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classification.py
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classification.py
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# -*- coding: utf-8 -*-
#
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import torch
import torch.nn as nn
from dgllife.model import load_pretrained
from dgllife.utils import EarlyStopping, Meter, SMILESToBigraph
from torch.optim import Adam
from torch.utils.data import DataLoader
from utils import collate_molgraphs, load_model, predict
def run_a_train_epoch(args, epoch, model, data_loader, loss_criterion, optimizer):
model.train()
train_meter = Meter()
for batch_id, batch_data in enumerate(data_loader):
smiles, bg, labels, masks = batch_data
if len(smiles) == 1:
# Avoid potential issues with batch normalization
continue
labels, masks = labels.to(args['device']), masks.to(args['device'])
logits = predict(args, model, bg)
# Mask non-existing labels
loss = (loss_criterion(logits, labels) * (masks != 0).float()).mean()
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_meter.update(logits, labels, masks)
if batch_id % args['print_every'] == 0:
print('epoch {:d}/{:d}, batch {:d}/{:d}, loss {:.4f}'.format(
epoch + 1, args['num_epochs'], batch_id + 1, len(data_loader), loss.item()))
train_score = np.mean(train_meter.compute_metric(args['metric']))
print('epoch {:d}/{:d}, training {} {:.4f}'.format(
epoch + 1, args['num_epochs'], args['metric'], train_score))
def run_an_eval_epoch(args, model, data_loader):
model.eval()
eval_meter = Meter()
with torch.no_grad():
for batch_id, batch_data in enumerate(data_loader):
smiles, bg, labels, masks = batch_data
labels = labels.to(args['device'])
logits = predict(args, model, bg)
eval_meter.update(logits, labels, masks)
return np.mean(eval_meter.compute_metric(args['metric']))
def main(args, exp_config, train_set, val_set, test_set):
if args['featurizer_type'] != 'pre_train':
exp_config['in_node_feats'] = args['node_featurizer'].feat_size()
if args['edge_featurizer'] is not None:
exp_config['in_edge_feats'] = args['edge_featurizer'].feat_size()
exp_config.update({
'n_tasks': args['n_tasks'],
'model': args['model']
})
train_loader = DataLoader(dataset=train_set, batch_size=exp_config['batch_size'], shuffle=True,
collate_fn=collate_molgraphs, num_workers=args['num_workers'])
val_loader = DataLoader(dataset=val_set, batch_size=exp_config['batch_size'],
collate_fn=collate_molgraphs, num_workers=args['num_workers'])
test_loader = DataLoader(dataset=test_set, batch_size=exp_config['batch_size'],
collate_fn=collate_molgraphs, num_workers=args['num_workers'])
if args['pretrain']:
args['num_epochs'] = 0
if args['featurizer_type'] == 'pre_train':
model = load_pretrained('{}_{}'.format(
args['model'], args['dataset'])).to(args['device'])
else:
model = load_pretrained('{}_{}_{}'.format(
args['model'], args['featurizer_type'], args['dataset'])).to(args['device'])
else:
model = load_model(exp_config).to(args['device'])
loss_criterion = nn.BCEWithLogitsLoss(reduction='none')
optimizer = Adam(model.parameters(), lr=exp_config['lr'],
weight_decay=exp_config['weight_decay'])
stopper = EarlyStopping(patience=exp_config['patience'],
filename=args['result_path'] + '/model.pth',
metric=args['metric'])
for epoch in range(args['num_epochs']):
# Train
run_a_train_epoch(args, epoch, model, train_loader, loss_criterion, optimizer)
# Validation and early stop
val_score = run_an_eval_epoch(args, model, val_loader)
early_stop = stopper.step(val_score, model)
print('epoch {:d}/{:d}, validation {} {:.4f}, best validation {} {:.4f}'.format(
epoch + 1, args['num_epochs'], args['metric'],
val_score, args['metric'], stopper.best_score))
if early_stop:
break
if not args['pretrain']:
stopper.load_checkpoint(model)
val_score = run_an_eval_epoch(args, model, val_loader)
test_score = run_an_eval_epoch(args, model, test_loader)
print('val {} {:.4f}'.format(args['metric'], val_score))
print('test {} {:.4f}'.format(args['metric'], test_score))
with open(args['result_path'] + '/eval.txt', 'w') as f:
if not args['pretrain']:
f.write('Best val {}: {}\n'.format(args['metric'], stopper.best_score))
f.write('Val {}: {}\n'.format(args['metric'], val_score))
f.write('Test {}: {}\n'.format(args['metric'], test_score))
if __name__ == '__main__':
from argparse import ArgumentParser
from utils import init_featurizer, mkdir_p, split_dataset, get_configure
parser = ArgumentParser('Multi-label Binary Classification')
parser.add_argument('-d', '--dataset', choices=['MUV', 'BACE', 'BBBP', 'ClinTox', 'SIDER',
'ToxCast', 'HIV', 'PCBA', 'Tox21'],
help='Dataset to use')
parser.add_argument('-mo', '--model', choices=['GCN', 'GAT', 'Weave', 'MPNN', 'AttentiveFP',
'gin_supervised_contextpred',
'gin_supervised_infomax',
'gin_supervised_edgepred',
'gin_supervised_masking',
'NF'],
help='Model to use')
parser.add_argument('-f', '--featurizer-type', choices=['canonical', 'attentivefp'],
help='Featurization for atoms (and bonds). This is required for models '
'other than gin_supervised_**.')
parser.add_argument('-p', '--pretrain', action='store_true',
help='Whether to skip the training and evaluate the pre-trained model '
'on the test set (default: False)')
parser.add_argument('-s', '--split', choices=['scaffold', 'random'], default='scaffold',
help='Dataset splitting method (default: scaffold)')
parser.add_argument('-sr', '--split-ratio', default='0.8,0.1,0.1', type=str,
help='Proportion of the dataset to use for training, validation and test, '
'(default: 0.8,0.1,0.1)')
parser.add_argument('-me', '--metric', choices=['roc_auc_score', 'pr_auc_score'],
default='roc_auc_score',
help='Metric for evaluation (default: roc_auc_score)')
parser.add_argument('-n', '--num-epochs', type=int, default=1000,
help='Maximum number of epochs for training. '
'We set a large number by default as early stopping '
'will be performed. (default: 1000)')
parser.add_argument('-nw', '--num-workers', type=int, default=0,
help='Number of processes for data loading (default: 0)')
parser.add_argument('-pe', '--print-every', type=int, default=20,
help='Print the training progress every X mini-batches')
parser.add_argument('-rp', '--result-path', type=str, default='classification_results',
help='Path to save training results (default: classification_results)')
args = parser.parse_args().__dict__
if torch.cuda.is_available():
args['device'] = torch.device('cuda:0')
else:
args['device'] = torch.device('cpu')
args = init_featurizer(args)
mkdir_p(args['result_path'])
smiles_to_g = SMILESToBigraph(add_self_loop=True, node_featurizer=args['node_featurizer'],
edge_featurizer=args['edge_featurizer'])
if args['dataset'] == 'MUV':
from dgllife.data import MUV
dataset = MUV(smiles_to_graph=smiles_to_g,
n_jobs=1 if args['num_workers'] == 0 else args['num_workers'])
elif args['dataset'] == 'BACE':
from dgllife.data import BACE
dataset = BACE(smiles_to_graph=smiles_to_g,
n_jobs=1 if args['num_workers'] == 0 else args['num_workers'])
elif args['dataset'] == 'BBBP':
from dgllife.data import BBBP
dataset = BBBP(smiles_to_graph=smiles_to_g,
n_jobs=1 if args['num_workers'] == 0 else args['num_workers'])
elif args['dataset'] == 'ClinTox':
from dgllife.data import ClinTox
dataset = ClinTox(smiles_to_graph=smiles_to_g,
n_jobs=1 if args['num_workers'] == 0 else args['num_workers'])
elif args['dataset'] == 'SIDER':
from dgllife.data import SIDER
dataset = SIDER(smiles_to_graph=smiles_to_g,
n_jobs=1 if args['num_workers'] == 0 else args['num_workers'])
elif args['dataset'] == 'ToxCast':
from dgllife.data import ToxCast
dataset = ToxCast(smiles_to_graph=smiles_to_g,
n_jobs=1 if args['num_workers'] == 0 else args['num_workers'])
elif args['dataset'] == 'HIV':
from dgllife.data import HIV
dataset = HIV(smiles_to_graph=smiles_to_g,
n_jobs=1 if args['num_workers'] == 0 else args['num_workers'])
elif args['dataset'] == 'PCBA':
from dgllife.data import PCBA
dataset = PCBA(smiles_to_graph=smiles_to_g,
n_jobs=1 if args['num_workers'] == 0 else args['num_workers'])
elif args['dataset'] == 'Tox21':
from dgllife.data import Tox21
dataset = Tox21(smiles_to_graph=smiles_to_g,
n_jobs=1 if args['num_workers'] == 0 else args['num_workers'])
else:
raise ValueError('Unexpected dataset: {}'.format(args['dataset']))
args['n_tasks'] = dataset.n_tasks
train_set, val_set, test_set = split_dataset(args, dataset)
exp_config = get_configure(args['model'], args['featurizer_type'], args['dataset'])
main(args, exp_config, train_set, val_set, test_set)