autofit.SamplesPDF#

class autofit.SamplesPDF(model: AbstractPriorModel, sample_list: List[Sample], samples_info: Dict | None = None)[source]#

Bases: Samples

Contains the samples of the non-linear search, including parameter values, log likelihoods, weights and other quantites.

For example, the output class can be used to load an instance of the best-fit model, get an instance of any individual sample by the NonLinearSearch and return information on the likelihoods, errors, etc.

This class stores samples of searches which provide the probability distribution function (PDF) of the model fit (e.g. nested samplers, MCMC).

Parameters:

model – Maps input vectors of unit parameter values to physical values and model instances via priors.
sample_list – The list of Samples which contains the paramoeters, likelihood, weights, etc. of every sample taken by the non-linear search.
samples_info – Contains information on the samples (e.g. total iterations, time to run the search, etc.).

Methods

`draw_randomly_via_pdf`	The parameter vector of an individual sample of the non-linear search drawn randomly from the PDF, returned as a 1D list.
`error_magnitudes_at_sigma`	The magnitude of every error after marginalization in 1D at an input sigma value of the probability density function (PDF), returned as two lists of values corresponding to the lower and upper errors.
`errors_at_lower_sigma`	The lower error of every parameter marginalized in 1D at an input sigma value of its probability density function (PDF), returned as a list.
`errors_at_sigma`	The lower and upper error of every parameter marginalized in 1D at an input sigma value of its probability density function (PDF), returned as a list.
`errors_at_upper_sigma`	The upper error of every parameter marginalized in 1D at an input sigma value of its probability density function (PDF), returned as a list.
`from_list_info_and_model`
`from_sample_index`	The parameters of an individual sample of the non-linear search, returned as a model instance.
`from_table`	Write a table of parameters, posteriors, priors and likelihoods
`info_to_json`
`max_log_likelihood`	The parameters of the maximum log likelihood sample of the NonLinearSearch returned as a model instance or list of values.
`max_log_posterior`	The parameters of the maximum log posterior sample of the NonLinearSearch returned as a model instance.
`median_pdf`	The median of the probability density function (PDF) of every parameter marginalized in 1D, returned as a model instance or list of values.
`minimise`	A copy of this object with only important samples retained
`model_centred_absolute`	Returns a model where every free parameter is a GaussianPrior with mean the previous result's inferred maximum log likelihood parameter values and sigma the input absolute value a.
`model_centred_max_lh_bounded`	Returns a model where every free parameter is a UniformPrior with lower_limit and upper_limit the previous result's inferred maximum log likelihood parameter values minus and plus the bound `b.
`model_centred_relative`	Returns a model where every free parameter is a GaussianPrior with mean the previous result's inferred maximum log likelihood parameter values and sigma a relative value from the result r.
`offset_values_via_input_values`	The values of an input_vector offset by the median_pdf(as_instance=False) (the PDF medians).
`path_map_for_model`
`samples_above_weight_threshold_from`	Returns a new Samples object containing only the samples with a weight above the input threshold.
`samples_drawn_randomly_via_pdf_from`	Draw one or more samples randomly from the PDF, weighted by the sample weights.
`save_covariance_matrix`	Save the covariance matrix as a CSV file.
`subsamples`
`summary`
`values_at_lower_sigma`	The lower value of every parameter marginalized in 1D at an input sigma value of its probability density function (PDF), returned as a list.
`values_at_sigma`	The value of every parameter marginalized in 1D at an input sigma value of its probability density function (PDF), returned as two lists of values corresponding to the lower and upper values parameter values.
`values_at_upper_sigma`	The upper value of every parameter marginalized in 1D at an input sigma value of its probability density function (PDF), returned as a list.
`values_for_path`	Returns the value for a variable with a given path for each sample in the model
`with_paths`	Create a copy of this object with only attributes specified by a list of paths.
`without_paths`	Create a copy of this object with only attributes not specified by a list of paths.
`write_table`	Write a table of parameters, posteriors, priors and likelihoods.

Attributes

`covariance_matrix`	Compute the covariance matrix of the non-linear search samples, using the method np.cov() which is described at the following link:
`instance`
`instances`	One model instance for each sample
`log_evidence`
`log_likelihood`
`log_likelihood_list`
`log_posterior_list`
`log_prior_list`
`max_log_likelihood_index`	The index of the sample with the highest log likelihood.
`max_log_likelihood_sample`	The index of the sample with the highest log likelihood.
`max_log_posterior_index`	The index of the sample with the highest log posterior.
`max_log_posterior_sample`
`model_centred`	Returns a model where every free parameter is a GaussianPrior with mean the previous result's inferred maximum log likelihood parameter values and sigma values determined from the errors on each parameter or the prior config files
`names`	A list of names of unique priors in the same order as prior ids (and therefore sample columns)
`parameter_lists`
`parameters_extract`
`paths`	A list of paths to unique priors in the same order as prior ids (and therefore sample columns)
`pdf_converged`	To analyse and visualize samples the analysis must be sufficiently converged to produce smooth enough PDF for error estimate and PDF generation.
`prior_means`	The mean of every parameter used to link its inferred values and errors to priors used to sample the same (or similar) parameters in a subsequent search, where:
`time`
`total_iterations`
`total_samples`
`unconverged_sample_size`	If a set of samples are unconverged, alternative methods to compute their means, errors, etc are used.
`weight_list`

classmethod from_table(filename: str, model)[source]#

Write a table of parameters, posteriors, priors and likelihoods

Parameters:: filename – Where the table is to be written

property unconverged_sample_size#

If a set of samples are unconverged, alternative methods to compute their means, errors, etc are used.

These use a subset of samples spanning the range from the most recent sample to the valaue of the unconverted_sample_size. However, if there are fewer samples than this size, we change the size to be the the size of the total number of samples.

property pdf_converged: bool#

To analyse and visualize samples the analysis must be sufficiently converged to produce smooth enough PDF for error estimate and PDF generation.

This property checks whether the non-linear search’s samples are sufficiently converged for this, by checking if one sample’s weight contains > 99% of the weight. If this is the case, it implies the convergence necessary for error estimate and visualization has not been met.

This does not necessarily imply the NonLinearSearch has converged overall, only that errors and visualization can be performed numerically.

median_pdf() → List[float][source]#: The median of the probability density function (PDF) of every parameter marginalized in 1D, returned as a model instance or list of values.

values_at_sigma(sigma: float) → [typing.Tuple, <class 'autofit.mapper.model.ModelInstance'>][source]#

The value of every parameter marginalized in 1D at an input sigma value of its probability density function (PDF), returned as two lists of values corresponding to the lower and upper values parameter values.

For example, if sigma is 1.0, the marginalized values of every parameter at 31.7% and 68.2% percentiles of each PDF is returned.

This does not account for covariance between parameters. For example, if two parameters (x, y) are degenerate whereby x decreases as y gets larger to give the same PDF, this function will still return both at their upper values. Thus, caution is advised when using the function to reperform a model-fits.

This is estimated using the quantile function if the samples have converged, by sampling the density function at an input PDF %. If not converged, a crude estimate using the range of values of the current physical live points is used.

Parameters:: sigma – The sigma within which the PDF is used to estimate errors (e.g. sigma = 1.0 uses 0.6826 of the PDF).

values_at_upper_sigma(sigma: float) → List | ModelInstance[source]#

The upper value of every parameter marginalized in 1D at an input sigma value of its probability density function (PDF), returned as a list.