7a82079d10379287ba4e6e42e21b5b3ce8f541bc,spotlight/sequence/implicit.py,ImplicitSequenceModel,fit,#ImplicitSequenceModel#Any#Any#,100

Before Change

                positive_prediction = self._net(user_representation,
                                                sequence_var)

                if self._loss == "adaptive_hinge":
                    raise NotImplementedError
                else:
                    negative_items = sample_items(
                        self._num_items,
                        batch_sequence.size(),
                        random_state=self._random_state)
                    negative_var = Variable(
                        gpu(torch.from_numpy(negative_items), self._use_cuda)
                    )
                    negative_prediction = self._net(user_representation,
                                                    negative_var)

                self._optimizer.zero_grad()

                loss = loss_fnc(positive_prediction,
                                negative_prediction,

After Change

                                                sequence_var)

                if self._loss == "adaptive_hinge":
                    negative_prediction = [self._get_negative_prediction(sequence_var.size(),
                                                                         user_representation)
                                           for _ in range(5)]
                else:
                    negative_prediction = self._get_negative_prediction(sequence_var.size(),
                                                                        user_representation)

Instances