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
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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