def setUp(self):
parser = ArgumentParser()
add_train_args(parser)
args = parser.parse_args([])
args.data_path = os.path.join(
os.path.dirname(os.path.abspath(__file__)), 'delaney_toy.csv')
args.dataset_type = 'regression'
args.batch_size = 2
args.hidden_size = 5
args.epochs = 1
args.quiet = True
self.temp_dir = TemporaryDirectory()
args.save_dir = self.temp_dir.name
logger = create_logger(name='train',
save_dir=args.save_dir,
quiet=args.quiet)
modify_train_args(args)
cross_validate(args, logger)
clear_cache()
parser = ArgumentParser()
add_predict_args(parser)
args = parser.parse_args([])
args.batch_size = 2
args.checkpoint_dir = self.temp_dir.name
args.preds_path = NamedTemporaryFile().name
args.test_path = os.path.join(
os.path.dirname(os.path.abspath(__file__)),
'delaney_toy_smiles.csv')
self.args = args
def test_hyperopt(self):
try:
parser = ArgumentParser()
add_train_args(parser)
parser.add_argument('--num_iters', type=int, default=20,
help='Number of hyperparameter choices to try')
parser.add_argument('--config_save_path', type=str,
help='Path to .json file where best hyperparameter settings will be written')
parser.add_argument('--log_dir', type=str,
help='(Optional) Path to a directory where all results of the hyperparameter optimization will be written')
args = parser.parse_args([])
args.data_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'delaney_toy.csv')
args.dataset_type = 'regression'
args.batch_size = 2
args.hidden_size = 5
args.epochs = 1
args.quiet = True
temp_file = NamedTemporaryFile()
args.config_save_path = temp_file.name
args.num_iters = 3
modify_train_args(args)
grid_search(args)
clear_cache()
except:
self.fail('hyperopt')
def setUp(self):
parser = ArgumentParser()
add_train_args(parser)
args = parser.parse_args([])
args.data_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'delaney_toy.csv')
args.dataset_type = 'regression'
args.batch_size = 2
args.hidden_size = 5
args.epochs = 1
args.quiet = True
self.args = args
logger = create_logger(name='train', save_dir=args.save_dir, quiet=args.quiet)
self.logger = logger
model = load_checkpoint(os.path.join(save_dir, 'model.pt'), cuda=args.cuda)
test_smiles, test_targets = test_data.smiles(), test_data.targets()
test_preds = predict(model, test_data, args.batch_size)
test_scores = evaluate_predictions(test_preds, test_targets,
args.num_tasks, metric_func,
args.dataset_type)
avg_test_score = np.nanmean(test_scores)
print(f'Test {args.metric} = {avg_test_score:.4f}')
return avg_test_score
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument('--source_data_path', required=True)
parser.add_argument('--src_batch_size', type=int, default=100)
parser.add_argument('--lambda_e', type=float, default=0.1)
add_train_args(parser)
args = parser.parse_args()
modify_train_args(args)
all_test_score = np.zeros((args.num_folds, ))
for i in range(args.num_folds):
fold_dir = os.path.join(args.save_dir, f'fold_{i}')
makedirs(fold_dir)
all_test_score[i] = run_training(args, fold_dir)
mean, std = np.mean(all_test_score), np.std(all_test_score)
print(f'{args.num_folds} fold average: {mean:.4f} +/- {std:.4f}')
def train():
global training_message
if request.method == 'GET':
return render_template('train.html',
datasets=get_datasets(),
started=False,
cuda=app.config['CUDA'],
gpus=app.config['GPUS'])
# Get arguments
data_name, epochs, checkpoint_name = \
request.form['dataName'], int(request.form['epochs']), request.form['checkpointName']
gpu = request.form.get('gpu', None)
dataset_type = request.form.get('datasetType', 'regression')
if not checkpoint_name.endswith('.pt'):
checkpoint_name += '.pt'
# Create and modify args
parser = ArgumentParser()
add_train_args(parser)
args = parser.parse_args()
args.data_path = os.path.join(app.config['DATA_FOLDER'], data_name)
args.dataset_type = dataset_type
args.epochs = epochs
target_set, all_targets_have_labels, has_invalid_targets = get_target_set(
args.data_path)
if len(target_set) == 0:
return render_template('train.html',
datasets=get_datasets(),
started=False,
cuda=app.config['CUDA'],
gpus=app.config['GPUS'],
error="No training labels provided")
if has_invalid_targets:
return render_template('train.html',
datasets=get_datasets(),
started=False,
cuda=app.config['CUDA'],
gpus=app.config['GPUS'],
error="Training data contains invalid labels")
classification_on_regression_dataset = ((not target_set <= set([0, 1]))
and args.dataset_type
== 'classification')
if classification_on_regression_dataset:
return render_template(
'train.html',
datasets=get_datasets(),
started=False,
cuda=app.config['CUDA'],
gpus=app.config['GPUS'],
error=
'Selected classification dataset, but not all labels are 0 or 1')
regression_on_classification_dataset = (target_set <= set([0, 1]) and
args.dataset_type == 'regression')
if not all_targets_have_labels:
training_message += 'One or more targets have no labels. \n' # TODO could have separate warning messages for each?
if regression_on_classification_dataset:
training_message += 'All labels are 0 or 1; did you mean to train classification instead of regression?\n'
if gpu is not None:
if gpu == 'None':
args.no_cuda = True
else:
args.gpu = int(gpu)
with TemporaryDirectory() as temp_dir:
args.save_dir = temp_dir
modify_train_args(args)
if os.path.isdir(args.save_dir):
training_message += 'Overwriting preexisting checkpoint with the same name.'
logger = logging.getLogger('train')
logger.setLevel(logging.DEBUG)
logger.propagate = False
set_logger(logger, args.save_dir, args.quiet)
global progress
process = mp.Process(target=progress_bar, args=(args, progress))
process.start()
global started
started = 1
# Run training
run_training(args, logger)
process.join()
# reset globals
started = 0
progress = mp.Value('d', 0.0)
# Move checkpoint
shutil.move(
os.path.join(args.save_dir, 'model_0', 'model.pt'),
os.path.join(app.config['CHECKPOINT_FOLDER'], checkpoint_name))
warning = training_message if len(training_message) > 0 else None
training_message = ""
return render_template('train.html',
datasets=get_datasets(),
cuda=app.config['CUDA'],
gpus=app.config['GPUS'],
trained=True,
warning=warning)
def train():
"""Renders the train page and performs training if request method is POST."""
global PROGRESS, TRAINING
warnings, errors = [], []
if request.method == 'GET':
return render_train()
# Get arguments
data_name, epochs, ensemble_size, checkpoint_name = \
request.form['dataName'], int(request.form['epochs']), \
int(request.form['ensembleSize']), request.form['checkpointName']
gpu = request.form.get('gpu')
data_path = os.path.join(app.config['DATA_FOLDER'], f'{data_name}.csv')
dataset_type = request.form.get('datasetType', 'regression')
# Create and modify args
parser = ArgumentParser()
add_train_args(parser)
args = parser.parse_args([])
args.data_path = data_path
args.dataset_type = dataset_type
args.epochs = epochs
args.ensemble_size = ensemble_size
# Check if regression/classification selection matches data
data = get_data(path=data_path)
targets = data.targets()
unique_targets = {target for row in targets for target in row if target is not None}
if dataset_type == 'classification' and len(unique_targets - {0, 1}) > 0:
errors.append('Selected classification dataset but not all labels are 0 or 1. Select regression instead.')
return render_train(warnings=warnings, errors=errors)
if dataset_type == 'regression' and unique_targets <= {0, 1}:
errors.append('Selected regression dataset but all labels are 0 or 1. Select classification instead.')
return render_train(warnings=warnings, errors=errors)
if gpu is not None:
if gpu == 'None':
args.no_cuda = True
else:
args.gpu = int(gpu)
current_user = request.cookies.get('currentUser')
if not current_user:
# Use DEFAULT as current user if the client's cookie is not set.
current_user = app.config['DEFAULT_USER_ID']
ckpt_id, ckpt_name = db.insert_ckpt(checkpoint_name,
current_user,
args.dataset_type,
args.epochs,
args.ensemble_size,
len(targets))
with TemporaryDirectory() as temp_dir:
args.save_dir = temp_dir
modify_train_args(args)
process = mp.Process(target=progress_bar, args=(args, PROGRESS))
process.start()
TRAINING = 1
# Run training
logger = create_logger(name='train', save_dir=args.save_dir, quiet=args.quiet)
task_scores = run_training(args, logger)
process.join()
# Reset globals
TRAINING = 0
PROGRESS = mp.Value('d', 0.0)
# Check if name overlap
if checkpoint_name != ckpt_name:
warnings.append(name_already_exists_message('Checkpoint', checkpoint_name, ckpt_name))
# Move models
for root, _, files in os.walk(args.save_dir):
for fname in files:
if fname.endswith('.pt'):
model_id = db.insert_model(ckpt_id)
save_path = os.path.join(app.config['CHECKPOINT_FOLDER'], f'{model_id}.pt')
shutil.move(os.path.join(args.save_dir, root, fname), save_path)
return render_train(trained=True,
metric=args.metric,
num_tasks=len(args.task_names),
task_names=args.task_names,
task_scores=format_float_list(task_scores),
mean_score=format_float(np.mean(task_scores)),
warnings=warnings,
errors=errors)
def train():
global progress, training
warnings, errors = [], []
if request.method == 'GET':
return render_train()
# Get arguments
data_name, epochs, checkpoint_name = \
request.form['dataName'], int(request.form['epochs']), request.form['checkpointName']
gpu = request.form.get('gpu')
data_path = os.path.join(app.config['DATA_FOLDER'], data_name)
dataset_type = request.form.get('datasetType', 'regression')
if not checkpoint_name.endswith('.pt'):
checkpoint_name += '.pt'
# Create and modify args
parser = ArgumentParser()
add_train_args(parser)
args = parser.parse_args()
args.data_path = data_path
args.dataset_type = dataset_type
args.epochs = epochs
# Check if regression/classification selection matches data
data = get_data(path=data_path)
targets = data.targets()
unique_targets = set(np.unique(targets))
if dataset_type == 'classification' and len(unique_targets - {0, 1}) > 0:
errors.append(
'Selected classification dataset but not all labels are 0 or 1. Select regression instead.'
)
return render_train(warnings=warnings, errors=errors)
if dataset_type == 'regression' and unique_targets <= {0, 1}:
errors.append(
'Selected regression dataset but all labels are 0 or 1. Select classification instead.'
)
return render_train(warnings=warnings, errors=errors)
if gpu is not None:
if gpu == 'None':
args.no_cuda = True
else:
args.gpu = int(gpu)
with TemporaryDirectory() as temp_dir:
args.save_dir = temp_dir
modify_train_args(args)
logger = logging.getLogger('train')
logger.setLevel(logging.DEBUG)
logger.propagate = False
set_logger(logger, args.save_dir, args.quiet)
process = mp.Process(target=progress_bar, args=(args, progress))
process.start()
training = 1
# Run training
task_scores = run_training(args, logger)
process.join()
# Reset globals
training = 0
progress = mp.Value('d', 0.0)
# Check if name overlap
original_save_path = os.path.join(app.config['CHECKPOINT_FOLDER'],
checkpoint_name)
save_path = find_unique_path(original_save_path)
if save_path != original_save_path:
warnings.append(
name_already_exists_message('Checkpoint', original_save_path,
save_path))
# Move checkpoint
shutil.move(os.path.join(args.save_dir, 'model_0', 'model.pt'),
save_path)
return render_train(trained=True,
metric=args.metric,
num_tasks=len(args.task_names),
task_names=args.task_names,
task_scores=format_float_list(task_scores),
mean_score=format_float(np.mean(task_scores)),
warnings=warnings,
errors=errors)
