Context-sensitive ranking and choice in Python with PyTorch
The library has been migrated to pytorch. This is a breaking change. You will likely need to adapt to this new version if you have been using estimators from version 1.x.
The RankNet and CmpNet estimators are now trained with a loss that applies to the whole result (the general/discrete choice or ranking). They were previously trained on object pairs with different loss functions.
Behavior and default parameters of the estimators may differ from the previous versions. For example the default activation for CmpNet and RankNet is now SELU instead of ReLU.