Source code for abydos.distance._baroni_urbani_buser_ii

# 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._baroni_urbani_buser_ii.

Baroni-Urbani & Buser II correlation
"""

from ._token_distance import _TokenDistance

__all__ = ['BaroniUrbaniBuserII']


[docs]class BaroniUrbaniBuserII(_TokenDistance): r"""Baroni-Urbani & Buser II correlation. For two sets X and Y and a population N, the Baroni-Urbani & Buser II correlation :cite:`BaroniUrbani:1976` is .. math:: corr_{BaroniUrbaniBuserII}(X, Y) = \frac{\sqrt{|X \cap Y| \cdot |(N \setminus X) \setminus Y|} + |X \cap Y| - |X \setminus Y| - |Y \setminus X|} {\sqrt{|X \cap Y| \cdot |(N \setminus X) \setminus Y|} + |X \cap Y| + |X \setminus Y| + |Y \setminus X|} This is the first, but less commonly used and referenced of the two similarities proposed by Baroni-Urbani & Buser. In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n, this is .. math:: corr_{BaroniUrbaniBuserII} = \frac{\sqrt{ad}+a-b-c}{\sqrt{ad}+a+b+c} .. versionadded:: 0.4.0 """ def __init__( self, alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs ): """Initialize BaroniUrbaniBuserII 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(BaroniUrbaniBuserII, self).__init__( alphabet=alphabet, tokenizer=tokenizer, intersection_type=intersection_type, **kwargs )
[docs] def corr(self, src, tar): """Return the Baroni-Urbani & Buser II 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 Baroni-Urbani & Buser II correlation Examples -------- >>> cmp = BaroniUrbaniBuserII() >>> cmp.corr('cat', 'hat') 0.8239675481756209 >>> cmp.corr('Niall', 'Neil') 0.7105646350028408 >>> cmp.corr('aluminum', 'Catalan') 0.31398542410970204 >>> cmp.corr('ATCG', 'TAGC') -1.0 .. versionadded:: 0.4.0 """ 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() return ((a * d) ** 0.5 + a - b - c) / ((a * d) ** 0.5 + a + b + c)
[docs] def sim(self, src, tar): """Return the Baroni-Urbani & Buser II 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 Baroni-Urbani & Buser II similarity Examples -------- >>> cmp = BaroniUrbaniBuserII() >>> cmp.sim('cat', 'hat') 0.9119837740878105 >>> cmp.sim('Niall', 'Neil') 0.8552823175014204 >>> cmp.sim('aluminum', 'Catalan') 0.656992712054851 >>> cmp.sim('ATCG', 'TAGC') 0.0 .. versionadded:: 0.4.0 """ return (self.corr(src, tar) + 1) / 2
if __name__ == '__main__': import doctest doctest.testmod()