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.
--
sean
Thanks for sharing. It might be indeed a pass to get rid of our
> liblinear bindings and move to a pure-cython implementation for
> LinearSVC and LogisticRegression.
>
> The ongoing work by Fabian and Gael on alternative optimizers for
> LogisticRegression to add support for warm restarts and regularization
> path would also probably benefit from non-memory-copy pure cython
> implementations of the liblinear algorithm.
>
> --
> Olivier
>
>
>
> I'd very much like to get rid of liblinear, but we really have to be
> careful
> in the analysis. I'm pretty sure they benchmarked with a lot of sparse
> and dense
> data with lots of different amounts of noise, regularization,
> n_features, n_samples.
>
> Also, thanks for sharing your results Mathieu, that looks really promising!
>
>
>
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