Source code for

# -*- coding: utf-8 -*-
# Copyright, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
# ToxCast from MoleculeNet for the prediction of toxicology data,
# from the Toxicology in the 21st Century initiative

import pandas as pd

from import get_download_dir, download, _get_dgl_url, extract_archive

from .csv_dataset import MoleculeCSVDataset
from ..utils.mol_to_graph import smiles_to_bigraph

__all__ = ['ToxCast']

[docs]class ToxCast(MoleculeCSVDataset): r"""ToxCast from MoleculeNet for the prediction of toxicology data The Toxicology in the 21st Century ( initiative created a data collection providing toxicology data for a large library of compounds based on in vitro high-throughput screening. The dataset includes qualitative results of over 600 experiments on 8615 compounds. References: * [1] MoleculeNet: A Benchmark for Molecular Machine Learning. * [2] ToxCast Chemical Landscape: Paving the Road to 21st Century Toxicology Parameters ---------- smiles_to_graph: callable, str -> DGLGraph A function turning a SMILES string into a DGLGraph. Default to :func:`dgllife.utils.smiles_to_bigraph`. node_featurizer : callable, rdkit.Chem.rdchem.Mol -> dict Featurization for nodes like atoms in a molecule, which can be used to update ndata for a DGLGraph. Default to None. edge_featurizer : callable, rdkit.Chem.rdchem.Mol -> dict Featurization for edges like bonds in a molecule, which can be used to update edata for a DGLGraph. Default to None. load : bool Whether to load the previously pre-processed dataset or pre-process from scratch. ``load`` should be False when we want to try different graph construction and featurization methods and need to preprocess from scratch. Default to False. log_every : bool Print a message every time ``log_every`` molecules are processed. Default to 1000. cache_file_path : str Path to the cached DGLGraphs, default to 'toxcast_dglgraph.bin'. n_jobs : int The maximum number of concurrently running jobs for graph construction and featurization, using joblib backend. Default to 1. Examples -------- >>> import torch >>> from import ToxCast >>> from dgllife.utils import smiles_to_bigraph, CanonicalAtomFeaturizer >>> dataset = ToxCast(smiles_to_bigraph, CanonicalAtomFeaturizer()) >>> # Get size of the dataset >>> len(dataset) 8576 >>> # Get the 0th datapoint, consisting of SMILES, DGLGraph, labels, and masks >>> dataset[0] ('[O-][N+](=O)C1=CC=C(Cl)C=C1', Graph(num_nodes=10, num_edges=20, ndata_schemes={'h': Scheme(shape=(74,), dtype=torch.float32)} edata_schemes={}) tensor([0., ..., 0.]), tensor([1., ..., 1.])) To address the imbalance between positive and negative samples, we can re-weight positive samples for each task based on the training datapoints. >>> train_ids = torch.arange(500) >>> dataset.task_pos_weights(train_ids) tensor([4.0435e+00, ..., 1.7500e+01]) """ def __init__(self, smiles_to_graph=smiles_to_bigraph, node_featurizer=None, edge_featurizer=None, load=False, log_every=1000, cache_file_path='./toxcast_dglgraph.bin', n_jobs=1): self._url = 'dataset/' data_path = get_download_dir() + '/' dir_path = get_download_dir() + '/toxcast' download(_get_dgl_url(self._url), path=data_path, overwrite=False) extract_archive(data_path, dir_path) df = pd.read_csv(dir_path + '/toxcast_data.csv') super(ToxCast, self).__init__(df=df, smiles_to_graph=smiles_to_graph, node_featurizer=node_featurizer, edge_featurizer=edge_featurizer, smiles_column='smiles', cache_file_path=cache_file_path, load=load, log_every=log_every, init_mask=True, n_jobs=n_jobs)
[docs] def __getitem__(self, item): """Get datapoint with index Parameters ---------- item : int Datapoint index Returns ------- str SMILES for the ith datapoint DGLGraph DGLGraph for the ith datapoint Tensor of dtype float32 and shape (T) Labels of the ith datapoint for all tasks. T for the number of tasks. Tensor of dtype float32 and shape (T) Binary masks of the ith datapoint indicating the existence of labels for all tasks. """ return self.smiles[item], self.graphs[item], self.labels[item], self.mask[item]