diff options
Diffstat (limited to 'lib')
| -rwxr-xr-x | lib/dfatool.py | 23 |
1 files changed, 21 insertions, 2 deletions
diff --git a/lib/dfatool.py b/lib/dfatool.py index 2b5d3f3..f0f71f1 100755 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -970,6 +970,10 @@ class EnergyModel: def param_dependence_ratio(self, state_or_trans, key, param): return 1 - self.param_independence_ratio(state_or_trans, key, param) + # This heuristic is very similar to the "function is not much better than + # median" checks in get_fitted. So far, doing it here as well is mostly + # a performance and not an algorithm quality decision. + # --df, 2018-04-18 def depends_on_param(self, state_or_trans, key, param): if self._use_corrcoef: return self.param_dependence_ratio(state_or_trans, key, param) > 0.1 @@ -987,6 +991,7 @@ class EnergyModel: def arg_dependence_ratio(self, state_or_trans, key, arg_index): return 1 - self.arg_independence_ratio(state_or_trans, key, arg_index) + # See notes on depends_on_param def depends_on_arg(self, state_or_trans, key, param): if self._use_corrcoef: return self.arg_dependence_ratio(state_or_trans, key, param) > 0.1 @@ -1091,7 +1096,8 @@ class EnergyModel: vprint(self.verbose, '[I] Not modeling {} {} as function of {}: best ({:.0f}) is worse than ref ({:.0f}, {:.0f})'.format( state_or_tran, model_attribute, result['key'][2], fit_result['best_rmsd'], fit_result['mean_rmsd'], fit_result['median_rmsd'])) - elif fit_result['best_rmsd'] >= 0.5 * min(fit_result['mean_rmsd'], fit_result['median_rmsd']): + # See notes on depends_on_param + elif fit_result['best_rmsd'] >= 0.8 * min(fit_result['mean_rmsd'], fit_result['median_rmsd']): vprint(self.verbose, '[I] Not modeling {} {} as function of {}: best ({:.0f}) is not much better than ({:.0f}, {:.0f})'.format( state_or_tran, model_attribute, result['key'][2], fit_result['best_rmsd'], fit_result['mean_rmsd'], fit_result['median_rmsd'])) @@ -1154,6 +1160,7 @@ class EnergyModel: detailed_results = {} model_energy_list = [] real_energy_list = [] + model_rel_energy_list = [] model_duration_list = [] real_duration_list = [] model_timeout_list = [] @@ -1169,22 +1176,32 @@ class EnergyModel: for rep_id in range(len(trace['trace'][0]['offline'])): model_energy = 0. real_energy = 0. + model_rel_energy = 0. model_duration = 0. real_duration = 0. model_timeout = 0. real_timeout = 0. - for trace_part in trace['trace']: + for i, trace_part in enumerate(trace['trace']): name = trace_part['name'] + prev_name = trace['trace'][i-1]['name'] isa = trace_part['isa'] if name != 'UNINITIALIZED': param = trace_part['offline_aggregates']['param'][rep_id] + prev_param = trace['trace'][i-1]['offline_aggregates']['param'][rep_id] power = trace_part['offline'][rep_id]['uW_mean'] duration = trace_part['offline'][rep_id]['us'] + prev_duration = trace['trace'][i-1]['offline'][rep_id]['us'] real_energy += power * duration if isa == 'state': model_energy += model_function(name, 'power', param=param) * duration else: model_energy += model_function(name, 'energy', param=param) + # If i == 1, the previous state was UNINITIALIZED, for which we do not have model data + if i == 1: + model_rel_energy += model_function(name, 'energy', param=param) + else: + model_rel_energy += model_function(prev_name, 'power', param=prev_param) * (prev_duration + duration) + model_rel_energy += model_function(name, 'rel_energy_prev', param=param) real_duration += duration model_duration += model_function(name, 'duration', param=param) if 'plan' in trace_part and trace_part['plan']['level'] == 'epilogue': @@ -1192,6 +1209,7 @@ class EnergyModel: model_timeout += model_function(name, 'timeout', param=param) real_energy_list.append(real_energy) model_energy_list.append(model_energy) + model_rel_energy_list.append(model_rel_energy) real_duration_list.append(real_duration) model_duration_list.append(model_duration) real_timeout_list.append(real_timeout) @@ -1202,6 +1220,7 @@ class EnergyModel: 'duration_by_trace' : regression_measures(np.array(model_duration_list), np.array(real_duration_list)), 'energy_by_trace' : regression_measures(np.array(model_energy_list), np.array(real_energy_list)), 'timeout_by_trace' : regression_measures(np.array(model_timeout_list), np.array(real_timeout_list)), + 'rel_energy_by_trace' : regression_measures(np.array(model_rel_energy_list), np.array(real_energy_list)), } |