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

[Joel Nothman]

I don't think we should assume that this is the only possible reason for
inconsistency. Could you give us a small snippet of data and code on which
you find this inconsistency?

On 27 August 2016 at 23:42, elgesto at gmail.com <elgesto at gmail.com> wrote:

> 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
>>> list scikit-learn at python.org https://mail.python.org/mailma
>>> n/listinfo/scikit-learn
>>
>> _______________________________________________
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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> <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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>
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