elicit.utils module#
- elicit.utils.one_forward_simulation(prior_model, trainer, model, targets)[source]#
One forward simulation from prior samples to elicited statistics.
- Parameters:
- prior_modelinstance of Priors class objects
initialized prior distributions which can be used for sampling.
- global_dictdict
global dictionary with all user input specifications.
- ground_truthbool, optional
Is true if model should be learned with simulated data that represent a pre-defined ground truth. The default is False.
- Returns:
- elicited_statisticsdict
dictionary containing the elicited statistics that can be used to compute the loss components
- elicit.utils.get_expert_data(trainer, model, targets, expert, parameters, network)[source]#
Wrapper for loading the training data which can be expert data or data simulations using a pre-defined ground truth.
- Parameters:
- global_dictdict
global dictionary with all user input specifications.
- path_to_expert_datastr, optional
path to file location where expert data has been saved
- Returns:
- expert_datadict
dictionary containing the training data. Must have same form as the model-simulated elicited statistics.