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
Best Regards,
Yury Zhauniarovich
On 11 May 2015 at 17:44, Andreas Mueller <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> 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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