def fit()

in recommenders/models/ncf/ncf_singlenode.py [0:0]

• 26 lines of code
• 12 McCabe index (conditional complexity)

    def fit(self, data):
        """Fit model with training data

        Args:
            data (NCFDataset): initilized Dataset in ./dataset.py
        """

        # get user and item mapping dict
        self.user2id = data.user2id
        self.item2id = data.item2id
        self.id2user = data.id2user
        self.id2item = data.id2item

        # loop for n_epochs
        for epoch_count in range(1, self.n_epochs + 1):

            # negative sampling for training
            train_begin = time()
            data.negative_sampling()

            # initialize
            train_loss = []

            # calculate loss and update NCF parameters
            for user_input, item_input, labels in data.train_loader(self.batch_size):

                user_input = np.array([self.user2id[x] for x in user_input])
                item_input = np.array([self.item2id[x] for x in item_input])
                labels = np.array(labels)

                feed_dict = {
                    self.user_input: user_input[..., None],
                    self.item_input: item_input[..., None],
                    self.labels: labels[..., None],
                }

                # get loss and execute optimization
                loss, _ = self.sess.run([self.loss, self.optimizer], feed_dict)
                train_loss.append(loss)
            train_time = time() - train_begin

            # output every self.verbose
            if self.verbose and epoch_count % self.verbose == 0:
                logger.info(
                    "Epoch %d [%.2fs]: train_loss = %.6f "
                    % (epoch_count, train_time, sum(train_loss) / len(train_loss))
                )