scikit-query v0.4

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.

scikit-query v0.3

• Added random sampling of triplet constraints

scikit-query v0.2

• Now with actual documentation at ReadTheDocs !
• 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

scikit-query v0.1.1

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

scikit-query v0.1

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