Edit distance, Similarity and 2 sequence differences printing.
pip install cdiffer- python3.6 or later
Compute absolute Levenshtein distance of two strings.
dist(sequence, sequence)
Help on built-in function dist in module cdiffer:
dist(...)
Compute absolute Levenshtein distance of two strings.
dist(sequence, sequence)
Examples (it's hard to spell Levenshtein correctly):
>>> dist('coffee', 'cafe')
4
>>> dist(list('coffee'), list('cafe'))
4
>>> dist(tuple('coffee'), tuple('cafe'))
4
>>> dist(iter('coffee'), iter('cafe'))
4
>>> dist(range(4), range(5))
1
>>> dist('coffee', 'xxxxxx')
12
>>> dist('coffee', 'coffee')
0Compute similarity of two strings.
similar(sequence, sequence)
The similarity is a number between 0 and 1, base on levenshtein edit distance.
>>> from cdiffer import similar
>>>
>>> similar('coffee', 'cafe')
0.6
>>> similar('hoge', 'bar')
0.0Find sequence of edit operations transforming one string to another.
differ(source_sequence, destination_sequence, diffonly=False, rep_rate=60)
>>> from cdiffer import differ
>>>
>>> for x in differ('coffee', 'cafe'):
... print(x)
...
['equal', 0, 0, 'c', 'c']
['delete', 1, None,'o',None]
['insert', None, 1,None,'a']
['equal', 2, 2, 'f', 'f']
['delete', 3, None,'f',None]
['delete', 4, None,'e',None]
['equal', 5, 3, 'e', 'e']
>>> for x in differ('coffee', 'cafe', diffonly=True):
... print(x)
...
['delete', 1, None,'o',None]
['insert', None, 1,None,'a']
['delete', 3, None,'f',None]
['delete', 4, None,'e',None]
>>> for x in differ('coffee', 'cafe', rep_rate = 0):
... print(x)
...
['equal', 0, 0, 'c', 'c']
['replace', 1, 1, 'o', 'a']
['equal', 2, 2, 'f', 'f']
['delete', 3, None,'f',None]
['delete', 4, None,'e',None]
['equal', 5, 3, 'e', 'e']
>>> for x in differ('coffee', 'cafe', diffonly=True, rep_rate = 0):
... print(x)
...
['replace', 1, 1, 'o', 'a']
['delete', 3, None,'f',None]
['delete', 4, None,'e',None]This Function is compare and prety printing 2 sequence data.
compare(source_sequence, destination_sequence, diffonly=False, rep_rate=60, condition_value=" ---> ")
arg1 -> iterable : left comare target data.
arg2 -> iterable : right comare target data.
keya -> callable one argument function : Using sort and compare with key about `a` object.
keyb -> callable one argument function : Using sort and compare with key about `a` object.
header -> bool : output data with header(True) or without header(False). <default True>
diffonly -> bool : output data with equal data(False) or without equal data(True). <default False>
rep_rate -> int: Threshold to be considered as replacement.(-1 ~ 100). -1: allways replacement.
startidx -> int: output record index starting number. <default `0`>
condition_value -> str : Conjunctions for comparison.
na_value -> str: if not found data when filled value.
delete_sign_value -> str: if deleted data when adding sign value.
insert_sign_value -> str: if insert data when adding sign value.
1st column -> matching rate (0 ~ 100).
2nd column -> matching tagname (unicode string).
3rd over -> compare data.
In [1]: from cdiffer import compare
... compare('coffee', 'cafe')
[['tag', 'index_a', 'index_b', 'data'],
['equal', 0, 0, 'c'],
['insert', '-', 1, 'ADD ---> a'],
['delete', 1, '-', 'o ---> DEL'],
['equal', 2, 2, 'f'],
['delete', 3, '-', 'f ---> DEL'],
['equal', 4, 3, 'e'],
['delete', 5, '-', 'e ---> DEL']]
In [2]: compare([list("abc"), list("abc")], [list("abc"), list("acc"), list("xtz")], rep_rate=50)
[['tag', 'index_a', 'index_b', 'COL_00', 'COL_01', 'COL_02', 'COL_03'],
['equal', 0, 0, 'a', 'b', 'c'],
['replace', 1, 1, 'a', 'b ---> DEL', 'ADD ---> c', 'c'],
['insert', '-', 2, 'ADD ---> x', 'ADD ---> t', 'ADD ---> z']]
In [3]: compare(["abc", "abc"], ["abc", "acc", "xtz"], rep_rate=40)
[['tag', 'index_a', 'index_b', 'data'],
['equal', 0, 0, 'abc'],
['replace', 1, 1, 'abc ---> acc'],
['insert', '-', 2, 'ADD ---> xtz']]
In [4]: compare(["abc", "abc"], ["abc", "acc", "xtz"], rep_rate=50)
[['tag', 'index_a', 'index_b', 'data'],
['equal', 0, 0, 'abc'],
['replace', 1, 1, 'abc ---> acc'],
['insert', '-', 2, 'ADD ---> xtz']]C:\Windows\system>ipython
Python 3.7.7 (tags/v3.7.7:d7c567b08f, Mar 10 2020, 10:41:24) [MSC v.1900 64 bit (AMD64)]
Type 'copyright', 'credits' or 'license' for more information
IPython 7.21.0 -- An enhanced Interactive Python. Type '?' for help.
In [1]: from cdiffer import *
In [2]: %timeit dist('coffee', 'cafe')
...: %timeit dist(list('coffee'), list('cafe'))
...: %timeit dist(tuple('coffee'), tuple('cafe'))
...: %timeit dist(iter('coffee'), iter('cafe'))
...: %timeit dist(range(4), range(5))
...: %timeit dist('coffee', 'xxxxxx')
...: %timeit dist('coffee', 'coffee')
125 ns ± 0.534 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
677 ns ± 2.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
638 ns ± 3.42 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
681 ns ± 2.16 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
843 ns ± 3.66 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
125 ns ± 0.417 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
50.5 ns ± 0.338 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
In [3]: %timeit similar('coffee', 'cafe')
...: %timeit similar(list('coffee'), list('cafe'))
...: %timeit similar(tuple('coffee'), tuple('cafe'))
...: %timeit similar(iter('coffee'), iter('cafe'))
...: %timeit similar(range(4), range(5))
...: %timeit similar('coffee', 'xxxxxx')
...: %timeit similar('coffee', 'coffee')
123 ns ± 0.301 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
680 ns ± 2.64 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
647 ns ± 1.78 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
680 ns ± 7.57 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
848 ns ± 4.19 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
130 ns ± 0.595 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
54.8 ns ± 0.691 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
In [4]: %timeit differ('coffee', 'cafe')
...: %timeit differ(list('coffee'), list('cafe'))
...: %timeit differ(tuple('coffee'), tuple('cafe'))
...: %timeit differ(iter('coffee'), iter('cafe'))
...: %timeit differ(range(4), range(5))
...: %timeit differ('coffee', 'xxxxxx')
...: %timeit differ('coffee', 'coffee')
735 ns ± 4.18 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
1.36 µs ± 5.17 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
1.31 µs ± 5.25 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
1.37 µs ± 5.04 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
1.33 µs ± 5.32 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
1.07 µs ± 6.75 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
638 ns ± 3.67 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [5]: a = dict(zip('012345', 'coffee'))
...: b = dict(zip('0123', 'cafe'))
...: %timeit dist(a, b)
...: %timeit similar(a, b)
...: %timeit differ(a, b)
524 ns ± 2.6 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
539 ns ± 2.23 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
1.07 µs ± 1.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [6]: %timeit compare("coffee", "cafe")
...: %timeit compare([list("abc"), list("abc")], [list("abc"), list("acc"), list("xtz")], rep_rate=50)
...: %timeit compare(["abc", "abc"], ["abc", "acc", "xtz"], rep_rate=40)
...: %timeit compare(["abc", "abc"], ["abc", "acc", "xtz"], rep_rate=50)
844 ns ± 3.88 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
3.32 µs ± 6.92 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
1.16 µs ± 3.94 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
1.3 µs ± 31.5 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)