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run_multiple_experiments_1step.py
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import configparser
import sys,os, getopt
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
from pathlib import Path
import Traffic_prediction_per_fold_1step as runexp2
def run_concurrent_exp(cfg, config_file,dataset_path, app, n_features, model, granularity,
fold=10, output_path='.', n_samples=0,win_size=10,
gpu_id=1, mode='EXP',
byproto=False, proto=None):
#for current_fold in range(fold):
local_cfg_file_name = os.path.join(os.path.dirname(config_file),'config_%s_W%s_%s.ini'%(model,win_size,app))
#shutil.copy(config_file, local_cfg_file_name)
cfg.set('BASE', 'app', app)
cfg.set('BASE', 'n_features', str(n_features))
cfg.set('BASE', 'win_size', str(win_size))
cfg.set('I/O', 'app_from_dataset', dataset_path)
cfg.set('I/O', 'output_path', output_path)
cfg.set('MODEL', 'model', model)
cfg.set('I/O', 'byproto',str(byproto))
cfg.set('BASE', 'proto',str(proto))
cfg.set('I/O', 'granularity', granularity)
cfg.set('BASE', 'fold', str(fold))
cfg.set('BASE', 'current_fold', str(0))
cfg.set('BASE', 'n_samples', str(n_samples))
out_feat=['L4_payload_bytes','iat_micros','packet_dir']
cfg.set('COMPILE', 'pl_weight', str(0.45))
cfg.set('COMPILE', 'iat_weight', str(0.1))
cfg.set('COMPILE', 'pd_weight', str(0.45))
cfg.set('BASE', 'out_fine_features', ','.join(out_feat))
print('INFO: Predicting %s'%out_feat)
with open(local_cfg_file_name, 'w') as cfgfile:
cfg.write(cfgfile)
print('Config File : %s'%local_cfg_file_name)
if mode=='EXP':
runexp2.main(cfg,gpu_id)
#if __name__ == "__main__":
def main(argv):
config_path='config_template.ini'
finalres=None
models=[]
apps=[]
sensitivity=False
win=10
gpu_id=None
validation=False
by_fold=False
#appsx=['Zoom','Teams','Skype','Slack','Webex','Meets','Messenger','GotoMeeting','Discrod']
appsx=['Zoom','Teams','Skype','Webex']
#appsx=['Teams','Skype','Webex']
activities=['audiocall','chat','videocall']
mode='EXP'
protosx=['UDP','TCP']
protos=[]
byproto=False
filtering='X'
sampling=False
print(argv)
try:
opts, args = getopt.getopt(argv, "hc:m:o:e:f:sw:g:a:vp:S", "[source_dir_path=]")
except getopt.GetoptError:
print('<script_name.py> -o <OUTDIR> -e <EXP OUT> -m <MODEL> -c <CONFIG_FILE> -f <FILTER (a,t,m) -s <Sensitivity> -w < window size (int) -g <GPU-ID (int)> -a <app name>')
sys.exit(2)
for opt, arg in opts:
if opt in ("-h", "--help"):
print('HELP: <script_name.py> -o <OUTDIR> -m <MODEL> -c <CONFIG_FILE> -f <FILTER (a,t,m) -s <Sensitivity> -w <window size (int)> -g<GPU-ID (int)> -a <app name>')
sys.exit()
if opt in ('-c','--config'):
config_path=arg
if opt in ('-p','--proto'):
byproto=True
if arg in protosx:
protos.append(arg)
if opt in ('-f','--finter'):
if arg in ['a']:
filtering='app'
elif arg in ['t']:
filtering='activity'
elif arg in ['m']:
filtering='appactivity'
else:
print('Wrong filter')
if opt in ('-o','--output'):
finalres=arg
if opt in ('-e','--exppath'):
exppath=arg
if opt in ('-w','--window'):
win=int(arg)
if opt in ('-g','--gpu-id'):
gpu_id=arg
if opt in ('-s','--sensitivity'):
sensitivity=True
if opt in ('-v','--validation'):
validation=True
if opt in ('-a','--app'):
if arg in appsx:
apps.append(arg)
if opt in ('-S','--sampling'):
sampling=True
if opt in ('-m','--model'):
if arg in ['CNN','LSTM','GRU','DSANET','GLOBAL_CNN_RNN','SERIES_NET','STN']:
models.append(arg)
else:
print('Wrong model')
exit()
config = configparser.ConfigParser()
config.read(config_path)
#apps = ['Discord','GotoMeeting','Meets','Messenger','Skype','Slack','Teams','Webex','Zoom']
if len(apps)==0:
print('Experiment on all apps')
apps=appsx
else:
print('Experiment on %s apps'%apps)
if byproto:
if len(protos)==0:
print('Experiment on all apps')
protos=protosx
else:
print('Experiment on %s protos'%protos)
root_dir_datasets = config['I/O']['root_datasets']
n_features = config['BASE'].getint('n_features')
granularity = 'packets'
if finalres is None:
finalres = config['I/O']['final_res_root']
total_fold = config['BASE'].getint('fold')
if sensitivity:
win_sizes=[2,5,10,20,30]
else:
win_sizes = [win]
Path(exppath).mkdir(parents=True, exist_ok=True)
config.set('I/O', 'output_path',exppath)
if finalres is None:
finalres=exppath
else:
Path(finalres).mkdir(parents=True, exist_ok=True)
config.set('I/O', 'final_res_root',finalres)
config.set('OPTIONS', 'validation_set',str(validation))
config.set('OPTIONS', 'validation_split',str(0.2))
config.set('OPTIONS', 'filtering',filtering+'proto' if byproto and filtering!='X' else 'proto' if filtering=='X' and byproto else
filtering)
config.set('OPTIONS', 'sampling',str(sampling))
n_samples = config['BASE'].getint('n_samples')
if filtering=='app':
for app in apps:
for win_size in win_sizes:
for model in models:
dataset_path = root_dir_datasets
if byproto:
for proto in protos:
run_concurrent_exp( cfg=config,
config_file=config_path,
dataset_path=dataset_path,
app=app,
n_features=n_features,
model=model,
granularity=granularity,
fold=total_fold,
output_path=exppath,
n_samples=n_samples,
win_size=win_size,
gpu_id=gpu_id,
mode=mode,
byproto=byproto,
proto=proto)
else:
run_concurrent_exp( cfg=config,
config_file=config_path,
dataset_path=dataset_path,
app=app,
n_features=n_features,
model=model,
granularity=granularity,
fold=total_fold,
output_path=exppath,
n_samples=n_samples,
win_size=win_size,
gpu_id=gpu_id,
mode=mode,
byproto=byproto)
elif filtering=='activity':
if classification_type is not None:
classification_type='app'
print('Model per ACTIVITY...Forcing to APP classification')
for act in activities:
for win_size in win_sizes:
for model in models:
dataset_path = root_dir_datasets
if byproto:
for proto in protos:
run_concurrent_exp( cfg=config,
config_file=config_path,
dataset_path=dataset_path,
app=act,
n_features=n_features,
model=model,
granularity=granularity,
fold=total_fold,
output_path=exppath,
n_samples=n_samples,
win_size=win_size,
gpu_id=gpu_id,
mode=mode,
byproto=byproto,
proto=proto)
else:
run_concurrent_exp(
cfg=config,
config_file=config_path,
dataset_path=dataset_path,
app=act,
n_features=n_features,
model=model,
granularity=granularity,
fold=total_fold,
output_path=exppath,
n_samples=n_samples,
win_size=win_size,
gpu_id=gpu_id,
mode=mode,
byproto=byproto)
else:
with open(config_path, 'w') as cfgfile:
config.write(cfgfile)
if not by_fold:
for win_size in win_sizes:
for model in models:
output_path = config['I/O']['output_path']
dataset_path = root_dir_datasets
if byproto:
for proto in protos:
run_concurrent_exp(
cfg=config,
config_file=config_path,
dataset_path=dataset_path,
app='ALL',
n_features=n_features,
model=model,
granularity=granularity,
fold=total_fold,
output_path=exppath,
n_samples=n_samples,
win_size=win_size,
gpu_id=gpu_id,
mode=mode,
byproto=byproto,
proto=proto,
)
else:
run_concurrent_exp(
cfg=config,
config_file=config_path,
dataset_path=dataset_path,
app='ALL',
n_features=n_features,
model=model,
granularity=granularity,
fold=total_fold,
output_path=exppath,
n_samples=n_samples,
win_size=win_size,
gpu_id=gpu_id,
mode=mode,
byproto=byproto)
else:
print('ToDo')
if __name__ == '__main__':
main(sys.argv[1:])