leaspy.algo.personalize.mode_realisations module

class ModeReal(settings)

Bases: AbstractMCMCPersonalizeAlgo

Sampler based algorithm, individual parameters are derived as the most frequent realization for n_iter samplings.

TODO? we could derive some confidence intervals on individual parameters thanks to this personalization algorithm…

TODO: harmonize naming in paths realiSation vs. realiZation…

Parameters

settingsAlgorithmSettings: Settings of the algorithm.

Attributes

log_noise_fmt: Getter

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

annealing_on: bool

current_iteration: int

deterministic: bool = False

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}}

property log_noise_fmt

Getter

Returns

formatstr: The format for the print of the loss

name: str = 'mode_real'

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

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, dataset)

Main personalize function, wraps the abstract _get_individual_parameters() method.

Parameters

modelAbstractModel: A subclass object of leaspy AbstractModel.
datasetDataset: Dataset object build with leaspy class objects Data, algo & model

Returns

individual_parametersIndividualParameters

Contains individual parameters.

noise_stdfloat or torch.FloatTensor

The estimated noise (is a tensor if model.noise_model is 'gaussian_diagonal')

$= \frac{1}{n_{visits} \times n_{dim}} \sqrt{\sum_{i, j \in [1, n_{visits}] \times [1, n_{dim}]} \varepsilon_{i,j}}$

where $\varepsilon_{i,j} = \left( f(\theta, (z_{i,j}), (t_{i,j})) - (y_{i,j}) \right)^2$ , where $\theta$ are the model’s fixed effect, $(z_{i,j})$ the model’s random effects, $(t_{i,j})$ the time-points and $f$ the model’s estimator.

samplers: Dict[str, AbstractSampler]

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))

temperature: float

temperature_inv: float