# Copyright 2014-2020 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.stats._pairwise.
The stats._pairwise module implements pairwise statistical algorithms.
"""
from ._mean import amean, hmean, std
from ..distance._levenshtein import sim_levenshtein as sim
__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.
Parameters
----------
collection : list
A collection of terms or a string that can be split
metric : function
A similarity metric function
mean_func : function
A mean function that takes a list of values and returns a float
symmetric : bool
Set to True if all pairwise similarities should be calculated in both
directions
Returns
-------
float
The mean pairwise similarity of a collection of strings
Raises
------
ValueError
mean_func must be a function
ValueError
metric must be a function
ValueError
collection is neither a string nor iterable type
ValueError
collection has fewer than two members
Examples
--------
>>> round(mean_pairwise_similarity(['Christopher', 'Kristof',
... 'Christobal']), 12)
0.519801980198
>>> round(mean_pairwise_similarity(['Niall', 'Neal', 'Neil']), 12)
0.545454545455
.. versionadded:: 0.1.0
"""
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.
Parameters
----------
src_collection : list
A collection of terms or a string that can be split
tar_collection : list
A collection of terms or a string that can be split
metric : function
A similarity metric function
mean_func : function
A mean function that takes a list of values and returns a float
symmetric : bool
Set to True if all pairwise similarities should be calculated in both
directions
Returns
-------
tuple
The max, min, mean, and standard deviation of similarities
Raises
------
ValueError
mean_func must be a function
ValueError
metric must be a function
ValueError
src_collection is neither a string nor iterable
ValueError
tar_collection is neither a string nor iterable
Example
-------
>>> tuple(round(_, 12) for _ in pairwise_similarity_statistics(
... ['Christopher', 'Kristof', 'Christobal'], ['Niall', 'Neal', 'Neil']))
(0.2, 0.0, 0.118614718615, 0.075070477184)
.. versionadded:: 0.3.0
"""
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()