diff options
Diffstat (limited to 'bin/test_corrcoef.py')
| -rwxr-xr-x | bin/test_corrcoef.py | 318 |
1 files changed, 200 insertions, 118 deletions
diff --git a/bin/test_corrcoef.py b/bin/test_corrcoef.py index 835e086..e389d01 100755 --- a/bin/test_corrcoef.py +++ b/bin/test_corrcoef.py @@ -9,76 +9,105 @@ from dfatool.dfatool import gplearn_to_function opts = {} + def print_model_quality(results): for state_or_tran in results.keys(): print() for key, result in results[state_or_tran].items(): - if 'smape' in result: - print('{:20s} {:15s} {:.2f}% / {:.0f}'.format( - state_or_tran, key, result['smape'], result['mae'])) + if "smape" in result: + print( + "{:20s} {:15s} {:.2f}% / {:.0f}".format( + state_or_tran, key, result["smape"], result["mae"] + ) + ) else: - print('{:20s} {:15s} {:.0f}'.format( - state_or_tran, key, result['mae'])) + print("{:20s} {:15s} {:.0f}".format(state_or_tran, key, result["mae"])) + def format_quality_measures(result): - if 'smape' in result: - return '{:6.2f}% / {:9.0f}'.format(result['smape'], result['mae']) + if "smape" in result: + return "{:6.2f}% / {:9.0f}".format(result["smape"], result["mae"]) else: - return '{:6} {:9.0f}'.format('', result['mae']) + return "{:6} {:9.0f}".format("", result["mae"]) + 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): info = info_list[i] - buf += ' ||| ' + buf += " ||| " if info == None or info(state_or_tran, key): - result = results['by_name'][state_or_tran][key] + result = results["by_name"][state_or_tran][key] buf += format_quality_measures(result) else: - buf += '{:6}----{:9}'.format('', '') + buf += "{:6}----{:9}".format("", "") print(buf) + def model_summary_table(result_list): - buf = 'transition duration' + buf = "transition duration" for results in result_list: if len(buf): - buf += ' ||| ' - buf += format_quality_measures(results['duration_by_trace']) + buf += " ||| " + buf += format_quality_measures(results["duration_by_trace"]) print(buf) - buf = 'total energy ' + buf = "total energy " for results in result_list: if len(buf): - buf += ' ||| ' - buf += format_quality_measures(results['energy_by_trace']) + buf += " ||| " + buf += format_quality_measures(results["energy_by_trace"]) print(buf) - buf = 'transition timeout ' + buf = "transition timeout " for results in result_list: if len(buf): - buf += ' ||| ' - buf += format_quality_measures(results['timeout_by_trace']) + buf += " ||| " + buf += format_quality_measures(results["timeout_by_trace"]) print(buf) def print_text_model_data(model, pm, pq, lm, lq, am, ai, aq): - print('') - print(r'key attribute $1 - \frac{\sigma_X}{...}$') + print("") + print(r"key attribute $1 - \frac{\sigma_X}{...}$") for state_or_tran in model.by_name.keys(): - for attribute in model.by_name[state_or_tran]['attributes']: - print('{} {} {:.8f}'.format(state_or_tran, attribute, model.generic_param_dependence_ratio(state_or_tran, attribute))) - - print('') - print(r'key attribute parameter $1 - \frac{...}{...}$') + for attribute in model.by_name[state_or_tran]["attributes"]: + print( + "{} {} {:.8f}".format( + state_or_tran, + attribute, + model.generic_param_dependence_ratio(state_or_tran, attribute), + ) + ) + + print("") + print(r"key attribute parameter $1 - \frac{...}{...}$") for state_or_tran in model.by_name.keys(): - for attribute in model.by_name[state_or_tran]['attributes']: + for attribute in model.by_name[state_or_tran]["attributes"]: for param in model.parameters(): - print('{} {} {} {:.8f}'.format(state_or_tran, attribute, param, model.param_dependence_ratio(state_or_tran, attribute, param))) + print( + "{} {} {} {:.8f}".format( + state_or_tran, + attribute, + param, + model.param_dependence_ratio(state_or_tran, attribute, param), + ) + ) if state_or_tran in model._num_args: for arg_index in range(model._num_args[state_or_tran]): - print('{} {} {:d} {:.8f}'.format(state_or_tran, attribute, arg_index, model.arg_dependence_ratio(state_or_tran, attribute, arg_index))) + print( + "{} {} {:d} {:.8f}".format( + state_or_tran, + attribute, + arg_index, + model.arg_dependence_ratio( + state_or_tran, attribute, arg_index + ), + ) + ) + -if __name__ == '__main__': +if __name__ == "__main__": ignored_trace_indexes = None discard_outliers = None @@ -89,36 +118,40 @@ if __name__ == '__main__': try: optspec = ( - 'plot-unparam= plot-param= show-models= show-quality= ' - 'ignored-trace-indexes= discard-outliers= function-override= ' - 'with-safe-functions' + "plot-unparam= plot-param= show-models= show-quality= " + "ignored-trace-indexes= discard-outliers= function-override= " + "with-safe-functions" ) - raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(' ')) + raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.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"]) - if 'function-override' in opts: - for function_desc in opts['function-override'].split(';'): - state_or_tran, attribute, *function_str = function_desc.split(' ') - function_override[(state_or_tran, attribute)] = ' '.join(function_str) + if "function-override" in opts: + for function_desc in opts["function-override"].split(";"): + state_or_tran, attribute, *function_str = function_desc.split(" ") + function_override[(state_or_tran, attribute)] = " ".join( + function_str + ) - if 'show-models' in opts: - show_models = opts['show-models'].split(',') + if "show-models" in opts: + show_models = opts["show-models"].split(",") - if 'show-quality' in opts: - show_quality = opts['show-quality'].split(',') + if "show-quality" in opts: + show_quality = opts["show-quality"].split(",") - if 'with-safe-functions' in opts: + if "with-safe-functions" in opts: safe_functions_enabled = True except getopt.GetoptError as err: @@ -131,105 +164,154 @@ if __name__ == '__main__': by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data) ref_model = PTAModel( - by_name, parameters, arg_count, - traces = preprocessed_data, - ignore_trace_indexes = ignored_trace_indexes, - discard_outliers = discard_outliers, - function_override = function_override, - use_corrcoef = False) + by_name, + parameters, + arg_count, + traces=preprocessed_data, + ignore_trace_indexes=ignored_trace_indexes, + discard_outliers=discard_outliers, + function_override=function_override, + use_corrcoef=False, + ) model = PTAModel( - by_name, parameters, arg_count, - traces = preprocessed_data, - ignore_trace_indexes = ignored_trace_indexes, - discard_outliers = discard_outliers, - function_override = function_override, - use_corrcoef = True) - - - if 'plot-unparam' in opts: - for kv in opts['plot-unparam'].split(';'): - state_or_trans, attribute = kv.split(' ') + by_name, + parameters, + arg_count, + traces=preprocessed_data, + ignore_trace_indexes=ignored_trace_indexes, + discard_outliers=discard_outliers, + function_override=function_override, + use_corrcoef=True, + ) + + if "plot-unparam" in opts: + for kv in opts["plot-unparam"].split(";"): + state_or_trans, attribute = kv.split(" ") plotter.plot_y(model.by_name[state_or_trans][attribute]) if len(show_models): - print('--- simple static model ---') + print("--- simple static model ---") static_model = model.get_static() ref_static_model = ref_model.get_static() - if 'static' in show_models or 'all' in show_models: + if "static" in show_models or "all" in show_models: for state in model.states(): - print('{:10s}: {:.0f} µW ({:.2f})'.format( - state, - static_model(state, 'power'), - model.generic_param_dependence_ratio(state, 'power'))) + print( + "{:10s}: {:.0f} µW ({:.2f})".format( + state, + static_model(state, "power"), + model.generic_param_dependence_ratio(state, "power"), + ) + ) for param in model.parameters(): - print('{:10s} dependence on {:15s}: {:.2f}'.format( - '', - param, - model.param_dependence_ratio(state, 'power', param))) + print( + "{:10s} dependence on {:15s}: {:.2f}".format( + "", param, model.param_dependence_ratio(state, "power", param) + ) + ) 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'), - static_model(trans, 'rel_energy_next'), - model.generic_param_dependence_ratio(trans, 'energy'), - model.generic_param_dependence_ratio(trans, 'rel_energy_prev'), - model.generic_param_dependence_ratio(trans, 'rel_energy_next'))) - print('{:10s}: {:.0f} µs'.format(trans, static_model(trans, 'duration'))) + print( + "{:10s}: {:.0f} / {:.0f} / {:.0f} pJ ({:.2f} / {:.2f} / {:.2f})".format( + trans, + static_model(trans, "energy"), + static_model(trans, "rel_energy_prev"), + static_model(trans, "rel_energy_next"), + model.generic_param_dependence_ratio(trans, "energy"), + model.generic_param_dependence_ratio(trans, "rel_energy_prev"), + model.generic_param_dependence_ratio(trans, "rel_energy_next"), + ) + ) + print("{:10s}: {:.0f} µs".format(trans, static_model(trans, "duration"))) static_quality = model.assess(static_model) ref_static_quality = ref_model.assess(ref_static_model) if len(show_models): - print('--- LUT ---') + print("--- LUT ---") lut_model = model.get_param_lut() lut_quality = model.assess(lut_model) ref_lut_model = ref_model.get_param_lut() ref_lut_quality = ref_model.assess(ref_lut_model) if len(show_models): - print('--- param model ---') - param_model, param_info = model.get_fitted(safe_functions_enabled = safe_functions_enabled) - ref_param_model, ref_param_info = ref_model.get_fitted(safe_functions_enabled = safe_functions_enabled) - print('') - print('') - print('state_or_trans attribute param stddev_ratio corrcoef') + print("--- param model ---") + param_model, param_info = model.get_fitted( + safe_functions_enabled=safe_functions_enabled + ) + ref_param_model, ref_param_info = ref_model.get_fitted( + safe_functions_enabled=safe_functions_enabled + ) + print("") + print("") + print("state_or_trans attribute param stddev_ratio corrcoef") for state in model.states(): for attribute in model.attributes(state): for param in model.parameters(): - print('{:10s} {:10s} {:10s} {:f} {:f}'.format(state, attribute, param, - ref_model.param_dependence_ratio(state, attribute, param), - model.param_dependence_ratio(state, attribute, param))) + print( + "{:10s} {:10s} {:10s} {:f} {:f}".format( + state, + attribute, + param, + ref_model.param_dependence_ratio(state, attribute, param), + model.param_dependence_ratio(state, attribute, param), + ) + ) for trans in model.transitions(): for attribute in model.attributes(trans): for param in model.parameters(): - print('{:10s} {:10s} {:10s} {:f} {:f}'.format(trans, attribute, param, - ref_model.param_dependence_ratio(trans, attribute, param), - model.param_dependence_ratio(trans, attribute, param))) - print('') - print('') + print( + "{:10s} {:10s} {:10s} {:f} {:f}".format( + trans, + attribute, + param, + ref_model.param_dependence_ratio(trans, attribute, param), + model.param_dependence_ratio(trans, attribute, param), + ) + ) + print("") + print("") analytic_quality = model.assess(param_model) ref_analytic_quality = ref_model.assess(ref_param_model) - if 'tex' in show_models or 'tex' in show_quality: - print_text_model_data(model, static_model, static_quality, lut_model, lut_quality, param_model, param_info, analytic_quality) + if "tex" in show_models or "tex" in show_quality: + print_text_model_data( + model, + static_model, + static_quality, + lut_model, + lut_quality, + param_model, + param_info, + analytic_quality, + ) - if 'table' in show_quality or 'all' in show_quality: - print('corrcoef:') - model_quality_table([static_quality, analytic_quality, lut_quality], [None, param_info, None]) - print('heuristic:') - model_quality_table([ref_static_quality, ref_analytic_quality, ref_lut_quality], [None, ref_param_info, None]) - if 'summary' in show_quality or 'all' in show_quality: - print('corrcoef:') + if "table" in show_quality or "all" in show_quality: + print("corrcoef:") + model_quality_table( + [static_quality, analytic_quality, lut_quality], [None, param_info, None] + ) + print("heuristic:") + model_quality_table( + [ref_static_quality, ref_analytic_quality, ref_lut_quality], + [None, ref_param_info, None], + ) + if "summary" in show_quality or "all" in show_quality: + print("corrcoef:") model_summary_table([static_quality, analytic_quality, lut_quality]) - print('heuristic:') + print("heuristic:") model_summary_table([ref_static_quality, ref_analytic_quality, ref_lut_quality]) - if 'plot-param' in opts: - for kv in opts['plot-param'].split(';'): - state_or_trans, attribute, param_name, *function = kv.split(' ') + if "plot-param" in opts: + for kv in opts["plot-param"].split(";"): + state_or_trans, attribute, param_name, *function = kv.split(" ") if len(function): - function = gplearn_to_function(' '.join(function)) + function = gplearn_to_function(" ".join(function)) else: function = None - plotter.plot_param(model, state_or_trans, attribute, model.param_index(param_name), extra_function=function) + plotter.plot_param( + model, + state_or_trans, + attribute, + model.param_index(param_name), + extra_function=function, + ) sys.exit(0) |