import os
import os.path as osp
import sys
from typing import Callable, List, Optional
import torch
import torch.nn.functional as F
from torch_scatter import scatter
from tqdm import tqdm
from torch_geometric.data import (Data, InMemoryDataset, download_url,
extract_zip)
HAR2EV = 27.211386246
KCALMOL2EV = 0.04336414
conversion = torch.tensor([
1., 1., HAR2EV, HAR2EV, HAR2EV, 1., HAR2EV, HAR2EV, HAR2EV, HAR2EV, HAR2EV,
1., KCALMOL2EV, KCALMOL2EV, KCALMOL2EV, KCALMOL2EV, 1., 1., 1.
])
atomrefs = {
6: [0., 0., 0., 0., 0.],
7: [
-13.61312172, -1029.86312267, -1485.30251237, -2042.61123593,
-2713.48485589
],
8: [
-13.5745904, -1029.82456413, -1485.26398105, -2042.5727046,
-2713.44632457
],
9: [
-13.54887564, -1029.79887659, -1485.2382935, -2042.54701705,
-2713.42063702
],
10: [
-13.90303183, -1030.25891228, -1485.71166277, -2043.01812778,
-2713.88796536
],
11: [0., 0., 0., 0., 0.],
}
[docs]class QM9(InMemoryDataset):
r"""The QM9 dataset from the `"MoleculeNet: A Benchmark for Molecular
Machine Learning" <https://arxiv.org/abs/1703.00564>`_ paper, consisting of
about 130,000 molecules with 19 regression targets.
Each molecule includes complete spatial information for the single low
energy conformation of the atoms in the molecule.
In addition, we provide the atom features from the `"Neural Message
Passing for Quantum Chemistry" <https://arxiv.org/abs/1704.01212>`_ paper.
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
| Target | Property | Description | Unit |
+========+==================================+===================================================================================+=============================================+
| 0 | :math:`\mu` | Dipole moment | :math:`\textrm{D}` |
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
| 1 | :math:`\alpha` | Isotropic polarizability | :math:`{a_0}^3` |
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
| 2 | :math:`\epsilon_{\textrm{HOMO}}` | Highest occupied molecular orbital energy | :math:`\textrm{eV}` |
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
| 3 | :math:`\epsilon_{\textrm{LUMO}}` | Lowest unoccupied molecular orbital energy | :math:`\textrm{eV}` |
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
| 4 | :math:`\Delta \epsilon` | Gap between :math:`\epsilon_{\textrm{HOMO}}` and :math:`\epsilon_{\textrm{LUMO}}` | :math:`\textrm{eV}` |
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
| 5 | :math:`\langle R^2 \rangle` | Electronic spatial extent | :math:`{a_0}^2` |
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
| 6 | :math:`\textrm{ZPVE}` | Zero point vibrational energy | :math:`\textrm{eV}` |
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
| 7 | :math:`U_0` | Internal energy at 0K | :math:`\textrm{eV}` |
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
| 8 | :math:`U` | Internal energy at 298.15K | :math:`\textrm{eV}` |
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
| 9 | :math:`H` | Enthalpy at 298.15K | :math:`\textrm{eV}` |
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
| 10 | :math:`G` | Free energy at 298.15K | :math:`\textrm{eV}` |
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
| 11 | :math:`c_{\textrm{v}}` | Heat capavity at 298.15K | :math:`\frac{\textrm{cal}}{\textrm{mol K}}` |
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
| 12 | :math:`U_0^{\textrm{ATOM}}` | Atomization energy at 0K | :math:`\textrm{eV}` |
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
| 13 | :math:`U^{\textrm{ATOM}}` | Atomization energy at 298.15K | :math:`\textrm{eV}` |
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
| 14 | :math:`H^{\textrm{ATOM}}` | Atomization enthalpy at 298.15K | :math:`\textrm{eV}` |
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
| 15 | :math:`G^{\textrm{ATOM}}` | Atomization free energy at 298.15K | :math:`\textrm{eV}` |
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
| 16 | :math:`A` | Rotational constant | :math:`\textrm{GHz}` |
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
| 17 | :math:`B` | Rotational constant | :math:`\textrm{GHz}` |
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
| 18 | :math:`C` | Rotational constant | :math:`\textrm{GHz}` |
+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
Args:
root (string): Root directory where the dataset should be saved.
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`)
Stats:
.. list-table::
:widths: 10 10 10 10 10
:header-rows: 1
* - #graphs
- #nodes
- #edges
- #features
- #tasks
* - 130,831
- ~18.0
- ~37.3
- 11
- 19
""" # noqa: E501
raw_url = ('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/'
'molnet_publish/qm9.zip')
raw_url2 = 'https://ndownloader.figshare.com/files/3195404'
processed_url = 'https://data.pyg.org/datasets/qm9_v3.zip'
def __init__(self, root: str, transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None):
super().__init__(root, transform, pre_transform, pre_filter)
self.data, self.slices = torch.load(self.processed_paths[0])
def mean(self, target: int) -> float:
y = torch.cat([self.get(i).y for i in range(len(self))], dim=0)
return float(y[:, target].mean())
def std(self, target: int) -> float:
y = torch.cat([self.get(i).y for i in range(len(self))], dim=0)
return float(y[:, target].std())
def atomref(self, target) -> Optional[torch.Tensor]:
if target in atomrefs:
out = torch.zeros(100)
out[torch.tensor([1, 6, 7, 8, 9])] = torch.tensor(atomrefs[target])
return out.view(-1, 1)
return None
@property
def raw_file_names(self) -> List[str]:
try:
import rdkit # noqa
return ['gdb9.sdf', 'gdb9.sdf.csv', 'uncharacterized.txt']
except ImportError:
return ['qm9_v3.pt']
@property
def processed_file_names(self) -> str:
return 'data_v3.pt'
def download(self):
try:
import rdkit # noqa
file_path = download_url(self.raw_url, self.raw_dir)
extract_zip(file_path, self.raw_dir)
os.unlink(file_path)
file_path = download_url(self.raw_url2, self.raw_dir)
os.rename(osp.join(self.raw_dir, '3195404'),
osp.join(self.raw_dir, 'uncharacterized.txt'))
except ImportError:
path = download_url(self.processed_url, self.raw_dir)
extract_zip(path, self.raw_dir)
os.unlink(path)
def process(self):
try:
import rdkit
from rdkit import Chem, RDLogger
from rdkit.Chem.rdchem import BondType as BT
from rdkit.Chem.rdchem import HybridizationType
RDLogger.DisableLog('rdApp.*')
except ImportError:
rdkit = None
if rdkit is None:
print(("Using a pre-processed version of the dataset. Please "
"install 'rdkit' to alternatively process the raw data."),
file=sys.stderr)
data_list = torch.load(self.raw_paths[0])
data_list = [Data(**data_dict) for data_dict in data_list]
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]
torch.save(self.collate(data_list), self.processed_paths[0])
return
types = {'H': 0, 'C': 1, 'N': 2, 'O': 3, 'F': 4}
bonds = {BT.SINGLE: 0, BT.DOUBLE: 1, BT.TRIPLE: 2, BT.AROMATIC: 3}
with open(self.raw_paths[1], 'r') as f:
target = f.read().split('\n')[1:-1]
target = [[float(x) for x in line.split(',')[1:20]]
for line in target]
target = torch.tensor(target, dtype=torch.float)
target = torch.cat([target[:, 3:], target[:, :3]], dim=-1)
target = target * conversion.view(1, -1)
with open(self.raw_paths[2], 'r') as f:
skip = [int(x.split()[0]) - 1 for x in f.read().split('\n')[9:-2]]
suppl = Chem.SDMolSupplier(self.raw_paths[0], removeHs=False,
sanitize=False)
data_list = []
for i, mol in enumerate(tqdm(suppl)):
if i in skip:
continue
N = mol.GetNumAtoms()
pos = suppl.GetItemText(i).split('\n')[4:4 + N]
pos = [[float(x) for x in line.split()[:3]] for line in pos]
pos = torch.tensor(pos, dtype=torch.float)
type_idx = []
atomic_number = []
aromatic = []
sp = []
sp2 = []
sp3 = []
num_hs = []
for atom in mol.GetAtoms():
type_idx.append(types[atom.GetSymbol()])
atomic_number.append(atom.GetAtomicNum())
aromatic.append(1 if atom.GetIsAromatic() else 0)
hybridization = atom.GetHybridization()
sp.append(1 if hybridization == HybridizationType.SP else 0)
sp2.append(1 if hybridization == HybridizationType.SP2 else 0)
sp3.append(1 if hybridization == HybridizationType.SP3 else 0)
z = torch.tensor(atomic_number, dtype=torch.long)
row, col, edge_type = [], [], []
for bond in mol.GetBonds():
start, end = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()
row += [start, end]
col += [end, start]
edge_type += 2 * [bonds[bond.GetBondType()]]
edge_index = torch.tensor([row, col], dtype=torch.long)
edge_type = torch.tensor(edge_type, dtype=torch.long)
edge_attr = F.one_hot(edge_type,
num_classes=len(bonds)).to(torch.float)
perm = (edge_index[0] * N + edge_index[1]).argsort()
edge_index = edge_index[:, perm]
edge_type = edge_type[perm]
edge_attr = edge_attr[perm]
row, col = edge_index
hs = (z == 1).to(torch.float)
num_hs = scatter(hs[row], col, dim_size=N).tolist()
x1 = F.one_hot(torch.tensor(type_idx), num_classes=len(types))
x2 = torch.tensor([atomic_number, aromatic, sp, sp2, sp3, num_hs],
dtype=torch.float).t().contiguous()
x = torch.cat([x1.to(torch.float), x2], dim=-1)
y = target[i].unsqueeze(0)
name = mol.GetProp('_Name')
data = Data(x=x, z=z, pos=pos, edge_index=edge_index,
edge_attr=edge_attr, y=y, name=name, idx=i)
if self.pre_filter is not None and not self.pre_filter(data):
continue
if self.pre_transform is not None:
data = self.pre_transform(data)
data_list.append(data)
torch.save(self.collate(data_list), self.processed_paths[0])