Source code for torch_geometric.datasets.rcdd

import os
from typing import Callable, List, Optional

import numpy as np
import torch

from import (
from torch_geometric.utils import index_to_mask

[docs]class RCDD(InMemoryDataset): r"""The risk commodity detection dataset (RCDD) from the `"Datasets and Interfaces for Benchmarking Heterogeneous Graph Neural Networks" <>`_ paper. RCDD is an industrial-scale heterogeneous graph dataset based on a real risk detection scenario from Alibaba's e-commerce platform. It consists of 13,806,619 nodes and 157,814,864 edges across 7 node types and 7 edge types, respectively. Args: root (str): Root directory where the dataset should be saved. transform (callable, optional): A function/transform that takes in an :obj:`` object and returns a transformed version. The data object will be transformed before every access. (default: :obj:`None`) pre_transform (callable, optional): A function/transform that takes in an :obj:`` object and returns a transformed version. The data object will be transformed before being saved to disk. (default: :obj:`None`) force_reload (bool, optional): Whether to re-process the dataset. (default: :obj:`False`) """ url = ('' 'openhgnn/') def __init__( self, root: str, transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None, force_reload: bool = False, ) -> None: super().__init__(root, transform, pre_transform, force_reload=force_reload) self.load(self.processed_paths[0], data_cls=HeteroData) @property def raw_file_names(self) -> List[str]: return [ 'AliRCD_ICDM_nodes.csv', 'AliRCD_ICDM_edges.csv', 'AliRCD_ICDM_train_labels.csv', 'AliRCD_ICDM_test_labels.csv', ] @property def processed_file_names(self) -> str: return '' def download(self) -> None: path = download_url(self.url, self.raw_dir) extract_zip(path, self.raw_dir) os.unlink(path) @property def num_classes(self) -> int: return 2 def process(self) -> None: import pandas as pd data = HeteroData() node_df = pd.read_csv( # AliRCD_ICDM_nodes.csv: self.raw_paths[0], header=None, names=['node_id', 'node_type', 'node_feat'], ) # Map global node IDs to local ones for each node type: mapping = torch.empty(len(node_df), dtype=torch.long) for node_type in node_df['node_type'].unique(): mask = node_df['node_type'] == node_type mask = torch.from_numpy(mask.values) num_nodes = int(mask.sum()) mapping[mask] = torch.arange(num_nodes) data[node_type].num_nodes = num_nodes x = np.vstack([ np.asarray(f.split(':'), dtype=np.float32) for f in node_df['node_feat'][mask.numpy()] ]) data[node_type].x = torch.from_numpy(x) edge_df = pd.read_csv( # AliRCD_ICDM_edges.csv: self.raw_paths[1], header=None, names=['src_id', 'dst_id', 'src_type', 'dst_type', 'edge_type'], ) for edge_type in edge_df['edge_type'].unique(): edge_type_df = edge_df[edge_df['edge_type'] == edge_type] src_type = edge_type_df['src_type'].iloc[0] dst_type = edge_type_df['dst_type'].iloc[0] src = mapping[torch.from_numpy(edge_type_df['src_id'].values)] dst = mapping[torch.from_numpy(edge_type_df['dst_id'].values)] edge_index = torch.stack([src, dst], dim=0) data[src_type, edge_type, dst_type].edge_index = edge_index train_df = pd.read_csv( # AliRCD_ICDM_train_labels.csv: self.raw_paths[2], header=None, names=['node_id', 'label'], dtype=int, ) test_df = pd.read_csv( # AliRCD_ICDM_test_labels.csv: self.raw_paths[3], header=None, sep='\t', names=['node_id', 'label'], dtype=int, ) train_idx = mapping[torch.from_numpy(train_df['node_id'].values)] test_idx = mapping[torch.from_numpy(test_df['node_id'].values)] y = torch.full((data['item'].num_nodes, ), -1, dtype=torch.long) y[train_idx] = torch.from_numpy(train_df['label'].values) y[test_idx] = torch.from_numpy(test_df['label'].values) train_mask = index_to_mask(train_idx, data['item'].num_nodes) test_mask = index_to_mask(test_idx, data['item'].num_nodes) data['item'].y = y data['item'].train_mask = train_mask data['item'].test_mask = test_mask if self.pre_transform is not None: data = self.pre_transform(data)[data], self.processed_paths[0])