Source code for MDAnalysis.analysis.encore.clustering.ClusterCollection
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"""
Cluster representation --- :mod:`MDAnalysis.analysis.encore.clustering.ClusterCollection`
=========================================================================================
The module contains the Cluster and ClusterCollection classes which are
designed to store results from clustering algorithms.
:Author: Matteo Tiberti, Wouter Boomsma, Tone Bengtsen
.. versionadded:: 0.16.0
"""
import numpy as np
[docs]class Cluster(object):
"""
Generic Cluster class for clusters with centroids.
Attributes
----------
id : int
Cluster ID number. Useful for the ClustersCollection class
metadata : iterable
dict of lists or numpy.array, containing metadata for the cluster
elements. The iterable must return the same number of elements as
those that belong to the cluster.
size : int
number of elements.
centroid : element object
cluster centroid.
elements : numpy.array
array containing the cluster elements.
"""
def __init__(self, elem_list=None, centroid=None, idn=None, metadata=None):
"""Class constructor. If elem_list is None, an empty cluster is created
and the remaining arguments ignored.
Parameters
----------
elem_list : numpy.array or None
numpy array of cluster elements
centroid : None or element object
centroid
idn : int
cluster ID
metadata : iterable
metadata, one value for each cluster element. The iterable
must have the same length as the elements array.
"""
self.id = idn
if elem_list is None:
self.size = 0
self.elements = np.array([])
self.centroid = None
self.metadata = {}
return
self.metadata = {}
self.elements = elem_list
if centroid not in self.elements:
raise LookupError("Centroid of cluster not found in the element list")
self.centroid = centroid
self.size = self.elements.shape[0]
if metadata:
for name, data in metadata.items():
if len(data) != self.size:
raise TypeError('Size of metadata having label "{0}"'
'is not equal to the number of cluster '
'elements'.format(name))
self.add_metadata(name, data)
def __iter__(self):
"""
Iterate over elements in cluster
"""
return iter(self.elements)
def __len__(self):
"""
Size of cluster
"""
return len(self.elements)
def add_metadata(self, name, data):
if len(data) != self.size:
raise TypeError("Size of metadata is not equal to the number of"
"cluster elmements")
self.metadata[name] = np.array(data)
def __repr__(self):
"""
Textual representation
"""
if self.size == 0:
return "<Cluster with no elements>"
else:
return "<Cluster with {0} elements, centroid={1}, id={2}>".format(
self.size,
self.centroid,
self.id)
[docs]class ClusterCollection(object):
"""Clusters collection class; this class represents the results of a full
clustering run. It stores a group of clusters defined as
encore.clustering.Cluster objects.
Attributes
----------
clusters : list
list of of Cluster objects which are part of the Cluster collection
"""
def __init__(self, elements=None, metadata=None):
"""Class constructor. If elements is None, an empty cluster collection
will be created. Otherwise, the constructor takes as input an
iterable of ints, for instance:
[ a, a, a, a, b, b, b, c, c, ... , z, z ]
the variables a,b,c,...,z are cluster centroids, here as cluster
element numbers (i.e. 3 means the 4th element of the ordered input
for clustering). The array maps a correspondence between
cluster elements (which are implicitly associated with the
position in the array) with centroids, i. e. defines clusters.
For instance:
[ 1, 1, 1, 4, 4, 5 ]
means that elements 0, 1, 2 form a cluster which has 1 as centroid,
elements 3 and 4 form a cluster which has 4 as centroid, and
element 5 has its own cluster.
Parameters
----------
elements : iterable of ints or None
clustering results. See the previous description for details
metadata : {str:list, str:list,...} or None
metadata for the data elements. The list must be of the same
size as the elements array, with one value per element.
"""
idn = 0
if elements is None:
self.clusters = None
return
if not len(set((type(el) for el in elements))) == 1:
raise TypeError("all the elements must have the same type")
self.clusters = []
elements_array = np.array(elements)
centroids = np.unique(elements_array)
for i in centroids:
if elements[i] != i:
raise ValueError("element {0}, which is a centroid, doesn't "
"belong to its own cluster".format(
elements[i]))
for c in centroids:
this_metadata = {}
this_array = np.where(elements_array == c)
if metadata:
for k, v in metadata.items():
this_metadata[k] = np.asarray(v)[this_array]
self.clusters.append(
Cluster(elem_list=this_array[0], idn=idn, centroid=c,
metadata=this_metadata))
idn += 1
[docs] def get_ids(self):
"""
Get the ID numbers of the clusters
Returns
-------
ids : list of int
list of cluster ids
"""
return [v.id for v in self.clusters]
[docs] def get_centroids(self):
"""
Get the centroids of the clusters
Returns
-------
centroids : list of cluster element objects
list of cluster centroids
"""
return [v.centroid for v in self.clusters]
def __iter__(self):
"""
Iterate over clusters
"""
return iter(self.clusters)
def __len__(self):
"""
Length of clustering collection
"""
return len(self.clusters)
def __repr__(self):
"""
Textual representation
"""
if self.clusters is None:
return "<ClusterCollection with no clusters>"
else:
return "<ClusterCollection with {0} clusters>".format(
len(self.clusters))