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import math
from py_stringmatching import utils
from py_stringmatching.similarity_measure.token_similarity_measure import \
TokenSimilarityMeasure
class Cosine(TokenSimilarityMeasure):
"""Computes a variant of cosine measure known as Ochiai coefficient.
This is not the cosine measure that computes the cosine of the angle between two given vectors. Rather, it computes a variant of cosine measure known as Ochiai coefficient (see the Wikipedia page "Cosine Similarity"). Specifically, for two sets X and Y, this measure computes:
:math:`cosine(X, Y) = \\frac{|X \\cap Y|}{\\sqrt{|X| \\cdot |Y|}}`
Note:
* In the case where one of X and Y is an empty set and the other is a non-empty set, we define their cosine score to be 0.
* In the case where both X and Y are empty sets, we define their cosine score to be 1.
"""
def __init__(self):
super(Cosine, self).__init__()
def get_raw_score(self, set1, set2):
"""Computes the raw cosine score between two sets.
Args:
set1,set2 (set or list): Input sets (or lists). Input lists are converted to sets.
Returns:
Cosine similarity (float)
Raises:
TypeError : If the inputs are not sets (or lists) or if one of the inputs is None.
Examples:
>>> cos = Cosine()
>>> cos.get_raw_score(['data', 'science'], ['data'])
0.7071067811865475
>>> cos.get_raw_score(['data', 'data', 'science'], ['data', 'management'])
0.4999999999999999
>>> cos.get_raw_score([], ['data'])
0.0
References:
* String similarity joins: An Experimental Evaluation (a paper appearing in the VLDB 2014 Conference).
* Project Flamingo at http://flamingo.ics.uci.edu.
"""
# input validations
utils.sim_check_for_none(set1, set2)
utils.sim_check_for_list_or_set_inputs(set1, set2)
# if exact match return 1.0
if utils.sim_check_for_exact_match(set1, set2):
return 1.0
# if one of the strings is empty return 0
if utils.sim_check_for_empty(set1, set2):
return 0
if not isinstance(set1, set):
set1 = set(set1)
if not isinstance(set2, set):
set2 = set(set2)
return float(len(set1 & set2)) / (math.sqrt(float(len(set1))) *
math.sqrt(float(len(set2))))
def get_sim_score(self, set1, set2):
"""Computes the normalized cosine similarity between two sets.
Args:
set1,set2 (set or list): Input sets (or lists). Input lists are converted to sets.
Returns:
Normalized cosine similarity (float)
Raises:
TypeError : If the inputs are not sets (or lists) or if one of the inputs is None.
Examples:
>>> cos = Cosine()
>>> cos.get_sim_score(['data', 'science'], ['data'])
0.7071067811865475
>>> cos.get_sim_score(['data', 'data', 'science'], ['data', 'management'])
0.4999999999999999
>>> cos.get_sim_score([], ['data'])
0.0
"""
return self.get_raw_score(set1, set2)
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