7a82079d10379287ba4e6e42e21b5b3ce8f541bc,spotlight/factorization/implicit.py,ImplicitFactorizationModel,fit,#ImplicitFactorizationModel#,102

Before Change

                        self._num_items,
                        len(batch_user),
                        random_state=self._random_state)
                    negative_var = Variable(
                        gpu(torch.from_numpy(negative_items))
                    )
                    negative_prediction = self._net(user_var, negative_var)

                self._optimizer.zero_grad()

                loss = loss_fnc(positive_prediction, negative_prediction)

After Change

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

Instances