import os.path as osp
from typing import Callable, List, Optional
import torch
from torch_geometric.data import InMemoryDataset, download_url
from torch_geometric.data.hypergraph_data import HyperGraphData
[docs]class CornellTemporalHyperGraphDataset(InMemoryDataset):
r"""A collection of temporal higher-order network datasets from the
`"Simplicial Closure and higher-order link prediction"
<https://arxiv.org/abs/1802.06916>`_ paper.
Each of the datasets is a timestamped sequence of simplices, where a
simplex is a set of :math:`k` nodes.
See the original `datasets page
<https://www.cs.cornell.edu/~arb/data/>`_ for more details about
individual datasets.
Args:
root (str): Root directory where the dataset should be saved.
name (str): The name of the dataset.
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"`)
setting (str, optional): If :obj:`"transductive"`, loads the dataset
for transductive training.
If :obj:`"inductive"`, loads the dataset for inductive training.
(default: :obj:`"transductive"`)
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`)
"""
names = [
'email-Eu',
'email-Enron',
'NDC-classes',
'tags-math-sx',
'email-Eu-25',
'NDC-substances',
'congress-bills',
'tags-ask-ubuntu',
'email-Enron-25',
'NDC-classes-25',
'threads-ask-ubuntu',
'contact-high-school',
'NDC-substances-25',
'congress-bills-25',
'contact-primary-school',
]
url = ('https://huggingface.co/datasets/SauravMaheshkar/{}/raw/main/'
'processed/{}/{}')
def __init__(
self,
root: str,
name: str,
split: str = 'train',
setting: str = 'transductive',
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
assert name in self.names
assert setting in ['transductive', 'inductive']
self.name = name
self.setting = setting
super().__init__(root, transform, pre_transform, pre_filter,
force_reload)
if split == 'train':
path = self.processed_paths[0]
elif split == 'val':
path = self.processed_paths[1]
elif split == 'test':
path = self.processed_paths[2]
else:
raise ValueError(f"Split '{split}' not found")
self.load(path)
@property
def raw_dir(self) -> str:
return osp.join(self.root, self.name, self.setting, 'raw')
@property
def raw_file_names(self) -> List[str]:
return ['train_df.csv', 'val_df.csv', 'test_df.csv']
@property
def processed_dir(self) -> str:
return osp.join(self.root, self.name, self.setting, 'processed')
@property
def processed_file_names(self) -> List[str]:
return ['train_data.pt', 'val_data.pt', 'test_data.pt']
def download(self) -> None:
for filename in self.raw_file_names:
url = self.url.format(self.name, self.setting, filename)
download_url(url, self.raw_dir)
def process(self) -> None:
import pandas as pd
for raw_path, path in zip(self.raw_paths, self.processed_paths):
df = pd.read_csv(raw_path)
data_list = []
for i, row in df.iterrows():
edge_indices: List[List[int]] = [[], []]
for node in eval(row['nodes']): # str(list) -> list:
edge_indices[0].append(node)
edge_indices[1].append(i) # Use `i` as hyper-edge index.
x = torch.tensor([[row['timestamp']]], dtype=torch.float)
edge_index = torch.tensor(edge_indices)
data = HyperGraphData(x=x, edge_index=edge_index)
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, path)