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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