Source code for abydos.distance._stuart_tau

# Copyright 2018-2020 by Christopher C. Little.
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"""abydos.distance._stuart_tau.

Stuart's Tau correlation
"""

from ._token_distance import _TokenDistance

__all__ = ['StuartTau']


[docs]class StuartTau(_TokenDistance): r"""Stuart's Tau correlation. For two sets X and Y and a population N, Stuart's Tau-C correlation :cite:`Stuart:1953` is .. math:: corr_{Stuart_{\tau_c}}(X, Y) = \frac{4 \cdot (|X \cap Y| + |(N \setminus X) \setminus Y| - |X \triangle Y|)}{|N|^2} In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n, this is .. math:: corr_{Stuart_{\tau_c}} = \frac{4 \cdot ((a+d)-(b+c))}{n^2} .. versionadded:: 0.4.0 """ def __init__( self, alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs ): """Initialize StuartTau 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(StuartTau, self).__init__( alphabet=alphabet, tokenizer=tokenizer, intersection_type=intersection_type, **kwargs )
[docs] def corr(self, src, tar): """Return the Stuart's Tau 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 Stuart's Tau correlation Examples -------- >>> cmp = StuartTau() >>> cmp.corr('cat', 'hat') 0.005049979175343606 >>> cmp.corr('Niall', 'Neil') 0.005010932944606414 >>> cmp.corr('aluminum', 'Catalan') 0.004900807334983164 >>> cmp.corr('ATCG', 'TAGC') 0.0049718867138692216 .. versionadded:: 0.4.0 """ 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() if not n: return 1.0 return max(-1.0, min(1.0, 4 * (a + d - b - c) / (n ** 2)))
[docs] def sim(self, src, tar): """Return the Stuart's Tau 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 Stuart's Tau similarity Examples -------- >>> cmp = StuartTau() >>> cmp.sim('cat', 'hat') 0.5025249895876718 >>> cmp.sim('Niall', 'Neil') 0.5025054664723032 >>> cmp.sim('aluminum', 'Catalan') 0.5024504036674916 >>> cmp.sim('ATCG', 'TAGC') 0.5024859433569346 .. versionadded:: 0.4.0 """ return (1.0 + self.corr(src, tar)) / 2.0
if __name__ == '__main__': import doctest doctest.testmod()