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import os.path as osp
from typing import Callable, List, Optional
import numpy as np
import torch
from torch_geometric.data import Data, InMemoryDataset, download_url
from torch_geometric.io import fs
from torch_geometric.utils import one_hot
class LINKXDataset(InMemoryDataset):
r"""A variety of non-homophilous graph datasets from the `"Large Scale
Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple
Methods" <https://arxiv.org/abs/2110.14446>`_ paper.
.. note::
Some of the datasets provided in :class:`LINKXDataset` are from other
sources, but have been updated with new features and/or labels.
Args:
root (str): Root directory where the dataset should be saved.
name (str): The name of the dataset (:obj:`"penn94"`, :obj:`"reed98"`,
:obj:`"amherst41"`, :obj:`"cornell5"`, :obj:`"johnshopkins55"`,
:obj:`"genius"`).
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`)
force_reload (bool, optional): Whether to re-process the dataset.
(default: :obj:`False`)
"""
github_url = ('https://github.com/CUAI/Non-Homophily-Large-Scale/'
'raw/master/data')
gdrive_url = 'https://drive.usercontent.google.com/download?confirm=t'
facebook_datasets = [
'penn94', 'reed98', 'amherst41', 'cornell5', 'johnshopkins55'
]
datasets = {
'penn94': {
'data.mat': f'{github_url}/facebook100/Penn94.mat'
},
'reed98': {
'data.mat': f'{github_url}/facebook100/Reed98.mat'
},
'amherst41': {
'data.mat': f'{github_url}/facebook100/Amherst41.mat',
},
'cornell5': {
'data.mat': f'{github_url}/facebook100/Cornell5.mat'
},
'johnshopkins55': {
'data.mat': f'{github_url}/facebook100/Johns%20Hopkins55.mat'
},
'genius': {
'data.mat': f'{github_url}/genius.mat'
},
'wiki': {
'wiki_views2M.pt':
f'{gdrive_url}&id=1p5DlVHrnFgYm3VsNIzahSsvCD424AyvP',
'wiki_edges2M.pt':
f'{gdrive_url}&id=14X7FlkjrlUgmnsYtPwdh-gGuFla4yb5u',
'wiki_features2M.pt':
f'{gdrive_url}&id=1ySNspxbK-snNoAZM7oxiWGvOnTRdSyEK'
}
}
splits = {
'penn94': f'{github_url}/splits/fb100-Penn94-splits.npy',
}
def __init__(
self,
root: str,
name: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
self.name = name.lower()
assert self.name in self.datasets.keys()
super().__init__(root, transform, pre_transform,
force_reload=force_reload)
self.load(self.processed_paths[0])
@property
def raw_dir(self) -> str:
return osp.join(self.root, self.name, 'raw')
@property
def processed_dir(self) -> str:
return osp.join(self.root, self.name, 'processed')
@property
def raw_file_names(self) -> List[str]:
names = list(self.datasets[self.name].keys())
if self.name in self.splits:
names += [self.splits[self.name].split('/')[-1]]
return names
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self) -> None:
for filename, path in self.datasets[self.name].items():
download_url(path, self.raw_dir, filename=filename)
if self.name in self.splits:
download_url(self.splits[self.name], self.raw_dir)
def _process_wiki(self) -> Data:
paths = {x.split('/')[-1]: x for x in self.raw_paths}
x = fs.torch_load(paths['wiki_features2M.pt'])
edge_index = fs.torch_load(paths['wiki_edges2M.pt']).t().contiguous()
y = fs.torch_load(paths['wiki_views2M.pt'])
return Data(x=x, edge_index=edge_index, y=y)
def _process_facebook(self) -> Data:
from scipy.io import loadmat
mat = loadmat(self.raw_paths[0])
A = mat['A'].tocsr().tocoo()
row = torch.from_numpy(A.row).to(torch.long)
col = torch.from_numpy(A.col).to(torch.long)
edge_index = torch.stack([row, col], dim=0)
metadata = torch.from_numpy(mat['local_info'].astype('int64'))
xs = []
y = metadata[:, 1] - 1 # gender label, -1 means unlabeled
x = torch.cat([metadata[:, :1], metadata[:, 2:]], dim=-1)
for i in range(x.size(1)):
_, out = x[:, i].unique(return_inverse=True)
xs.append(one_hot(out))
x = torch.cat(xs, dim=-1)
data = Data(x=x, edge_index=edge_index, y=y)
if self.name in self.splits:
splits = np.load(self.raw_paths[1], allow_pickle=True)
assert data.num_nodes is not None
sizes = (data.num_nodes, len(splits))
data.train_mask = torch.zeros(sizes, dtype=torch.bool)
data.val_mask = torch.zeros(sizes, dtype=torch.bool)
data.test_mask = torch.zeros(sizes, dtype=torch.bool)
for i, split in enumerate(splits):
data.train_mask[:, i][torch.tensor(split['train'])] = True
data.val_mask[:, i][torch.tensor(split['valid'])] = True
data.test_mask[:, i][torch.tensor(split['test'])] = True
return data
def _process_genius(self) -> Data:
from scipy.io import loadmat
mat = loadmat(self.raw_paths[0])
edge_index = torch.from_numpy(mat['edge_index']).to(torch.long)
x = torch.from_numpy(mat['node_feat']).to(torch.float)
y = torch.from_numpy(mat['label']).squeeze().to(torch.long)
return Data(x=x, edge_index=edge_index, y=y)
def process(self) -> None:
if self.name in self.facebook_datasets:
data = self._process_facebook()
elif self.name == 'genius':
data = self._process_genius()
elif self.name == 'wiki':
data = self._process_wiki()
else:
raise NotImplementedError(
f"chosen dataset '{self.name}' is not implemented")
if self.pre_transform is not None:
data = self.pre_transform(data)
self.save([data], self.processed_paths[0])
def __repr__(self) -> str:
return f'{self.name.capitalize()}({len(self)})'
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