gistfile1.txt

data_list = []

      # process by session_id
      grouped = df.groupby('session_id')
      for session_id, group in tqdm(grouped):
          sess_item_id = LabelEncoder().fit_transform(group.item_id)
          group = group.reset_index(drop=True)
          group['sess_item_id'] = sess_item_id
          node_features = group.loc[group.session_id==session_id,['sess_item_id','item_id']].sort_values('sess_item_id').item_id.drop_duplicates().values

          node_features = torch.LongTensor(node_features).unsqueeze(1)
          target_nodes = group.sess_item_id.values[1:]
          source_nodes = group.sess_item_id.values[:-1]

          edge_index = torch.tensor([source_nodes,
                                 target_nodes], dtype=torch.long)
          x = node_features

          y = torch.FloatTensor([group.label.values[0]])

          data = Data(x=x, edge_index=edge_index, y=y)
          data_list.append(data)

      data, slices = self.collate(data_list)
      torch.save((data, slices), self.processed_paths[0])