Dice

class py_stringmatching.similarity_measure.dice.Dice

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:

\(dice(X, Y) = \frac{2 * |X \cap Y|}{|X| + |Y|}\)

Note

In the case where both X and Y are empty sets, we define their Dice score to be 1.

get_raw_score(set1, set2)

Computes the raw Dice score between two sets. This score is already in [0,1].

Parameters: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.

get_sim_score(set1, set2)

Computes the normalized dice similarity score between two sets. Simply call get_raw_score.

Parameters: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