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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