from py_stringmatching import utils
from py_stringmatching.similarity_measure.token_similarity_measure import \
TokenSimilarityMeasure
[docs]class Dice(TokenSimilarityMeasure):
"""Returns the Dice score between two strings.
The Dice similarity score is defined as twice the shared information (intersection) divided by sum of cardinalities.
For two sets X and Y, the Dice similarity score is:
:math:`dice(X, Y) = \\frac{2 * |X \\cap Y|}{|X| + |Y|}`
Note:
In the case where both X and Y are empty sets, we define their Dice score to be 1.
"""
def __init__(self):
super(Dice, self).__init__()
[docs] def get_raw_score(self, set1, set2):
"""Computes the raw Dice score between two sets. This score is already in [0,1].
Args:
set1,set2 (set or list): Input sets (or lists). Input lists are converted to sets.
Returns:
Dice similarity score (float).
Raises:
TypeError : If the inputs are not sets (or lists) or if one of the inputs is None.
Examples:
>>> dice = Dice()
>>> dice.get_raw_score(['data', 'science'], ['data'])
0.6666666666666666
>>> dice.get_raw_score({1, 1, 2, 3, 4}, {2, 3, 4, 5, 6, 7, 7, 8})
0.5454545454545454
>>> dice.get_raw_score(['data', 'management'], ['data', 'data', 'science'])
0.5
References:
* Wikipedia article : https://en.wikibooks.org/wiki/Algorithm_Implementation/Strings/Dice%27s_coefficient
* SimMetrics library.
"""
# 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 2.0 * float(len(set1 & set2)) / float(len(set1) + len(set2))
[docs] def get_sim_score(self, set1, set2):
"""Computes the normalized dice similarity score 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 dice similarity (float).
Raises:
TypeError : If the inputs are not sets (or lists) or if one of the inputs is None.
Examples:
>>> dice = Dice()
>>> dice.get_sim_score(['data', 'science'], ['data'])
0.6666666666666666
>>> dice.get_sim_score({1, 1, 2, 3, 4}, {2, 3, 4, 5, 6, 7, 7, 8})
0.5454545454545454
>>> dice.get_sim_score(['data', 'management'], ['data', 'data', 'science'])
0.5
"""
return self.get_raw_score(set1, set2)