nessai.samplers.base
Base nested sampler object
Module Contents
Classes
Base nested sampler class. |
- class nessai.samplers.base.BaseNestedSampler(model: nessai.model.Model, nlive: int, output: str = None, seed: int = None, checkpointing: bool = True, checkpoint_interval: int = 600, checkpoint_on_iteration: bool = False, checkpoint_callback: Callable | None = None, logging_interval: int = None, log_on_iteration: bool = True, resume_file: str = None, plot: bool = True, n_pool: int | None = None, pool: Any | None = None)
Bases:
abc.ABCBase nested sampler class.
- Parameters:
- model
nessai.model.Model User-defined model
- outputstr, optional
Path for the output. If not specified the current working directory is used.
- seedint, optional
Seed used to seed numpy and torch.
- checkpointingbool, optional
Boolean to toggle checkpointing, must be enabled to resume the sampler. If false the sampler is still saved at the end of sampling.
- checkpoint_intervalint
The interval used for checkpointing. By default this is a time interval in seconds. If
checkpoint_on_iteration=Truethis corresponds to the number of iterations between checkpointing.- checkpoint_on_iterationbool
If true the checkpointing interval is checked against the number of iterations
- checkpoint_callbackCallback
Callback function to be used instead of the default function. The function will be called in the code:checkpoint method as: :code`checkpoint_callback(self)`.
- logging_intervalint, optional
The interval in seconds used for periodic logging. If not specified, then periodic logging is disabled.
- log_on_iterationbool
If true logging will occur based on the iteration. If false logging will be periodic if logging_interval is set. In case where neither logging is enabled, log_on_iteration will be set to true with an interval of
nlive.- resume_filestr, optional
Name of the file the sampler will be saved to and resumed from.
- plotbool, optional
Boolean to enable or disable plotting.
- n_poolint, optional
Number of threads to when for creating the multiprocessing pool.
- poolobject
User defined multiprocessing pool that will be used when evaluating the likelihood.
- model
- property likelihood_evaluation_time
Current log-likelihood time
- property total_likelihood_evaluations
Total number of likelihood evaluations
- abstract property posterior_effective_sample_size
The effective sample size of the posterior distribution
- likelihood_calls
Alias for
total_likelihood_evaluations
- configure_output(output: str | None, resume_file: str | None = None)
Configure the output folder
- Parameters:
- outputstr
Directory where the results will be stored
- resume_filestr, optional
Specific file to use for checkpointing. If not specified the default is used (nested_sampler_resume.pkl)
- configure_random_seed(seed: int | None)
Initialise the random seed.
- Parameters:
- seedOptional[int]
The random seed. If not specified, a random seed is generated.
- configure_periodic_logging(logging_interval, log_on_iteration)
Configure the periodic logging.
- Parameters:
- logging_intervalint, optional
The interval in seconds used for periodic logging. If not specified, then periodic logging is disabled.
- log_on_iterationbool
If true logging will occur based on the iteration. If false logging will be periodic if logging_interval is set. In case where neither logging is enabled, log_on_iteration will be set to true with an interval of
nlive.
- periodically_log_state()
Log the state of the sampler.
Calls
log_stateif the elapsed interval in time (or iterations) is more than the specified interval.
- initialise_history() None
Initialise the dictionary to store history
- update_history() None
Update the history dictionary
- checkpoint(periodic: bool = False, force: bool = False, save_existing: bool = True)
Checkpoint the classes internal state.
- Parameters:
- periodicbool
Indicates if the checkpoint is regular periodic checkpointing or forced by a signal. If forced by a signal, it will show up on the state plot.
- forcebool
Force the sampler to checkpoint.
- save_existingbool
If True, the previous checkpoint will be copied to .old. If False, the new checkpoint will override the previous file.
- classmethod resume_from_pickled_sampler(sampler: Any, model: nessai.model.Model, checkpoint_callback: Callable | None = None)
Resume from pickle data.
- Parameters:
- dataAny
Pickle data
- model
nessai.model.Model User-defined model
- checkpoint_callbackOptional[Callable]
Checkpoint callback function. If not specified, the default method will be used.
- Returns:
- Instance of BaseNestedSampler
- classmethod resume(filename: str, model: nessai.model.Model, **kwargs)
Resumes the interrupted state from a checkpoint pickle file.
- Parameters:
- filenamestr
Pickle file to resume from
- model
nessai.model.Model User-defined model
- Returns:
- obj
Instance of BaseNestedSampler
- close_pool(code=None)
Close the multiprocessing pool.
- get_result_dictionary()
Return a dictionary that contains results.
Only includes version, seed and sampling time. Child classes should call this method and add to the dictionary.