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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 json
import re
import runner
import sys
import time
import io
import yaml
from aspectc import Repo
from automata import PTA
from codegen import *
from harness import OnboardTimerHarness
from dfatool import regression_measures
opt = dict()
if __name__ == '__main__':
try:
optspec = (
'accounting= '
'arch= '
'app= '
'depth= '
'dummy= '
'instance= '
'repeat= '
'run= '
'sleep= '
'timer-pin= '
'trace-filter= '
)
raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(' '))
for option, parameter in raw_opts:
optname = re.sub(r'^--', '', option)
opt[optname] = parameter
if 'depth' in opt:
opt['depth'] = int(opt['depth'])
else:
opt['depth'] = 3
if 'sleep' in opt:
opt['sleep'] = int(opt['sleep'])
else:
opt['sleep'] = 0
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]
with open(modelfile, 'r') as f:
if '.json' in modelfile:
pta = PTA.from_json(json.load(f))
else:
pta = PTA.from_yaml(yaml.safe_load(f))
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']
repo = Repo('/home/derf/var/projects/multipass/build/repo.acp')
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, 1e6, 'uint32_t', 'uint32_t', 'uint32_t', 'uint32_t')
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)
print('actual energy {:.0f} µJ, modeled energy {:.0f} µJ'.format(real_energy / 1e6, model_energy / 1e6))
measures = regression_measures(np.array(model_energies), np.array(real_energies))
print('SMAPE {:.0f}%, MAE {}'.format(measures['smape'], measures['mae']))
sys.exit(0)
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