diff options
Diffstat (limited to 'lib/dfatool.py')
| -rwxr-xr-x | lib/dfatool.py | 58 |
1 files changed, 46 insertions, 12 deletions
diff --git a/lib/dfatool.py b/lib/dfatool.py index 616e6fd..ac0885b 100755 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -15,7 +15,7 @@ from multiprocessing import Pool from automata import PTA from functions import analytic from functions import AnalyticFunction -from utils import vprint, is_numeric, soft_cast_int, param_slice_eq, compute_param_statistics, remove_index_from_tuple +from utils import vprint, is_numeric, soft_cast_int, param_slice_eq, compute_param_statistics, remove_index_from_tuple, is_power_of_two, distinct_param_values arg_support_enabled = True @@ -430,6 +430,48 @@ class ParamStats: """ return 1 - self._generic_param_independence_ratio(state_or_trans, attribute) + def _reduce_param_matrix(self, matrix: np.ndarray, parameter_names: list) -> list: + """ + :param matrix: parameter dependence matrix, M[(...)] == 1 iff (model attribute) is influenced by (parameter) for other parameter value indxe == (...) + :param parameter_names: names of parameters in the order in which they appear in the matrix index. The first entry corresponds to the first axis, etc. + :returns: parameters which determine whether (parameter) has an effect on (model attribute). If a parameter is not part of this list, its value does not + affect (parameter)'s influence on (model attribute) -- it either always or never has an influence + """ + if np.all(matrix == True) or np.all(matrix == False): + return list() + + if not is_power_of_two(np.count_nonzero(matrix)): + # cannot be reliably reduced to a list of parameters + return list() + + if np.count_nonzero(matrix) == 1: + influential_parameters = list() + for i, parameter_name in enumerate(parameter_names): + if matrix.shape[i] > 1: + influential_parameters.append(parameter_name) + return influential_parameters + + for axis in range(matrix.ndim): + candidate = self._reduce_param_matrix(np.all(matrix, axis=axis), remove_index_from_tuple(parameter_names, axis)) + if len(candidate): + return candidate + + return list() + + def _get_codependent_parameters(self, stats, param): + """ + Return list of parameters which affect whether `param` influences the model attribute described in `stats` or not. + """ + safe_div = np.vectorize(lambda x,y: 0. if x == 0 else 1 - x/y) + ratio_by_value = safe_div(stats['lut_by_param_values'][param], stats['std_by_param_values'][param]) + err_mode = np.seterr('ignore') + dep_by_value = ratio_by_value > 0.5 + np.seterr(**err_mode) + + other_param_list = list(filter(lambda x: x != param, self._parameter_names)) + influencer_parameters = self._reduce_param_matrix(dep_by_value, other_param_list) + return influencer_parameters + def _param_independence_ratio(self, state_or_trans, attribute, param): """ Return the heuristic ratio of parameter independence for state_or_trans, attribute, and param. @@ -446,17 +488,9 @@ class ParamStats: # This means that the variation of param does not affect the model quality -> no influence, return 1 return 1. - safe_div = np.vectorize(lambda x,y: 1. if x == 0 else x/y) - std_by_value = safe_div(statistics['lut_by_param_values'][param], statistics['std_by_param_values'][param]) - - i = 0 - for other_param in self._parameter_names: - if param != other_param and not np.any(np.isnan(std_by_value)) and std_by_value.shape[i] > 1: - dep1 = np.all(std_by_value < 0.5, axis=i) - dep2 = np.all(std_by_value >= 0.5, axis=i) - if np.any(dep1 | dep2 == False): - print('possible correlation {}/{} {} <-> {}'.format(state_or_trans, attribute, param, other_param)) - i += 1 + influencer_parameters = self._get_codependent_parameters(statistics, param) + if len(influencer_parameters): + print('{}/{} {} <-> {}'.format(state_or_trans, attribute, param, influencer_parameters)) return statistics['std_param_lut'] / statistics['std_by_param'][param] |