def run(self):
sys.stdout = Logger("train_record.txt")
start = time.clock()
if self.type_name == 'HA':
self.l_name1 = [
'H1', 'H3', 'H4', 'H5', 'H6', 'H7', 'H9', 'H10', 'H11', 'H2',
'H13', 'H16'
]
self.l_name2 = ['H2', 'H13', 'H16']
# self.num_classes = 12
elif self.type_name == 'NA':
self.l_name1 = ['N1', 'N2', 'N3', 'N6', 'N7', 'N8', 'N9', 'N5']
self.l_name2 = ['N5']
# self.num_classes = 8
# if self.multi==0:
for l in self.l_name1:
sequences, label, max_len, num_acid = load_dataset(
l, self.type_name)
# split train and valid data
x_train, x_val, y_train, y_val = train_test_split(sequences,
label,
test_size=0.2)
x_val = x_val.reshape(x_val.shape[0], max_len, num_acid, 1)
x_train = x_train.reshape(x_train.shape[0], max_len, num_acid, 1)
print('x_val shape is', x_val.shape)
print('x_train shape is', x_train.shape)
# make a history object
history = LossHistory()
resnet = Resnet(l, history, self.args)
model = resnet.set_model(max_len, num_acid)
if l in self.l_name2:
model.load_weights('./result/' + self.type_name +
'/weight/model_weights_' + self.best_l +
'_8:2.h5')
resnet.run(model, x_train, x_val, y_train, y_val)
if l in self.l_name1:
if self.min_loss > history.val_loss['epoch'][-1]:
self.min_loss = history.val_loss['epoch'][-1]
self.best_l = l
print('the training is ok!')
end = time.clock()
print('Time cost is:', end - start)
history.acc_plot('epoch', l, self.type_name)
history.loss_plot('epoch', l, self.type_name)
'''
def run(self):
'''
if self.multi==1:
sequences, label, max_len, num_acid = load_multidata(self.type_name, isTrain=False)
seq_test = sequences.reshape(sequences.shape[0], max_len, num_acid, 1)
print('the shape of test seqs is', seq_test.shape)
print('label is', label)
history = LossHistory()
res = Resnet('test', history, self.args)
model = res.set_multimodel(max_len, num_acid)
model.load_weights('./result/'+self.type_name+'/weight/multi_model_weights_8:2.h5')
result = model.predict(seq_test, batch_size=64, verbose=0)
predict = [-1 for i in range(len(result))]
print('result is:', result)
for i in range(len(result)):
predict[i] = np.argmax(result[i])
print('predict is:', predict)
# evaluate
accuracy = accuracy_score(label, predict)
print('accuracy of test is', accuracy)
print('class accuracy is:\n',classification_report(label, predict))
'''
result_final = []
predict = []
if self.type_name == 'HA':
self.label_list = [
'H1', 'H10', 'H11', 'H13', 'H16', 'H2', 'H3', 'H4', 'H5', 'H6',
'H7', 'H9'
]
elif self.type_name == 'NA':
self.label_list = ['N1', 'N2', 'N3', 'N5', 'N6', 'N7', 'N8', 'N9']
label_dic = labels_dic(self.label_list)
sequences, label, max_len, num_acid = load_dataset('test',
self.type_name,
isTrain=False)
seq_test = sequences.reshape(sequences.shape[0], max_len, num_acid, 1)
print('the shape of test seqs is', seq_test.shape)
print('label is', label)
print('set label is', set(label))
history = LossHistory()
res = Resnet('test', history, self.args)
model = res.set_model(max_len, num_acid)
# for each subtype, calculate voting score
for l_name in self.label_list:
print('current l_name is', l_name)
model.load_weights('./result/' + self.type_name +
'/weight/model_weights_' + l_name + '_8:2.h5')
result = model.predict(seq_test, batch_size=64, verbose=0)
result_final.append(result)
# print('result is', result)
# print('the shape of result is', result.shape)
result_final = np.array(result_final)
print('final result shape is', result_final.shape)
# print('final result is', result_final)
# for each test example, cal the predict label
for i in range(result_final.shape[1]):
classes = []
score = []
# for each classifer, cal the possibility
for j in range(len(self.label_list)):
if result_final[j][i] > 0.5:
classes.append(j)
score.append(result_final[j][i])
# print('score is', score)
# print('classes shape is', len(classes))
if len(classes) == 0:
classes.append(-1)
if len(classes) == 1:
predict.append(classes[0])
else:
predict.append(max_class(score, classes))
print('predict is', predict)
predict = np.array(predict)
print('set predict is', set(predict))
# evaluate
accuracy = accuracy_score(label, predict)
print('accuracy of test is', accuracy)
print('class accuracy is:\n', classification_report(label, predict))