def evaluate(args, model, tcrs, peps, signs):
# 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, signs, tcr_atox,
pep_atox, batch_size, max_len)
auc, roc = ae.evaluate_full(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, signs, batch_size,
amino_to_ix)
auc, roc = lstm.evaluate_full(model, test_batches, args.device)
return auc, roc
def single_protein_score(args, model, test_data, protein, protein_peps):
# positive examples - tcr in test that bind a pep belongs to the protein
# negative examples - tcr in test that do not bind a pep belongs to the protein
# Get pep-relevant data
tcrs = [p[0] for p in test_data if p[1][0] in protein_peps[protein]]
signs_to_prob = {'n': 0.0, 'p': 1.0}
signs = [
signs_to_prob[p[2]] for p in test_data
if p[1][0] in protein_peps[protein]
]
peps = [p[1][0] for p in test_data if p[1][0] in protein_peps[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'])
}
max_len = 28
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)
test_auc, roc = ae.evaluate_full(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, signs, batch_size,
amino_to_ix)
test_auc, roc = lstm.evaluate_full(model, test_batches, args.device)
return test_auc, roc
def single_peptide_score(args, model, test_data, pep, neg_type=None):
# positive examples - tcr in test that bind this pep
# negative examples - tcr in test that do not bind this pep
# negs could be from test pairs, or naive, or memory
# Get pep-relevant data
tcrs = [p[0] for p in test_data if p[1][0] == pep]
signs_to_prob = {'n': 0.0, 'p': 1.0}
signs = [signs_to_prob[p[2]] for p in test_data if p[1][0] == pep]
peps = [pep] * 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
if args.model_type == 'ae':
test_batches = ae.get_full_batches(tcrs, peps, signs, tcr_atox,
pep_atox, batch_size, max_len)
test_auc, roc = ae.evaluate_full(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, signs, batch_size,
amino_to_ix)
test_auc, roc = lstm.evaluate_full(model, test_batches, args.device)
return test_auc, roc
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)]))
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'))
