I do not think it is real bad :) However, I opened a new issue on githput as advised.
Thanks PhD Candidate Mathematical and Computer Sciences and Engineering King Abdullah University of Science and Technology Thuwal 23955-6900 KAUST Mail Box # 2620 Kingdom of Saudi Arabia Tel.: (+966) 506134003 On Wed, Aug 19, 2015 at 12:04 AM, Andreas Mueller <t3k...@gmail.com> wrote: > That seems real bad. Can you please open an issue on github? > > > On 08/18/2015 01:24 PM, Othman Soufan wrote: > > Hi Andreas, > > Indeed, I was not yet registered with the mail-list. > > The sklearn version I have installed is 0.16.1 > > I did not get an error when inputing 1d X and what I receive back are > predictions as many as the length of this 1d list. > > For example: > > >>> from sklearn import datasets > > >>> from sklearn.multiclass import OneVsOneClassifier > > >>> from sklearn.svm import LinearSVC > > >>> iris = datasets.load_iris() > > >>> X, y = iris.data, iris.target > > >>> OneVsOneClassifier(LinearSVC(random_state=0)).fit(X, y).predict(X[1,:] > ) > > Out[*1*]: array([0, 1, 1, 1]) > > > And by replacing X[1,:] to be X[1:2,:] which in terms of values are the > same: > > >>> OneVsOneClassifier(LinearSVC(random_state=0)).fit(X, y).predict(X[ > *1:2*,:]) > > Out[*2*]: array([0]) # Proper output > > > Regards, > > Othman Soufan > > > > PhD Candidate > Mathematical and Computer Sciences and Engineering > King Abdullah University of Science and Technology > Thuwal 23955-6900 > KAUST Mail Box # 2620 > Kingdom of Saudi Arabia > Tel.: (+966) 506134003 > > On Tue, Aug 18, 2015 at 6:54 PM, Andreas Mueller <t3k...@gmail.com> wrote: > >> Hi. >> I just replied to the thread above, maybe you weren't subscribed to the >> ml yet. >> >> Did you get an error when inputting a 1d X? >> Which version of scikit-learn are you on? >> >> X should really always be 2d. Unfortunately that is currently >> inconsistent, and will be fixed soon. >> >> So yes, that will be fixed, but it would be great to know the exact >> behavior you encountered, >> and the version. >> >> Thanks, >> Andy >> >> >> >> On 08/18/2015 11:50 AM, Othman Soufan wrote: >> >> Greetings Guys, >> >> I came through the contributed implementation to multiclass.py in >> Scikit-learn. I just have a suggestion for you to consider the case when >> only one testing sample is passed to decision_function "Decision >> function for the OneVsOneClassifier". As for the current implementation, >> an undesirable output comes since n_samples = X.shape[0] will take a >> number larger than one when X is only a single list vector with some >> values. I may suggest you check the shape of X before parsing it in a >> particular way, or update the documentation to advise the user on a >> suggested way to get the prediction for one testing sample. >> >> In a sense, it is true to say that usually, there is a testing set of >> many samples but in a specific case of mine, it was preferable to predict >> sample by sample. I overcome this by using X[0:1,:] instead of X[0,:] where >> X is a testing set of several samples. >> >> Regards, >> Othman Soufan >> >> >> ------------------------------------------------------------------------------ >> >> >> >> _______________________________________________ >> Scikit-learn-general mailing >> listScikit-learn-general@lists.sourceforge.nethttps://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> >> >> >> >> ------------------------------------------------------------------------------ >> >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> >> > > > ------------------------------------------------------------------------------ > > > > _______________________________________________ > Scikit-learn-general mailing > listScikit-learn-general@lists.sourceforge.nethttps://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > > > > ------------------------------------------------------------------------------ > > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > >
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