[scikit-learn] Fwd: inconsistency between libsvm and scikit-learn.svc results

[elgesto at gmail.com]

So there is no possibility to reach a consistency?

2016-08-27 15:36 GMT+03:00 olologin <olologin at gmail.com>:

> On 08/27/2016 02:19 PM, elgesto at gmail.com wrote:
>
> Can I update the libsvm version by myself?
>
> 2016-08-27 12:49 GMT+03:00 olologin <olologin at gmail.com>:
>
>> On 08/27/2016 12:33 PM, elgesto at gmail.com wrote:
>>
>> I have a project that is based on SVM algorithm implemented by libsvm
>> <https://www.csie.ntu.edu.tw/%7Ecjlin/libsvm/>. Recently I decided to
>> try several other classification algorithm, this is where scikit-learn
>> <http://scikit-learn.org/> comes to the picture.
>>
>> The connection to the scikit was pretty straightforward, it supports
>> libsvm format by load_svmlight_file routine. Ans it's svm implementation
>> is based on the same libsvm.
>>
>> When everything was done, I decided to the check the consistence of the
>> results by directly running libsvm and via scikit-learn, and the results
>> were different. Among 18 measures in learning curves, 7 were different, and
>> the difference is located at the small steps of the learning curve. The
>> libsvm results seems much more stable, but scikit-learn results have some
>> drastic fluctuation.
>>
>> The classifiers have exactly the same parameters of course. I tried to
>> check the version of libsvm in scikit-learn implementation, but I din't
>> find it, the only thing I found was libsvm.so file.
>>
>> Currently I am using libsvm 3.21 version, and scikit-learn 0.17.1 version.
>>
>> I wound appreciate any help in addressing this issue.
>>
>>
>> size    libsvm                  scikit-learn
>> 1       0.1336239435355727      0.1336239435355727
>> 2       0.08699516468193455     0.08699516468193455
>> 3       0.32928301642777424     0.2117238289550198      #different
>> 4       0.2835688734876902      0.2835688734876902
>> 5       0.27846766962743097     0.26651875338163966     #different
>> 6       0.2853854654662907      0.18898048915599963     #different
>> 7       0.28196058132165136     0.28196058132165136
>> 8       0.31473956032575623     0.1958710201604552      #different
>> 9       0.33588303670653136     0.2101641630182972      #different
>> 10      0.4075242509025311      0.2997807499800962      #different
>> 15      0.4391771087975972      0.4391771087975972
>> 20      0.3837789445609818      0.2713167833345173      #different
>> 25      0.4252154334940311      0.4252154334940311
>> 30      0.4256407777477492      0.4256407777477492
>> 35      0.45314944605858387     0.45314944605858387
>> 40      0.4278633233755064      0.4278633233755064
>> 45      0.46174762022239796     0.46174762022239796
>> 50      0.45370452524846866     0.45370452524846866
>>
>>
>>
>>
>> _______________________________________________
>> scikit-learn mailing listscikit-learn at python.orghttps://mail.python.org/mailman/listinfo/scikit-learn
>>
>> This might be because current version of libsvm used in scikit is 3.10
>> from 2011. With some patch imported from upstream.
>> _______________________________________________ scikit-learn mailing
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>
> _______________________________________________
> scikit-learn mailing listscikit-learn at python.orghttps://mail.python.org/mailman/listinfo/scikit-learn
>
> I don't think it is so easy, version which is used in scikit-learn has
> many additional modifications.
>
> from header of svm.cpp: /*    Modified 2010:    - Support for dense data
> by Ming-Fang Weng    - Return indices for support vectors, Fabian Pedregosa
>      <fabian.pedregosa at inria.fr> <fabian.pedregosa at inria.fr>    - Fixes
> to avoid name collision, Fabian Pedregosa    - Add support for instance
> weights, Fabian Pedregosa based on work      by Ming-Wei Chang, Hsuan-Tien
> Lin, Ming-Hen Tsai, Chia-Hua Ho and      Hsiang-Fu Yu,
> <http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#weights_
> for_data_instances>
> <http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#weights_for_data_instances>.
>    - Make labels sorted in svm_group_classes, Fabian Pedregosa.  */
>
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