# -*- coding: utf-8 -*-
# Copyright 2014-2018 by Christopher C. Little.
# This file is part of Abydos.
#
# Abydos 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.
#
# Abydos 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 Abydos. If not, see <http://www.gnu.org/licenses/>.
"""abydos.clustering.
The clustering module implements clustering algorithms such as:
- mean pair-wise similarity
"""
from __future__ import division, unicode_literals
from six.moves import range
from .distance import sim
from .stats import amean, hmean, std
__all__ = ['mean_pairwise_similarity', 'pairwise_similarity_statistics']
[docs]def mean_pairwise_similarity(collection, metric=sim,
mean_func=hmean, symmetric=False):
"""Calculate the mean pairwise similarity of a collection of strings.
Takes the mean of the pairwise similarity between each member of a
collection, optionally in both directions (for asymmetric similarity
metrics.
:param list collection: a collection of terms or a string that can be split
:param function metric: a similarity metric function
:param function mean_func: a mean function that takes a list of values and
returns a float
:param bool symmetric: set to True if all pairwise similarities should be
calculated in both directions
:returns: the mean pairwise similarity of a collection of strings
:rtype: float
>>> round(mean_pairwise_similarity(['Christopher', 'Kristof',
... 'Christobal']), 12)
0.519801980198
>>> round(mean_pairwise_similarity(['Niall', 'Neal', 'Neil']), 12)
0.545454545455
"""
if not callable(mean_func):
raise ValueError('mean_func must be a function')
if not callable(metric):
raise ValueError('metric must be a function')
if hasattr(collection, 'split'):
collection = collection.split()
if not hasattr(collection, '__iter__'):
raise ValueError('collection is neither a string nor iterable type')
elif len(collection) < 2:
raise ValueError('collection has fewer than two members')
collection = list(collection)
pairwise_values = []
for i in range(len(collection)):
for j in range(i+1, len(collection)):
pairwise_values.append(metric(collection[i], collection[j]))
if symmetric:
pairwise_values.append(metric(collection[j], collection[i]))
return mean_func(pairwise_values)
[docs]def pairwise_similarity_statistics(src_collection, tar_collection, metric=sim,
mean_func=amean, symmetric=False):
"""Calculate the pairwise similarity statistics a collection of strings.
Calculate pairwise similarities among members of two collections,
returning the maximum, minimum, mean (according to a supplied function,
arithmetic mean, by default), and (population) standard deviation
of those similarities.
:param list src_collection: a collection of terms or a string that can be
split
:param list tar_collection: a collection of terms or a string that can be
split
:param function metric: a similarity metric function
:param function mean_func: a mean function that takes a list of values and
returns a float
:param bool symmetric: set to True if all pairwise similarities should be
calculated in both directions
:returns: the max, min, mean, and standard deviation of similarities
:rtype: tuple
>>> tuple(round(_, 12) for _ in pairwise_similarity_statistics(
... ['Christopher', 'Kristof', 'Christobal'], ['Niall', 'Neal', 'Neil']))
(0.2, 0.0, 0.118614718615, 0.075070477184)
"""
if not callable(mean_func):
raise ValueError('mean_func must be a function')
if not callable(metric):
raise ValueError('metric must be a function')
if hasattr(src_collection, 'split'):
src_collection = src_collection.split()
if not hasattr(src_collection, '__iter__'):
raise ValueError('src_collection is neither a string nor iterable')
if hasattr(tar_collection, 'split'):
tar_collection = tar_collection.split()
if not hasattr(tar_collection, '__iter__'):
raise ValueError('tar_collection is neither a string nor iterable')
src_collection = list(src_collection)
tar_collection = list(tar_collection)
pairwise_values = []
for src in src_collection:
for tar in tar_collection:
pairwise_values.append(metric(src, tar))
if symmetric:
pairwise_values.append(metric(tar, src))
return (max(pairwise_values), min(pairwise_values),
mean_func(pairwise_values), std(pairwise_values, mean_func, 0))
if __name__ == '__main__':
import doctest
doctest.testmod()