# 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._unknown_f.
Unknown F similarity
"""
from math import log
from ._token_distance import _TokenDistance
__all__ = ['UnknownF']
[docs]class UnknownF(_TokenDistance):
r"""Unknown F similarity.
For two sets X and Y and a population N, Unknown F similarity, which
:cite:`Choi:2010` attributes to :cite:`Gilbert:1966` but could not be
located in that source, is given as
.. math::
sim(X, Y) =
log(|X \cap Y|) - log(|N|) - log\Big(\frac{|X|}{|N|}\Big) -
log\Big(\frac{|Y|}{|N|}\Big)
In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n,
this is
.. math::
sim =
log(a) - log(n) - log\Big(\frac{a+b}{n}\Big) -
log\Big(\frac{a+c}{n}\Big)
This formula is not very normalizable, so the following formula is used
instead:
.. math::
sim_{UnknownF}(X, Y) =
min\Bigg(1, 1+log\Big(\frac{|X \cap Y|}{|N|}\Big) -
\frac{1}{2}\Bigg(log\Big(\frac{|X|}{|N|}\Big) +
log\Big(\frac{|Y|}{|N|}\Big)\Bigg)\Bigg)
In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n,
this is
.. math::
sim_{UnknownF} =
min\Bigg(1, 1+log\Big(\frac{a}{n}\Big) -
\frac{1}{2}\Bigg(log\Big(\frac{a+b}{n}\Big) +
log\Big(\frac{a+c}{n}\Big)\Bigg)\Bigg)
.. versionadded:: 0.4.0
"""
def __init__(
self,
alphabet=None,
tokenizer=None,
intersection_type='crisp',
**kwargs
):
"""Initialize UnknownF instance.
Parameters
----------
alphabet : Counter, collection, int, or None
This represents the alphabet of possible tokens.
See :ref:`alphabet <alphabet>` description in
:py:class:`_TokenDistance` for details.
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.
**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(UnknownF, self).__init__(
alphabet=alphabet,
tokenizer=tokenizer,
intersection_type=intersection_type,
**kwargs
)
[docs] def sim_score(self, src, tar):
"""Return the Unknown F similarity between 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
Unknown F similarity
Examples
--------
>>> cmp = UnknownF()
>>> cmp.sim_score('cat', 'hat')
0.3068528194400555
>>> cmp.sim_score('Niall', 'Neil')
-0.007451510271132555
>>> cmp.sim_score('aluminum', 'Catalan')
-1.1383330595080272
>>> cmp.sim_score('ATCG', 'TAGC')
1.0
.. versionadded:: 0.4.0
"""
if src == tar:
return 1.0
if not src or not tar:
return 0.0
self._tokenize(src, tar)
a = self._intersection_card()
b = self._src_only_card()
c = self._tar_only_card()
n = self._population_unique_card()
part1 = a / n
if part1 == 0:
part1 = 1
return min(
1.0, 1 + log(part1) - (log((a + b) / n) + log((a + c) / n)) / 2
)
[docs] def sim(self, *args, **kwargs):
"""Raise exception when called.
Parameters
----------
*args
Variable length argument list
**kwargs
Arbitrary keyword arguments
Raises
------
NotImplementedError
Method disabled for Unknown F similarity
.. versionadded:: 0.4.0
"""
raise NotImplementedError('Method disabled for Unknown F similarity.')
[docs] def dist(self, *args, **kwargs):
"""Raise exception when called.
Parameters
----------
*args
Variable length argument list
**kwargs
Arbitrary keyword arguments
Raises
------
NotImplementedError
Method disabled for Unknown F similarity
.. versionadded:: 0.4.0
"""
raise NotImplementedError('Method disabled for Unknown F similarity.')
if __name__ == '__main__':
import doctest
doctest.testmod()