diff --git a/index.html b/index.html index 83aa121b..1a6cc116 100644 --- a/index.html +++ b/index.html @@ -1,3 +1,2 @@ - diff --git a/previews/PR144/.documenter-siteinfo.json b/previews/PR144/.documenter-siteinfo.json index a19b48ca..fafd0018 100644 --- a/previews/PR144/.documenter-siteinfo.json +++ b/previews/PR144/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.10.5","generation_timestamp":"2024-09-09T09:39:57","documenter_version":"1.7.0"}} \ No newline at end of file +{"documenter":{"julia_version":"1.10.5","generation_timestamp":"2024-09-09T09:49:40","documenter_version":"1.7.0"}} \ No newline at end of file diff --git a/previews/PR144/api/index.html b/previews/PR144/api/index.html index f0557125..aea75c30 100644 --- a/previews/PR144/api/index.html +++ b/previews/PR144/api/index.html @@ -1,475 +1,17 @@ -API · AbstractMCMC - - - - - -
GitHub

API

AbstractMCMC defines an interface for sampling Markov chains.

Model

AbstractMCMC.LogDensityModel — Type
LogDensityModel <: AbstractMCMC.AbstractModel

Wrapper around something that implements the LogDensityProblem.jl interface.

Note that this does not implement the LogDensityProblems.jl interface itself, but it simply useful for indicating to the sample and other AbstractMCMC methods that the wrapped object implements the LogDensityProblems.jl interface.

Fields

  • logdensity: The object that implements the LogDensityProblems.jl interface.
source

Sampler

AbstractMCMC.AbstractSampler — Type
AbstractSampler

The AbstractSampler type is intended to be inherited from when implementing a custom sampler. Any persistent state information should be saved in a subtype of AbstractSampler.

When defining a new sampler, you should also overload the function transition_type, which tells the sample function what type of parameter it should expect to receive.

source

Sampling a single chain

StatsBase.sample — Method
sample(
+API · AbstractMCMC
GitHub
API
AbstractMCMC defines an interface for sampling Markov chains.
Model
AbstractMCMC.LogDensityModel — Type
LogDensityModel <: AbstractMCMC.AbstractModel
Wrapper around something that implements the LogDensityProblem.jl interface.
Note that this does not implement the LogDensityProblems.jl interface itself, but it simply useful for indicating to the sample and other AbstractMCMC methods that the wrapped object implements the LogDensityProblems.jl interface.
Fields
  • logdensity: The object that implements the LogDensityProblems.jl interface.
source
Sampler
AbstractMCMC.AbstractSampler — Type
AbstractSampler
The AbstractSampler type is intended to be inherited from when implementing a custom sampler. Any persistent state information should be saved in a subtype of AbstractSampler.
When defining a new sampler, you should also overload the function transition_type, which tells the sample function what type of parameter it should expect to receive.
source
Sampling a single chain
StatsBase.sample — Method
sample(
     rng::Random.AbatractRNG=Random.default_rng(),
     model::AbstractModel,
     sampler::AbstractSampler,
     N_or_isdone;
     kwargs...,
-)
Sample from the model with the Markov chain Monte Carlo sampler and return the samples.
If N_or_isdone is an Integer, exactly N_or_isdone samples are returned.
Otherwise, sampling is performed until a convergence criterion N_or_isdone returns true. The convergence criterion has to be a function with the signature
isdone(rng, model, sampler, samples, state, iteration; kwargs...)
where state and iteration are the current state and iteration of the sampler, respectively. It should return true when sampling should end, and false otherwise.
source
StatsBase.sample — Method
sample(
+)
Sample from the model with the Markov chain Monte Carlo sampler and return the samples.
If N_or_isdone is an Integer, exactly N_or_isdone samples are returned.
Otherwise, sampling is performed until a convergence criterion N_or_isdone returns true. The convergence criterion has to be a function with the signature
isdone(rng, model, sampler, samples, state, iteration; kwargs...)
where state and iteration are the current state and iteration of the sampler, respectively. It should return true when sampling should end, and false otherwise.
source
StatsBase.sample — Method
sample(
     rng::Random.AbstractRNG=Random.default_rng(),
     logdensity,
     sampler::AbstractSampler,
     N_or_isdone;
     kwargs...,
-)
Wrap the logdensity function in a LogDensityModel, and call sample with the resulting model instead of logdensity.
The logdensity function has to support the LogDensityProblems.jl interface.
source
Iterator
Iterator
AbstractMCMC.steps — Method
steps(
     rng::Random.AbstractRNG=Random.default_rng(),
     model::AbstractModel,
     sampler::AbstractSampler;
@@ -486,12 +28,12 @@
 julia> iterator = steps(MyModel(), MySampler());
 
 julia> collect(Iterators.take(iterator, 10)) == zeros(10)
-true
source
AbstractMCMC.steps — Method
steps(
     rng::Random.AbstractRNG=Random.default_rng(),
     logdensity,
     sampler::AbstractSampler;
     kwargs...,
-)
Wrap the logdensity function in a LogDensityModel, and call steps with the resulting model instead of logdensity.
The logdensity function has to support the LogDensityProblems.jl interface.
source
Transducer
Transducer
AbstractMCMC.Sample — Method
Sample(
     rng::Random.AbstractRNG=Random.default_rng(),
     model::AbstractModel,
     sampler::AbstractSampler;
@@ -508,12 +50,12 @@
 julia> transducer = Sample(MyModel(), MySampler());
 
 julia> collect(transducer(1:10)) == zeros(10)
-true
source
AbstractMCMC.Sample — Method
Sample(
     rng::Random.AbstractRNG=Random.default_rng(),
     logdensity,
     sampler::AbstractSampler;
     kwargs...,
-)
Wrap the logdensity function in a LogDensityModel, and call Sample with the resulting model instead of logdensity.
The logdensity function has to support the LogDensityProblems.jl interface.
source
Sampling multiple chains in parallel
Sampling multiple chains in parallel
StatsBase.sample — Method
sample(
     rng::Random.AbstractRNG=Random.default_rng(),
     model::AbstractModel,
     sampler::AbstractSampler,
@@ -521,7 +63,7 @@
     N::Integer,
     nchains::Integer;
     kwargs...,
-)
Sample nchains Monte Carlo Markov chains from the model with the sampler in parallel using the parallel algorithm, and combine them into a single chain.
source
StatsBase.sample — Method
sample(
+)
Sample nchains Monte Carlo Markov chains from the model with the sampler in parallel using the parallel algorithm, and combine them into a single chain.
source
StatsBase.sample — Method
sample(
     rng::Random.AbstractRNG=Random.default_rng(),
     logdensity,
     sampler::AbstractSampler,
@@ -529,5 +71,4 @@
     N::Integer,
     nchains::Integer;
     kwargs...,
-)
Wrap the logdensity function in a LogDensityModel, and call sample with the resulting model instead of logdensity.
The logdensity function has to support the LogDensityProblems.jl interface.
source
Two algorithms are provided for parallel sampling with multiple threads and multiple processes, and one allows for the user to sample multiple chains in serial (no parallelization):
Common keyword arguments
Common keyword arguments for regular and parallel sampling are:
  • progress (default: AbstractMCMC.PROGRESS[] which is true initially):  toggles progress logging
  • chain_type (default: Any): determines the type of the returned chain
  • callback (default: nothing): if callback !== nothing, then callback(rng, model, sampler, sample, state, iteration) is called after every sampling step, where sample is the most recent sample of the Markov chain and state and iteration are the current state and iteration of the sampler
  • discard_initial (default: 0): number of initial samples that are discarded
  • thinning (default: 1): factor by which to thin samples.
  • initial_state (default: nothing): if initial_state !== nothing, the first call to AbstractMCMC.step is passed initial_state as the state argument.
Info
The common keyword arguments progress, chain_type, and callback are not supported by the iterator AbstractMCMC.steps and the transducer AbstractMCMC.Sample.
There is no "official" way for providing initial parameter values yet. However, multiple packages such as EllipticalSliceSampling.jl and AdvancedMH.jl support an initial_params keyword argument for setting the initial values when sampling a single chain. To ensure that sampling multiple chains "just works" when sampling of a single chain is implemented, we decided to support initial_params in the default implementations of the ensemble methods:
  • initial_params (default: nothing): if initial_params isa AbstractArray, then the ith element of initial_params is used as initial parameters of the ith chain. If one wants to use the same initial parameters x for every chain, one can specify e.g. initial_params = FillArrays.Fill(x, N).
Progress logging can be enabled and disabled globally with AbstractMCMC.setprogress!(progress).
AbstractMCMC.setprogress! — Function
setprogress!(progress::Bool; silent::Bool=false)
Enable progress logging globally if progress is true, and disable it otherwise.  Optionally disable informational message if silent is true.
source
Chains
The chain_type keyword argument allows to set the type of the returned chain. A common choice is to return chains of type Chains from MCMCChains.jl.
AbstractMCMC defines the abstract type AbstractChains for Markov chains.
AbstractMCMC.AbstractChains — Type
AbstractChains
AbstractChains is an abstract type for an object that stores parameter samples generated through a MCMC process.
source
For chains of this type, AbstractMCMC defines the following two methods.
AbstractMCMC.chainscat — Function
chainscat(c::AbstractChains...)
Concatenate multiple chains.
By default, the chains are concatenated along the third dimension by calling cat(c...; dims=3).
source
AbstractMCMC.chainsstack — Function
chainsstack(c::AbstractVector)
Stack chains in c.
By default, the vector of chains is returned unmodified. If eltype(c) <: AbstractChains, then reduce(chainscat, c) is called.
source

-
+)

Wrap the logdensity function in a LogDensityModel, and call sample with the resulting model instead of logdensity.

The logdensity function has to support the LogDensityProblems.jl interface.

source

Two algorithms are provided for parallel sampling with multiple threads and multiple processes, and one allows for the user to sample multiple chains in serial (no parallelization):

Common keyword arguments

Common keyword arguments for regular and parallel sampling are:

  • progress (default: AbstractMCMC.PROGRESS[] which is true initially): toggles progress logging
  • chain_type (default: Any): determines the type of the returned chain
  • callback (default: nothing): if callback !== nothing, then callback(rng, model, sampler, sample, state, iteration) is called after every sampling step, where sample is the most recent sample of the Markov chain and state and iteration are the current state and iteration of the sampler
  • discard_initial (default: 0): number of initial samples that are discarded
  • thinning (default: 1): factor by which to thin samples.
  • initial_state (default: nothing): if initial_state !== nothing, the first call to AbstractMCMC.step is passed initial_state as the state argument.
Info

The common keyword arguments progress, chain_type, and callback are not supported by the iterator AbstractMCMC.steps and the transducer AbstractMCMC.Sample.

There is no "official" way for providing initial parameter values yet. However, multiple packages such as EllipticalSliceSampling.jl and AdvancedMH.jl support an initial_params keyword argument for setting the initial values when sampling a single chain. To ensure that sampling multiple chains "just works" when sampling of a single chain is implemented, we decided to support initial_params in the default implementations of the ensemble methods:

  • initial_params (default: nothing): if initial_params isa AbstractArray, then the ith element of initial_params is used as initial parameters of the ith chain. If one wants to use the same initial parameters x for every chain, one can specify e.g. initial_params = FillArrays.Fill(x, N).

Progress logging can be enabled and disabled globally with AbstractMCMC.setprogress!(progress).

AbstractMCMC.setprogress! — Function
setprogress!(progress::Bool; silent::Bool=false)

Enable progress logging globally if progress is true, and disable it otherwise. Optionally disable informational message if silent is true.

source

Chains

The chain_type keyword argument allows to set the type of the returned chain. A common choice is to return chains of type Chains from MCMCChains.jl.

AbstractMCMC defines the abstract type AbstractChains for Markov chains.

AbstractMCMC.AbstractChains — Type
AbstractChains

AbstractChains is an abstract type for an object that stores parameter samples generated through a MCMC process.

source

For chains of this type, AbstractMCMC defines the following two methods.

AbstractMCMC.chainscat — Function
chainscat(c::AbstractChains...)

Concatenate multiple chains.

By default, the chains are concatenated along the third dimension by calling cat(c...; dims=3).

source
AbstractMCMC.chainsstack — Function
chainsstack(c::AbstractVector)

Stack chains in c.

By default, the vector of chains is returned unmodified. If eltype(c) <: AbstractChains, then reduce(chainscat, c) is called.

source
diff --git a/previews/PR144/design/index.html b/previews/PR144/design/index.html index e75ed4bb..879e6b10 100644 --- a/previews/PR144/design/index.html +++ b/previews/PR144/design/index.html @@ -1,463 +1,5 @@ -Design · AbstractMCMC - - - - - -
GitHub

Design

This page explains the default implementations and design choices of AbstractMCMC. It is not intended for users but for developers that want to implement the AbstractMCMC interface for Markov chain Monte Carlo sampling. The user-facing API is explained in API.

Overview

AbstractMCMC provides a default implementation of the user-facing interface described in API. You can completely neglect these and define your own implementation of the interface. However, as described below, in most use cases the default implementation allows you to obtain support of parallel sampling, progress logging, callbacks, iterators, and transducers for free by just defining the sampling step of your inference algorithm, drastically reducing the amount of code you have to write. In general, the docstrings of the functions described below might be helpful if you intend to make use of the default implementations.

Basic structure

The simplified structure for regular sampling (the actual implementation contains some additional error checks and support for progress logging and callbacks) is

StatsBase.sample(
+Design · AbstractMCMC
GitHub
Design
This page explains the default implementations and design choices of AbstractMCMC. It is not intended for users but for developers that want to implement the AbstractMCMC interface for Markov chain Monte Carlo sampling. The user-facing API is explained in API.
Overview
AbstractMCMC provides a default implementation of the user-facing interface described in API. You can completely neglect these and define your own implementation of the interface. However, as described below, in most use cases the default implementation allows you to obtain support of parallel sampling, progress logging, callbacks, iterators, and transducers for free by just defining the sampling step of your inference algorithm, drastically reducing the amount of code you have to write. In general, the docstrings of the functions described below might be helpful if you intend to make use of the default implementations.
Basic structure
The simplified structure for regular sampling (the actual implementation contains some additional error checks and support for progress logging and callbacks) is
StatsBase.sample(
     rng::Random.AbstractRNG,
     model::AbstractMCMC.AbstractModel,
     sampler::AbstractMCMC.AbstractSampler,
@@ -482,5 +24,4 @@
     end
 
     return AbstractMCMC.bundle_samples(samples, model, sampler, state, chain_type; kwargs...)
-end
All other default implementations make use of the same structure and in particular call the same methods.
Sampling step
The only method for which no default implementation is provided (and hence which downstream packages have to implement) is AbstractMCMC.step. It defines the sampling step of the inference method.
AbstractMCMC.step — Function
step(rng, model, sampler[, state; kwargs...])
Return a 2-tuple of the next sample and the next state of the MCMC sampler for model.
Samples describe the results of a single step of the sampler. As an example, a sample might include a vector of parameters sampled from a prior distribution.
When sampling using sample, every step call after the first has access to the current state of the sampler.
source
Collecting samples
Note
This section does not apply to the iterator and transducer interface.
After the initial sample is obtained, the default implementations for regular and parallel sampling (not for the iterator and the transducer since it is not needed there) create a container for all samples (the initial one and all subsequent samples) using AbstractMCMC.samples.
AbstractMCMC.samples — Function
samples(sample, model, sampler[, N; kwargs...])
Generate a container for the samples of the MCMC sampler for the model, whose first sample is sample.
The method can be called with and without a predefined number N of samples.
source
In each step, the sample is saved in the container by AbstractMCMC.save!!. The notation !! follows the convention of the package BangBang.jl which is used in the default implementation of AbstractMCMC.save!!. It indicates that the sample is pushed to the container but a "widening" fallback is used if the container type does not allow to save the sample. Therefore AbstractMCMC.save!! always has to return the container.
AbstractMCMC.save!! — Function
save!!(samples, sample, iteration, model, sampler[, N; kwargs...])
Save the sample of the MCMC sampler at the current iteration in the container of samples.
The function can be called with and without a predefined number N of samples. By default, AbstractMCMC uses push!! from the Julia package BangBang to append to the container, and widen its type if needed.
source
For most use cases the default implementation of AbstractMCMC.samples and AbstractMCMC.save!! should work out of the box and hence need not to be overloaded in downstream code.
Creating chains
Note
This section does not apply to the iterator and transducer interface.
At the end of the sampling procedure for regular and paralle sampling we transform the collection of samples to the desired output type by calling AbstractMCMC.bundle_samples.
AbstractMCMC.bundle_samples — Function
bundle_samples(samples, model, sampler, state, chain_type[; kwargs...])
Bundle all samples that were sampled from the model with the given sampler in a chain.
The final state of the sampler can be included in the chain. The type of the chain can be specified with the chain_type argument.
By default, this method returns samples.
source
The default implementation should be fine in most use cases, but downstream packages could, e.g., save the final state of the sampler as well if they overload AbstractMCMC.bundle_samples.

-
+end

All other default implementations make use of the same structure and in particular call the same methods.

Sampling step

The only method for which no default implementation is provided (and hence which downstream packages have to implement) is AbstractMCMC.step. It defines the sampling step of the inference method.

AbstractMCMC.step — Function
step(rng, model, sampler[, state; kwargs...])

Return a 2-tuple of the next sample and the next state of the MCMC sampler for model.

Samples describe the results of a single step of the sampler. As an example, a sample might include a vector of parameters sampled from a prior distribution.

When sampling using sample, every step call after the first has access to the current state of the sampler.

source

Collecting samples

Note

This section does not apply to the iterator and transducer interface.

After the initial sample is obtained, the default implementations for regular and parallel sampling (not for the iterator and the transducer since it is not needed there) create a container for all samples (the initial one and all subsequent samples) using AbstractMCMC.samples.

AbstractMCMC.samples — Function
samples(sample, model, sampler[, N; kwargs...])

Generate a container for the samples of the MCMC sampler for the model, whose first sample is sample.

The method can be called with and without a predefined number N of samples.

source

In each step, the sample is saved in the container by AbstractMCMC.save!!. The notation !! follows the convention of the package BangBang.jl which is used in the default implementation of AbstractMCMC.save!!. It indicates that the sample is pushed to the container but a "widening" fallback is used if the container type does not allow to save the sample. Therefore AbstractMCMC.save!! always has to return the container.

AbstractMCMC.save!! — Function
save!!(samples, sample, iteration, model, sampler[, N; kwargs...])

Save the sample of the MCMC sampler at the current iteration in the container of samples.

The function can be called with and without a predefined number N of samples. By default, AbstractMCMC uses push!! from the Julia package BangBang to append to the container, and widen its type if needed.

source

For most use cases the default implementation of AbstractMCMC.samples and AbstractMCMC.save!! should work out of the box and hence need not to be overloaded in downstream code.

Creating chains

Note

This section does not apply to the iterator and transducer interface.

At the end of the sampling procedure for regular and paralle sampling we transform the collection of samples to the desired output type by calling AbstractMCMC.bundle_samples.

AbstractMCMC.bundle_samples — Function
bundle_samples(samples, model, sampler, state, chain_type[; kwargs...])

Bundle all samples that were sampled from the model with the given sampler in a chain.

The final state of the sampler can be included in the chain. The type of the chain can be specified with the chain_type argument.

By default, this method returns samples.

source

The default implementation should be fine in most use cases, but downstream packages could, e.g., save the final state of the sampler as well if they overload AbstractMCMC.bundle_samples.

diff --git a/previews/PR144/gibbs/index.html b/previews/PR144/gibbs/index.html index af5f67bb..4d514b8b 100644 --- a/previews/PR144/gibbs/index.html +++ b/previews/PR144/gibbs/index.html @@ -1,463 +1,5 @@ -state Interface Functions · AbstractMCMC - - - - - -
GitHub

state Interface Functions

We encourage sampler packages to implement the following interface functions for the state type(s) they maintain:

get_logprob
+state Interface Functions · AbstractMCMC
GitHub
state Interface Functions
We encourage sampler packages to implement the following interface functions for the state type(s) they maintain:
get_logprob
 set_logprob!!
 get_params
 set_params!!
These function will provide a minimum interface to interact with the state datatype, which a sampler package doesn't have to expose.
Using the state Interface for block sampling within Gibbs
In this sections, we will demonstrate how a model package may use this state interface to support a Gibbs sampler that can support blocking sampling using different inference algorithms.
We consider a simple hierarchical model with a normal likelihood, with unknown mean and variance parameters.
\[\begin{align}
@@ -722,5 +264,4 @@
 tau2_samples = [sample.values.tau2 for sample in samples][20001:end]
 
 mean(mu_samples)
-mean(tau2_samples)
- +mean(tau2_samples) diff --git a/previews/PR144/index.html b/previews/PR144/index.html index 02cbc374..d6f14943 100644 --- a/previews/PR144/index.html +++ b/previews/PR144/index.html @@ -1,461 +1,2 @@ -Home · AbstractMCMC - - - - - -
GitHub

AbstractMCMC.jl

Abstract types and interfaces for Markov chain Monte Carlo methods.

AbstractMCMC defines an interface for sampling and combining Markov chains. It comes with a default sampling algorithm that provides support of progress bars, parallel sampling (multithreaded and multicore), and user-provided callbacks out of the box. Typically developers only have to define the sampling step of their inference method in an iterator-like fashion to make use of this functionality. Additionally, the package defines an iterator and a transducer for sampling Markov chains based on the interface.

- +Home · AbstractMCMC
GitHub

AbstractMCMC.jl

Abstract types and interfaces for Markov chain Monte Carlo methods.

AbstractMCMC defines an interface for sampling and combining Markov chains. It comes with a default sampling algorithm that provides support of progress bars, parallel sampling (multithreaded and multicore), and user-provided callbacks out of the box. Typically developers only have to define the sampling step of their inference method in an iterator-like fashion to make use of this functionality. Additionally, the package defines an iterator and a transducer for sampling Markov chains based on the interface.