autofit.Emcee#
- class Emcee[source]#
Bases:
AbstractMCMC
An Emcee non-linear search.
For a full description of Emcee, checkout its Github and readthedocs webpages:
https://emcee.readthedocs.io/en/stable/
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.initializer (
Optional
[Initializer
]) – Generates the initialize samples of non-linear parameter space (see autofit.non_linear.initializer).auto_correlation_settings – Customizes and performs auto correlation calculations performed during and after the search.
number_of_cores (
Optional
[int
]) – The number of cores sampling is performed using a Python multiprocessing Pool instance.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.
make_pool
Make the pool instance used to parallelize a NonLinearSearch alongside a set of unique ids for every process in the pool.
make_sneakier_pool
- rtype
SneakierPool
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 non-linear search's model-fitting results.
perform_visualization
Perform visualization of the non-linear search's model-fitting results.
plot_results
post_fit_output
Cleans up the output folderds after a completed non-linear search.
pre_fit_output
Outputs attributes of fit before the non-linear search begins.
remove_state_files
result_via_completed_fit
Returns the result of the non-linear search of a completed model-fit.
samples_from
Loads the samples of a non-linear search from its output files.
samples_via_csv_from
Returns a Samples object from the samples.csv and samples_info.json files.
Returns a Samples object from the emcee internal results.
start_resume_fit
Start a non-linear search from scratch, or resumes one which was previously terminated mid-way through.
Attributes
auto_correlations
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
config_type
logger
Log 'msg % args' with severity 'DEBUG'.
name
paths
- rtype
Optional
[AbstractPaths
]
samples_cls
samples_info
timer
using_mpi
Whether the search is being performing using MPI for parallelisation or not.
- samples_via_internal_from(model)[source]#
Returns a Samples object from the emcee internal results.
The samples contain all information on the parameter space sampling (e.g. the parameters, log likelihoods, etc.).
The internal search results are converted from the native format used by the search to lists of values (e.g. parameter_lists, log_likelihood_list).
- Parameters
model – Maps input vectors of unit parameter values to physical values and model instances via priors.
- 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
- Return type
HDFBackend