gismo 0.4.3

Features

Gismo combines three main ideas:

• TF-IDTF: a symmetric version of the TF-IDF embedding.

• DIteration: a fast, push-based, variant of the PageRank algorithm.

• Fuzzy dendrogram: a variant of the Louvain clustering algorithm.

Quickstart

Install gismo:

$ pip install gismo

Import gismo in a Python project:

import gismo as gs

To get the hang of a typical Gismo workflow, you can check the Toy Example notebook. For more advanced uses, look at the other tutorials or directly the reference section.

Credits

Thomas Bonald, Anne Bouillard, Marc-Olivier Buob, Dohy Hong.

This package was created with Cookiecutter and the francois-durand/package_helper project template.

X.X.X (TODO-List)

• Rethink distortion on both vectors normalization and IDTF/query trade-off.

• Accelerate similarity computation (currently sklearn-based) in clustering.

0.4.X (2023-0X-XX) (tentative)

• Context manager for FileSource (e.g. with FileSource(...) as source:)

• 3.9 compatibility issues rechecked

• Wheels

• Minor change in test_dblp.py

0.4.3 (2022-12-26)

• Refresh dependencies, compatibilities, and such.

• Gismo is tested up to Python 3.10.

• Patch sklearn change of API (ngram_range must be a tuple, get_feature_names has been renamed get_feature_names_out)

• Updates MixInIO logic: you now save with the dump method and load with the load class method.

• Package management now uses Github actions.

0.4.2 (2021-05-05)

Minor patch

• Signature of the Gismo rank method changed to allow to enter directly a query vector instead of a string query (useful if one wants to craft a custom query vector).

• Original source of the Reuters 50/50 dataset was discontinued; changed to an alternate source.

• Fix change in spacy API

0.4.1 (2020-11-25)

Minor update.

• DBLP API modified to you can specify the set of fields you want to retrieve.

• Minor update in doctests.

• Python 3.9 compatibility added.

0.4.0 (2020-07-21)

0.4 is a big update. Lot of things added, lot of things changed.

• New API for Gismo runtime parameters (see new parameters module for details). Short version:

  • gismo = Gismo(corpus, embedding, alpha=0.85): create a gismo with damping factor set to 0.85 instead of default value.

  • gismo.parameters.alpha = 0.85: set the damping factor of the gismo to 0.85.

  • gismo.rank(query, alpha=0.85): makes a query with damping factor temporarily set to 0.85.

• Landmarks! Half Corpus, half Gismo, the Landmarks class can simplify many analysis tasks.

  • Landmarks are (small) corpus where each entry is augmented with the computation of an associated gismo query;

  • Landmarks can be used to refine the analysis around a part of your data;

  • They can be used as soft and fast classifiers.

  • Landmarks’ runtime parameters follow the same approach than for Gismo instances (cf above).

  • See the dedicated tutorial to learn more!

• Documentation summer cleaning.

• query_distortion parameter (reshape subspace for clustering) is renamed distortion and is now a float instead of a bool (e.g. you can apply distortion in a non-binary way).

• Full refactoring of get_*** and post_*** methods and objects.

  • The good news is that they are now more natural, self-describing, and unified.

  • The bad news is that there is no backward-compatibility with previous Gismo versions. Hopefully this refactoring will last for some time!

• Gismo logo added!

0.3.1 (2020-06-12)

• New dataset: Reuters C50

• New module: sentencizer

0.3.0 (2020-05-13)

• dblp module: url2source function added to directly load a small dblp source in memory instead of using a FileSource approach.

• Possibility to disable query distortion in gismo.

• XGismo class to cross analyze embeddings.

• Tutorials updated

0.2.5 (2020-05-11)

• auto_k feature: if not specified, a query-dependent, reasonable, number of results k is estimated.

• covering methods added to gismo. It is now possible to use get_covering_* instead of get_ranked_* to maximize coverage and/or eliminate redundancy.

0.2.4 (2020-05-07)

• Tutorials for ACM and DBLP added. After cleaning, there is currently 3 tutorials:

  • Toy model, to get the hang of Gismo on a tiny example,

  • ACM, to play with Gismo on a small example,

  • DBLP, to play with a large dataset.

0.2.3 (2020-05-04)

• ACM and DBLP dataset creation added.

0.2.2 (2020-05-04)

• Notebook tutorials added (early version)

0.2.1 (2020-05-03)

• Actual code

• Coverage badge

0.1.0 (2020-04-30)

• First release on PyPI.