Source code for abydos.distance._bennet

# Copyright 2019-2020 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._bennet.

Bennet's S correlation
"""

from ._token_distance import _TokenDistance

__all__ = ['Bennet']

[docs]class Bennet(_TokenDistance):
r"""Bennet's S correlation.

For two sets X and Y and a population N, Bennet's :math:S
correlation :cite:Bennet:1954 is

.. math::

corr_{Bennet}(X, Y) = S =
\frac{p_o - p_e^S}{1 - p_e^S}

where

.. math::

p_o = \frac{|X \cap Y| + |(N \setminus X) \setminus Y|}{|N|}

p_e^S = \frac{1}{2}

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

.. math::

p_o = \frac{a+d}{n}

p_e^S = \frac{1}{2}

"""

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

[docs]    def corr(self, src, tar):
"""Return the Bennet's S 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
Bennet's S correlation

Examples
--------
>>> cmp = Bennet()
>>> cmp.corr('cat', 'hat')
0.989795918367347
>>> cmp.corr('Niall', 'Neil')
0.9821428571428572
>>> cmp.corr('aluminum', 'Catalan')
0.9617834394904459
>>> cmp.corr('ATCG', 'TAGC')
0.9744897959183674

"""
if src == tar:
return 1.0

self._tokenize(src, tar)

a = self._intersection_card()
d = self._total_complement_card()
n = self._population_unique_card()

return 2 * (a + d) / n - 1

[docs]    def sim(self, src, tar):
"""Return the Bennet's S 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
Bennet's S similarity

Examples
--------
>>> cmp = Bennet()
>>> cmp.sim('cat', 'hat')
0.9948979591836735
>>> cmp.sim('Niall', 'Neil')
0.9910714285714286
>>> cmp.sim('aluminum', 'Catalan')
0.9808917197452229
>>> cmp.sim('ATCG', 'TAGC')
0.9872448979591837