Tversky Index
Tversky index similarity measure
- class py_stringmatching.similarity_measure.tversky_index.TverskyIndex(alpha=0.5, beta=0.5)[source]
Tversky index similarity measure class.
- Parameters:
alpha (float) – Tversky index parameters (defaults to 0.5).
beta (float) – 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: \(tversky_index(X, Y) = \frac{|X \cap Y|}{|X \cap Y| + \alpha |X-Y| + \beta |Y-X|}\) where, :math: alpha, beta >=0
- Parameters:
set1 (set or list) – Input sets (or lists). Input lists are converted to sets.
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
- get_sim_score(set1, set2)[source]
Computes the normalized tversky index similarity between two sets.
- Parameters:
set1 (set or list) – Input sets (or lists). Input lists are converted to sets.
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