It work now... Thanks all...
…
X_test = np.r_[ND, AD]
# fit the model
clf = svm.OneClassSVM(nu=float(NU), kernel="rbf", gamma=float(GA))
clf.fit(LD)
pred = clf.predict(X_test)
y_true = np.array([1]*500+[-1]*500)
fpr,tpr,thresholds = roc_curve(y_true,pred)
roc_auc = auc(fpr,tpr)
plt.figure()
plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
plt.show()
…
Best,
Ady
On 10/23/15, Ady Wahyudi Paundu <awpau...@gmail.com> wrote:
> Hi Nicolas, Thank you for the pointer.
>
> Since i didn’t know where to take the value of ‘scoring’ variable,
> this is what I did (by adapting to the example in scikit-learn web)
>
> …
> X_test = np.r_[ND, AD]
>
> # fit the model
> clf = svm.OneClassSVM(nu=float(NU), kernel="rbf", gamma=float(GA))
> scoring = clf.fit(LD).decision_function(X_test)
>
> pred = clf.predict(X_test)
> y_true = np.array([0]*500+[1]*500)
> fpr,tpr,thresholds = roc_curve(y_true,scoring)
> roc_auc = auc(fpr,tpr)
>
> plt.figure()
> plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)
> plt.plot([0, 1], [0, 1], 'k--')
> plt.xlim([0.0, 1.0])
> plt.ylim([0.0, 1.05])
> plt.xlabel('False Positive Rate')
> plt.ylabel('True Positive Rate')
> plt.title('Receiver operating characteristic')
> plt.legend(loc="lower right")
> plt.show()
>
> …
>
> this however showed only the axis and diagonal dots without ROC curve
> (auc=0).
> How to fix this?
>
> Best,
> Ady
>
> On 10/20/15, Nicolas Goix <goix.nico...@gmail.com> wrote:
>> Hi,
>> You can do:
>>
>> ```
>> from sklearn.metrics import roc_curve, auc
>>
>> X_test = np.r_[ND, AD]
>> pred = clf.predict(X_test)
>>
>> y_true = np.array([0] * 500 + [1] * 500)
>>
>> fpr, tpr, thresholds = roc_curve(y_true, scoring)
>>
>> # then you can plot(fpr, tpr) to get the roc curve and compute the AUC
>> with:
>> AUC = auc(fpr, tpr)
>>
>> ```
>>
>> Best,
>> Nicolas
>>
>> 2015-10-20 3:41 GMT+02:00 Ady Wahyudi Paundu <awpau...@gmail.com>:
>>
>>> Hi all,
>>>
>>> Can I create ROC curve for one_class_SVM classifier?
>>> If I can, can you give pointer on how to do this? (or a link?)
>>>
>>> for example now i have:
>>> LD: normal data for learning (100 item)
>>> ND: normal data for evaluation (500 item)
>>> AD: abnormal data for evaluation (500 item)
>>>
>>> one_class_SVM code for evaluation would be something like this (NU and
>>> GA is user input):
>>>
>>> clf = svm.OneClassSVM(nu=float(NU), kernel="rbf", gamma=float(GA))
>>> clf.fit(LD)
>>> y_pred_train = clf.predict(LD)
>>> y_pred_test = clf.predict(ND)
>>> y_pred_outliers = clf.predict(AD)
>>> n_error_train = y_pred_train[y_pred_train == -1].size
>>> n_error_test = y_pred_test[y_pred_test == -1].size
>>> n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size
>>>
>>> how to produce ROC curve from there?
>>>
>>> Thanks in advance
>>> ~Ady
>>>
>>>
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>>>
>>
>
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