed\n # with value `100.0`, and similarly for `m=1.0`.\n fixed_model = fix(model, x=100.0, m=1.0);\n\njulia> m, x = fixed_model(); (m == 1.0 && x == 100.0)\ntrue\n\njulia> # Let's only fix on `x = 100.0`.\n fixed_model = fix(model, x = 100.0);\n\njulia> m, x = fixed_model(); (m ≠ 1.0 && x == 100.0)\ntrue\n\nThe above uses a NamedTuple to hold the fixed variables, which allows us to perform some additional optimizations; in many cases, the above has zero runtime-overhead.\n\nBut we can also use a Dict, which offers more flexibility in the fixing (see examples further below) but generally has worse performance than the NamedTuple approach:\n\njulia> fixed_model_dict = fix(model, Dict(@varname(x) => 100.0));\n\njulia> m, x = fixed_model_dict(); (m ≠ 1.0 && x == 100.0)\ntrue\n\njulia> # Alternative: pass `Pair{<:VarName}` as positional argument.\n fixed_model_dict = fix(model, @varname(x) => 100.0, );\n\njulia> m, x = fixed_model_dict(); (m ≠ 1.0 && x == 100.0)\ntrue\n\nFix only a part of a multivariate variable\n\nWe can not only fix multivariate random variables, but we can also use the standard mechanism of setting something to missing in the call to fix to only fix a part of the variable.\n\njulia> @model function demo_mv(::Type{TV}=Float64) where {TV}\n m = Vector{TV}(undef, 2)\n m[1] ~ Normal()\n m[2] ~ Normal()\n return m\n end\ndemo_mv (generic function with 4 methods)\n\njulia> model = demo_mv();\n\njulia> fixed_model = fix(model, m = [missing, 1.0]);\n\njulia> # (✓) `m[1]` sampled while `m[2]` is fixed\n m = fixed_model(); (m[1] ≠ 1.0 && m[2] == 1.0)\ntrue\n\nIntuitively one might also expect to be able to write something like fix(model, var\"m[1]\" = 1.0, ). Unfortunately this is not supported as it has the potential of increasing compilation times but without offering any benefit with respect to runtime:\n\njulia> # (×) `m[2]` is not set to 1.0.\n m = fix(model, var\"m[2]\" = 1.0)(); m[2] == 1.0\nfalse\n\nBut you can do this if you use a Dict as the underlying storage instead:\n\njulia> # Alternative: `fix(model, Dict(@varname(m[2] => 1.0)))`\n # (✓) `m[2]` is set to 1.0.\n m = fix(model, @varname(m[2]) => 1.0)(); (m[1] ≠ 1.0 && m[2] == 1.0)\ntrue\n\nNested models\n\nfix of course also supports the use of nested models through the use of @submodel.\n\njulia> @model demo_inner() = m ~ Normal()\ndemo_inner (generic function with 2 methods)\n\njulia> @model function demo_outer()\n @submodel m = demo_inner()\n return m\n end\ndemo_outer (generic function with 2 methods)\n\njulia> model = demo_outer();\n\njulia> model() ≠ 1.0\ntrue\n\njulia> fixed_model = model | (m = 1.0, );\n\njulia> fixed_model()\n1.0\n\nBut one needs to be careful when prefixing variables in the nested models:\n\njulia> @model function demo_outer_prefix()\n @submodel prefix=\"inner\" m = demo_inner()\n return m\n end\ndemo_outer_prefix (generic function with 2 methods)\n\njulia> # (×) This doesn't work now!\n fixed_model = demo_outer_prefix() | (m = 1.0, );\n\njulia> fixed_model() == 1.0\nfalse\n\njulia> # (✓) `m` in `demo_inner` is referred to as `inner.m` internally, so we do:\n fixed_model = demo_outer_prefix() | (var\"inner.m\" = 1.0, );\n\njulia> fixed_model()\n1.0\n\njulia> # Note that the above `var\"...\"` is just standard Julia syntax:\n keys((var\"inner.m\" = 1.0, ))\n(Symbol(\"inner.m\"),)\n\nAnd similarly when using Dict:\n\njulia> fixed_model_dict = demo_outer_prefix() | (@varname(var\"inner.m\") => 1.0);\n\njulia> fixed_model_dict()\n1.0\n\nThe difference is maybe more obvious once we look at how these different in their trace/VarInfo:\n\njulia> keys(VarInfo(demo_outer()))\n1-element Vector{VarName{:m, Setfield.IdentityLens}}:\n m\n\njulia> keys(VarInfo(demo_outer_prefix()))\n1-element Vector{VarName{Symbol(\"inner.m\"), Setfield.IdentityLens}}:\n inner.m\n\nFrom this we can tell what the correct way to fix m within demo_inner is in the two different models.\n\nDifference from condition\n\nA very similar functionality is also provided by condition which, not surprisingly, conditions variables instead of fixing them. The only difference between fixing and conditioning is as follows:\n\nconditioned variables are considered to be observations, and are thus included in the computation logjoint and loglikelihood, but not in logprior.\nfixed variables are considered to be constant, and are thus not included in any log-probability computations.\n\njulia> @model function demo()\n m ~ Normal()\n x ~ Normal(m, 1)\n return (; m=m, x=x)\n end\ndemo (generic function with 2 methods)\n\njulia> model = demo();\n\njulia> model_fixed = fix(model, m = 1.0);\n\njulia> model_conditioned = condition(model, m = 1.0);\n\njulia> logjoint(model_fixed, (x=1.0,))\n-0.9189385332046728\n\njulia> # Different!\n logjoint(model_conditioned, (x=1.0,))\n-2.3378770664093453\n\njulia> # And the difference is the missing log-probability of `m`:\n logjoint(model_fixed, (x=1.0,)) + logpdf(Normal(), 1.0) == logjoint(model_conditioned, (x=1.0,))\ntrue\n\n\n\n\n\nfix([context::AbstractContext,] values::NamedTuple)\nfix([context::AbstractContext]; values...)\n\nReturn FixedContext with values and context if values is non-empty, otherwise return context which is DefaultContext by default.\n\nSee also: unfix\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.fixed","page":"API","title":"DynamicPPL.fixed","text":"fixed(model::Model)\n\nReturn the fixed values in model.\n\nExamples\n\njulia> using Distributions\n\njulia> using DynamicPPL: fixed, contextualize\n\njulia> @model function demo()\n m ~ Normal()\n x ~ Normal(m, 1)\n end\ndemo (generic function with 2 methods)\n\njulia> m = demo();\n\njulia> # Returns all the variables we have fixed on + their values.\n fixed(fix(m, x=100.0, m=1.0))\n(x = 100.0, m = 1.0)\n\njulia> # Nested ones also work (note that `PrefixContext` does nothing to the result).\n cm = fix(contextualize(m, PrefixContext{:a}(fix(m=1.0))), x=100.0);\n\njulia> fixed(cm)\n(x = 100.0, m = 1.0)\n\njulia> # Since we fixed on `m`, not `a.m` as it will appear after prefixed,\n # `a.m` is treated as a random variable.\n keys(VarInfo(cm))\n1-element Vector{VarName{Symbol(\"a.m\"), Setfield.IdentityLens}}:\n a.m\n\njulia> # If we instead fix on `a.m`, `m` in the model will be considered an observation.\n cm = fix(contextualize(m, PrefixContext{:a}(fix(var\"a.m\"=1.0))), x=100.0);\n\njulia> fixed(cm).x\n100.0\n\njulia> fixed(cm).var\"a.m\"\n1.0\n\njulia> keys(VarInfo(cm)) # <= no variables are sampled\nVarName[]\n\n\n\n\n\nfixed(context::AbstractContext)\n\nReturn the values that are fixed under context.\n\nNote that this will recursively traverse the context stack and return a merged version of the fix values.\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"The difference between fix and condition is described in the docstring of fix above.","category":"page"},{"location":"api/","page":"API","title":"API","text":"Similarly, we can unfix variables, i.e. return them to their original meaning:","category":"page"},{"location":"api/","page":"API","title":"API","text":"unfix","category":"page"},{"location":"api/#DynamicPPL.unfix","page":"API","title":"DynamicPPL.unfix","text":"unfix(model::Model)\nunfix(model::Model, variables...)\n\nReturn a Model for which variables... are not considered fixed. If no variables are provided, then all variables currently considered fixed will no longer be.\n\nThis is essentially the inverse of fix. This also means that it suffers from the same limitiations.\n\nNote that currently we only support variables to take on explicit values provided to fix.\n\nExamples\n\njulia> using Distributions\n\njulia> @model function demo()\n m ~ Normal()\n x ~ Normal(m, 1)\n return (; m=m, x=x)\n end\ndemo (generic function with 2 methods)\n\njulia> fixed_model = fix(demo(), m = 1.0, x = 10.0);\n\njulia> fixed_model()\n(m = 1.0, x = 10.0)\n\njulia> # By specifying the `VarName` to `unfix`.\n model = unfix(fixed_model, @varname(m));\n\njulia> (m, x) = model(); (m ≠ 1.0 && x == 10.0)\ntrue\n\njulia> # When `NamedTuple` is used as the underlying, you can also provide\n # the symbol directly (though the `@varname` approach is preferable if\n # if the variable is known at compile-time).\n model = unfix(fixed_model, :m);\n\njulia> (m, x) = model(); (m ≠ 1.0 && x == 10.0)\ntrue\n\njulia> # `unfix` multiple at once:\n (m, x) = unfix(model, :m, :x)(); (m ≠ 1.0 && x ≠ 10.0)\ntrue\n\njulia> # `unfix` without any symbols will `unfix` all variables.\n (m, x) = unfix(model)(); (m ≠ 1.0 && x ≠ 10.0)\ntrue\n\njulia> # Usage of `Val` to perform `unfix` at compile-time if possible\n # is also supported.\n model = unfix(fixed_model, Val{:m}());\n\njulia> (m, x) = model(); (m ≠ 1.0 && x == 10.0)\ntrue\n\nSimilarly when using a Dict:\n\njulia> fixed_model_dict = fix(demo(), @varname(m) => 1.0, @varname(x) => 10.0);\n\njulia> fixed_model_dict()\n(m = 1.0, x = 10.0)\n\njulia> unfixed_model_dict = unfix(fixed_model_dict, @varname(m));\n\njulia> (m, x) = unfixed_model_dict(); m ≠ 1.0 && x == 10.0\ntrue\n\nBut, as mentioned, unfix is only supported for variables explicitly provided to fix earlier:\n\njulia> @model function demo_mv(::Type{TV}=Float64) where {TV}\n m = Vector{TV}(undef, 2)\n m[1] ~ Normal()\n m[2] ~ Normal()\n return m\n end\ndemo_mv (generic function with 4 methods)\n\njulia> model = demo_mv();\n\njulia> fixed_model = fix(model, @varname(m) => [1.0, 2.0]);\n\njulia> fixed_model()\n2-element Vector{Float64}:\n 1.0\n 2.0\n\njulia> unfixed_model = unfix(fixed_model, @varname(m[1]));\n\njulia> unfixed_model() # (×) `m[1]` is still fixed\n2-element Vector{Float64}:\n 1.0\n 2.0\n\njulia> # (✓) this works though\n unfixed_model_2 = fix(unfixed_model, @varname(m[1]) => missing);\n\njulia> m = unfixed_model_2(); (m[1] ≠ 1.0 && m[2] == 2.0)\ntrue\n\n\n\n\n\nunfix(context::AbstractContext, syms...)\n\nReturn context but with syms no longer fixed.\n\nNote that this recursively traverses contexts, unfixing all along the way.\n\nSee also: fix\n\n\n\n\n\n","category":"function"},{"location":"api/#Utilities","page":"API","title":"Utilities","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"It is possible to manually increase (or decrease) the accumulated log density from within a model function.","category":"page"},{"location":"api/","page":"API","title":"API","text":"@addlogprob!","category":"page"},{"location":"api/#DynamicPPL.@addlogprob!","page":"API","title":"DynamicPPL.@addlogprob!","text":"@addlogprob!(ex)\n\nAdd the result of the evaluation of ex to the joint log probability.\n\nExamples\n\nThis macro allows you to include arbitrary terms in the likelihood\n\njulia> myloglikelihood(x, μ) = loglikelihood(Normal(μ, 1), x);\n\njulia> @model function demo(x)\n μ ~ Normal()\n @addlogprob! myloglikelihood(x, μ)\n end;\n\njulia> x = [1.3, -2.1];\n\njulia> loglikelihood(demo(x), (μ=0.2,)) ≈ myloglikelihood(x, 0.2)\ntrue\n\nand to reject samples:\n\njulia> @model function demo(x)\n m ~ MvNormal(zero(x), I)\n if dot(m, x) < 0\n @addlogprob! -Inf\n # Exit the model evaluation early\n return\n end\n x ~ MvNormal(m, I)\n return\n end;\n\njulia> logjoint(demo([-2.1]), (m=[0.2],)) == -Inf\ntrue\n\nnote: Note\nThe @addlogprob! macro increases the accumulated log probability regardless of the evaluation context, i.e., regardless of whether you evaluate the log prior, the log likelihood or the log joint density. If you would like to avoid this behaviour you should check the evaluation context. It can be accessed with the internal variable __context__. For instance, in the following example the log density is not accumulated when only the log prior is computed: julia> myloglikelihood(x, μ) = loglikelihood(Normal(μ, 1), x);\n\njulia> @model function demo(x)\n μ ~ Normal()\n if DynamicPPL.leafcontext(__context__) !== PriorContext()\n @addlogprob! myloglikelihood(x, μ)\n end\n end;\n\njulia> x = [1.3, -2.1];\n\njulia> logprior(demo(x), (μ=0.2,)) ≈ logpdf(Normal(), 0.2)\ntrue\n\njulia> loglikelihood(demo(x), (μ=0.2,)) ≈ myloglikelihood(x, 0.2)\ntrue\n\n\n\n\n\n","category":"macro"},{"location":"api/","page":"API","title":"API","text":"Return values of the model function for a collection of samples can be obtained with generated_quantities.","category":"page"},{"location":"api/","page":"API","title":"API","text":"generated_quantities","category":"page"},{"location":"api/#DynamicPPL.generated_quantities","page":"API","title":"DynamicPPL.generated_quantities","text":"generated_quantities(model::Model, chain::AbstractChains)\n\nExecute model for each of the samples in chain and return an array of the values returned by the model for each sample.\n\nExamples\n\nGeneral\n\nOften you might have additional quantities computed inside the model that you want to inspect, e.g.\n\n@model function demo(x)\n # sample and observe\n θ ~ Prior()\n x ~ Likelihood()\n return interesting_quantity(θ, x)\nend\nm = demo(data)\nchain = sample(m, alg, n)\n# To inspect the `interesting_quantity(θ, x)` where `θ` is replaced by samples\n# from the posterior/`chain`:\ngenerated_quantities(m, chain) # <= results in a `Vector` of returned values\n # from `interesting_quantity(θ, x)`\n\nConcrete (and simple)\n\njulia> using DynamicPPL, Turing\n\njulia> @model function demo(xs)\n s ~ InverseGamma(2, 3)\n m_shifted ~ Normal(10, √s)\n m = m_shifted - 10\n\n for i in eachindex(xs)\n xs[i] ~ Normal(m, √s)\n end\n\n return (m, )\n end\ndemo (generic function with 1 method)\n\njulia> model = demo(randn(10));\n\njulia> chain = sample(model, MH(), 10);\n\njulia> generated_quantities(model, chain)\n10×1 Array{Tuple{Float64},2}:\n (2.1964758025119338,)\n (2.1964758025119338,)\n (0.09270081916291417,)\n (0.09270081916291417,)\n (0.09270081916291417,)\n (0.09270081916291417,)\n (0.09270081916291417,)\n (0.043088571494005024,)\n (-0.16489786710222099,)\n (-0.16489786710222099,)\n\n\n\n\n\ngenerated_quantities(model::Model, parameters::NamedTuple)\ngenerated_quantities(model::Model, values, keys)\ngenerated_quantities(model::Model, values, keys)\n\nExecute model with variables keys set to values and return the values returned by the model.\n\nIf a NamedTuple is given, keys=keys(parameters) and values=values(parameters).\n\nExample\n\njulia> using DynamicPPL, Distributions\n\njulia> @model function demo(xs)\n s ~ InverseGamma(2, 3)\n m_shifted ~ Normal(10, √s)\n m = m_shifted - 10\n for i in eachindex(xs)\n xs[i] ~ Normal(m, √s)\n end\n return (m, )\n end\ndemo (generic function with 2 methods)\n\njulia> model = demo(randn(10));\n\njulia> parameters = (; s = 1.0, m_shifted=10);\n\njulia> generated_quantities(model, parameters)\n(0.0,)\n\njulia> generated_quantities(model, values(parameters), keys(parameters))\n(0.0,)\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"For a chain of samples, one can compute the pointwise log-likelihoods of each observed random variable with pointwise_loglikelihoods.","category":"page"},{"location":"api/","page":"API","title":"API","text":"pointwise_loglikelihoods","category":"page"},{"location":"api/#DynamicPPL.pointwise_loglikelihoods","page":"API","title":"DynamicPPL.pointwise_loglikelihoods","text":"pointwise_loglikelihoods(model::Model, chain::Chains, keytype = String)\n\nRuns model on each sample in chain returning a OrderedDict{String, Matrix{Float64}} with keys corresponding to symbols of the observations, and values being matrices of shape (num_chains, num_samples).\n\nkeytype specifies what the type of the keys used in the returned OrderedDict are. Currently, only String and VarName are supported.\n\nNotes\n\nSay y is a Vector of n i.i.d. Normal(μ, σ) variables, with μ and σ both being <:Real. Then the observe (i.e. when the left-hand side is an observation) statements can be implemented in three ways:\n\nusing a for loop:\n\nfor i in eachindex(y)\n y[i] ~ Normal(μ, σ)\nend\n\nusing .~:\n\ny .~ Normal(μ, σ)\n\nusing MvNormal:\n\ny ~ MvNormal(fill(μ, n), σ^2 * I)\n\nIn (1) and (2), y will be treated as a collection of n i.i.d. 1-dimensional variables, while in (3) y will be treated as a single n-dimensional observation.\n\nThis is important to keep in mind, in particular if the computation is used for downstream computations.\n\nExamples\n\nFrom chain\n\njulia> using DynamicPPL, Turing\n\njulia> @model function demo(xs, y)\n s ~ InverseGamma(2, 3)\n m ~ Normal(0, √s)\n for i in eachindex(xs)\n xs[i] ~ Normal(m, √s)\n end\n\n y ~ Normal(m, √s)\n end\ndemo (generic function with 1 method)\n\njulia> model = demo(randn(3), randn());\n\njulia> chain = sample(model, MH(), 10);\n\njulia> pointwise_loglikelihoods(model, chain)\nOrderedDict{String,Array{Float64,2}} with 4 entries:\n \"xs[1]\" => [-1.42932; -2.68123; … ; -1.66333; -1.66333]\n \"xs[2]\" => [-1.6724; -0.861339; … ; -1.62359; -1.62359]\n \"xs[3]\" => [-1.42862; -2.67573; … ; -1.66251; -1.66251]\n \"y\" => [-1.51265; -0.914129; … ; -1.5499; -1.5499]\n\njulia> pointwise_loglikelihoods(model, chain, String)\nOrderedDict{String,Array{Float64,2}} with 4 entries:\n \"xs[1]\" => [-1.42932; -2.68123; … ; -1.66333; -1.66333]\n \"xs[2]\" => [-1.6724; -0.861339; … ; -1.62359; -1.62359]\n \"xs[3]\" => [-1.42862; -2.67573; … ; -1.66251; -1.66251]\n \"y\" => [-1.51265; -0.914129; … ; -1.5499; -1.5499]\n\njulia> pointwise_loglikelihoods(model, chain, VarName)\nOrderedDict{VarName,Array{Float64,2}} with 4 entries:\n xs[1] => [-1.42932; -2.68123; … ; -1.66333; -1.66333]\n xs[2] => [-1.6724; -0.861339; … ; -1.62359; -1.62359]\n xs[3] => [-1.42862; -2.67573; … ; -1.66251; -1.66251]\n y => [-1.51265; -0.914129; … ; -1.5499; -1.5499]\n\nBroadcasting\n\nNote that x .~ Dist() will treat x as a collection of independent observations rather than as a single observation.\n\njulia> @model function demo(x)\n x .~ Normal()\n end;\n\njulia> m = demo([1.0, ]);\n\njulia> ℓ = pointwise_loglikelihoods(m, VarInfo(m)); first(ℓ[@varname(x[1])])\n-1.4189385332046727\n\njulia> m = demo([1.0; 1.0]);\n\njulia> ℓ = pointwise_loglikelihoods(m, VarInfo(m)); first.((ℓ[@varname(x[1])], ℓ[@varname(x[2])]))\n(-1.4189385332046727, -1.4189385332046727)\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"For converting a chain into a format that can more easily be fed into a Model again, for example using condition, you can use value_iterator_from_chain.","category":"page"},{"location":"api/","page":"API","title":"API","text":"value_iterator_from_chain\n","category":"page"},{"location":"api/#DynamicPPL.value_iterator_from_chain","page":"API","title":"DynamicPPL.value_iterator_from_chain","text":"value_iterator_from_chain(model::Model, chain)\nvalue_iterator_from_chain(varinfo::AbstractVarInfo, chain)\n\nReturn an iterator over the values in chain for each variable in model/varinfo.\n\nExample\n\njulia> using MCMCChains, DynamicPPL, Distributions, StableRNGs\n\njulia> rng = StableRNG(42);\n\njulia> @model function demo_model(x)\n s ~ InverseGamma(2, 3)\n m ~ Normal(0, sqrt(s))\n for i in eachindex(x)\n x[i] ~ Normal(m, sqrt(s))\n end\n\n return s, m\n end\ndemo_model (generic function with 2 methods)\n\njulia> model = demo_model([1.0, 2.0]);\n\njulia> chain = Chains(rand(rng, 10, 2, 3), [:s, :m]);\n\njulia> iter = value_iterator_from_chain(model, chain);\n\njulia> first(iter)\nOrderedDict{VarName, Any} with 2 entries:\n s => 0.580515\n m => 0.739328\n\njulia> collect(iter)\n10×3 Matrix{OrderedDict{VarName, Any}}:\n OrderedDict(s=>0.580515, m=>0.739328) … OrderedDict(s=>0.186047, m=>0.402423)\n OrderedDict(s=>0.191241, m=>0.627342) OrderedDict(s=>0.776277, m=>0.166342)\n OrderedDict(s=>0.971133, m=>0.637584) OrderedDict(s=>0.651655, m=>0.712044)\n OrderedDict(s=>0.74345, m=>0.110359) OrderedDict(s=>0.469214, m=>0.104502)\n OrderedDict(s=>0.170969, m=>0.598514) OrderedDict(s=>0.853546, m=>0.185399)\n OrderedDict(s=>0.704776, m=>0.322111) … OrderedDict(s=>0.638301, m=>0.853802)\n OrderedDict(s=>0.441044, m=>0.162285) OrderedDict(s=>0.852959, m=>0.0956922)\n OrderedDict(s=>0.803972, m=>0.643369) OrderedDict(s=>0.245049, m=>0.871985)\n OrderedDict(s=>0.772384, m=>0.646323) OrderedDict(s=>0.906603, m=>0.385502)\n OrderedDict(s=>0.70882, m=>0.253105) OrderedDict(s=>0.413222, m=>0.953288)\n\njulia> # This can be used to `condition` a `Model`.\n conditioned_model = model | first(iter);\n\njulia> conditioned_model() # <= results in same values as the `first(iter)` above\n(0.5805148626851955, 0.7393275279160691)\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"Sometimes it can be useful to extract the priors of a model. This is the possible using extract_priors.","category":"page"},{"location":"api/","page":"API","title":"API","text":"extract_priors","category":"page"},{"location":"api/#DynamicPPL.extract_priors","page":"API","title":"DynamicPPL.extract_priors","text":"extract_priors([rng::Random.AbstractRNG, ]model::Model)\n\nExtract the priors from a model.\n\nThis is done by sampling from the model and recording the distributions that are used to generate the samples.\n\nwarning: Warning\nBecause the extraction is done by execution of the model, there are several caveats:If one variable, say, y ~ Normal(0, x), where x ~ Normal() is also a random variable, then the extracted prior will have different parameters in every extraction!\nIf the model does not have static support, say, n ~ Categorical(1:10); x ~ MvNormmal(zeros(n), I), then the extracted priors themselves will be different between extractions, not just their parameters.Both of these caveats are demonstrated below.\n\nExamples\n\nChanging parameters\n\njulia> using Distributions, StableRNGs\n\njulia> rng = StableRNG(42);\n\njulia> @model function model_dynamic_parameters()\n x ~ Normal(0, 1)\n y ~ Normal(x, 1)\n end;\n\njulia> model = model_dynamic_parameters();\n\njulia> extract_priors(rng, model)[@varname(y)]\nNormal{Float64}(μ=-0.6702516921145671, σ=1.0)\n\njulia> extract_priors(rng, model)[@varname(y)]\nNormal{Float64}(μ=1.3736306979834252, σ=1.0)\n\nChanging support\n\njulia> using LinearAlgebra, Distributions, StableRNGs\n\njulia> rng = StableRNG(42);\n\njulia> @model function model_dynamic_support()\n n ~ Categorical(ones(10) ./ 10)\n x ~ MvNormal(zeros(n), I)\n end;\n\njulia> model = model_dynamic_support();\n\njulia> length(extract_priors(rng, model)[@varname(x)])\n6\n\njulia> length(extract_priors(rng, model)[@varname(x)])\n9\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"NamedDist","category":"page"},{"location":"api/#DynamicPPL.NamedDist","page":"API","title":"DynamicPPL.NamedDist","text":"A named distribution that carries the name of the random variable with it.\n\n\n\n\n\n","category":"type"},{"location":"api/#Testing-Utilities","page":"API","title":"Testing Utilities","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"DynamicPPL provides several demo models and helpers for testing samplers in the DynamicPPL.TestUtils submodule.","category":"page"},{"location":"api/","page":"API","title":"API","text":"DynamicPPL.TestUtils.test_sampler\nDynamicPPL.TestUtils.test_sampler_on_demo_models\nDynamicPPL.TestUtils.test_sampler_continuous\nDynamicPPL.TestUtils.marginal_mean_of_samples","category":"page"},{"location":"api/#DynamicPPL.TestUtils.test_sampler","page":"API","title":"DynamicPPL.TestUtils.test_sampler","text":"test_sampler(models, sampler, args...; kwargs...)\n\nTest that sampler produces correct marginal posterior means on each model in models.\n\nIn short, this method iterates through models, calls AbstractMCMC.sample on the model and sampler to produce a chain, and then checks marginal_mean_of_samples(chain, vn) for every (leaf) varname vn against the corresponding value returned by posterior_mean for each model.\n\nTo change how comparison is done for a particular chain type, one can overload marginal_mean_of_samples for the corresponding type.\n\nArguments\n\nmodels: A collection of instaces of DynamicPPL.Model to test on.\nsampler: The AbstractMCMC.AbstractSampler to test.\nargs...: Arguments forwarded to sample.\n\nKeyword arguments\n\nvarnames_filter: A filter to apply to varnames(model), allowing comparison for only a subset of the varnames.\natol=1e-1: Absolute tolerance used in @test.\nrtol=1e-3: Relative tolerance used in @test.\nkwargs...: Keyword arguments forwarded to sample.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.TestUtils.test_sampler_on_demo_models","page":"API","title":"DynamicPPL.TestUtils.test_sampler_on_demo_models","text":"test_sampler_on_demo_models(meanfunction, sampler, args...; kwargs...)\n\nTest sampler on every model in DEMO_MODELS.\n\nThis is just a proxy for test_sampler(meanfunction, DEMO_MODELS, sampler, args...; kwargs...).\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.TestUtils.test_sampler_continuous","page":"API","title":"DynamicPPL.TestUtils.test_sampler_continuous","text":"test_sampler_continuous(sampler, args...; kwargs...)\n\nTest that sampler produces the correct marginal posterior means on all models in demo_models.\n\nAs of right now, this is just an alias for test_sampler_on_demo_models.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.TestUtils.marginal_mean_of_samples","page":"API","title":"DynamicPPL.TestUtils.marginal_mean_of_samples","text":"marginal_mean_of_samples(chain, varname)\n\nReturn the mean of variable represented by varname in chain.\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"DynamicPPL.TestUtils.DEMO_MODELS","category":"page"},{"location":"api/#DynamicPPL.TestUtils.DEMO_MODELS","page":"API","title":"DynamicPPL.TestUtils.DEMO_MODELS","text":"A collection of models corresponding to the posterior distribution defined by the generative process\n\ns ~ InverseGamma(2, 3)\nm ~ Normal(0, √s)\n1.5 ~ Normal(m, √s)\n2.0 ~ Normal(m, √s)\n\nor by\n\ns[1] ~ InverseGamma(2, 3)\ns[2] ~ InverseGamma(2, 3)\nm[1] ~ Normal(0, √s)\nm[2] ~ Normal(0, √s)\n1.5 ~ Normal(m[1], √s[1])\n2.0 ~ Normal(m[2], √s[2])\n\nThese are examples of a Normal-InverseGamma conjugate prior with Normal likelihood, for which the posterior is known in closed form.\n\nIn particular, for the univariate model (the former one):\n\nmean(s) == 49 / 24\nmean(m) == 7 / 6\n\nAnd for the multivariate one (the latter one):\n\nmean(s[1]) == 19 / 8\nmean(m[1]) == 3 / 4\nmean(s[2]) == 8 / 3\nmean(m[2]) == 1\n\n\n\n\n\n","category":"constant"},{"location":"api/","page":"API","title":"API","text":"For every demo model, one can define the true log prior, log likelihood, and log joint probabilities.","category":"page"},{"location":"api/","page":"API","title":"API","text":"DynamicPPL.TestUtils.logprior_true\nDynamicPPL.TestUtils.loglikelihood_true\nDynamicPPL.TestUtils.logjoint_true","category":"page"},{"location":"api/#DynamicPPL.TestUtils.logprior_true","page":"API","title":"DynamicPPL.TestUtils.logprior_true","text":"logprior_true(model, args...)\n\nReturn the logprior of model for args.\n\nThis should generally be implemented by hand for every specific model.\n\nSee also: logjoint_true, loglikelihood_true.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.TestUtils.loglikelihood_true","page":"API","title":"DynamicPPL.TestUtils.loglikelihood_true","text":"loglikelihood_true(model, args...)\n\nReturn the loglikelihood of model for args.\n\nThis should generally be implemented by hand for every specific model.\n\nSee also: logjoint_true, logprior_true.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.TestUtils.logjoint_true","page":"API","title":"DynamicPPL.TestUtils.logjoint_true","text":"logjoint_true(model, args...)\n\nReturn the logjoint of model for args.\n\nDefaults to logprior_true(model, args...) + loglikelihood_true(model, args..).\n\nThis should generally be implemented by hand for every specific model so that the returned value can be used as a ground-truth for testing things like:\n\nValidity of evaluation of model using a particular implementation of AbstractVarInfo.\nValidity of a sampler when combined with DynamicPPL by running the sampler twice: once targeting ground-truth functions, e.g. logjoint_true, and once targeting model.\n\nAnd more.\n\nSee also: logprior_true, loglikelihood_true.\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"And in the case where the model includes constrained variables, it can also be useful to define","category":"page"},{"location":"api/","page":"API","title":"API","text":"DynamicPPL.TestUtils.logprior_true_with_logabsdet_jacobian\nDynamicPPL.TestUtils.logjoint_true_with_logabsdet_jacobian","category":"page"},{"location":"api/#DynamicPPL.TestUtils.logprior_true_with_logabsdet_jacobian","page":"API","title":"DynamicPPL.TestUtils.logprior_true_with_logabsdet_jacobian","text":"logprior_true_with_logabsdet_jacobian(model::Model, args...)\n\nReturn a tuple (args_unconstrained, logprior_unconstrained) of model for args....\n\nUnlike logprior_true, the returned logprior computation includes the log-absdet-jacobian adjustment, thus computing logprior for the unconstrained variables.\n\nNote that args are assumed be in the support of model, while args_unconstrained will be unconstrained.\n\nSee also: logprior_true.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.TestUtils.logjoint_true_with_logabsdet_jacobian","page":"API","title":"DynamicPPL.TestUtils.logjoint_true_with_logabsdet_jacobian","text":"logjoint_true_with_logabsdet_jacobian(model::Model, args...)\n\nReturn a tuple (args_unconstrained, logjoint) of model for args.\n\nUnlike logjoint_true, the returned logjoint computation includes the log-absdet-jacobian adjustment, thus computing logjoint for the unconstrained variables.\n\nNote that args are assumed be in the support of model, while args_unconstrained will be unconstrained.\n\nThis should generally not be implemented directly, instead one should implement logprior_true_with_logabsdet_jacobian for a given model.\n\nSee also: logjoint_true, logprior_true_with_logabsdet_jacobian.\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"Finally, the following methods can also be of use:","category":"page"},{"location":"api/","page":"API","title":"API","text":"DynamicPPL.TestUtils.varnames\nDynamicPPL.TestUtils.posterior_mean\nDynamicPPL.TestUtils.setup_varinfos\nDynamicPPL.TestUtils.update_values!!\nDynamicPPL.TestUtils.test_values","category":"page"},{"location":"api/#DynamicPPL.TestUtils.varnames","page":"API","title":"DynamicPPL.TestUtils.varnames","text":"varnames(model::Model)\n\nReturn a collection of VarName as they are expected to appear in the model.\n\nEven though it is recommended to implement this by hand for a particular Model, a default implementation using SimpleVarInfo{<:Dict} is provided.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.TestUtils.posterior_mean","page":"API","title":"DynamicPPL.TestUtils.posterior_mean","text":"posterior_mean(model::Model)\n\nReturn a NamedTuple compatible with varnames(model) where the values represent the posterior mean under model.\n\n\"Compatible\" means that a varname from varnames(model) can be used to extract the corresponding value using get, e.g. get(posterior_mean(model), varname).\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.TestUtils.setup_varinfos","page":"API","title":"DynamicPPL.TestUtils.setup_varinfos","text":"setup_varinfos(model::Model, example_values::NamedTuple, varnames; include_threadsafe::Bool=false)\n\nReturn a tuple of instances for different implementations of AbstractVarInfo with each vi, supposedly, satisfying vi[vn] == get(example_values, vn) for vn in varnames.\n\nIf include_threadsafe is true, then the returned tuple will also include thread-safe versions of the varinfo instances.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.TestUtils.update_values!!","page":"API","title":"DynamicPPL.TestUtils.update_values!!","text":"update_values!!(vi::AbstractVarInfo, vals::NamedTuple, vns)\n\nReturn instance similar to vi but with vns set to values from vals.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.TestUtils.test_values","page":"API","title":"DynamicPPL.TestUtils.test_values","text":"test_values(vi::AbstractVarInfo, vals::NamedTuple, vns)\n\nTest that vi[vn] corresponds to the correct value in vals for every vn in vns.\n\n\n\n\n\n","category":"function"},{"location":"api/#Advanced","page":"API","title":"Advanced","text":"","category":"section"},{"location":"api/#Variable-names","page":"API","title":"Variable names","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"Names and possibly nested indices of variables are described with AbstractPPL.VarName. They can be defined with AbstractPPL.@varname. Please see the documentation of AbstractPPL.jl for further information.","category":"page"},{"location":"api/#Data-Structures-of-Variables","page":"API","title":"Data Structures of Variables","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"DynamicPPL provides different data structures for samples from the model and their log density. All of them are subtypes of AbstractVarInfo.","category":"page"},{"location":"api/","page":"API","title":"API","text":"AbstractVarInfo","category":"page"},{"location":"api/#DynamicPPL.AbstractVarInfo","page":"API","title":"DynamicPPL.AbstractVarInfo","text":"AbstractVarInfo\n\nAbstract supertype for data structures that capture random variables when executing a probabilistic model and accumulate log densities such as the log likelihood or the log joint probability of the model.\n\nSee also: VarInfo, SimpleVarInfo.\n\n\n\n\n\n","category":"type"},{"location":"api/#Common-API","page":"API","title":"Common API","text":"","category":"section"},{"location":"api/#Accumulation-of-log-probabilities","page":"API","title":"Accumulation of log-probabilities","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"getlogp\nsetlogp!!\nacclogp!!\nresetlogp!!","category":"page"},{"location":"api/#DynamicPPL.getlogp","page":"API","title":"DynamicPPL.getlogp","text":"getlogp(vi::AbstractVarInfo)\n\nReturn the log of the joint probability of the observed data and parameters sampled in vi.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.setlogp!!","page":"API","title":"DynamicPPL.setlogp!!","text":"setlogp!!(vi::AbstractVarInfo, logp)\n\nSet the log of the joint probability of the observed data and parameters sampled in vi to logp, mutating if it makes sense.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.acclogp!!","page":"API","title":"DynamicPPL.acclogp!!","text":"acclogp!!(vi::AbstractVarInfo, logp)\n\nAdd logp to the value of the log of the joint probability of the observed data and parameters sampled in vi, mutating if it makes sense.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.resetlogp!!","page":"API","title":"DynamicPPL.resetlogp!!","text":"resetlogp!!(vi::AbstractVarInfo)\n\nReset the value of the log of the joint probability of the observed data and parameters sampled in vi to 0, mutating if it makes sense.\n\n\n\n\n\n","category":"function"},{"location":"api/#Variables-and-their-realizations","page":"API","title":"Variables and their realizations","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"keys\ngetindex\nDynamicPPL.getindex_raw\npush!!\nempty!!\nisempty","category":"page"},{"location":"api/#Base.keys","page":"API","title":"Base.keys","text":"keys(vi::AbstractVarInfo)\n\nReturn an iterator over all vns in vi.\n\n\n\n\n\n","category":"function"},{"location":"api/#Base.getindex","page":"API","title":"Base.getindex","text":"getindex(vi::AbstractVarInfo, vn::VarName[, dist::Distribution])\ngetindex(vi::AbstractVarInfo, vns::Vector{<:VarName}[, dist::Distribution])\n\nReturn the current value(s) of vn (vns) in vi in the support of its (their) distribution(s).\n\nIf dist is specified, the value(s) will be reshaped accordingly.\n\nSee also: getindex_raw(vi::AbstractVarInfo, vn::VarName, dist::Distribution)\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.getindex_raw","page":"API","title":"DynamicPPL.getindex_raw","text":"getindex_raw(vi::AbstractVarInfo, vn::VarName[, dist::Distribution])\ngetindex_raw(vi::AbstractVarInfo, vns::Vector{<:VarName}[, dist::Distribution])\n\nReturn the current value(s) of vn (vns) in vi.\n\nIf dist is specified, the value(s) will be reshaped accordingly.\n\nSee also: getindex(vi::AbstractVarInfo, vn::VarName, dist::Distribution)\n\nnote: Note\nThe difference between getindex(vi, vn, dist) and getindex_raw is that getindex will also transform the value(s) to the support of the distribution(s). This is not the case for getindex_raw.\n\n\n\n\n\n","category":"function"},{"location":"api/#BangBang.push!!","page":"API","title":"BangBang.push!!","text":"push!!(vi::AbstractVarInfo, vn::VarName, r, dist::Distribution)\n\nPush a new random variable vn with a sampled value r from a distribution dist to the VarInfo vi, mutating if it makes sense.\n\n\n\n\n\npush!!(vi::AbstractVarInfo, vn::VarName, r, dist::Distribution, spl::AbstractSampler)\n\nPush a new random variable vn with a sampled value r sampled with a sampler spl from a distribution dist to VarInfo vi, if it makes sense.\n\nThe sampler is passed here to invalidate its cache where defined.\n\nwarning: Warning\nThis method is considered legacy, and is likely to be deprecated in the future.\n\n\n\n\n\npush!!(vi::AbstractVarInfo, vn::VarName, r, dist::Distribution, gid::Selector)\n\nPush a new random variable vn with a sampled value r sampled with a sampler of selector gid from a distribution dist to VarInfo vi.\n\nwarning: Warning\nThis method is considered legacy, and is likely to be deprecated in the future.\n\n\n\n\n\n","category":"function"},{"location":"api/#BangBang.empty!!","page":"API","title":"BangBang.empty!!","text":"empty!!(vi::AbstractVarInfo)\n\nEmpty the fields of vi.metadata and reset vi.logp[] and vi.num_produce[] to zeros.\n\nThis is useful when using a sampling algorithm that assumes an empty vi, e.g. SMC.\n\n\n\n\n\n","category":"function"},{"location":"api/#Base.isempty","page":"API","title":"Base.isempty","text":"isempty(vi::AbstractVarInfo)\n\nReturn true if vi is empty and false otherwise.\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"values_as","category":"page"},{"location":"api/#DynamicPPL.values_as","page":"API","title":"DynamicPPL.values_as","text":"values_as(varinfo[, Type])\n\nReturn the values/realizations in varinfo as Type, if implemented.\n\nIf no Type is provided, return values as stored in varinfo.\n\nExamples\n\nSimpleVarInfo with NamedTuple:\n\njulia> data = (x = 1.0, m = [2.0]);\n\njulia> values_as(SimpleVarInfo(data))\n(x = 1.0, m = [2.0])\n\njulia> values_as(SimpleVarInfo(data), NamedTuple)\n(x = 1.0, m = [2.0])\n\njulia> values_as(SimpleVarInfo(data), OrderedDict)\nOrderedDict{VarName{sym, Setfield.IdentityLens} where sym, Any} with 2 entries:\n x => 1.0\n m => [2.0]\n\njulia> values_as(SimpleVarInfo(data), Vector)\n2-element Vector{Float64}:\n 1.0\n 2.0\n\nSimpleVarInfo with OrderedDict:\n\njulia> data = OrderedDict{Any,Any}(@varname(x) => 1.0, @varname(m) => [2.0]);\n\njulia> values_as(SimpleVarInfo(data))\nOrderedDict{Any, Any} with 2 entries:\n x => 1.0\n m => [2.0]\n\njulia> values_as(SimpleVarInfo(data), NamedTuple)\n(x = 1.0, m = [2.0])\n\njulia> values_as(SimpleVarInfo(data), OrderedDict)\nOrderedDict{Any, Any} with 2 entries:\n x => 1.0\n m => [2.0]\n\njulia> values_as(SimpleVarInfo(data), Vector)\n2-element Vector{Float64}:\n 1.0\n 2.0\n\nTypedVarInfo:\n\njulia> # Just use an example model to construct the `VarInfo` because we're lazy.\n vi = VarInfo(DynamicPPL.TestUtils.demo_assume_dot_observe());\n\njulia> vi[@varname(s)] = 1.0; vi[@varname(m)] = 2.0;\n\njulia> # For the sake of brevity, let's just check the type.\n md = values_as(vi); md.s isa DynamicPPL.Metadata\ntrue\n\njulia> values_as(vi, NamedTuple)\n(s = 1.0, m = 2.0)\n\njulia> values_as(vi, OrderedDict)\nOrderedDict{VarName{sym, Setfield.IdentityLens} where sym, Float64} with 2 entries:\n s => 1.0\n m => 2.0\n\njulia> values_as(vi, Vector)\n2-element Vector{Float64}:\n 1.0\n 2.0\n\nUntypedVarInfo:\n\njulia> # Just use an example model to construct the `VarInfo` because we're lazy.\n vi = VarInfo(); DynamicPPL.TestUtils.demo_assume_dot_observe()(vi);\n\njulia> vi[@varname(s)] = 1.0; vi[@varname(m)] = 2.0;\n\njulia> # For the sake of brevity, let's just check the type.\n values_as(vi) isa DynamicPPL.Metadata\ntrue\n\njulia> values_as(vi, NamedTuple)\n(s = 1.0, m = 2.0)\n\njulia> values_as(vi, OrderedDict)\nOrderedDict{VarName{sym, Setfield.IdentityLens} where sym, Float64} with 2 entries:\n s => 1.0\n m => 2.0\n\njulia> values_as(vi, Vector)\n2-element Vector{Real}:\n 1.0\n 2.0\n\n\n\n\n\n","category":"function"},{"location":"api/#Transformations","page":"API","title":"Transformations","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"DynamicPPL.AbstractTransformation\nDynamicPPL.NoTransformation\nDynamicPPL.DynamicTransformation\nDynamicPPL.StaticTransformation","category":"page"},{"location":"api/#DynamicPPL.AbstractTransformation","page":"API","title":"DynamicPPL.AbstractTransformation","text":"abstract type AbstractTransformation\n\nRepresents a transformation to be used in link!! and invlink!!, amongst others.\n\nA concrete implementation of this should implement the following methods:\n\nlink!!: transforms the AbstractVarInfo to the unconstrained space.\ninvlink!!: transforms the AbstractVarInfo to the constrained space.\n\nAnd potentially:\n\nmaybe_invlink_before_eval!!: hook to decide whether to transform before evaluating the model.\n\nSee also: link!!, invlink!!, maybe_invlink_before_eval!!.\n\n\n\n\n\n","category":"type"},{"location":"api/#DynamicPPL.NoTransformation","page":"API","title":"DynamicPPL.NoTransformation","text":"struct NoTransformation <: DynamicPPL.AbstractTransformation\n\nTransformation which applies the identity function.\n\n\n\n\n\n","category":"type"},{"location":"api/#DynamicPPL.DynamicTransformation","page":"API","title":"DynamicPPL.DynamicTransformation","text":"struct DynamicTransformation <: DynamicPPL.AbstractTransformation\n\nTransformation which transforms the variables on a per-need-basis in the execution of a given Model.\n\nThis is in constrast to StaticTransformation which transforms all variables before the execution of a given Model.\n\nSee also: StaticTransformation.\n\n\n\n\n\n","category":"type"},{"location":"api/#DynamicPPL.StaticTransformation","page":"API","title":"DynamicPPL.StaticTransformation","text":"struct StaticTransformation{F} <: DynamicPPL.AbstractTransformation\n\nTransformation which transforms all variables before the execution of a given Model.\n\nThis is done through the maybe_invlink_before_eval!! method.\n\nSee also: DynamicTransformation, maybe_invlink_before_eval!!.\n\nFields\n\nbijector::Any: The function, assumed to implement the Bijectors interface, to be applied to the variables\n\n\n\n\n\n","category":"type"},{"location":"api/","page":"API","title":"API","text":"DynamicPPL.istrans\nDynamicPPL.settrans!!\nDynamicPPL.transformation\nDynamicPPL.link\nDynamicPPL.invlink\nDynamicPPL.link!!\nDynamicPPL.invlink!!\nDynamicPPL.default_transformation\nDynamicPPL.maybe_invlink_before_eval!!\nDynamicPPL.reconstruct","category":"page"},{"location":"api/#DynamicPPL.istrans","page":"API","title":"DynamicPPL.istrans","text":"istrans(vi::AbstractVarInfo[, vns::Union{VarName, AbstractVector{<:Varname}}])\n\nReturn true if vi is working in unconstrained space, and false if vi is assuming realizations to be in support of the corresponding distributions.\n\nIf vns is provided, then only check if this/these varname(s) are transformed.\n\nwarning: Warning\nNot all implementations of AbstractVarInfo support transforming only a subset of the variables.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.settrans!!","page":"API","title":"DynamicPPL.settrans!!","text":"settrans!!(vi::AbstractVarInfo, trans::Bool[, vn::VarName])\n\nReturn vi with istrans(vi, vn) evaluating to true.\n\nIf vn is not specified, then istrans(vi) evaluates to true for all variables.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.transformation","page":"API","title":"DynamicPPL.transformation","text":"transformation(vi::AbstractVarInfo)\n\nReturn the AbstractTransformation related to vi.\n\n\n\n\n\n","category":"function"},{"location":"api/#Bijectors.link","page":"API","title":"Bijectors.link","text":"link([t::AbstractTransformation, ]vi::AbstractVarInfo, model::Model)\nlink([t::AbstractTransformation, ]vi::AbstractVarInfo, spl::AbstractSampler, model::Model)\n\nTransform the variables in vi to their linked space without mutating vi, using the transformation t. \n\nIf t is not provided, default_transformation(model, vi) will be used.\n\nSee also: default_transformation, invlink.\n\n\n\n\n\n","category":"function"},{"location":"api/#Bijectors.invlink","page":"API","title":"Bijectors.invlink","text":"invlink([t::AbstractTransformation, ]vi::AbstractVarInfo, model::Model)\ninvlink([t::AbstractTransformation, ]vi::AbstractVarInfo, spl::AbstractSampler, model::Model)\n\nTransform the variables in vi to their constrained space without mutating vi, using the (inverse of) transformation t.\n\nIf t is not provided, default_transformation(model, vi) will be used.\n\nSee also: default_transformation, link.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.link!!","page":"API","title":"DynamicPPL.link!!","text":"link!!([t::AbstractTransformation, ]vi::AbstractVarInfo, model::Model)\nlink!!([t::AbstractTransformation, ]vi::AbstractVarInfo, spl::AbstractSampler, model::Model)\n\nTransform the variables in vi to their linked space, using the transformation t, mutating vi if possible.\n\nIf t is not provided, default_transformation(model, vi) will be used.\n\nSee also: default_transformation, invlink!!.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.invlink!!","page":"API","title":"DynamicPPL.invlink!!","text":"invlink!!([t::AbstractTransformation, ]vi::AbstractVarInfo, model::Model)\ninvlink!!([t::AbstractTransformation, ]vi::AbstractVarInfo, spl::AbstractSampler, model::Model)\n\nTransform the variables in vi to their constrained space, using the (inverse of) transformation t, mutating vi if possible.\n\nIf t is not provided, default_transformation(model, vi) will be used.\n\nSee also: default_transformation, link!!.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.default_transformation","page":"API","title":"DynamicPPL.default_transformation","text":"default_transformation(model::Model[, vi::AbstractVarInfo])\n\nReturn the AbstractTransformation currently related to model and, potentially, vi.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.maybe_invlink_before_eval!!","page":"API","title":"DynamicPPL.maybe_invlink_before_eval!!","text":"maybe_invlink_before_eval!!([t::Transformation,] vi, context, model)\n\nReturn a possibly invlinked version of vi.\n\nThis will be called prior to model evaluation, allowing one to perform a single invlink!! before evaluation rather than lazyily evaluating the transforms on as-we-need basis as is done with DynamicTransformation.\n\nSee also: StaticTransformation, DynamicTransformation.\n\nExamples\n\njulia> using DynamicPPL, Distributions, Bijectors\n\njulia> @model demo() = x ~ Normal()\ndemo (generic function with 2 methods)\n\njulia> # By subtyping `Transform`, we inherit the `(inv)link!!`.\n struct MyBijector <: Bijectors.Transform end\n\njulia> # Define some dummy `inverse` which will be used in the `link!!` call.\n Bijectors.inverse(f::MyBijector) = identity\n\njulia> # We need to define `with_logabsdet_jacobian` for `MyBijector`\n # (`identity` already has `with_logabsdet_jacobian` defined)\n function Bijectors.with_logabsdet_jacobian(::MyBijector, x)\n # Just using a large number of the logabsdet-jacobian term\n # for demonstration purposes.\n return (x, 1000)\n end\n\njulia> # Change the `default_transformation` for our model to be a\n # `StaticTransformation` using `MyBijector`.\n function DynamicPPL.default_transformation(::Model{typeof(demo)})\n return DynamicPPL.StaticTransformation(MyBijector())\n end\n\njulia> model = demo();\n\njulia> vi = SimpleVarInfo(x=1.0)\nSimpleVarInfo((x = 1.0,), 0.0)\n\njulia> # Uses the `inverse` of `MyBijector`, which we have defined as `identity`\n vi_linked = link!!(vi, model)\nTransformed SimpleVarInfo((x = 1.0,), 0.0)\n\njulia> # Now performs a single `invlink!!` before model evaluation.\n logjoint(model, vi_linked)\n-1001.4189385332047\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.reconstruct","page":"API","title":"DynamicPPL.reconstruct","text":"reconstruct([f, ]dist, val)\n\nReconstruct val so that it's compatible with dist.\n\nIf f is also provided, the reconstruct value will be such that f(reconstruct_val) is compatible with dist.\n\n\n\n\n\n","category":"function"},{"location":"api/#Utils","page":"API","title":"Utils","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"Base.merge(::AbstractVarInfo)\nDynamicPPL.subset\nDynamicPPL.unflatten\nDynamicPPL.tonamedtuple\nDynamicPPL.varname_leaves\nDynamicPPL.varname_and_value_leaves","category":"page"},{"location":"api/#Base.merge-Tuple{AbstractVarInfo}","page":"API","title":"Base.merge","text":"merge(varinfo, other_varinfos...)\n\nMerge varinfos into one, giving precedence to the right-most varinfo when sensible.\n\nThis is particularly useful when combined with subset(varinfo, vns).\n\nSee docstring of subset(varinfo, vns) for examples.\n\n\n\n\n\n","category":"method"},{"location":"api/#DynamicPPL.subset","page":"API","title":"DynamicPPL.subset","text":"subset(varinfo::AbstractVarInfo, vns::AbstractVector{<:VarName})\n\nSubset a varinfo to only contain the variables vns.\n\nwarning: Warning\nThe ordering of the variables in the resulting varinfo is not guaranteed to follow the ordering of the variables in varinfo. Hence care must be taken, in particular when used in conjunction with other methods which uses the vector-representation of the varinfo, e.g. getindex(varinfo, sampler).\n\nExamples\n\njulia> @model function demo()\n s ~ InverseGamma(2, 3)\n m ~ Normal(0, sqrt(s))\n x = Vector{Float64}(undef, 2)\n x[1] ~ Normal(m, sqrt(s))\n x[2] ~ Normal(m, sqrt(s))\n end\ndemo (generic function with 2 methods)\n\njulia> model = demo();\n\njulia> varinfo = VarInfo(model);\n\njulia> keys(varinfo)\n4-element Vector{VarName}:\n s\n m\n x[1]\n x[2]\n\njulia> for (i, vn) in enumerate(keys(varinfo))\n varinfo[vn] = i\n end\n\njulia> varinfo[[@varname(s), @varname(m), @varname(x[1]), @varname(x[2])]]\n4-element Vector{Float64}:\n 1.0\n 2.0\n 3.0\n 4.0\n\njulia> # Extract one with only `m`.\n varinfo_subset1 = subset(varinfo, [@varname(m),]);\n\n\njulia> keys(varinfo_subset1)\n1-element Vector{VarName{:m, Setfield.IdentityLens}}:\n m\n\njulia> varinfo_subset1[@varname(m)]\n2.0\n\njulia> # Extract one with both `s` and `x[2]`.\n varinfo_subset2 = subset(varinfo, [@varname(s), @varname(x[2])]);\n\njulia> keys(varinfo_subset2)\n2-element Vector{VarName}:\n s\n x[2]\n\njulia> varinfo_subset2[[@varname(s), @varname(x[2])]]\n2-element Vector{Float64}:\n 1.0\n 4.0\n\nsubset is particularly useful when combined with merge(varinfo::AbstractVarInfo)\n\njulia> # Merge the two.\n varinfo_subset_merged = merge(varinfo_subset1, varinfo_subset2);\n\njulia> keys(varinfo_subset_merged)\n3-element Vector{VarName}:\n m\n s\n x[2]\n\njulia> varinfo_subset_merged[[@varname(s), @varname(m), @varname(x[2])]]\n3-element Vector{Float64}:\n 1.0\n 2.0\n 4.0\n\njulia> # Merge the two with the original.\n varinfo_merged = merge(varinfo, varinfo_subset_merged);\n\njulia> keys(varinfo_merged)\n4-element Vector{VarName}:\n s\n m\n x[1]\n x[2]\n\njulia> varinfo_merged[[@varname(s), @varname(m), @varname(x[1]), @varname(x[2])]]\n4-element Vector{Float64}:\n 1.0\n 2.0\n 3.0\n 4.0\n\nNotes\n\nType-stability\n\nwarning: Warning\nThis function is only type-stable when vns contains only varnames with the same symbol. For exmaple, [@varname(m[1]), @varname(m[2])] will be type-stable, but [@varname(m[1]), @varname(x)] will not be.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.unflatten","page":"API","title":"DynamicPPL.unflatten","text":"unflatten(original, x::AbstractVector)\n\nReturn instance of original constructed from x.\n\n\n\n\n\nunflatten(vi::AbstractVarInfo[, context::AbstractContext], x::AbstractVector)\n\nReturn a new instance of vi with the values of x assigned to the variables.\n\nIf context is provided, x is assumed to be realizations only for variables not filtered out by context.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.tonamedtuple","page":"API","title":"DynamicPPL.tonamedtuple","text":"tonamedtuple(vi::AbstractVarInfo)\n\nConvert a vi into a NamedTuple where each variable symbol maps to the values and indexing string of the variable.\n\nFor example, a model that had a vector of vector-valued variables x would return\n\n(x = ([1.5, 2.0], [3.0, 1.0], [\"x[1]\", \"x[2]\"]), )\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.varname_leaves","page":"API","title":"DynamicPPL.varname_leaves","text":"varname_leaves(vn::VarName, val)\n\nReturn an iterator over all varnames that are represented by vn on val.\n\nExamples\n\njulia> using DynamicPPL: varname_leaves\n\njulia> foreach(println, varname_leaves(@varname(x), rand(2)))\nx[1]\nx[2]\n\njulia> foreach(println, varname_leaves(@varname(x[1:2]), rand(2)))\nx[1:2][1]\nx[1:2][2]\n\njulia> x = (y = 1, z = [[2.0], [3.0]]);\n\njulia> foreach(println, varname_leaves(@varname(x), x))\nx.y\nx.z[1][1]\nx.z[2][1]\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.varname_and_value_leaves","page":"API","title":"DynamicPPL.varname_and_value_leaves","text":"varname_and_value_leaves(vn::VarName, val)\n\nReturn an iterator over all varname-value pairs that are represented by vn on val.\n\nExamples\n\njulia> using DynamicPPL: varname_and_value_leaves\n\njulia> foreach(println, varname_and_value_leaves(@varname(x), 1:2))\n(x[1], 1)\n(x[2], 2)\n\njulia> foreach(println, varname_and_value_leaves(@varname(x[1:2]), 1:2))\n(x[1:2][1], 1)\n(x[1:2][2], 2)\n\njulia> x = (y = 1, z = [[2.0], [3.0]]);\n\njulia> foreach(println, varname_and_value_leaves(@varname(x), x))\n(x.y, 1)\n(x.z[1][1], 2.0)\n(x.z[2][1], 3.0)\n\nThere are also some special handling for certain types:\n\njulia> using LinearAlgebra\n\njulia> x = reshape(1:4, 2, 2);\n\njulia> # `LowerTriangular`\n foreach(println, varname_and_value_leaves(@varname(x), LowerTriangular(x)))\n(x[1,1], 1)\n(x[2,1], 2)\n(x[2,2], 4)\n\njulia> # `UpperTriangular`\n foreach(println, varname_and_value_leaves(@varname(x), UpperTriangular(x)))\n(x[1,1], 1)\n(x[1,2], 3)\n(x[2,2], 4)\n\njulia> # `Cholesky` with lower-triangular\n foreach(println, varname_and_value_leaves(@varname(x), Cholesky([1.0 0.0; 0.0 1.0], 'L', 0)))\n(x.L[1,1], 1.0)\n(x.L[2,1], 0.0)\n(x.L[2,2], 1.0)\n\njulia> # `Cholesky` with upper-triangular\n foreach(println, varname_and_value_leaves(@varname(x), Cholesky([1.0 0.0; 0.0 1.0], 'U', 0)))\n(x.U[1,1], 1.0)\n(x.U[1,2], 0.0)\n(x.U[2,2], 1.0)\n\n\n\n\n\n","category":"function"},{"location":"api/#SimpleVarInfo","page":"API","title":"SimpleVarInfo","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"SimpleVarInfo","category":"page"},{"location":"api/#DynamicPPL.SimpleVarInfo","page":"API","title":"DynamicPPL.SimpleVarInfo","text":"struct SimpleVarInfo{NT, T, C<:DynamicPPL.AbstractTransformation} <: AbstractVarInfo\n\nA simple wrapper of the parameters with a logp field for accumulation of the logdensity.\n\nCurrently only implemented for NT<:NamedTuple and NT<:AbstractDict.\n\nFields\n\nvalues: underlying representation of the realization represented\nlogp: holds the accumulated log-probability\ntransformation: represents whether it assumes variables to be transformed\n\nNotes\n\nThe major differences between this and TypedVarInfo are:\n\nSimpleVarInfo does not require linearization.\nSimpleVarInfo can use more efficient bijectors.\nSimpleVarInfo is only type-stable if NT<:NamedTuple and either a) no indexing is used in tilde-statements, or b) the values have been specified with the correct shapes.\n\nExamples\n\nGeneral usage\n\njulia> using StableRNGs\n\njulia> @model function demo()\n m ~ Normal()\n x = Vector{Float64}(undef, 2)\n for i in eachindex(x)\n x[i] ~ Normal()\n end\n return x\n end\ndemo (generic function with 2 methods)\n\njulia> m = demo();\n\njulia> rng = StableRNG(42);\n\njulia> ### Sampling ###\n ctx = SamplingContext(rng, SampleFromPrior(), DefaultContext());\n\njulia> # In the `NamedTuple` version we need to provide the place-holder values for\n # the variables which are using \"containers\", e.g. `Array`.\n # In this case, this means that we need to specify `x` but not `m`.\n _, vi = DynamicPPL.evaluate!!(m, SimpleVarInfo((x = ones(2), )), ctx);\n\njulia> # (✓) Vroom, vroom! FAST!!!\n vi[@varname(x[1])]\n0.4471218424633827\n\njulia> # We can also access arbitrary varnames pointing to `x`, e.g.\n vi[@varname(x)]\n2-element Vector{Float64}:\n 0.4471218424633827\n 1.3736306979834252\n\njulia> vi[@varname(x[1:2])]\n2-element Vector{Float64}:\n 0.4471218424633827\n 1.3736306979834252\n\njulia> # (×) If we don't provide the container...\n _, vi = DynamicPPL.evaluate!!(m, SimpleVarInfo(), ctx); vi\nERROR: type NamedTuple has no field x\n[...]\n\njulia> # If one does not know the varnames, we can use a `OrderedDict` instead.\n _, vi = DynamicPPL.evaluate!!(m, SimpleVarInfo{Float64}(OrderedDict()), ctx);\n\njulia> # (✓) Sort of fast, but only possible at runtime.\n vi[@varname(x[1])]\n-1.019202452456547\n\njulia> # In addtion, we can only access varnames as they appear in the model!\n vi[@varname(x)]\nERROR: KeyError: key x not found\n[...]\n\njulia> vi[@varname(x[1:2])]\nERROR: KeyError: key x[1:2] not found\n[...]\n\nTechnically, it's possible to use any implementation of AbstractDict in place of OrderedDict, but OrderedDict ensures that certain operations, e.g. linearization/flattening of the values in the varinfo, are consistent between evaluations. Hence OrderedDict is the preferred implementation of AbstractDict to use here.\n\nYou can also sample in transformed space:\n\njulia> @model demo_constrained() = x ~ Exponential()\ndemo_constrained (generic function with 2 methods)\n\njulia> m = demo_constrained();\n\njulia> _, vi = DynamicPPL.evaluate!!(m, SimpleVarInfo(), ctx);\n\njulia> vi[@varname(x)] # (✓) 0 ≤ x < ∞\n1.8632965762164932\n\njulia> _, vi = DynamicPPL.evaluate!!(m, DynamicPPL.settrans!!(SimpleVarInfo(), true), ctx);\n\njulia> vi[@varname(x)] # (✓) -∞ < x < ∞\n-0.21080155351918753\n\njulia> xs = [last(DynamicPPL.evaluate!!(m, DynamicPPL.settrans!!(SimpleVarInfo(), true), ctx))[@varname(x)] for i = 1:10];\n\njulia> any(xs .< 0) # (✓) Positive probability mass on negative numbers!\ntrue\n\njulia> # And with `OrderedDict` of course!\n _, vi = DynamicPPL.evaluate!!(m, DynamicPPL.settrans!!(SimpleVarInfo(OrderedDict()), true), ctx);\n\njulia> vi[@varname(x)] # (✓) -∞ < x < ∞\n0.6225185067787314\n\njulia> xs = [last(DynamicPPL.evaluate!!(m, DynamicPPL.settrans!!(SimpleVarInfo(), true), ctx))[@varname(x)] for i = 1:10];\n\njulia> any(xs .< 0) # (✓) Positive probability mass on negative numbers!\ntrue\n\nEvaluation in transformed space of course also works:\n\njulia> vi = DynamicPPL.settrans!!(SimpleVarInfo((x = -1.0,)), true)\nTransformed SimpleVarInfo((x = -1.0,), 0.0)\n\njulia> # (✓) Positive probability mass on negative numbers!\n getlogp(last(DynamicPPL.evaluate!!(m, vi, DynamicPPL.DefaultContext())))\n-1.3678794411714423\n\njulia> # While if we forget to indicate that it's transformed:\n vi = DynamicPPL.settrans!!(SimpleVarInfo((x = -1.0,)), false)\nSimpleVarInfo((x = -1.0,), 0.0)\n\njulia> # (✓) No probability mass on negative numbers!\n getlogp(last(DynamicPPL.evaluate!!(m, vi, DynamicPPL.DefaultContext())))\n-Inf\n\nIndexing\n\nUsing NamedTuple as underlying storage.\n\njulia> svi_nt = SimpleVarInfo((m = (a = [1.0], ), ));\n\njulia> svi_nt[@varname(m)]\n(a = [1.0],)\n\njulia> svi_nt[@varname(m.a)]\n1-element Vector{Float64}:\n 1.0\n\njulia> svi_nt[@varname(m.a[1])]\n1.0\n\njulia> svi_nt[@varname(m.a[2])]\nERROR: BoundsError: attempt to access 1-element Vector{Float64} at index [2]\n[...]\n\njulia> svi_nt[@varname(m.b)]\nERROR: type NamedTuple has no field b\n[...]\n\nUsing OrderedDict as underlying storage.\n\njulia> svi_dict = SimpleVarInfo(OrderedDict(@varname(m) => (a = [1.0], )));\n\njulia> svi_dict[@varname(m)]\n(a = [1.0],)\n\njulia> svi_dict[@varname(m.a)]\n1-element Vector{Float64}:\n 1.0\n\njulia> svi_dict[@varname(m.a[1])]\n1.0\n\njulia> svi_dict[@varname(m.a[2])]\nERROR: BoundsError: attempt to access 1-element Vector{Float64} at index [2]\n[...]\n\njulia> svi_dict[@varname(m.b)]\nERROR: type NamedTuple has no field b\n[...]\n\n\n\n\n\n","category":"type"},{"location":"api/#VarInfo","page":"API","title":"VarInfo","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"Another data structure is VarInfo.","category":"page"},{"location":"api/","page":"API","title":"API","text":"VarInfo\nTypedVarInfo","category":"page"},{"location":"api/#DynamicPPL.VarInfo","page":"API","title":"DynamicPPL.VarInfo","text":"struct VarInfo{Tmeta, Tlogp} <: AbstractVarInfo\n metadata::Tmeta\n logp::Base.RefValue{Tlogp}\n num_produce::Base.RefValue{Int}\nend\n\nA light wrapper over one or more instances of Metadata. Let vi be an instance of VarInfo. If vi isa VarInfo{<:Metadata}, then only one Metadata instance is used for all the sybmols. VarInfo{<:Metadata} is aliased UntypedVarInfo. If vi isa VarInfo{<:NamedTuple}, then vi.metadata is a NamedTuple that maps each symbol used on the LHS of ~ in the model to its Metadata instance. The latter allows for the type specialization of vi after the first sampling iteration when all the symbols have been observed. VarInfo{<:NamedTuple} is aliased TypedVarInfo.\n\nNote: It is the user's responsibility to ensure that each \"symbol\" is visited at least once whenever the model is called, regardless of any stochastic branching. Each symbol refers to a Julia variable and can be a hierarchical array of many random variables, e.g. x[1] ~ ... and x[2] ~ ... both have the same symbol x.\n\n\n\n\n\n","category":"type"},{"location":"api/#DynamicPPL.TypedVarInfo","page":"API","title":"DynamicPPL.TypedVarInfo","text":"TypedVarInfo(vi::UntypedVarInfo)\n\nThis function finds all the unique syms from the instances of VarName{sym} found in vi.metadata.vns. It then extracts the metadata associated with each symbol from the global vi.metadata field. Finally, a new VarInfo is created with a new metadata as a NamedTuple mapping from symbols to type-stable Metadata instances, one for each symbol.\n\n\n\n\n\n","category":"type"},{"location":"api/","page":"API","title":"API","text":"One main characteristic of VarInfo is that samples are stored in a linearized form.","category":"page"},{"location":"api/","page":"API","title":"API","text":"link!\ninvlink!","category":"page"},{"location":"api/#DynamicPPL.link!","page":"API","title":"DynamicPPL.link!","text":"link!(vi::VarInfo, spl::Sampler)\n\nTransform the values of the random variables sampled by spl in vi from the support of their distributions to the Euclidean space and set their corresponding \"trans\" flag values to true.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.invlink!","page":"API","title":"DynamicPPL.invlink!","text":"invlink!(vi::VarInfo, spl::AbstractSampler)\n\nTransform the values of the random variables sampled by spl in vi from the Euclidean space back to the support of their distributions and sets their corresponding \"trans\" flag values to false.\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"set_flag!\nunset_flag!\nis_flagged","category":"page"},{"location":"api/#DynamicPPL.set_flag!","page":"API","title":"DynamicPPL.set_flag!","text":"set_flag!(vi::VarInfo, vn::VarName, flag::String)\n\nSet vn's value for flag to true in vi.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.unset_flag!","page":"API","title":"DynamicPPL.unset_flag!","text":"unset_flag!(vi::VarInfo, vn::VarName, flag::String)\n\nSet vn's value for flag to false in vi.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.is_flagged","page":"API","title":"DynamicPPL.is_flagged","text":"is_flagged(vi::VarInfo, vn::VarName, flag::String)\n\nCheck whether vn has a true value for flag in vi.\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"For Gibbs sampling the following functions were added.","category":"page"},{"location":"api/","page":"API","title":"API","text":"setgid!\nupdategid!","category":"page"},{"location":"api/#DynamicPPL.setgid!","page":"API","title":"DynamicPPL.setgid!","text":"setgid!(vi::VarInfo, gid::Selector, vn::VarName)\n\nAdd gid to the set of sampler selectors associated with vn in vi.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.updategid!","page":"API","title":"DynamicPPL.updategid!","text":"updategid!(vi::VarInfo, vn::VarName, spl::Sampler)\n\nSet vn's gid to Set([spl.selector]), if vn does not have a sampler selector linked and vn's symbol is in the space of spl.\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"The following functions were used for sequential Monte Carlo methods.","category":"page"},{"location":"api/","page":"API","title":"API","text":"get_num_produce\nset_num_produce!\nincrement_num_produce!\nreset_num_produce!\nsetorder!\nset_retained_vns_del_by_spl!","category":"page"},{"location":"api/#DynamicPPL.get_num_produce","page":"API","title":"DynamicPPL.get_num_produce","text":"get_num_produce(vi::VarInfo)\n\nReturn the num_produce of vi.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.set_num_produce!","page":"API","title":"DynamicPPL.set_num_produce!","text":"set_num_produce!(vi::VarInfo, n::Int)\n\nSet the num_produce field of vi to n.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.increment_num_produce!","page":"API","title":"DynamicPPL.increment_num_produce!","text":"increment_num_produce!(vi::VarInfo)\n\nAdd 1 to num_produce in vi.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.reset_num_produce!","page":"API","title":"DynamicPPL.reset_num_produce!","text":"reset_num_produce!(vi::VarInfo)\n\nReset the value of num_produce the log of the joint probability of the observed data and parameters sampled in vi to 0.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.setorder!","page":"API","title":"DynamicPPL.setorder!","text":"setorder!(vi::VarInfo, vn::VarName, index::Int)\n\nSet the order of vn in vi to index, where order is the number of observe statements run before samplingvn`.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.set_retained_vns_del_by_spl!","page":"API","title":"DynamicPPL.set_retained_vns_del_by_spl!","text":"set_retained_vns_del_by_spl!(vi::VarInfo, spl::Sampler)\n\nSet the \"del\" flag of variables in vi with order > vi.num_produce[] to true.\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"Base.empty!","category":"page"},{"location":"api/#Base.empty!","page":"API","title":"Base.empty!","text":"empty!(meta::Metadata)\n\nEmpty the fields of meta.\n\nThis is useful when using a sampling algorithm that assumes an empty meta, e.g. SMC.\n\n\n\n\n\n","category":"function"},{"location":"api/#Evaluation-Contexts","page":"API","title":"Evaluation Contexts","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"Internally, both sampling and evaluation of log densities are performed with AbstractPPL.evaluate!!.","category":"page"},{"location":"api/","page":"API","title":"API","text":"AbstractPPL.evaluate!!","category":"page"},{"location":"api/#AbstractPPL.evaluate!!","page":"API","title":"AbstractPPL.evaluate!!","text":"evaluate!!(model::Model[, rng, varinfo, sampler, context])\n\nSample from the model using the sampler with random number generator rng and the context, and store the sample and log joint probability in varinfo.\n\nReturns both the return-value of the original model, and the resulting varinfo.\n\nThe method resets the log joint probability of varinfo and increases the evaluation number of sampler.\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"The behaviour of a model execution can be changed with evaluation contexts that are passed as additional argument to the model function. Contexts are subtypes of AbstractPPL.AbstractContext.","category":"page"},{"location":"api/","page":"API","title":"API","text":"SamplingContext\nDefaultContext\nLikelihoodContext\nPriorContext\nMiniBatchContext\nPrefixContext","category":"page"},{"location":"api/#DynamicPPL.SamplingContext","page":"API","title":"DynamicPPL.SamplingContext","text":"SamplingContext(\n [rng::Random.AbstractRNG=Random.default_rng()],\n [sampler::AbstractSampler=SampleFromPrior()],\n [context::AbstractContext=DefaultContext()],\n)\n\nCreate a context that allows you to sample parameters with the sampler when running the model. The context determines how the returned log density is computed when running the model.\n\nSee also: DefaultContext, LikelihoodContext, PriorContext\n\n\n\n\n\n","category":"type"},{"location":"api/#DynamicPPL.DefaultContext","page":"API","title":"DynamicPPL.DefaultContext","text":"struct DefaultContext <: AbstractContext end\n\nThe DefaultContext is used by default to compute log the joint probability of the data and parameters when running the model.\n\n\n\n\n\n","category":"type"},{"location":"api/#DynamicPPL.LikelihoodContext","page":"API","title":"DynamicPPL.LikelihoodContext","text":"struct LikelihoodContext{Tvars} <: AbstractContext\n vars::Tvars\nend\n\nThe LikelihoodContext enables the computation of the log likelihood of the parameters when running the model. vars can be used to evaluate the log likelihood for specific values of the model's parameters. If vars is nothing, the parameter values inside the VarInfo will be used by default.\n\n\n\n\n\n","category":"type"},{"location":"api/#DynamicPPL.PriorContext","page":"API","title":"DynamicPPL.PriorContext","text":"struct PriorContext{Tvars} <: AbstractContext\n vars::Tvars\nend\n\nThe PriorContext enables the computation of the log prior of the parameters vars when running the model.\n\n\n\n\n\n","category":"type"},{"location":"api/#DynamicPPL.MiniBatchContext","page":"API","title":"DynamicPPL.MiniBatchContext","text":"struct MiniBatchContext{Tctx, T} <: AbstractContext\n context::Tctx\n loglike_scalar::T\nend\n\nThe MiniBatchContext enables the computation of log(prior) + s * log(likelihood of a batch) when running the model, where s is the loglike_scalar field, typically equal to the number of data points / batch size. This is useful in batch-based stochastic gradient descent algorithms to be optimizing log(prior) + log(likelihood of all the data points) in the expectation.\n\n\n\n\n\n","category":"type"},{"location":"api/#DynamicPPL.PrefixContext","page":"API","title":"DynamicPPL.PrefixContext","text":"PrefixContext{Prefix}(context)\n\nCreate a context that allows you to use the wrapped context when running the model and adds the Prefix to all parameters.\n\nThis context is useful in nested models to ensure that the names of the parameters are unique.\n\nSee also: @submodel\n\n\n\n\n\n","category":"type"},{"location":"api/#Samplers","page":"API","title":"Samplers","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"In DynamicPPL two samplers are defined that are used to initialize unobserved random variables: SampleFromPrior which samples from the prior distribution, and SampleFromUniform which samples from a uniform distribution.","category":"page"},{"location":"api/","page":"API","title":"API","text":"SampleFromPrior\nSampleFromUniform","category":"page"},{"location":"api/#DynamicPPL.SampleFromPrior","page":"API","title":"DynamicPPL.SampleFromPrior","text":"SampleFromPrior\n\nSampling algorithm that samples unobserved random variables from their prior distribution.\n\n\n\n\n\n","category":"type"},{"location":"api/#DynamicPPL.SampleFromUniform","page":"API","title":"DynamicPPL.SampleFromUniform","text":"SampleFromUniform\n\nSampling algorithm that samples unobserved random variables from a uniform distribution.\n\nReferences\n\nStan reference manual\n\n\n\n\n\n","category":"type"},{"location":"api/","page":"API","title":"API","text":"Additionally, a generic sampler for inference is implemented.","category":"page"},{"location":"api/","page":"API","title":"API","text":"Sampler","category":"page"},{"location":"api/#DynamicPPL.Sampler","page":"API","title":"DynamicPPL.Sampler","text":"Sampler{T}\n\nGeneric sampler type for inference algorithms of type T in DynamicPPL.\n\nSampler should implement the AbstractMCMC interface, and in particular AbstractMCMC.step. A default implementation of the initial sampling step is provided that supports resuming sampling from a previous state and setting initial parameter values. It requires to overload loadstate and initialstep for loading previous states and actually performing the initial sampling step, respectively. Additionally, sometimes one might want to implement initialsampler that specifies how the initial parameter values are sampled if they are not provided. By default, values are sampled from the prior.\n\n\n\n\n\n","category":"type"},{"location":"api/","page":"API","title":"API","text":"The default implementation of Sampler uses the following unexported functions.","category":"page"},{"location":"api/","page":"API","title":"API","text":"DynamicPPL.initialstep\nDynamicPPL.loadstate\nDynamicPPL.initialsampler","category":"page"},{"location":"api/#DynamicPPL.initialstep","page":"API","title":"DynamicPPL.initialstep","text":"initialstep(rng, model, sampler, varinfo; kwargs...)\n\nPerform the initial sampling step of the sampler for the model.\n\nThe varinfo contains the initial samples, which can be provided by the user or sampled randomly.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.loadstate","page":"API","title":"DynamicPPL.loadstate","text":"loadstate(data)\n\nLoad sampler state from data.\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.initialsampler","page":"API","title":"DynamicPPL.initialsampler","text":"initialsampler(sampler::Sampler)\n\nReturn the sampler that is used for generating the initial parameters when sampling with sampler.\n\nBy default, it returns an instance of SampleFromPrior.\n\n\n\n\n\n","category":"function"},{"location":"api/#model_internal","page":"API","title":"Model-Internal Functions","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"tilde_assume\ndot_tilde_assume","category":"page"},{"location":"api/#DynamicPPL.tilde_assume","page":"API","title":"DynamicPPL.tilde_assume","text":"tilde_assume(context::SamplingContext, right, vn, vi)\n\nHandle assumed variables, e.g., x ~ Normal() (where x does occur in the model inputs), accumulate the log probability, and return the sampled value with a context associated with a sampler.\n\nFalls back to\n\ntilde_assume(context.rng, context.context, context.sampler, right, vn, vi)\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.dot_tilde_assume","page":"API","title":"DynamicPPL.dot_tilde_assume","text":"dot_tilde_assume(context::SamplingContext, right, left, vn, vi)\n\nHandle broadcasted assumed variables, e.g., x .~ MvNormal() (where x does not occur in the model inputs), accumulate the log probability, and return the sampled value for a context associated with a sampler.\n\nFalls back to\n\ndot_tilde_assume(context.rng, context.context, context.sampler, right, left, vn, vi)\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"tilde_observe\ndot_tilde_observe","category":"page"},{"location":"api/#DynamicPPL.tilde_observe","page":"API","title":"DynamicPPL.tilde_observe","text":"tilde_observe(context::SamplingContext, right, left, vi)\n\nHandle observed constants with a context associated with a sampler.\n\nFalls back to tilde_observe(context.context, context.sampler, right, left, vi).\n\n\n\n\n\n","category":"function"},{"location":"api/#DynamicPPL.dot_tilde_observe","page":"API","title":"DynamicPPL.dot_tilde_observe","text":"dot_tilde_observe(context::SamplingContext, right, left, vi)\n\nHandle broadcasted observed constants, e.g., [1.0] .~ MvNormal(), accumulate the log probability, and return the observed value for a context associated with a sampler.\n\nFalls back to dot_tilde_observe(context.context, context.sampler, right, left, vi).\n\n\n\n\n\n","category":"function"},{"location":"#DynamicPPL.jl","page":"Home","title":"DynamicPPL.jl","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"A domain-specific language and backend for probabilistic programming languages, used by Turing.jl.","category":"page"},{"location":"tutorials/prob-interface/#The-Probability-Interface","page":"The Probability Interface","title":"The Probability Interface","text":"","category":"section"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"The easiest way to manipulate and query DynamicPPL models is via the DynamicPPL probability interface.","category":"page"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"Let's use a simple model of normally-distributed data as an example.","category":"page"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"using DynamicPPL\nusing Distributions\nusing FillArrays\n\nusing LinearAlgebra\nusing Random\n\n@model function gdemo(n)\n μ ~ Normal(0, 1)\n x ~ MvNormal(Fill(μ, n), I)\n return nothing\nend\nnothing # hide","category":"page"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"We generate some data using μ = 0:","category":"page"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"Random.seed!(1776)\ndataset = randn(100)\nnothing # hide","category":"page"},{"location":"tutorials/prob-interface/#Conditioning-and-Deconditioning","page":"The Probability Interface","title":"Conditioning and Deconditioning","text":"","category":"section"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"Bayesian models can be transformed with two main operations, conditioning and deconditioning (also known as marginalization). Conditioning takes a variable and fixes its value as known. We do this by passing a model and a collection of conditioned variables to | or its alias condition:","category":"page"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"model = gdemo(length(dataset)) | (x=dataset, μ=0)\nnothing # hide","category":"page"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"This operation can be reversed by applying decondition:","category":"page"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"decondition(model)\nnothing # hide","category":"page"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"We can also decondition only some of the variables:","category":"page"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"decondition(model, :μ)\nnothing # hide","category":"page"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"note: Note\nSometimes it is helpful to define convenience functions for conditioning on some variable(s). For instance, in this example we might want to define a version of gdemo that conditions on some observations of x:gdemo(x::AbstractVector{<:Real}) = gdemo(length(x)) | (; x)For illustrative purposes, however, we do not use this function in the examples below.","category":"page"},{"location":"tutorials/prob-interface/#Probabilities-and-Densities","page":"The Probability Interface","title":"Probabilities and Densities","text":"","category":"section"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"We often want to calculate the (unnormalized) probability density for an event. This probability might be a prior, a likelihood, or a posterior (joint) density. DynamicPPL provides convenient functions for this. For example, we can calculate the joint probability of a set of samples (here drawn from the prior) with logjoint:","category":"page"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"model = gdemo(length(dataset)) | (x=dataset,)\n\nRandom.seed!(124)\nsample = rand(model)\nlogjoint(model, sample)","category":"page"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"For models with many variables rand(model) can be prohibitively slow since it returns a NamedTuple of samples from the prior distribution of the unconditioned variables. We recommend working with samples of type DataStructures.OrderedDict in this case:","category":"page"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"using DataStructures\n\nRandom.seed!(124)\nsample_dict = rand(OrderedDict, model)\nlogjoint(model, sample_dict)","category":"page"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"The prior probability and the likelihood of a set of samples can be calculated with the functions loglikelihood and logjoint, respectively:","category":"page"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"logjoint(model, sample) ≈ loglikelihood(model, sample) + logprior(model, sample)","category":"page"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"logjoint(model, sample_dict) ≈\nloglikelihood(model, sample_dict) + logprior(model, sample_dict)","category":"page"},{"location":"tutorials/prob-interface/#Example:-Cross-validation","page":"The Probability Interface","title":"Example: Cross-validation","text":"","category":"section"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"To give an example of the probability interface in use, we can use it to estimate the performance of our model using cross-validation. In cross-validation, we split the dataset into several equal parts. Then, we choose one of these sets to serve as the validation set. Here, we measure fit using the cross entropy (Bayes loss).[1]","category":"page"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"using MLUtils\n\nfunction cross_val(\n dataset::AbstractVector{<:Real};\n nfolds::Int=5,\n nsamples::Int=1_000,\n rng::Random.AbstractRNG=Random.default_rng(),\n)\n # Initialize `loss` in a way such that the loop below does not change its type\n model = gdemo(1) | (x=[first(dataset)],)\n loss = zero(logjoint(model, rand(rng, model)))\n\n for (train, validation) in kfolds(dataset, nfolds)\n # First, we train the model on the training set, i.e., we obtain samples from the posterior.\n # For normally-distributed data, the posterior can be computed in closed form.\n # For general models, however, typically samples will be generated using MCMC with Turing.\n posterior = Normal(mean(train), 1)\n samples = rand(rng, posterior, nsamples)\n\n # Evaluation on the validation set.\n validation_model = gdemo(length(validation)) | (x=validation,)\n loss += sum(samples) do sample\n logjoint(validation_model, (μ=sample,))\n end\n end\n\n return loss\nend\n\ncross_val(dataset)","category":"page"},{"location":"tutorials/prob-interface/","page":"The Probability Interface","title":"The Probability Interface","text":"[1]: See ParetoSmooth.jl for a faster and more accurate implementation of cross-validation than the one provided here.","category":"page"}] +} diff --git a/previews/PR546/siteinfo.js b/previews/PR546/siteinfo.js new file mode 100644 index 000000000..810bddda7 --- /dev/null +++ b/previews/PR546/siteinfo.js @@ -0,0 +1 @@ +var DOCUMENTER_CURRENT_VERSION = "previews/PR546"; diff --git a/previews/PR546/tutorials/prob-interface/index.html b/previews/PR546/tutorials/prob-interface/index.html new file mode 100644 index 000000000..d0f151acd --- /dev/null +++ b/previews/PR546/tutorials/prob-interface/index.html @@ -0,0 +1,52 @@ + +The Probability Interface · DynamicPPL
GitHub

The Probability Interface

The easiest way to manipulate and query DynamicPPL models is via the DynamicPPL probability interface.

Let's use a simple model of normally-distributed data as an example.

using DynamicPPL
+using Distributions
+using FillArrays
+
+using LinearAlgebra
+using Random
+
+@model function gdemo(n)
+    μ ~ Normal(0, 1)
+    x ~ MvNormal(Fill(μ, n), I)
+    return nothing
+end

We generate some data using μ = 0:

Random.seed!(1776)
+dataset = randn(100)

Conditioning and Deconditioning

Bayesian models can be transformed with two main operations, conditioning and deconditioning (also known as marginalization). Conditioning takes a variable and fixes its value as known. We do this by passing a model and a collection of conditioned variables to | or its alias condition:

model = gdemo(length(dataset)) | (x=dataset, μ=0)

This operation can be reversed by applying decondition:

decondition(model)

We can also decondition only some of the variables:

decondition(model, :μ)
Note

Sometimes it is helpful to define convenience functions for conditioning on some variable(s). For instance, in this example we might want to define a version of gdemo that conditions on some observations of x:

gdemo(x::AbstractVector{<:Real}) = gdemo(length(x)) | (; x)

For illustrative purposes, however, we do not use this function in the examples below.

Probabilities and Densities

We often want to calculate the (unnormalized) probability density for an event. This probability might be a prior, a likelihood, or a posterior (joint) density. DynamicPPL provides convenient functions for this. For example, we can calculate the joint probability of a set of samples (here drawn from the prior) with logjoint:

model = gdemo(length(dataset)) | (x=dataset,)
+
+Random.seed!(124)
+sample = rand(model)
+logjoint(model, sample)
-181.7247437162069

For models with many variables rand(model) can be prohibitively slow since it returns a NamedTuple of samples from the prior distribution of the unconditioned variables. We recommend working with samples of type DataStructures.OrderedDict in this case:

using DataStructures
+
+Random.seed!(124)
+sample_dict = rand(OrderedDict, model)
+logjoint(model, sample_dict)
-181.7247437162069

The prior probability and the likelihood of a set of samples can be calculated with the functions loglikelihood and logjoint, respectively:

logjoint(model, sample) ≈ loglikelihood(model, sample) + logprior(model, sample)
true
logjoint(model, sample_dict) ≈
+loglikelihood(model, sample_dict) + logprior(model, sample_dict)
true

Example: Cross-validation

To give an example of the probability interface in use, we can use it to estimate the performance of our model using cross-validation. In cross-validation, we split the dataset into several equal parts. Then, we choose one of these sets to serve as the validation set. Here, we measure fit using the cross entropy (Bayes loss).[1]

using MLUtils
+
+function cross_val(
+    dataset::AbstractVector{<:Real};
+    nfolds::Int=5,
+    nsamples::Int=1_000,
+    rng::Random.AbstractRNG=Random.default_rng(),
+)
+    # Initialize `loss` in a way such that the loop below does not change its type
+    model = gdemo(1) | (x=[first(dataset)],)
+    loss = zero(logjoint(model, rand(rng, model)))
+
+    for (train, validation) in kfolds(dataset, nfolds)
+        # First, we train the model on the training set, i.e., we obtain samples from the posterior.
+        # For normally-distributed data, the posterior can be computed in closed form.
+        # For general models, however, typically samples will be generated using MCMC with Turing.
+        posterior = Normal(mean(train), 1)
+        samples = rand(rng, posterior, nsamples)
+
+        # Evaluation on the validation set.
+        validation_model = gdemo(length(validation)) | (x=validation,)
+        loss += sum(samples) do sample
+            logjoint(validation_model, (μ=sample,))
+        end
+    end
+
+    return loss
+end
+
+cross_val(dataset)
-212760.30282411768
  • 1See ParetoSmooth.jl for a faster and more accurate implementation of cross-validation than the one provided here.