Sequence Prediction with Co-Occurring Item Groups

[redemption-star]

One example input of the predictor seems to be model.fit([["A", "B", "C"], ["A", "C"], ["A", "B"]]) , here "A", "B", "C" can be products purchased by three customers and each sublist is a ordered sequence of each customer's purchase history. But your model seems unable to deal with model.fit([["A", {"B", "C"}, "D"], ["A", {"A", "B"} "C"], ["A", "B"]]) where each set is a basket of products that are purchased at the same timestamp. How can I do sequence prediction with the input data that have the form like this: http://philippe-fournier-viger.com/spmf/CPTPlus.php

[bluesheeptoken]

Hello, sorry for the delay, I did not receive the GitHub notifications.

The way subseq or CPT are working internally, I don't think this is doable unfortunately.
Do you have the example where CPT would work?

An alternative could be introducing a new word "D" that represents "A" or "C". But this has drawbacks.