def main(_):
"""TODO(nh2tran): docstring."""
print("main()")
if deepnovo_config.FLAGS.knapsack_build:
deepnovo_main_modules.knapsack_build()
elif deepnovo_config.FLAGS.train:
deepnovo_main_modules.train()
elif deepnovo_config.FLAGS.test_true_feeding:
deepnovo_main_modules.test_true_feeding()
elif deepnovo_config.FLAGS.decode:
deepnovo_main_modules.decode()
elif deepnovo_config.FLAGS.search_db:
model = deepnovo_model.DecodingModel()
worker_io = deepnovo_worker_io.WorkerIO(
input_file=deepnovo_config.input_file,
output_file=deepnovo_config.output_file)
worker_db = deepnovo_worker_db.WorkerDB()
worker_db.build_db()
worker_db.search_db(model, worker_io)
# due to some mistakes in cleavage rules, we need worker_db.peptide_list to
# check for consistency
worker_test = deepnovo_worker_test.WorkerTest()
worker_test.test_accuracy(worker_db.peptide_list)
elif deepnovo_config.FLAGS.test:
pass
else:
print("ERROR: wrong option!")
sys.exit()
def main(_):
"""TODO(nh2tran): docstring."""
print("main()")
if deepnovo_config.FLAGS.knapsack_build:
deepnovo_main_modules.knapsack_build()
elif deepnovo_config.FLAGS.train:
deepnovo_main_modules.train()
elif deepnovo_config.FLAGS.test_true_feeding:
deepnovo_main_modules.test_true_feeding()
elif deepnovo_config.FLAGS.decode:
deepnovo_main_modules.decode()
elif deepnovo_config.FLAGS.search_denovo:
model = deepnovo_model.ModelInference()
model.build_model()
worker_io = deepnovo_worker_io.WorkerIO(
input_file=deepnovo_config.denovo_input_file,
output_file=deepnovo_config.denovo_output_file)
worker_denovo = deepnovo_worker_denovo.WorkerDenovo()
worker_denovo.search_denovo(model, worker_io)
elif deepnovo_config.FLAGS.search_db:
model = deepnovo_model.ModelInference()
model.build_model()
worker_io = deepnovo_worker_io.WorkerIO(
input_file=deepnovo_config.db_input_file,
output_file=deepnovo_config.db_output_file)
worker_db = deepnovo_worker_db.WorkerDB()
worker_db.build_db()
worker_db.search_db(model, worker_io)
elif deepnovo_config.FLAGS.search_hybrid:
model = deepnovo_model.ModelInference()
model.build_model()
# denovo search
worker_io = deepnovo_worker_io.WorkerIO(
input_file=deepnovo_config.hybrid_input_file,
output_file=deepnovo_config.hybrid_denovo_file)
worker_denovo = deepnovo_worker_denovo.WorkerDenovo()
predicted_denovo_list = worker_denovo.search_denovo(model, worker_io)
# db search with predicted_denovo_list
worker_io = deepnovo_worker_io.WorkerIO(
input_file=deepnovo_config.hybrid_input_file,
output_file=deepnovo_config.hybrid_output_file)
worker_db = deepnovo_worker_db.WorkerDB()
worker_db.build_db()
worker_db.search_db(model, worker_io, predicted_denovo_list)
elif deepnovo_config.FLAGS.test:
# test 1%FDR
#~ worker_db = deepnovo_worker_db.WorkerDB()
#~ worker_db.build_db()
#~ worker_test = deepnovo_worker_test.WorkerTest()
#~ worker_test.test_accuracy(worker_db.peptide_list)
worker_test = deepnovo_worker_test.WorkerTest()
worker_test.test_accuracy()
else:
print("ERROR: wrong option!")
sys.exit()
def main():
if deepnovo_config.args.train:
logger.info("training mode")
train()
elif deepnovo_config.args.search_denovo:
logger.info("denovo mode")
data_reader = DeepNovoDenovoDataset(
feature_filename=deepnovo_config.denovo_input_feature_file,
spectrum_filename=deepnovo_config.denovo_input_spectrum_file)
denovo_worker = IonCNNDenovo(deepnovo_config.MZ_MAX,
deepnovo_config.knapsack_file,
beam_size=deepnovo_config.args.beam_size)
forward_deepnovo, backward_deepnovo, init_net = build_model(
training=False)
model_wrapper = InferenceModelWrapper(forward_deepnovo,
backward_deepnovo, init_net)
writer = DenovoWriter(deepnovo_config.denovo_output_file)
denovo_worker.search_denovo(model_wrapper, data_reader, writer)
elif deepnovo_config.args.valid:
valid_set = DeepNovoTrainDataset(
deepnovo_config.input_feature_file_valid,
deepnovo_config.input_spectrum_file_valid)
valid_data_loader = torch.utils.data.DataLoader(
dataset=valid_set,
batch_size=deepnovo_config.batch_size,
shuffle=False,
num_workers=deepnovo_config.num_workers,
collate_fn=collate_func)
forward_deepnovo, backward_deepnovo, init_net = build_model(
training=False)
forward_deepnovo.eval()
backward_deepnovo.eval()
validation_loss = validation(forward_deepnovo, backward_deepnovo,
init_net, valid_data_loader)
logger.info(f"validation perplexity: {perplexity(validation_loss)}")
elif deepnovo_config.args.test:
logger.info("test mode")
worker_test = deepnovo_worker_test.WorkerTest()
worker_test.test_accuracy()
# show 95 accuracy score threshold
accuracy_cutoff = 0.95
accuracy_file = deepnovo_config.accuracy_file
score_cutoff = find_score_cutoff(accuracy_file, accuracy_cutoff)
else:
raise RuntimeError("unspecified mode")
def main(_):
hostname = socket.gethostname()
print('hostname:', hostname)
print("main()")
if deepnovo_config.FLAGS.knapsack_build:
deepnovo_main_modules.knapsack_build()
elif deepnovo_config.FLAGS.train:
deepnovo_main_modules.train()
elif deepnovo_config.FLAGS.test_true_feeding:
deepnovo_main_modules.test_true_feeding()
elif deepnovo_config.FLAGS.decode:
deepnovo_main_modules.decode()
elif deepnovo_config.FLAGS.multi_decode:
deepnovo_main_modules.multi_decode()
elif deepnovo_config.FLAGS.multi_train:
if deepnovo_config.data_format == 'mgf':
deepnovo_main_modules.multi_train()
elif deepnovo_config.data_format == 'pickle':
deepnovo_main_modules.multi_train_pickles()
elif deepnovo_config.FLAGS.sigopt:
deepnovo_main_modules.sigopt()
elif deepnovo_config.FLAGS.search_denovo:
model = deepnovo_model.ModelInference()
model.build_model()
worker_io = deepnovo_worker_io.WorkerIO(
input_file=deepnovo_config.denovo_input_file,
output_file=deepnovo_config.denovo_output_file)
worker_denovo = deepnovo_worker_denovo.WorkerDenovo()
worker_denovo.search_denovo(model, worker_io)
elif deepnovo_config.FLAGS.search_db:
model = deepnovo_model.ModelInference()
model.build_model()
worker_io = deepnovo_worker_io.WorkerIO(
input_file=deepnovo_config.db_input_file,
output_file=deepnovo_config.db_output_file)
worker_db = deepnovo_worker_db.WorkerDB()
worker_db.build_db()
worker_db.search_db(model, worker_io)
elif deepnovo_config.FLAGS.search_hybrid:
model = deepnovo_model.ModelInference()
model.build_model()
# denovo search
worker_io = deepnovo_worker_io.WorkerIO(
input_file=deepnovo_config.hybrid_input_file,
output_file=deepnovo_config.hybrid_denovo_file)
worker_denovo = deepnovo_worker_denovo.WorkerDenovo()
predicted_denovo_list = worker_denovo.search_denovo(model, worker_io)
# db search with predicted_denovo_list
worker_io = deepnovo_worker_io.WorkerIO(
input_file=deepnovo_config.hybrid_input_file,
output_file=deepnovo_config.hybrid_output_file)
worker_db = deepnovo_worker_db.WorkerDB()
worker_db.build_db()
worker_db.search_db(model, worker_io, predicted_denovo_list)
elif deepnovo_config.FLAGS.test:
worker_test = deepnovo_worker_test.WorkerTest()
worker_test.test_accuracy()
else:
print("ERROR: wrong option!")
sys.exit()