`leaspy.algo.abstract_algo`.AbstractAlgo

class AbstractAlgo(settings: AlgorithmSettings)

Bases: ABC

Abstract class containing common methods for all algorithm classes. These classes are child classes of AbstractAlgo.

Parameters:

settingsAlgorithmSettings: The specifications of the algorithm as a AlgorithmSettings instance.

Attributes:

namestr

Name of the algorithm.

familystr

Family of the algorithm. For now, valid families are:

'fit'`
'personalize'`
'simulate'

deterministicbool

True, if and only if algorithm does not involve in randomness. Setting a seed and such algorithms will be useless.

algo_parametersdict

Contains the algorithm’s parameters. Those are controlled by the AlgorithmSettings.parameters class attribute.

seedint, optional

Seed used by numpy and torch.

output_managerFitOutputManager

Optional output manager of the algorithm

Methods

`load_parameters`(parameters)	Update the algorithm's parameters by the ones in the given dictionary.
`run`(model, *args[, return_loss])	Main method, run the algorithm.
`run_impl`(model, args, *extra_kwargs)	Run the algorithm (actual implementation), to be implemented in children classes.
`set_output_manager`(output_settings)	Set a `FitOutputManager` object for the run of the algorithm

load_parameters(parameters: dict)

Update the algorithm’s parameters by the ones in the given dictionary. The keys in the io which does not belong to the algorithm’s parameters keys are ignored.

Parameters:

parametersdict: Contains the pairs (key, value) of the wanted parameters

Examples

>>> settings = leaspy.io.settings.algorithm_settings.AlgorithmSettings("mcmc_saem")
>>> my_algo = leaspy.algo.fit.tensor_mcmcsaem.TensorMCMCSAEM(settings)
>>> my_algo.algo_parameters
{'n_iter': 10000,
 'n_burn_in_iter': 9000,
 'eps': 0.001,
 'L': 10,
 'sampler_ind': 'Gibbs',
 'sampler_pop': 'Gibbs',
 'annealing': {'do_annealing': False,
  'initial_temperature': 10,
  'n_plateau': 10,
  'n_iter': 200}}
>>> parameters = {'n_iter': 5000, 'n_burn_in_iter': 4000}
>>> my_algo.load_parameters(parameters)
>>> my_algo.algo_parameters
{'n_iter': 5000,
 'n_burn_in_iter': 4000,
 'eps': 0.001,
 'L': 10,
 'sampler_ind': 'Gibbs',
 'sampler_pop': 'Gibbs',
 'annealing': {'do_annealing': False,
  'initial_temperature': 10,
  'n_plateau': 10,
  'n_iter': 200}}

run(model: AbstractModel, *args, return_loss: bool = False, **extra_kwargs) → Any

Main method, run the algorithm.

TODO fix proper abstract class method: input depends on algorithm… (esp. simulate != from others…)

Parameters:

modelAbstractModel: The used model.
datasetDataset: Contains all the subjects’ observations with corresponding timepoints, in torch format to speed up computations.
return_lossbool (default False), keyword only: Should the algorithm return main output and optional loss output as a 2-tuple?

Returns:

Depends on algorithm class: TODO change?

leaspy.algo.abstract_algo.AbstractAlgo

`leaspy.algo.abstract_algo`.AbstractAlgo