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import os.path as osp
from typing import Callable, List, Optional
from torch_geometric.data import Data, InMemoryDataset
from torch_geometric.io import fs, read_tu_data
class TUDataset(InMemoryDataset):
r"""A variety of graph kernel benchmark datasets, *.e.g.*,
:obj:`"IMDB-BINARY"`, :obj:`"REDDIT-BINARY"` or :obj:`"PROTEINS"`,
collected from the `TU Dortmund University
<https://chrsmrrs.github.io/datasets>`_.
In addition, this dataset wrapper provides `cleaned dataset versions
<https://github.com/nd7141/graph_datasets>`_ as motivated by the
`"Understanding Isomorphism Bias in Graph Data Sets"
<https://arxiv.org/abs/1910.12091>`_ paper, containing only non-isomorphic
graphs.
.. note::
Some datasets may not come with any node labels.
You can then either make use of the argument :obj:`use_node_attr`
to load additional continuous node attributes (if present) or provide
synthetic node features using transforms such as
:class:`torch_geometric.transforms.Constant` or
:class:`torch_geometric.transforms.OneHotDegree`.
Args:
root (str): Root directory where the dataset should be saved.
name (str): The `name
<https://chrsmrrs.github.io/datasets/docs/datasets/>`_ 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`)
force_reload (bool, optional): Whether to re-process the dataset.
(default: :obj:`False`)
use_node_attr (bool, optional): If :obj:`True`, the dataset will
contain additional continuous node attributes (if present).
(default: :obj:`False`)
use_edge_attr (bool, optional): If :obj:`True`, the dataset will
contain additional continuous edge attributes (if present).
(default: :obj:`False`)
cleaned (bool, optional): If :obj:`True`, the dataset will
contain only non-isomorphic graphs. (default: :obj:`False`)
**STATS:**
.. list-table::
:widths: 20 10 10 10 10 10
:header-rows: 1
* - Name
- #graphs
- #nodes
- #edges
- #features
- #classes
* - MUTAG
- 188
- ~17.9
- ~39.6
- 7
- 2
* - ENZYMES
- 600
- ~32.6
- ~124.3
- 3
- 6
* - PROTEINS
- 1,113
- ~39.1
- ~145.6
- 3
- 2
* - COLLAB
- 5,000
- ~74.5
- ~4914.4
- 0
- 3
* - IMDB-BINARY
- 1,000
- ~19.8
- ~193.1
- 0
- 2
* - REDDIT-BINARY
- 2,000
- ~429.6
- ~995.5
- 0
- 2
* - ...
-
-
-
-
-
"""
url = 'https://www.chrsmrrs.com/graphkerneldatasets'
cleaned_url = ('https://raw.githubusercontent.com/nd7141/'
'graph_datasets/master/datasets')
def __init__(
self,
root: str,
name: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
force_reload: bool = False,
use_node_attr: bool = False,
use_edge_attr: bool = False,
cleaned: bool = False,
) -> None:
self.name = name
self.cleaned = cleaned
super().__init__(root, transform, pre_transform, pre_filter,
force_reload=force_reload)
out = fs.torch_load(self.processed_paths[0])
if not isinstance(out, tuple) or len(out) < 3:
raise RuntimeError(
"The 'data' object was created by an older version of PyG. "
"If this error occurred while loading an already existing "
"dataset, remove the 'processed/' directory in the dataset's "
"root folder and try again.")
assert len(out) == 3 or len(out) == 4
if len(out) == 3: # Backward compatibility.
data, self.slices, self.sizes = out
data_cls = Data
else:
data, self.slices, self.sizes, data_cls = out
if not isinstance(data, dict): # Backward compatibility.
self.data = data
else:
self.data = data_cls.from_dict(data)
assert isinstance(self._data, Data)
if self._data.x is not None and not use_node_attr:
num_node_attributes = self.num_node_attributes
self._data.x = self._data.x[:, num_node_attributes:]
if self._data.edge_attr is not None and not use_edge_attr:
num_edge_attrs = self.num_edge_attributes
self._data.edge_attr = self._data.edge_attr[:, num_edge_attrs:]
@property
def raw_dir(self) -> str:
name = f'raw{"_cleaned" if self.cleaned else ""}'
return osp.join(self.root, self.name, name)
@property
def processed_dir(self) -> str:
name = f'processed{"_cleaned" if self.cleaned else ""}'
return osp.join(self.root, self.name, name)
@property
def num_node_labels(self) -> int:
return self.sizes['num_node_labels']
@property
def num_node_attributes(self) -> int:
return self.sizes['num_node_attributes']
@property
def num_edge_labels(self) -> int:
return self.sizes['num_edge_labels']
@property
def num_edge_attributes(self) -> int:
return self.sizes['num_edge_attributes']
@property
def raw_file_names(self) -> List[str]:
names = ['A', 'graph_indicator']
return [f'{self.name}_{name}.txt' for name in names]
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self) -> None:
url = self.cleaned_url if self.cleaned else self.url
fs.cp(f'{url}/{self.name}.zip', self.raw_dir, extract=True)
for filename in fs.ls(osp.join(self.raw_dir, self.name)):
fs.mv(filename, osp.join(self.raw_dir, osp.basename(filename)))
fs.rm(osp.join(self.raw_dir, self.name))
def process(self) -> None:
self.data, self.slices, sizes = read_tu_data(self.raw_dir, self.name)
if self.pre_filter is not None or self.pre_transform is not None:
data_list = [self.get(idx) for idx in range(len(self))]
if self.pre_filter is not None:
data_list = [d for d in data_list if self.pre_filter(d)]
if self.pre_transform is not None:
data_list = [self.pre_transform(d) for d in data_list]
self.data, self.slices = self.collate(data_list)
self._data_list = None # Reset cache.
assert isinstance(self._data, Data)
fs.torch_save(
(self._data.to_dict(), self.slices, sizes, self._data.__class__),
self.processed_paths[0],
)
def __repr__(self) -> str:
return f'{self.name}({len(self)})'
|
