elicit.configs module

elicit.configs.save_history(loss: bool = True, loss_component: bool = True, time: bool = True, hyperparameter: bool = True, hyperparameter_gradient: bool = True)

Controls whether sub-results of the history object should be included or excluded. Results are saved across epochs. By default all sub-results are included.

Parameters:

lossbool, optional

total loss per epoch. The default is True.

loss_componentbool, optional

loss per loss-component per epoch. The default is True.

timebool, optional

time in sec per epoch. The default is True.

hyperparameterbool, optional

‘parametric_prior’ method: Trainable hyperparameters of parametric prior distributions. ‘deep_prior’ method: Mean and standard deviation of each marginal from the joint prior. The default is True.

hyperparameter_gradientbool, optional

Gradients of the hyperparameter. Only for ‘parametric_prior’ method. The default is True.

Returns:

save_hist_dictdict

dictionary with inclusion/exclusion settings for each sub-result in history object.

elicit.configs.save_results(target_quantities: bool = True, elicited_statistics: bool = True, prior_samples: bool = True, model_samples: bool = True, model: bool = True, expert_elicited_statistics: bool = True, expert_prior_samples: bool = True, init_loss_list: bool = True, init_prior: bool = True, init_matrix: bool = True, loss_tensor_expert: bool = True, loss_tensor_model: bool = True)

Controls whether sub-results of the result object should be included or excluded in the final result file. Results are based on the computation of the last epoch. By default all sub-results are included.

Parameters:

target_quantitiesbool, optional

simulation-based target quantities. The default is True.

elicited_statisticsbool, optional

simulation-based elicited statistics. The default is True.

prior_samplesbool, optional

samples from simulation-based prior distributions. The default is True.

model_samplesbool, optional

output variables from the simulation-based generative model. The default is True.

modelbool, optional

fitted elicit model object including the trainable variables. The default is True.

expert_elicited_statisticsbool, optional

expert-elicited statistics. The default is True.

expert_prior_samplesbool, optional

if oracle is used: samples from the true prior distribution, otherwise it is None. The default is True.

init_loss_listbool, optional

initialization phase: Losses related to the samples drawn from the initialization distribution. Only included for method ‘parametric_prior’. The default is True.

init_priorbool, optional

initialized elicit model object including the trainable variables. Only included for method ‘parametric_prior’. The default is True.

init_matrixbool, optional

initialization phase: samples drawn from the initialization distribution for each hyperparameter. Only included for method ‘parametric_prior’. The default is True.

loss_tensor_expertbool, optional

expert term in loss component for computing the discrepancy. The default is True.

loss_tensor_modelbool, optional

simulation-based term in loss component for computing the discrepancy. The default is True.

Returns:

save_res_dictdict

dictionary with inclusion/exclusion settings for each sub-result in results object.