import os.path as osp
from typing import Callable, Optional
import torch
from torch_geometric.data import InMemoryDataset, TemporalData, download_url
[docs]class JODIEDataset(InMemoryDataset):
r"""The temporal graph datasets
from the `"JODIE: Predicting Dynamic Embedding
Trajectory in Temporal Interaction Networks"
<https://cs.stanford.edu/~srijan/pubs/jodie-kdd2019.pdf>`_ paper.
Args:
root (str): Root directory where the dataset should be saved.
name (str): The name of the dataset (:obj:`"Reddit"`,
:obj:`"Wikipedia"`, :obj:`"MOOC"`, and :obj:`"LastFM"`).
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`)
**STATS:**
.. list-table::
:widths: 10 10 10 10 10
:header-rows: 1
* - Name
- #nodes
- #edges
- #features
- #classes
* - Reddit
- 6,509
- 25,470
- 172
- 1
* - Wikipedia
- 9,227
- 157,474
- 172
- 2
* - MOOC
- 7,144
- 411,749
- 4
- 2
* - LastFM
- 1,980
- 1,293,103
- 2
- 1
"""
url = 'http://snap.stanford.edu/jodie/{}.csv'
names = ['reddit', 'wikipedia', 'mooc', 'lastfm']
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.names
super().__init__(root, transform, pre_transform,
force_reload=force_reload)
self.load(self.processed_paths[0], data_cls=TemporalData)
@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) -> str:
return f'{self.name}.csv'
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self) -> None:
download_url(self.url.format(self.name), self.raw_dir)
def process(self) -> None:
import pandas as pd
df = pd.read_csv(self.raw_paths[0], skiprows=1, header=None)
src = torch.from_numpy(df.iloc[:, 0].values).to(torch.long)
dst = torch.from_numpy(df.iloc[:, 1].values).to(torch.long)
dst += int(src.max()) + 1
t = torch.from_numpy(df.iloc[:, 2].values).to(torch.long)
y = torch.from_numpy(df.iloc[:, 3].values).to(torch.long)
msg = torch.from_numpy(df.iloc[:, 4:].values).to(torch.float)
data = TemporalData(src=src, dst=dst, t=t, msg=msg, y=y)
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()}()'