"""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!
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.
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
discrepancies = np.concatenate([s.discrepancies for s in sample_objs])
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()