nessai.samplers.base ==================== .. py:module:: nessai.samplers.base .. autoapi-nested-parse:: Base nested sampler object .. !! processed by numpydoc !! Classes ------- .. autoapisummary:: nessai.samplers.base.BaseNestedSampler Module Contents --------------- .. py:class:: BaseNestedSampler(model: nessai.model.Model, nlive: int, output: str = None, seed: int = None, rng: Optional[numpy.random.Generator] = None, checkpointing: bool = True, checkpoint_interval: int = 600, checkpoint_on_iteration: bool = False, checkpoint_callback: Optional[Callable] = None, logging_interval: int = None, log_on_iteration: bool = True, resume_file: str = None, plot: bool = True, n_pool: Optional[int] = None, pool: Optional[Any] = None) Bases: :py:obj:`abc.ABC` Base nested sampler class. :Parameters: **model** : :obj:`nessai.model.Model` User-defined model **output** : str, optional Path for the output. If not specified the current working directory is used. **seed** : int, optional Seed used to seed numpy and torch. **checkpointing** : bool, 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_interval** : int The interval used for checkpointing. By default this is a time interval in seconds. If :code:`checkpoint_on_iteration=True` this corresponds to the number of iterations between checkpointing. **checkpoint_on_iteration** : bool If true the checkpointing interval is checked against the number of iterations **checkpoint_callback** : Callback 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_interval** : int, optional The interval in seconds used for periodic logging. If not specified, then periodic logging is disabled. **log_on_iteration** : bool 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 :code:`nlive`. **resume_file** : str, optional Name of the file the sampler will be saved to and resumed from. **plot** : bool, optional Boolean to enable or disable plotting. **n_pool** : int, optional Number of threads to when for creating the multiprocessing pool. **pool** : object User defined multiprocessing pool that will be used when evaluating the likelihood. .. !! processed by numpydoc !! .. py:property:: likelihood_evaluation_time Current log-likelihood time .. !! processed by numpydoc !! .. py:property:: total_likelihood_evaluations Total number of likelihood evaluations .. !! processed by numpydoc !! .. py:attribute:: likelihood_calls Alias for :code:`total_likelihood_evaluations` .. !! processed by numpydoc !! .. py:property:: posterior_effective_sample_size :abstractmethod: The effective sample size of the posterior distribution .. !! processed by numpydoc !! .. py:method:: configure_output(output: Union[str, None], resume_file: Union[str, None] = None) Configure the output folder :Parameters: **output** : str Directory where the results will be stored **resume_file** : str, optional Specific file to use for checkpointing. If not specified the default is used (nested_sampler_resume.pkl) .. !! processed by numpydoc !! .. py:method:: update_output(output: str) -> None Update the output directory and resume file. :Parameters: **output: str** Path to the output directory .. !! processed by numpydoc !! .. py:method:: configure_rng(seed: Optional[int] = None, rng: Optional[numpy.random.Generator] = None) Configure the random number generation. If a seed is not specified, a random seed is generated using the specified random number generator, or the default numpy random number if not specified. In the latter case, the seed is then used to seed the new numpy random number generator. ..versionadded:: 0.14.0 :Parameters: **seed** : Optional[int] The random seed. If not specified, a random seed is generated. **rng: Optional[np.random.Generator]** Random number generator. If not specified, the default numpy random number generator is used with the specified seed. .. !! processed by numpydoc !! .. py:method:: configure_random_seed(seed: Optional[int] = None) -> None Initialise the random seed ..deprecated:: 0.14.0 Deprecated in favour of :py:meth:`~nessai.samplers.base.BaseNestedSampler.configure_rng`. :Parameters: **seed** : Optional[int] The random seed. If not specified, a random seed is generated. .. !! processed by numpydoc !! .. py:method:: configure_periodic_logging(logging_interval, log_on_iteration) Configure the periodic logging. :Parameters: **logging_interval** : int, optional The interval in seconds used for periodic logging. If not specified, then periodic logging is disabled. **log_on_iteration** : bool 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 :code:`nlive`. .. !! processed by numpydoc !! .. py:method:: periodically_log_state() Log the state of the sampler. Calls :code:`log_state` if the elapsed interval in time (or iterations) is more than the specified interval. .. !! processed by numpydoc !! .. py:method:: initialise_history() -> None Initialise the dictionary to store history .. !! processed by numpydoc !! .. py:method:: update_history() -> None Update the history dictionary .. !! processed by numpydoc !! .. py:method:: checkpoint(periodic: bool = False, force: bool = False, save_existing: bool = True) Checkpoint the classes internal state. :Parameters: **periodic** : bool 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. **force** : bool Force the sampler to checkpoint. **save_existing** : bool If True, the previous checkpoint will be copied to `.old`. If False, the new checkpoint will override the previous file. .. !! processed by numpydoc !! .. py:method:: resume_from_pickled_sampler(sampler: Any, model: nessai.model.Model, output: Optional[str] = None, checkpoint_callback: Optional[Callable] = None) :classmethod: Resume from pickle data. :Parameters: **data** : Any Pickle data **model** : :obj:`nessai.model.Model` User-defined model **output** : Optional[str] New output directory. If not specified, the output directory will be the same as the previous run. **checkpoint_callback** : Optional[Callable] Checkpoint callback function. If not specified, the default method will be used. :Returns: Instance of BaseNestedSampler .. .. !! processed by numpydoc !! .. py:method:: resume(filename: str, model: nessai.model.Model, output: Optional[str] = None, **kwargs) :classmethod: Resumes the interrupted state from a checkpoint pickle file. :Parameters: **filename** : str Pickle file to resume from **model** : :obj:`nessai.model.Model` User-defined model **output** : Optional[str] New output directory. If not specified, the output directory will be the same as the previous run. :Returns: obj Instance of BaseNestedSampler .. !! processed by numpydoc !! .. py:method:: close_pool(code=None) Close the multiprocessing pool. .. !! processed by numpydoc !! .. py:method:: 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. .. !! processed by numpydoc !!