import os
import shutil
import os.path as osp
from typing import Callable, Optional, List
from ggfm.data import download_url, extract_zip
[docs]class AMiner:
r"""The heterogeneous AMiner dataset from the `"metapath2vec: Scalable
Representation Learning for Heterogeneous Networks"
<https://dl.acm.org/doi/pdf/10.1145/3097983.3098036>`_ paper, consisting of nodes from
type :obj:`"paper"`, :obj:`"author"` and :obj:`"venue"`.
Aminer is a heterogeneous graph containing three types of entities - author
(1,693,531 nodes), venue (3,883 nodes), and paper (3,194,405 nodes).
Venue categories and author research interests are available as ground
truth labels for a subset of nodes.
"""
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: Optional[str] = None, transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None, pre_filter: Optional[Callable] = None,
force_reload: bool = False):
super().__init__(root, transform, pre_transform, pre_filter, force_reload = force_reload)
self.data, self.slices = self.load_data(self.processed_paths[0])
@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 'Aminer_pre_data.pt'
def download(self):
shutil.rmtree(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):
# data = gg()
#
# # 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 = tlx.convert_to_tensor(df['y'].values) - 1
# data['author'].y_index = tlx.convert_to_tensor(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 = tlx.convert_to_tensor(df['y'].values) - 1
# data['venue'].y_index = tlx.convert_to_tensor(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 = tlx.convert_to_tensor(paper_author.values)
# if tlx.BACKEND == 'mindspore':
# paper_author = paper_author.T
# else:
# paper_author = tlx.ops.transpose(paper_author)
# M, N = int(max(paper_author[0]) + 1), int(max(paper_author[1]) + 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
# paper_author = tlx.convert_to_numpy(paper_author)
# data['author', 'writes', 'paper'].edge_index = tlx.convert_to_tensor(np.flip(paper_author, axis=0).copy())
#
# # 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 = tlx.convert_to_tensor(paper_venue.values)
# if tlx.BACKEND == 'mindspore':
# paper_venue = paper_venue.T
# else:
# paper_venue = tlx.ops.transpose(paper_venue)
# M, N = int(max(paper_venue[0]) + 1), int(max(paper_venue[1]) + 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
# paper_venue = tlx.convert_to_numpy(paper_venue)
# data['venue', 'publishes', 'paper'].edge_index = tlx.convert_to_tensor(np.flip(paper_venue, axis=0).copy())
#
#
# if self.pre_transform is not None:
# data = self.pre_transform(data)
#
# self.save_data(self.collate([data]), self.processed_paths[0])
pass
# todo