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A Python script for finding duplicate text files.
$ python3 -m venv .$ source bin/activate(dedup) $ pip install -r requirements.txtdedup.py can find candidate duplicate pairs in a way that avoids searching the full set of pair-wise combinations:
(dedup) $ ./dedup.py -husage: dedup.py [-h] [-n N_GRAM_SIZE] [-b {32,64,128}] [-r THRESHOLD] [-f {NFC,NFKC,NFD,NFKD}] [-t {.adm.json,.txt}] [-w WINDOW] filenamespositional arguments: filenames file listing a set of filenames to check for duplicates; one filename per line (use "-" to read filenames as lines from stdin)optional arguments: -h, --help show this help message and exit -n N_GRAM_SIZE, --n-gram-size N_GRAM_SIZE size of character n-grams (default: 2) -b {32,64,128}, --bits {32,64,128} hash size (in bits) (default: 128) -r THRESHOLD, --threshold THRESHOLD minimum bitwise difference threshold for considering two LSHs equivalent (lower thresholds are more strict; higher thresholds are more lenient; thresholds must be between 0 and -b/--bits) (default: 128) -f {NFC,NFKC,NFD,NFKD}, --normalization-form {NFC,NFKC,NFD,NFKD} Unicode normalization option; refer to unicodedata.normalize for explanation of options (default: NFKD) -t {.adm.json,.txt}, --doc-type {.adm.json,.txt} the type of documents to compare (default: .txt) -w WINDOW, --window WINDOW window size to check for candidates in the sorted list of pairs (default: 2)(dedup) $ find test -name "*.txt" | sort | xargs head==> test/1.txt <==the cat sat in the hat==> test/2.txt <==the cat sat on the mat==> test/3.txt <==we all scream for ice scream==> test/4.txt <==we all scream for ice cream==> test/5.txt <==we all like ice scream==> test/6.txt <==abcdefg==> test/7.txt <==abcdefghijklmnop(dedup) $ find test -name '*.txt' | ./dedup.py -100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 1489.30files/s]test/1.txttest/2.txt17test/3.txttest/6.txt55test/3.txttest/7.txt56test/4.txttest/6.txt56test/2.txttest/5.txt58test/5.txttest/7.txt61(dedup) $ find test -name '*.txt' | ./dedup.py - -b 64 -r 64100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 2830.44files/s]test/3.txttest/4.txt4test/1.txttest/2.txt7test/7.txttest/6.txt12test/4.txttest/5.txt14test/3.txttest/2.txt29test/1.txttest/6.txt32(dedup) $ find test -name '*.txt' | ./dedup.py - -b 32 -r 32100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 4707.41files/s]test/3.txttest/4.txt3test/3.txttest/5.txt5test/1.txttest/2.txt6test/4.txttest/6.txt13test/5.txttest/7.txt13test/1.txttest/6.txt15If you have a list of filename pairs (such as the output fromdedup.py), thenmatching_substrings.py can be used to inspect the substrings that match between the candidate files:
(dedup) $ ./matching_substrings.py -husage: matching_substrings.py [-h] [-r THRESHOLD] [-n MATCHES] [-j] inputRead pairs of files from stdin and report matching substrings across thefiles. Output is TSV with the following fields: 1.filename 2.filename1.startOffset 1.endOffset 2.startOffset 2.endOffset text size or JSON like thefollowing: { "1.filename": "/path/to/1.txt", "2.filename": "/path/to/2.txt","1.startOffset": 0, "1.endOffset": 4, "2.startOffset": 10, "2.endOffset": 14,"text": "the", "size": 3 }positional arguments: input input file (use "-" to read from stdin)optional arguments: -h, --help show this help message and exit -r THRESHOLD, --threshold THRESHOLD minimum matching substring length to report (default: 5) -n MATCHES, --matches MATCHES number of substring matches to report per file pair (default is to report all substring matches) (default: None) -j, --json output as JSON lines (default is TSV) (default: False)(dedup) $ find test -name '*.txt' | ./dedup.py - -b 32 -r 32 | cut -f 1,2 | ./matching_substrings.py - -j | jq .100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 4758.53files/s]{ "1.filename": "test/3.txt", "2.filename": "test/4.txt", "1.startOffset": 0, "1.endOffset": 22, "2.startOffset": 0, "2.endOffset": 22, "text": "we all scream for ice ", "size": 22}{ "1.filename": "test/3.txt", "2.filename": "test/4.txt", "1.startOffset": 23, "1.endOffset": 29, "2.startOffset": 22, "2.endOffset": 28, "text": "cream\n", "size": 6}{ "1.filename": "test/3.txt", "2.filename": "test/5.txt", "1.startOffset": 17, "1.endOffset": 29, "2.startOffset": 11, "2.endOffset": 23, "text": " ice scream\n", "size": 12}{ "1.filename": "test/3.txt", "2.filename": "test/5.txt", "1.startOffset": 0, "1.endOffset": 7, "2.startOffset": 0, "2.endOffset": 7, "text": "we all ", "size": 7}{ "1.filename": "test/1.txt", "2.filename": "test/2.txt", "1.startOffset": 0, "1.endOffset": 12, "2.startOffset": 0, "2.endOffset": 12, "text": "the cat sat ", "size": 12}{ "1.filename": "test/1.txt", "2.filename": "test/2.txt", "1.startOffset": 13, "1.endOffset": 19, "2.startOffset": 13, "2.endOffset": 19, "text": "n the ", "size": 6}The script uses locality sensitive hashing (LSH) to compare texts. LSHs are a kind of hashing function that, unlike normal hashing functions, have the property that the probability of a hash collision is proportional to the similarity of objects hashed. In this case, the LSH implemented is simhash (describedhere).
Runningdedup.py with the-v/--verbose option prints the binary values of the computed LSHs and shows the similarity scores:
In [1]: from cityhash import CityHash32, CityHash64, CityHash128 In [2]: from dedup import simhash# 32 bit LSHIn [3]: '{:0>32b}'.format(simhash('the cat sat in the hat', hashf=CityHash32))Out[3]: '00001001110100111100101111111010'# 64 bit LSHIn [4]: '{:0>64b}'.format(simhash('the cat sat in the hat', hashf=CityHash64))Out[4]: '0011010110100111001010111110011101000010100111001101111110100010'#128 bit LSHIn [5]: '{:0>128b}'.format(simhash('the cat sat in the hat', hashf=CityHash128))Out[5]: '00110010010110111010000110100100000111111101100011001010000000100110111000111010010101011001101101011011011000001110001010111001'After computing an LSH for each document, the list of documents are sorted by their LSH and pairs of adjacent LSHs in the list are compared. If an adjacent pair's LSHs are similar enough (based on a threshold), then the pair is considered a duplicate. Considering the sorted list of LSHs once is not sufficient however, because if a pair of documents have high bits that differ, but most bits are still the same, they won't be considered, because they will be unlikely to be adjacent in the sorted list. To mitigate this, each LSH is rotated and the lists of LSHs are sorted again, and again adjacent pairs are checked.
This strategy requires a pass over each of N documents to compute the LSHs, but once the LSHs are computed, it requires only N-1 * M comparisons where M is size of the LSH in bits. When N is much larger than M, this is a significant improvement in time complexity over the naive approach of comparing N documents pairwise, which would require N choose 2 or N! / 2! * (N - 2)! comparisons.
If we have, say, over 100k documents to compare, performing N choose 2 comparisons becomes very expensive:
# ~241 hours is a long time(dedup) $ find ~/Desktop/words -type f | ./dedup_pairwise.py - > ~/Desktop/duplicates_pairwise.tsv100%|███████████████████████████████████████████████████████████████████████████████████████████| 128959/128959 [00:20<00:00, 6251.87files/s] 0%| | 1272202/8315147361 [01:58<241:35:38, 9559.05pairs/sBut, if we use the strategy of rotating the LSHs, we can find duplicates in a reasonable amount of time:
# <10 minutes is acceptable(dedup) $ find ~/Desktop/words -type f | ./dedup.py - -r 12 -b 128 >| ~/Desktop/duplicates.tsv 100%|███████████████████████████████████████████████████████████████████████████████████████████| 128959/128959 [00:35<00:00, 3623.02files/s](dedup) $ wc -l ~/Desktop/duplicates.tsv 3841 /Users/zach/Desktop/duplicates.tsv(dedup) $ head ~/Desktop/duplicates.tsv | while read a b r; do cat "$a"; cat "$b"; echo "-----"; donekaraitrait-----bulldull-----moldtold-----purveyedsurveyed-----salutelute-----blizzardizzard-----blizzardlizard-----aspoutpout-----exclaimingclaiming-----durantjurant-----