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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 __future__ import print_function
from sys import stderr
from . import clustvalidation
from ..coretype.tree import _translate_nodes
from .. import TreeNode, ArrayTable
from .. import numpy
from six.moves import range
__all__ = ["ClusterNode", "ClusterTree"]
class ClusterNode(TreeNode):
""" Creates a new Cluster Tree object, which is a collection
of ClusterNode instances connected in a hierarchical way, and
representing a clustering result.
a newick file or string can be passed as the first argument. An
ArrayTable file or instance can be passed as a second argument.
Examples:
t1 = Tree() # creates an empty tree
t2 = Tree( '(A:1,(B:1,(C:1,D:1):0.5):0.5);' )
t3 = Tree( '/home/user/myNewickFile.txt' )
"""
def _set_forbidden(self, value):
raise ValueError("This attribute can not be manually set.")
def _get_intra(self):
if self._silhouette is None:
self.get_silhouette()
return self._intracluster_dist
def _get_inter(self):
if self._silhouette is None:
self.get_silhouette()
return self._intercluster_dist
def _get_silh(self):
if self._silhouette is None:
self.get_silhouette()
return self._silhouette
def _get_prof(self):
if self._profile is None:
self._calculate_avg_profile()
return self._profile
def _get_std(self):
if self._std_profile is None:
self._calculate_avg_profile()
return self._std_profile
def _set_profile(self, value):
self._profile = value
intracluster_dist = property(fget=_get_intra, fset=_set_forbidden)
intercluster_dist = property(fget=_get_inter, fset=_set_forbidden)
silhouette = property(fget=_get_silh, fset=_set_forbidden)
profile = property(fget=_get_prof, fset=_set_profile)
deviation = property(fget=_get_std, fset=_set_forbidden)
def __init__(self, newick = None, text_array = None, \
fdist=clustvalidation.default_dist):
# Default dist is spearman_dist when scipy module is loaded
# otherwise, it is set to euclidean_dist.
# Initialize basic tree features and loads the newick (if any)
TreeNode.__init__(self, newick)
self._fdist = None
self._silhouette = None
self._intercluster_dist = None
self._intracluster_dist = None
self._profile = None
self._std_profile = None
# Cluster especific features
self.features.add("intercluster_dist")
self.features.add("intracluster_dist")
self.features.add("silhouette")
self.features.add("profile")
self.features.add("deviation")
# Initialize tree with array data
if text_array:
self.link_to_arraytable(text_array)
if newick:
self.set_distance_function(fdist)
def __repr__(self):
return "ClusterTree node (%s)" %hex(self.__hash__())
def set_distance_function(self, fn):
""" Sets the distance function used to calculate cluster
distances and silouette index.
ARGUMENTS:
fn: a pointer to python function acepting two arrays (numpy) as
arguments.
EXAMPLE:
# A simple euclidean distance
my_dist_fn = lambda x,y: abs(x-y)
tree.set_distance_function(my_dist_fn)
"""
for n in self.traverse():
n._fdist = fn
n._silhouette = None
n._intercluster_dist = None
n._intracluster_dist = None
def link_to_arraytable(self, arraytbl):
""" Allows to link a given arraytable object to the tree
structure under this node. Row names in the arraytable object
are expected to match leaf names.
Returns a list of nodes for with profiles could not been found
in arraytable.
"""
# Initialize tree with array data
if type(arraytbl) == ArrayTable:
array = arraytbl
else:
array = ArrayTable(arraytbl)
missing_leaves = []
matrix_values = [i for r in range(len(array.matrix))\
for i in array.matrix[r] if numpy.isfinite(i)]
array._matrix_min = min(matrix_values)
array._matrix_max = max(matrix_values)
for n in self.traverse():
n.arraytable = array
if n.is_leaf() and n.name in array.rowNames:
n._profile = array.get_row_vector(n.name)
elif n.is_leaf():
n._profile = [numpy.nan]*len(array.colNames)
missing_leaves.append(n)
if len(missing_leaves)>0:
print("""[%d] leaf names could not be mapped to the matrix rows.""" %\
len(missing_leaves), file=stderr)
self.arraytable = array
def iter_leaf_profiles(self):
""" Returns an iterator over all the profiles associated to
the leaves under this node."""
for l in self.iter_leaves():
yield l.get_profile()[0]
def get_leaf_profiles(self):
""" Returns the list of all the profiles associated to the
leaves under this node."""
return [l.get_profile()[0] for l in self.iter_leaves()]
def get_silhouette(self, fdist=None):
""" Calculates the node's silhouette value by using a given
distance function. By default, euclidean distance is used. It
also calculates the deviation profile, mean profile, and
inter/intra-cluster distances.
It sets the following features into the analyzed node:
- node.intracluster
- node.intercluster
- node.silhouete
intracluster distances a(i) are calculated as the Centroid
Diameter
intercluster distances b(i) are calculated as the Centroid linkage distance
** Rousseeuw, P.J. (1987) Silhouettes: A graphical aid to the
interpretation and validation of cluster analysis.
J. Comput. Appl. Math., 20, 53-65.
"""
if fdist is None:
fdist = self._fdist
# Updates internal values
self._silhouette, self._intracluster_dist, self._intercluster_dist = \
clustvalidation.get_silhouette_width(fdist, self)
# And returns them
return self._silhouette, self._intracluster_dist, self._intercluster_dist
def get_dunn(self, clusters, fdist=None):
""" Calculates the Dunn index for the given set of descendant
nodes.
"""
if fdist is None:
fdist = self._fdist
nodes = _translate_nodes(self, *clusters)
return clustvalidation.get_dunn_index(fdist, *nodes)
def _calculate_avg_profile(self):
""" This internal function updates the mean profile
associated to an internal node. """
# Updates internal values
self._profile, self._std_profile = clustvalidation.get_avg_profile(self)
# cosmetic alias
#: .. currentmodule:: ete3
#
ClusterTree = ClusterNode
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