Source code for abydos.distance._goodman_kruskal_lambda

# Copyright 2018-2020 by Christopher C. Little.
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Goodman & Kruskal's Lambda similarity

from ._token_distance import _TokenDistance

__all__ = ['GoodmanKruskalLambda']

[docs]class GoodmanKruskalLambda(_TokenDistance): r"""Goodman & Kruskal's Lambda similarity. For two sets X and Y and a population N, Goodman & Kruskal's lambda :cite:`Goodman:1954` is .. math:: sim_{GK_\lambda}(X, Y) = \frac{\frac{1}{2}(max(|X \cap Y|, |X \setminus Y|)+ max(|Y \setminus X|, |(N \setminus X) \setminus Y|)+ max(|X \cap Y|, |Y \setminus X|)+ max(|X \setminus Y|, |(N \setminus X) \setminus Y|))- (max(|X|, |N \setminus X|)+max(|Y|, |N \setminus Y|))} {|N|-\frac{1}{2}(max(|X|, |N \setminus X|)+ max(|Y|, |N \setminus Y|))} In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n, this is .. math:: sim_{GK_\lambda} = \frac{\frac{1}{2}((max(a,b)+max(c,d)+max(a,c)+max(b,d))- (max(a+b,c+d)+max(a+c,b+d)))} {n-\frac{1}{2}(max(a+b,c+d)+max(a+c,b+d))} .. versionadded:: 0.4.0 """ def __init__( self, alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs ): """Initialize GoodmanKruskalLambda 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(GoodmanKruskalLambda, self).__init__( alphabet=alphabet, tokenizer=tokenizer, intersection_type=intersection_type, **kwargs )
[docs] def sim(self, src, tar): """Return Goodman & Kruskal's Lambda 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 Goodman & Kruskal's Lambda similarity Examples -------- >>> cmp = GoodmanKruskalLambda() >>> cmp.sim('cat', 'hat') 0.0 >>> cmp.sim('Niall', 'Neil') 0.0 >>> cmp.sim('aluminum', 'Catalan') 0.0 >>> cmp.sim('ATCG', 'TAGC') 0.0 .. 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() sigma = max(a, b) + max(c, d) + max(a, c) + max(b, d) sigma_prime = max(a + c, b + d) + max(a + b, c + d) num = sigma - sigma_prime if num: return num / (2 * (a + b + c + d) - sigma_prime) return 0.0
if __name__ == '__main__': import doctest doctest.testmod()