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dataset_utils.py
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#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
import torch
import ipdb
import math
import ipdb
import os.path as osp
import numpy as np
import torch.nn.functional as F
import torch_geometric.transforms as T
from cSBM_dataset import dataset_ContextualSBM
from torch_geometric.datasets import Planetoid
from torch_geometric.datasets import Coauthor
from torch_geometric.datasets import Amazon
from torch_geometric.nn import APPNP
from torch_sparse import coalesce
from torch_geometric.data import InMemoryDataset, download_url, Data
from torch_geometric.utils.undirected import is_undirected, to_undirected
from torch_geometric.io import read_npz
class dataset_heterophily(InMemoryDataset):
def __init__(self, root='data/', name=None,
p2raw=None,
train_percent=0.01,
transform=None, pre_transform=None):
existing_dataset = ['chameleon', 'film', 'squirrel']
if name not in existing_dataset:
raise ValueError(
f'name of hypergraph dataset must be one of: {existing_dataset}')
else:
self.name = name
self._train_percent = train_percent
if (p2raw is not None) and osp.isdir(p2raw):
self.p2raw = p2raw
elif p2raw is None:
self.p2raw = None
elif not osp.isdir(p2raw):
raise ValueError(
f'path to raw hypergraph dataset "{p2raw}" does not exist!')
if not osp.isdir(root):
os.makedirs(root)
self.root = root
super(dataset_heterophily, self).__init__(
root, transform, pre_transform)
self.data, self.slices = torch.load(self.processed_paths[0])
self.train_percent = self.data.train_percent.item()
@property
def raw_dir(self):
return osp.join(self.root, self.name, 'raw')
@property
def processed_dir(self):
return osp.join(self.root, self.name, 'processed')
@property
def raw_file_names(self):
file_names = [self.name]
return file_names
@property
def processed_file_names(self):
return ['data.pt']
def download(self):
pass
def process(self):
p2f = osp.join(self.raw_dir, self.name)
with open(p2f, 'rb') as f:
data = pickle.load(f)
data = data if self.pre_transform is None else self.pre_transform(data)
torch.save(self.collate([data]), self.processed_paths[0])
def __repr__(self):
return '{}()'.format(self.name)
class WebKB(InMemoryDataset):
r"""The WebKB datasets used in the
`"Geom-GCN: Geometric Graph Convolutional Networks"
<https://openreview.net/forum?id=S1e2agrFvS>`_ paper.
Nodes represent web pages and edges represent hyperlinks between them.
Node features are the bag-of-words representation of web pages.
The task is to classify the nodes into one of the five categories, student,
project, course, staff, and faculty.
Args:
root (string): Root directory where the dataset should be saved.
name (string): The name of the dataset (:obj:`"Cornell"`,
:obj:`"Texas"` :obj:`"Washington"`, :obj:`"Wisconsin"`).
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`)
"""
url = ('https://raw.githubusercontent.com/graphdml-uiuc-jlu/geom-gcn/'
'master/new_data')
def __init__(self, root, name, transform=None, pre_transform=None):
self.name = name.lower()
assert self.name in ['cornell', 'texas', 'washington', 'wisconsin']
super(WebKB, self).__init__(root, transform, pre_transform)
self.data, self.slices = torch.load(self.processed_paths[0])
@property
def raw_dir(self):
return osp.join(self.root, self.name, 'raw')
@property
def processed_dir(self):
return osp.join(self.root, self.name, 'processed')
@property
def raw_file_names(self):
return ['out1_node_feature_label.txt', 'out1_graph_edges.txt']
@property
def processed_file_names(self):
return 'data.pt'
def download(self):
for name in self.raw_file_names:
download_url(f'{self.url}/{self.name}/{name}', self.raw_dir)
def process(self):
with open(self.raw_paths[0], 'r') as f:
data = f.read().split('\n')[1:-1]
x = [[float(v) for v in r.split('\t')[1].split(',')] for r in data]
x = torch.tensor(x, dtype=torch.float)
y = [int(r.split('\t')[2]) for r in data]
y = torch.tensor(y, dtype=torch.long)
with open(self.raw_paths[1], 'r') as f:
data = f.read().split('\n')[1:-1]
data = [[int(v) for v in r.split('\t')] for r in data]
edge_index = torch.tensor(data, dtype=torch.long).t().contiguous()
edge_index = to_undirected(edge_index)
edge_index, _ = coalesce(edge_index, None, x.size(0), x.size(0))
data = Data(x=x, edge_index=edge_index, y=y)
data = data if self.pre_transform is None else self.pre_transform(data)
torch.save(self.collate([data]), self.processed_paths[0])
def __repr__(self):
return '{}()'.format(self.name)
def DataLoader(name):
# assert name in ['cSBM_data_Aug_19_2020-13:06',
# 'cSBM_data_Aug_18_2020-18:50',
# 'cSBM_data_Aug_21_2020-10:06',
# 'cSBM_data_Aug_19_2020-20:41',
# 'cSBM_data_Aug_21_2020-11:04',
# 'cSBM_data_Aug_21_2020-11:21',
# 'cSBM_data_Sep_01_2020-14:15',
# 'cSBM_data_Sep_01_2020-14:18',
# 'cSBM_data_Sep_01_2020-14:19',
# 'cSBM_data_Sep_01_2020-14:32',
# 'cSBM_data_Sep_01_2020-14:22',
# 'cSBM_data_Sep_01_2020-14:23',
# 'cSBM_data_Sep_01_2020-14:27',
# 'cSBM_data_Sep_01_2020-14:29',
# 'Cora', 'Citeseer', 'PubMed',
# 'Computers', 'Photo',
# 'chameleon', 'film', 'squirrel',
# 'Texas', 'Cornell']
# if name in ['cSBM_data_Aug_19_2020-13:06',
# 'cSBM_data_Aug_18_2020-18:50',
# 'cSBM_data_Aug_21_2020-10:06',
# 'cSBM_data_Aug_19_2020-20:41',
# 'cSBM_data_Aug_21_2020-11:04',
# 'cSBM_data_Aug_21_2020-11:21',
# 'cSBM_data_Sep_01_2020-14:15',
# 'cSBM_data_Sep_01_2020-14:18',
# 'cSBM_data_Sep_01_2020-14:19',
# 'cSBM_data_Sep_01_2020-14:32',
# 'cSBM_data_Sep_01_2020-14:22',
# 'cSBM_data_Sep_01_2020-14:23',
# 'cSBM_data_Sep_01_2020-14:27',
# 'cSBM_data_Sep_01_2020-14:29']:
'''
if 'cSBM_data' in name:
path = 'data_cSBM/'
dataset = dataset_ContextualSBM(path, name=name)
else:
name = name.lower()
if name in ['cora', 'citeseer', 'pubmed']:
root_path = '../'
path = osp.join(root_path, 'data', name)
dataset = Planetoid(path, name, transform=T.NormalizeFeatures())
elif name in ['computers', 'photo']:
root_path = '../'
path = osp.join(root_path, 'data', name)
dataset = Amazon(path, name, T.NormalizeFeatures())
elif name in ['chameleon', 'film', 'squirrel']:
dataset = dataset_heterophily(
root='../data/', name=name, transform=T.NormalizeFeatures())
elif name in ['texas', 'cornell']:
dataset = WebKB(root='../data/',
name=name, transform=T.NormalizeFeatures())
else:
raise ValueError(f'dataset {name} not supported in dataloader')
'''
path = 'data_cSBM/'
dataset = dataset_ContextualSBM(path, name=name)
return dataset