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# #START_LICENSE###########################################################
#
#
# This file is part of the Environment for Tree Exploration program
# (ETE). http://etetoolkit.org
#
# ETE is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# ETE is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
# or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public
# License for more details.
#
# You should have received a copy of the GNU General Public License
# along with ETE. If not, see <http://www.gnu.org/licenses/>.
#
#
# ABOUT THE ETE PACKAGE
# =====================
#
# ETE is distributed under the GPL copyleft license (2008-2015).
#
# If you make use of ETE in published work, please cite:
#
# Jaime Huerta-Cepas, Joaquin Dopazo and Toni Gabaldon.
# ETE: a python Environment for Tree Exploration. Jaime BMC
# Bioinformatics 2010,:24doi:10.1186/1471-2105-11-24
#
# Note that extra references to the specific methods implemented in
# the toolkit may be available in the documentation.
#
# More info at http://etetoolkit.org. Contact: huerta@embl.de
#
#
# #END_LICENSE#############################################################
from __future__ import absolute_import
from .. import numpy
from math import sqrt
from six.moves import range
def safe_mean(values):
""" Returns mean value discarding non finite values """
valid_values = []
for v in values:
if numpy.isfinite(v):
valid_values.append(v)
return numpy.mean(valid_values), numpy.std(valid_values)
def safe_mean_vector(vectors):
""" Returns mean profile discarding non finite values.
"""
# if only one vector, avg = itself
if len(vectors)==1:
return vectors[0], numpy.zeros(len(vectors[0]))
# Takes the vector length form the first item
length = len(vectors[0])
safe_mean = []
safe_std = []
for pos in range(length):
pos_mean = []
for v in vectors:
if numpy.isfinite(v[pos]):
pos_mean.append(v[pos])
safe_mean.append(numpy.mean(pos_mean))
safe_std.append(numpy.std(pos_mean))
return numpy.array(safe_mean), numpy.array(safe_std)
def get_silhouette_width(fdist, cluster):
sisters = cluster.get_sisters()
# Calculates silhouette
silhouette = []
intra_dist = []
inter_dist = []
for st in sisters:
if st.profile is None:
continue
for i in cluster.iter_leaves():
# Skip nodes without profile
if i._profile is not None:
# item intraclsuterdist -> Centroid Diameter
a = fdist(i.profile, cluster.profile)*2
# intracluster dist -> Centroid Linkage
b = fdist(i.profile, st.profile)
if (b-a) == 0.0:
s = 0.0
else:
s = (b-a) / max(a,b)
intra_dist.append(a)
inter_dist.append(b)
silhouette.append(s)
silhouette, std = safe_mean(silhouette)
intracluster_dist, std = safe_mean(intra_dist)
intercluster_dist, std = safe_mean(inter_dist)
return silhouette, intracluster_dist, intercluster_dist
def get_avg_profile(node):
""" This internal function updates the mean profile
associated to an internal node. """
if not node.is_leaf():
leaf_vectors = [n._profile for n in node.get_leaves() \
if n._profile is not None]
if len(leaf_vectors)>0:
node._profile, node._std_profile = safe_mean_vector(leaf_vectors)
else:
node._profile, node._std_profile = None, None
return node._profile, node._std_profile
else:
node._std_profile = [0.0]*len(node._profile)
return node._profile, [0.0]*len(node._profile)
def get_dunn_index(fdist, *clusters):
"""
Returns the Dunn index for the given selection of nodes.
J.C. Dunn. Well separated clusters and optimal fuzzy
partitions. 1974. J.Cybern. 4. 95-104.
"""
if len(clusters)<2:
raise ValueError("At least 2 clusters are required")
intra_dist = []
for c in clusters:
for i in c.get_leaves():
if i is not None:
# item intraclsuterdist -> Centroid Diameter
a = fdist(i.profile, c.profile)*2
intra_dist.append(a)
max_a = numpy.max(intra_dist)
inter_dist = []
for i, ci in enumerate(clusters):
for cj in clusters[i+1:]:
# intracluster dist -> Centroid Linkage
b = fdist(ci.profile, cj.profile)
inter_dist.append(b)
min_b = numpy.min(inter_dist)
if max_a == 0.0:
D = 0.0
else:
D = min_b / max_a
return D
# ####################
# distance functions
# ####################
def pearson_dist(v1, v2):
try:
from scipy.stats import pearsonr
except ImportError:
raise RuntimeError("scipy is required to execute this function. Please install it an try again")
if (v1 == v2).all():
return 0.0
else:
return 1.0 - pearsonr(list(v1), list(v2))[0]
def spearman_dist(v1, v2):
try:
from scipy.stats import spearmanr
except ImportError:
raise RuntimeError("scipy is required to execute this function. Please install it an try again")
if (v1 == v2).all():
return 0.0
else:
return 1.0 - spearmanr(list(v1), list(v2))[0]
def euclidean_dist(v1, v2):
if (v1 == v2).all():
return 0.0
else:
return sqrt( square_euclidean_dist(v1,v2) )
def square_euclidean_dist(v1,v2):
if (v1 == v2).all():
return 0.0
valids = 0
distance= 0.0
for i in range(len(v1)):
if numpy.isfinite(v1[i]) and numpy.isfinite(v2[i]):
valids += 1
d = v1[i]-v2[i]
distance += d*d
if valids==0:
raise ValueError("Cannot calculate values")
return distance/valids
default_dist = spearman_dist
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