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Extract common code to methods
Signed-off-by: abigailt <abigailt@il.ibm.com>
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abigailt
parent
570c6f8966
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1 changed files with 39 additions and 61 deletions
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@ -315,17 +315,10 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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self._attach_cells_representatives(x_prepared, used_x_train, y_train, nodes)
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self._attach_cells_representatives(x_prepared, used_x_train, y_train, nodes)
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# self._cells currently holds the generalization created from the tree leaves
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# self._cells currently holds the generalization created from the tree leaves
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self._calculate_generalizations(x_test)
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generalized = self._generalize(x_test, x_prepared_test, nodes)
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if self.generalize_using_transform:
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generalized = self._generalize_from_tree(x_test, x_prepared_test, nodes, self.cells, self._cells_by_id)
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else:
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generalized = self._generalize_from_generalizations(x_test, self.generalizations)
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# check accuracy
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# check accuracy
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if self.encoder:
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accuracy = self._calculate_accuracy(generalized, y_test, self.estimator, self.encoder)
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accuracy = self.estimator.score(ArrayDataset(self.encoder.transform(generalized), y_test))
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else:
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accuracy = self.estimator.score(ArrayDataset(generalized, y_test))
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print('Initial accuracy of model on generalized data, relative to original model predictions '
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print('Initial accuracy of model on generalized data, relative to original model predictions '
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'(base generalization derived from tree, before improvements): %f' % accuracy)
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'(base generalization derived from tree, before improvements): %f' % accuracy)
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@ -348,17 +341,8 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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self._attach_cells_representatives(x_prepared, used_x_train, y_train, nodes)
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self._attach_cells_representatives(x_prepared, used_x_train, y_train, nodes)
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self._calculate_generalizations(x_test)
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generalized = self._generalize(x_test, x_prepared_test, nodes)
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if self.generalize_using_transform:
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accuracy = self._calculate_accuracy(generalized, y_test, self.estimator, self.encoder)
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generalized = self._generalize_from_tree(x_test, x_prepared_test, nodes, self.cells,
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self._cells_by_id)
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else:
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generalized = self._generalize_from_generalizations(x_test, self.generalizations)
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if self.encoder:
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accuracy = self.estimator.score(ArrayDataset(self.encoder.transform(generalized), y_test))
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else:
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accuracy = self.estimator.score(ArrayDataset(generalized, y_test))
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# if accuracy passed threshold roll back to previous iteration generalizations
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# if accuracy passed threshold roll back to previous iteration generalizations
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if accuracy < self.target_accuracy:
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if accuracy < self.target_accuracy:
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self.cells = cells_previous_iter
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self.cells = cells_previous_iter
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@ -381,17 +365,8 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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if removed_feature is None:
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if removed_feature is None:
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break
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break
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self._calculate_generalizations(x_test)
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generalized = self._generalize(x_test, x_prepared_test, nodes)
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if self.generalize_using_transform:
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accuracy = self._calculate_accuracy(generalized, y_test, self.estimator, self.encoder)
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generalized = self._generalize_from_tree(x_test, x_prepared_test, nodes, self.cells,
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self._cells_by_id)
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else:
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generalized = self._generalize_from_generalizations(x_test, self.generalizations)
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if self.encoder:
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accuracy = self.estimator.score(ArrayDataset(self.encoder.transform(generalized), y_test))
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else:
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accuracy = self.estimator.score(ArrayDataset(generalized, y_test))
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print('Removed feature: %s, new relative accuracy: %f' % (removed_feature, accuracy))
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print('Removed feature: %s, new relative accuracy: %f' % (removed_feature, accuracy))
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# self._cells currently holds the chosen generalization based on target accuracy
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# self._cells currently holds the chosen generalization based on target accuracy
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@ -918,6 +893,15 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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return original_data_generalized
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return original_data_generalized
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def _generalize(self, data, data_prepared, nodes):
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self._calculate_generalizations(data)
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if self.generalize_using_transform:
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generalized = self._generalize_from_tree(data, data_prepared, nodes, self.cells,
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self._cells_by_id)
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else:
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generalized = self._generalize_from_generalizations(data, self.generalizations)
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return generalized
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@staticmethod
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@staticmethod
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def _map_to_ranges_categories(samples, ranges, categories):
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def _map_to_ranges_categories(samples, ranges, categories):
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all_sample_indexes = []
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all_sample_indexes = []
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@ -987,21 +971,8 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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feature_data[feature],
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feature_data[feature],
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total)
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total)
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if feature_ncp > 0:
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if feature_ncp > 0:
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# divide by accuracy gain
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feature_ncp = self._normalize_ncp_by_accuracy_gain(original_data, prepared_data, nodes, feature,
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new_cells = copy.deepcopy(self.cells)
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feature_ncp, labels, current_accuracy)
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cells_by_id = copy.deepcopy(self._cells_by_id)
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GeneralizeToRepresentative._remove_feature_from_cells(new_cells, cells_by_id, feature)
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generalized = self._generalize_from_tree(original_data, prepared_data, nodes, new_cells,
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cells_by_id)
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if self.encoder:
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accuracy_gain = self.estimator.score(ArrayDataset(self.encoder.transform(generalized),
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labels)) - current_accuracy
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else:
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accuracy_gain = self.estimator.score(ArrayDataset(generalized, labels)) - current_accuracy
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if accuracy_gain < 0:
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accuracy_gain = 0
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if accuracy_gain != 0:
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feature_ncp = feature_ncp / accuracy_gain
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if feature_ncp < range_min:
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if feature_ncp < range_min:
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range_min = feature_ncp
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range_min = feature_ncp
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@ -1017,22 +988,9 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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feature_data[feature],
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feature_data[feature],
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total)
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total)
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if feature_ncp > 0:
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if feature_ncp > 0:
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# divide by accuracy loss
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feature_ncp = self._normalize_ncp_by_accuracy_gain(original_data, prepared_data, nodes, feature,
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new_cells = copy.deepcopy(self.cells)
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feature_ncp, labels, current_accuracy)
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cells_by_id = copy.deepcopy(self._cells_by_id)
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GeneralizeToRepresentative._remove_feature_from_cells(new_cells, cells_by_id, feature)
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generalized = self._generalize_from_tree(original_data, prepared_data, nodes, new_cells,
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cells_by_id)
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if self.encoder:
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accuracy_gain = self.estimator.score(ArrayDataset(self.encoder.transform(generalized),
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labels)) - current_accuracy
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else:
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accuracy_gain = self.estimator.score(ArrayDataset(generalized, labels)) - current_accuracy
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if accuracy_gain < 0:
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accuracy_gain = 0
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if accuracy_gain != 0:
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feature_ncp = feature_ncp / accuracy_gain
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if feature_ncp < range_min:
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if feature_ncp < range_min:
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range_min = feature_ncp
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range_min = feature_ncp
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remove_feature = feature
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remove_feature = feature
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@ -1062,6 +1020,21 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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feature_ncp += cell_ncp
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feature_ncp += cell_ncp
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return feature_ncp
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return feature_ncp
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def _normalize_ncp_by_accuracy_gain(self, original_data, prepared_data, nodes, feature, feature_ncp, labels,
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current_accuracy):
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new_cells = copy.deepcopy(self.cells)
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cells_by_id = copy.deepcopy(self._cells_by_id)
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GeneralizeToRepresentative._remove_feature_from_cells(new_cells, cells_by_id, feature)
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generalized = self._generalize_from_tree(original_data, prepared_data, nodes, new_cells,
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cells_by_id)
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accuracy = self._calculate_accuracy(generalized, labels, self.estimator, self.encoder)
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accuracy_gain = accuracy - current_accuracy
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if accuracy_gain < 0:
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accuracy_gain = 0
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if accuracy_gain != 0:
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feature_ncp = feature_ncp / accuracy_gain
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return feature_ncp
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def _calculate_generalizations(self, samples: Optional[pd.DataFrame] = None):
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def _calculate_generalizations(self, samples: Optional[pd.DataFrame] = None):
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ranges, range_representatives = self._calculate_ranges(self.cells)
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ranges, range_representatives = self._calculate_ranges(self.cells)
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categories, category_representatives = self._calculate_categories(self.cells)
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categories, category_representatives = self._calculate_categories(self.cells)
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@ -1281,3 +1254,8 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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for feature in to_remove:
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for feature in to_remove:
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del generalizations['categories'][feature]
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del generalizations['categories'][feature]
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@staticmethod
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def _calculate_accuracy(generalized, y_test, estimator, encoder):
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generalized_data = encoder.transform(generalized) if encoder else generalized
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return estimator.score(ArrayDataset(generalized_data, y_test))
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