I thought the same about the correpondence between SVC and nuSVC. Any ideas why lowering the value might also help? http://stackoverflow.com/questions/35221433/error-in-using-non-linear-svm-in-scikit-learn
He apparently used a very low value for nu (0.01) and the error vanished. Greets, Piotr On 08.12.2016 11:18, Michael Eickenberg wrote: Ah, sorry, true. It is the error fraction instead of the number of errors. In any case, try varying this quantity. At one point I thought that nuSVC is just the constrained optimization version of the lagrange-style (penalized) normal SVC. That would mean that there is a correspondence between C for SVC and nu for nuSVC, leading to the conclusion that there must be nus that are feasible. So setting to nu=1. should always lead to feasibility. Now, looking at the docstring, since the nu controls two quantities at the same time, I am not entirely 1000% sure of this anymore, but I think it still holds. Michael On Thu, Dec 8, 2016 at 11:08 AM, Piotr Bialecki <piotr.biale...@hotmail.de<mailto:piotr.biale...@hotmail.de>> wrote: Hi Michael, hi Thomas, I think the nu value is bound to (0, 1]. So the code will result in a ValueError (at least in sklearn 0.18). @Thomas I still think the optimization problem is not feasible due to your data. Have you tried balancing the dataset as I mentioned in your other question regarding the MLPClassifier? Greets, Piotr On 08.12.2016 10:57, Michael Eickenberg wrote: You have to set a bigger \nu. Try nus =2 ** np.arange(-1, 10) # starting at .5 (default), going to 512 for nu in nus: clf = svm.NuSVC(nu=nu) try: clf.fit ... except ValueError as e: print("nu {} not feasible".format(nu)) At some point it should start working. Hope that helps, Michael On Thu, Dec 8, 2016 at 10:49 AM, Thomas Evangelidis <teva...@gmail.com<mailto:teva...@gmail.com>> wrote: Hi Piotr, the SVC performs quite well, slightly better than random forests on the same data. By training error do you mean this? clf = svm.SVC(probability=True) clf.fit(train_list_resampled3, train_activity_list_resampled3) print "training error=", clf.score(train_list_resampled3, train_activity_list_resampled3) If this is what you mean by "skip the sample_weights": clf = svm.NuSVC(probability=True) clf.fit(train_list_resampled3, train_activity_list_resampled3, sample_weight=None) then no, it does not converge. After all "sample_weight=None" is the default value. I am out of ideas about what may be the problem. Thomas On 8 December 2016 at 08:56, Piotr Bialecki <piotr.biale...@hotmail.de<mailto:piotr.biale...@hotmail.de>> wrote: Hi Thomas, the doc says, that nu gives an upper bound on the fraction of training errors and a lower bound of the fractions of support vectors. http://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html Therefore, it acts as a hard bound on the allowed misclassification on your dataset. To me it seems as if the error bound is not feasible. How well did the SVC perform? What was your training error there? Will the NuSVC converge when you skip the sample_weights? Greets, Piotr On 08.12.2016 00:07, Thomas Evangelidis wrote: Greetings, I want to use the Nu-Support Vector Classifier with the following input data: X= [ array([ 3.90387012, 1.60732281, -0.33315799, 4.02770896, 1.82337731, -0.74007214, 6.75989219, 3.68538903, .................. 0. , 11.64276776, 0. , 0. ]), array([ 3.36856769e+00, 1.48705816e+00, 4.28566992e-01, 3.35622071e+00, 1.64046508e+00, 5.66879661e-01, ..................... 4.25335335e+00, 1.96508829e+00, 8.63453394e-06]), array([ 3.74986249e+00, 1.69060713e+00, -5.09921270e-01, 3.76320781e+00, 1.67664455e+00, -6.21126735e-01, .......................... 4.16700259e+00, 1.88688784e+00, 7.34729942e-06]), ....... ] and Y= [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ............................ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] Each array of X contains 60 numbers and the dataset consists of 48 positive and 1230 negative observations. When I train an svm.SVC() classifier I get quite good predictions, but wit the svm.NuSVC() I keep getting the following error no matter which value of nu in [0.1, ..., 0.9, 0.99, 0.999, 0.9999] I try: /usr/local/lib/python2.7/dist-packages/sklearn/svm/base.pyc in fit(self, X, y, sample_weight) 187 188 seed = rnd.randint(np.iinfo('i').max) --> 189 fit(X, y, sample_weight, solver_type, kernel, random_seed=seed) 190 # see comment on the other call to np.iinfo in this file 191 /usr/local/lib/python2.7/dist-packages/sklearn/svm/base.pyc in _dense_fit(self, X, y, sample_weight, solver_type, kernel, random_seed) 254 cache_size=self.cache_size, coef0=self.coef0, 255 gamma=self._gamma, epsilon=self.epsilon, --> 256 max_iter=self.max_iter, random_seed=random_seed) 257 258 self._warn_from_fit_status() /usr/local/lib/python2.7/dist-packages/sklearn/svm/libsvm.so in sklearn.svm.libsvm.fit (sklearn/svm/libsvm.c:2501)() ValueError: specified nu is infeasible Does anyone know what might be wrong? Could it be the input data? thanks in advance for any advice Thomas -- ====================================================================== Thomas Evangelidis Research Specialist CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/1S081, 62500 Brno, Czech Republic email: <mailto:tev...@pharm.uoa.gr> tev...@pharm.uoa.gr<mailto:tev...@pharm.uoa.gr> <mailto:teva...@gmail.com> teva...@gmail.com<mailto:teva...@gmail.com> website: <https://sites.google.com/site/thomasevangelidishomepage/> <https://sites.google.com/site/> https://sites.google.com/site/thomasevangelidishomepage/ _______________________________________________ scikit-learn mailing list scikit-learn@python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn -- ====================================================================== Thomas Evangelidis Research Specialist CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/1S081, 62500 Brno, Czech Republic email: tev...@pharm.uoa.gr<mailto:tev...@pharm.uoa.gr> teva...@gmail.com<mailto:teva...@gmail.com> website: https://sites.google.com/site/thomasevangelidishomepage/ _______________________________________________ scikit-learn mailing list scikit-learn@python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn
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