BayesPermus 1.0.0

Bayesian inference of algorithm performance using permutation models.

BayesPermus

Bayesian inference of algorithm performance using permutation models.

Installation

pip install BayesPermus

Usage

1. Prepare permutation data:

permus = np.array([[1,2,3], [1,3,2]])

2. Obtain the marginal probabilities:

from BayesPermus.models.BradleyTerry import BradleyTerry

# BT Dirichlet hyper-priors
dirichlet_alpha_bt = [1, 1, 1]

# Create Bayesian inference model
bradleyTerry = BradleyTerry(dirichlet_alpha_bt, num_samples=1000)

# Calculate the marginal probabilities
probs = bradleyTerry.calculate_top_ranking_probs(permus)

Additional available models

• Bradley-Terry:

from BayesPermus.models.BradleyTerry import BradleyTerry

• Plackett-Luce:

from BayesPermus.models.PlackettLuce import PlackettLuceDirichlet
from BayesPermus.models.PlackettLuce import PlackettLuceGamma

• Mallows Model:

from BayesPermus.models.MallowsModel import MallowsModel

Additional available marginals

• Probability of an algorithm to be in the first position: model.calculate_top_ranking_probs(...).
• Probability of an algorithm to outperform another: model.calculate_better_than_probs(...).
• Probability of an algorithm to be in the top-k ranking: model.calculate_top_k_probs(...).