Enrich your train fold with a custom sampler inside an imblearn pipeline

Once you have a set of augmented data to enrich your original data set, you will ask yourself how and at which point to merge them. Typically you are using sklearn and its modules to evaluate your estimator or search for optimal hyper-parameters. Popular modules including RandomizedSearchCV or cross_validate have the option to pass a cross validation method like KFold. By utilizing a cross validation method to measure the performance of your estimator, your data is split in a train and a test set. This is done dynamically under the hood of the sklearn methods.