Welcome to seqsim’s documentation!¶
Python library for computing measures of distance and similarity for sequences of hashable data types.
While developed as a general-purpose library, seqsim is mostly
designed for usage in research within the field of cultural evolution,
and particularly of the cultural evolution of textual traditions. Some
methods act as a thin-wrapper to either the standard Python library or
of to other libraries such as textdistance.
Installation¶
In any standard Python environment, seqsim can be installed with:
$ pip install seqsim
Usage¶
The library offers different methods to compare sequences of arbitrary hashable elements. It is possible to mix sequence and element types.
Full documentation is offered at ReadTheDocs and code with almost
complete coverage is offered in the tests. For most common usages, a
wrapper .distance() function can be used.
>>> import seqsim
>>> seqsim.edit.levenshtein_dist("kitten", "string")
5
>>> seqsim.edit.levenshtein_dist("kitten", "string", normal=True)
>>> 0.8333333333333334
>>> seqsim.sequence.ratcliff_obershelp([1,2,3,4], [2,4,3,5])
0.5
>>> seqsim.compression.entropy_ncd([1,2,3,4], [2,4,3,5])
0.08333333333333333
Demonstration¶
The core of the library are the metrics for sequence distance/similarity on arbitrary data types, as in the table below.
Method |
“kitten” / “sitting” |
(1, 2, 3, 4) / (3, 4, 2, 1) |
|---|---|---|
arith_ncd |
1.25 |
0.888889 |
arith_ncd_normal |
1.25 |
0.888889 |
birnbaum_simil |
10 |
5 |
b irnbaum_simil_normal |
0.3125 |
0.5 |
birnbaun |
0.565217 |
0.5 |
birnbaun_normal |
0.565217 |
0.5 |
bulk_delete |
3 |
3 |
bulk_delete_normal |
0.428571 |
0.75 |
damerau |
3 |
4 |
damerau_normal |
0.428571 |
1 |
entropy |
0.101341 |
0 |
entropy_normal |
0.101341 |
0 |
fast_birnbaum |
0.666667 |
0.7 |
fast_birnbaum_normal |
0.666667 |
0.7 |
fast_birnbaum_simil |
7 |
3 |
fast_b irnbaum_simil_normal |
0.25 |
0.3 |
fragile_ends_simil |
3 |
3.5 |
fragi le_ends_simil_normal |
0.5 |
1 |
jaccard |
0.7 |
0 |
jaccard_normal |
0.7 |
0 |
jaro |
0.253968 |
0.5 |
jaro_normal |
0.253968 |
0.5 |
jaro_winkler |
0.253968 |
0.5 |
jaro_winkler_normal |
0.253968 |
0.5 |
levenshtein |
3 |
4 |
levenshtein_normal |
0.428571 |
1 |
mmcwpa |
0.538462 |
0.387628 |
mmcwpa_normal |
0.538462 |
0.387628 |
ratcliff_obershelp |
0.384615 |
0.5 |
Authors and citation¶
The library is developed in the context of “Cultural Evolution of Text”, project, with funding from the Riksbankens Jubileumsfond (grant agreement ID: MXM19-1087:1).
If you use seqsim, please cite it as:
Tresoldi, Tiago; Maurits, Luke; Dunn, Michael. (2021). seqsim, a library for computing measures of distance and similarity for sequences of hashable data types. Version 0.3. Uppsala: Uppsala universitet. Available at: https://github.com/evotext/seqsim
In BibTeX:
@misc{Tresoldi2021seqsim,
author = {Tresoldi, Tiago; Maurits, Luke; Dunn, Michael},
title = {seqsim, a library for computing measures of distance and similarity for sequences of hashable data types. Version 0.3},
howpublished = {\url{https://github.com/evotext/seqsim}},
address = {Uppsala},
publisher = {Uppsala universitet},
year = {2021},
}
References¶
The image at the top of this file is derived from Yves de Saint-Denis, Vie et martyre de saint Denis et de ses compagnons, versions latine et française. It is available in high resolution from Bibliothèque nationale de France, Département des Manuscrits, Français 2090, fol. 12v.
References to the various implementation are available in the source code comments and in the online documentation.
Contents: