mirror of
https://github.com/IBM/ai-privacy-toolkit.git
synced 2026-07-11 16:22:10 +02:00
No default encoder, if none provided data is supplied to the model as is. Fix data type of representative values. Fix and add more tests.
Signed-off-by: abigailt <abigailt@il.ibm.com>
This commit is contained in:
abigailt
parent
ef406cea62
commit
30cb705062
2 changed files with 134 additions and 84 deletions
|
|
@ -57,7 +57,7 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
|
|||
:param categorical_features: The list of categorical features (if supplied, these featurtes will be one-hot
|
||||
encoded before using them to train the decision tree model).
|
||||
:param encoder: Optional encoder for encoding data before feeding it into the estimator (e.g., for categorical
|
||||
features)
|
||||
features). If not provided, the data will be fed as is directly to the estimator.
|
||||
:type encoder: sklearn OrdinalEncoder or OneHotEncoder
|
||||
:type categorical_features: list of strings, optional
|
||||
:param features_to_minimize: The features to be minimized.
|
||||
|
|
@ -256,7 +256,6 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
|
|||
# Going to fit
|
||||
# (currently not dealing with option to fit with only X and y and no estimator)
|
||||
if self.estimator and dataset and dataset.get_samples() is not None and dataset.get_labels() is not None:
|
||||
dtype = dataset.get_samples().dtype
|
||||
x = pd.DataFrame(dataset.get_samples(), columns=self._features)
|
||||
if not self.features_to_minimize:
|
||||
self.features_to_minimize = self._features
|
||||
|
|
@ -293,21 +292,6 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
|
|||
# collect feature data (such as min, max)
|
||||
self._feature_data = self._get_feature_data(x)
|
||||
|
||||
# default encoder in case none provided
|
||||
if self.encoder is None:
|
||||
numeric_features = [f for f in self._features if f not in self.categorical_features]
|
||||
numeric_transformer = Pipeline(
|
||||
steps=[('imputer', SimpleImputer(strategy='constant', fill_value=0))]
|
||||
)
|
||||
categorical_transformer = OneHotEncoder(handle_unknown="ignore", sparse=False)
|
||||
self.encoder = ColumnTransformer(
|
||||
transformers=[
|
||||
("num", numeric_transformer, numeric_features),
|
||||
("cat", categorical_transformer, self.categorical_features),
|
||||
]
|
||||
)
|
||||
self.encoder.fit(x)
|
||||
|
||||
self.cells = []
|
||||
self._categorical_values = {}
|
||||
|
||||
|
|
@ -341,7 +325,10 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
|
|||
generalized = self._generalize_from_generalizations(x_test, self.generalizations)
|
||||
|
||||
# check accuracy
|
||||
accuracy = self.estimator.score(ArrayDataset(self.encoder.transform(generalized).astype(dtype), y_test))
|
||||
if self.encoder:
|
||||
accuracy = self.estimator.score(ArrayDataset(self.encoder.transform(generalized), y_test))
|
||||
else:
|
||||
accuracy = self.estimator.score(ArrayDataset(generalized, y_test))
|
||||
print('Initial accuracy of model on generalized data, relative to original model predictions '
|
||||
'(base generalization derived from tree, before improvements): %f' % accuracy)
|
||||
|
||||
|
|
@ -371,8 +358,10 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
|
|||
else:
|
||||
generalized = self._generalize_from_generalizations(x_test, self.generalizations)
|
||||
|
||||
accuracy = self.estimator.score(ArrayDataset(self.encoder.transform(generalized).astype(dtype),
|
||||
y_test))
|
||||
if self.encoder:
|
||||
accuracy = self.estimator.score(ArrayDataset(self.encoder.transform(generalized), y_test))
|
||||
else:
|
||||
accuracy = self.estimator.score(ArrayDataset(generalized, y_test))
|
||||
# if accuracy passed threshold roll back to previous iteration generalizations
|
||||
if accuracy < self.target_accuracy:
|
||||
self.cells = cells_previous_iter
|
||||
|
|
@ -401,8 +390,11 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
|
|||
self._cells_by_id)
|
||||
else:
|
||||
generalized = self._generalize_from_generalizations(x_test, self.generalizations)
|
||||
accuracy = self.estimator.score(ArrayDataset(self.encoder.transform(generalized).astype(dtype),
|
||||
y_test))
|
||||
|
||||
if self.encoder:
|
||||
accuracy = self.estimator.score(ArrayDataset(self.encoder.transform(generalized), y_test))
|
||||
else:
|
||||
accuracy = self.estimator.score(ArrayDataset(generalized, y_test))
|
||||
print('Removed feature: %s, new relative accuracy: %f' % (removed_feature, accuracy))
|
||||
|
||||
# self._cells currently holds the chosen generalization based on target accuracy
|
||||
|
|
@ -893,7 +885,11 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
|
|||
|
||||
def _generalize_indexes(self, original_data, cells, all_indexes):
|
||||
# prepared data include one hot encoded categorical data + QI
|
||||
representatives = pd.DataFrame(columns=self._features) # empty except for columns
|
||||
dtypes = original_data.dtypes.to_dict()
|
||||
new_dtypes = {}
|
||||
for t in dtypes.keys():
|
||||
new_dtypes[t] = pd.Series(dtype=dtypes[t].name)
|
||||
representatives = pd.DataFrame(new_dtypes) # empty except for columns
|
||||
original_data_generalized = pd.DataFrame(original_data, columns=self._features, copy=True)
|
||||
|
||||
# iterate over cells (leaves in decision tree)
|
||||
|
|
@ -1000,8 +996,11 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
|
|||
GeneralizeToRepresentative._remove_feature_from_cells(new_cells, cells_by_id, feature)
|
||||
generalized = self._generalize_from_tree(original_data, prepared_data, nodes, new_cells,
|
||||
cells_by_id)
|
||||
accuracy_gain = self.estimator.score(ArrayDataset(self.encoder.transform(generalized),
|
||||
labels)) - current_accuracy
|
||||
if self.encoder:
|
||||
accuracy_gain = self.estimator.score(ArrayDataset(self.encoder.transform(generalized),
|
||||
labels)) - current_accuracy
|
||||
else:
|
||||
accuracy_gain = self.estimator.score(ArrayDataset(generalized, labels)) - current_accuracy
|
||||
if accuracy_gain < 0:
|
||||
accuracy_gain = 0
|
||||
if accuracy_gain != 0:
|
||||
|
|
@ -1027,8 +1026,11 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
|
|||
GeneralizeToRepresentative._remove_feature_from_cells(new_cells, cells_by_id, feature)
|
||||
generalized = self._generalize_from_tree(original_data, prepared_data, nodes, new_cells,
|
||||
cells_by_id)
|
||||
accuracy_gain = self.estimator.score(ArrayDataset(self.encoder.transform(generalized),
|
||||
labels)) - current_accuracy
|
||||
if self.encoder:
|
||||
accuracy_gain = self.estimator.score(ArrayDataset(self.encoder.transform(generalized),
|
||||
labels)) - current_accuracy
|
||||
else:
|
||||
accuracy_gain = self.estimator.score(ArrayDataset(generalized, labels)) - current_accuracy
|
||||
|
||||
if accuracy_gain < 0:
|
||||
accuracy_gain = 0
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue