leaspy.algo.others.lme_personalize module

class LMEPersonalizeAlgorithm(settings: AlgorithmSettings)

Bases: AbstractAlgo

Personalization algorithm associated to LMEModel

TODO: it should be a child of AbstractPersonalizeAlgorithm (refactoring needed)

Parameters:

settingsAlgorithmSettings: Algorithm settings (none yet). Most LME parameters are defined within LME model and LME fit algorithm.

Attributes:

name'lme_personalize'

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, dataset)	Main method, refer to abstract definition in `run()`.
`set_output_manager`(output_settings)	Not implemented.

deterministic: bool = True

family: str = 'personalize'

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}}

name: str = 'lme_personalize'

output_manager: FitOutputManager | None

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?