One logical possibility is if svm would accept the scikit-learn changes.
On 8/27/16 6:42 AM, [email protected] wrote:
So there is no possibility to reach a consistency?
2016-08-27 15:36 GMT+03:00 olologin <[email protected]
<mailto:[email protected]>>:
On 08/27/2016 02:19 PM, [email protected]
<mailto:[email protected]> wrote:
Can I update the libsvm version by myself?
2016-08-27 12:49 GMT+03:00 olologin <[email protected]
<mailto:[email protected]>>:
On 08/27/2016 12:33 PM, [email protected]
<mailto:[email protected]> 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 <[email protected]>
<mailto:[email protected]> - 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/%7Ecjlin/libsvmtools/#weights_for_data_instances>.
- Make labels sorted in svm_group_classes, Fabian Pedregosa. */
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