TuringLang/Turing.jl: v0.41.0

Turing v0.41.0

Diff since v0.40.5

DynamicPPL 0.38

Turing.jl v0.41 brings with it all the underlying changes in DynamicPPL 0.38. Please see the DynamicPPL changelog for full details: in this section we only describe the changes that will directly affect end-users of Turing.jl.

Performance

A number of functions such as returned and predict will have substantially better performance in this release.

ProductNamedTupleDistribution

Distributions.ProductNamedTupleDistribution can now be used on the right-hand side of ~ in Turing models.

Initial parameters

Initial parameters for MCMC sampling must now be specified in a different form. You still need to use the initial_params keyword argument to sample, but the allowed values are different. For almost all samplers in Turing.jl (except Emcee) this should now be a DynamicPPL.AbstractInitStrategy.

There are three kinds of initialisation strategies provided out of the box with Turing.jl (they are exported so you can use these directly with using Turing):

• InitFromPrior(): Sample from the prior distribution. This is the default for most samplers in Turing.jl (if you don't specify initial_params).

• InitFromUniform(a, b): Sample uniformly from [a, b] in linked space. This is the default for Hamiltonian samplers. If a and b are not specified it defaults to [-2, 2], which preserves the behaviour in previous versions (and mimics that of Stan).

• InitFromParams(p): Explicitly provide a set of initial parameters. Note: p must be either a NamedTuple or an AbstractDict{<:VarName}; it can no longer be a Vector. Parameters must be provided in unlinked space, even if the sampler later performs linking.

  • For this release of Turing.jl, you can also provide a NamedTuple or AbstractDict{<:VarName} and this will be automatically wrapped in InitFromParams for you. This is an intermediate measure for backwards compatibility, and will eventually be removed.

This change is made because Vectors are semantically ambiguous. It is not clear which element of the vector corresponds to which variable in the model, nor is it clear whether the parameters are in linked or unlinked space. Previously, both of these would depend on the internal structure of the VarInfo, which is an implementation detail. In contrast, the behaviour of AbstractDicts and NamedTuples is invariant to the ordering of variables and it is also easier for readers to understand which variable is being set to which value.

If you were previously using varinfo[:] to extract a vector of initial parameters, you can now use Dict(k => varinfo[k] for k in keys(varinfo) to extract a Dict of initial parameters.

For more details about initialisation you can also refer to the main TuringLang docs, and/or the DynamicPPL API docs.

resume_from and loadstate

The resume_from keyword argument to sample is now removed. Instead of sample(...; resume_from=chain) you can use sample(...; initial_state=loadstate(chain)) which is entirely equivalent. loadstate is exported from Turing now instead of in DynamicPPL.

Note that loadstate only works for MCMCChains.Chains. For FlexiChains users please consult the FlexiChains docs directly where this functionality is described in detail.

pointwise_logdensities

pointwise_logdensities(model, chn), pointwise_loglikelihoods(...), and pointwise_prior_logdensities(...) now return an MCMCChains.Chains object if chn is itself an MCMCChains.Chains object. The old behaviour of returning an OrderedDict is still available: you just need to pass OrderedDict as the third argument, i.e., pointwise_logdensities(model, chn, OrderedDict).

Initial step in MCMC sampling

HMC and NUTS samplers no longer take an extra single step before starting the chain. This means that if you do not discard any samples at the start, the first sample will be the initial parameters (which may be user-provided).

Note that if the initial sample is included, the corresponding sampler statistics will be missing. Due to a technical limitation of MCMCChains.jl, this causes all indexing into MCMCChains to return Union{Float64, Missing} or similar. If you want the old behaviour, you can discard the first sample (e.g. using discard_initial=1).

Merged pull requests:

• [breaking] v0.41 (#2667) (@penelopeysm)
• Compatibility with DynamicPPL 0.38 + InitContext (#2676) (@penelopeysm)
• Remove Sampler, remove InferenceAlgorithm, transfer initialstep, init_strategy, and other functions from DynamicPPL to Turing (#2689) (@penelopeysm)

Closed issues:

• Do we need resume_from now that we have initial_state? (#2171)
• Introduce a docs FAQ section (#2431)
• These tests should be in DynamicPPL or removed (#2475)
• Support for Distributions.ProductNamedTupleDistribution (#2659)
• Overly aggressive concretisation in bundle_samples (#2666)
• broken docs on website (#2698)