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import os
import os.path as osp
from typing import Callable, List, Optional
import torch
from torch_geometric.data import (
HeteroData,
InMemoryDataset,
download_url,
extract_zip,
)
from torch_geometric.io import fs
from torch_geometric.utils import coalesce
class AMiner(InMemoryDataset):
r"""The heterogeneous AMiner dataset from the `"metapath2vec: Scalable
Representation Learning for Heterogeneous Networks"
<https://ericdongyx.github.io/papers/
KDD17-dong-chawla-swami-metapath2vec.pdf>`_ paper, consisting of nodes from
type :obj:`"paper"`, :obj:`"author"` and :obj:`"venue"`.
Venue categories and author research interests are available as ground
truth labels for a subset of nodes.
Args:
root (str): Root directory where the dataset should be saved.
transform (callable, optional): A function/transform that takes in an
:obj:`torch_geometric.data.HeteroData` 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.HeteroData` 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`)
"""
url = 'https://www.dropbox.com/s/1bnz8r7mofx0osf/net_aminer.zip?dl=1'
y_url = 'https://www.dropbox.com/s/nkocx16rpl4ydde/label.zip?dl=1'
def __init__(
self,
root: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
super().__init__(root, transform, pre_transform,
force_reload=force_reload)
self.load(self.processed_paths[0], data_cls=HeteroData)
@property
def raw_file_names(self) -> List[str]:
return [
'id_author.txt', 'id_conf.txt', 'paper.txt', 'paper_author.txt',
'paper_conf.txt', 'label'
]
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self) -> None:
fs.rm(self.raw_dir)
path = download_url(self.url, self.root)
extract_zip(path, self.root)
os.rename(osp.join(self.root, 'net_aminer'), self.raw_dir)
os.unlink(path)
path = download_url(self.y_url, self.raw_dir)
extract_zip(path, self.raw_dir)
os.unlink(path)
def process(self) -> None:
import pandas as pd
data = HeteroData()
# Get author labels.
path = osp.join(self.raw_dir, 'id_author.txt')
author = pd.read_csv(path, sep='\t', names=['idx', 'name'],
index_col=1)
path = osp.join(self.raw_dir, 'label',
'googlescholar.8area.author.label.txt')
df = pd.read_csv(path, sep=' ', names=['name', 'y'])
df = df.join(author, on='name')
data['author'].y = torch.from_numpy(df['y'].values) - 1
data['author'].y_index = torch.from_numpy(df['idx'].values)
# Get venue labels.
path = osp.join(self.raw_dir, 'id_conf.txt')
venue = pd.read_csv(path, sep='\t', names=['idx', 'name'], index_col=1)
path = osp.join(self.raw_dir, 'label',
'googlescholar.8area.venue.label.txt')
df = pd.read_csv(path, sep=' ', names=['name', 'y'])
df = df.join(venue, on='name')
data['venue'].y = torch.from_numpy(df['y'].values) - 1
data['venue'].y_index = torch.from_numpy(df['idx'].values)
# Get paper<->author connectivity.
path = osp.join(self.raw_dir, 'paper_author.txt')
paper_author = pd.read_csv(path, sep='\t', header=None)
paper_author = torch.from_numpy(paper_author.values)
paper_author = paper_author.t().contiguous()
M, N = int(paper_author[0].max() + 1), int(paper_author[1].max() + 1)
paper_author = coalesce(paper_author, num_nodes=max(M, N))
data['paper'].num_nodes = M
data['author'].num_nodes = N
data['paper', 'written_by', 'author'].edge_index = paper_author
data['author', 'writes', 'paper'].edge_index = paper_author.flip([0])
# Get paper<->venue connectivity.
path = osp.join(self.raw_dir, 'paper_conf.txt')
paper_venue = pd.read_csv(path, sep='\t', header=None)
paper_venue = torch.from_numpy(paper_venue.values)
paper_venue = paper_venue.t().contiguous()
M, N = int(paper_venue[0].max() + 1), int(paper_venue[1].max() + 1)
paper_venue = coalesce(paper_venue, num_nodes=max(M, N))
data['venue'].num_nodes = N
data['paper', 'published_in', 'venue'].edge_index = paper_venue
data['venue', 'publishes', 'paper'].edge_index = paper_venue.flip([0])
if self.pre_transform is not None:
data = self.pre_transform(data)
self.save([data], self.processed_paths[0])
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