Btw I saw that you were using the following params
> > params = {
> > 'kernel': 'rbf',
> > 'C': 1.0,
> > 'shrinking': False,
> > 'degree': 3,
> > 'probability' : True,
> > 'gamma' : 0.0,
> > 'coef0' : 0.0,
> > 'cache_size' : 300,
> > 'class_weight' : None,
> > 'max_iter' : -1,
> > 'random_state' : 123,
> > 'penalty' : 'l2',
> > 'dual' : False,
> > }
but sklearn.svm.SVC does not have a 'penalty' and 'dual' parameter. I think you
accidentally mixed it with the parameters from sklearn.svm.linearSVC. Probably
just a copy&paste error when you posted the code.
Best,
Sebastian
> On May 11, 2015, at 12:07 PM, Andreas Mueller <t3k...@gmail.com> wrote:
>
> It looks like that was for LinearSVC.
> You can still do it using OneVsRestClassifier(SVC()) btw.
>
> On 05/11/2015 12:05 PM, Yury Zhauniarovich wrote:
>> Thank you, Andreas!
>>
>> However, this is quite strange to me because I read the blog post [1] and it
>> seems that there it was working.
>>
>> [1] https://jmetzen.github.io/2015-04-14/calibration.html
>> <https://jmetzen.github.io/2015-04-14/calibration.html>
>>
>>
>> Best Regards,
>> Yury Zhauniarovich
>>
>> On 11 May 2015 at 17:44, Andreas Mueller <t3k...@gmail.com
>> <mailto:t3k...@gmail.com>> wrote:
>> Indeed, the CalibratedClassifierCV does currently not work on SVC.
>> This is an unfortunate and known issue and we'll fix it soon.
>>
>>
>> On 05/11/2015 09:53 AM, Sebastian Raschka wrote:
>> > Hi, Yuri,
>> >
>> > Can you provide the shapes of val_x and val_y via val_x.shape and
>> > val_y.shape? Scikit-learn expects "X" to have the shape (n_samples,
>> > n_samples), and "y" should have the shape (n_samples,).
>> > For example, if your training dataset only consists of 1 column, it can be
>> > easily lead to problems. E.g., instead of
>> >
>> > array([[1, 2, 3, 4]])
>> >>>> X = np.array([1,2,3,4])
>> >>>> X.shape
>> > (4,)
>> >
>> > you can transform the array as follows:
>> >>>> X.reshape(-1, 1)
>> > array([[1],
>> > [2],
>> > [3],
>> > [4]])
>> >
>> > Best,
>> > Sebastian
>> >
>> >> On May 11, 2015, at 9:30 AM, Yury Zhauniarovich <y.zhalnerov...@gmail.com
>> >> <mailto:y.zhalnerov...@gmail.com>> wrote:
>> >>
>> >> Dear all,
>> >>
>> >> I am quite new to sklearn and I do not know precisely but it seems that I
>> >> found a potential issue in CalibratedClassifierCV. I run result
>> >> calibration on SVC and get the following error:
>> >> Traceback (most recent call last):
>> >> File "svc_test_with_calibration.py", line 99, in <module>
>> >> cal_clf = CalibratedClassifierCV(clf, method='sigmoid', cv='prefit')
>> >> File "/usr/local/lib/python2.7/dist-packages/sklearn/calibration.py",
>> >> line 137, in fit
>> >> calibrated_classifier.fit(X, y)
>> >> File "/usr/local/lib/python2.7/dist-packages/sklearn/calibration.py",
>> >> line 309, in fit
>> >> calibrator.fit(this_df, Y[:, k], sample_weight)
>> >> IndexError: index 9 is out of bounds for axis 1 with size 9
>> >>
>> >> Here is the code that I use:
>> >> #parameters
>> >> params = {
>> >> 'kernel': 'rbf',
>> >> 'C': 1.0,
>> >> 'shrinking': False,
>> >> 'degree': 3,
>> >> 'probability' : True,
>> >> 'gamma' : 0.0,
>> >> 'coef0' : 0.0,
>> >> 'cache_size' : 300,
>> >> 'class_weight' : None,
>> >> 'max_iter' : -1,
>> >> 'random_state' : 123,
>> >> 'penalty' : 'l2',
>> >> 'dual' : False,
>> >> }
>> >>
>> >> print "SVC..."
>> >> pretty_print(params)
>> >>
>> >> print "Training uncalibrated..."
>> >> clf = SVC(**params)
>> >> clf.fit(train_x, train_y)
>> >> uncal_clf_probs = clf.predict_proba(test_x)
>> >>
>> >> print "Calibrating..."
>> >> cal_clf = CalibratedClassifierCV(clf, method='sigmoid', cv='prefit')
>> >> cal_clf.fit(val_x, val_y)
>> >> cal_clf_probs = cal_clf.predict_proba(test_x)
>> >>
>> >> ll_uncal = log_loss(test_y, uncal_clf_probs)
>> >> ll_cal = log_loss(test_y, cal_clf_probs)
>> >>
>> >> The error happens in line: cal_clf.fit(val_x, val_y) However, if I run
>> >> similar code on ExtraTreesClassifier everything works as expected. Could
>> >> someone tell me if it is a bug s.t. I can report this issue on github? Or
>> >> am I doing something wrong?
>> >>
>> >> Best Regards,
>> >> Yury Zhauniarovich
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