Tversky Index¶
Tversky index similarity measure
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class
py_stringmatching.similarity_measure.tversky_index.TverskyIndex(alpha=0.5, beta=0.5)[source]¶ Tversky index similarity measure class.
Parameters: beta (alpha,) – Tversky index parameters (defaults to 0.5). -
get_raw_score(set1, set2)[source]¶ Computes the Tversky index similarity between two sets.
The Tversky index is an asymmetric similarity measure on sets that compares a variant to a prototype. The Tversky index can be seen as a generalization of Dice’s coefficient and Tanimoto coefficient.
For sets X and Y the Tversky index is a number between 0 and 1 given by: \(tversky_index(X, Y) = \frac{|X \cap Y|}{|X \cap Y| + lpha |X-Y| + eta |Y-X|}\) where, :math: lpha, eta >=0
Parameters: set1,set2 (set or list) – Input sets (or lists). Input lists are converted to sets. Returns: Tversly index similarity (float) Raises: TypeError– If the inputs are not sets (or lists) or if one of the inputs is None.Examples
>>> tvi = TverskyIndex() >>> tvi.get_raw_score(['data', 'science'], ['data']) 0.6666666666666666 >>> tvi.get_raw_score(['data', 'management'], ['data', 'data', 'science']) 0.5 >>> tvi.get_raw_score({1, 1, 2, 3, 4}, {2, 3, 4, 5, 6, 7, 7, 8}) 0.5454545454545454 >>> tvi = TverskyIndex(0.5, 0.5) >>> tvi.get_raw_score({1, 1, 2, 3, 4}, {2, 3, 4, 5, 6, 7, 7, 8}) 0.5454545454545454 >>> tvi = TverskyIndex(beta=0.5) >>> tvi.get_raw_score(['data', 'management'], ['data', 'data', 'science']) 0.5
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get_sim_score(set1, set2)[source]¶ Computes the normalized tversky index similarity between two sets.
Parameters: set1,set2 (set or list) – Input sets (or lists). Input lists are converted to sets. Returns: Normalized tversky index similarity (float) Raises: TypeError– If the inputs are not sets (or lists) or if one of the inputs is None.Examples
>>> tvi = TverskyIndex() >>> tvi.get_sim_score(['data', 'science'], ['data']) 0.6666666666666666 >>> tvi.get_sim_score(['data', 'management'], ['data', 'data', 'science']) 0.5 >>> tvi.get_sim_score({1, 1, 2, 3, 4}, {2, 3, 4, 5, 6, 7, 7, 8}) 0.5454545454545454 >>> tvi = TverskyIndex(0.5, 0.5) >>> tvi.get_sim_score({1, 1, 2, 3, 4}, {2, 3, 4, 5, 6, 7, 7, 8}) 0.5454545454545454 >>> tvi = TverskyIndex(beta=0.5) >>> tvi.get_sim_score(['data', 'management'], ['data', 'data', 'science']) 0.5
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