# Copyright 2019-2020 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._tf_idf.
TF-IDF similarity
"""
from math import log1p
from ._token_distance import _TokenDistance
from ..corpus import UnigramCorpus
__all__ = ['TFIDF']
[docs]class TFIDF(_TokenDistance):
r"""TF-IDF similarity.
For two sets X and Y and a population N, TF-IDF similarity
:cite:`Cohen:2003` is
.. math::
sim_{TF-IDF}(X, Y) = \sum_{w \in X \cap Y} V(w, X) \cdot V(w, Y)
V(w, S) = \frac{V'(w, S)}{\sqrt{\sum_{w \in S} V'(w, S)^2}}
V'(w, S) = log(1+TF_{w,S}) \cdot log(1+IDF_w)
Notes
-----
One is added to both the TF & IDF values before taking the logarithm to
ensure the logarithms do not fall to 0, which will tend to result in 0.0
similarities even when there is a degree of matching.
.. versionadded:: 0.4.0
"""
def __init__(self, tokenizer=None, corpus=None, **kwargs):
"""Initialize TFIDF instance.
Parameters
----------
tokenizer : _Tokenizer
A tokenizer instance from the :py:mod:`abydos.tokenizer` package
corpus : UnigramCorpus
A unigram corpus :py:class:`UnigramCorpus`. If None, a corpus will
be created from the two words when a similarity function is called.
**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.
.. versionadded:: 0.4.0
"""
super(TFIDF, self).__init__(tokenizer=tokenizer, **kwargs)
self._corpus = corpus
[docs] def sim(self, src, tar):
"""Return the TF-IDF 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
TF-IDF similarity
Examples
--------
>>> cmp = TFIDF()
>>> cmp.sim('cat', 'hat')
0.30404449697373
>>> cmp.sim('Niall', 'Neil')
0.20108911303601
>>> cmp.sim('aluminum', 'Catalan')
0.05355175631194
>>> cmp.sim('ATCG', 'TAGC')
0.0
.. versionadded:: 0.4.0
"""
self._tokenize(src, tar)
src_tok, tar_tok = self._get_tokens()
if self._corpus is None:
corpus = UnigramCorpus(word_tokenizer=self.params['tokenizer'])
corpus.add_document(src)
corpus.add_document(tar)
else:
corpus = self._corpus
vws_dict = {}
vwt_dict = {}
for token in src_tok.keys():
vws_dict[token] = log1p(src_tok[token]) * corpus.idf(token)
for token in tar_tok.keys():
vwt_dict[token] = log1p(tar_tok[token]) * corpus.idf(token)
vws_rss = sum(score ** 2 for score in vws_dict.values()) ** 0.5
vwt_rss = sum(score ** 2 for score in vwt_dict.values()) ** 0.5
return float(
round(
sum(
vws_dict[token] / vws_rss * vwt_dict[token] / vwt_rss
for token in self._intersection().keys()
),
14,
)
)
if __name__ == '__main__':
import doctest
doctest.testmod()