[scikit-learn] Fwd: inconsistency between libsvm and scikit-learn.svc results
elgesto at gmail.com
elgesto at gmail.com
Sat Aug 27 09:42:20 EDT 2016
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.
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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,
> - Make labels sorted in svm_group_classes, Fabian Pedregosa. */
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