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fix notebook and add features_names to ArrayDataset

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and allow providing features names in QI and Cat features not just indexes
This commit is contained in:
olasaadi 2022-03-24 19:32:24 +02:00
parent 137167fb0c
commit 66c86dc595
5 changed files with 89 additions and 74 deletions
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15
apt/anonymization/anonymizer.py
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@ -22,19 +22,19 @@ class Anonymize:
"""
:param k: The privacy parameter that determines the number of records that will be indistinguishable from each
other (when looking at the quasi identifiers). Should be at least 2.
:param quasi_identifiers: The indexes of features that need to be minimized.
:param categorical_features: The list of categorical features indexes
:param quasi_identifiers: The features that need to be minimized.
:param categorical_features: The list of categorical features.
:param is_regression: Boolean param indicates that is is a regression problem.
"""
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:
raise ValueError("The list of quasi-identifiers cannot be empty")
self.k = k
self.quasi_identifiers = quasi_identifiers
self.categorical_features = categorical_features
self.is_regression = is_regression
self.features_names = None
def anonymize(self, dataset: ArrayDataset) -> DATA_PANDAS_NUMPY_TYPE:
"""
@ -51,6 +51,15 @@ class Anonymize:
self._features = [i for i in range(dataset.get_samples().shape[0])]
else:
raise ValueError('No data provided')
if not set(self.quasi_identifiers).issubset(set(self.features_names)):
raise ValueError('Quasi identifiers should bs a subset of the supplied features or indexes in range of '
'the data columns')
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')
self.quasi_identifiers = [i for i, v in enumerate(self.features_names) if v in self.quasi_identifiers]
if self.categorical_features:
self.categorical_features = [i for i, v in enumerate(self.features_names) if v in self.categorical_features]
transformed = self._anonymize(dataset.get_samples().copy(), dataset.get_labels())
if dataset.is_pandas: