leaspy.algo.personalize.abstract_mcmc_personalize module
- class AbstractMCMCPersonalizeAlgo(settings)
Bases:
AlgoWithAnnealingMixin
,AlgoWithSamplersMixin
,AlgoWithDeviceMixin
,AbstractPersonalizeAlgo
Base class for MCMC-based personalization algorithms.
Individual parameters are derived from realizations of individual variables of the model.
- Parameters
- settings
AlgorithmSettings
Settings of the algorithm.
- settings
- Attributes
log_noise_fmt
Getter
- name
Methods
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 personalize function, wraps the abstract
_get_individual_parameters()
method.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}}
- property log_noise_fmt
Getter
- Returns
- formatstr
The format for the print of the loss
- output_manager: Optional[FitOutputManager]
- 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
- model
AbstractModel
The used model.
- dataset
Dataset
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?
- model
- Returns
- Depends on algorithm class: TODO change?
- run_impl(model, dataset)
Main personalize function, wraps the abstract
_get_individual_parameters()
method.- Parameters
- model
AbstractModel
A subclass object of leaspy AbstractModel.
- dataset
Dataset
Dataset object build with leaspy class objects Data, algo & model
- model
- Returns
- individual_parameters
IndividualParameters
Contains individual parameters.
- noise_stdfloat or
torch.FloatTensor
The estimated noise (is a tensor if model.noise_model is
'gaussian_diagonal'
)where , where are the model’s fixed effect, the model’s random effects, the time-points and the model’s estimator.
- individual_parameters
- set_output_manager(output_settings)
Set a
FitOutputManager
object for the run of the algorithm- Parameters
- output_settings
OutputsSettings
Contains the logs settings for the computation run (console print periodicity, plot periodicity …)
- output_settings
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))