The algorithm used for approximate string matching
doesn't find the optimal edit sequence (it finds
longest blocks instead).
Example:
>>> from difflib import SequenceMatcher
>>> sm = SequenceMatcher()
>>> sm.set_seqs('axfot', 'aoftax')
>>> sm.ratio()
0.36363636363636365
>>> sm.get_matching_blocks()
[(0, 4, 2), (5, 6, 0)]
>>> sm.get_opcodes()
[('insert', 0, 0, 0, 4), ('equal', 0, 2, 4, 6),
('delete', 2, 5, 6, 6)]
What's wrong?
Levenshtein distance with weight 2 for item replacement
is only 5 (the weight 2 corresponds to what ratio() is
supposed to compute, the classic Levenshtein distance
is 4), so one would expect to get similarity (i.e.
ratio()) (115)/11 = 6/11 = 0.545454545454..., and not
only 4/11.
And really, the maximal matching blocks are:
[(0, 0, 1), (2, 2, 1), (4, 3, 1)]
and the minimal edit sequence is:
[('equal', 0, 1, 0, 1), ('replace', 1, 2, 1, 2),
('equal', 2, 3, 2, 3), ('delete', 3, 4, 3, 3),
('equal', 4, 5, 3, 4), ('insert', 5, 5, 4, 6)]
The impact of this ``feature'' on difflike
applications may be even positive, beause the edit
sequence then consists of smaller number of operations
on lager chunks. Thus I'm not sure if this is
something which should be fixed. However, it should be
at least noted in the documentation the ratio()
function gives only a lower bound of the string
similarity (so people like me won't be tempted to use
it to check results of their own Levenshtein
distance/string similarity implementation).
