strs = 'lookuptable_5folds_'+str(i)+'.json'
jsonfiles.append(os.path.join(temp_path,strs))
#print(jsonfiles)
temp_path = os.path.join(os.getcwd(),'files_h5')
h5folders = []
for i in (5,10,15):
strs = 'subjects_'+str(i)
h5folders.append(os.path.join(temp_path,strs))
# print(h5folders)
file_trans_input = os.path.join(os.getcwd(),'dataset','scattered','standard1' )
file_trans_output = os.path.join(os.getcwd(),'dataset','difsizesSVM')
valpath = os.path.join(file_trans_output, 'val')
testpath = os.path.join(file_trans_output, 'test')
report1 = [valpath, 'validation']
report2 = [testpath, 'test']
sys.stdout = logger(filename=os.path.join(os.getcwd(),'log&&materials','Supploopsvm_multipledesignevalutionresults.log'))
# This loop is for different sizess of datasets
topCon = {'SVM':[]}
wholest = time.time()
for jsonfile, h5folder, flag in zip(jsonfiles,h5folders,(5,10,15,20)):
datalooper = trainvalFormation(file_trans_input, None, 5, 'specified')
# jsonfile = os.path.join(os.getcwd(),'files_json', 'lookuptable_5folds_10.json')
datalooper.specifybyloading(path=jsonfile)
dur = time.time()
datalooper.subjects_transfer(file_trans_output)
features, targets = skstyleDataset(file_trans_output,flag)
print('dataset formation time: {}s.'.format(round(time.time() - dur, 2)))
print()
print()
print()
print('#'*80)
file_trans_input = os.path.join(os.getcwd(), 'dataset', 'scattered',
'standard1')
file_trans_output = os.path.join(os.getcwd(), 'dataset', 'constructed2')
looper = trainvalFormation(file_trans_input, file_trans_output, 5,
'specified')
looper.specifybyloading()
basedatapath = '/home/zhaok14/example/PycharmProjects/setsail/5foldCNNdesign/dataset/constructed2'
valdatapath = os.path.join(os.getcwd(), 'dataset', 'constructed2', 'val')
testdatapath = os.path.join(os.getcwd(), 'dataset', 'constructed2', 'test')
report1 = [valdatapath, 'validation']
report2 = [testdatapath, 'test']
ev = time.time()
# 1. initialize the dataset
sys.stdout = logger(filename=os.path.join(os.getcwd(), 'log&&materials',
'spec_lstmresults.log'))
# 2. for every single rounds of evaluation, we need to train the models.
print('Note this time lstm is with the upgraded spec feature....')
for i in (0, 1, 2, 3, 4):
strg = 'NEWCHECKING:ROUND_{}'.format(str(i))
print()
print(40 * '-' + strg + 40 * '-')
print()
# 2.1 generate different data
dur = time.time()
looper.loop_files_transfer(i)
dur = round(time.time() - dur, 2)
print('file transformation finished. time:{}s'.format(dur))
# 2.2 build individual networks
nn = comparativeNetwork()
nn.CreateLstmModel()
#print(jsonfiles)
temp_path = os.path.join(os.getcwd(), 'files_h5')
h5folders = []
for i in (5, 10, 15, 20):
strs = 'subjects_' + str(i)
h5folders.append(os.path.join(temp_path, strs))
# print(h5folders)
file_trans_input = os.path.join(os.getcwd(), 'dataset', 'scattered',
'standard1')
file_trans_output = os.path.join(os.getcwd(), 'dataset', 'difsizes')
valpath = os.path.join(file_trans_output, 'val')
testpath = os.path.join(file_trans_output, 'test')
report1 = [valpath, 'validation']
report2 = [testpath, 'test']
sys.stdout = logger(
filename=os.path.join(os.getcwd(), 'log&&materials',
'Bigloop_20subjectsdesignevalutionresults.log'))
# This loop is for different sizess of datasets
topCon = {'CNN': [], 'LSTM': [], 'FC': []}
wholest = time.time()
for jsonfile, h5folder, flag in zip(jsonfiles, h5folders, (5, 10, 15, 20)):
if flag != 20:
print('We just skip the {}-scale dataset..'.format(str(flag)))
continue
datalooper = trainvalFormation(file_trans_input, file_trans_output, 5,
'specified')
datalooper.specifybyloading(path=jsonfile)
print()
print()
print()
print('#' * 80)
#define the file transfer object
file_trans_input = os.path.join(os.getcwd(), 'dataset', 'scattered', 'standard1')
file_trans_output = os.path.join(os.getcwd(), 'dataset', 'constructed')
looper = trainvalFormation(file_trans_input, file_trans_output, 5, 'specified')
looper.specifybyloading()
basedatapath = '/home/zhaok14/example/PycharmProjects/setsail/5foldCNNdesign/dataset/constructed'
valdatapath = os.path.join(os.getcwd(),'dataset','constructed','val')
testdatapath = os.path.join(os.getcwd(),'dataset','constructed','test')
report1 = [valdatapath,'validation']
report2 = [testdatapath, 'test']
ev = time.time()
# 1. initialize the dataset
sys.stdout = logger(filename=os.path.join(os.getcwd(),'log&&materials','Newfeature_multipleensemble_evaluationresults.log'))
print('This time the corrected features are applied....')
for i in (0, 1, 2, 3, 4):
print()
print(40 * '-' + 'NEWCHECKING:ROUND_{}'.format(str(i)) + 40 * '-')
print()
looper.loop_files_transfer(i)
nn = flexible_average()
# 2. create individual and ensemble model
regudir = os.path.join(os.getcwd(),'CNNevaluation','regular','regi='+ str(i))
residir = os.path.join(os.getcwd(), 'CNNevaluation', 'residual','resi='+ str(i))
incedir = os.path.join(os.getcwd(),'CNNevaluation','inception','inci='+ str(i))
nn.ensembleForward(regudir=regudir, residir=residir, incedir=incedir)
regAresModel, regAresName = nn.average(regudir=True,residir=True)
regAincModel, regAincName = nn.average(regudir=True,incedir=True)
resAincModel, resAincName = nn.average(residir=True,incedir=True)
jsonfiles.append(os.path.join(temp_path, strs))
# print(jsonfiles)
temp_path = os.path.join(os.getcwd(), 'files_h5')
h5folders = []
for i in (5, 10, 15, 20):
strs = 'subjects_' + str(i)
h5folders.append(os.path.join(temp_path, strs))
# print(h5folders)
file_trans_input = os.path.join(os.getcwd(), 'dataset', 'scattered',
'standard_aligned')
file_trans_output = os.path.join(os.getcwd(), 'dataset', 'difsizes')
valpath = os.path.join(file_trans_output, 'val')
testpath = os.path.join(file_trans_output, 'test')
report1 = [valpath, 'validation']
report2 = [testpath, 'test']
sys.stdout = logger(filename=os.path.join(
os.getcwd(), 'log&&materials', 'std150_designevalutionresults.log'))
# This loop is for different sizess of datasets
topCon = {'CNN': [], 'LSTM': [], 'FC': []}
wholest = time.time()
for jsonfile, h5folder, flag in zip(jsonfiles, h5folders, (5, 10, 15, 20)):
if flag != 20:
print('We just skip the {}-scale dataset..'.format(str(flag)))
continue
datalooper = trainvalFormation(file_trans_input, file_trans_output, 5,
'specified')
datalooper.specifybyloading(path=jsonfile)
print()
print()
print()
print('#' * 80)
print('The followed data formation jsonfile:{}'.format(