abydos/distance/_stiles.py
# Copyright 2018-2020 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._stiles.
Stiles similarity
"""
from math import copysign, log10
from typing import Any, Counter as TCounter, Optional, Sequence, Set, Union
from ._token_distance import _TokenDistance
from ..tokenizer import _Tokenizer
__all__ = ['Stiles']
class Stiles(_TokenDistance):
r"""Stiles similarity.
For two sets X and Y and a population N, Stiles similarity
:cite:`Stiles:1961` is
.. math::
sim_{Stiles}(X, Y) = log_{10}
\frac{|N| \Big(||X \cap Y| \cdot
|N| -
|X \setminus Y| \cdot |Y \setminus X|| -
\frac{|N|}{2}\Big)^2}
{|X \setminus Y| \cdot |Y \setminus X| \cdot
(|N| - |X \setminus Y|) \cdot
(|N| - |Y \setminus X|)}
In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n,
this is
.. math::
sim_{Stiles} =
log_{10} \frac{n(|an-bc|-\frac{1}{2}n)^2}{bc(n-b)(n-c)}
.. versionadded:: 0.4.0
"""
def __init__(
self,
alphabet: Optional[
Union[TCounter[str], Sequence[str], Set[str], int]
] = None,
tokenizer: Optional[_Tokenizer] = None,
intersection_type: str = 'crisp',
**kwargs: Any
) -> None:
"""Initialize Stiles 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(Stiles, self).__init__(
alphabet=alphabet,
tokenizer=tokenizer,
intersection_type=intersection_type,
**kwargs
)
def sim_score(self, src: str, tar: str) -> float:
"""Return the Stiles 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
Stiles similarity
Examples
--------
>>> cmp = Stiles()
>>> cmp.sim_score('cat', 'hat')
2.6436977886009236
>>> cmp.sim_score('Niall', 'Neil')
2.1622951406967723
>>> cmp.sim_score('aluminum', 'Catalan')
0.41925115106844024
>>> cmp.sim_score('ATCG', 'TAGC')
-0.8426334527850912
.. versionadded:: 0.4.0
"""
self._tokenize(src, tar)
eps = 0.0000001
a = max(self._intersection_card(), eps)
b = max(self._src_only_card(), eps)
c = max(self._tar_only_card(), eps)
n = max(self._total_complement_card(), eps) + a + b + c
anmbc = a * n - b * c
return copysign(
log10(
n
* max((abs(anmbc) - n / 2) ** 2, eps)
/ (b * (n - b) * c * (n - c))
),
anmbc,
)
def corr(self, src: str, tar: str) -> float:
"""Return the Stiles correlation 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
Stiles correlation
Examples
--------
>>> cmp = Stiles()
>>> cmp.corr('cat', 'hat')
0.14701542182970487
>>> cmp.corr('Niall', 'Neil')
0.11767566062554877
>>> cmp.corr('aluminum', 'Catalan')
0.022355640924908403
>>> cmp.corr('ATCG', 'TAGC')
-0.046296656196428934
.. versionadded:: 0.4.0
"""
return self.sim_score(src, tar) / max(
self.sim_score(src, src), self.sim_score(tar, tar)
)
def sim(self, src: str, tar: str) -> float:
"""Return the normalized Stiles 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 Stiles similarity
Examples
--------
>>> cmp = Stiles()
>>> cmp.sim('cat', 'hat')
0.5735077109148524
>>> cmp.sim('Niall', 'Neil')
0.5588378303127743
>>> cmp.sim('aluminum', 'Catalan')
0.5111778204624542
>>> cmp.sim('ATCG', 'TAGC')
0.4768516719017855
.. versionadded:: 0.4.0
"""
return (1.0 + self.corr(src, tar)) / 2.0
if __name__ == '__main__':
import doctest
doctest.testmod()