autofit.Analysis#

class autofit.Analysis[source]#

Bases: ABC

Protocol for an analysis. Defines methods that can or must be implemented to define a class that compute the likelihood that some instance fits some data.

Methods

`compute_latent_samples`	Internal method that manages computation of latent samples from samples.
`compute_latent_variables`	Override to compute latent variables from the instance.
`log_likelihood_function`
`make_result`	Returns the Result of the non-linear search after it is completed.
`modify_after_fit`	Overwrite this method to modify the attributes of the Analysis class before the non-linear search begins.
`modify_before_fit`	Overwrite this method to modify the attributes of the Analysis class before the non-linear search begins.
`modify_model`
`profile_log_likelihood_function`	Overwrite this function for profiling of the log likelihood function to be performed every update of a non-linear search.
`save_attributes`
`save_results`
`save_results_combined`
`with_model`	Associate an explicit model with this analysis.

class Result(samples_summary: SamplesSummary, paths: Optional[AbstractPaths] = None, samples: Optional[Samples] = None, search_internal: Optional[object] = None, analysis: Optional[Analysis] = None)#

Bases: AbstractResult

The result of a non-linear search.

The default behaviour is for all key results to be in the samples_summary attribute, which is a concise summary of the results of the non-linear search. The reasons for this to be the main attribute are:

It is concise and therefore has minimal I/O overhead, which is important because when runs are resumed

the results are loaded often, which can become very slow for large results via a samples.csv.

The output.yaml config files can be used to disable the output of the samples.csv file

and search_internal.dill files. This means in order for results to be loaded in a way that allows a run to resume, the samples_summary must contain all results necessary to resume the run.

For this reason, the samples and search_internal attributes are optional. On the first run of a model-fit, they will always contain values as they are passed in via memory from the results of the search. However, if a run is resumed they are no longer available in memory, and they will only be available if their corresponding samples.csv and search_internal.dill files are output on disk and available to load.

This object includes:

The samples_summary attribute, which is a summary of the results of the non-linear search.
The paths attribute, which contains the path structure to the results of the search on the hard-disk and

is used to load the samples and search internal attributes if they are required and not available in memory.

The samples of the non-linear search (E.g. MCMC chains, nested sampling samples) which are used to compute

the maximum likelihood model, posteriors and other properties.

The non-linear search used to perform the model fit in its internal format (e.g. the Dynesty sampler used

by dynesty itself as opposed to PyAutoFit abstract classes).

Parameters:

samples_summary – A summary of the most important samples of the non-linear search (e.g. maximum log likelihood, median PDF).
paths – The paths to the results of the search, used to load the samples and search internal attributes if they are required and not available in memory.
samples – The samples of the non-linear search, for example the MCMC chains.
search_internal – The non-linear search used to perform the model fit in its internal format.
analysis – The Analysis object that was used to perform the model-fit from which this result is inferred.

dict() → dict#: Human-readable dictionary representation of the results

property projected_model: AbstractPriorModel#: Create a new model with the same structure as the previous model, replacing each prior with a new prior created by calculating sufficient statistics from samples and corresponding weights for that prior.

property samples: Optional[Samples]#

Returns the samples of the non-linear search, for example the MCMC chains or nested sampling samples.

When a model-fit is run the first time, the samples are passed into the result via memory and therefore always available.

However, if a model-fit is resumed the samples are not available in memory and they only way to load them is via the samples.csv file output on the hard-disk. This property handles the loading of the samples from the samples.csv file if they are not available in memory.

Return type:: The samples of the non-linear search.

property search_internal#

Returns the non-linear search used to perform the model fit in its internal sampler format.

When a model-fit is run the first time, the search internal is passed into the result via memory and therefore always available.

However, if a model-fit is resumed the search internal is not available in memory and they only way to load it is via the search_internal.dill file output on the hard-disk. This property handles the loading of the search internal from the search_internal.dill file if it is not available in memory.

Return type:: The non-linear search used to perform the model fit in its internal sampler format.

compute_latent_samples(samples: Samples) → Optional[Samples][source]#

Internal method that manages computation of latent samples from samples.

Parameters:: samples – The samples from the non-linear search.
Return type:: The computed latent samples or None if compute_latent_variable is not implemented.

compute_latent_variables(instance) → Dict[str, float][source]#

Override to compute latent variables from the instance.

Latent variables are expressed as a dictionary: {“name”: value}

More complex models can be expressed by separating variables names by ‘.’ {“name.attribute”: value}

Parameters:: instance – An instance of the model.
Return type:: The computed latent variables.

with_model(model)[source]#

Associate an explicit model with this analysis. Instances of the model will be used to compute log likelihood in place of the model passed from the search.

Parameters:: model – A model to associate with this analysis
Return type:: An analysis for that model

modify_before_fit(paths: AbstractPaths, model: AbstractPriorModel)[source]#

Overwrite this method to modify the attributes of the Analysis class before the non-linear search begins.

An example use-case is using properties of the model to alter the Analysis class in ways that can speed up the fitting performed in the log_likelihood_function.

modify_after_fit(paths: AbstractPaths, model: AbstractPriorModel, result: Result)[source]#

Overwrite this method to modify the attributes of the Analysis class before the non-linear search begins.

An example use-case is using properties of the model to alter the Analysis class in ways that can speed up the fitting performed in the log_likelihood_function.

make_result(samples_summary: SamplesSummary, paths: AbstractPaths, samples: Optional[SamplesPDF] = None, search_internal: Optional[object] = None, analysis: Optional[object] = None) → Result[source]#

Returns the Result of the non-linear search after it is completed.

The result type is defined as a class variable in the Analysis class. It can be manually overwritten by a user to return a user-defined result object, which can be extended with additional methods and attributes specific to the model-fit.

The standard Result object may include:

The samples summary, which contains the maximum log likelihood instance and median PDF model.
The paths of the search, which are used for loading the samples and search internal below when a search

is resumed.

The samples of the non-linear search (e.g. MCMC chains) also stored in samples.csv.
The non-linear search used for the fit in its internal representation, which is used for resuming a search

and making bespoke visualization using the search’s internal results.

The analysis used to fit the model (default disabled to save memory, but option may be useful for certain

projects).

Parameters:

samples_summary – The summary of the samples of the non-linear search, which include the maximum log likelihood instance and median PDF model.
paths – An object describing the paths for saving data (e.g. hard-disk directories or entries in sqlite database).
samples – The samples of the non-linear search, for example the chains of an MCMC run.
search_internal – The internal representation of the non-linear search used to perform the model-fit.
analysis – The analysis used to fit the model.

Returns:

The result of the non-linear search, which is defined as a class variable in the Analysis class.

Return type:

Result

profile_log_likelihood_function(paths: AbstractPaths, instance)[source]#

Overwrite this function for profiling of the log likelihood function to be performed every update of a non-linear search.

This behaves analogously to overwriting the visualize function of the Analysis class, whereby the user fills in the project-specific behaviour of the profiling.

Parameters:

paths – An object describing the paths for saving data (e.g. hard-disk directories or entries in sqlite database).
instance – The maximum likliehood instance of the model so far in the non-linear search.

__add__(other: Analysis)[source]#

Analyses can be added together. The resultant log likelihood function returns the sum of the underlying log likelihood functions.

Parameters:: other – Another analysis class
Return type:: A class that computes log likelihood based on both analyses