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, [email protected] <[email protected]> wrote: > So there is no possibility to reach a consistency? > > 2016-08-27 15:36 GMT+03:00 olologin <[email protected]>: > >> On 08/27/2016 02:19 PM, [email protected] wrote: >> >> Can I update the libsvm version by myself? >> >> 2016-08-27 12:49 GMT+03:00 olologin <[email protected]>: >> >>> On 08/27/2016 12:33 PM, [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 >>> >>> >>> >>> >>> _______________________________________________ >>> scikit-learn mailing >>> [email protected]https://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 [email protected] https://mail.python.org/mailma >>> n/listinfo/scikit-learn >> >> _______________________________________________ >> scikit-learn mailing >> [email protected]https://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 >> <[email protected]> <[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/~cjlin/libsvmtools/#weights_for_data_instances>. >> - Make labels sorted in svm_group_classes, Fabian Pedregosa. */ >> >> _______________________________________________ >> scikit-learn mailing list >> [email protected] >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > >
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