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polyfuzz.models.cosine_similarity

Calculate similarity between two matrices/vectors and return best matches

Parameters

Name	Type	Description	Default
from_vector	ndarray	the matrix or vector representing the embedded strings to map from	required
to_vector	ndarray	the matrix or vector representing the embedded strings to map to	required
from_list	List[str]	The list from which you want mappings	required
to_list	List[str]	The list where you want to map to	required
min_similarity	float	The minimum similarity between strings, otherwise return 0 similarity	0.75
top_n	int	The number of best matches you want returned	1
method	str	The method/package for calculating the cosine similarity. Options: "sparse", "sklearn", "knn". Sparse is the fastest and most memory efficient but requires a package that might be difficult to install. Sklearn is a bit slower than sparse and requires significantly more memory as the distance matrix is not sparse Knn uses 1-nearest neighbor to extract the most similar strings it is significantly slower than both methods but requires little memory	'sparse'

Returns

Type	Description
DataFrame	matches: The best matches between the lists of strings

Usage:

Make sure to fill the to_vector and from_vector with vector representations of to_list and from_list respectively:

from polyfuzz.models import extract_best_matches
indices, similarity = extract_best_matches(from_vector, to_vector, method="sparse")