gistfile1.txt

import torch
from torch_geometric.data import InMemoryDataset
from tqdm import tqdm

class YooChooseBinaryDataset(InMemoryDataset):
    
    def process(self):
        
        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])