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Compute distance between the two texts.

Project description

TextDistance logo

TextDistance logo

Build Status PyPI version Status Code size License

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.

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