"""QM7b dataset for graph property prediction (regression)."""
import os
from scipy import io
from .. import backend as F
from ..convert import graph as dgl_graph
from .dgl_dataset import DGLDataset
from .utils import check_sha1, download, load_graphs, save_graphs
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class QM7bDataset(DGLDataset):
r"""QM7b dataset for graph property prediction (regression)
This dataset consists of 7,211 molecules with 14 regression targets.
Nodes means atoms and edges means bonds. Edge data 'h' means
the entry of Coulomb matrix.
Reference: `<http://quantum-machine.org/datasets/>`_
Statistics:
- Number of graphs: 7,211
- Number of regression targets: 14
- Average number of nodes: 15
- Average number of edges: 245
- Edge feature size: 1
Parameters
----------
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: True.
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_tasks : int
Number of prediction tasks
num_labels : int
(DEPRECATED, use num_tasks instead) Number of prediction tasks
Raises
------
UserWarning
If the raw data is changed in the remote server by the author.
Examples
--------
>>> data = QM7bDataset()
>>> data.num_tasks
14
>>>
>>> # iterate over the dataset
>>> for g, label in data:
... edge_feat = g.edata['h'] # get edge feature
... # your code here...
...
>>>
"""
_url = (
"http://deepchem.io.s3-website-us-west-1.amazonaws.com/"
"datasets/qm7b.mat"
)
_sha1_str = "4102c744bb9d6fd7b40ac67a300e49cd87e28392"
def __init__(
self, raw_dir=None, force_reload=False, verbose=False, transform=None
):
super(QM7bDataset, self).__init__(
name="qm7b",
url=self._url,
raw_dir=raw_dir,
force_reload=force_reload,
verbose=verbose,
transform=transform,
)
def process(self):
mat_path = os.path.join(self.raw_dir, self.name + ".mat")
self.graphs, self.label = self._load_graph(mat_path)
def _load_graph(self, filename):
data = io.loadmat(filename)
labels = F.tensor(data["T"], dtype=F.data_type_dict["float32"])
feats = data["X"]
num_graphs = labels.shape[0]
graphs = []
for i in range(num_graphs):
edge_list = feats[i].nonzero()
g = dgl_graph(edge_list)
g.edata["h"] = F.tensor(
feats[i][edge_list[0], edge_list[1]].reshape(-1, 1),
dtype=F.data_type_dict["float32"],
)
graphs.append(g)
return graphs, labels
def save(self):
"""save the graph list and the labels"""
graph_path = os.path.join(self.save_path, "dgl_graph.bin")
save_graphs(str(graph_path), self.graphs, {"labels": self.label})
def has_cache(self):
graph_path = os.path.join(self.save_path, "dgl_graph.bin")
return os.path.exists(graph_path)
def load(self):
graphs, label_dict = load_graphs(
os.path.join(self.save_path, "dgl_graph.bin")
)
self.graphs = graphs
self.label = label_dict["labels"]
def download(self):
file_path = os.path.join(self.raw_dir, self.name + ".mat")
download(self.url, path=file_path)
if not check_sha1(file_path, self._sha1_str):
raise UserWarning(
"File {} is downloaded but the content hash does not match."
"The repo may be outdated or download may be incomplete. "
"Otherwise you can create an issue for it.".format(self.name)
)
@property
def num_tasks(self):
"""Number of prediction tasks."""
return self.num_labels
@property
def num_labels(self):
"""Number of prediction tasks."""
return 14
@property
def num_classes(self):
"""Number of prediction tasks."""
return 14
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def __getitem__(self, idx):
r"""Get graph and label by index
Parameters
----------
idx : int
Item index
Returns
-------
(:class:`dgl.DGLGraph`, Tensor)
"""
if self._transform is None:
g = self.graphs[idx]
else:
g = self._transform(self.graphs[idx])
return g, self.label[idx]
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def __len__(self):
r"""Number of graphs in the dataset.
Return
-------
int
"""
return len(self.graphs)
QM7b = QM7bDataset