Cosine

class py_stringmatching.similarity_measure.cosine.Cosine

Computes a variant of cosine measure known as Ochiai coefficient.

This is not the cosine measure that computes the cosine of the angle between two given vectors. Rather, it computes a variant of cosine measure known as Ochiai coefficient (see the Wikipedia page “Cosine Similarity”). Specifically, for two sets X and Y, this measure computes:

\(cosine(X, Y) = \frac{|X \cap Y|}{\sqrt{|X| \cdot |Y|}}\)

Note

• In the case where one of X and Y is an empty set and the other is a non-empty set, we define their cosine score to be 0.
• In the case where both X and Y are empty sets, we define their cosine score to be 1.

get_raw_score(set1, set2)

Computes the raw cosine score between two sets.

Parameters:set1,set2 (set or list) – Input sets (or lists). Input lists are converted to sets. Returns:Cosine similarity (float) Raises:TypeError – If the inputs are not sets (or lists) or if one of the inputs is None.

Examples

>>> cos = Cosine()
>>> cos.get_raw_score(['data', 'science'], ['data'])
0.7071067811865475
>>> cos.get_raw_score(['data', 'data', 'science'], ['data', 'management'])
0.4999999999999999
>>> cos.get_raw_score([], ['data'])
0.0

References

• String similarity joins: An Experimental Evaluation (a paper appearing in the VLDB 2014 Conference).
• Project Flamingo at http://flamingo.ics.uci.edu.

get_sim_score(set1, set2)

Computes the normalized cosine similarity between two sets.

Parameters:set1,set2 (set or list) – Input sets (or lists). Input lists are converted to sets. Returns:Normalized cosine similarity (float) Raises:TypeError – If the inputs are not sets (or lists) or if one of the inputs is None.

Examples

>>> cos = Cosine()
>>> cos.get_sim_score(['data', 'science'], ['data'])
0.7071067811865475
>>> cos.get_sim_score(['data', 'data', 'science'], ['data', 'management'])
0.4999999999999999
>>> cos.get_sim_score([], ['data'])
0.0