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Minimization fixes (#12)
* Fixes related to corner cases in calculating generalizations * Fix print * Fix corner cases in transform as well * Improve prints + bug fixes in calculation of feature to remove * Notebook demonstrating ai minimization
This commit is contained in:
abigailgold
GitHub
parent
d2591d7840
commit
43952e2332
2 changed files with 326 additions and 47 deletions
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@ -117,6 +117,10 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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self.cells = params['cells']
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return self
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@property
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def generalizations(self):
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return self.generalizations_
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def fit_transform(self, X=None, y=None):
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"""Learns the generalizations based on training data, and applies them to the data.
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@ -206,40 +210,45 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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nodes = self._get_nodes_level(0)
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self._attach_cells_representatives(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._calculate_generalizations()
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# apply generalizations to test data
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generalized = self._generalize(X_test, nodes, self.cells_, self.cells_by_id_)
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# check accuracy
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accuracy = self.estimator.score(generalized, y_test)
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print('Initial accuracy is %f' % accuracy)
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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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# if accuracy above threshold, improve generalization
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if accuracy > self.target_accuracy:
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print('Improving generalizations')
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level = 1
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while accuracy > self.target_accuracy:
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nodes = self._get_nodes_level(level)
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self._calculate_level_cells(level)
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self._attach_cells_representatives(X_train, y_train, nodes)
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self._calculate_generalizations()
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generalized = self._generalize(X_test, nodes, self.cells_,
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self.cells_by_id_)
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accuracy = self.estimator.score(generalized, y_test)
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print('Level: %d, accuracy: %f' % (level, accuracy))
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print('Pruned tree to level: %d, new relative accuracy: %f' % (level, accuracy))
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level+=1
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# if accuracy below threshold, improve accuracy by removing features from generalization
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if accuracy < self.target_accuracy:
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print('Improving accuracy')
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while accuracy < self.target_accuracy:
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self._calculate_generalizations()
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removed_feature = self._remove_feature_from_generalization(X_test,
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nodes, y_test,
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feature_data)
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if not removed_feature:
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feature_data, accuracy)
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if removed_feature is None:
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break
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generalized = self._generalize(X_test, nodes, self.cells_,
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self.cells_by_id_)
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self._calculate_generalizations()
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generalized = self._generalize(X_test, nodes, self.cells_, self.cells_by_id_)
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accuracy = self.estimator.score(generalized, y_test)
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print('Removed feature: %s, 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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@ -304,12 +313,12 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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# replace the values in the representative columns with the representative
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# values (leaves others untouched)
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if not representatives.columns.empty:
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if indexes and not representatives.columns.empty:
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if len(indexes) > 1:
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replace = pd.concat([representatives.loc[i].to_frame().T]*len(indexes)).reset_index(drop=True)
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replace.index = indexes
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else:
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replace = representatives.loc[i].to_frame().T
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replace = representatives.loc[i].to_frame().T.reset_index(drop=True)
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replace.index = indexes
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generalized.loc[indexes, representatives.columns] = replace
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return generalized.to_numpy()
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@ -409,30 +418,31 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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new_cells = []
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new_cells_by_id = {}
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nodes = self._get_nodes_level(level)
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for node in nodes:
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if self.dt_.tree_.feature[node] == -2: # leaf node
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new_cell = self.cells_by_id_[node]
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else:
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left_child = self.dt_.tree_.children_left[node]
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right_child = self.dt_.tree_.children_right[node]
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left_cell = self.cells_by_id_[left_child]
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right_cell = self.cells_by_id_[right_child]
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new_cell = {'id': int(node), 'ranges': {}, 'categories': {},
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'label': None, 'representative': None}
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for feature in left_cell['ranges'].keys():
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new_cell['ranges'][feature] = {}
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new_cell['ranges'][feature]['start'] = left_cell['ranges'][feature]['start']
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new_cell['ranges'][feature]['end'] = right_cell['ranges'][feature]['start']
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for feature in left_cell['categories'].keys():
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new_cell['categories'][feature] = \
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list(set(left_cell['categories'][feature]) |
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set(right_cell['categories'][feature]))
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self._calculate_level_cell_label(left_cell, right_cell, new_cell)
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new_cells.append(new_cell)
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new_cells_by_id[new_cell['id']] = new_cell
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self.cells_ = new_cells
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self.cells_by_id_ = new_cells_by_id
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# else: nothing to do, stay with previous cells
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if nodes:
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for node in nodes:
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if self.dt_.tree_.feature[node] == -2: # leaf node
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new_cell = self.cells_by_id_[node]
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else:
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left_child = self.dt_.tree_.children_left[node]
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right_child = self.dt_.tree_.children_right[node]
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left_cell = self.cells_by_id_[left_child]
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right_cell = self.cells_by_id_[right_child]
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new_cell = {'id': int(node), 'ranges': {}, 'categories': {},
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'label': None, 'representative': None}
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for feature in left_cell['ranges'].keys():
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new_cell['ranges'][feature] = {}
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new_cell['ranges'][feature]['start'] = left_cell['ranges'][feature]['start']
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new_cell['ranges'][feature]['end'] = right_cell['ranges'][feature]['start']
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for feature in left_cell['categories'].keys():
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new_cell['categories'][feature] = \
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list(set(left_cell['categories'][feature]) |
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set(right_cell['categories'][feature]))
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self._calculate_level_cell_label(left_cell, right_cell, new_cell)
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new_cells.append(new_cell)
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new_cells_by_id[new_cell['id']] = new_cell
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self.cells_ = new_cells
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self.cells_by_id_ = new_cells_by_id
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# else: nothing to do, stay with previous cells
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def _calculate_level_cell_label(self, left_cell, right_cell, new_cell):
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new_cell['hist'] = [x + y for x, y in zip(left_cell['hist'], right_cell['hist'])]
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@ -445,6 +455,7 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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stack = [(0, -1)] # seed is the root node id and its parent depth
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while len(stack) > 0:
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node_id, parent_depth = stack.pop()
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# depth = distance from root
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node_depth[node_id] = parent_depth + 1
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if self.dt_.tree_.children_left[node_id] != self.dt_.tree_.children_right[node_id]:
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@ -453,10 +464,14 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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else:
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is_leaves[node_id] = True
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# depth of entire tree
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max_depth = max(node_depth)
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# depth of current level
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depth = max_depth - level
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# level is higher than root
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if depth < 0:
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return None
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# return all nodes with depth == level or leaves higher than level
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return [i for i, x in enumerate(node_depth) if x == depth or (x < depth and is_leaves[i])]
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def _attach_cells_representatives(self, samples, labels, level_nodes):
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@ -518,12 +533,12 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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indexes = [j for j in range(len(mapping_to_cells)) if mapping_to_cells[j]['id'] == cells[i]['id']]
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# replaces the values in the representative columns with the representative values
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# (leaves others untouched)
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if not representatives.columns.empty:
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if indexes and not representatives.columns.empty:
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if len(indexes) > 1:
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replace = pd.concat([representatives.loc[i].to_frame().T]*len(indexes)).reset_index(drop=True)
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replace.index = indexes
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else:
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replace = representatives.loc[i].to_frame().T
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replace = representatives.loc[i].to_frame().T.reset_index(drop=True)
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replace.index = indexes
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generalized.loc[indexes, representatives.columns] = replace
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return generalized.to_numpy()
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@ -539,14 +554,16 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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node_ids = self._find_sample_nodes(samples, nodes)
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return [cells_by_id[nodeId] for nodeId in node_ids]
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def _remove_feature_from_generalization(self, samples, nodes, labels, feature_data):
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feature = self._get_feature_to_remove(samples, nodes, labels, feature_data)
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if not feature:
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def _remove_feature_from_generalization(self, samples, nodes, labels, feature_data, current_accuracy):
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feature = self._get_feature_to_remove(samples, nodes, labels, feature_data, current_accuracy)
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if feature is None:
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return None
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GeneralizeToRepresentative._remove_feature_from_cells(self.cells_, self.cells_by_id_, feature)
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# del self.generalizations_['ranges'][feature]
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# self.generalizations_['untouched'].append(feature)
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return feature
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def _get_feature_to_remove(self, samples, nodes, labels, feature_data):
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def _get_feature_to_remove(self, samples, nodes, labels, feature_data, current_accuracy):
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# We want to remove features with low iLoss (NCP) and high accuracy gain
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# (after removing them)
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ranges = self.generalizations_['ranges']
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@ -567,13 +584,17 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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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(samples, nodes, new_cells, cells_by_id)
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accuracy = self.estimator.score(generalized, labels)
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feature_ncp = feature_ncp / accuracy
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accuracy_gain = self.estimator.score(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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range_min = feature_ncp
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remove_feature = feature
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print('feature to remove: ' + (remove_feature if remove_feature else ''))
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print('feature to remove: ' + (str(remove_feature) if remove_feature is not None else 'none'))
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return remove_feature
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def _calculate_generalizations(self):
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@ -660,5 +681,3 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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del cell['categories'][feature]
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cell['untouched'].append(feature)
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cells_by_id[cell['id']] = cell.copy()
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260
notebooks/minimization_adult.ipynb
Normal file
260
notebooks/minimization_adult.ipynb
Normal file
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@ -0,0 +1,260 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Applying data minimization to a trained ML model"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this tutorial we will show how to perform data minimization for ML models using the minimization module. \n",
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"\n",
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"This will be demonstarted using the Adult dataset (original dataset can be found here: https://archive.ics.uci.edu/ml/datasets/adult). \n",
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"\n",
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"We use only the numerical features in the dataset because this is what is currently supported by the module."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[3.9000e+01 1.3000e+01 2.1740e+03 0.0000e+00 4.0000e+01]\n",
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" [5.0000e+01 1.3000e+01 0.0000e+00 0.0000e+00 1.3000e+01]\n",
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" [3.8000e+01 9.0000e+00 0.0000e+00 0.0000e+00 4.0000e+01]\n",
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" ...\n",
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" [5.8000e+01 9.0000e+00 0.0000e+00 0.0000e+00 4.0000e+01]\n",
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" [2.2000e+01 9.0000e+00 0.0000e+00 0.0000e+00 2.0000e+01]\n",
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" [5.2000e+01 9.0000e+00 1.5024e+04 0.0000e+00 4.0000e+01]]\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"# Use only numeric features (age, education-num, capital-gain, capital-loss, hours-per-week)\n",
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"x_train = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
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" usecols=(0, 4, 10, 11, 12), delimiter=\", \")\n",
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"\n",
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"y_train = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
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" usecols=14, dtype=str, delimiter=\", \")\n",
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"\n",
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"\n",
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"x_test = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
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" usecols=(0, 4, 10, 11, 12), delimiter=\", \", skiprows=1)\n",
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"\n",
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"y_test = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
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" usecols=14, dtype=str, delimiter=\", \", skiprows=1)\n",
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"\n",
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"# Trim trailing period \".\" from label\n",
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"y_test = np.array([a[:-1] for a in y_test])\n",
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"\n",
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"y_train[y_train == '<=50K'] = 0\n",
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"y_train[y_train == '>50K'] = 1\n",
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"y_train = y_train.astype(np.int)\n",
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"\n",
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"y_test[y_test == '<=50K'] = 0\n",
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"y_test[y_test == '>50K'] = 1\n",
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"y_test = y_test.astype(np.int)\n",
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"\n",
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"print(x_train)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Train decision tree model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Base model accuracy: 0.8189914624408821\n"
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]
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}
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],
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"source": [
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"from sklearn.tree import DecisionTreeClassifier\n",
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"\n",
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"model = DecisionTreeClassifier()\n",
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"model.fit(x_train, y_train)\n",
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"\n",
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"print('Base model accuracy: ', model.score(x_test, y_test))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Run minimization\n",
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"We will try to run minimization with different possible values of target accuracy (how close to the original model's accuracy we want to get, 1 being same accuracy as for original data)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Initial accuracy of model on generalized data, relative to original model predictions (base generalization derived from tree, before improvements): 0.929376\n",
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"Improving accuracy\n",
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"feature to remove: 0\n",
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"Removed feature: 0, new relative accuracy: 0.939867\n",
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"feature to remove: 4\n",
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"Removed feature: 4, new relative accuracy: 0.967247\n",
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"feature to remove: 2\n",
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"Removed feature: 2, new relative accuracy: 0.972620\n",
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"feature to remove: 1\n",
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"Removed feature: 1, new relative accuracy: 0.992323\n",
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"feature to remove: 3\n",
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"Removed feature: 3, new relative accuracy: 1.000000\n",
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"Accuracy on minimized data: 0.8237371411024106\n"
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]
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}
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],
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"source": [
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"import sys\n",
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"import os\n",
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"sys.path.insert(0, os.path.abspath('..'))\n",
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"\n",
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"from apt.minimization import GeneralizeToRepresentative\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"# default target_accuracy is 0.998\n",
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"minimizer = GeneralizeToRepresentative(model)\n",
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"# Can be done either on training or test data. Doing it with test data is better as the resulting accuracy on test\n",
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"# data will be closer to the desired target accuracy (when working with training data it could result in a larger gap)\n",
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"# Don't forget to leave a hold-out set for final validation!\n",
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"X_generalizer_train, x_test, y_generalizer_train, y_test = train_test_split(x_test, y_test, stratify=y_test,\n",
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" test_size = 0.4, random_state = 38)\n",
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"x_train_predictions = model.predict(X_generalizer_train)\n",
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"minimizer.fit(X_generalizer_train, x_train_predictions)\n",
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"transformed = minimizer.transform(x_test)\n",
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"\n",
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"print('Accuracy on minimized data: ', model.score(transformed, y_test))"
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]
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},
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{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Let's see what features were generalized"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'ranges': {}, 'untouched': [0, 1, 2, 3, 4]}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"generalizations = minimizer.generalizations\n",
|
||||
"print(generalizations)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can see that for the default target accuracy of 0.998 of the original accuracy, no generalizations are possible (all features are left untouched, i.e., not generalized).\n",
|
||||
"\n",
|
||||
"Let's change to a slightly lower target accuracy."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Initial accuracy of model on generalized data, relative to original model predictions (base generalization derived from tree, before improvements): 0.929376\n",
|
||||
"Improving accuracy\n",
|
||||
"feature to remove: 0\n",
|
||||
"Removed feature: 0, new relative accuracy: 0.939867\n",
|
||||
"feature to remove: 4\n",
|
||||
"Removed feature: 4, new relative accuracy: 0.967247\n",
|
||||
"feature to remove: 2\n",
|
||||
"Removed feature: 2, new relative accuracy: 0.972620\n",
|
||||
"feature to remove: 1\n",
|
||||
"Removed feature: 1, new relative accuracy: 0.992323\n",
|
||||
"Accuracy on minimized data: 0.820205742361431\n",
|
||||
"{'ranges': {3: [546.0, 704.0, 705.5, 742.5, 782.0, 834.0, 870.0, 1446.5, 1538.5, 1612.5, 1699.0, 1744.0, 1801.0, 1814.0, 1846.0, 1881.5, 1978.5, 2248.0, 2298.5, 2537.5]}, 'untouched': [0, 1, 2, 4]}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# We allow a 2.5% deviation in accuracy from the original model accuracy\n",
|
||||
"minimizer2 = GeneralizeToRepresentative(model, target_accuracy=0.975)\n",
|
||||
"\n",
|
||||
"minimizer2.fit(X_generalizer_train, x_train_predictions)\n",
|
||||
"transformed2 = minimizer2.transform(x_test)\n",
|
||||
"print('Accuracy on minimized data: ', model.score(transformed2, y_test))\n",
|
||||
"generalizations2 = minimizer2.generalizations\n",
|
||||
"print(generalizations2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This time we were able to generalize one feature, feature number 3 (capital-loss)."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue