Refreshing API and

[TianyuDu]

Towards BEMB v1.0.

Corresponding branch: api-update

We are planning to refine and expand the current API of BEMBFlex.

The pred_item and multiple class prediction.

• Note: this is a non-trivial extension: for each observation (consumer pick an item), we compute a scalar utility function for each available item. There is a single scalar utility computed from the chosen item (item_index[i]). This only allows us to do binary classification . We might have to drop this feature.

• Currently the model supports predicting binary batch.label or multi-class batch.item_index. We plan to support arbitrary multi-class classifications.
  • In particular, you don't need to change anything if pred_item=True, the model will know the number of classes is exactly the num_items parameter. Also, in this case, your ChoiceDataset object does not need to have a label attribute, since the model will look for the item_index as the ground truth for training.
  • In contrast, if pred_item=False, now you need to supply a num_classes to the BEMBFlex.__init__() method. Also, you would need a label attribute in the ChoiceDataset object. The label attribute should be a LongTensor with values from {0, 1, ..., num_classes}.

Post-Estimation

• Thanks to feedbacks from our valued users, we are planning to reorganize our post-estimation prediction methods for better user experience.
  • We will implement a method called predict_proba(), the same name as inference methods of scikit-learn models.
  • This method will have @torch.no_grad() as a decorator, so you can use it however you want without being worried about gradient tracking.
  • With pred_items = True, the batch needs item_index attribute only if it's involved in the utility computation (e.g., within-category computation).
  • With pred_items = False, the batch does not need to have a label attribute.
  • The preliminary API of predict_proba() is used as the following:

batch = ChoiceDataset(...)
bemb = BEMBFlex(..., pred_item=True, ...)
proba = bemb.predict_proba(batch)  # shape = (len(batch), num_items)

batch = ChoiceDataset(...)
# not that batch doesn't need to have a label attribute.
bemb = BEMBFlex(..., pred_item=False, num_classes=..., ...)
proba = bemb.predict_proba(batch)  # shape = (len(batch), num_classes)

Renaming Variables.

• We received feedbacks that the naming of price-variation is ambiguous, we propose to change it to sessionitem-variation instead (this is precisely the definition of such variables).