BTW, the advantages of scikit-learn's approach over text-file based
programs are also briefly discussed in our recent paper:
http://staff.science.uva.nl/~buitinck/papers/scikit-learn-api.pdf
Mathieu
On Thu, Aug 22, 2013 at 9:20 PM, Mathieu Blondel <[email protected]>wrote:
> 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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