diff options
Diffstat (limited to 'bin/eval-outlier-removal.py')
| -rwxr-xr-x | bin/eval-outlier-removal.py | 187 |
1 files changed, 129 insertions, 58 deletions
diff --git a/bin/eval-outlier-removal.py b/bin/eval-outlier-removal.py index 3a4aa28..d8b0e9d 100755 --- a/bin/eval-outlier-removal.py +++ b/bin/eval-outlier-removal.py @@ -7,63 +7,73 @@ from dfatool.dfatool import PTAModel, RawData, pta_trace_to_aggregate opts = {} + def model_quality_table(result_lists, info_list): - for state_or_tran in result_lists[0]['by_name'].keys(): - for key in result_lists[0]['by_name'][state_or_tran].keys(): - buf = '{:20s} {:15s}'.format(state_or_tran, key) + for state_or_tran in result_lists[0]["by_name"].keys(): + for key in result_lists[0]["by_name"][state_or_tran].keys(): + buf = "{:20s} {:15s}".format(state_or_tran, key) for i, results in enumerate(result_lists): - results = results['by_name'] + results = results["by_name"] info = info_list[i] - buf += ' ||| ' + buf += " ||| " if info == None or info(state_or_tran, key): result = results[state_or_tran][key] - if 'smape' in result: - buf += '{:6.2f}% / {:9.0f}'.format(result['smape'], result['mae']) + if "smape" in result: + buf += "{:6.2f}% / {:9.0f}".format( + result["smape"], result["mae"] + ) else: - buf += '{:6} {:9.0f}'.format('', result['mae']) + buf += "{:6} {:9.0f}".format("", result["mae"]) else: - buf += '{:6}----{:9}'.format('', '') + buf += "{:6}----{:9}".format("", "") print(buf) + def combo_model_quality_table(result_lists, info_list): - for state_or_tran in result_lists[0][0]['by_name'].keys(): - for key in result_lists[0][0]['by_name'][state_or_tran].keys(): + for state_or_tran in result_lists[0][0]["by_name"].keys(): + for key in result_lists[0][0]["by_name"][state_or_tran].keys(): for sub_result_lists in result_lists: - buf = '{:20s} {:15s}'.format(state_or_tran, key) + buf = "{:20s} {:15s}".format(state_or_tran, key) for i, results in enumerate(sub_result_lists): - results = results['by_name'] + results = results["by_name"] info = info_list[i] - buf += ' ||| ' + buf += " ||| " if info == None or info(state_or_tran, key): result = results[state_or_tran][key] - if 'smape' in result: - buf += '{:6.2f}% / {:9.0f}'.format(result['smape'], result['mae']) + if "smape" in result: + buf += "{:6.2f}% / {:9.0f}".format( + result["smape"], result["mae"] + ) else: - buf += '{:6} {:9.0f}'.format('', result['mae']) + buf += "{:6} {:9.0f}".format("", result["mae"]) else: - buf += '{:6}----{:9}'.format('', '') + buf += "{:6}----{:9}".format("", "") print(buf) -if __name__ == '__main__': + +if __name__ == "__main__": ignored_trace_indexes = [] discard_outliers = None try: - raw_opts, args = getopt.getopt(sys.argv[1:], "", - 'plot ignored-trace-indexes= discard-outliers='.split(' ')) + raw_opts, args = getopt.getopt( + sys.argv[1:], "", "plot ignored-trace-indexes= discard-outliers=".split(" ") + ) for option, parameter in raw_opts: - optname = re.sub(r'^--', '', option) + optname = re.sub(r"^--", "", option) opts[optname] = parameter - if 'ignored-trace-indexes' in opts: - ignored_trace_indexes = list(map(int, opts['ignored-trace-indexes'].split(','))) + if "ignored-trace-indexes" in opts: + ignored_trace_indexes = list( + map(int, opts["ignored-trace-indexes"].split(",")) + ) if 0 in ignored_trace_indexes: - print('[E] arguments to --ignored-trace-indexes start from 1') + print("[E] arguments to --ignored-trace-indexes start from 1") - if 'discard-outliers' in opts: - discard_outliers = float(opts['discard-outliers']) + if "discard-outliers" in opts: + discard_outliers = float(opts["discard-outliers"]) except getopt.GetoptError as err: print(err) @@ -72,19 +82,29 @@ if __name__ == '__main__': raw_data = RawData(args) preprocessed_data = raw_data.get_preprocessed_data() - by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data, ignored_trace_indexes) - m1 = PTAModel(by_name, parameters, arg_count, - traces = preprocessed_data, - ignore_trace_indexes = ignored_trace_indexes) - m2 = PTAModel(by_name, parameters, arg_count, - traces = preprocessed_data, - ignore_trace_indexes = ignored_trace_indexes, - discard_outliers = discard_outliers) - - print('--- simple static model ---') + by_name, parameters, arg_count = pta_trace_to_aggregate( + preprocessed_data, ignored_trace_indexes + ) + m1 = PTAModel( + by_name, + parameters, + arg_count, + traces=preprocessed_data, + ignore_trace_indexes=ignored_trace_indexes, + ) + m2 = PTAModel( + by_name, + parameters, + arg_count, + traces=preprocessed_data, + ignore_trace_indexes=ignored_trace_indexes, + discard_outliers=discard_outliers, + ) + + print("--- simple static model ---") static_m1 = m1.get_static() static_m2 = m2.get_static() - #for state in model.states(): + # for state in model.states(): # print('{:10s}: {:.0f} µW ({:.2f})'.format( # state, # static_model(state, 'power'), @@ -94,7 +114,7 @@ if __name__ == '__main__': # '', # param, # model.param_dependence_ratio(state, 'power', param))) - #for trans in model.transitions(): + # for trans in model.transitions(): # print('{:10s}: {:.0f} / {:.0f} / {:.0f} pJ ({:.2f} / {:.2f} / {:.2f})'.format( # trans, static_model(trans, 'energy'), # static_model(trans, 'rel_energy_prev'), @@ -107,36 +127,84 @@ if __name__ == '__main__': static_q2 = m2.assess(static_m2) static_q12 = m1.assess(static_m2) - print('--- LUT ---') + print("--- LUT ---") lut_m1 = m1.get_param_lut() lut_m2 = m2.get_param_lut() lut_q1 = m1.assess(lut_m1) lut_q2 = m2.assess(lut_m2) lut_q12 = m1.assess(lut_m2) - print('--- param model ---') + print("--- param model ---") param_m1, param_i1 = m1.get_fitted() for state in m1.states(): - for attribute in ['power']: + for attribute in ["power"]: if param_i1(state, attribute): - print('{:10s}: {}'.format(state, param_i1(state, attribute)['function']._model_str)) - print('{:10s} {}'.format('', param_i1(state, attribute)['function']._regression_args)) + print( + "{:10s}: {}".format( + state, param_i1(state, attribute)["function"]._model_str + ) + ) + print( + "{:10s} {}".format( + "", param_i1(state, attribute)["function"]._regression_args + ) + ) for trans in m1.transitions(): - for attribute in ['energy', 'rel_energy_prev', 'rel_energy_next', 'duration', 'timeout']: + for attribute in [ + "energy", + "rel_energy_prev", + "rel_energy_next", + "duration", + "timeout", + ]: if param_i1(trans, attribute): - print('{:10s}: {:10s}: {}'.format(trans, attribute, param_i1(trans, attribute)['function']._model_str)) - print('{:10s} {:10s} {}'.format('', '', param_i1(trans, attribute)['function']._regression_args)) + print( + "{:10s}: {:10s}: {}".format( + trans, + attribute, + param_i1(trans, attribute)["function"]._model_str, + ) + ) + print( + "{:10s} {:10s} {}".format( + "", "", param_i1(trans, attribute)["function"]._regression_args + ) + ) param_m2, param_i2 = m2.get_fitted() for state in m2.states(): - for attribute in ['power']: + for attribute in ["power"]: if param_i2(state, attribute): - print('{:10s}: {}'.format(state, param_i2(state, attribute)['function']._model_str)) - print('{:10s} {}'.format('', param_i2(state, attribute)['function']._regression_args)) + print( + "{:10s}: {}".format( + state, param_i2(state, attribute)["function"]._model_str + ) + ) + print( + "{:10s} {}".format( + "", param_i2(state, attribute)["function"]._regression_args + ) + ) for trans in m2.transitions(): - for attribute in ['energy', 'rel_energy_prev', 'rel_energy_next', 'duration', 'timeout']: + for attribute in [ + "energy", + "rel_energy_prev", + "rel_energy_next", + "duration", + "timeout", + ]: if param_i2(trans, attribute): - print('{:10s}: {:10s}: {}'.format(trans, attribute, param_i2(trans, attribute)['function']._model_str)) - print('{:10s} {:10s} {}'.format('', '', param_i2(trans, attribute)['function']._regression_args)) + print( + "{:10s}: {:10s}: {}".format( + trans, + attribute, + param_i2(trans, attribute)["function"]._model_str, + ) + ) + print( + "{:10s} {:10s} {}".format( + "", "", param_i2(trans, attribute)["function"]._regression_args + ) + ) analytic_q1 = m1.assess(param_m1) analytic_q2 = m2.assess(param_m2) @@ -144,10 +212,13 @@ if __name__ == '__main__': model_quality_table([static_q1, analytic_q1, lut_q1], [None, param_i1, None]) model_quality_table([static_q2, analytic_q2, lut_q2], [None, param_i2, None]) model_quality_table([static_q12, analytic_q12, lut_q12], [None, param_i2, None]) - combo_model_quality_table([ - [static_q1, analytic_q1, lut_q1], - [static_q2, analytic_q2, lut_q2], - [static_q12, analytic_q12, lut_q12]], - [None, param_i1, None]) + combo_model_quality_table( + [ + [static_q1, analytic_q1, lut_q1], + [static_q2, analytic_q2, lut_q2], + [static_q12, analytic_q12, lut_q12], + ], + [None, param_i1, None], + ) sys.exit(0) |