Negative dimensions error when predicting svm

Question

I have trained an SVM using the NSL-KDD dataset but when I try to make a prediction on it, it comes up with the error "negative dimensions are not allowed".

Below is my code:

`#Indexing of features and labels 
features = dataset[:,:-2]
labels = dataset[:,-2]

#split data
train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.2)

#train model
model = SVC(C=1.0, kernel='rbf')

#create classifier
classifier = SklearnClassifier(model=model)

#Train  classifier
model.fit(train_features, train_labels)


predictions = classifier.predict(test_features)`

full error with traceback:

`File ~\OneDrive\Documents\ids\fgsm.py:50 in <module>
   predictions = classifier.predict(test_features)

File ~\AppData\Roaming\Python\Python39\site-packages\art\estimators\classification\classifier.py:73 in replacement_function
   return fdict[func_name](self, *args, **kwargs)

File ~\AppData\Roaming\Python\Python39\site-packages\art\estimators\classification\scikitlearn.py:1418 in predict
   one_hot_targets = np.eye(self.nb_classes)[targets]

 File C:\ProgramData\Anaconda3\lib\site-packages\numpy\lib\twodim_base.py:214 in eye
   m = zeros((N, M), dtype=dtype, order=order)

ValueError: negative dimensions are not allowed`

This is the line where the error took place:

`predictions = classifier.predict(test_features)`

I thought that maybe there would be a data mismatch but when I run the following code, I get this:

`print(np.shape(test_features))
(5039, 38)

print(np.shape(test_labels))
(5039,) `

The main reason why im not using model.predict is because im using the classifier estimator from the Adversarial Robustness Toolbox. But i get a negative dimension when i run this code:

`In [4]: classifier.nb_classes
 Out[4]: -1`