def leave_one_out(
newpath,
path,
hyperparameters,
iedb_data_file,
mhc_sequence_fasta_file=None,
peptide_length = 9,
max_sequence_length=None,
nb_epoch=10, max_ic50 = 5000.0,):
log_transformed_ic50_cutoff = 1 - np.log(500)/np.log(max_ic50)
create_fasta_file(path, remove_residues = True, consensus_cutoff = 0)
##Load files
allele_binding_data, df = load_binding_data(iedb_data_file)
allele_sequence_data, max_allele_length = load_allele_sequence_data(mhc_sequence_fasta_file)
max_sequence_length= max_allele_length
#optimizer = keras.optimizers.Adagrad(lr=0.01, beta_1=0.9, beta_2=0.999, epsilon=1e-8)
#optimizer = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-8)
#optimizer = keras.optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=1e-6)
##build models
for learn_rate in [ 0.001]:
for peptide_activation in [ 'tanh']:
for mhc_activation in [ 'tanh']:
total_AUC_score = []
print (peptide_activation, mhc_activation, learn_rate)
optimizer = keras.optimizers.RMSprop(lr=learn_rate, rho=0.9, epsilon=1e-6)
graph = build_graph_native_sequence_model(
maxlen_mhc = max_sequence_length,
optimizer=optimizer,
mhc_activation=mhc_activation,
peptide_activation= peptide_activation )
initial_weights = graph.get_weights()
allele_list = create_allele_list(allele_binding_data, allele_sequence_data)
formatted_allele_list = ['A0101','A0201','A2902','A3101','A6801','B0801','B1501','B1801','B2705','B3901']
#formatted_allele_list = ['B2705']
# formatted_allele_list = ['A0101','A0201','A0202','A0203',
# 'A0205','A0206','A0207','A0211',
# 'A0212','A0216','A0219','A0301',
# 'A1101','A2301','A2402','A2403',
# 'A2501','A2601','A2602','A2603',
# 'A2902','A3001','A3002','A3101',
# 'A3201','A3301','A6801','A6802',
# 'A6901','A8001','B0702','B0801',
# 'B0802','B0803','B1501','B1502',
# 'B1503','B1509','B1517','B1801',
# 'B2705','B3501','B3801','B3901',
# 'B4001','B4002','B4402','B4403',
# 'B4501','B4601','B4801','B5101',
# 'B5301','B5401','B5701','B5801',
# 'B5802','B7301']
formatted_allele_list = allele_list
##leave one out validaiton
for allele in allele_list:
if not os.path.exists(newpath):
os.makedirs(newpath)
f = open(newpath+ '/' + allele,'wb')
#split into training_list and test_list, where the test_list consists of a single allele
training_list = create_allele_list(allele_binding_data, allele_sequence_data)
training_list.remove(allele)
peptides_train, mhc_train, Y_train = get_model_data(training_list,
allele_sequence_data,
allele_binding_data,
dense_mhc_model=None,
peptide_length = peptide_length,
mhc_length=max_sequence_length,
mhc_dense = None
)
peptides_test, mhc_test, Y_test = get_model_data([allele],
allele_sequence_data,
allele_binding_data,
dense_mhc_model=None,
peptide_length = peptide_length,
mhc_length=max_sequence_length,
mhc_dense=None)
#convert ic50 values into binary binders(1) or non-binders(0)
Y_true = 1 * np.greater(Y_test,log_transformed_ic50_cutoff)
Y_train = 1 * np.greater(Y_train,log_transformed_ic50_cutoff)
history = LossHistory()
history.get_data(peptides_train, mhc_train, Y_train, peptides_test, mhc_test, Y_true)
if(not np.all([Y_true[0]]*len(Y_true) == Y_true) ): #check if all values in Y_true are not the same
graph.set_weights(initial_weights)
graph.fit(
{'peptide':peptides_train,'mhc':mhc_train, 'output': Y_train},
batch_size=32,
nb_epoch=nb_epoch,
verbose = 1,
callbacks = [history]
)
Y_true = 1 * np.greater(Y_test,log_transformed_ic50_cutoff)
Y_pred = graph.predict({'peptide':peptides_test,'mhc':mhc_test})['output']
Y_pred = Y_pred.reshape(Y_pred.shape[0])
AUC = roc_auc_score(Y_true, Y_pred)
#convert Y_pred to binary
Y_pred_binary = 1 * np.greater(Y_pred,log_transformed_ic50_cutoff)
ACC = accuracy_score(Y_true, Y_pred_binary)
print ("Allele: ",allele, "\t#entries :", len(peptides_test) ,"\tAUC: ", AUC, "\tACC:", ACC)
total_AUC_score.append(AUC)
print(history.test_error,'\n',history.training_error, file=f)
print ("\nAllele: ",allele, "\t#entries :", len(peptides_test) ,"\tAUC: ", AUC, "\tACC:", ACC, file=f)
f.close()
print (1 - sum(total_AUC_score)/len(total_AUC_score))
def leave_one_out(
path,
hyperparameters,
iedb_data_file,
mhc_sequence_fasta_file=None,
peptide_length = 9,
max_sequence_length=None,
nb_epoch=30, max_ic50 = 5000.0,):
log_transformed_ic50_cutoff = 1 - np.log(500)/np.log(max_ic50)
create_fasta_file(path, remove_residues = True, consensus_cutoff = 0)
##Load files
allele_binding_data, df = load_binding_data(iedb_data_file)
allele_sequence_data, max_allele_length = load_allele_sequence_data(mhc_sequence_fasta_file)
max_sequence_length= max_allele_length
for learn_rate in [ 0.001]:
for peptide_activation in [ 'tanh']:
for mhc_activation in [ 'tanh']:
optimizer = keras.optimizers.RMSprop(lr=learn_rate, rho=0.9, epsilon=1e-6)
print (peptide_activation, mhc_activation, learn_rate)
graph = ffn_matrix( maxlen_mhc = max_sequence_length,
optimizer=optimizer,
mhc_activation=mhc_activation,
peptide_activation= peptide_activation )
initial_weights = graph.get_weights()
allele_list = create_allele_list(allele_binding_data, allele_sequence_data)
formatted_allele_list = ['A0101', 'A0201']
##leave one out validaiton
for allele in formatted_allele_list:
print(allele)
#split into training_list and test_list, where the test_list consists of a single allele
training_list = create_allele_list(allele_binding_data, allele_sequence_data)
training_list.remove(allele)
peptides_train, mhc_train, Y_train = get_model_data(training_list,
allele_sequence_data,
allele_binding_data,
dense_mhc_model=None,
peptide_length = peptide_length,
mhc_length=max_sequence_length,
mhc_dense = None
)
peptides_test, mhc_test, Y_test = get_model_data([allele],
allele_sequence_data,
allele_binding_data,
dense_mhc_model=None,
peptide_length = peptide_length,
mhc_length=max_sequence_length,
mhc_dense=None)
#convert ic50 values into binary binders(1) or non-binders(0)
Y_true = 1 * np.greater(Y_test,log_transformed_ic50_cutoff)
Y_train = 1 * np.greater(Y_train,log_transformed_ic50_cutoff)
if(not np.all([Y_true[0]]*len(Y_true) == Y_true) ): #check if all values in Y_true are not the same
graph.set_weights(initial_weights)
history = LossHistory()
history.get_data(peptides_train, mhc_train, Y_train, peptides_test, mhc_test, Y_true)
graph.fit(
{'peptide':peptides_train,'mhc':mhc_train, 'output': Y_train},
batch_size=32,
nb_epoch=nb_epoch,
verbose = 1,
callbacks=[history]
)
