ffd72221f97a6c94170127b18ecd74e0d2fa8048,spotlight/factorization/explicit.py,ExplicitFactorizationModel,fit,#ExplicitFactorizationModel#Any#Any#,95

Before Change

            for (batch_user,
                 batch_item,
                 batch_ratings) in minibatch(user_ids_tensor,
                                             item_ids_tensor,
                                             ratings_tensor,
                                             batch_size=self._batch_size):

                user_var = Variable(batch_user)
                item_var = Variable(batch_item)
                ratings_var = Variable(batch_ratings)

                predictions = self._net(user_var, item_var)

                if self._loss == "poisson":
                    predictions = torch.exp(predictions)

                self._optimizer.zero_grad()

                loss = loss_fnc(ratings_var, predictions)
                epoch_loss += loss.data[0]

                loss.backward()
                self._optimizer.step()

            if verbose:
                print("Epoch {}: loss {}".format(epoch_num,
                                                 epoch_loss / (epoch_num + 1)))

    def predict(self, user_ids, item_ids):
        

After Change

            for (minibatch_num,
                 (batch_user,
                  batch_item,
                  batch_ratings)) in enumerate(minibatch(user_ids_tensor,
                                                         item_ids_tensor,
                                                         ratings_tensor,
                                                         batch_size=self._batch_size)):

                user_var = Variable(batch_user)
                item_var = Variable(batch_item)
                ratings_var = Variable(batch_ratings)

                predictions = self._net(user_var, item_var)

                if self._loss == "poisson":
                    predictions = torch.exp(predictions)

                self._optimizer.zero_grad()

                loss = loss_fnc(ratings_var, predictions)
                epoch_loss += loss.data[0]

                loss.backward()
                self._optimizer.step()

            epoch_loss /= minibatch_num + 1

            if verbose:
                print("Epoch {}: loss {}".format(epoch_num, epoch_loss))

Instances