Hi. Which version of scikit-learn are you using? Andy
On 03/26/2012 03:07 PM, xinfan meng wrote:
Alex, I am afraid some codes have been broken... In [127]: X = [[0], [1]] In [129]: Y = [0, 1] In [130]: clf.fit(X, Y) Out[130]:SVC(C=1.0, cache_size=200, coef0=0.0, degree=3, gamma=0.0, kernel='linear',probability=False, scale_C=False, shrinking=True, tol=0.001) In [132]: clf.decision_function(X) Out[132]: array([[-0.5], [ 0.5]]) In [133]: X2 = [[1], [0]] In [134]: Y2 = [1, 0] In [135]: clf.fit(X2, Y2) Out[135]:SVC(C=1.0, cache_size=200, coef0=0.0, degree=3, gamma=0.0, kernel='linear',probability=False, scale_C=False, shrinking=True, tol=0.001) In [137]: clf.decision_function(X2) Out[137]: array([[ 0.5], [-0.5]])On Mon, Mar 26, 2012 at 8:59 PM, Alexandre Gramfort <[email protected] <mailto:[email protected]>> wrote:hi, from what I remember we fixed the random label ordering problem at least for the 2 classes case. can you check that things behave fine and the same way with Y = [0, 1] and Y = [1, 0]? Alex On Mon, Mar 26, 2012 at 5:00 AM, xinfan meng <[email protected] <mailto:[email protected]>> wrote: > I use the following codes to obtain decision values for SVC classifier clf. > > ----------------------------------------------------------------------------------------------- > > In [5]: >>> clf = svm.SVC() > > In [23]: >>> X = [[0], [1], [2]] > > In [24]: >>> Y = [0, 1, 2] > > In [25]: clf.fit(X, Y) > Out[25]: > SVC(C=1.0, cache_size=200, coef0=0.0, degree=3, gamma=1.0, kernel='rbf', > probability=False, scale_C=False, shrinking=True, tol=0.001) > > In [26]: clf.predict([[0]]) > Out[26]: array([ 0.]) > > In [27]: clf.predict(X) > Out[27]: array([ 0., 1., 2.]) > > In [28]: clf.decision_function(X) > Out[28]: > array([[-0.63212056, -0.98168436, -0.3495638 ], > [ 0.63212056, -0. , -0.63212056], > [ 0.3495638 , 0.98168436, 0.63212056]]) > > ----------------------------------------------------------------------------------------------- > > > > The decision_function return confusing results. Why [-0.63212056, > -0.98168436, -0.3495638 ] corresponds to label 0 ? > The encoding of labels seems to be different from the natural orders of (0, > 1, 2 ...) . > After reading the README file of LibSVM, I found the label encoding can be > obtained by calling svm_get_labels(). > Where can I find this function wrapper in sklearn? Without that, the return > results of decision_function() are difficult to interpret. > Thanks! > > -- > Best Wishes > -------------------------------------------- > Meng Xinfan（蒙新泛） > Institute of Computational Linguistics > Department of Computer Science & Technology > School of Electronic Engineering & Computer Science > Peking University > Beijing, 100871 > China > > ------------------------------------------------------------------------------ > This SF email is sponsosred by: > Try Windows Azure free for 90 days Click Here > http://p.sf.net/sfu/sfd2d-msazure > _______________________________________________ > Scikit-learn-general mailing list > [email protected] <mailto:[email protected]> > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > ------------------------------------------------------------------------------ This SF email is sponsosred by: Try Windows Azure free for 90 days Click Here http://p.sf.net/sfu/sfd2d-msazure _______________________________________________ Scikit-learn-general mailing list [email protected] <mailto:[email protected]> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- Best Wishes -------------------------------------------- Meng Xinfan（蒙新泛） Institute of Computational Linguistics Department of Computer Science & Technology School of Electronic Engineering & Computer Science Peking University Beijing, 100871 China ------------------------------------------------------------------------------ This SF email is sponsosred by: Try Windows Azure free for 90 days Click Here http://p.sf.net/sfu/sfd2d-msazure _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
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