# 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._fellegi_sunter.
Fellegi-Sunter similarity
"""
from math import exp, log
from sys import float_info
from ._token_distance import _TokenDistance
__all__ = ['FellegiSunter']
[docs]class FellegiSunter(_TokenDistance):
r"""Fellegi-Sunter similarity.
Fellegi-Sunter similarity is based on the description in
:cite:`Cohen:2003` and implementation in :cite:`Cohen:2003b`.
.. versionadded:: 0.4.0
"""
def __init__(
self,
tokenizer=None,
intersection_type='crisp',
simplified=False,
mismatch_factor=0.5,
**kwargs
):
"""Initialize FellegiSunter instance.
Parameters
----------
tokenizer : _Tokenizer
A tokenizer instance from the :py:mod:`abydos.tokenizer` package
intersection_type : str
Specifies the intersection type, and set type as a result:
See :ref:`intersection_type <intersection_type>` description in
:py:class:`_TokenDistance` for details.
simplified : bool
Specifies to use the simplified scoring variant
mismatch_factor : float
Specifies the penalty factor for mismatches
**kwargs
Arbitrary keyword arguments
Other Parameters
----------------
qval : int
The length of each q-gram. Using this parameter and tokenizer=None
will cause the instance to use the QGram tokenizer with this
q value.
metric : _Distance
A string distance measure class for use in the ``soft`` and
``fuzzy`` variants.
threshold : float
A threshold value, similarities above which are counted as
members of the intersection for the ``fuzzy`` variant.
.. versionadded:: 0.4.0
"""
super(FellegiSunter, self).__init__(
tokenizer=tokenizer, intersection_type=intersection_type, **kwargs
)
self._simplified = simplified
self._mismatch_factor = mismatch_factor
[docs] def sim_score(self, src, tar):
"""Return the Fellegi-Sunter 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
Fellegi-Sunter similarity
Examples
--------
>>> cmp = FellegiSunter()
>>> cmp.sim_score('cat', 'hat')
0.8803433378011485
>>> cmp.sim_score('Niall', 'Neil')
0.6958768466635681
>>> cmp.sim_score('aluminum', 'Catalan')
0.45410905865149187
>>> cmp.sim_score('ATCG', 'TAGC')
0.0
.. versionadded:: 0.4.0
"""
self._tokenize(src, tar)
src_tokens, tar_tokens = self._get_tokens()
src_total = sum(src_tokens.values())
tar_total = sum(tar_tokens.values())
src_unique = len(src_tokens)
tar_unique = len(tar_tokens)
similarity = 0.0
for _tok, count in self._intersection().items():
if self._simplified:
similarity += -log(count / tar_total)
else:
prob = count / tar_total
similarity -= log(
1
+ float_info.epsilon
- exp(
src_unique
* tar_unique
* log(1 + float_info.epsilon - prob * prob)
)
)
for _tok, count in self._src_only().items():
if self._simplified:
similarity -= -log(count / src_total) * self._mismatch_factor
return similarity
[docs] def sim(self, src, tar):
"""Return the normalized Fellegi-Sunter 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 Fellegi-Sunter similarity
Examples
--------
>>> cmp = FellegiSunter()
>>> cmp.sim('cat', 'hat')
0.2934477792670495
>>> cmp.sim('Niall', 'Neil')
0.13917536933271363
>>> cmp.sim('aluminum', 'Catalan')
0.056763632331436484
>>> cmp.sim('ATCG', 'TAGC')
0.0
.. versionadded:: 0.4.0
"""
score = self.sim_score(src, tar)
if score == 0.0:
return 0.0
if self._simplified:
return max(0.0, score / (len(src) + len(tar)))
return max(0.0, score / max(len(src), len(tar)))
if __name__ == '__main__':
import doctest
doctest.testmod()