Developing efficient Bayesian phylogenetic inference methods
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The goal of this package is to develop efficient approximate inference methods for Bayesian phylogenetics. So far, we have developed a probablistic path Hamiltonian Monte Carlo method for Bayesian phylogenetic inference.
PhyloInfer is a python package that is built on ETE toolkit and biopython
To install, simply run
pip install phyloinfer
Please install pyTQDist if you want to use the related features.
In what follows, we present some simple examples on simulated data and real data.
First, we simulate data from a given phylogeny tree with 50 tips
# load modules
import phyloinfer as pinf
import numpy as np
# set model parameters
pden = np.array([.25,.25,.25,.25])
# decompose the rate matrix (JC model)
D, U, U_inv, rate_matrix = pinf.rateM.decompJC()
# sample a random tree from the prior
ntips = 50
true_tree = pinf.tree.create(ntips, branch='random')
# simulate Data
data = pinf.data.treeSimu(true_tree, D, U, U_inv, pden, 1000)
Now, you may want to take a look at the negative log-posterior or the log-likelihood of the true tree
L = pinf.Loglikelihood.initialCLV(data)
true_branch = pinf.branch.get(true_tree)
print "The negative log-posterior of the true tree: {}".format(pinf.Logposterior.Logpost(true_tree, true_branch, D, U, U_inv, pden, L))
print "The log-likelihood of the true tree: {}".format(pinf.Loglikelihood.phyloLoglikelihood(true_tree, true_branch, D, U, U_inv, pden, L))
Next, we sample a starting tree from the prior
init_tree = pinf.tree.create(ntips, branch='random')
Again, you may want to see its negative log-posterior or log-likelihood
init_branch = pinf.branch.get(init_tree)
print "The negative log-posterior of the init tree: {}".format(pinf.Logposterior.Logpost(init_tree, init_branch, D, U, U_inv, pden, L))
print "The log-likelihood of the init tree: {}".format(pinf.Loglikelihood.phyloLoglikelihood(init_tree, init_branch, D, U, U_inv, pden, L))
Now, we are ready to run ppHMC to sample from the posterior!!!
samp_res = pinf.phmc.hmc(init_tree, init_branch, (pden, 1.0), data, 100, 0.001, 100, subModel='JC', surrogate=True, burnin_frac=0.2, adap_stepsz_rate = 0.4, delta=0.002, monitor_event=True, printfreq=50)
Load primates data set
data, taxon = pinf.data.loadData('../datasets/primates.nex','nexus')
Again, initialize the tree from the prior
ntips = len(taxon)
init_tree = pinf.tree.create(ntips, branch='random')
init_branch = pinf.branch.get(init_tree)
Run ppHMC to sample from the posterior
samp_res = pinf.phmc.hmc(init_tree, init_branch, (pden, 1.0), data, 100, 0.004, 100, subModel='JC', surrogate=True, burnin_frac=0.5, delta=0.008, adap_stepsz_rate=0.8, printfreq=20)
For more details, see the notebooks in examples.
Content type
Image
Digest
Size
2 GB
Last updated
almost 7 years ago
docker pull matsen/phyloinfer