diff options
| -rwxr-xr-x | lib/dfatool.py | 56 |
1 files changed, 34 insertions, 22 deletions
diff --git a/lib/dfatool.py b/lib/dfatool.py index 1dc5df5..907612c 100755 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -285,8 +285,9 @@ def _preprocess_measurement(measurement): class ParamStats: - def __init__(self, by_name, by_param, parameter_names, arg_count): + def __init__(self, by_name, by_param, parameter_names, arg_count, use_corrcoef = False): self.stats = dict() + self.use_corrcoef = use_corrcoef # Note: This is deliberately single-threaded. The overhead incurred # by multiprocessing is higher than the speed gained by parallel # computation of statistics measures. @@ -295,39 +296,56 @@ class ParamStats: for attribute in by_name[state_or_tran]['attributes']: self.stats[state_or_tran][attribute] = compute_param_statistics(by_name, by_param, parameter_names, arg_count, state_or_tran, attribute) - def generic_param_independence_ratio(self, state_or_trans, attribute, use_corrcoef = False): + def generic_param_independence_ratio(self, state_or_trans, attribute): statistics = self.stats[state_or_trans][attribute] - if use_corrcoef: + if self.use_corrcoef: # not supported return 0 if statistics['std_static'] == 0: return 0 return statistics['std_param_lut'] / statistics['std_static'] - def generic_param_dependence_ratio(self, state_or_trans, attribute, use_corrcoef = False): - return 1 - self.generic_param_independence_ratio(state_or_trans, attribute, use_corrcoef) + def generic_param_dependence_ratio(self, state_or_trans, attribute): + return 1 - self.generic_param_independence_ratio(state_or_trans, attribute) - def param_independence_ratio(self, state_or_trans, attribute, param, use_corrcoef = False): + def param_independence_ratio(self, state_or_trans, attribute, param): statistics = self.stats[state_or_trans][attribute] - if use_corrcoef: + 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] - def param_dependence_ratio(self, state_or_trans, attribute, param, use_corrcoef = False): - return 1 - self.param_independence_ratio(state_or_trans, attribute, param, use_corrcoef) + def param_dependence_ratio(self, state_or_trans, attribute, param): + return 1 - self.param_independence_ratio(state_or_trans, attribute, param) - def arg_independence_ratio(self, state_or_trans, attribute, arg_index, use_corrcoef = False): + def arg_independence_ratio(self, state_or_trans, attribute, arg_index): statistics = self.stats[state_or_trans][attribute] - if use_corrcoef: + 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] - def arg_dependence_ratio(self, state_or_trans, attribute, arg_index, use_corrcoef = False): - return 1 - self.arg_independence_ratio(state_or_trans, attribute, arg_index, use_corrcoef) + def arg_dependence_ratio(self, state_or_trans, attribute, arg_index): + return 1 - self.arg_independence_ratio(state_or_trans, attribute, arg_index) + + # 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 + else: + return self.param_dependence_ratio(state_or_trans, key, param) > 0.5 + + # 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 + else: + return self.arg_dependence_ratio(state_or_trans, key, param) > 0.5 class RawData: """ @@ -827,7 +845,7 @@ class EnergyModel: def _compute_all_param_statistics(self): - self.stats = ParamStats(self.by_name, self.by_param, self._parameter_names, self._num_args) + self.stats = ParamStats(self.by_name, self.by_param, self._parameter_names, self._num_args, self._use_corrcoef) @classmethod def from_model(self, model_data, parameter_names): @@ -891,17 +909,11 @@ class EnergyModel: # 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.stats.param_dependence_ratio(state_or_trans, key, param, self._use_corrcoef) > 0.1 - else: - return self.stats.param_dependence_ratio(state_or_trans, key, param, self._use_corrcoef) > 0.5 + return self.stats.depends_on_param(state_or_trans, key, param) # See notes on depends_on_param def depends_on_arg(self, state_or_trans, key, param): - if self._use_corrcoef: - return self.stats.arg_dependence_ratio(state_or_trans, key, param, self._use_corrcoef) > 0.1 - else: - return self.stats.arg_dependence_ratio(state_or_trans, key, param, self._use_corrcoef) > 0.5 + return self.stats.depends_on_arg(state_or_trans, key, param) def _get_model_from_dict(self, model_dict, model_function): model = {} |