Hi Mathieu, Thanks for the feedback and great to hear that I am not alone in this pursuit!!! More comments are below
On Sat, 29 Aug 2015, Mathieu Blondel wrote: > I am a bit concerned that most people wouldn't want or wouldn't be able to > install an external program, though. As long as installation is straightforward, I think it should be a minor hurdle. It will be by default (Recommends) installed with scikit-learn, pymvpa, and any other related package I am maintaining in Debian/Ubuntu. It is already available from pypi although installation there could be a bit problematic due to external depends indeed. We will look into minimizing possibility for issues and will also look into packaging within conda universe. Happen it is a no brainer to have it installed -- installation of an external tool, especially if recommended by the project, should not be a big issue. > For this reason, I think the ideal > solution should be web based. This could for example take the form of a > sphinx plugin for easily integrating with the project's documentation. We > could maintain a BibTeX file and reference BibTeX entries from within the > documentation. The sphinx plugin would make it easier to find relevant > citations from various places in the documentation (class reference, user > guide). Although sound idea on its own, even if complementary to duecredit, it IMHO would not be as "productive". Sure thing some determined users will look up references for pieces they used, but not exhaustively and not for core functions which they might have not even knew have called (indirectly). That is exactly what duecredit tries to address -- automate that collection of references. > One difficulty, though, is that the relevant citations in scikit-learn > estimators often depends on constructor options. For example, in > LinearSVC, the paper to cite is not the same whether we use dual=True or > dual=False, penalty="l1" or penalty="l2", etc. That is already partially handled, e.g. https://github.com/duecredit/duecredit/blob/master/duecredit/injections/mod_scipy.py#L134 injector.add('scipy.cluster.hierarchy', 'linkage', BibTeX(""" @article{ward1963hierarchical, title={Hierarchical grouping to optimize an objective function}, author={Ward Jr, Joe H}, journal={Journal of the American statistical association}, volume={58}, number={301}, pages={236--244}, year={1963}, publisher={Taylor \& Francis} }"""), conditions={(1, 'method'): {'ward'}}, description="Ward hierarchical clustering", min_version='0.4.3', tags=['reference']) says to reference that publication only if method='ward' to the linkage call. Similarly I can decorate __init__. But thus partially -- since I don't want to cite merely if __init__ was called, I would like to cite only if actual computation has happened, so it should also be conditioned on some methods of the class being called... We will look into supporting that. -- 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
