diff options
Diffstat (limited to 'bin/eval-online-model-accuracy.py')
| -rwxr-xr-x | bin/eval-online-model-accuracy.py | 45 |
1 files changed, 20 insertions, 25 deletions
diff --git a/bin/eval-online-model-accuracy.py b/bin/eval-online-model-accuracy.py index 75e2a51..49157e5 100755 --- a/bin/eval-online-model-accuracy.py +++ b/bin/eval-online-model-accuracy.py @@ -22,18 +22,14 @@ Options: """ import getopt -import json import re -import runner import sys -import time -import io import itertools import yaml from automata import PTA -from codegen import * -from harness import OnboardTimerHarness +from codegen import get_simulated_accountingmethod from dfatool import regression_measures +import numpy as np opt = dict() @@ -64,16 +60,16 @@ if __name__ == '__main__': 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, + '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: @@ -121,7 +117,7 @@ if __name__ == '__main__': 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'])) + runs = list(pta.dfs(opt['depth'], with_arguments=True, with_parameters=True, trace_filter=opt['trace-filter'], sleep=opt['sleep'])) num_transitions = len(runs) @@ -134,8 +130,8 @@ if __name__ == '__main__': 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) + 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) @@ -144,7 +140,6 @@ if __name__ == '__main__': 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() @@ -161,7 +156,7 @@ if __name__ == '__main__': base_weight += 8 return base_weight - #sys.exit(0) + # 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): @@ -171,10 +166,10 @@ if __name__ == '__main__': # duration in µs # Bei kurzer Dauer (z.B. nur [1e2]) performt auc 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) + 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) + 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) @@ -182,8 +177,8 @@ if __name__ == '__main__': 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']) + 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 |