Source code for torch_geometric.datasets.ppi

from itertools import product
import os
import os.path as osp
import json

import torch
import numpy as np
import networkx as nx
from networkx.readwrite import json_graph
from import (InMemoryDataset, Data, download_url,
from torch_geometric.utils import remove_self_loops

[docs]class PPI(InMemoryDataset): r"""The protein-protein interaction networks from the `"Predicting Multicellular Function through Multi-layer Tissue Networks" <>`_ paper, containing positional gene sets, motif gene sets and immunological signatures as features (50 in total) and gene ontology sets as labels (121 in total). Args: root (string): Root directory where the dataset should be saved. split (string): If :obj:`"train"`, loads the training dataset. If :obj:`"val"`, loads the validation dataset. If :obj:`"test"`, loads the test dataset. (default: :obj:`"train"`) 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`) pre_filter (callable, optional): A function that takes in an :obj:`` object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default: :obj:`None`) """ url = '' def __init__(self, root, split='train', transform=None, pre_transform=None, pre_filter=None): assert split in ['train', 'val', 'test'] super(PPI, self).__init__(root, transform, pre_transform, pre_filter) if split == 'train':, self.slices = torch.load(self.processed_paths[0]) elif split == 'val':, self.slices = torch.load(self.processed_paths[1]) elif split == 'test':, self.slices = torch.load(self.processed_paths[2]) @property def raw_file_names(self): splits = ['train', 'valid', 'test'] files = ['feats.npy', 'graph_id.npy', 'graph.json', 'labels.npy'] return ['{}_{}'.format(s, f) for s, f in product(splits, files)] @property def processed_file_names(self): return ['', '', ''] def download(self): path = download_url(self.url, self.root) extract_zip(path, self.raw_dir) os.unlink(path) def process(self): for s, split in enumerate(['train', 'valid', 'test']): path = osp.join(self.raw_dir, '{}_graph.json').format(split) with open(path, 'r') as f: G = nx.DiGraph(json_graph.node_link_graph(json.load(f))) x = np.load(osp.join(self.raw_dir, '{}_feats.npy').format(split)) x = torch.from_numpy(x).to(torch.float) y = np.load(osp.join(self.raw_dir, '{}_labels.npy').format(split)) y = torch.from_numpy(y).to(torch.float) data_list = [] path = osp.join(self.raw_dir, '{}_graph_id.npy').format(split) idx = torch.from_numpy(np.load(path)).to(torch.long) idx = idx - idx.min() for i in range(idx.max().item() + 1): mask = idx == i G_s = G.subgraph(mask.nonzero().view(-1).tolist()) edge_index = torch.tensor(list(G_s.edges)).t().contiguous() edge_index = edge_index - edge_index.min() edge_index, _ = remove_self_loops(edge_index) data = Data(edge_index=edge_index, x=x[mask], y=y[mask]) if self.pre_filter is not None and not self.pre_filter(data): continue if self.pre_transform is not None: data = self.pre_transform(data) data_list.append(data), self.processed_paths[s])