# -*- coding: utf-8 -*-
#
# Copyright Amazon.com, 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 dgl.data.utils import get_download_dir, download, _get_dgl_url, extract_archive
from .csv_dataset import MoleculeCSVDataset
__all__ = ['ToxCast']
[docs]class ToxCast(MoleculeCSVDataset):
r"""ToxCast from MoleculeNet for the prediction of toxicology data
The Toxicology in the 21st Century (https://tripod.nih.gov/tox21/challenge/)
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
* [3] DeepChem
Parameters
----------
smiles_to_graph: callable, str -> DGLGraph
A function turning a SMILES string into a DGLGraph. If None, it uses
:func:`dgllife.utils.SMILESToBigraph` by default.
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 dgllife.data import ToxCast
>>> from dgllife.utils import SMILESToBigraph, CanonicalAtomFeaturizer
>>> smiles_to_g = SMILESToBigraph(node_featurizer=CanonicalAtomFeaturizer())
>>> dataset = ToxCast(smiles_to_g)
>>> # 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=None,
node_featurizer=None,
edge_featurizer=None,
load=False,
log_every=1000,
cache_file_path='./toxcast_dglgraph.bin',
n_jobs=1):
self._url = 'dataset/toxcast.zip'
data_path = get_download_dir() + '/toxcast.zip'
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]