diff options
Diffstat (limited to 'lib/dfatool.py')
| -rwxr-xr-x | lib/dfatool.py | 55 |
1 files changed, 39 insertions, 16 deletions
diff --git a/lib/dfatool.py b/lib/dfatool.py index 06f81c4..2b5d3f3 100755 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -777,20 +777,11 @@ def _compute_param_statistics(by_name, by_param, parameter_names, num_args, stat for param_idx, param in enumerate(parameter_names): ret['std_by_param'][param] = _mean_std_by_param(by_param, state_or_trans, key, param_idx) - if all_params_are_numeric(by_name[state_or_trans], param_idx): - param_values = np.array(list((map(lambda x: x[param_idx], by_name[state_or_trans]['param'])))) - try: - ret['corr_by_param'][param] = np.corrcoef(by_name[state_or_trans][key], param_values)[0, 1] - except FloatingPointError as fpe: - # Typically happens when all parameter values are identical. - # Building a correlation coefficient is pointless in this case - # -> assume no correlation - ret['corr_by_param'][param] = 0. - else: - ret['corr_by_param'][param] = 0. + ret['corr_by_param'][param] = _corr_by_param(by_name, state_or_trans, key, param_idx) if arg_support_enabled and state_or_trans in num_args: for arg_index in range(num_args[state_or_trans]): ret['std_by_arg'].append(_mean_std_by_param(by_param, state_or_trans, key, len(parameter_names) + arg_index)) + ret['corr_by_arg'].append(_corr_by_param(by_name, state_or_trans, key, len(parameter_names) + arg_index)) return ret @@ -813,12 +804,22 @@ def _mean_std_by_param(by_param, state_or_tran, key, param_index): print('[W] parameter value partition for {} is empty'.format(param_value)) return np.mean([np.std(partition) for partition in partitions]) -#def _corr_by_param(by_name, state_or_tran, key, param_index): -# +def _corr_by_param(by_name, state_or_trans, key, param_index): + if all_params_are_numeric(by_name[state_or_trans], param_index): + param_values = np.array(list((map(lambda x: x[param_index], by_name[state_or_trans]['param'])))) + try: + return np.corrcoef(by_name[state_or_trans][key], param_values)[0, 1] + except FloatingPointError as fpe: + # Typically happens when all parameter values are identical. + # Building a correlation coefficient is pointless in this case + # -> assume no correlation + return 0. + else: + return 0. class EnergyModel: - def __init__(self, preprocessed_data, ignore_trace_indexes = None, discard_outliers = None, function_override = {}, verbose = True): + def __init__(self, preprocessed_data, ignore_trace_indexes = None, discard_outliers = None, function_override = {}, verbose = True, use_corrcoef = False): self.traces = preprocessed_data self.by_name = {} self.by_param = {} @@ -829,6 +830,7 @@ class EnergyModel: self._parameter_names = sorted(self.traces[0]['trace'][0]['parameter'].keys()) self._num_args = {} self._outlier_threshold = discard_outliers + self._use_corrcoef = use_corrcoef self.function_override = function_override self.verbose = verbose if discard_outliers != None: @@ -948,6 +950,8 @@ class EnergyModel: def generic_param_independence_ratio(self, state_or_trans, key): statistics = self.stats[state_or_trans][key] + if self._use_corrcoef: + return 0 if statistics['std_static'] == 0: return 0 return statistics['std_param_lut'] / statistics['std_static'] @@ -957,6 +961,8 @@ class EnergyModel: def param_independence_ratio(self, state_or_trans, key, param): statistics = self.stats[state_or_trans][key] + if self._use_corrcoef: + return 1 - np.abs(statistics['corr_by_param'][param]) if statistics['std_by_param'][param] == 0: return 0 return statistics['std_param_lut'] / statistics['std_by_param'][param] @@ -964,8 +970,16 @@ class EnergyModel: def param_dependence_ratio(self, state_or_trans, key, param): return 1 - self.param_independence_ratio(state_or_trans, key, param) + 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 + else: + return self.param_dependence_ratio(state_or_trans, key, param) > 0.5 + def arg_independence_ratio(self, state_or_trans, key, arg_index): statistics = self.stats[state_or_trans][key] + if self._use_corrcoef: + return 1 - np.abs(statistics['corr_by_arg'][arg_index]) if statistics['std_by_arg'][arg_index] == 0: return 0 return statistics['std_param_lut'] / statistics['std_by_arg'][arg_index] @@ -973,6 +987,12 @@ 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) + 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 + else: + return self.arg_dependence_ratio(state_or_trans, key, param) > 0.5 + def _get_model_from_dict(self, model_dict, model_function): model = {} for name, elem in model_dict.items(): @@ -1038,7 +1058,7 @@ class EnergyModel: for model_attribute in self.by_name[state_or_tran]['attributes']: fit_results = {} for parameter_index, parameter_name in enumerate(self._parameter_names): - if self.param_dependence_ratio(state_or_tran, model_attribute, parameter_name) > 0.5: + if self.depends_on_param(state_or_tran, model_attribute, parameter_name): fit_queue.append({ 'key' : [state_or_tran, model_attribute, parameter_name], 'args' : [self.by_param, state_or_tran, model_attribute, parameter_index, safe_functions_enabled] @@ -1047,7 +1067,7 @@ class EnergyModel: #print('{} {} is {}'.format(state_or_tran, parameter_name, fit_results[parameter_name]['best'])) if arg_support_enabled and self.by_name[state_or_tran]['isa'] == 'transition': for arg_index in range(self._num_args[state_or_tran]): - if self.arg_dependence_ratio(state_or_tran, model_attribute, arg_index) > 0.5: + if self.depends_on_arg(state_or_tran, model_attribute, arg_index): fit_queue.append({ 'key' : [state_or_tran, model_attribute, arg_index], 'args' : [param_subdict, state_or_tran, model_attribute, len(self._parameter_names) + arg_index, safe_functions_enabled] @@ -1127,6 +1147,9 @@ class EnergyModel: def parameters(self): return self._parameter_names + def attributes(self, state_or_trans): + return self.by_name[state_or_trans]['attributes'] + def assess(self, model_function): detailed_results = {} model_energy_list = [] |