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Support 1-hot encoded features in anonymization + fixes related to encoding in minimization (#86)

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* Support 1-hot encoded features in anonymization (#72)
* Fix anonymization adult notebook + new notebook to demonstrate anonymization on 1-hot encoded data

* Minimizer: 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>
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abigailgold 2023-10-19 11:48:15 +03:00 • committed by GitHub
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7 changed files with 670 additions and 255 deletions
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76
apt/anonymization/anonymizer.py
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@ -23,7 +23,11 @@ class Anonymize:
:type k: int
:param quasi_identifiers: The features that need to be minimized in case of pandas data, and indexes of features
in case of numpy data.
:type quasi_identifiers: np.ndarray or list
:type quasi_identifiers: np.ndarray or list of strings or integers.
:param quasi_identifer_slices: If some of the quasi-identifiers represent 1-hot encoded features that need to remain
consistent after anonymization, provide a list containing the list of column names
or indexes that represent a single feature.
:type quasi_identifer_slices: list of lists of strings or integers.
: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).
:type categorical_features: list, optional
@ -35,8 +39,12 @@ class Anonymize:
:type train_only_QI: boolean, optional
"""
def __init__(self, k: int, quasi_identifiers: Union[np.ndarray, list], categorical_features: Optional[list] = None,
is_regression: Optional[bool] = False, train_only_QI: Optional[bool] = False):
def __init__(self, k: int,
quasi_identifiers: Union[np.ndarray, list],
quasi_identifer_slices: Optional[list] = None,
categorical_features: Optional[list] = None,
is_regression: Optional[bool] = False,
train_only_QI: Optional[bool] = False):
if k < 2:
raise ValueError("k should be a positive integer with a value of 2 or higher")
if quasi_identifiers is None or len(quasi_identifiers) < 1:
@ -49,6 +57,7 @@ class Anonymize:
self.train_only_QI = train_only_QI
self.features_names = None
self.features = None
self.quasi_identifer_slices = quasi_identifer_slices
def anonymize(self, dataset: ArrayDataset) -> DATA_PANDAS_NUMPY_TYPE:
"""
@ -76,7 +85,14 @@ class Anonymize:
if self.categorical_features and not set(self.categorical_features).issubset(set(self.features_names)):
raise ValueError('Categorical features should bs a subset of the supplied features or indexes in range of '
'the data columns')
# transform quasi identifiers to indexes
self.quasi_identifiers = [i for i, v in enumerate(self.features_names) if v in self.quasi_identifiers]
if self.quasi_identifer_slices:
temp_list = []
for slice in self.quasi_identifer_slices:
new_slice = [i for i, v in enumerate(self.features_names) if v in slice]
temp_list.append(new_slice)
self.quasi_identifer_slices = temp_list
if self.categorical_features:
self.categorical_features = [i for i, v in enumerate(self.features_names) if v in self.categorical_features]
@ -126,31 +142,49 @@ class Anonymize:
return cells_by_id
def _find_representatives(self, x, x_anonymizer_train, cells):
# x is original data, x_anonymizer_train is only QIs + 1-hot encoded
# x is original data (always numpy), x_anonymizer_train is only QIs + 1-hot encoded
node_ids = self._find_sample_nodes(x_anonymizer_train)
if self.quasi_identifer_slices:
all_one_hot_features = set([feature for encoded in self.quasi_identifer_slices for feature in encoded])
else:
all_one_hot_features = set()
for cell in cells:
cell['representative'] = {}
# get all rows in cell
indexes = [index for index, node_id in enumerate(node_ids) if node_id == cell['id']]
# TODO: should we filter only those with majority label? (using hist)
rows = x[indexes]
for feature in self.quasi_identifiers:
values = rows[:, feature]
if self.categorical_features and feature in self.categorical_features:
# find most common value
cell['representative'][feature] = Counter(values).most_common(1)[0][0]
else:
# find the mean value (per feature)
median = np.median(values)
min_value = max(values)
min_dist = float("inf")
for value in values:
# euclidean distance between two floating point values
dist = abs(value - median)
if dist < min_dist:
min_dist = dist
min_value = value
cell['representative'][feature] = min_value
done = set()
for feature in self.quasi_identifiers: # self.quasi_identifiers are numerical indexes
if feature not in done:
# deal with 1-hot encoded features
if feature in all_one_hot_features:
# find features that belong together
for encoded in self.quasi_identifer_slices:
if feature in encoded:
values = rows[:, encoded]
unique_rows, counts = np.unique(values, axis=0, return_counts=True)
rep = unique_rows[np.argmax(counts)]
for i, e in enumerate(encoded):
done.add(e)
cell['representative'][e] = rep[i]
else: # rest of features
values = rows[:, feature]
if self.categorical_features and feature in self.categorical_features:
# find most common value
cell['representative'][feature] = Counter(values).most_common(1)[0][0]
else:
# find the mean value (per feature)
median = np.median(values)
min_value = max(values)
min_dist = float("inf")
for value in values:
# euclidean distance between two floating point values
dist = abs(value - median)
if dist < min_dist:
min_dist = dist
min_value = value
cell['representative'][feature] = min_value
def _find_sample_nodes(self, samples):
paths = self._anonymizer.decision_path(samples).toarray()