Changelog

Changelog#

[0.2.2] - 2024-XX-XX#

[0.2.1] - 2024-07-31#

Bugfixes

  • Minor bug fixed with final step of algorithm (path rescaling).

Features

  • Initial support for dating with unphased (or poorly phased) singleton mutations via singletons_phased=False option. The API is preliminary and may change.

Documentation

  • Fixed description of priors for variational gamma method, which were referred to a ‘flat’ or improper but are actually empirical Bayes priors on root node ages, fit by expectation maximization.

[0.2.0] - 2024-06-10#

Bugfixes

  • Variational gamma uses a rescaling approach which helps considerably if e.g. population sizes vary over time

  • Variational gamma does not use mutational area of branches, but average path length, which reduces bias in tree sequences containing polytomies

Breaking changes

  • The default method has been changed to variational_gamma.

  • Variational gamma uses an improper (flat) prior, and therefore no longer needs population_size specifying.

  • The standalone preprocess_ts function also applies the split_disjoint_nodes method, which creates extra nodes but improves dating accuracy.

  • Json metadata for mean time and variance in the mutation and node tables is now saved with a suitable schema. This means json.loads() is no longer needed to read it.

  • The mutation_rate and population_size parameters are now keyword-only, and therefore these parameter names need to be explicitly typed out.

  • The ignore-oldest option has been removed from the command-line interface, as it is no longer very helpful with new tsinfer output, which has the root node split. The option is still accessible from the Python API.

[0.1.7] - 2024-01-11#

Bugfixes

  • In variational gamma, rescale messages at end of each iteration to avoid numerical instability.

[0.1.6] - 2024-01-07#

Breaking changes

  • The standalone preprocess_ts function now defaults to not removing unreferenced individuals, populations, or sites, aiming to change the tree sequence tables as little as possible.

  • get_dates (previously undocumented) has been removed, as posteriors can be obtained using return_posterior. The normalize terminology previously used in get_dates is changed to standardize to better reflect the fact that the maximum (not sum) is one, and exposed via the outside_standardize parameter.

  • The Ne argument to date has been deprecated (although it is still in the API for backward compatibility). The equivalent argument population_size should be used instead.

  • The CLI -verbosity flag no longer takes a number, but uses action="count", so -v turns verbosity to INFO level, whereas -vv turns verbosity to DEBUG level.

  • The return_posteriors=True option with method="inside_outside" previously returned a dict that included keys start_time and end_time, giving the impression that the posterior for node age is discretized over time slices in this algorithm. In actuality, the posterior is discretized atomically over time points, so start_time and end_time have been replaced by a single key time.

  • The return_posteriors=True option with method="maximization" is no longer accepted (previously simply returned None)

  • Python 3.7 is no longer supported.

Features

  • A new continuous-time method, "variational_gamma" has been introduced, which uses an iterative expectation propagation approach. Tests show this increases accuracy, especially at older times. A Laplace approximation and damping are used to ensure numerical stability. After testing, the node priors used in this method are based on a global mixture prior, which can be refined during iteration. Future releases may switch to using this as the default method.

  • Priors may be calculated using a piecewise-constant effective population trajectory, which is implemented in the demography.PopulationSizeHistory class. The population_size argument to date accepts either a single scalar effective population size, or a PopulationSizeHistory instance.

  • Added support and wheels for Python 3.11

  • The .date() function is now a wrapper for the individual dating methods (accessible using tsdate.core.dating_methods), which can be called independently. (e.g. tsdate.inside_outside(...)). This makes it easier to document method-specific options. The API docs have been revised accordingly. Provenance is now saved with the name of the method used as the celled command, rather than "command": "date".

  • Major re-write of documentation (now at https://tskit.dev/tsdate/docs/), to use the standard tskit jupyterbook framework.

Bugfixes

  • The returned posteriors when return_posteriors=True now return actual probabilities (scaled so that they sum to one) rather than standardized “probabilities” whose maximum value is one.

  • The population size is saved in provenance metadata (as a dictionary if it is a PopulationSizeHistory instance)

  • preprocess_ts always records provenance as being from the preprocess_ts command, even if no gaps are removed. The command now has a (rarely used) delete_intervals parameter, which is normally filled out and saved in provenance (as it was before). If no gap deletion is done, the param is saved as []

[0.1.5] - 2022-06-07#

Features

  • Added the time_units parameter to tsdate.date, allowing users to specify the time units of the dated tree sequence. Default is "generations".

  • Added the return_posteriors parameter to tsdate.date. If True, the function returns a tuple of (dated_ts, posteriors).

  • mutation_rate is now a required argument in tsdate.date and tsdate.get_dates

  • tsdate returns an error if users attempt to date an unsimplified tree sequence.

  • Updated tsdate citation information to cite the recent Science paper

  • Built wheel on Python 3.10

[0.1.4] - 2021-06-30#

Features

  • The algorithm now operates completely in unscaled time (in units of generations) under the hood, which means that tsdate.build_prior_grid now requires the parameter Ne.

  • Users now have access to the marginal posterior distributions on node age by running tsdate.get_dates, though this is undocumented for now.

Bugfixes

  • A fix to the way likelihoods are added should eliminate numerical errors that are sometimes encountered when dating very large tree sequences.

[0.1.3] - 2021-02-15#

Features

  • Two new methods, tsdate.sites_time_from_ts and tsdate.add_sampledata_times, support inference of tree sequences from non-contemporaneous samples.

  • New tutorial on inferring tree sequences from modern and historic/ancient samples explains how to use these functions in conjunction with tsinfer.

  • tsdate.preprocess_ts supports dating inferred tree sequences which include large, uninformative stretches (i.e. centromeres and telomeres). Simply run this function on the tree sequence before dating it.

  • ignore_outside is a new parameter in the outside pass which tells tsdate to ignore edges from oldest root (these edges are often of low quality in tsinfer inferred tree sequences)

  • Development environment is now equivalent to other tskit-dev projects

[0.1.2] - 2020-02-28#

  • Improve user experience with more progress bars and logging.

  • Slightly change traversal method in outside and outside maximization algorithms, this should only affect inference on inferred tree sequences with large numbers of nodes at the same frequency.

  • Improve reporting of current project version

  • Use appdirs for default caching location

  • Prevent dating tree sequences with dangling nodes

[0.1.1] - 2020-02-25#

  • Bugfix release: resolve issue with precalculating prior values.

€# [0.1.0] - 2020-02-24

Early Alpha release made available via PyPI for community testing and evaluation.

Please don’t use this version in published works.