nessai.flowsampler ================== .. py:module:: nessai.flowsampler .. autoapi-nested-parse:: Main code that handles running and checkpoiting the sampler. .. !! processed by numpydoc !! Classes ------- .. autoapisummary:: nessai.flowsampler.FlowSampler Module Contents --------------- .. py:class:: FlowSampler(model, output=None, importance_nested_sampler=False, resume=True, resume_file='nested_sampler_resume.pkl', resume_data=None, weights_file=None, weights_path=None, signal_handling=True, exit_code=130, pytorch_threads=1, close_pool=True, eps=None, torch_dtype=None, disable_vectorisation=False, likelihood_chunksize=None, allow_multi_valued_likelihood=None, parallelise_prior=None, result_extension='hdf5', **kwargs) Main class to handle running the nested sampler. :Parameters: **model** : :obj:`nessai.model.Model` User-defined model. **output** : str, optional Output directory **resume** : bool, optional If True try to resume the sampler is the resume file exists. **resume_file** : str, optional File to resume sampler from. **resume_data: Any, optional** Data to resume the sampler from instead of a resume file. The data will be passed to the :code:`resume_from_pickled_sampler` of the relevant class. **weights_path** : str, optional Path to either the weights file or directory containing subdirectories with weight files. **weights_file** : str, optional Weights file to use, same as :code:`weights_path` and included for backwards_compatibility. Will be ignored is :code:`weights_paths` has also been specified. **pytorch_threads** : int Maximum number of threads to use for torch. If ``None`` torch uses all available threads. **signal_handling** : bool Enable or disable signal handling. **exit_code** : int, optional Exit code to use when forcibly exiting the sampler. **close_pool** : bool If True, the multiprocessing pool will be closed once the run method has been called. Disables the option in :code:`NestedSampler` if enabled. **disable_vectorisation** : bool Disable likelihood vectorisation. Overrides the value of :py:attr:`nessai.model.Model.allow_vectorised`. **likelihood_chunksize** : Optional[int] Chunksize used when evaluating a vectorised likelihood. Overrides the of :py:attr:`nessai.model.Model.likelihood_chunksize`. Set to None to evaluate the likelihood with all available points. **allow_multi_valued_likelihood** : Optional[bool] Allow for a multi-valued likelihood function that will return different likelihood values for the same point in parameter space. See :py:attr:`nessai.model.Model.allow_multi_valued_likelihood` for more details. **parallelise_prior** : Optional[bool] If true, and a multiprocessing pool has been specified, then the log-prior calculation will be parallelised using multiprocessing. If false, then the pool with not be used. Overrides the value of the `parallelise_prior` attribute in the model class, which is false by default. **result_extension** : str Extension used when saving the result format. Defaults to HDF5, but also supports JSON. **kwargs** Keyword arguments passed to :obj:`~nessai.samplers.nestedsampler.NestedSampler`. .. !! processed by numpydoc !! .. py:method:: check_resume(resume_file, resume_data) Check if it is possible to resume the sampler .. !! processed by numpydoc !! .. py:property:: log_evidence Return the most recent log evidence .. !! processed by numpydoc !! .. py:property:: log_evidence_error Return the most recent log evidence error .. !! processed by numpydoc !! .. py:property:: nested_samples Return the nested samples .. !! processed by numpydoc !! .. py:property:: rng Return the random number generator .. !! processed by numpydoc !! .. py:method:: run(plot=True, save=True, posterior_sampling_method=None, close_pool=None, **kwargs) Run the nested sampler. Will pick the correct run method given the configuration used. :Parameters: **plot** : bool Toggle all plots produced once the sampler has converged. **save** : bool, optional Toggle automatic saving of results **posterior_sampling_method** : str, optional Method used for drawing posterior samples. Defaults to rejection sampling. **close_pool** : bool, optional Boolean to indicated if the pool should be closed at the end of the run function. If False, the user must manually close the pool. If specified, this value overrides the value passed when initialising the FlowSampler class. .. !! processed by numpydoc !! .. py:method:: run_standard_sampler(plot=True, plot_indices=True, plot_posterior=True, plot_logXlogL=True, save=True, posterior_sampling_method=None, close_pool=None) Run the standard nested sampler. :Parameters: **plot** : bool Toggle all plots produced once the sampler has converged. **plot_indices** : bool Toggle the insertion indices plot. **plot_posterior** : bool Toggle the posterior distribution plot. **plot_logXlogL** : bool Toggle the log-prior volume vs log-likelihood plot. **save** Enable or disable saving of a results file. **posterior_sampling_method** Method used for drawing posterior samples. Defaults to rejection sampling. **close_pool** : bool Boolean to indicated if the pool should be closed at the end of the run function. If False, the user must manually close the pool. If specified, this value overrides the value passed when initialising the class. .. !! processed by numpydoc !! .. py:method:: run_importance_nested_sampler(plot=True, plot_posterior=True, save=True, posterior_sampling_method=None, redraw_samples=False, n_posterior_samples=None, compute_initial_posterior=False, close_pool=None, **kwargs) Run the importance nested sampler. :Parameters: **plot** Enable or disable plotting. Independent of the value passed to the :code:`NestedSampler` object. **save** Enable or disable saving of a results file. **posterior_sampling_method** Method used for drawing posterior samples. Defaults to importance sampling. **redraw_samples** If True after the sampling is finished, samples are redrawn from the meta proposal and used to compute an updated evidence estimate and posterior. This can reduce biases in the results. **n_posterior_samples** Number of posterior samples to draw when when redrawing samples. **compute_initial_posterior** Enables or disables computing the posterior before redrawing samples. If :code:`redraw_samples` is False, then this flag is ignored. **close_pool** : bool Boolean to indicated if the pool should be closed at the end of the run function. If False, the user must manually close the pool. If specified, this value overrides the value passed when initialising the class. **kwargs** Keyword arguments passed to :py:meth:`~nessai.importancesampler.ImportanceNestedSampler.draw_final_samples` .. !! processed by numpydoc !! .. py:method:: save_kwargs(kwargs: dict) -> None Save the dictionary of keyword arguments used. Uses an encoder class to handle numpy arrays. :Parameters: **kwargs** : dict Dictionary of kwargs to save. .. !! processed by numpydoc !! .. py:method:: save_results(filename: str, extension: Optional[str] = None) -> None Save the results from sampling to a specific results file. :Parameters: **filename** : str Name of file to save results to. **extension** : Optional[str] File extension to used. If not specified, it will be inferred from the filename. .. !! processed by numpydoc !! .. py:method:: terminate_run(code=None) Terminate a sampling run. :Parameters: **code** : int, optional Code passed to :code:`close_pool` .. !! processed by numpydoc !! .. py:method:: safe_exit(signum=None, frame=None) Safely exit. This includes closing the multiprocessing pool. .. !! processed by numpydoc !!