[scikit-learn] Fwd: inconsistency between libsvm and scikit-learn.svc results
olologin at gmail.com
Sat Aug 27 08:36:56 EDT 2016
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
>> 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
>> scikit-learn mailing list
>> scikit-learn at python.org <mailto:scikit-learn at python.org>
> 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,
- Make labels sorted in svm_group_classes, Fabian Pedregosa. */
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