def main():
# Parse the arguments.
args = parse_arguments()
augment = False if args.augment == 'False' else True
multi_gpu = False if args.multi_gpu == 'False' else True
if args.label:
labels = args.label
class_num = len(labels) if isinstance(labels, list) else 1
else:
raise ValueError('No target label was specified.')
# Dataset preparation. Postprocessing is required for the regression task.
def postprocess_label(label_list):
label_arr = np.asarray(label_list, dtype=np.int32)
return label_arr
# Apply a preprocessor to the dataset.
logging.info('Preprocess train dataset and test dataset...')
preprocessor = preprocess_method_dict[args.method]()
parser = CSVFileParserForPair(preprocessor, postprocess_label=postprocess_label,
labels=labels, smiles_cols=['smiles_1', 'smiles_2'])
train = parser.parse(args.train_datafile)['dataset']
valid = parser.parse(args.valid_datafile)['dataset']
if augment:
logging.info('Utilizing data augmentation in train set')
train = augment_dataset(train)
num_train = train.get_datasets()[0].shape[0]
num_valid = valid.get_datasets()[0].shape[0]
logging.info('Train/test split: {}/{}'.format(num_train, num_valid))
if len(args.net_hidden_dims):
net_hidden_dims = tuple([int(net_hidden_dim) for net_hidden_dim in args.net_hidden_dims.split(',')])
else:
net_hidden_dims = ()
fp_attention = True if args.fp_attention else False
update_attention = True if args.update_attention else False
weight_tying = False if args.weight_tying == 'False' else True
attention_tying = False if args.attention_tying == 'False' else True
fp_batch_normalization = True if args.fp_bn == 'True' else False
layer_aggregator = None if args.layer_aggregator == '' else args.layer_aggregator
context = False if args.context == 'False' else True
output_activation = functions.relu if args.output_activation == 'relu' else None
predictor = set_up_predictor(method=args.method,
fp_hidden_dim=args.fp_hidden_dim, fp_out_dim=args.fp_out_dim, conv_layers=args.conv_layers,
concat_hidden=args.concat_hidden, layer_aggregator=layer_aggregator,
fp_dropout_rate=args.fp_dropout_rate, fp_batch_normalization=fp_batch_normalization,
net_hidden_dims=net_hidden_dims, class_num=class_num,
sim_method=args.sim_method, fp_attention=fp_attention, weight_typing=weight_tying, attention_tying=attention_tying,
update_attention=update_attention,
context=context, context_layers=args.context_layers, context_dropout=args.context_dropout,
message_function=args.message_function, readout_function=args.readout_function,
num_timesteps=args.num_timesteps, num_output_hidden_layers=args.num_output_hidden_layers,
output_hidden_dim=args.output_hidden_dim, output_activation=output_activation,
symmetric=args.symmetric
)
train_iter = SerialIterator(train, args.batchsize)
test_iter = SerialIterator(valid, args.batchsize,
repeat=False, shuffle=False)
metrics_fun = {'accuracy': F.binary_accuracy}
classifier = Classifier(predictor, lossfun=F.sigmoid_cross_entropy,
metrics_fun=metrics_fun, device=args.gpu)
# Set up the optimizer.
optimizer = optimizers.Adam(alpha=args.learning_rate, weight_decay_rate=args.weight_decay_rate)
# optimizer = optimizers.Adam()
# optimizer = optimizers.SGD(lr=args.learning_rate)
optimizer.setup(classifier)
# add regularization
if args.max_norm > 0:
optimizer.add_hook(chainer.optimizer.GradientClipping(threshold=args.max_norm))
if args.l2_rate > 0:
optimizer.add_hook(chainer.optimizer.WeightDecay(rate=args.l2_rate))
if args.l1_rate > 0:
optimizer.add_hook(chainer.optimizer.Lasso(rate=args.l1_rate))
# Set up the updater.
if multi_gpu:
logging.info('Using multiple GPUs')
updater = training.ParallelUpdater(train_iter, optimizer, devices={'main': 0, 'second': 1},
converter=concat_mols)
else:
logging.info('Using single GPU')
updater = training.StandardUpdater(train_iter, optimizer, device=args.gpu,
converter=concat_mols)
# Set up the trainer.
logging.info('Training...')
# add stop_trigger parameter
early_stop = triggers.EarlyStoppingTrigger(monitor='validation/main/loss', patients=30, max_trigger=(500, 'epoch'))
out = 'output' + '/' + args.out
trainer = training.Trainer(updater, stop_trigger=early_stop, out=out)
# trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
trainer.extend(E.Evaluator(test_iter, classifier,
device=args.gpu, converter=concat_mols))
train_eval_iter = SerialIterator(train, args.batchsize,
repeat=False, shuffle=False)
trainer.extend(AccuracyEvaluator(
train_eval_iter, classifier, eval_func=predictor,
device=args.gpu, converter=concat_mols, name='train_acc',
pos_labels=1, ignore_labels=-1, raise_value_error=False))
# extension name='validation' is already used by `Evaluator`,
# instead extension name `val` is used.
trainer.extend(AccuracyEvaluator(
test_iter, classifier, eval_func=predictor,
device=args.gpu, converter=concat_mols, name='val_acc',
pos_labels=1, ignore_labels=-1))
trainer.extend(ROCAUCEvaluator(
train_eval_iter, classifier, eval_func=predictor,
device=args.gpu, converter=concat_mols, name='train_roc',
pos_labels=1, ignore_labels=-1, raise_value_error=False))
# extension name='validation' is already used by `Evaluator`,
# instead extension name `val` is used.
trainer.extend(ROCAUCEvaluator(
test_iter, classifier, eval_func=predictor,
device=args.gpu, converter=concat_mols, name='val_roc',
pos_labels=1, ignore_labels=-1))
trainer.extend(PRCAUCEvaluator(
train_eval_iter, classifier, eval_func=predictor,
device=args.gpu, converter=concat_mols, name='train_prc',
pos_labels=1, ignore_labels=-1, raise_value_error=False))
# extension name='validation' is already used by `Evaluator`,
# instead extension name `val` is used.
trainer.extend(PRCAUCEvaluator(
test_iter, classifier, eval_func=predictor,
device=args.gpu, converter=concat_mols, name='val_prc',
pos_labels=1, ignore_labels=-1))
trainer.extend(F1Evaluator(
train_eval_iter, classifier, eval_func=predictor,
device=args.gpu, converter=concat_mols, name='train_f',
pos_labels=1, ignore_labels=-1, raise_value_error=False))
# extension name='validation' is already used by `Evaluator`,
# instead extension name `val` is used.
trainer.extend(F1Evaluator(
test_iter, classifier, eval_func=predictor,
device=args.gpu, converter=concat_mols, name='val_f',
pos_labels=1, ignore_labels=-1))
# apply shift strategy to learning rate every 10 epochs
# trainer.extend(E.ExponentialShift('alpha', args.exp_shift_rate), trigger=(10, 'epoch'))
if args.exp_shift_strategy == 1:
trainer.extend(E.ExponentialShift('alpha', args.exp_shift_rate),
trigger=triggers.ManualScheduleTrigger([10, 20, 30, 40, 50, 60], 'epoch'))
elif args.exp_shift_strategy == 2:
trainer.extend(E.ExponentialShift('alpha', args.exp_shift_rate),
trigger=triggers.ManualScheduleTrigger([5, 10, 15, 20, 25, 30], 'epoch'))
elif args.exp_shift_strategy == 3:
trainer.extend(E.ExponentialShift('alpha', args.exp_shift_rate),
trigger=triggers.ManualScheduleTrigger([5, 10, 15, 20, 25, 30, 40, 50, 60, 70], 'epoch'))
else:
raise ValueError('No such strategy to adapt learning rate')
# # observation of learning rate
trainer.extend(E.observe_lr(), trigger=(1, 'iteration'))
entries = [
'epoch',
'main/loss', 'train_acc/main/accuracy', 'train_roc/main/roc_auc', 'train_prc/main/prc_auc',
# 'train_p/main/precision', 'train_r/main/recall',
'train_f/main/f1',
'validation/main/loss', 'val_acc/main/accuracy', 'val_roc/main/roc_auc', 'val_prc/main/prc_auc',
# 'val_p/main/precision', 'val_r/main/recall',
'val_f/main/f1',
'lr',
'elapsed_time']
trainer.extend(E.PrintReport(entries=entries))
# change from 10 to 2 on Mar. 1 2019
trainer.extend(E.snapshot(), trigger=(2, 'epoch'))
trainer.extend(E.LogReport())
trainer.extend(E.ProgressBar())
trainer.extend(E.PlotReport(['main/loss', 'validation/main/loss'], 'epoch', file_name='loss.png'))
trainer.extend(E.PlotReport(['train_acc/main/accuracy', 'val_acc/main/accuracy'], 'epoch', file_name='accuracy.png'))
if args.resume:
resume_path = os.path.join(out, args.resume)
logging.info('Resume training according to snapshot in {}'.format(resume_path))
chainer.serializers.load_npz(resume_path, trainer)
trainer.run()
# Save the regressor's parameters.
model_path = os.path.join(out, args.model_filename)
logging.info('Saving the trained models to {}...'.format(model_path))
classifier.save_pickle(model_path, protocol=args.protocol)
def main():
# Parse the arguments.
args = parse_arguments()
if args['label']:
labels = args['label']
class_num = len(labels) if isinstance(labels, list) else 1
else:
raise ValueError('No target label was specified.')
# Dataset preparation. Postprocessing is required for the regression task.
def postprocess_label(label_list):
label_arr = np.asarray(label_list, dtype=np.int32)
return label_arr
# Apply a preprocessor to the dataset.
logging.info('Preprocess train dataset and valid dataset...')
# use `ggnn` for the time being
preprocessor = preprocess_method_dict['ggnn']()
# parser = CSVFileParserForPair(preprocessor, postprocess_label=postprocess_label,
# labels=labels, smiles_cols=['smiles_1', 'smiles_2'])
if args['feature'] == 'molenc':
parser = MolAutoencoderParserForPair(
preprocessor,
postprocess_label=postprocess_label,
labels=labels,
smiles_cols=['smiles_1', 'smiles_2'])
if args['feature'] == 'ssp':
parser = SSPParserForPair(preprocessor,
postprocess_label=postprocess_label,
labels=labels,
smiles_cols=['smiles_1', 'smiles_2'])
else:
parser = Mol2VecParserForPair(preprocessor,
postprocess_label=postprocess_label,
labels=labels,
smiles_cols=['smiles_1', 'smiles_2'])
train = parser.parse(args['train_datafile'])['dataset']
valid = parser.parse(args['valid_datafile'])['dataset']
if args['augment']:
logging.info('Utilizing data augmentation in train set')
train = augment_dataset(train)
num_train = train.get_datasets()[0].shape[0]
num_valid = valid.get_datasets()[0].shape[0]
logging.info('Train/test split: {}/{}'.format(num_train, num_valid))
if len(args['net_hidden_dims']):
net_hidden_dims = tuple([
int(net_hidden_dim)
for net_hidden_dim in args['net_hidden_dims'].split(',')
])
else:
net_hidden_dims = ()
predictor = set_up_predictor(fp_out_dim=args['fp_out_dim'],
net_hidden_dims=net_hidden_dims,
class_num=class_num,
sim_method=args['sim_method'],
symmetric=args['symmetric'])
train_iter = SerialIterator(train, args['batchsize'])
test_iter = SerialIterator(valid,
args['batchsize'],
repeat=False,
shuffle=False)
metrics_fun = {'accuracy': F.binary_accuracy}
classifier = Classifier(predictor,
lossfun=F.sigmoid_cross_entropy,
metrics_fun=metrics_fun,
device=args['gpu'])
# Set up the optimizer.
optimizer = optimizers.Adam(alpha=args['learning_rate'],
weight_decay_rate=args['weight_decay_rate'])
# optimizer = optimizers.Adam()
# optimizer = optimizers.SGD(lr=args.learning_rate)
optimizer.setup(classifier)
# add regularization
if args['max_norm'] > 0:
optimizer.add_hook(
chainer.optimizer.GradientClipping(threshold=args['max_norm']))
if args['l2_rate'] > 0:
optimizer.add_hook(chainer.optimizer.WeightDecay(rate=args['l2_rate']))
if args['l1_rate'] > 0:
optimizer.add_hook(chainer.optimizer.Lasso(rate=args['l1_rate']))
updater = training.StandardUpdater(train_iter,
optimizer,
device=args['gpu'],
converter=concat_mols)
# Set up the trainer.
logging.info('Training...')
# add stop_trigger parameter
early_stop = triggers.EarlyStoppingTrigger(monitor='validation/main/loss',
patients=10,
max_trigger=(500, 'epoch'))
out = 'output' + '/' + args['out']
trainer = training.Trainer(updater, stop_trigger=early_stop, out=out)
trainer.extend(
E.Evaluator(test_iter,
classifier,
device=args['gpu'],
converter=concat_mols))
train_eval_iter = SerialIterator(train,
args['batchsize'],
repeat=False,
shuffle=False)
trainer.extend(
AccuracyEvaluator(train_eval_iter,
classifier,
eval_func=predictor,
device=args['gpu'],
converter=concat_mols,
name='train_acc',
pos_labels=1,
ignore_labels=-1,
raise_value_error=False))
# extension name='validation' is already used by `Evaluator`,
# instead extension name `val` is used.
trainer.extend(
AccuracyEvaluator(test_iter,
classifier,
eval_func=predictor,
device=args['gpu'],
converter=concat_mols,
name='val_acc',
pos_labels=1,
ignore_labels=-1))
trainer.extend(
ROCAUCEvaluator(train_eval_iter,
classifier,
eval_func=predictor,
device=args['gpu'],
converter=concat_mols,
name='train_roc',
pos_labels=1,
ignore_labels=-1,
raise_value_error=False))
# extension name='validation' is already used by `Evaluator`,
# instead extension name `val` is used.
trainer.extend(
ROCAUCEvaluator(test_iter,
classifier,
eval_func=predictor,
device=args['gpu'],
converter=concat_mols,
name='val_roc',
pos_labels=1,
ignore_labels=-1))
trainer.extend(
PRCAUCEvaluator(train_eval_iter,
classifier,
eval_func=predictor,
device=args['gpu'],
converter=concat_mols,
name='train_prc',
pos_labels=1,
ignore_labels=-1,
raise_value_error=False))
# extension name='validation' is already used by `Evaluator`,
# instead extension name `val` is used.
trainer.extend(
PRCAUCEvaluator(test_iter,
classifier,
eval_func=predictor,
device=args['gpu'],
converter=concat_mols,
name='val_prc',
pos_labels=1,
ignore_labels=-1))
trainer.extend(
F1Evaluator(train_eval_iter,
classifier,
eval_func=predictor,
device=args['gpu'],
converter=concat_mols,
name='train_f',
pos_labels=1,
ignore_labels=-1,
raise_value_error=False))
# extension name='validation' is already used by `Evaluator`,
# instead extension name `val` is used.
trainer.extend(
F1Evaluator(test_iter,
classifier,
eval_func=predictor,
device=args['gpu'],
converter=concat_mols,
name='val_f',
pos_labels=1,
ignore_labels=-1))
# apply shift strategy to learning rate every 10 epochs
# trainer.extend(E.ExponentialShift('alpha', args.exp_shift_rate), trigger=(10, 'epoch'))
if args['exp_shift_strategy'] == 1:
trainer.extend(E.ExponentialShift('alpha', args['exp_shift_rate']),
trigger=triggers.ManualScheduleTrigger(
[10, 20, 30, 40, 50, 60], 'epoch'))
elif args['exp_shift_strategy'] == 2:
trainer.extend(E.ExponentialShift('alpha', args['exp_shift_rate']),
trigger=triggers.ManualScheduleTrigger(
[5, 10, 15, 20, 25, 30], 'epoch'))
elif args['exp_shift_strategy'] == 3:
trainer.extend(E.ExponentialShift('alpha', args['exp_shift_rate']),
trigger=triggers.ManualScheduleTrigger(
[5, 10, 15, 20, 25, 30, 40, 50, 60, 70], 'epoch'))
else:
raise ValueError('No such strategy to adapt learning rate')
# # observation of learning rate
trainer.extend(E.observe_lr(), trigger=(1, 'iteration'))
entries = [
'epoch',
'main/loss',
'train_acc/main/accuracy',
'train_roc/main/roc_auc',
'train_prc/main/prc_auc',
# 'train_p/main/precision', 'train_r/main/recall',
'train_f/main/f1',
'validation/main/loss',
'val_acc/main/accuracy',
'val_roc/main/roc_auc',
'val_prc/main/prc_auc',
# 'val_p/main/precision', 'val_r/main/recall',
'val_f/main/f1',
'lr',
'elapsed_time'
]
trainer.extend(E.PrintReport(entries=entries))
# change from 10 to 2 on Mar. 1 2019
trainer.extend(E.snapshot(), trigger=(2, 'epoch'))
trainer.extend(E.LogReport())
trainer.extend(E.ProgressBar())
trainer.extend(
E.PlotReport(['main/loss', 'validation/main/loss'],
'epoch',
file_name='loss.png'))
trainer.extend(
E.PlotReport(['train_acc/main/accuracy', 'val_acc/main/accuracy'],
'epoch',
file_name='accuracy.png'))
if args['resume']:
resume_path = os.path.join(out, args['resume'])
logging.info(
'Resume training according to snapshot in {}'.format(resume_path))
chainer.serializers.load_npz(resume_path, trainer)
trainer.run()
# Save the regressor's parameters.
model_path = os.path.join(out, args['model_filename'])
logging.info('Saving the trained models to {}...'.format(model_path))
classifier.save_pickle(model_path, protocol=args['protocol'])
def main():
# Parse the arguments.
args = parse_arguments()
if args.label:
labels = args.label
class_num = len(labels) if isinstance(labels, list) else 1
else:
raise ValueError('No target label was specified.')
# Dataset preparation. Postprocessing is required for the regression task.
def postprocess_label(label_list):
label_arr = np.asarray(label_list, dtype=np.int32)
return label_arr
# Apply a preprocessor to the dataset.
print('Preprocessing dataset...')
preprocessor = preprocess_method_dict[args.method]()
parser = CSVFileParserForPair(preprocessor,
postprocess_label=postprocess_label,
labels=labels,
smiles_cols=['smiles_1', 'smiles_2'])
dataset = parser.parse(args.datafile)['dataset']
# Split the dataset into training and validation.
train_data_size = int(len(dataset) * args.train_data_ratio)
train, val = split_dataset_random(dataset, train_data_size, args.seed)
# Set up the predictor.
# def set_up_predictor(method, fp_hidden_dim, fp_out_dim, conv_layers, net_hidden_num, class_num, net_layers):
# predictor = set_up_predictor(args.method, args.unit_num,
# args.conv_layers, class_num)
if len(args.net_hidden_dims):
net_hidden_dims = tuple([
int(net_hidden_dim)
for net_hidden_dim in args.net_hidden_dims.split(',')
])
else:
net_hidden_dims = ()
predictor = set_up_predictor(method=args.method,
fp_hidden_dim=args.fp_hidden_dim,
fp_out_dim=args.fp_out_dim,
conv_layers=args.conv_layers,
concat_hidden=args.concat_hidden,
fp_dropout_rate=args.fp_dropout_rate,
net_hidden_dims=net_hidden_dims,
class_num=class_num,
sim_method=args.sim_method)
# Set up the iterator.
train_iter = SerialIterator(train, args.batchsize)
val_iter = SerialIterator(val, args.batchsize, repeat=False, shuffle=False)
metrics_fun = {'accuracy': F.binary_accuracy}
classifier = Classifier(predictor,
lossfun=F.sigmoid_cross_entropy,
metrics_fun=metrics_fun,
device=args.gpu)
# Set up the optimizer.
optimizer = optimizers.Adam(alpha=args.learning_rate,
weight_decay_rate=args.weight_decay_rate)
# optimizer = optimizers.Adam()
# optimizer = optimizers.SGD(lr=args.learning_rate)
optimizer.setup(classifier)
# Set up the updater.
updater = training.StandardUpdater(train_iter,
optimizer,
device=args.gpu,
converter=concat_mols)
# Set up the trainer.
print('Training...')
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
trainer.extend(
E.Evaluator(val_iter,
classifier,
device=args.gpu,
converter=concat_mols))
train_eval_iter = SerialIterator(train,
args.batchsize,
repeat=False,
shuffle=False)
trainer.extend(
AccuracyEvaluator(train_eval_iter,
classifier,
eval_func=predictor,
device=args.gpu,
converter=concat_mols,
name='train_acc',
pos_labels=1,
ignore_labels=-1,
raise_value_error=False))
# extension name='validation' is already used by `Evaluator`,
# instead extension name `val` is used.
trainer.extend(
AccuracyEvaluator(val_iter,
classifier,
eval_func=predictor,
device=args.gpu,
converter=concat_mols,
name='val_acc',
pos_labels=1,
ignore_labels=-1))
trainer.extend(
ROCAUCEvaluator(train_eval_iter,
classifier,
eval_func=predictor,
device=args.gpu,
converter=concat_mols,
name='train_roc',
pos_labels=1,
ignore_labels=-1,
raise_value_error=False))
# extension name='validation' is already used by `Evaluator`,
# instead extension name `val` is used.
trainer.extend(
ROCAUCEvaluator(val_iter,
classifier,
eval_func=predictor,
device=args.gpu,
converter=concat_mols,
name='val_roc',
pos_labels=1,
ignore_labels=-1))
trainer.extend(
PRCAUCEvaluator(train_eval_iter,
classifier,
eval_func=predictor,
device=args.gpu,
converter=concat_mols,
name='train_prc',
pos_labels=1,
ignore_labels=-1,
raise_value_error=False))
# extension name='validation' is already used by `Evaluator`,
# instead extension name `val` is used.
trainer.extend(
PRCAUCEvaluator(val_iter,
classifier,
eval_func=predictor,
device=args.gpu,
converter=concat_mols,
name='val_prc',
pos_labels=1,
ignore_labels=-1))
# trainer.extend(PrecisionEvaluator(
# train_eval_iter, classifier, eval_func=predictor,
# device=args.gpu, converter=concat_mols, name='train_p',
# pos_labels=1, ignore_labels=-1, raise_value_error=False))
# # extension name='validation' is already used by `Evaluator`,
# # instead extension name `val` is used.
# trainer.extend(PrecisionEvaluator(
# val_iter, classifier, eval_func=predictor,
# device=args.gpu, converter=concat_mols, name='val_p',
# pos_labels=1, ignore_labels=-1))
#
# trainer.extend(RecallEvaluator(
# train_eval_iter, classifier, eval_func=predictor,
# device=args.gpu, converter=concat_mols, name='train_r',
# pos_labels=1, ignore_labels=-1, raise_value_error=False))
# # extension name='validation' is already used by `Evaluator`,
# # instead extension name `val` is used.
# trainer.extend(RecallEvaluator(
# val_iter, classifier, eval_func=predictor,
# device=args.gpu, converter=concat_mols, name='val_r',
# pos_labels=1, ignore_labels=-1))
trainer.extend(
F1Evaluator(train_eval_iter,
classifier,
eval_func=predictor,
device=args.gpu,
converter=concat_mols,
name='train_f',
pos_labels=1,
ignore_labels=-1,
raise_value_error=False))
# extension name='validation' is already used by `Evaluator`,
# instead extension name `val` is used.
trainer.extend(
F1Evaluator(val_iter,
classifier,
eval_func=predictor,
device=args.gpu,
converter=concat_mols,
name='val_f',
pos_labels=1,
ignore_labels=-1))
# apply shift strategy to learning rate every 10 epochs
# trainer.extend(E.ExponentialShift('alpha', args.exp_shift_rate), trigger=(10, 'epoch'))
trainer.extend(E.ExponentialShift('alpha', args.exp_shift_rate),
trigger=triggers.ManualScheduleTrigger([10, 20, 30, 40, 50],
'epoch'))
# # observation of learning rate
trainer.extend(E.observe_lr(), trigger=(1, 'iteration'))
entries = [
'epoch',
'main/loss',
'train_acc/main/accuracy',
'train_roc/main/roc_auc',
'train_prc/main/prc_auc',
# 'train_p/main/precision', 'train_r/main/recall',
'train_f/main/f1',
'validation/main/loss',
'val_acc/main/accuracy',
'val_roc/main/roc_auc',
'val_prc/main/prc_auc',
# 'val_p/main/precision', 'val_r/main/recall',
'val_f/main/f1',
'lr',
'elapsed_time'
]
trainer.extend(E.PrintReport(entries=entries))
trainer.extend(E.snapshot(), trigger=(args.epoch, 'epoch'))
trainer.extend(E.LogReport())
trainer.extend(E.ProgressBar())
if args.resume:
resume_path = os.path.join(args.out, args.resume)
logging.info(
'Resume training according to snapshot in {}'.format(resume_path))
chainer.serializers.load_npz(resume_path, trainer)
trainer.run()
# Save the regressor's parameters.
model_path = os.path.join(args.out, args.model_filename)
print('Saving the trained models to {}...'.format(model_path))
classifier.save_pickle(model_path, protocol=args.protocol)
def main():
# Parse the arguments.
args = parse_arguments()
if args.label:
labels = args.label
class_num = len(labels) if isinstance(labels, list) else 1
else:
raise ValueError('No target label was specified.')
# Dataset preparation. Postprocessing is required for the regression task.
def postprocess_label(label_list):
label_arr = np.asarray(label_list, dtype=np.int32)
return label_arr
# Apply a preprocessor to the dataset.
print('Preprocessing dataset...')
preprocessor = preprocess_method_dict[args.method]()
parser = CSVFileParserForPair(preprocessor,
postprocess_label=postprocess_label,
labels=labels,
smiles_cols=['smiles_1', 'smiles_2'])
dataset = parser.parse(args.datafile)['dataset']
# Split the dataset into training and validation.
train_data_size = int(len(dataset) * args.train_data_ratio)
train, val = split_dataset_random(dataset, train_data_size, args.seed)
# Set up the predictor.
predictor = set_up_predictor(args.method, args.unit_num, args.conv_layers,
class_num)
# Set up the iterator.
train_iter = SerialIterator(train, args.batchsize)
val_iter = SerialIterator(val, args.batchsize, repeat=False, shuffle=False)
# Set up the regressor.
metric_fun = {
'accuracy': F.accuracy,
# 'precision': F.precision,
# 'recall': F.recall,
# 'F1-score': F.f1_score,
}
classifier = Classifier(predictor,
lossfun=F.sigmoid_cross_entropy,
metrics_fun=F.accuracy,
device=args.gpu)
# Set up the optimizer.
optimizer = optimizers.Adam()
optimizer.setup(classifier)
# Set up the updater.
updater = training.StandardUpdater(train_iter,
optimizer,
device=args.gpu,
converter=concat_mols)
# updater = training.ParallelUpdater(train_iter, optimizer, devices={'main': 0, 'second': 1},
# converter=concat_mols)
# Set up the trainer.
print('Training...')
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
trainer.extend(
E.Evaluator(val_iter,
classifier,
device=args.gpu,
converter=concat_mols))
train_eval_iter = SerialIterator(train,
args.batchsize,
repeat=False,
shuffle=False)
trainer.extend(
ROCAUCEvaluator(train_eval_iter,
classifier,
eval_func=predictor,
device=args.gpu,
converter=concat_mols,
name='train_roc',
pos_labels=1,
ignore_labels=-1,
raise_value_error=False))
# extension name='validation' is already used by `Evaluator`,
# instead extension name `val` is used.
trainer.extend(
ROCAUCEvaluator(val_iter,
classifier,
eval_func=predictor,
device=args.gpu,
converter=concat_mols,
name='val_roc',
pos_labels=1,
ignore_labels=-1))
trainer.extend(
PRCAUCEvaluator(train_eval_iter,
classifier,
eval_func=predictor,
device=args.gpu,
converter=concat_mols,
name='train_prc',
pos_labels=1,
ignore_labels=-1,
raise_value_error=False))
# extension name='validation' is already used by `Evaluator`,
# instead extension name `val` is used.
trainer.extend(
PRCAUCEvaluator(val_iter,
classifier,
eval_func=predictor,
device=args.gpu,
converter=concat_mols,
name='val_prc',
pos_labels=1,
ignore_labels=-1))
trainer.extend(
E.PrintReport([
'epoch',
'main/loss',
'main/accuracy',
# 'train_roc/main/roc_auc', 'train_prc/main/prc_auc',
'validation/main/loss',
'validation/main/accuracy',
# 'val_roc/main/roc_auc', 'val_prc/main/prc_auc',
'elapsed_time'
]))
trainer.extend(E.snapshot(), trigger=(args.epoch, 'epoch'))
trainer.extend(E.LogReport())
trainer.extend(E.ProgressBar())
trainer.run()
# Save the regressor's parameters.
model_path = os.path.join(args.out, args.model_filename)
print('Saving the trained models to {}...'.format(model_path))
classifier.save_pickle(model_path, protocol=args.protocol)
def main():
# Parse the arguments.
args = parse_arguments()
gpu = False
if args.gpu >= 0:
import cupy
gpu = True
if args.label:
labels = args.label
class_num = len(labels) if isinstance(labels, list) else 1
else:
raise ValueError('No target label was specified.')
# Dataset preparation. Postprocessing is required for the regression task.
def postprocess_label(label_list):
if gpu:
return cupy.asarray(label_list, dtype=cupy.int32)
else:
return numpy.asarray(label_list, dtype=numpy.int32)
# Apply a preprocessor to the dataset.
print('Preprocessing dataset...')
preprocessor = set_up_preprocessor(args.method, args.max_atoms)
parser = CSVFileParser(preprocessor,
postprocess_label=postprocess_label,
labels=labels,
smiles_col='SMILES')
train = parser.parse(args.train_datafile)['dataset']
# Validation
preprocessor = set_up_preprocessor(args.method, args.max_atoms)
parser = CSVFileParser(preprocessor,
postprocess_label=postprocess_label,
labels=labels,
smiles_col='SMILES')
val = parser.parse(args.val_datafile)['dataset']
# Set up the predictor.
predictor = set_up_predictor(args.method,
args.unit_num,
args.conv_layers,
class_num,
max_atoms=args.max_atoms)
if gpu:
predictor = predictor.to_gpu()
# Set up the iterator.
train_iter = SerialIterator(train, args.batchsize)
val_iter = SerialIterator(val, args.batchsize, repeat=False, shuffle=False)
# Set up the classifier.
classifier = Classifier(predictor,
lossfun=F.sigmoid_cross_entropy,
metrics_fun=F.binary_accuracy,
device=args.gpu)
# Set up the optimizer.
optimizer = optimizers.Adam()
optimizer.setup(classifier)
optimizer.add_hook(chainer.optimizer.WeightDecay(rate=args.l2reg))
# Set up the updater.
updater = training.StandardUpdater(train_iter,
optimizer,
device=args.gpu,
converter=concat_mols)
# Set up the trainer.
print('Training...')
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
#metrics
trainer.extend(E.Evaluator(val_iter, classifier))
train_eval_iter = SerialIterator(train,
args.batchsize,
repeat=False,
shuffle=False)
trainer.extend(
ROCAUCEvaluator(train_eval_iter,
classifier,
eval_func=predictor,
device=args.gpu,
converter=concat_mols,
name='train',
pos_labels=1,
ignore_labels=-1,
raise_value_error=False))
# extension name='validation' is already used by `Evaluator`,
# instead extension name `val` is used.
trainer.extend(
ROCAUCEvaluator(val_iter,
classifier,
eval_func=predictor,
device=args.gpu,
converter=concat_mols,
name='val',
pos_labels=1,
ignore_labels=-1))
trainer.extend(E.snapshot(), trigger=(args.epoch, 'epoch'))
trainer.extend(E.LogReport())
trainer.extend(
E.PrintReport([
'epoch', 'main/loss', 'main/accuracy', 'train/main/roc_auc',
'validation/main/loss', 'validation/main/accuracy',
'val/main/roc_auc', 'elapsed_time'
]))
trainer.extend(E.ProgressBar())
trainer.run()
# Save the classifier's parameters.
model_path = os.path.join(args.out, args.model_filename)
print('Saving the trained model to {}...'.format(model_path))
classifier.save_pickle(model_path, protocol=args.protocol)