I agree with Olivier's remarks.
lightning supports a rudimentary command-line interface [*] but that's
because I want to make it easy to non-Python users to try my algorithm on
their data.
Mathieu
[*] http://www.mblondel.org/code/mlj2013/
On Thu, Aug 22, 2013 at 9:10 PM, Olivier Grisel <[email protected]>wrote:
> 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
>
>
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