Hello Sebastian,
I think you made a mistake, the shape of X should be (n_samples,
n_features). Am I right?
Here are the shapes of val_x and val_y in SVC:
val_x.shape: (6186, 93)
val_y.shape: (6186,)
And for ExtraTrees I have the same shape of val_x and val_y as I described
above.
Best Regards,
Yury Zhauniarovich
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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