Source code for abydos.distance._goodman_kruskal_lambda_r

# Copyright 2019-2020 by Christopher C. Little.
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"""abydos.distance._goodman_kruskal_lambda_r.

Goodman & Kruskal Lambda-r correlation.
"""

from ._token_distance import _TokenDistance

__all__ = ['GoodmanKruskalLambdaR']

[docs]class GoodmanKruskalLambdaR(_TokenDistance): r"""Goodman & Kruskal Lambda-r correlation. For two sets X and Y and a population N, Goodman & Kruskal :math:\lambda_r correlation :cite:Goodman:1954 is .. math:: corr_{GK_{\lambda_r}}(X, Y) = \frac{|X \cap Y| + |(N \setminus X) \setminus Y| - \frac{1}{2}(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:: corr_{GK_{\lambda_r}} = \frac{a + d - \frac{1}{2}(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 GoodmanKruskalLambdaR 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(GoodmanKruskalLambdaR, self).__init__( alphabet=alphabet, tokenizer=tokenizer, intersection_type=intersection_type, **kwargs )
[docs] def corr(self, src, tar): """Return Goodman & Kruskal Lambda-r 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 Goodman & Kruskal Lambda-r correlation Examples -------- >>> cmp = GoodmanKruskalLambdaR() >>> cmp.corr('cat', 'hat') 0.0 >>> cmp.corr('Niall', 'Neil') -0.2727272727272727 >>> cmp.corr('aluminum', 'Catalan') -0.7647058823529411 >>> cmp.corr('ATCG', 'TAGC') -1.0 .. versionadded:: 0.4.0 """ if src == tar: return 1.0 self._tokenize(src, tar) if not self._src_card() or not self._tar_card(): return -1.0 a = self._intersection_card() b = self._src_only_card() c = self._tar_only_card() d = self._total_complement_card() n = self._population_unique_card() sigma_prime = max(a + b, c + d) + max(a + c, b + d) num = 2 * (a + d) - sigma_prime if num: return num / (2 * n - sigma_prime) return 0.0
[docs] def sim(self, src, tar): """Return Goodman & Kruskal Lambda-r 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 Lambda-r similarity Examples -------- >>> cmp = GoodmanKruskalLambdaR() >>> cmp.sim('cat', 'hat') 0.5 >>> cmp.sim('Niall', 'Neil') 0.36363636363636365 >>> cmp.sim('aluminum', 'Catalan') 0.11764705882352944 >>> cmp.sim('ATCG', 'TAGC') 0.0 .. versionadded:: 0.4.0 """ return (1.0 + self.corr(src, tar)) / 2.0
if __name__ == '__main__': import doctest doctest.testmod()