2013/8/22 Sean Violante <[email protected]>: > I'm sure you will hate this suggestion, but what about creating a text > file/command line "interface" to existing machine learning executables. > advantages: > a) no problem with data copy: the executable loads data from file (you don't > need to keep in sklearn) > b) most ML algos are available from command line with text file input. > c) sklearn is great for the added extras ( cross validation, metrics, grid > search, feature selection etc) > d) less time to integrate new algos, in fact algo development is left to > original authors.
The goal of scikit-learn is to work well in the numpy / scipy ecosystem, typically in an interactive IPython shell session where the user is responsible to load the data in memory as a numpy array and never touch the disk again after that (assuming the data is small enough to fit in memory). We don't want to wrap external libraries written in c++ or anything else. Quite the opposite, we would like to move away from that paradigm and have fine control of the memory layout of the data. What you describe is reasonable but at the opposite end of the current interests of the scikit-learn developers team: it means that its a good opportunity to start your own project ;) -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ Introducing Performance Central, a new site from SourceForge and AppDynamics. Performance Central is your source for news, insights, analysis and resources for efficient Application Performance Management. Visit us today! http://pubads.g.doubleclick.net/gampad/clk?id=48897511&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
