base_rating.py 文件源码

def fix_predictions(self, X, predictions, bias):
        idxs_users_missing, idxs_items_missing = self.indices_missing

        # Set average when neither the user nor the item exist
        g_avg = bias['globalAvg']
        common_indices = np.intersect1d(idxs_users_missing, idxs_items_missing)
        predictions[common_indices] = g_avg

        # Only users exist (return average + {dUser})
        if 'dUsers' in bias:
            missing_users = np.setdiff1d(idxs_users_missing, common_indices)
            if len(missing_users) > 0:
                user_idxs = X[missing_users, self.order[0]]
                predictions[missing_users] = g_avg + bias['dUsers'][user_idxs]

        # Only items exist (return average + {dItem})
        if 'dItems' in bias:
            missing_items = np.setdiff1d(idxs_items_missing, common_indices)
            if len(missing_items) > 0:
                item_idxs = X[missing_items, self.order[1]]
                predictions[missing_items] = g_avg + bias['dItems'][item_idxs]

        return predictions