seqsim

Python library for computing measures of similarity for sequences of hashable data types

Installation

In any standard Python environment, seqsim can be installed with:

$ pip install seqsim

Usage

The library offers different methods to compare sequences of hashable Python elements (not only characters in a string).

>> import seqsim
>> test1_seq_a = "kitten"
>> test1_seq_b = "sitting"
>> test2_seq_a = (1, 2, 3)
>> test2_seq_b = [1, 2, 3]
>> test3_seq_a = (1, 2, 3, 4, 5)
>> test3_seq_b = (1, 2, 4, 3, 6, 7)
>> test4_seq_a = (1, 2, 3)
>> test4_seq_b = ["a", "b", "c", "d"]
>> seqsim.distance.edit_distance(test1_seq_a, test1_seq_b)
3.0
>> seqsim.distance.jaccard_distance(test3_seq_a,test3_seq_b)
0.4285714285714286
>> seqsim.distance.mmcwpa_distance(test3_seq_a, test3_seq_b)
0.5546382285848768

Full documentation will be offered in future releases. For the moment, the library usage is illustrated by set of tests in the tests/ directory.

Changelog

Version 0.2:

• First release for new roadmap supporting sequences of any hashable Python datatype, importing code from other projects (mostly from titivillus)

Community guidelines

While the author can be contacted directly for support, it is recommended that third parties use GitHub standard features, such as issues and pull requests, to contribute, report problems, or seek support.

Contributing guidelines, including a code of conduct, can be found in the CONTRIBUTING.md file.

Author and citation

The library is developed by Tiago Tresoldi (tiago.tresoldi@lingfil.uu.se).

If you use seqsim, please cite it as:

Tresoldi, Tiago (2021). seqsim, a library for computing measures of similarity for sequences of hashable data types. Version 0.2. Uppsala: Uppsala Universitet. Available at: https://github.com/tresoldi/seqsim

In BibTeX:

@misc{Tresoldi2021titivillus,
  author = {Tresoldi, Tiago},
  title = {seqsim, a library for computing measures of similarity for sequences of hashable data types},
  howpublished = {\url{https://github.com/tresoldi/seqsim}},
  address = {Uppsala},
  publisher = {Uppsala Universitet},
  year = {2021},
}