Imbalanced dataset: how to deal with test data?
You should use the testing set without any change, as answered by others. But it is very important to understand the difference between average accuracy and overall accuracy. 
In overall accuracy you find ( number of samples predicted correctly/ total number of samples) in average accuracy, you find the overall accuracy per class and then you find the average of these overall accuracies. 
When you know that you are working with imbalanced database, where all classes are important, you should use the average accuracy 

To understand what  this means: imagine you have two classes, class A and class B , and the ratio is 90 to 10 .
If you are sampling randomly for the training and testing, then the ratio is still 90:10 in the testing set. If your model is very biased , that predicts all the samples to be class A , then:
Overall accuracy = 90% 
Average accuracy = 50 % 
( 100% for class A + 0% for class B) / 2 

The overall accuracy is really high but it does not reflect the actual quality of the model. The average accuracy gives you a better indication of the quality