def train_model(batches, test_batches, device, args, params):
"""
Train and evaluate the model
"""
losses = []
# We use Binary-Cross-Entropy loss
loss_function = nn.BCELoss()
# Set model with relevant parameters
model = AutoencoderLSTMClassifier(params['emb_dim'], device,
params['max_len'], 21, params['enc_dim'],
params['batch_size'], args['ae_file'],
params['train_ae'])
# Move to GPU
model.to(device)
# We use Adam optimizer
optimizer = optim.Adam(model.parameters(),
lr=params['lr'],
weight_decay=params['wd'])
# Train several epochs
best_auc = 0
best_roc = None
for epoch in range(params['epochs']):
print('epoch:', epoch + 1)
epoch_time = time.time()
# Train model and get loss
loss = train_epoch(batches, model, loss_function, optimizer, device)
losses.append(loss)
# Compute auc
train_auc = evaluate(model, batches, device)[0]
print('train auc:', train_auc)
with open(args['train_auc_file'], 'a+') as file:
file.write(str(train_auc) + '\n')
test_auc, roc = evaluate(model, test_batches, device)
# nni.report_intermediate_result(test_auc)
if test_auc > best_auc:
best_auc = test_auc
best_roc = roc
# print(roc)
# plt.plot(roc[0], roc[1])
# plt.show()
print('test auc:', test_auc)
with open(args['test_auc_file'], 'a+') as file:
file.write(str(test_auc) + '\n')
print('one epoch time:', time.time() - epoch_time)
return model, best_auc, best_roc
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)]))
def protein_test(args):
assert args.protein
# 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.test_data_file == 'auto':
dir = 'memory_and_protein'
p_key = 'protein' if args.protein else ''
args.test_data_file = dir + '/' + '_'.join([
args.model_type, args.dataset, args.sampling, p_key, 'test.pickle'
])
if args.model_file == 'auto':
dir = 'memory_and_protein'
p_key = 'protein' if args.protein else ''
args.model_file = dir + '/' + '_'.join(
[args.model_type, args.dataset, args.sampling, p_key, 'model.pt'])
# Read test data
with open(args.test_data_file, 'rb') as handle:
test = pickle.load(handle)
device = args.device
if args.model_type == 'ae':
test_tcrs, test_peps, test_signs = ae_get_lists_from_pairs(test, 28)
model = AutoencoderLSTMClassifier(10, device, 28, 21, 30, 50,
args.ae_file, False)
checkpoint = torch.load(args.model_file)
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
model.eval()
if args.model_type == 'lstm':
test_tcrs, test_peps, test_signs = lstm_get_lists_from_pairs(test)
model = DoubleLSTMClassifier(10, 30, 0.1, device)
checkpoint = torch.load(args.model_file)
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
model.eval()
pass
# Get frequent peps list
if args.dataset == 'mcpas':
datafile = 'McPAS-TCR.csv'
p = []
protein_peps = {}
with open(datafile, 'r', encoding='unicode_escape') as file:
file.readline()
reader = csv.reader(file)
for line in reader:
pep, protein = line[11], line[9]
if protein == 'NA' or pep == 'NA':
continue
p.append(protein)
try:
protein_peps[protein].append(pep)
except KeyError:
protein_peps[protein] = [pep]
d = {t: p.count(t) for t in set(p)}
sorted_d = sorted(d.items(), key=lambda k: k[1], reverse=True)
proteins = [t[0] for t in sorted_d]
"""
McPAS most frequent proteins
NP177 Influenza
Matrix protein (M1) Influenza
pp65 Cytomegalovirus (CMV)
BMLF-1 Epstein Barr virus (EBV)
PB1 Influenza
"""
rocs = []
for protein in proteins[:50]:
protein_shows = [
i for i in range(len(test_peps))
if test_peps[i] in protein_peps[protein]
]
test_tcrs_protein = [test_tcrs[i] for i in protein_shows]
test_peps_protein = [test_peps[i] for i in protein_shows]
test_signs_protein = [test_signs[i] for i in protein_shows]
if args.model_type == 'ae':
test_batches_protein = ae.get_full_batches(test_tcrs_protein,
test_peps_protein,
test_signs_protein,
tcr_atox, pep_atox, 50,
28)
if args.model_type == 'lstm':
lstm.convert_data(test_tcrs_protein, test_peps_protein,
amino_to_ix)
test_batches_protein = lstm.get_full_batches(
test_tcrs_protein, test_peps_protein, test_signs_protein, 50,
amino_to_ix)
if len(protein_shows):
try:
if args.model_type == 'ae':
test_auc, roc = ae.evaluate_full(model,
test_batches_protein,
device)
if args.model_type == 'lstm':
test_auc, roc = lstm.evaluate_full(model,
test_batches_protein,
device)
rocs.append((pep, roc))
# print(protein)
print(str(test_auc))
# print(protein + ', ' + str(test_auc))
except ValueError:
# print(protein)
print('NA')
# print(protein + ', ' 'NA')
pass
return rocs
def pep_test(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_ae_dim_100.pt'
if args.test_data_file == 'auto':
dir = 'final_results'
p_key = 'protein' if args.protein else ''
args.test_data_file = dir + '/' + '_'.join([
args.model_type, args.dataset, args.sampling, p_key, 'test.pickle'
])
if args.model_file == 'auto':
dir = 'final_results'
p_key = 'protein' if args.protein else ''
args.model_file = dir + '/' + '_'.join(
[args.model_type, args.dataset, args.sampling, p_key, 'model.pt'])
# Read test data
with open(args.test_data_file, 'rb') as handle:
test = pickle.load(handle)
device = args.device
if args.model_type == 'ae':
test_tcrs, test_peps, test_signs = ae_get_lists_from_pairs(test, 28)
model = AutoencoderLSTMClassifier(10, device, 28, 21, 100, 50,
args.ae_file, False)
checkpoint = torch.load(args.model_file)
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
model.eval()
if args.model_type == 'lstm':
test_tcrs, test_peps, test_signs = lstm_get_lists_from_pairs(test)
model = DoubleLSTMClassifier(10, 500, 0.1, device)
checkpoint = torch.load(args.model_file)
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
model.eval()
pass
# Get frequent peps list
if args.dataset == 'mcpas':
datafile = 'nine_class_testdata.csv'
p = []
with open(datafile, 'r', encoding='unicode_escape') as file:
file.readline()
reader = csv.reader(file)
for line in reader:
pep = line[1]
if pep == 'NA':
continue
p.append(pep)
d = {t: p.count(t) for t in set(p)}
sorted_d = sorted(d.items(), key=lambda k: k[1], reverse=True)
peps = [t[0] for t in sorted_d]
"""
McPAS most frequent peps
LPRRSGAAGA Influenza
GILGFVFTL Influenza
GLCTLVAML Epstein Barr virus (EBV)
NLVPMVATV Cytomegalovirus (CMV)
SSYRRPVGI Influenza
"""
rocs = []
auc = []
for pep in peps[:249]:
pep_shows = [i for i in range(len(test_peps)) if pep == test_peps[i]]
test_tcrs_pep = [test_tcrs[i] for i in pep_shows]
test_peps_pep = [test_peps[i] for i in pep_shows]
test_signs_pep = [test_signs[i] for i in pep_shows]
if args.model_type == 'ae':
test_batches_pep = ae.get_full_batches(test_tcrs_pep,
test_peps_pep,
test_signs_pep, tcr_atox,
pep_atox, 50, 28)
if args.model_type == 'lstm':
lstm.convert_data(test_tcrs_pep, test_peps_pep, amino_to_ix)
test_batches_pep = lstm.get_full_batches(test_tcrs_pep,
test_peps_pep,
test_signs_pep, 50,
amino_to_ix)
if len(pep_shows):
try:
if args.model_type == 'ae':
test_auc, roc = ae.evaluate_full(model, test_batches_pep,
device)
filename = 'pep_test_result_ae2'
if args.model_type == 'lstm':
test_auc, roc = lstm.evaluate_full(model, test_batches_pep,
device)
filename = 'pep_test_result_lstm2'
rocs.append((pep, roc))
auc.append([pep, roc, test_auc])
print(str(test_auc))
# print(pep + ', ' + str(test_auc))
except ValueError:
print('NA')
# print(pep + ', ' 'NA')
pass
pickle.dump(auc, open(filename, 'wb'))
def pep_test(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_ae_dim_100.pt'
if args.test_data_file == 'auto':
dir = 'final_results'
p_key = 'protein' if args.protein else ''
args.test_data_file = dir + '/' + '_'.join([
args.model_type, args.dataset, args.sampling, p_key, 'test.pickle'
])
if args.model_file == 'auto':
dir = 'final_results'
p_key = 'protein' if args.protein else ''
args.model_file = dir + '/' + '_'.join(
[args.model_type, args.dataset, args.sampling, p_key, 'model.pt'])
# Read test data
with open(args.test_data_file, 'rb') as handle:
test = pickle.load(handle)
device = args.device
if args.model_type == 'ae':
test_tcrs, test_peps, test_signs = ae_get_lists_from_pairs(test, 28)
model = AutoencoderLSTMClassifier(10, device, 28, 21, 100, 50,
args.ae_file, False)
checkpoint = torch.load(args.model_file)
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
model.eval()
if args.model_type == 'lstm':
test_tcrs, test_peps, test_signs = lstm_get_lists_from_pairs(test)
model = DoubleLSTMClassifier(10, 500, 0.1, device)
checkpoint = torch.load(args.model_file)
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
model.eval()
pass
rocs = []
auc = []
if args.model_type == 'ae':
test_batches_pep = ae.get_full_batches(test_tcrs, test_peps,
test_signs, tcr_atox, pep_atox,
50, 28)
test_auc, roc = ae.evaluate_full(model, test_batches_pep, device)
filename = 'tpp_test_result_ae2'
if args.model_type == 'lstm':
lstm.convert_data(test_tcrs, test_peps, amino_to_ix)
test_batches_pep = lstm.get_full_batches(test_tcrs, test_peps,
test_signs, 50, amino_to_ix)
test_auc, roc = lstm.evaluate_full(model, test_batches_pep, device)
filename = 'tpp_test_result_lstm2'
rocs.append(roc)
auc.append([roc, test_auc])
print(str(test_auc))
pickle.dump(auc, open(filename, 'wb'))
def load_model_and_data(args):
# train
if args.train_data_file == 'auto':
dir = 'save_results'
p_key = 'protein' if args.protein else ''
args.train_data_file = dir + '/' + '_'.join([
args.model_type, args.dataset, args.sampling, p_key, 'train.pickle'
])
# test
if args.test_data_file == 'auto':
dir = 'save_results'
p_key = 'protein' if args.protein else ''
args.test_data_file = dir + '/' + '_'.join([
args.model_type, args.dataset, args.sampling, p_key, 'test.pickle'
])
# Read train data
with open(args.train_data_file, "rb") as file:
train_data = pickle.load(file)
# Read test data
with open(args.test_data_file, "rb") as file:
test_data = pickle.load(file)
# trained model
if args.model_file == 'auto':
dir = 'save_results'
p_key = 'protein' if args.protein else ''
args.model_file = dir + '/' + '_'.join(
[args.model_type, args.dataset, args.sampling, p_key, 'model.pt'])
# enc_dim = 30
# Load model
device = args.device
if args.model_type == 'ae':
checkpoint = torch.load(args.model_file, map_location=device)
params = checkpoint['params']
args.ae_file = 'TCR_Autoencoder/tcr_ae_dim_' + str(
params['enc_dim']) + '.pt'
model = AutoencoderLSTMClassifier(params['emb_dim'], device, 28, 21,
params['enc_dim'],
params['batch_size'], args.ae_file,
False)
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
model.eval()
if args.model_type == 'lstm':
checkpoint = torch.load(args.model_file, map_location=device)
params = checkpoint['params']
model = DoubleLSTMClassifier(params['emb_dim'], params['lstm_dim'],
params['dropout'], device)
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
model.eval()
data = [train_data, test_data]
return model, data