recbole.utils.case_study

recbole.utils.case_study.full_sort_scores(uid_series, model, test_data, device=None)[source]

Calculate the scores of all items for each user in uid_series.

Note

The score of [pad] and history items will be set into -inf.

Parameters

  • uid_series (numpy.ndarray or list) – User id series.

  • model (AbstractRecommender) – Model to predict.

  • test_data (FullSortEvalDataLoader) – The test_data of model.

  • device (torch.device, optional) – The device which model will run on. Defaults to None. Note: device=None is equivalent to device=torch.device('cpu').

Returns

the scores of all items for each user in uid_series.

Return type

torch.Tensor

recbole.utils.case_study.full_sort_topk(uid_series, model, test_data, k, device=None)[source]

Calculate the top-k items’ scores and ids for each user in uid_series.

Note

The score of [pad] and history items will be set into -inf.

Parameters

  • uid_series (numpy.ndarray) – User id series.

  • model (AbstractRecommender) – Model to predict.

  • test_data (FullSortEvalDataLoader) – The test_data of model.

  • k (int) – The top-k items.

  • device (torch.device, optional) – The device which model will run on. Defaults to None. Note: device=None is equivalent to device=torch.device('cpu').

Returns

  • topk_scores (torch.Tensor): The scores of topk items.

  • topk_index (torch.Tensor): The index of topk items, which is also the internal ids of items.

Return type

tuple