wehub-resource-sync
138 lines
3.6 KiB
Python
138 lines
3.6 KiB
Python
import os
|
|
|
|
from .dgl_dataset import DGLBuiltinDataset
|
|
from .utils import _get_dgl_url, load_graphs
|
|
|
|
|
|
class ZINCDataset(DGLBuiltinDataset):
|
|
r"""ZINC dataset for the graph regression task.
|
|
|
|
A subset (12K) of ZINC molecular graphs (250K) dataset is used to
|
|
regress a molecular property known as the constrained solubility.
|
|
For each molecular graph, the node features are the types of heavy
|
|
atoms, between which the edge features are the types of bonds.
|
|
Each graph contains 9-37 nodes and 16-84 edges.
|
|
|
|
Reference `<https://arxiv.org/pdf/2003.00982.pdf>`_
|
|
|
|
Statistics:
|
|
|
|
Train examples: 10,000
|
|
Valid examples: 1,000
|
|
Test examples: 1,000
|
|
Average number of nodes: 23.16
|
|
Average number of edges: 39.83
|
|
Number of atom types: 28
|
|
Number of bond types: 4
|
|
|
|
Parameters
|
|
----------
|
|
mode : str, optional
|
|
Should be chosen from ["train", "valid", "test"]
|
|
Default: "train".
|
|
raw_dir : str
|
|
Raw file directory to download/contains the input data directory.
|
|
Default: "~/.dgl/".
|
|
force_reload : bool
|
|
Whether to reload the dataset.
|
|
Default: False.
|
|
verbose : bool
|
|
Whether to print out progress information.
|
|
Default: False.
|
|
transform : callable, optional
|
|
A transform that takes in a :class:`~dgl.DGLGraph` object and returns
|
|
a transformed version. The :class:`~dgl.DGLGraph` object will be
|
|
transformed before every access.
|
|
|
|
Attributes
|
|
----------
|
|
num_atom_types : int
|
|
Number of atom types.
|
|
num_bond_types : int
|
|
Number of bond types.
|
|
|
|
Examples
|
|
---------
|
|
>>> from dgl.data import ZINCDataset
|
|
|
|
>>> training_set = ZINCDataset(mode="train")
|
|
>>> training_set.num_atom_types
|
|
28
|
|
>>> len(training_set)
|
|
10000
|
|
>>> graph, label = training_set[0]
|
|
>>> graph
|
|
Graph(num_nodes=29, num_edges=64,
|
|
ndata_schemes={'feat': Scheme(shape=(), dtype=torch.int64)}
|
|
edata_schemes={'feat': Scheme(shape=(), dtype=torch.int64)})
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
mode="train",
|
|
raw_dir=None,
|
|
force_reload=False,
|
|
verbose=False,
|
|
transform=None,
|
|
):
|
|
self._url = _get_dgl_url("dataset/ZINC12k.zip")
|
|
self.mode = mode
|
|
|
|
super(ZINCDataset, self).__init__(
|
|
name="zinc",
|
|
url=self._url,
|
|
raw_dir=raw_dir,
|
|
force_reload=force_reload,
|
|
verbose=verbose,
|
|
transform=transform,
|
|
)
|
|
|
|
def process(self):
|
|
self.load()
|
|
|
|
@property
|
|
def graph_path(self):
|
|
return os.path.join(self.save_path, "ZincDGL_{}.bin".format(self.mode))
|
|
|
|
def has_cache(self):
|
|
return os.path.exists(self.graph_path)
|
|
|
|
def load(self):
|
|
self._graphs, self._labels = load_graphs(self.graph_path)
|
|
|
|
@property
|
|
def num_atom_types(self):
|
|
return 28
|
|
|
|
@property
|
|
def num_bond_types(self):
|
|
return 4
|
|
|
|
def __len__(self):
|
|
return len(self._graphs)
|
|
|
|
def __getitem__(self, idx):
|
|
r"""Get one example by index.
|
|
|
|
Parameters
|
|
----------
|
|
idx : int
|
|
The sample index.
|
|
|
|
Returns
|
|
-------
|
|
dgl.DGLGraph
|
|
Each graph contains:
|
|
|
|
- ``ndata['feat']``: Types of heavy atoms as node features
|
|
- ``edata['feat']``: Types of bonds as edge features
|
|
|
|
Tensor
|
|
Constrained solubility as graph label
|
|
"""
|
|
labels = self._labels["g_label"]
|
|
if self._transform is None:
|
|
return self._graphs[idx], labels[idx]
|
|
else:
|
|
return self._transform(self._graphs[idx]), labels[idx]
|