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olasaadi 2022-03-15 10:52:45 +02:00
parent c5d3be2390
commit a2b560920f
1 changed files with 8 additions and 8 deletions
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16
apt/anonymization/anonymizer.py
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@ -5,7 +5,7 @@ from collections import Counter
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.preprocessing import OneHotEncoder
from apt.utils.datasets import ArrayDataset
from apt.utils.datasets import ArrayDataset, DATA_ARRAY_TYPE
from typing import Union, Optional
@ -38,21 +38,21 @@ class Anonymize:
self.categorical_features = categorical_features
self.is_regression = is_regression
def anonymize(self, dataset: ArrayDataset) -> Union[np.ndarray, pd.DataFrame]:
def anonymize(self, dataset: ArrayDataset) -> DATA_ARRAY_TYPE:
"""
Method for performing model-guided anonymization.
:param dataset: Data wrapper Containing The training data for the model and ehe predictions of the
original model on the training data. If provided as a pandas
dataframe, may contain both numeric and categorical data.
:param dataset: Data wrapper containing the training data for the model and the predictions of the
original model on the training data. If implemented with a pandas dataframe, may
contain both numeric and categorical data.
:return: An array containing the anonymized training dataset.
"""
if type(dataset.x) == np.ndarray:
return self._anonymize_ndarray(dataset.x.copy(), dataset.y)
if type(dataset.get_samples()) == np.ndarray:
return self._anonymize_ndarray(dataset.get_samples().copy(), dataset.get_labels())
else: # pandas
if not self.categorical_features:
raise ValueError('When supplying a pandas dataframe, categorical_features must be defined')
return self._anonymize_pandas(dataset.x.copy(), dataset.y)
return self._anonymize_pandas(dataset.get_samples().copy(), dataset.get_labels())
def _anonymize_ndarray(self, x, y):
if x.shape[0] != y.shape[0]: