Source code for elfi.methods.model_selection

"""This module contains methods for model comparison and selection."""

import numpy as np

[docs]def compare_models(sample_objs, model_priors=None): """Find posterior probabilities for different models. The algorithm requires elfi.Sample objects from prerun inference methods. For example the output from elfi.Rejection.sample is valid. The portion of samples for each model in the top discrepancies are adjusted by each models acceptance ratio and prior probability. The discrepancies (including summary statistics) must be comparable so that it is meaningful to sort them! Parameters ---------- sample_objs : list of elfi.Sample Resulting Sample objects from prerun inference models. The objects must include a valid `discrepancies` attribute. model_priors : array_like, optional Prior probability of each model. Defaults to 1 / n_models. Returns ------- np.array Posterior probabilities for the considered models. """ n_models = len(sample_objs) n_min = min([s.n_samples for s in sample_objs]) # concatenate discrepancy vectors try: discrepancies = np.concatenate([s.discrepancies for s in sample_objs]) except ValueError: raise ValueError("All Sample objects must include valid discrepancies.") # sort and take the smallest n_min inds = np.argsort(discrepancies)[:n_min] # calculate the portions of accepted samples for each model in the top discrepancies p_models = np.empty(n_models) up_bound = 0 for i in range(n_models): low_bound = up_bound up_bound += sample_objs[i].n_samples p_models[i] = np.logical_and(inds >= low_bound, inds < up_bound).sum() # adjust by the number of simulations run p_models[i] /= sample_objs[i].n_sim # adjust by the prior model probability if model_priors is not None: p_models[i] *= model_priors[i] p_models = p_models / p_models.sum() return p_models