Scikit-query

Latest version: v0.4

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0.4

New module for informative subset selection, currently implementing 3 strategies :
- random selection
- selection based on nearest neighbors (Cai et al. 2016)
- selection based on Shannon entropy (Chen and Jin 2020)
These methods reduce the size of the query space by focusing on informative points, leading to a faster runtime of the query strategies. They can also serve as a warm start for neighborhood-based methods.

0.3

- Added random sampling of triplet constraints

0.2

- Now with actual documentation at [ReadTheDocs](https://scikit-query.readthedocs.io/en/latest/index.html) !
- fit now takes exactly four 4 arguments : data, oracle, partition and number of clusters. The latest two are optional. This shouldn't change anymore to avoid breaking compatibility with previous versions.
- Significantly improved performance of FFQS and MinMax
- FFQS and MinMax can now be initialized with a precomputed pairwise distance matrix of the data to avoid unnecessary recomputations
- Added automatic computation of epsilon threshold in AIPC
- All implementations can now write selected constraints in a text file

0.1.1

- Added options for choice between standard and incremental settings in FFQS, MinMax and NPU.

0.1

- Added random sampling, FFQS, MinMax, NPU, AIPC, SASC algorithms.
- Jupyter notebook giving a use case of the library.

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