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import json
import os
import os.path as osp
from itertools import product
from typing import Callable, List, Optional
import numpy as np
import torch
from torch_geometric.data import (
Data,
InMemoryDataset,
download_url,
extract_zip,
)
from torch_geometric.utils import remove_self_loops
class PPI(InMemoryDataset):
r"""The protein-protein interaction networks from the `"Predicting
Multicellular Function through Multi-layer Tissue Networks"
<https://arxiv.org/abs/1707.04638>`_ 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 (str): Root directory where the dataset should be saved.
split (str, optional): 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:`torch_geometric.data.Data` 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:`torch_geometric.data.Data` 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:`torch_geometric.data.Data` object and returns a boolean
value, indicating whether the data object should be included in the
final dataset. (default: :obj:`None`)
force_reload (bool, optional): Whether to re-process the dataset.
(default: :obj:`False`)
**STATS:**
.. list-table::
:widths: 10 10 10 10 10
:header-rows: 1
* - #graphs
- #nodes
- #edges
- #features
- #tasks
* - 20
- ~2,245.3
- ~61,318.4
- 50
- 121
"""
url = 'https://data.dgl.ai/dataset/ppi.zip'
def __init__(
self,
root: str,
split: str = 'train',
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
assert split in ['train', 'val', 'test']
super().__init__(root, transform, pre_transform, pre_filter,
force_reload=force_reload)
if split == 'train':
self.load(self.processed_paths[0])
elif split == 'val':
self.load(self.processed_paths[1])
elif split == 'test':
self.load(self.processed_paths[2])
@property
def raw_file_names(self) -> List[str]:
splits = ['train', 'valid', 'test']
files = ['feats.npy', 'graph_id.npy', 'graph.json', 'labels.npy']
return [f'{split}_{name}' for split, name in product(splits, files)]
@property
def processed_file_names(self) -> List[str]:
return ['train.pt', 'val.pt', 'test.pt']
def download(self) -> None:
path = download_url(self.url, self.root)
extract_zip(path, self.raw_dir)
os.unlink(path)
def process(self) -> None:
import networkx as nx
from networkx.readwrite import json_graph
for s, split in enumerate(['train', 'valid', 'test']):
path = osp.join(self.raw_dir, f'{split}_graph.json')
with open(path) as f:
G = nx.DiGraph(json_graph.node_link_graph(json.load(f)))
x = np.load(osp.join(self.raw_dir, f'{split}_feats.npy'))
x = torch.from_numpy(x).to(torch.float)
y = np.load(osp.join(self.raw_dir, f'{split}_labels.npy'))
y = torch.from_numpy(y).to(torch.float)
data_list = []
path = osp.join(self.raw_dir, f'{split}_graph_id.npy')
idx = torch.from_numpy(np.load(path)).to(torch.long)
idx = idx - idx.min()
for i in range(int(idx.max()) + 1):
mask = idx == i
G_s = G.subgraph(
mask.nonzero(as_tuple=False).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.save(data_list, self.processed_paths[s])
|
