On Fri, Nov 4, 2011 at 12:25 PM, Mathieu Blondel <[email protected]> wrote: > Another possibility is to host a Theanos-based implementation as a > side project on github and make the API scikit-learn compatible. > > # In general, I don't really buy the "why implement X if it already > exists in Y" argument because it can be said of pretty much every > module in scikit-learn. Since we came up with a quite rigorous review > process, even if we reimplement something that already exists > elsewhere, in the end we usually obtain a very high-quality module (in > code and documentation). Think of the tree module :)
+1 for the sklearn review process AND for cooperating with other projects and sharing a good API. It would be great to be able to prototype things in sklearn and then drop in something like Theano or a map-reduce implementation or some experimental new algorithm to improve speed or accuracy. The more the ecosystem converges on quality code with similar APIs, the closer we are to that. There's currently a wiki page for other libraries with compatible APIs. It only had one project on it last I checked. Perhaps that could be extended to "other projects that can help you get your machine learning job done in Python, whom we're talking to about API alignment" :) -Ken ------------------------------------------------------------------------------ RSA(R) Conference 2012 Save $700 by Nov 18 Register now http://p.sf.net/sfu/rsa-sfdev2dev1 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
