On Thu, 05 Nov 2015, Andreas Mueller wrote: > I'm happy to announce the release of scikit-learn 0.17.
Congrats! FWIW, for those using your apt/deb package manager, 0.17 is available (including python3-sklearn package) from neurodebian. Moreover, in duecredit project (available from github/pypi) we have added some more injections for sklearn citations [1]. So if you are interested to easily get citations for sklearn and used by your analysis script methods papers, just run your script with "python -m duecredit". (if interested to see your publication listed -- contribute!) So if I run scikit-learn's tests this way python -m duecredit /usr/bin/nosetests -s -v sklearn I get: DueCredit Report: - Data analysis library for tabular data / pandas (v 0.17.0+git8-gcac4ad2) [1] - Scientific tools library / scipy (v 0.16) [2] - Machine Learning library / sklearn (v 0.17) [3] - Affinity propagation clustering algorithm / sklearn.cluster.affinity_propagation_ (v 0.17) [4] - Spectral Biclustering algorithm / sklearn.cluster.bicluster:SpectralBiclustering._fit (v 0.17) [5] - Spectral Coclustering algorithm / sklearn.cluster.bicluster:SpectralCoclustering._fit (v 0.17) [5] - dbscan clustering algorithm / sklearn.cluster.dbscan_:dbscan (v 0.17) [6] - Mean shift clustering algorithm / sklearn.cluster.mean_shift_:mean_shift (v 0.17) [7] - Spectral clustering / sklearn.cluster.spectral:spectral_clustering (v 0.17) [8] - Random forest classifiers / sklearn.ensemble.forest:RandomForestClassifier.predict_proba (v 0.17) [9] - Random forest regressions / sklearn.ensemble.forest:RandomForestRegressor.predict (v 0.17) [9] - Classification and regression trees / sklearn.tree.tree:DecisionTreeClassifier.predict_proba (v 0.17) [10] 3 packages cited 1 modules cited 8 functions cited References ---------- [1] McKinney, 2010. Data Structures for Statistical Computing in Python . In S. van der Walt & Millman, eds. Proceedings of the 9th Python in Science Conference . pp. 51–56. [2] Jones, E. et al., 2001. SciPy: Open source scientific tools for Python. [3] Pedregosa, F. et al., 2011. Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12, pp.2825–2830. [4] Frey, B.J. & Dueck, D., 2007. Clustering by Passing Messages Between Data Points. Science, 315(5814), pp.972–976. [5] Kluger, Y., 2003. Spectral Biclustering of Microarray Data: Coclustering Genes and Conditions. Genome Research, 13(4), pp.703–716. [6] Ester, M. et al., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise.. In Kdd. pp. 226–231. [7] Comaniciu, D. & Meer, P., 2002. Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Machine Intell., 24(5), pp.603–619. [8] von Luxburg, U., 2007. A tutorial on spectral clustering. Stat Comput, 17(4), pp.395–416. [9] Breiman, L., 2001. Machine Learning, 45(1), pp.5–32. [10] Breiman, L. et al., 1984. Classification and Regression Trees, Monterey, CA: Wadsworth and Brooks. Cheers and sorry for a shameless plug! ;) [1] https://github.com/duecredit/duecredit/blob/master/duecredit/injections/mod_sklearn.py -- Yaroslav O. Halchenko Center for Open Neuroscience http://centerforopenneuroscience.org Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755 Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419 WWW: http://www.linkedin.com/in/yarik ------------------------------------------------------------------------------ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
