diff options
| -rwxr-xr-x | bin/analyze-archive.py | 57 | ||||
| -rwxr-xr-x | lib/dfatool.py | 22 |
2 files changed, 48 insertions, 31 deletions
diff --git a/bin/analyze-archive.py b/bin/analyze-archive.py index 81d6f54..ca043c5 100755 --- a/bin/analyze-archive.py +++ b/bin/analyze-archive.py @@ -3,6 +3,31 @@ import sys from dfatool import EnergyModel, RawData +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'])) + else: + print('{:20s} {:15s} {:.0f}'.format( + state_or_tran, key, result['mae'])) + + +def model_quality_table(result_lists): + for state_or_tran in result_lists[0].keys(): + for key in result_lists[0][state_or_tran].keys(): + buf = '{:20s} {:15s}'.format(state_or_tran, key) + for results in result_lists: + buf += ' ||| ' + result = results[state_or_tran][key] + if 'smape' in result: + buf += '{:6.2f}% / {:9.0f}'.format(result['smape'], result['mae']) + else: + buf += '{:6} {:9.0f}'.format('', result['mae']) + print(buf) + if __name__ == '__main__': filenames = sys.argv[1:] raw_data = RawData(filenames) @@ -10,8 +35,8 @@ if __name__ == '__main__': preprocessed_data = raw_data.get_preprocessed_data() model = EnergyModel(preprocessed_data) - #print('--- simple static model ---') - #static_model = model.get_static() + print('--- simple static model ---') + static_model = model.get_static() #for state in model.states(): # print('{:10s}: {:.0f} µW ({:.2f})'.format( # state, @@ -31,21 +56,25 @@ if __name__ == '__main__': # 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'))) - #model.assess(static_model) + static_quality = model.assess(static_model) - #print('--- LUT ---') - #lut_model = model.get_param_lut() - #model.assess(lut_model) + print('--- LUT ---') + lut_model = model.get_param_lut() + lut_quality = model.assess(lut_model) print('--- param model ---') param_model, param_info = model.get_fitted() - for state in model.states(): - for attribute in ['power']: - if param_info(state, attribute): - print('{:10s}: {}'.format(state, param_info(state, attribute)['function']._model_str)) - for trans in model.transitions(): - for attribute in ['energy', 'rel_energy_prev', 'rel_energy_next', 'duration', 'timeout']: - if param_info(trans, attribute): - print('{:10s}: {:10s}: {}'.format(trans, attribute, param_info(trans, attribute)['function']._model_str)) + #for state in model.states(): + # for attribute in ['power']: + # if param_info(state, attribute): + # print('{:10s}: {}'.format(state, param_info(state, attribute)['function']._model_str)) + # print('{:10s} {}'.format('', param_info(state, attribute)['function']._regression_args)) + #for trans in model.transitions(): + # for attribute in ['energy', 'rel_energy_prev', 'rel_energy_next', 'duration', 'timeout']: + # if param_info(trans, attribute): + # print('{:10s}: {:10s}: {}'.format(trans, attribute, param_info(trans, attribute)['function']._model_str)) + # print('{:10s} {:10s} {}'.format('', '', param_info(trans, attribute)['function']._regression_args)) + analytic_quality = model.assess(param_model) + model_quality_table([static_quality, lut_quality, analytic_quality]) sys.exit(0) diff --git a/lib/dfatool.py b/lib/dfatool.py index 3387de0..1699a1d 100755 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -798,31 +798,19 @@ class EnergyModel: return self._parameter_names def assess(self, model_function): + results = {} for name, elem in sorted(self.by_name.items()): - print('{}:'.format(name)) + results[name] = {} if elem['isa'] == 'state': predicted_data = np.array(list(map(lambda i: model_function(name, 'power', param=elem['param'][i]), range(len(elem['power']))))) measures = regression_measures(predicted_data, elem['power']) - if 'smape' in measures: - print(' power: {:.2f}% / {:.0f} µW'.format( - measures['smape'], measures['mae'] - )) - else: - print(' power: {:.0f} µW'.format( - measures['mae'] - )) + results[name]['power'] = measures else: for key in ['duration', 'energy', 'rel_energy_prev', 'rel_energy_next', 'timeout']: predicted_data = np.array(list(map(lambda i: model_function(name, key, param=elem['param'][i]), range(len(elem[key]))))) measures = regression_measures(predicted_data, elem[key]) - if 'smape' in measures: - print(' {:15s}: {:.2f}% / {:.0f}'.format( - key, measures['smape'], measures['mae'] - )) - else: - print(' {:15s}: {:.0f}'.format( - key, measures['mae'] - )) + results[name][key] = measures + return results |