TextDistance

TextDistance -- python library for compare distance between two or more sequences by many algorithms.

Features:

• 30+ algorithms
• Pure python implementation
• Simple usage
• More than two sequences comparing
• Some algorithms have more than one implementation in one class.
• Optional numpy usage for maximum speed.

Algorithms

Edit based

Algorithm	Class	Functions
Hamming	Hamming	hamming
MLIPNS	Mlipns	mlipns
Levenshtein	Levenshtein	levenshtein
Damerau-Levenshtein	DamerauLevenshtein	damerau_levenshtein
Jaro-Winkler	JaroWinkler	jaro_winkler, jaro
Strcmp95	StrCmp95	strcmp95
Needleman-Wunsch	NeedlemanWunsch	needleman_wunsch
Gotoh	Gotoh	gotoh
Smith-Waterman	SmithWaterman	smith_waterman

Token based

Algorithm	Class	Functions
Jaccard index	Jaccard	jaccard
Sørensen–Dice coefficient	Sorensen	sorensen, sorensen_dice, dice
Tversky index	Tversky	tversky
Overlap coefficient	Overlap	overlap
Tanimoto distance	Tanimoto	tanimoto
Cosine similarity	Cosine	cosine
Monge-Elkan	MongeElkan	monge_elkan
Bag distance	Bag	bag

Sequence based

Algorithm	Class	Functions
longest common subsequence similarity	LCSSeq	lcsseq
longest common substring similarity	LCSStr	lcsstr
Ratcliff-Obershelp similarity	RatcliffObershelp	ratcliff_obershelp

Compression based

Work in progress. Now all algorithms compare two strings as array of bits, not by chars.

NCD - normalized compression distance.

Functions:

1. bz2_ncd
2. lzma_ncd
3. arith_ncd
4. rle_ncd
5. bwtrle_ncd
6. zlib_ncd

Phonetic

Algorithm	Class	Functions
MRA	MRA	mra
Editex	Editex	editex

Simple

Algorithm	Class	Functions
Prefix similarity	Prefix	prefix
Postfix similarity	Postfix	postfix
Length distance	Length	length
Identity similarity	Identity	identity
Matrix similarity	Matrix	matrix

Installation

Stable:

pip install textdistance

Dev:

pip install -e git+https://github.com/orsinium/textdistance.git#egg=textdistance

Usage

All algorithms have 2 interfaces:

1. Class with algorithm-specific params for customizing.
2. Class instance with default params for quick and simple usage.

All algorithms have some common methods:

1. .distance(*sequences) -- calculate distance between sequences.
2. .similarity(*sequences) -- calculate similarity for sequences.
3. .maximum(*sequences) -- maximum possible value for distance and similarity. For any sequence: distance + similarity == maximum.
4. .normalized_distance(*sequences) -- normalized distance between sequences. The return value is a float between 0 and 1, where 0 means equal, and 1 totally different.
5. .normalized_similarity(*sequences) -- normalized similarity for sequences. The return value is a float between 0 and 1, where 0 means totally different, and 1 equal.

Most common init arguments:

1. qval -- q-value for split sequences into q-grams. Possible values:
   • 1 (default) -- compare sequences by chars.
   • 2 or more -- transform sequences to q-grams.
   • None -- split sequences by words.
2. as_set -- for token-based algorithms:
   • True -- t and ttt is equal.
   • False (default) -- t and ttt is different.

Example

For example, Hamming distance:

import textdistance

textdistance.hamming('test', 'text')
# 1

textdistance.hamming.distance('test', 'text')
# 1

textdistance.hamming.similarity('test', 'text')
# 3

textdistance.hamming.normalized_distance('test', 'text')
# 0.25

textdistance.hamming.normalized_similarity('test', 'text')
# 0.75

textdistance.Hamming(qval=2).distance('test', 'text')
# 2

Any other algorithms have same interface.