autofit.Emcee#

Bases: AbstractMCMC

An Emcee non-linear search.

For a full description of Emcee, checkout its Github and readthedocs webpages:

If you use Emcee as part of a published work, please cite the package following the instructions under the Attribution section of the GitHub page.

Parameters

name (Optional[str]) – The name of the search, controlling the last folder results are output.
path_prefix (Optional[str]) – The path of folders prefixing the name folder where results are output.
unique_tag (Optional[str]) – The name of a unique tag for this model-fit, which will be given a unique entry in the sqlite database and also acts as the folder after the path prefix and before the search name.
prior_passer (Optional[PriorPasser]) – Controls how priors are passed from the results of this NonLinearSearch to a subsequent non-linear search.
initializer (Optional[Initializer]) – Generates the initialize samples of non-linear parameter space (see autofit.non_linear.initializer).
auto_correlations_settings – Customizes and performs auto correlation calculations performed during and after the search.
number_of_cores (Optional[int]) – The number of cores Emcee sampling is performed using a Python multiprocessing Pool instance. If 1, a pool instance is not created and the job runs in serial.
session (Optional[Session]) – An SQLalchemy session instance so the results of the model-fit are written to an SQLite database.

Methods

`check_model`
`config_dict_with_test_mode_settings_from`
`copy_with_paths`
`exact_fit`	rtype `Tuple`[`MeanField`, `Status`]
`fit`	Fit a model, M with some function f that takes instances of the class represented by model M and gives a score for their fitness.
`fit_sequential`	Fit multiple analyses contained within the analysis sequentially.
`fitness_function_from_model_and_analysis`
`make_pool`	Make the pool instance used to parallelize a NonLinearSearch alongside a set of unique ids for every process in the pool.
`make_sneaky_pool`	Create a pool for multiprocessing that uses slight-of-hand to avoid copying the fitness function between processes multiple times.
`optimise`	Perform optimisation for expectation propagation.
`perform_update`	Perform an update of the NonLinearSearch results, which occurs every iterations_per_update of the non-linear search.
`plot_results`
`remove_state_files`
`samples_from`

Attributes

`backend`	The Emcee hdf5 backend, which provides access to all samples, likelihoods, etc.
`backend_filename`
`config_dict_run`	A property that is only computed once per instance and then replaces itself with an ordinary attribute.
`config_dict_search`	A property that is only computed once per instance and then replaces itself with an ordinary attribute.
`config_dict_settings`	rtype `Dict`
`config_type`
`logger`	Log 'msg % args' with severity 'DEBUG'.
`name`
`paths`	rtype `Optional`[`AbstractPaths`]
`samples_cls`
`timer`

property backend: HDFBackend#

The Emcee hdf5 backend, which provides access to all samples, likelihoods, etc. of the non-linear search.

The sampler is described in the “Results” section at https://dynesty.readthedocs.io/en/latest/quickstart.html