def main(args):
if args.method == 'dann':
train_fn = train_dann.train_dann
elif args.method == 'adda':
train_fn = train_adda.train_adda
elif args.method == 'dcc':
train_fn = train_dcc.train_dcc
elif args.method == 'cleitcs':
train_fn = train_cleitcs.train_cleitcs
else:
train_fn = train_coral.train_coral
device = 'cuda' if torch.cuda.is_available() else 'cpu'
with open(os.path.join('model_save', 'train_params.json'), 'r') as f:
training_params = json.load(f)
training_params.update(
{
'device': device,
'model_save_folder': os.path.join('model_save', args.method, 'labeled'),
})
safe_make_dir(training_params['model_save_folder'])
data_provider = DataProvider(batch_size=training_params['labeled']['batch_size'],
target=args.measurement)
training_params.update(
{
'input_dim': data_provider.shape_dict['gex'],
'output_dim': data_provider.shape_dict['target']
}
)
s_labeled_dataloader = data_provider.get_labeled_gex_dataloader()
labeled_dataloader_generator = data_provider.get_drug_labeled_mut_dataloader()
s_ft_evaluation_metrics = defaultdict(list)
t_ft_evaluation_metrics = defaultdict(list)
val_ft_evaluation_metrics = defaultdict(list)
test_ft_evaluation_metrics = defaultdict(list)
fold_count = 0
for train_labeled_dataloader, val_labeled_dataloader, test_labeled_dataloader in labeled_dataloader_generator:
target_regressor, train_historys = train_fn(s_dataloaders=s_labeled_dataloader,
t_dataloaders=train_labeled_dataloader,
val_dataloader=val_labeled_dataloader,
test_dataloader=test_labeled_dataloader,
metric_name=args.metric,
seed = fold_count,
**wrap_training_params(training_params, type='labeled'))
for metric in ['dpearsonr', 'dspearmanr', 'drmse', 'cpearsonr', 'cspearmanr', 'crmse']:
val_ft_evaluation_metrics[metric].append(train_historys[-2][metric][train_historys[-2]['best_index']])
test_ft_evaluation_metrics[metric].append(train_historys[-1][metric][train_historys[-2]['best_index']])
fold_count += 1
with open(os.path.join(training_params['model_save_folder'], f'test_ft_evaluation_results.json'), 'w') as f:
json.dump(test_ft_evaluation_metrics, f)
with open(os.path.join(training_params['model_save_folder'], f'ft_evaluation_results.json'), 'w') as f:
json.dump(val_ft_evaluation_metrics, f)
def main(args):
device = 'cuda' if torch.cuda.is_available() else 'cpu'
with open(os.path.join('model_save', 'train_params.json'), 'r') as f:
training_params = json.load(f)
training_params.update(
{
'device': device,
'model_save_folder': os.path.join('model_save', 'mlp', args.omics),
})
data_provider = DataProvider(batch_size=training_params['unlabeled']['batch_size'],
target=args.measurement)
training_params.update(
{
'input_dim': data_provider.shape_dict[args.omics],
'output_dim': data_provider.shape_dict['target']
}
)
ft_evaluation_metrics = defaultdict(list)
if args.omics == 'gex':
labeled_dataloader_generator = data_provider.get_drug_labeled_gex_dataloader()
fold_count = 0
for train_labeled_dataloader, val_labeled_dataloader in labeled_dataloader_generator:
target_regressor, ft_historys = fine_tune_encoder(
train_dataloader=train_labeled_dataloader,
val_dataloader=val_labeled_dataloader,
test_dataloader=val_labeled_dataloader,
seed=fold_count,
metric_name=args.metric,
**wrap_training_params(training_params, type='labeled')
)
for metric in ['dpearsonr', 'dspearmanr','drmse', 'cpearsonr', 'cspearmanr','crmse']:
ft_evaluation_metrics[metric].append(ft_historys[-2][metric][ft_historys[-2]['best_index']])
fold_count += 1
else:
labeled_dataloader_generator = data_provider.get_drug_labeled_mut_dataloader()
fold_count = 0
test_ft_evaluation_metrics = defaultdict(list)
for train_labeled_dataloader, val_labeled_dataloader, test_labeled_dataloader in labeled_dataloader_generator:
target_regressor, ft_historys = fine_tune_encoder(
train_dataloader=train_labeled_dataloader,
val_dataloader=val_labeled_dataloader,
test_dataloader=test_labeled_dataloader,
seed=fold_count,
metric_name=args.metric,
**wrap_training_params(training_params, type='labeled')
)
for metric in ['dpearsonr', 'dspearmanr','drmse', 'cpearsonr', 'cspearmanr','crmse']:
ft_evaluation_metrics[metric].append(ft_historys[-2][metric][ft_historys[-2]['best_index']])
test_ft_evaluation_metrics[metric].append(ft_historys[-1][metric][ft_historys[-2]['best_index']])
fold_count += 1
with open(os.path.join(training_params['model_save_folder'], f'test_ft_evaluation_results.json'), 'w') as f:
json.dump(test_ft_evaluation_metrics, f)
with open(os.path.join(training_params['model_save_folder'], f'ft_evaluation_results.json'), 'w') as f:
json.dump(ft_evaluation_metrics, f)
