Unbalanced training set from balanced data
With a straightforward approach, you can just oversample one of the classes to get the imbalance. You can achieve it with using SMOTE for example. Usually this technique is used to get a balanced set from imbalanced, but it can also work vice-versa, just oversample only once class.
Some links to check:
https://imbalanced-learn.org/stable/over_sampling.html
https://machinelearningmastery.com/random-oversampling-and-undersampling-for-imbalanced-classification/
https://towardsdatascience.com/how-to-effortlessly-handle-class-imbalance-with-python-and-smote-9b715ca8e5a7