Thank you for your interest in our paper: Generalizing Tree Probability Estimation via Bayesian Networks.
Please consider citing the paper when any of the material is used for your research.
@incollection{NIPS2018_7418,
title = {Generalizing Tree Probability Estimation via Bayesian Networks},
author = {Zhang, Cheng and Matsen IV, Frederick A},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {1449--1458},
year = {2018},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/7418-generalizing-tree-probability-estimation-via-bayesian-networks.pdf}
}
Load MCMC sample
from utils import summary, mcmc_treeprob
# for golden runs
tree_dict_total, tree_names_total, tree_wts_total = summary(dataname, data_directory)
# for sample runs
tree_dict, tree_names, tree_wts = mcmc_treeprob(path_to_data, 'nexus')Run SBN
from models import SBN
# parameters to set up the model
# @taxa is the taxa list of the dataset
# @emp_tree_freq is the empirical frequency dictionary of the trees, can be left None if kl divergence computation is not required.
model = SBN(taxa, emp_tree_freq)
# parameters to train the model
# @tree_dict is the unique tree dictionary
# @tree_names is the name list of the trees
# @tree_wts is the corresponding frequencies for the trees with names in tree_names
# run sbn-sa
model.bn_train_prob(tree_dict, tree_names, tree_wts)
# run sbn-em
logp = model.bn_em_prob(tree_dict, tree_names, tree_wts, maxiter=200, abstol=1e-05, monitor=True, MAP=False)Once trained, one can compute the sbn probablities of trees
sbn_est_prob = model.bn_estimate(tree)
When emp_tree_freq is provided, one can evaluate the kl divergence
sbn_kl_div = model.kl_div(method='bn')['bn']
See more detailed examples in the jupyter notebooks.