How to interpret PR and ROC Curve for an unbalanced test set
For binary-class classification problems and data being highly unbalanced, 


Go for AUC and f1 score as metrics.
Plot the confusion matrix.
Split the data into train:valid:test::60:20:20 or 80:10:10, and do Cross-validation and hyperparameter tuning on train and valid sets. Then go for test set.
You could also try bootstrap resampling.