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#!/usr/bin/env python3
"""
Evaluate accuracy of online model for DFA/PTA traces.
Usage:
PYTHONPATH=lib bin/eval-online-model-accuracy.py [options] <pta/dfa definition>
Options:
--accounting=static_state|static_state_immediate|static_statetransition|static_statetransition_immedate
Select accounting method
--depth=<depth> (default: 3)
Maximum number of function calls per run
--sleep=<ms> (default: 0)
How long to sleep between simulated function calls.
--trace-filter=<transition,transition,transition,...>[ <transition,transition,transition,...> ...]
Only consider traces whose beginning matches one of the provided transition sequences.
E.g. --trace-filter='init,foo init,bar' will only consider traces with init as first and foo or bar as second transition,
and --trace-filter='init,foo,$ init,bar,$' will only consider the traces init -> foo and init -> bar.
"""
import getopt
import re
import sys
import itertools
import yaml
from dfatool.automata import PTA
from dfatool.codegen import get_simulated_accountingmethod
from dfatool.model import regression_measures
import numpy as np
opt = dict()
if __name__ == "__main__":
try:
optspec = (
"accounting= "
"arch= "
"app= "
"depth= "
"dummy= "
"instance= "
"repeat= "
"run= "
"sleep= "
"timer-pin= "
"trace-filter= "
"timer-freq= "
"timer-type= "
"timestamp-type= "
"energy-type= "
"power-type= "
"timestamp-granularity= "
"energy-granularity= "
"power-granularity= "
)
raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(" "))
opt_default = {
"depth": 3,
"sleep": 0,
"timer-freq": 1e6,
"timer-type": "uint16_t",
"timestamp-type": "uint16_t",
"energy-type": "uint32_t",
"power-type": "uint16_t",
"timestamp-granularity": 1e-6,
"power-granularity": 1e-6,
"energy-granularity": 1e-12,
}
for option, parameter in raw_opts:
optname = re.sub(r"^--", "", option)
opt[optname] = parameter
for key in "depth sleep".split():
if key in opt:
opt[key] = int(opt[key])
else:
opt[key] = opt_default[key]
for (
key
) in "timer-freq timestamp-granularity energy-granularity power-granularity".split():
if key in opt:
opt[key] = float(opt[key])
else:
opt[key] = opt_default[key]
for key in "timer-type timestamp-type energy-type power-type".split():
if key not in opt:
opt[key] = opt_default[key]
if "trace-filter" in opt:
trace_filter = []
for trace in opt["trace-filter"].split():
trace_filter.append(trace.split(","))
opt["trace-filter"] = trace_filter
else:
opt["trace-filter"] = None
except getopt.GetoptError as err:
print(err)
sys.exit(2)
modelfile = args[0]
pta = PTA.from_file(modelfile)
enum = dict()
if ".json" not in modelfile:
with open(modelfile, "r") as f:
driver_definition = yaml.safe_load(f)
if "dummygen" in driver_definition and "enum" in driver_definition["dummygen"]:
enum = driver_definition["dummygen"]["enum"]
pta.set_random_energy_model()
runs = list(
pta.dfs(
opt["depth"],
with_arguments=True,
with_parameters=True,
trace_filter=opt["trace-filter"],
sleep=opt["sleep"],
)
)
num_transitions = len(runs)
if len(runs) == 0:
print(
"DFS returned no traces -- perhaps your trace-filter is too restrictive?",
file=sys.stderr,
)
sys.exit(1)
real_energies = list()
real_durations = list()
model_energies = list()
for run in runs:
accounting_method = get_simulated_accountingmethod(opt["accounting"])(
pta,
opt["timer-freq"],
opt["timer-type"],
opt["timestamp-type"],
opt["power-type"],
opt["energy-type"],
)
real_energy, real_duration, _, _ = pta.simulate(
run, accounting=accounting_method
)
model_energy = accounting_method.get_energy()
real_energies.append(real_energy)
real_durations.append(real_duration)
model_energies.append(model_energy)
measures = regression_measures(np.array(model_energies), np.array(real_energies))
print("SMAPE {:.0f}%, MAE {}".format(measures["smape"], measures["mae"]))
timer_freqs = [1e3, 2e3, 5e3, 1e4, 2e4, 5e4, 1e5, 2e5, 5e5, 1e6, 2e6, 5e6]
timer_types = (
timestamp_types
) = power_types = energy_types = "uint8_t uint16_t uint32_t uint64_t".split()
def config_weight(timer_freq, timer_type, ts_type, power_type, energy_type):
base_weight = 0
for var_type in timer_type, ts_type, power_type, energy_type:
if var_type == "uint8_t":
base_weight += 1
elif var_type == "uint16_t":
base_weight += 2
elif var_type == "uint32_t":
base_weight += 4
elif var_type == "uint64_t":
base_weight += 8
return base_weight
# sys.exit(0)
mean_errors = list()
for timer_freq, timer_type, ts_type, power_type, energy_type in itertools.product(
timer_freqs, timer_types, timestamp_types, power_types, energy_types
):
real_energies = list()
real_durations = list()
model_energies = list()
# duration in µs
# Bei kurzer Dauer (z.B. nur [1e2]) performt auch uint32_t für Energie gut, sonst nicht so (weil overflow)
for sleep_duration in [1e2, 1e3, 1e4, 1e5, 1e6]:
runs = pta.dfs(
opt["depth"],
with_arguments=True,
with_parameters=True,
trace_filter=opt["trace-filter"],
sleep=sleep_duration,
)
for run in runs:
accounting_method = get_simulated_accountingmethod(opt["accounting"])(
pta, timer_freq, timer_type, ts_type, power_type, energy_type
)
real_energy, real_duration, _, _ = pta.simulate(
run, accounting=accounting_method
)
model_energy = accounting_method.get_energy()
real_energies.append(real_energy)
real_durations.append(real_duration)
model_energies.append(model_energy)
measures = regression_measures(
np.array(model_energies), np.array(real_energies)
)
mean_errors.append(
(
(timer_freq, timer_type, ts_type, power_type, energy_type),
config_weight(timer_freq, timer_type, ts_type, power_type, energy_type),
measures,
)
)
mean_errors.sort(key=lambda x: x[1])
mean_errors.sort(key=lambda x: x[2]["mae"])
for result in mean_errors:
config, weight, measures = result
print("{} -> {:.0f}% / {}".format(config, measures["smape"], measures["mae"]))
sys.exit(0)
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