Open in new window / Try shogun cloud
--- Log opened Mon Mar 19 00:00:19 2012
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hpsoarhi, my github id is hpsoar08:48
hpsoartoday, I compiled and installed shogun on my notebook with linux os08:49
hpsoarjust now, i set up my github tools, and successfully sent a pull request test08:49
hpsoarI had encountered some problems when install shogun, and run the examples08:50
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hpsoar1. firstly, I install through apt-get, but the examples doesn't work with errors like "cannot import name AveragedPerceptron"08:52
n4nd0hpsoar: hey! so the pacakge is not really up to date08:54
n4nd0hpsoar: you should try to compile and install from source, the code in github08:55
hpsoaryes, I reinstalled through the source code08:55
hpsoarthe examples can run, but some ends up unconverged08:56
n4nd0hpsoar: I am not sure if I follow08:56
n4nd0hpsoar: so it seems that you are trying out python examples, did you install with python support?08:57
hpsoaryes08:57
n4nd0./configure --interfaces=python_modular ??08:57
hpsoaryes08:57
n4nd0ok08:57
n4nd0and you are able or not to run examples?08:57
hpsoaryes08:57
n4nd0then everything is good :)08:58
hpsoarok, I want participate in the gsoc project, I think I need to spend some time getting familiar with the project. thanks for your help~09:02
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n4nd0no problem, just ask around here if you have any problem so we can try to help you ;)09:05
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blackburnhpsoar: it seems we've got some misunderstanding at pull request point09:27
blackburnhave you subscribed to the mailing list?09:27
hpsoaryes09:27
blackburnhpsoar: I clarified it a little, we intent that students could help with some improvements09:28
blackburnbut not some test thing09:28
hpsoardoesn't the project require a pull request test to make sure that the version control can work correctly09:28
hpsoarI know that, I have already viewed the ideas proposed09:29
blackburnno, github works well anyway09:30
blackburnwe suggest students to make real pull requests09:30
blackburnthat's what I mean09:30
blackburnhpsoar: according to things you said before (I checked logs) - contact mentor(s) of idea(s) you are interested with09:34
hpsoaroh, sorry09:35
blackburnno problem09:35
hpsoarI haved already pull the source to my machine, ok, thank you09:35
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sonne|workblackburn: thx for the clarification email09:44
blackburnsonne|work: lol09:44
sonne|workbtw - I think I know what is wrong with tests09:44
blackburnwhat?09:44
sonne|workserialization seems to not work09:44
blackburnoh awesome09:44
sonne|workno idea why though09:44
sonne|workpickle.dumps()/loads() work...09:44
blackburnsonne|work: I am tending to abandon release right now09:45
sonne|workso all tests fail that serialize shogun objects09:45
sonne|work?09:45
blackburnwell we may not release this time I mean09:45
sonne|workif I figure out what is wrong - I think we are in good shape for a release09:45
blackburnok, I don't mind release but I mean I hardly can find much time to fix some serialization errors09:46
n4nd0if I can help with sth guys, let me know please09:47
sonne|workblackburn: it is clear that I have to figure out what is wrong... - well I can try09:48
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blackburnsonne|work: not so clear if you have no time for that :)09:49
flxbHello!09:49
flxbIs it possible to serialize a GMNPSVM classifier? And if so, how do you do it in python_modular?09:49
sonne|workn4nd0: it is not so easy - I think I have to do it this time09:50
sonne|workpickle.dump(classifier)09:50
blackburnyes it is possible09:50
n4nd0sonne|work: ok09:50
blackburnn4nd0: what about multiclassmachine?09:55
blackburnI was lazy to implement OvO training09:55
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n4nd0blackburn: do you mean to prepare an interface similar to LinearMachine or the like for multiclass?09:57
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blackburnwrong click lol09:58
n4nd0blackburn: I don't know if you get it before09:58
n4nd0blackburn: do you mean to prepare an interface similar to LinearMachine or the like for multiclass?09:58
blackburnn4nd0: check shogun/machine/MulticlassMachine.cpp09:58
blackburnn4nd0: there is a code training machines with OvR scheme09:58
blackburnso approach is similar09:59
blackburnbut you need to subset features instance: i.e. select only feature vectors that belongs to 2 classes you dichotomize right now09:59
blackburnLibSVMMultiClass has some that kind of code10:00
blackburnand MultiClassSVM class has apply for that scheme10:00
n4nd0blackburn: 0vR is the same as one vs. all?10:01
blackburnyeah10:01
blackburnI used to say OvR cause 'all' sounds like you try to construct hyperplane between set X and set X+Y - this makes no sense :)10:02
n4nd0haha ok, I see your porint One vs. Rest10:02
n4nd0point*10:02
n4nd0so this 0v0 scheme is implemented in LibSVM but not in shogun?10:04
blackburnI want to port all multiclass things into multiclass folder10:05
blackburnGMNPSVM in fact10:06
blackburnand LibSVMMulticlass as well10:06
blackburnlet me check how it trains10:06
blackburnsonne|work: about labels I postpone it to some day after release10:07
blackburnbinarylabels, multiclasslabels, probabilisticlabels, etc10:07
blackburnn4nd0: ok multiclass libsvm trains 1-vs-110:10
n4nd0n4nd0: all right, and that's already in shogun10:11
blackburnyes, but it should change its base10:11
n4nd0blackburn: how should it be?10:12
blackburnI introduced MulticlassMachine10:12
blackburnprobably MulticlassSVM should be derived from KernelMulticlassMachine10:12
blackburnand all these things should -> multiclass instead of shogun/classifier/svm10:13
n4nd0blackburn: got it10:14
blackburnn4nd0: but this time you probably better implement that generic thing10:14
blackburnin multiclassmachine10:14
blackburndo you know how 1-vs-1 works?10:14
n4nd0blackburn: what is that generic thing?10:15
n4nd0blackburn: no, I have just heard of 1vsR10:15
blackburnboth LinearMulticlassMachine and KernelMulticlassMachine can use any classifier10:15
blackburnlinear or kernel based accordingly10:15
blackburni.e. you may10:16
blackburnLinearMulticlassMachine(ONE_VS_REST_STRATEGY, QDA(), train_features, train_labels)10:16
blackburnah yes QDA is not linera10:16
blackburnthen LDA()10:16
n4nd0:)10:16
blackburnso when you train it10:17
blackburnit constructs 1-vs-rest linear classifiers using provided lda10:17
blackburnand then classifies using these constructed hyperplanes :)10:17
n4nd0I think I get the idea10:18
n4nd0I guess this 1vs1 is another strategy of doing multiclass instead os 1vsR10:18
blackburnyes10:19
blackburntake a look no train_one_vs_rest10:19
blackburnimagine you've got labels [0,0,0,1,1,1,2,2,2]10:19
blackburnit constructs labels10:19
blackburn[-1,-1,-1,+1,+1,+1,+1,+1,+1]10:19
blackburn[+1,+1,+1,-1,-1,-1,+1,+1,+1]10:19
blackburn[+1,+1,+1,+1,+1,+1,-1,-1,-1]10:20
n4nd0I see10:20
blackburnand trains provided machine using ^10:20
blackburnso 3 planes separating each class from rest10:20
blackburntrain_one_vs_one should be a little different10:20
blackburnimagine again you've got labels [0,0,0,1,1,1,2,2,2]10:21
blackburn[-1,-1,-1,+1,+1,+1,0,0,0]10:21
blackburn[-1,-1,-1,0,0,0,+1,+1,+1]10:21
blackburn[0,0,0,-1,-1,-1,+1,+1,+1]10:22
blackburnwe should exclude vectors with 0 label10:22
blackburnthey are not considered by machine at all10:22
blackburngot it?10:22
n4nd0yes10:22
blackburnthis can be done with subsetting10:23
n4nd0in a general case 1vs1 generates more separating hyperplanes right?10:23
blackburnif there are K classes10:24
blackburnit constructs K*(K-1)/210:24
n4nd0while the other always does K10:24
blackburnOvR constructs K10:25
blackburnyes10:25
n4nd0good10:25
n4nd0so the idea is first to move all the stuff to multiclass10:25
n4nd0and later implement this one vs. one10:25
blackburnno10:26
blackburnjust implement one vs one10:26
blackburnin case Soeren would manage to repair serialization10:27
blackburnwe release, so no need to do such intrusive changes10:27
n4nd0ok10:27
blackburnn4nd0: btw there should be for i=1:K for j=i+1:K10:28
blackburnlittle hint :)10:29
blackburni.e. no need to construct classifiers between both (0,1) and (1,0)10:29
blackburnthey are equal up to sign10:29
n4nd0:)10:29
n4nd0good you said, I might have repeated stuff10:30
blackburnfor i = 1 : K10:30
blackburn for j = i + 1 : K10:30
n4nd0so simplying the idea is to add two methods in MulticlassMachine10:31
blackburn   mark labels == i as -110:31
blackburn   mark labels == j as +110:31
blackburn   subset feature vectors that belong to i and j classes (exclude others)10:31
blackburn   train classifier, store it10:31
blackburnthat's THE MARVELLOUS algorithm  ;)10:31
n4nd0:)10:32
blackburnn4nd0: yes, train_one_vs_one10:32
blackburnapply_one_vs_one10:32
n4nd0ok10:32
blackburnapply_one_vs_one is implemented in MultiClassSVM10:32
blackburnjust copy it10:32
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n4nd0blackburn: ok, it doesn't look like much work then (one never knows though)10:33
blackburnn4nd0: yes it is pretty simpel10:33
blackburn:)10:33
blackburnI did not implement it cause I am having some research on task with 43 classes10:33
n4nd0blackburn: I will try to do it and test it, comparing it to another example using 1vsR and hope to get same results10:33
blackburnso damn hard to train 903 classifiers10:33
blackburnapplying with libsvmmulticlass takes ONE HOUR10:34
blackburnthanks to wiking we've got a way to make it linear10:34
blackburnnow it takes 100s10:34
blackburn:D10:34
blackburnto train*10:34
blackburnand 4s to apply10:34
n4nd0cool10:35
blackburnn4nd0: ok I probably should quit irc cause it is pretty distracting and I can't start working :)10:44
blackburnto get fired during this crazy pre-gsoc is not my intention :D10:44
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blackburnah nevermind I'll just stop staring10:46
blackburn:D10:46
blackburnhighlight me if needed10:47
n4nd0ok, will do that10:50
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harshit_is there any function in Shogun or in any c++ library to check whether matrix is sparse or not12:54
harshit_?12:54
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hpsoarwhy the 1.2.0 version can not configure with cmdline_modular?14:20
blackburncmdline_modular? there is no such interface14:21
wikingyo14:22
blackburnhey14:22
wikingwazza man14:26
wikingfinally i'm work mode again14:26
wikinginstead of writing paper mode14:26
n4nd0wiking: what was your paper about?14:27
wikingn4nd0: image similarity measures14:27
wikingbasically extending my prev algorithms on computer vision for getting better accuracy14:27
wikingnothing fancy :|14:27
wikingit's one of those need-to-publish papers in my opinion14:28
n4nd0wiking: aham hope you get a good publication anyway :)14:28
wikingheheh let's hope so14:28
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blackburnwiking: n4nd0 guys last year we had some troubles with git, I just noticed you have no troubles at all :)14:43
n4nd0:)14:45
n4nd0let's hope it continues like this, without problems14:45
wikingblackburn: ?14:49
blackburnwiking: well we had some issues with branching etc, we all were new to git :D14:49
n4nd0svn before?14:50
wikinghahahah ah ok14:51
wiking:)14:51
blackburnactually there are some pitfalls14:52
blackburne.g. you may see Merge branch ... " commits14:52
blackburnsometimes14:52
blackburnthey can be avoided with proper rebasing14:52
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wikingheheh yeah rebasing is gooood15:51
wikingblackburn: branching and merging is like amaaaazing compared to svn ...!16:04
blackburnyou tend to make words long today ;)16:05
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blackburnwiking: one more wiking and you all will break the ice and sink16:08
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blackburnoh no16:08
blackburnone sinked16:08
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blackburnanother one bites the dust16:09
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blackburnand another one comes16:09
wiking_hahahah16:09
wiking_oh sorry to be proper.... aaaaaahhahahaa16:09
blackburn;)16:09
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CIA-64shogun: Soeren Sonnenburg master * r5efddaa / src/shogun/evaluation/CrossValidation.cpp : fix spelling mistake -> warning - http://git.io/xTbBTg20:10
CIA-64shogun: Soeren Sonnenburg master * r7bb576d / (2 files in 2 dirs): extract mahalanobis description and don't print anything in example - http://git.io/sC6bKg20:10
CIA-64shogun: Soeren Sonnenburg master * r7329de6 / testsuite/python_modular/tester.py : compare pickled version of SGObject instances not just ptrs - http://git.io/OS3iVg20:10
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CIA-64shogun: Soeren Sonnenburg master * rd4bb942 / applications/vw/Makefile : fix vw makefile - http://git.io/Bw5r8A21:02
CIA-64shogun: Soeren Sonnenburg master * reca0d2d / (4 files in 4 dirs):21:02
CIA-64shogun: fix a couple of issues like in applications21:02
CIA-64shogun: most notably the SVM object is gone and replaced by KernelMachine - http://git.io/XTq-mQ21:02
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PhilTilletHello21:26
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@sonney2kPhilTillet, hi21:32
blackburnhey guys21:32
blackburnsonney2k: did you manage to fix tester?21:32
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blackburnbam21:32
blackburnanother one bites the dust21:32
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blackburnsonney2k: I mean I have seen you fixed something21:34
CIA-64shogun: Soeren Sonnenburg master * rf081ae0 / testsuite/python_modular/tester.py : only import modshogun - http://git.io/dkMc5Q21:34
CIA-64shogun: Soeren Sonnenburg master * raba8078 / (4 files): comment print functions in examples - http://git.io/mnNbyw21:34
blackburnand again21:35
@sonney2kblackburn, do you know whether heiko will be around?21:35
blackburnsonney2k: he said this week is pretty hard21:35
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blackburnso probably next week only21:35
@sonney2kI think there is some breakage in this data lock stuff / at least kernels etc are no longer set in gmnpsvm after de-serialization21:36
blackburnsonney2k: row subset thing?21:36
@sonney2kno21:36
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@sonney2kmaybe related - I don't know for sure21:36
blackburnsonney2k: actually I asked him to fix this parameters thing21:36
@sonney2kbasically all applications are broken now21:37
@sonney2kbut tester.py -t 1e-10 runs many examples fine now21:37
blackburnsonney2k: I probably know the error - we need to add new field to parameter map21:37
@sonney2kso some improvements but not down to 0 errors21:38
blackburnI see21:38
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blackburnsonney2k: I'll try to fix it but tomorrow or later21:38
blackburnhave to go back to studies again damn21:38
blackburnsonney2k: time to study signal processing! see you ;)21:40
n4nd0blackburn: signal processing, cool stuff :P21:40
blackburnn4nd0: I am not a big fan but you are probably right ;)21:41
blackburnn4nd0: so see you too :D21:41
blackburnbye21:41
n4nd0:) bye!21:41
n4nd0sonney2k: nico answered today, you were right :)21:42
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PhilTilletNobody's insomniac today :D23:13
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n4nd0PhilTillet: there's always someone :P23:17
PhilTilletHaha :D23:18
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n4nd0but time to go now23:31
n4nd0good night guys23:31
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--- Log closed Tue Mar 20 00:00:19 2012