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tu_dataset.py
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tu_dataset.py
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from torch_geometric.datasets import TUDataset
import torch
from itertools import repeat, product
import numpy as np
class TUDatasetExt(TUDataset):
r"""A variety of graph kernel benchmark datasets, *.e.g.* "IMDB-BINARY",
"REDDIT-BINARY" or "PROTEINS", collected from the `TU Dortmund University
<http://graphkernels.cs.tu-dortmund.de>`_.
Args:
root (string): Root directory where the dataset should be saved.
name (string): The `name <http://graphkernels.cs.tu-dortmund.de>`_ of
the dataset.
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`)
use_node_attr (bool, optional): If :obj:`True`, the dataset will
contain additional continuous node features (if present).
(default: :obj:`False`)
"""
url = 'https://ls11-www.cs.tu-dortmund.de/people/morris/' \
'graphkerneldatasets'
def __init__(self,
root,
name,
transform=None,
pre_transform=None,
pre_filter=None,
use_node_attr=False,
processed_filename='data.pt',
aug="none", aug_ratio=None):
self.processed_filename = processed_filename
self.aug = "none"
self.aug_ratio = None
super(TUDatasetExt, self).__init__(root, name, transform, pre_transform,
pre_filter, use_node_attr)
@property
def processed_file_names(self):
return self.processed_filename
def get(self, idx):
data = self.data.__class__()
if hasattr(self.data, '__num_nodes__'):
data.num_nodes = self.data.__num_nodes__[idx]
for key in self.data.keys:
item, slices = self.data[key], self.slices[key]
if torch.is_tensor(item):
s = list(repeat(slice(None), item.dim()))
s[self.data.__cat_dim__(key,
item)] = slice(slices[idx],
slices[idx + 1])
else:
s = slice(slices[idx], slices[idx + 1])
data[key] = item[s]
if self.aug == 'dropN':
data = drop_nodes(data, self.aug_ratio)
elif self.aug == 'wdropN':
data = weighted_drop_nodes(data, self.aug_ratio, self.npower)
elif self.aug == 'permE':
data = permute_edges(data, self.aug_ratio)
elif self.aug == 'subgraph':
data = subgraph(data, self.aug_ratio)
elif self.aug == 'maskN':
data = mask_nodes(data, self.aug_ratio)
elif self.aug == 'none':
data = data
elif self.aug == 'random4':
ri = np.random.randint(4)
if ri == 0:
data = drop_nodes(data, self.aug_ratio)
elif ri == 1:
data = subgraph(data, self.aug_ratio)
elif ri == 2:
data = permute_edges(data, self.aug_ratio)
elif ri == 3:
data = mask_nodes(data, self.aug_ratio)
else:
print('sample augmentation error')
assert False
elif self.aug == 'random3':
ri = np.random.randint(3)
if ri == 0:
data = drop_nodes(data, self.aug_ratio)
elif ri == 1:
data = subgraph(data, self.aug_ratio)
elif ri == 2:
data = permute_edges(data, self.aug_ratio)
else:
print('sample augmentation error')
assert False
elif self.aug == 'random2':
ri = np.random.randint(2)
if ri == 0:
data = drop_nodes(data, self.aug_ratio)
elif ri == 1:
data = subgraph(data, self.aug_ratio)
else:
print('sample augmentation error')
assert False
else:
print('augmentation error')
assert False
# print(data)
# print(self.aug)
# assert False
return data
def drop_nodes(data, aug_ratio):
node_num, _ = data.x.size()
_, edge_num = data.edge_index.size()
drop_num = int(node_num * aug_ratio)
idx_perm = np.random.permutation(node_num)
idx_drop = idx_perm[:drop_num]
idx_nondrop = idx_perm[drop_num:]
idx_nondrop.sort()
idx_dict = {idx_nondrop[n]:n for n in list(range(idx_nondrop.shape[0]))}
edge_index = data.edge_index.numpy()
adj = torch.zeros((node_num, node_num))
adj[edge_index[0], edge_index[1]] = 1
adj = adj[idx_nondrop, :][:, idx_nondrop]
edge_index = adj.nonzero().t()
try:
data.edge_index = edge_index
data.x = data.x[idx_nondrop]
except:
data = data
return data
def weighted_drop_nodes(data, aug_ratio, npower):
node_num, _ = data.x.size()
_, edge_num = data.edge_index.size()
drop_num = int(node_num * aug_ratio)
adj = np.zeros((node_num, node_num))
adj[data.edge_index[0], data.edge_index[1]] = 1
deg = adj.sum(axis=1)
deg[deg==0] = 0.1
# print(deg)
# deg = deg ** (-1)
deg = deg ** (npower)
# print(deg)
# print(deg / deg.sum())
# assert False
idx_drop = np.random.choice(node_num, drop_num, replace=False, p=deg / deg.sum())
# idx_perm = np.random.permutation(node_num)
# idx_drop = idx_perm[:drop_num]
# idx_nondrop = idx_perm[drop_num:]
idx_nondrop = np.array([n for n in range(node_num) if not n in idx_drop])
# idx_nondrop.sort()
idx_dict = {idx_nondrop[n]:n for n in list(range(idx_nondrop.shape[0]))}
edge_index = data.edge_index.numpy()
###
adj = torch.zeros((node_num, node_num))
adj[edge_index[0], edge_index[1]] = 1
adj = adj[idx_nondrop, :][:, idx_nondrop]
edge_index = adj.nonzero().t()
###
# edge_index = [[idx_dict[edge_index[0, n]], idx_dict[edge_index[1, n]]] for n in range(edge_num) if (not edge_index[0, n] in idx_drop) and (not edge_index[1, n] in idx_drop)]
try:
data.edge_index = edge_index
data.x = data.x[idx_nondrop]
except:
data = data
return data
def permute_edges(data, aug_ratio):
node_num, _ = data.x.size()
_, edge_num = data.edge_index.size()
permute_num = int(edge_num * aug_ratio)
edge_index = data.edge_index.numpy()
idx_add = np.random.choice(node_num, (2, permute_num))
# idx_add = [[idx_add[0, n], idx_add[1, n]] for n in range(permute_num) if not (idx_add[0, n], idx_add[1, n]) in edge_index]
# edge_index = [edge_index[n] for n in range(edge_num) if not n in np.random.choice(edge_num, permute_num, replace=False)] + idx_add
edge_index = np.concatenate((edge_index[:, np.random.choice(edge_num, (edge_num - permute_num), replace=False)], idx_add), axis=1)
data.edge_index = torch.tensor(edge_index)
return data
def subgraph(data, aug_ratio):
node_num, _ = data.x.size()
_, edge_num = data.edge_index.size()
sub_num = int(node_num * aug_ratio)
edge_index = data.edge_index.numpy()
idx_sub = [np.random.randint(node_num, size=1)[0]]
idx_neigh = set([n for n in edge_index[1][edge_index[0]==idx_sub[0]]])
count = 0
while len(idx_sub) <= sub_num:
count = count + 1
if count > node_num:
break
if len(idx_neigh) == 0:
break
sample_node = np.random.choice(list(idx_neigh))
if sample_node in idx_sub:
continue
idx_sub.append(sample_node)
idx_neigh.union(set([n for n in edge_index[1][edge_index[0]==idx_sub[-1]]]))
idx_drop = [n for n in range(node_num) if not n in idx_sub]
idx_nondrop = idx_sub
data.x = data.x[idx_nondrop]
idx_dict = {idx_nondrop[n]:n for n in list(range(len(idx_nondrop)))}
edge_index = data.edge_index.numpy()
adj = torch.zeros((node_num, node_num))
adj[edge_index[0], edge_index[1]] = 1
adj[list(range(node_num)), list(range(node_num))] = 1
adj = adj[idx_nondrop, :][:, idx_nondrop]
edge_index = adj.nonzero().t()
# edge_index = [[idx_dict[edge_index[0, n]], idx_dict[edge_index[1, n]]] for n in range(edge_num) if (not edge_index[0, n] in idx_drop) and (not edge_index[1, n] in idx_drop)] + [[n, n] for n in idx_nondrop]
data.edge_index = edge_index
return data
def mask_nodes(data, aug_ratio):
node_num, feat_dim = data.x.size()
mask_num = int(node_num * aug_ratio)
token = data.x.mean(dim=0)
idx_mask = np.random.choice(node_num, mask_num, replace=False)
data.x[idx_mask] = torch.tensor(token, dtype=torch.float32)
return data