An implementation of the generalized merged distance to evaluate entity resolution
A python package to evaluate entity resolution
This package allows to evaluate entity resolution by efficiently computing several state of the art metrics : basic merge distance, precision, recall, variation of information. It's using the slice algorithm from the paper :
Menestrina, David and Whang, Steven Euijong and Garcia-Molina, Hector (2010) Evaluating Entity Resolution Results http://ilpubs.stanford.edu:8090/975/3/ERMetricVLDB.pdf
pip install entity-resolution-evaluation
Evaluate your resolution R against the gold standard S using a metric.
S = [[0, 1], [2, 3, 4], ] R = [[0, 1, 2], [3, 4], ] evaluate(R,S, 'bmd') # returns 2
To go from R to S, you have to do 1 split and 1 merge.
evaluate(R,S,'precision') # returns 0.5,
Half of the pairs of R are incorrect. (0,2) and (1,2) are incorrect. (0,1) and (3,4) are correct
evaluate(R,S,'recall') # returns 0.5
Half of the pairs of S are present in R. (0,1) and (3,4) are present. (2,3) and (2,4) are absent.
evaluate(R,S,'variation of information') # returns 0.6365141682948129
You can currently compute the following metrics :
|metric||value if perfect||bounds||intepretation|
|'bmd'||0||[0,infinity]||basic merge distance : the number of split and merge necessary to go from R to S|
|'precision'||1||[0,1]||proportion of pairs in R present in S|
|'recall'||1||[0,1]||proportion of pairs in S present in R|
|'f1'||1||[0,1]||harmonic mean of precision and recall|
|'variation_of_information'||0||[0,infinity]||amount of information that is lost and added to go from R to S|
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Copyright (c) 2018 Ministère de l'Action et des Comptes Publics, Paul Boosz, Benoît Guigal
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