Source code for abydos.distance._cohen_kappa

# Copyright 2018-2020 by Christopher C. Little.
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Cohen's Kappa similarity

from ._token_distance import _TokenDistance

__all__ = ['CohenKappa']

[docs]class CohenKappa(_TokenDistance): r"""Cohen's Kappa similarity. For two sets X and Y and a population N, Cohen's \kappa similarity :cite:`Cohen:1960` is .. math:: sim_{Cohen_\kappa}(X, Y) = \kappa = \frac{p_o - p_e^\kappa}{1 - p_e^\kappa} where .. math:: \begin{array}{l} p_o = \frac{|X \cap Y| + |(N \setminus X) \setminus Y|}{|N|}\\ \\ p_e^\kappa = \frac{|X|}{|N|} \cdot \frac{|Y|}{|N|} + \frac{|N \setminus X|}{|N|} \cdot \frac{|N \setminus Y|}{|N|} \end{array} In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n, this is .. math:: \begin{array}{l} p_o = \frac{a+d}{n}\\ \\ p_e^\kappa = \frac{a+b}{n} \cdot \frac{a+c}{n} + \frac{c+d}{n} \cdot \frac{b+d}{n} \end{array} .. versionadded:: 0.4.0 """ def __init__( self, alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs ): """Initialize CohenKappa 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(CohenKappa, self).__init__( alphabet=alphabet, tokenizer=tokenizer, intersection_type=intersection_type, **kwargs )
[docs] def sim(self, src, tar): """Return Cohen's Kappa 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 Cohen's Kappa similarity Examples -------- >>> cmp = CohenKappa() >>> cmp.sim('cat', 'hat') 0.9974358974358974 >>> cmp.sim('Niall', 'Neil') 0.9955041746949261 >>> cmp.sim('aluminum', 'Catalan') 0.9903412749517064 >>> cmp.sim('ATCG', 'TAGC') 0.993581514762516 .. versionadded:: 0.4.0 """ if src == tar: return 1.0 self._tokenize(src, tar) b = self._src_only_card() c = self._tar_only_card() d = self._total_complement_card() if d: return 2 * d / (b + c + 2 * d) return 0.0
if __name__ == '__main__': import doctest doctest.testmod()