BayesPermus 0.0.1

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 = num_algorithms * [1]


bradleyTerry = BradleyTerry(dirichlet_alpha_bt, num_samples=1000)

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

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(...).