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difflib-like library supporting moved blocks detection?

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  difflib-like library supporting moved blocks detection? Vlastimil Brom <vlastimil.brom@gmail.com> - 2011-07-13 23:13 +0200
    Re: difflib-like library supporting moved blocks detection? Chris Torek <nospam@torek.net> - 2011-07-14 04:47 +0000
      Re: difflib-like library supporting moved blocks detection? Vlastimil Brom <vlastimil.brom@gmail.com> - 2011-07-15 23:49 +0200

#9439 — difflib-like library supporting moved blocks detection?

Hi all,
I'd like to ask about the availability of a text diff library, like
difflib, which would support the detection of moved text blocks.
Currently I am almost happy with the results of
difflib.SequenceMatcher in my app (comparing different versions of
natural language texts), however the only drawback seems to be the
missing detection of moves of the text parts. I was thinking of simply
recomparing the deleted and inserted blocks using difflib again, but
this obviously won't be a general solution.
I found several algorithm discussions, but unfortunately no suitable
python implementation. (E.g. Medite -
http://www-poleia.lip6.fr/~ganascia/Medite_Project - seems to be
implemented in Python but it targets some rather special and probably
much more complex textological issues, than my current needs.)
Does maybe someone know such python library (or possibly a way of
enhancing difflib) for this task (i.e character-wise comparing of
texts - detecting insertion, deletion, substitution and move of text
blocks)?

Thanks in advance,
      Vlastimil Brom

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#9460

In article <mailman.1002.1310591600.1164.python-list@python.org>
Vlastimil Brom  <vlastimil.brom@gmail.com> wrote:
>I'd like to ask about the availability of a text diff library, like
>difflib, which would support the detection of moved text blocks.

If you allow arbitrary moves, the "minimal edit distance" problem
(string-to-string edit) becomes substantially harder.  If you only
allow insert, delete, or in-place-substitute, you have what is
called the "Levenshtein distance" case.  If you allow transpositions
you get "Damerau-Levenshtein".  These are both solveable with a
dynamic programming algorithm.  Once you allow move operations,
though, the problem becomes NP-complete.

See http://pages.cs.brandeis.edu/~shapird/publications/JDAmoves.pdf
for instance.  (They give an algorithm that produces "usually
acceptable" results in polynomial time.)
-- 
In-Real-Life: Chris Torek, Wind River Systems
Intel require I note that my opinions are not those of WRS or Intel
Salt Lake City, UT, USA (40°39.22'N, 111°50.29'W)  +1 801 277 2603
email: gmail (figure it out)      http://web.torek.net/torek/index.html

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#9574

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2011/7/14 Chris Torek <nospam@torek.net>:
> In article <mailman.1002.1310591600.1164.python-list@python.org>
> Vlastimil Brom  <vlastimil.brom@gmail.com> wrote:
>>I'd like to ask about the availability of a text diff library, like
>>difflib, which would support the detection of moved text blocks.
>
> If you allow arbitrary moves, the "minimal edit distance" problem
> (string-to-string edit) becomes substantially harder.  If you only
> allow insert, delete, or in-place-substitute, you have what is
> called the "Levenshtein distance" case.  If you allow transpositions
> you get "Damerau-Levenshtein".  These are both solveable with a
> dynamic programming algorithm.  Once you allow move operations,
> though, the problem becomes NP-complete.
>
> See http://pages.cs.brandeis.edu/~shapird/publications/JDAmoves.pdf
> for instance.  (They give an algorithm that produces "usually
> acceptable" results in polynomial time.)
> --
> In-Real-Life: Chris Torek, Wind River Systems
>
>
Thanks for the references and explanation!
I do realise the added complexity with taking the moves into account;
given that, my current needs and the usually satisfying results
obtained easily with difflib, I am not going to try to implement some
more complex diffing algorithm.
However, it turns out that the mentioned naive approach with just
recomparing the text additions and removals may be partially viable -
with some luck, i.e. given, the relevant segments are identified as
deletions and inserts and isolated by difflib in the first place (and
not subsumed under larger changes or split).

For illustration, the rough simplified code is attached (sorry for the
style and possible quirks...)
Just now the more similar text segments are just collected, it would
be also possible to sort them on their similarity ratio; the current
approach also allows to highlight potentially multiple moved segments.

Comments and suggestions are, of course, welcome,
                        regards,
                             vbr

# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# # # # # # # # # # # # # # # # # # # # #

#! Python
# -*- coding: utf-8 -*-

import difflib
import itertools

def compare_moves(a, b, similarity_threshold=0.6):
    """
    Poor man's text comparison with simple moves check. Compares two
strings using difflib
    and additionally tries to detect moved blocks
    by comparing similar deleted and inserted segments with each other
- given the similarity_threshold.
    """

    seq_matcher = difflib.SequenceMatcher(isjunk=None, a=a, b=b, autojunk=False)
    diff_raw = [[tag, i1, i2, j1, j2, a[i1:i2], b[j1:j2]] for tag, i1,
i2, j1, j2 in seq_matcher.get_opcodes()]

    deleted, inserted = {}, {}
    for tag, i1, i2, j1, j2 in seq_matcher.get_opcodes():
        if tag == 'delete':
            deleted[(i1, i2)] = [tag, i1, i2, j1, j2, a[i1:i2]]
        elif tag == 'insert':
            inserted[(i1, i2)] = [tag, i1, i2, j1, j2, b[j1:j2]]

    possibly_moved_blocks = []
    for deleted_item, inserted_item in
itertools.product(deleted.values(), inserted.values()):
        similarity_ratio = difflib.SequenceMatcher(isjunk=None,
a=deleted_item[5], b=inserted_item[5], autojunk=False).ratio()
        if similarity_ratio >= similarity_threshold:
            possibly_moved_blocks.append([deleted_item, inserted_item,
similarity_ratio])

    print diff_raw
    print possibly_moved_blocks


if __name__ == "__main__":
    compare_moves("abcXYZdeABfghijklmnopABBCq",
"ABCDabcdeACfgXYXZhijklmnopq", similarity_threshold = 0.6)

# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# # # # # #
# output: #
[['insert', 0, 0, 0, 4, '', 'ABCD'], ['equal', 0, 3, 4, 7, 'abc',
'abc'], ['delete', 3, 6, 7, 7, 'XYZ', ''], ['equal', 6, 9, 7, 10,
'deA', 'deA'], ['replace', 9, 10, 10, 11, 'B', 'C'], ['equal', 10, 12,
11, 13, 'fg', 'fg'], ['insert', 12, 12, 13, 17, '', 'XYXZ'], ['equal',
12, 21, 17, 26, 'hijklmnop', 'hijklmnop'], ['delete', 21, 25, 26, 26,
'ABBC', ''], ['equal', 25, 26, 26, 27, 'q', 'q']]

[[['delete', 21, 25, 26, 26, 'ABBC'], ['insert', 0, 0, 0, 4, 'ABCD'],
0.75], [['delete', 3, 6, 7, 7, 'XYZ'], ['insert', 12, 12, 13, 17,
'XYXZ'], 0.8571428571428571]]

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