def bks(limit, fmt, cstats, stats, train, test, profile):
src = ArffReader.ArffReader(sys.stdin)
dst = sys.stdout
cs = BKS(profile)
st = Stats()
# setup proto filter
if hasattr(P, "skip"):
check = lambda gt: gt not in P.skip
elif hasattr(P, "select"):
check = lambda gt: gt in P.select
else:
check = lambda: True
# arff header?
if fmt == "arff":
src.printh(nodata=True, dst=dst)
dst.write("%% Multilevel Traffic Classifier: BKS approach\n")
dst.write("% bks_proto: the identified protocol\n")
dst.write("@attribute bks_proto string\n\n")
dst.write("@data\n")
# load a model?
if test:
cs.train_load(test)
# go!
for d in src:
# check if gt is enabled
gt = d[P.gtcol]
if not check(gt): continue
# parse and pass to classifiers
f = Flow(src, d, gt)
s = cs.ask(f)
if train:
cs.train(s, f.gt)
elif test:
cs.classify(f, s)
# print it to the user?
if limit:
if f.isunk():
if "unk" not in limit: continue
elif "ans" not in limit:
if f.isok() and "ok" not in limit: continue
if f.iserr() and "err" not in limit: continue
# print and count
f.write(fmt, dst)
st.count(f)
# finish training
if train:
cs.train_finish()
cs.train_store(train)
# finish profiling
if profile:
cs.profile_store(profile)
# algo stats?
if cstats:
print("BKS statistics")
print("==============")
print(cs.get_stats())
# stats?
if stats:
if train:
cs.train_show()
else:
print("Performance statistics")
print("======================")
print(st.get_cm())
def waterfall(dump, dumpl, limit, fmt, cstats, stats):
# init modules
src = ArffReader.ArffReader(sys.stdin)
dst = sys.stdout
cs = Cascade()
st = Stats()
# setup proto filter
if hasattr(P, "skip"):
check = lambda gt: gt not in P.skip
elif hasattr(P, "select"):
check = lambda gt: gt in P.select
else:
check = lambda: True
# arff header?
if fmt == "arff":
src.printh(nodata=True, dst=dst)
dst.write("%% Multilevel Traffic Classifier: Waterfall approach\n")
dst.write("% waterfall_module: classifier that contributed the answer\n")
dst.write("% waterfall_proto: the identified protocol\n")
dst.write("@attribute waterfall_module string\n")
dst.write("@attribute waterfall_proto string\n\n")
dst.write("@data\n")
# go!
for d in src:
# check if gt is enabled
gt = d[P.gtcol]
if not check(gt): continue
# parse and classify
f = Flow(src, d, gt)
show = cs.classify(f, dump, dumpl)
# print it to the user?
if not show:
continue
if limit:
if f.isunk():
if "unk" not in limit: continue
elif "ans" not in limit:
if f.isok() and "ok" not in limit: continue
if f.iserr() and "err" not in limit: continue
# print and count
f.write(fmt, dst)
st.count(f)
# cascade stats?
if cstats:
if fmt != "none": print("")
print("Cascade statistics")
print("==================")
print(cs.get_stats())
# perf stats?
if stats:
if cstats: print("")
print("Performance statistics")
print("======================")
print(st.get_cm())
