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beta_metrics.py
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beta_metrics.py
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#!/usr/bin/env python
__author__ = "Rob Knight, Justin Kuczynski"
__copyright__ = "Copyright 2011, The QIIME Project"
__credits__ = ["Rob Knight", "Justin Kuczynski"]
__license__ = "GPL"
__version__ = "1.8.0"
__maintainer__ = "Justin Kuczynski"
__email__ = "justinak@gmail.com"
"""contains metrics for use with beta_diversity.py
most metrics are from cogent.math.distance_transform.py,
but some need wrappers to look like f(data, taxon_names, tree)-> dist_mtx
"""
import cogent.maths.unifrac.fast_tree as fast_tree
# (unifrac, unnormalized_unifrac,
# G, unnormalized_G, weighted_unifrac)
from cogent.maths.unifrac.fast_unifrac import fast_unifrac, fast_unifrac_one_sample
from qiime.parse import make_envs_dict
import numpy
import warnings
def make_unifrac_metric(weighted, metric, is_symmetric):
"""Make a unifrac-like metric.
Parameters:
sample_names: list of unique strings
weighted: bool, if True does the necessary root to tip calcs.
metric: f(branch_lengths, i, j) -> distance
is_symmetric: saves calc time if metric is symmetric.
kwargs passed to fast_unifrac
"""
def result(data, taxon_names, tree, sample_names, **kwargs):
""" wraps the fast_unifrac fn to return just a matrix, in correct order
sample_names: list of unique strings
"""
envs = make_envs_dict(data, sample_names, taxon_names)
unifrac_res = fast_unifrac(tree, envs, weighted=weighted, metric=metric,
is_symmetric=is_symmetric, modes=["distance_matrix"],**kwargs)
dist_mtx = _reorder_unifrac_res(unifrac_res['distance_matrix'],
sample_names)
return dist_mtx
return result
# these should start with dist_ to be discoverable by beta_diversity.py
# unweighted full tree => keep the full tree relating all samples.
# Compute how much branch
# length is present in one sample but not (both samples OR NEITHER
# SAMPLE). Divide by total branch length of full tree.
# G is asymmetric unifrac
dist_unweighted_unifrac = make_unifrac_metric(False, fast_tree.unifrac, True)
dist_unifrac = dist_unweighted_unifrac # default unifrac is just unifrac
dist_unweighted_unifrac_full_tree = make_unifrac_metric(False,
fast_tree.unnormalized_unifrac, True)
dist_weighted_unifrac = make_unifrac_metric(True,
fast_tree.weighted_unifrac, True)
dist_weighted_normalized_unifrac = make_unifrac_metric('correct',
fast_tree.weighted_unifrac, True)
dist_unifrac_g = make_unifrac_metric(False, fast_tree.G, False)
dist_unifrac_g_full_tree = make_unifrac_metric(False,
fast_tree.unnormalized_G, False)
def make_unifrac_row_metric(weighted, metric, is_symmetric):
"""Make a unifrac-like metric, for only one row of the dissm mtx
Parameters:
sample_names: list of unique strings
weighted: bool, if True does the necessary root to tip calcs.
metric: f(branch_lengths, i, j) -> distance
is_symmetric: ignored
sample_name: of the sample corresponding to the row of the dissim mtx
"""
def result(data, taxon_names, tree, sample_names, one_sample_name,**kwargs):
""" wraps the fast_unifrac fn to return just a matrix, in correct order
sample_names: list of unique strings
"""
envs = make_envs_dict(data, sample_names, taxon_names)
try:
unifrac_res = fast_unifrac_one_sample(one_sample_name,
tree, envs, weighted=weighted, metric=metric,**kwargs)
except ValueError as e:
if 'one_sample_name not found' in str(e):
warnings.warn('unifrac had no information on sample ' +\
one_sample_name +\
". Distances involving that sample aren't meaningful")
unifrac_res = (numpy.array([0.0]),[one_sample_name]) # self only
else:
raise e
dist_mtx = _reorder_unifrac_res_one_sample(unifrac_res,
sample_names)
return dist_mtx
return result
one_sample_unweighted_unifrac = make_unifrac_row_metric(False, fast_tree.unifrac, True)
one_sample_unifrac = one_sample_unweighted_unifrac # default unifrac is just unifrac
one_sample_unweighted_unifrac_full_tree = make_unifrac_row_metric(False,
fast_tree.unnormalized_unifrac, True)
one_sample_weighted_unifrac = make_unifrac_row_metric(True,
fast_tree.weighted_unifrac, True)
one_sample_weighted_normalized_unifrac = make_unifrac_row_metric('correct',
fast_tree.weighted_unifrac, True)
one_sample_unifrac_g = make_unifrac_row_metric(False, fast_tree.G, False)
one_sample_unifrac_g_full_tree = make_unifrac_row_metric(False,
fast_tree.unnormalized_G, False)
def _reorder_unifrac_res(unifrac_res, sample_names_in_desired_order):
""" reorder unifrac result
unifrac res is distmtx,sample_names. sample names not in unifrac's
sample names (not in tree, all zeros in otu table(?)) will be included,
with a user warning.
"""
sample_names = sample_names_in_desired_order
unifrac_dist_mtx = unifrac_res[0]
unifrac_sample_names = unifrac_res[1]
unifrac_sample_names_idx = dict([(n,i) for i,n in enumerate(unifrac_sample_names)])
if unifrac_sample_names == sample_names:
dist_mtx = unifrac_dist_mtx
else:
dist_mtx = numpy.zeros((len(sample_names), len(sample_names)))
for i, sam_i in enumerate(sample_names):
# make dist zero if both absent, else dist=1. dist to self is 0
if sam_i not in unifrac_sample_names_idx:
warnings.warn('unifrac had no information for sample ' +\
sam_i + ". Distances involving that sample aren't meaningful")
for j, sam_j in enumerate(sample_names):
if sam_j not in unifrac_sample_names_idx:
dist_mtx[i,j] = 0.0
else:
dist_mtx[i,j] = 1.0
# sam_i is present, so get unifrac dist
else:
unifrac_i = unifrac_sample_names_idx[sam_i]
for j, sam_j in enumerate(sample_names):
if sam_j not in unifrac_sample_names_idx:
dist_mtx[i,j] = 1.0
else:
unifrac_j = unifrac_sample_names_idx[sam_j]
dist_mtx[i,j] = unifrac_dist_mtx[unifrac_i, unifrac_j]
return dist_mtx
def _reorder_unifrac_res_one_sample(unifrac_res, sample_names_in_desired_order):
""" reorder unifrac result
unifrac res is distmtx,sample_names. sample names not in unifrac's
sample names (not in tree, all zeros in otu table(?)) will be included.
No user warning is printed for this one_sample case, that must be handled
by this function's caller.
"""
sample_names = sample_names_in_desired_order
unifrac_dist_arry = unifrac_res[0]
unifrac_sample_names = unifrac_res[1]
unifrac_sample_names_idx = dict([(n,i) for i,n in enumerate(unifrac_sample_names)])
if unifrac_sample_names == sample_names:
dist_arry = unifrac_dist_arry
else:
dist_arry= numpy.zeros(len(sample_names))
for i, sam_i in enumerate(sample_names):
# make dist zero if both absent, else dist=1. dist to self is 0
if sam_i not in unifrac_sample_names_idx:
dist_arry[i] = 1.0
# sam_i is present, so get unifrac dist
else:
unifrac_i = unifrac_sample_names_idx[sam_i]
dist_arry[i] = unifrac_dist_arry[unifrac_i]
return dist_arry