diff options
Diffstat (limited to 'lib/parameters.py')
| -rw-r--r-- | lib/parameters.py | 120 |
1 files changed, 2 insertions, 118 deletions
diff --git a/lib/parameters.py b/lib/parameters.py index 9ecd7e9..e516926 100644 --- a/lib/parameters.py +++ b/lib/parameters.py @@ -587,124 +587,6 @@ class ModelAttribute: return np.mean(self.by_param[param]) return np.median(self.by_param[param]) - def build_dtree(self): - split_param_index = self.get_split_param_index() - if split_param_index is None: - return - - distinct_values = self.stats.distinct_values_by_param_index[split_param_index] - tt1 = list( - map( - lambda i: self.param_values[i][split_param_index] == distinct_values[0], - range(len(self.param_values)), - ) - ) - tt2 = np.invert(tt1) - - pv1 = list() - pv2 = list() - - for i, param_tuple in enumerate(self.param_values): - if tt1[i]: - pv1.append(param_tuple) - else: - pv2.append(param_tuple) - - # print( - # f">>> split {self.name} {self.attr} by param #{split_param_index}" - # ) - - child1 = ModelAttribute( - self.name, - self.attr, - self.data[tt1], - pv1, - self.param_names, - self.arg_count, - codependent_param_dict(pv1), - ) - child2 = ModelAttribute( - self.name, - self.attr, - self.data[tt2], - pv2, - self.param_names, - self.arg_count, - codependent_param_dict(pv2), - ) - - ParamStats.compute_for_attr(child1) - ParamStats.compute_for_attr(child2) - - child1.build_dtree() - child2.build_dtree() - - self.split = ( - split_param_index, - {distinct_values[0]: child1, distinct_values[1]: child2}, - ) - - # print( - # f"<<< split {self.name} {self.attr} by param #{split_param_index}" - # ) - - # None -> kein split notwendig - # andernfalls: Parameter-Index, anhand dessen eine Decision Tree-Ebene aufgespannt wird - # (Kinder sind wiederum ModelAttributes, in denen dieser Parameter konstant ist) - def get_split_param_index(self): - if not self.param_names: - return None - std_by_param = list() - for param_index, param_name in enumerate(self.param_names): - distinct_values = self.stats.distinct_values_by_param_index[param_index] - if ( - self.stats.depends_on_param(param_name) - and len(distinct_values) == 2 - and not param_index in self.ignore_param - ): - val1 = list( - map( - lambda i: self.param_values[i][param_index] - == distinct_values[0], - range(len(self.param_values)), - ) - ) - val2 = np.invert(val1) - val1_std = np.std(self.data[val1]) - val2_std = np.std(self.data[val2]) - std_by_param.append(np.mean([val1_std, val2_std])) - else: - std_by_param.append(np.inf) - for arg_index in range(self.arg_count): - distinct_values = self.stats.distinct_values_by_param_index[ - len(self.param_names) + arg_index - ] - if ( - self.stats.depends_on_arg(arg_index) - and len(distinct_values) == 2 - and not len(self.param_names) + arg_index in self.ignore_param - ): - val1 = list( - map( - lambda i: self.param_values[i][ - len(self.param_names) + arg_index - ] - == distinct_values[0], - range(len(self.param_values)), - ) - ) - val2 = np.invert(val1) - val1_std = np.std(self.data[val1]) - val2_std = np.std(self.data[val2]) - std_by_param.append(np.mean([val1_std, val2_std])) - else: - std_by_param.append(np.inf) - split_param_index = np.argmin(std_by_param) - split_std = std_by_param[split_param_index] - if split_std == np.inf: - return None - return split_param_index - def get_data_for_paramfit(self, safe_functions_enabled=False): if self.split: return self.get_data_for_paramfit_split( @@ -716,6 +598,7 @@ class ModelAttribute: ) def get_data_for_paramfit_split(self, safe_functions_enabled=False): + # currently unused split_param_index, child_by_param_value = self.split ret = list() for param_value, child in child_by_param_value.items(): @@ -798,6 +681,7 @@ class ModelAttribute: self.set_data_from_paramfit_this(paramfit, prefix) def set_data_from_paramfit_split(self, paramfit, prefix): + # currently unused split_param_index, child_by_param_value = self.split function_map = { "split_by": split_param_index, |