wehub-resource-sync
297 lines
14 KiB
Python
297 lines
14 KiB
Python
""" QM9 dataset for graph property prediction (regression) """
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import os
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import numpy as np
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from .. import backend as F
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from ..convert import graph as dgl_graph
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from .dgl_dataset import DGLDataset
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from .utils import _get_dgl_url, download, extract_archive
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class QM9EdgeDataset(DGLDataset):
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r"""QM9Edge dataset for graph property prediction (regression)
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This dataset consists of 130,831 molecules with 19 regression targets.
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Nodes correspond to atoms and edges correspond to bonds.
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This dataset differs from :class:`~dgl.data.QM9Dataset` in the following aspects:
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1. It includes the bonds in a molecule in the edges of the corresponding graph while the edges in :class:`~dgl.data.QM9Dataset` are purely distance-based.
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2. It provides edge features, and node features in addition to the atoms' coordinates and atomic numbers.
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3. It provides another 7 regression tasks(from 12 to 19).
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This class is built based on a preprocessed version of the dataset, and we provide the preprocessing datails `here <https://gist.github.com/hengruizhang98/a2da30213b2356fff18b25385c9d3cd2>`_.
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Reference:
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- `"MoleculeNet: A Benchmark for Molecular Machine Learning" <https://arxiv.org/abs/1703.00564>`_
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- `"Neural Message Passing for Quantum Chemistry" <https://arxiv.org/abs/1704.01212>`_
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For
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Statistics:
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- Number of graphs: 130,831.
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- Number of regression targets: 19.
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Node attributes:
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- pos: the 3D coordinates of each atom.
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- attr: the 11D atom features.
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Edge attributes:
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- edge_attr: the 4D bond features.
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Regression targets:
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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| Keys | Property | Description | Unit |
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+========+==================================+===================================================================================+=============================================+
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| mu | :math:`\mu` | Dipole moment | :math:`\textrm{D}` |
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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| alpha | :math:`\alpha` | Isotropic polarizability | :math:`{a_0}^3` |
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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| homo | :math:`\epsilon_{\textrm{HOMO}}` | Highest occupied molecular orbital energy | :math:`\textrm{eV}` |
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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| lumo | :math:`\epsilon_{\textrm{LUMO}}` | Lowest unoccupied molecular orbital energy | :math:`\textrm{eV}` |
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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| gap | :math:`\Delta \epsilon` | Gap between :math:`\epsilon_{\textrm{HOMO}}` and :math:`\epsilon_{\textrm{LUMO}}` | :math:`\textrm{eV}` |
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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| r2 | :math:`\langle R^2 \rangle` | Electronic spatial extent | :math:`{a_0}^2` |
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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| zpve | :math:`\textrm{ZPVE}` | Zero point vibrational energy | :math:`\textrm{eV}` |
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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| U0 | :math:`U_0` | Internal energy at 0K | :math:`\textrm{eV}` |
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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| U | :math:`U` | Internal energy at 298.15K | :math:`\textrm{eV}` |
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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| H | :math:`H` | Enthalpy at 298.15K | :math:`\textrm{eV}` |
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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| G | :math:`G` | Free energy at 298.15K | :math:`\textrm{eV}` |
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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| Cv | :math:`c_{\textrm{v}}` | Heat capavity at 298.15K | :math:`\frac{\textrm{cal}}{\textrm{mol K}}` |
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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| U0_atom| :math:`U_0^{\textrm{ATOM}}` | Atomization energy at 0K | :math:`\textrm{eV}` |
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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| U_atom | :math:`U^{\textrm{ATOM}}` | Atomization energy at 298.15K | :math:`\textrm{eV}` |
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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| H_atom | :math:`H^{\textrm{ATOM}}` | Atomization enthalpy at 298.15K | :math:`\textrm{eV}` |
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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| G_atom | :math:`G^{\textrm{ATOM}}` | Atomization free energy at 298.15K | :math:`\textrm{eV}` |
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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| A | :math:`A` | Rotational constant | :math:`\textrm{GHz}` |
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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| B | :math:`B` | Rotational constant | :math:`\textrm{GHz}` |
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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| C | :math:`C` | Rotational constant | :math:`\textrm{GHz}` |
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+--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
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Parameters
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----------
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label_keys : list
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Names of the regression property, which should be a subset of the keys in the table above.
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If not provided, it will load all the labels.
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raw_dir : str
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Raw file directory to download/contains the input data directory.
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Default: ~/.dgl/
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force_reload : bool
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Whether to reload the dataset. Default: False.
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verbose : bool
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Whether to print out progress information. Default: True.
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transform : callable, optional
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A transform that takes in a :class:`~dgl.DGLGraph` object and returns
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a transformed version. The :class:`~dgl.DGLGraph` object will be
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transformed before every access.
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Attributes
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----------
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num_tasks : int
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Number of prediction tasks
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num_labels : int
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(DEPRECATED, use num_tasks instead) Number of prediction tasks
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Raises
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------
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UserWarning
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If the raw data is changed in the remote server by the author.
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Examples
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--------
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>>> data = QM9EdgeDataset(label_keys=['mu', 'alpha'])
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>>> data.num_tasks
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2
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>>> # iterate over the dataset
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>>> for graph, labels in data:
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... print(graph) # get information of each graph
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... print(labels) # get labels of the corresponding graph
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... # your code here...
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>>>
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"""
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keys = [
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"mu",
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"alpha",
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"homo",
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"lumo",
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"gap",
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"r2",
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"zpve",
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"U0",
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"U",
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"H",
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"G",
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"Cv",
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"U0_atom",
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"U_atom",
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"H_atom",
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"G_atom",
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"A",
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"B",
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"C",
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]
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map_dict = {}
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for i, key in enumerate(keys):
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map_dict[key] = i
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def __init__(
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self,
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label_keys=None,
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raw_dir=None,
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force_reload=False,
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verbose=True,
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transform=None,
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):
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if label_keys is None:
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self.label_keys = None
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self.num_labels = 19
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else:
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self.label_keys = [self.map_dict[i] for i in label_keys]
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self.num_labels = len(label_keys)
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self._url = _get_dgl_url("dataset/qm9_edge.npz")
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super(QM9EdgeDataset, self).__init__(
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name="qm9Edge",
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raw_dir=raw_dir,
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url=self._url,
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force_reload=force_reload,
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verbose=verbose,
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transform=transform,
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)
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def download(self):
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if not os.path.exists(self.npz_path):
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download(self._url, path=self.npz_path)
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def process(self):
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self.load()
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@property
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def npz_path(self):
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return f"{self.raw_dir}/qm9_edge.npz"
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def has_cache(self):
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return os.path.exists(self.npz_path)
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def save(self):
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np.savez_compressed(
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self.npz_path,
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n_node=self.n_node,
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n_edge=self.n_edge,
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node_attr=self.node_attr,
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node_pos=self.node_pos,
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edge_attr=self.edge_attr,
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src=self.src,
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dst=self.dst,
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targets=self.targets,
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)
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def load(self):
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data_dict = np.load(self.npz_path, allow_pickle=True)
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self.n_node = data_dict["n_node"]
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self.n_edge = data_dict["n_edge"]
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self.node_attr = data_dict["node_attr"]
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self.node_pos = data_dict["node_pos"]
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self.edge_attr = data_dict["edge_attr"]
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self.targets = data_dict["targets"]
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self.src = data_dict["src"]
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self.dst = data_dict["dst"]
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self.n_cumsum = np.concatenate([[0], np.cumsum(self.n_node)])
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self.ne_cumsum = np.concatenate([[0], np.cumsum(self.n_edge)])
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def __getitem__(self, idx):
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r"""Get graph and label by index
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Parameters
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----------
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idx : int
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Item index
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Returns
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-------
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dgl.DGLGraph
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The graph contains:
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- ``ndata['pos']``: the coordinates of each atom
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- ``ndata['attr']``: the features of each atom
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- ``edata['edge_attr']``: the features of each bond
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Tensor
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Property values of molecular graphs
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"""
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pos = self.node_pos[self.n_cumsum[idx] : self.n_cumsum[idx + 1]]
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src = self.src[self.ne_cumsum[idx] : self.ne_cumsum[idx + 1]]
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dst = self.dst[self.ne_cumsum[idx] : self.ne_cumsum[idx + 1]]
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g = dgl_graph((src, dst))
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g.ndata["pos"] = F.tensor(pos, dtype=F.data_type_dict["float32"])
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g.ndata["attr"] = F.tensor(
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self.node_attr[self.n_cumsum[idx] : self.n_cumsum[idx + 1]],
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dtype=F.data_type_dict["float32"],
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)
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g.edata["edge_attr"] = F.tensor(
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self.edge_attr[self.ne_cumsum[idx] : self.ne_cumsum[idx + 1]],
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dtype=F.data_type_dict["float32"],
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)
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label = F.tensor(
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self.targets[idx][self.label_keys],
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dtype=F.data_type_dict["float32"],
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)
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if self._transform is not None:
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g = self._transform(g)
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return g, label
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def __len__(self):
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r"""Number of graphs in the dataset.
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Returns
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-------
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int
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"""
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return self.n_node.shape[0]
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@property
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def num_tasks(self):
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r"""
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Returns
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-------
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int
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Number of prediction tasks
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"""
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return self.num_labels
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QM9Edge = QM9EdgeDataset
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