Source code for abydos.distance._clement

# Copyright 2018-2020 by Christopher C. Little.
# This file is part of Abydos.
#
# Abydos is free software: you can redistribute it and/or modify
# 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._clement.

Clement similarity
"""

from ._token_distance import _TokenDistance

__all__ = ['Clement']

[docs]class Clement(_TokenDistance):
r"""Clement similarity.

For two sets X and Y and a population N, Clement similarity
:cite:Clement:1976 is defined as

.. math::

sim_{Clement}(X, Y) =
\frac{|X \cap Y|}{|X|}\Big(1-\frac{|X|}{|N|}\Big) +
\frac{|(N \setminus X) \setminus Y|}{|N \setminus X|}
\Big(1-\frac{|N \setminus X|}{|N|}\Big)

In :ref:2x2 confusion table terms <confusion_table>, where a+b+c+d=n,
this is

.. math::

sim_{Clement} =
\frac{a}{a+b}\Big(1 - \frac{a+b}{n}\Big) +
\frac{d}{c+d}\Big(1 - \frac{c+d}{n}\Big)

"""

def __init__(
self,
alphabet=None,
tokenizer=None,
intersection_type='crisp',
**kwargs
):
"""Initialize Clement 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.

"""
super(Clement, self).__init__(
alphabet=alphabet,
tokenizer=tokenizer,
intersection_type=intersection_type,
**kwargs
)

[docs]    def sim(self, src, tar):
"""Return the Clement 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
Clement similarity

Examples
--------
>>> cmp = Clement()
>>> cmp.sim('cat', 'hat')
0.5025379382522239
>>> cmp.sim('Niall', 'Neil')
0.33840586363079933
>>> cmp.sim('aluminum', 'Catalan')
0.12119877280918714
>>> cmp.sim('ATCG', 'TAGC')
0.006336616803332366

"""
if src == tar:
return 1.0

self._tokenize(src, tar)

a = self._intersection_card()
b = self._src_only_card()
c = self._tar_only_card()
d = self._total_complement_card()
n = self._population_unique_card()

score = 0.0
if a + b:
score += (a / (a + b)) * (1 - (a + b) / n)
if c + d:
score += (d / (c + d)) * (1 - (c + d) / n)

return score

if __name__ == '__main__':
import doctest

doctest.testmod()