from py_stringmatching import utils
from py_stringmatching.similarity_measure.token_similarity_measure import \
TokenSimilarityMeasure
[docs]class Jaccard(TokenSimilarityMeasure):
"""Computes Jaccard measure.
For two sets X and Y, the Jaccard similarity score is:
:math:`jaccard(X, Y) = \\frac{|X \\cap Y|}{|X \\cup Y|}`
Note:
In the case where both X and Y are empty sets, we define their Jaccard score to be 1.
"""
def __init__(self):
super(Jaccard, self).__init__()
[docs] def get_raw_score(self, set1, set2):
"""Computes the raw Jaccard score between two sets.
Args:
set1,set2 (set or list): Input sets (or lists). Input lists are converted to sets.
Returns:
Jaccard similarity score (float).
Raises:
TypeError : If the inputs are not sets (or lists) or if one of the inputs is None.
Examples:
>>> jac = Jaccard()
>>> jac.get_raw_score(['data', 'science'], ['data'])
0.5
>>> jac.get_raw_score({1, 1, 2, 3, 4}, {2, 3, 4, 5, 6, 7, 7, 8})
0.375
>>> jac.get_raw_score(['data', 'management'], ['data', 'data', 'science'])
0.3333333333333333
"""
# 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)) / float(len(set1 | set2))
[docs] def get_sim_score(self, set1, set2):
"""Computes the normalized Jaccard similarity between two sets. Simply call get_raw_score.
Args:
set1,set2 (set or list): Input sets (or lists). Input lists are converted to sets.
Returns:
Normalized Jaccard similarity (float).
Raises:
TypeError : If the inputs are not sets (or lists) or if one of the inputs is None.
Examples:
>>> jac = Jaccard()
>>> jac.get_sim_score(['data', 'science'], ['data'])
0.5
>>> jac.get_sim_score({1, 1, 2, 3, 4}, {2, 3, 4, 5, 6, 7, 7, 8})
0.375
>>> jac.get_sim_score(['data', 'management'], ['data', 'data', 'science'])
0.3333333333333333
"""
return self.get_raw_score(set1, set2)