Don't understand why I get an inverse ROC curve for SVM (Python)
For any classification problem if AUC&lt;0.5, you are not performing better than random(0.5).
Reason could be:

Your classifier is over-fitted on the training set and performs very poorly on the test set.
Your test sample might be very small.
Your classifier is giving you the probability that the class is -1. Thus, you get a prediction (close to) 0 for a class 1, and 1 for a class -1 prediction. If your ROC method expects positive (+1) predictions to be higher than negative (-1) ones, you get a reversed curve.

A valid strategy is to simply invert the predictions as:
invert_prob=1-prob 

Reference: ROC