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Fix handling of categorical features

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abigailt 2022-04-25 17:41:15 +03:00
parent a37ff06df8
commit b8d2535231
1 changed files with 20 additions and 16 deletions
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36
apt/anonymization/anonymizer.py
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@ -48,8 +48,9 @@ class Anonymize:
self.quasi_identifiers = quasi_identifiers
self.categorical_features = categorical_features
self.is_regression = is_regression
self.features_names = None
self.train_only_QI = train_only_QI
self.features_names = None
self.features = None
def anonymize(self, dataset: ArrayDataset) -> DATA_PANDAS_NUMPY_TYPE:
"""
@ -59,13 +60,16 @@ class Anonymize:
original model on the training data.
:return: An array containing the anonymized training dataset.
"""
if dataset.features_names is not None:
self.features_names = dataset.features_names
# if features is None, use numbers instead of names
elif dataset.get_samples().shape[1] != 0:
self.features_names = [i for i in range(dataset.get_samples().shape[1])]
if dataset.get_samples().shape[1] != 0:
self.features = [i for i in range(dataset.get_samples().shape[1])]
else:
raise ValueError('No data provided')
if dataset.features_names is not None:
self.features_names = dataset.features_names
else: # if no names provided, use numbers instead
self.features_names = self.features
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')
@ -85,24 +89,24 @@ class Anonymize:
def _anonymize(self, x, y):
if x.shape[0] != y.shape[0]:
raise ValueError("x and y should have same number of rows")
x_anonymizer_train = x
if self.train_only_QI:
# build DT just on QI features
x_anonymizer_train = x[:, self.quasi_identifiers]
if x.dtype.kind not in 'iufc':
if not self.categorical_features:
raise ValueError('when supplying an array with non-numeric data, categorical_features must be defined')
x_prepared = self._modify_categorical_features(x_anonymizer_train)
x_prepared = self._modify_categorical_features(x)
else:
x_prepared = x_anonymizer_train
x_prepared = x
x_anonymizer_train = x_prepared
if self.train_only_QI:
# build DT just on QI features
x_anonymizer_train = x_prepared[:, self.quasi_identifiers]
if self.is_regression:
self.anonymizer = DecisionTreeRegressor(random_state=10, min_samples_split=2, min_samples_leaf=self.k)
else:
self.anonymizer = DecisionTreeClassifier(random_state=10, min_samples_split=2, min_samples_leaf=self.k)
self.anonymizer.fit(x_prepared, y)
cells_by_id = self._calculate_cells(x, x_prepared)
return self._anonymize_data(x, x_prepared, cells_by_id)
self.anonymizer.fit(x_anonymizer_train, y)
cells_by_id = self._calculate_cells(x, x_anonymizer_train)
return self._anonymize_data(x, x_anonymizer_train, cells_by_id)
def _calculate_cells(self, x, x_anonymizer_train):
# x is original data, x_anonymizer_train is only QIs + 1-hot encoded
@ -171,7 +175,7 @@ class Anonymize:
used_features = self.features
if self.train_only_QI:
used_features = self.quasi_identifiers
numeric_features = [f for f in x.columns if f in used_features and f not in self.categorical_features]
numeric_features = [f for f in self.features if f in used_features and f not in self.categorical_features]
categorical_features = [f for f in self.categorical_features if f in used_features]
numeric_transformer = Pipeline(
steps=[('imputer', SimpleImputer(strategy='constant', fill_value=0))]