leaspy.algo.abstract_algo module
- class AbstractAlgo(settings: AlgorithmSettings)
Bases:
ABC
Abstract class containing common methods for all algorithm classes. These classes are child classes of AbstractAlgo.
- Parameters:
- settings
AlgorithmSettings
The specifications of the algorithm as a
AlgorithmSettings
instance.
- settings
- 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
- output_manager
FitOutputManager
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:
- model
AbstractModel
The used model.
- dataset
Dataset
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?
- model
- Returns:
- Depends on algorithm class: TODO change?
- abstract run_impl(model: AbstractModel, *args, **extra_kwargs) Tuple[Any, torch.FloatTensor | None]
Run the algorithm (actual implementation), to be implemented in children classes.
TODO fix proper abstract class
- Parameters:
- model
AbstractModel
The used model.
- dataset
Dataset
Contains all the subjects’ observations with corresponding timepoints, in torch format to speed up computations.
- model
- Returns:
- A 2-tuple containing:
the result to send back to user
optional float tensor representing loss (to be printed)
- set_output_manager(output_settings: OutputsSettings) None
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))