nessai.proposal.flowproposal.base ================================= .. py:module:: nessai.proposal.flowproposal.base .. autoapi-nested-parse:: Base proposal class that contains common methods. .. !! processed by numpydoc !! Classes ------- .. autoapisummary:: nessai.proposal.flowproposal.base.BaseFlowProposal Module Contents --------------- .. py:class:: BaseFlowProposal(model, rng: Optional[numpy.random.Generator] = None, flow_config=None, training_config=None, output=None, poolsize=None, plot='min', check_acceptance=False, max_poolsize_scale=10, update_poolsize=True, accumulate_weights=False, save_training_data=False, reparameterisations=None, fallback_reparameterisation='zscore', use_default_reparameterisations=None, reverse_reparameterisations=False, map_to_unit_hypercube=False) Bases: :py:obj:`nessai.proposal.rejection.RejectionProposal` Object that handles training and proposal points :Parameters: **model** : :obj:`nessai.model.Model` User defined model. **flow_config** : dict, optional Configuration for training the normalising flow. If None, uses default settings. Defaults to None. **output** : str, optional Path to output directory. **plot** : {True, False, 'all', 'min'}, optional Controls the plotting level: ``True`` - all plots; ``False`` - no plots; ``'all'`` - all plots and ``'min'`` - 1d plots and loss. **poolsize** : int, optional Size of the proposal pool. Defaults to 10000. **update_poolsize** : bool, optional If True the poolsize is updated using the current acceptance of the nested sampler. **max_poolsize_scale** : int, optional Maximum scale for increasing the poolsize. E.g. if this value is 10 and the poolsize is 1000 the maximum number of points in the pool is 10,000. **check_acceptance** : bool, optional If True the acceptance is computed after populating the pool. This includes computing the likelihood for every point. Default False. **reparameterisations** : Union[dict, str], optional Dictionary for configure more flexible reparameterisations. This ignores any of the other settings related to rescaling. For more details see the documentation. **fallback_reparameterisation** : None or str Name of the reparameterisation to be used for parameters that have not been specified in the reparameterisations dictionary. If None, the :py:class:`~nessai.reparameterisations.NullReparameterisation` is used. Reparameterisation should support multiple parameters. **use_default_reparameterisations** : bool, optional If True then reparameterisations will be used even if ``reparameterisations`` is None. The exact reparameterisations used will depend on :py:func:`~nessai.proposal.flowproposal.FlowProposal.add_default_reparameterisations` which may be overloaded by child classes. If not specified then the value of the attribute :py:attr:`~nessai.proposal.flowproposal.FlowProposal.use_default_reparameterisations` is used. **reverse_reparameterisations** : bool Passed to :code:`reverse_order` in :py:obj:`~nessai.reparameterisations.combined.CombinedReparameterisation`. Reverses the order of the reparameterisations. **map_to_unit_hypercube** : bool If true, samples will be mapped to the unit hypercube before any reparameterisations are applied. .. !! processed by numpydoc !! .. py:attribute:: use_default_reparameterisations :value: False Indicates whether reparameterisations will be used be default in this class. Child classes can change this value a force the default behaviour to change without changing the keyword arguments. .. !! processed by numpydoc !! .. py:property:: flow_config Return the configuration for the flow .. !! processed by numpydoc !! .. py:property:: training_config Return the configuration for the flow .. !! processed by numpydoc !! .. py:property:: poolsize Return the poolsize based of the base value and the current value of the scaling .. !! processed by numpydoc !! .. py:property:: dims Return the number of dimensions .. !! processed by numpydoc !! .. py:property:: rescaled_dims Return the number of rescaled dimensions .. !! processed by numpydoc !! .. py:property:: x_dtype Return the dtype for the x space .. !! processed by numpydoc !! .. py:property:: x_prime_dtype Return the dtype for the x prime space .. !! processed by numpydoc !! .. py:property:: population_dtype dtype used for populating the proposal, depends on if the prior is defined in the x space or x-prime space .. !! processed by numpydoc !! .. py:property:: prior_bounds The priors bounds used when computing the priors. If :code:`map_to_unit_hypercube` is true, these will be [0, 1] .. !! processed by numpydoc !! .. py:method:: configure_poolsize(poolsize, update_poolsize, max_poolsize_scale) Configure settings related to the pool size .. !! processed by numpydoc !! .. py:method:: configure_plotting(plot) Configure plotting. Plotting is split into training and pool. Training refers to plots produced during training and pool refers to plots produces during the population stage. :Parameters: **plot** : {True, False, 'all', 'train', 'pool', 'min', 'minimal'} Level of plotting. `all`, `train` and `pool` enable corner style plots. All other values that evaluate to True enable 1d histogram plots. False disables all plotting. .. !! processed by numpydoc !! .. py:method:: update_flow_config() Update the flow configuration dictionary. .. !! processed by numpydoc !! .. py:method:: initialise(resumed: bool = False) -> None Initialise the proposal class. This includes: * Setting up the rescaling * Verifying the rescaling is invertible * Initialising the FlowModel :Parameters: **resumed** : bool Indicates if the proposal is being initialised after being resumed or not. When true, the reparameterisations will not be reinitialised. .. !! processed by numpydoc !! .. py:method:: update_poolsize_scale(acceptance) Update poolsize given the current acceptance. :Parameters: **acceptance** : float Current acceptance. .. !! processed by numpydoc !! .. py:method:: add_default_reparameterisations() Add any reparameterisations which are assumed by default .. !! processed by numpydoc !! .. py:method:: get_reparameterisation(name) Get the reparameterisation from the name .. !! processed by numpydoc !! .. py:method:: configure_reparameterisations(reparameterisations) Configure the reparameterisations. :Parameters: **reparameterisations** : Union[dict, str, None] Dictionary of reparameterisations. If None, then the defaults from :py:func`get_default_reparameterisations` are used. .. !! processed by numpydoc !! .. py:method:: set_rescaling() Set function and parameter names for rescaling .. !! processed by numpydoc !! .. py:method:: verify_rescaling() Verify the rescaling functions are invertible .. !! processed by numpydoc !! .. py:method:: rescale(x, **kwargs) Rescale from the physical space to the primed physical space :Parameters: **x: array_like** Array of live points to rescale **compute_radius: bool (False)** Used to indicate when rescaling is being used for computing the radius for population. This is important for rescaling that uses inversions. :Returns: array Array of rescaled values array Array of log det|J| .. !! processed by numpydoc !! .. py:method:: inverse_rescale(x_prime, return_unit_hypercube=False, **kwargs) Rescale from the primed physical space to the original physical space. :Parameters: **x_prime** : array_like Array of live points to rescale. :Returns: array Array of rescaled values in the data space. array Array of log-Jacobian determinants. .. !! processed by numpydoc !! .. py:method:: check_state(x) Update the state of the proposal given some training data. Includes updating the reparameterisations. :Parameters: **x: array_like** Array of training live points which can be used to set parameters .. !! processed by numpydoc !! .. py:method:: train(x, plot=True) Train the normalising flow given some of the live points. :Parameters: **x** : structured_array Array of live points **plot** : {True, False, 'all'} Enable or disable plots for during training. By default the plots are only one-dimensional histograms, `'all'` includes corner plots with samples, these are often a few MB in size so proceed with caution! .. !! processed by numpydoc !! .. py:method:: reset_model_weights(**kwargs) Reset the flow weights. :Parameters: **kwargs** Keyword arguments passed to :meth:`nessai.flowmodel.FlowModel.reset_model`. .. !! processed by numpydoc !! .. py:method:: check_prior_bounds(x, *args) Return only values that are within the prior bounds :Parameters: **x: array_like** Array of live points which will compared against prior bounds **\*args:** Additional arrays which correspond to the array of live points. Only those corresponding to points within the prior bounds are returned :Returns: out: tuple of arrays Array containing the subset of the original arrays which fall within the prior bounds .. !! processed by numpydoc !! .. py:method:: forward_pass(x, rescale=True, **kwargs) Pass a vector of points through the model :Parameters: **x** : array_like Live points to map to the latent space **rescale** : bool, optional (True) Apply rescaling function **compute_radius** : bool, optional (True) Flag parsed to rescaling for rescaling specific to radius computation :Returns: **x** : array_like Samples in the latent space **log_prob** : array_like Log probabilities corresponding to each sample (including the jacobian) .. !! processed by numpydoc !! .. py:method:: backward_pass(z, rescale=True, **kwargs) A backwards pass from the model (latent -> real) :Parameters: **z** : array_like Structured array of points in the latent space **rescale** : bool, optional (True) Apply inverse rescaling function :Returns: **x** : array_like Samples in the data space **log_j** : array_like Log Jacobian determinant .. !! processed by numpydoc !! .. py:method:: log_prior(x) Compute the prior probability using the user-defined model :Parameters: **x** : structured_array Array of samples :Returns: array_like Array of log prior probabilities .. !! processed by numpydoc !! .. py:method:: unit_hypercube_log_prior(x) Compute the prior in the unit hypercube space. :Parameters: **x** : array Samples in the unit hypercube. .. !! processed by numpydoc !! .. py:method:: compute_weights(x, log_q, return_log_prior=False) Compute weights for the samples. Does NOT normalise the weights :Parameters: **x** : structured_arrays Array of points **log_q** : array_like Array of log proposal probabilities. **return_log_prior: bool** If true, the log-prior probability is also returned. :Returns: array_like Log-weights for rejection sampling. .. !! processed by numpydoc !! .. py:method:: convert_to_samples(x, plot=True) Convert the array to samples ready to be used. This removes are auxiliary parameters, (e.g. auxiliary radial parameters) and ensures the prior is computed. These samples can be directly used in the nested sampler. :Parameters: **x** : array_like Array of samples **plot** : bool, optional If true a 1d histogram for each parameter of the pool is plotted. This includes a comparison the live points used to train the current realisation of the flow. :Returns: array Structured array of samples. .. !! processed by numpydoc !! .. py:method:: populate(worst_point, n_samples=10000) :abstractmethod: Populate the pool by drawing from the proposal distribution and using rejection sampling. Will also evaluate the likelihoods if the proposal contains a multiprocessing pool. :Parameters: **N** : int, optional Number of samples to draw. Not all samples will be accepted to the number of samples saved will be less than N. If not specified ``poolsize`` will be used. .. !! processed by numpydoc !! .. py:method:: compute_acceptance(logL) Compute how many of the current pool have log-likelihoods greater than the specified log-likelihood using the current value in the `logL` field. :Parameters: **float** : logL Log-likelihood to use as the lower bound :Returns: float Acceptance defined on [0, 1] .. !! processed by numpydoc !! .. py:method:: draw(worst_point) Draw a replacement point. The new point is independent of the worst point. The worst point is only used during population. :Parameters: **worst_point** : structured_array The current worst point used to compute the radius of the contour in the latent space. :Returns: structured_array New live point .. !! processed by numpydoc !! .. py:method:: plot_pool(x) Plot the pool of points. :Parameters: **x** : array_like Corresponding samples to plot in the physical space. .. !! processed by numpydoc !! .. py:method:: resume(model, flow_config, weights_file=None) Resume the proposal. The model and config are not stored so these must be provided. :Parameters: **model** : :obj:`nessai.model.Model` User-defined model used. **flow_config** : dict Configuration dictionary for the flow. **weights_files** : str, optional Weights file to try and load. If not provided the proposal tries to load the last weights file. .. !! processed by numpydoc !! .. py:method:: reset() Reset the proposal .. !! processed by numpydoc !!