leaspy.algo.fit.tensor_mcmcsaem.TensorMCMCSAEM

class TensorMCMCSAEM(settings)

Bases: AbstractFitMCMC

Main algorithm for MCMC-SAEM.

Parameters
settingsAlgorithmSettings

MCMC fit algorithm settings

See also

AbstractFitMCMC
Attributes
log_noise_fmt

Getter

Methods

iteration(dataset, model, realizations)

MCMC-SAEM iteration.

load_parameters(parameters)

Update the algorithm's parameters by the ones in the given dictionary.

run(model, *args[, return_noise])

Main method, run the algorithm.

run_impl(model, dataset)

Main method, run the algorithm.

set_output_manager(output_settings)

Set a FitOutputManager object for the run of the algorithm
iteration(dataset: Dataset, model: AbstractModel, realizations: CollectionRealization)

MCMC-SAEM iteration.

  1. Sample : MC sample successively of the population and individual variables

  2. Maximization step : update model parameters from current population/individual variables values.

Parameters
datasetDataset
modelAbstractModel
realizationsCollectionRealization
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}}
property log_noise_fmt

Getter

Returns
formatstr

The format for the print of the loss

run(model: AbstractModel, *args, return_noise: 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_noisebool (default False), keyword only

Should the algorithm return main output and optional noise output as a 2-tuple?

Returns
Depends on algorithm class: TODO change?

See also

AbstractFitAlgo
AbstractPersonalizeAlgo
SimulationAlgorithm
run_impl(model: AbstractModel, dataset: Dataset)

Main method, run the algorithm.

Basically, it initializes the CollectionRealization object, updates it using the iteration method then returns it.

TODO fix proper abstract class

Parameters
modelAbstractModel

The used model.

datasetDataset

Contains the subjects’ observations in torch format to speed up computation.

Returns
2-tuple:
set_output_manager(output_settings)

Set a FitOutputManager object for the run of the algorithm

Parameters
output_settingsOutputsSettings

Contains the logs settings for the computation run (console print periodicity, plot periodicity …)

Examples

>>> from leaspy import AlgorithmSettings
>>> from leaspy.io.settings.outputs_settings import OutputsSettings
>>> from leaspy.algo.fit.tensor_mcmcsaem import TensorMCMCSAEM
>>> algo_settings = AlgorithmSettings("mcmc_saem")
>>> my_algo = TensorMCMCSAEM(algo_settings)
>>> settings = {'path': 'brouillons',
                'console_print_periodicity': 50,
                'plot_periodicity': 100,
                'save_periodicity': 50
                }
>>> my_algo.set_output_manager(OutputsSettings(settings))