# Copyright 2019-2020 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.distance._ssk.
String subsequence kernel (SSK) similarity
"""
from ._token_distance import _TokenDistance
from ..tokenizer import QSkipgrams
__all__ = ['SSK']
[docs]class SSK(_TokenDistance):
r"""String subsequence kernel (SSK) similarity.
This is based on :cite:`Lodhi:2002`.
.. versionadded:: 0.4.1
"""
def __init__(self, tokenizer=None, ssk_lambda=0.9, **kwargs):
"""Initialize SSK instance.
Parameters
----------
tokenizer : _Tokenizer
A tokenizer instance from the :py:mod:`abydos.tokenizer` package
ssk_lambda : float or Iterable
A value in the range (0.0, 1.0) used for discouting gaps between
characters according to the method described in :cite:`Lodhi:2002`.
To supply multiple values of lambda, provide an Iterable of numeric
values, such as (0.5, 0.05) or np.arange(0.05, 0.5, 0.05)
**kwargs
Arbitrary keyword arguments
Other Parameters
----------------
qval : int
The length of each q-skipgram. Using this parameter and
tokenizer=None will cause the instance to use the QGramskipgrams
tokenizer with this q value.
.. versionadded:: 0.4.1
"""
super(SSK, self).__init__(
tokenizer=tokenizer, ssk_lambda=ssk_lambda, **kwargs
)
qval = 2 if 'qval' not in self.params else self.params['qval']
self.params['tokenizer'] = (
tokenizer
if tokenizer is not None
else QSkipgrams(
qval=qval, start_stop='', scaler='SSK', ssk_lambda=ssk_lambda
)
)
[docs] def sim_score(self, src, tar):
"""Return the SSK similarity of two strings.
Parameters
----------
src : str
Source string for comparison
tar : str
Target string for comparison
Returns
-------
float
String subsequence kernel similarity
Examples
--------
>>> cmp = SSK()
>>> cmp.dist_abs('cat', 'hat')
0.6441281138790036
>>> cmp.dist_abs('Niall', 'Neil')
0.5290992177869402
>>> cmp.dist_abs('aluminum', 'Catalan')
0.862398428061774
>>> cmp.dist_abs('ATCG', 'TAGC')
0.38591004719395017
.. versionadded:: 0.4.1
"""
self._tokenize(src, tar)
src_wts = self._src_tokens
tar_wts = self._tar_tokens
score = sum(
src_wts[token] * tar_wts[token] for token in src_wts & tar_wts
)
return score
[docs] def sim(self, src, tar):
"""Return the normalized SSK similarity of two strings.
Parameters
----------
src : str
Source string (or QGrams/Counter objects) for comparison
tar : str
Target string (or QGrams/Counter objects) for comparison
Returns
-------
float
Normalized string subsequence kernel similarity
Examples
--------
>>> cmp = SSK()
>>> cmp.sim('cat', 'hat')
0.3558718861209964
>>> cmp.sim('Niall', 'Neil')
0.4709007822130597
>>> cmp.sim('aluminum', 'Catalan')
0.13760157193822603
>>> cmp.sim('ATCG', 'TAGC')
0.6140899528060498
.. versionadded:: 0.4.1
"""
if src == tar:
return 1.0
self._tokenize(src, tar)
src_wts = self._src_tokens
tar_wts = self._tar_tokens
score = sum(
src_wts[token] * tar_wts[token] for token in src_wts & tar_wts
)
norm = (
sum(src_wts[token] * src_wts[token] for token in src_wts)
* sum(tar_wts[token] * tar_wts[token] for token in tar_wts)
) ** 0.5
if not score:
return 0.0
return score / norm
if __name__ == '__main__':
import doctest
doctest.testmod()