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 > >
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