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

[olologin]

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 
> <mailto:olologin at gmail.com>>:
>
>     On 08/27/2016 12:33 PM, elgesto at gmail.com
>     <mailto: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|
>>
>>
>>
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>
>     This might be because current version of libsvm used in scikit is
>     3.10 from 2011. With some patch imported from upstream.
>
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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>    - 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>. 
    - Make labels sorted in svm_group_classes, Fabian Pedregosa.  */

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