def predict(args, model, tcrs, peps):
assert len(tcrs) == len(peps)
tcrs_copy = tcrs.copy()
peps_copy = peps.copy()
dummy_signs = [0.0] * len(tcrs)
# Word to index dictionary
amino_acids = [letter for letter in 'ARNDCEQGHILKMFPSTWYV']
if args.model_type == 'lstm':
amino_to_ix = {
amino: index
for index, amino in enumerate(['PAD'] + amino_acids)
}
if args.model_type == 'ae':
pep_atox = {
amino: index
for index, amino in enumerate(['PAD'] + amino_acids)
}
tcr_atox = {
amino: index
for index, amino in enumerate(amino_acids + ['X'])
}
max_len = 28
batch_size = 50
# Predict
if args.model_type == 'ae':
test_batches = ae.get_full_batches(tcrs, peps, dummy_signs, tcr_atox,
pep_atox, batch_size, max_len)
preds = ae.predict(model, test_batches, args.device)
if args.model_type == 'lstm':
lstm.convert_data(tcrs, peps, amino_to_ix)
test_batches = lstm.get_full_batches(tcrs, peps, dummy_signs,
batch_size, amino_to_ix)
preds = lstm.predict(model, test_batches, args.device)
# Print predictions
# for tcr, pep, pred in zip(tcrs_copy, peps_copy, preds):
# print('\t'.join([tcr, pep, str(pred)]))
return tcrs_copy, peps_copy, preds
def predict(args):
# Word to index dictionary
amino_acids = [letter for letter in 'ARNDCEQGHILKMFPSTWYV']
if args.model_type == 'lstm':
amino_to_ix = {
amino: index
for index, amino in enumerate(['PAD'] + amino_acids)
}
if args.model_type == 'ae':
pep_atox = {
amino: index
for index, amino in enumerate(['PAD'] + amino_acids)
}
tcr_atox = {
amino: index
for index, amino in enumerate(amino_acids + ['X'])
}
if args.ae_file == 'auto':
args.ae_file = 'TCR_Autoencoder/tcr_autoencoder.pt'
if args.model_file == 'auto':
dir = 'models'
p_key = 'protein' if args.protein else ''
args.model_file = dir + '/' + '_'.join(
[args.model_type, args.dataset, args.sampling, p_key, 'model.pt'])
if args.test_data_file == 'auto':
args.test_data_file = 'pairs_example.csv'
# Read test data
tcrs = []
peps = []
signs = []
max_len = 28
with open(args.test_data_file, 'r') as csv_file:
reader = csv.reader(csv_file)
for line in reader:
tcr, pep = line
if args.model_type == 'ae' and len(tcr) >= max_len:
continue
tcrs.append(tcr)
peps.append(pep)
signs.append(0.0)
tcrs_copy = tcrs.copy()
peps_copy = peps.copy()
# Load model
device = args.device
if args.model_type == 'ae':
model = AutoencoderLSTMClassifier(10, device, 28, 21, 30, 50,
args.ae_file, False)
checkpoint = torch.load(args.model_file, map_location=device)
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
model.eval()
if args.model_type == 'lstm':
model = DoubleLSTMClassifier(10, 30, 0.1, device)
checkpoint = torch.load(args.model_file, map_location=device)
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
model.eval()
pass
# Predict
batch_size = 50
if args.model_type == 'ae':
test_batches = ae.get_full_batches(tcrs, peps, signs, tcr_atox,
pep_atox, batch_size, max_len)
preds = ae.predict(model, test_batches, device)
if args.model_type == 'lstm':
lstm.convert_data(tcrs, peps, amino_to_ix)
test_batches = lstm.get_full_batches(tcrs, peps, signs, batch_size,
amino_to_ix)
preds = lstm.predict(model, test_batches, device)
# Print predictions
for tcr, pep, pred in zip(tcrs_copy, peps_copy, preds):
print('\t'.join([tcr, pep, str(pred)]))