Source code for torch_geometric.datasets.molecule_net

import os
import os.path as osp
import re

import torch

from torch_geometric.data import InMemoryDataset, download_url, extract_gz
from torch_geometric.utils import from_smiles


[docs]class MoleculeNet(InMemoryDataset): r"""The `MoleculeNet <http://moleculenet.ai/datasets-1>`_ benchmark collection from the `"MoleculeNet: A Benchmark for Molecular Machine Learning" <https://arxiv.org/abs/1703.00564>`_ paper, containing datasets from physical chemistry, biophysics and physiology. All datasets come with the additional node and edge features introduced by the `Open Graph Benchmark <https://ogb.stanford.edu/docs/graphprop/>`_. Args: root (string): Root directory where the dataset should be saved. name (string): The name of the dataset (:obj:`"ESOL"`, :obj:`"FreeSolv"`, :obj:`"Lipo"`, :obj:`"PCBA"`, :obj:`"MUV"`, :obj:`"HIV"`, :obj:`"BACE"`, :obj:`"BBPB"`, :obj:`"Tox21"`, :obj:`"ToxCast"`, :obj:`"SIDER"`, :obj:`"ClinTox"`). 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`) """ url = 'https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/{}' # Format: name: [display_name, url_name, csv_name, smiles_idx, y_idx] names = { 'esol': ['ESOL', 'delaney-processed.csv', 'delaney-processed', -1, -2], 'freesolv': ['FreeSolv', 'SAMPL.csv', 'SAMPL', 1, 2], 'lipo': ['Lipophilicity', 'Lipophilicity.csv', 'Lipophilicity', 2, 1], 'pcba': ['PCBA', 'pcba.csv.gz', 'pcba', -1, slice(0, 128)], 'muv': ['MUV', 'muv.csv.gz', 'muv', -1, slice(0, 17)], 'hiv': ['HIV', 'HIV.csv', 'HIV', 0, -1], 'bace': ['BACE', 'bace.csv', 'bace', 0, 2], 'bbbp': ['BBPB', 'BBBP.csv', 'BBBP', -1, -2], 'tox21': ['Tox21', 'tox21.csv.gz', 'tox21', -1, slice(0, 12)], 'toxcast': ['ToxCast', 'toxcast_data.csv.gz', 'toxcast_data', 0, slice(1, 618)], 'sider': ['SIDER', 'sider.csv.gz', 'sider', 0, slice(1, 28)], 'clintox': ['ClinTox', 'clintox.csv.gz', 'clintox', 0, slice(1, 3)], } def __init__(self, root, name, transform=None, pre_transform=None, pre_filter=None): self.name = name.lower() assert self.name in self.names.keys() super().__init__(root, transform, pre_transform, pre_filter) self.data, self.slices = torch.load(self.processed_paths[0]) @property def raw_dir(self): return osp.join(self.root, self.name, 'raw') @property def processed_dir(self): return osp.join(self.root, self.name, 'processed') @property def raw_file_names(self): return f'{self.names[self.name][2]}.csv' @property def processed_file_names(self): return 'data.pt' def download(self): url = self.url.format(self.names[self.name][1]) path = download_url(url, self.raw_dir) if self.names[self.name][1][-2:] == 'gz': extract_gz(path, self.raw_dir) os.unlink(path) def process(self): with open(self.raw_paths[0], 'r') as f: dataset = f.read().split('\n')[1:-1] dataset = [x for x in dataset if len(x) > 0] # Filter empty lines. data_list = [] for line in dataset: line = re.sub(r'\".*\"', '', line) # Replace ".*" strings. line = line.split(',') smiles = line[self.names[self.name][3]] ys = line[self.names[self.name][4]] ys = ys if isinstance(ys, list) else [ys] ys = [float(y) if len(y) > 0 else float('NaN') for y in ys] y = torch.tensor(ys, dtype=torch.float).view(1, -1) data = from_smiles(smiles) data.y = y 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]) def __repr__(self) -> str: return f'{self.names[self.name][0]}({len(self)})'