diff --git a/apt/anonymization/anonymizer.py b/apt/anonymization/anonymizer.py index 99c898a..8a4f95d 100644 --- a/apt/anonymization/anonymizer.py +++ b/apt/anonymization/anonymizer.py @@ -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: diff --git a/apt/utils/dataset_utils.py b/apt/utils/dataset_utils.py index f99c6cc..2405f8f 100644 --- a/apt/utils/dataset_utils.py +++ b/apt/utils/dataset_utils.py @@ -273,7 +273,7 @@ def get_nursery_dataset(raw: bool = True, test_set: float = 0.2, transform_socia raise Exception("Bad label value: %s" % value) data["label"] = data["label"].apply(modify_label) - data["children"] = data["children"].apply(lambda x: 4 if x == "more" else x) + data["children"] = data["children"].apply(lambda x: "4" if x == "more" else x) if transform_social: diff --git a/apt/utils/datasets/datasets.py b/apt/utils/datasets/datasets.py index ebcd7cb..29dd4e9 100644 --- a/apt/utils/datasets/datasets.py +++ b/apt/utils/datasets/datasets.py @@ -18,7 +18,6 @@ from torch import Tensor logger = logging.getLogger(__name__) - INPUT_DATA_ARRAY_TYPE = Union[np.ndarray, pd.DataFrame, List, Tensor] OUTPUT_DATA_ARRAY_TYPE = np.ndarray DATA_PANDAS_NUMPY_TYPE = Union[np.ndarray, pd.DataFrame] @@ -113,7 +112,6 @@ class StoredDataset(Dataset): if unzip: StoredDataset.extract_archive(zip_path=file_path, dest_path=dest_path, remove_archive=False) - @staticmethod def extract_archive(zip_path: str, dest_path=None, remove_archive=False): """ @@ -164,7 +162,8 @@ class StoredDataset(Dataset): class ArrayDataset(Dataset): """Dataset that is based on x and y arrays (e.g., numpy/pandas/list...)""" - def __init__(self, x: INPUT_DATA_ARRAY_TYPE, y: Optional[INPUT_DATA_ARRAY_TYPE] = None, **kwargs): + def __init__(self, x: INPUT_DATA_ARRAY_TYPE, y: Optional[INPUT_DATA_ARRAY_TYPE] = None, features_names=None, + **kwargs): """ ArrayDataset constructor. :param x: collection of data samples @@ -172,10 +171,12 @@ class ArrayDataset(Dataset): :param kwargs: dataset parameters """ self.is_pandas = False - self.features_names = None + self.features_names = features_names self._y = array2numpy(self, y) if y is not None else None self._x = array2numpy(self, x) if self.is_pandas: + if features_names and not np.array_equal(features_names, x.columns): + raise ValueError("The supplied features are not the same as in the data features") self.features_names = x.columns if y is not None and len(self._x) != len(self._y): @@ -213,7 +214,6 @@ class PytorchData(Dataset): else: self.__getitem__ = self.get_sample_item - def get_samples(self) -> OUTPUT_DATA_ARRAY_TYPE: """Return data samples as numpy array""" return array2numpy(self._x) @@ -244,6 +244,7 @@ class DatasetFactory: :param name: dataset name :return: """ + def inner_wrapper(wrapped_class: Dataset) -> Any: if name in cls.registry: logger.warning('Dataset %s already exists. Will replace it', name) diff --git a/notebooks/attribute_inference_anonymization_nursery.ipynb b/notebooks/attribute_inference_anonymization_nursery.ipynb index 34fa296..bfba540 100644 --- a/notebooks/attribute_inference_anonymization_nursery.ipynb +++ b/notebooks/attribute_inference_anonymization_nursery.ipynb @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -37,7 +37,7 @@ "text/plain": " parents has_nurs form children housing finance \\\n8450 pretentious very_crit foster 1 less_conv convenient \n12147 great_pret very_crit complete 1 critical inconv \n2780 usual critical complete 4 less_conv convenient \n11924 great_pret critical foster 1 critical convenient \n59 usual proper complete 2 convenient convenient \n... ... ... ... ... ... ... \n5193 pretentious less_proper complete 1 convenient inconv \n1375 usual less_proper incomplete 2 less_conv convenient \n10318 great_pret less_proper foster 4 convenient convenient \n6396 pretentious improper completed 3 less_conv convenient \n485 usual proper incomplete 1 critical inconv \n\n social health \n8450 1 not_recom \n12147 1 recommended \n2780 1 not_recom \n11924 1 not_recom \n59 0 not_recom \n... ... ... \n5193 0 recommended \n1375 1 priority \n10318 0 priority \n6396 1 recommended \n485 1 not_recom \n\n[10366 rows x 8 columns]", "text/html": "
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parentshas_nursformchildrenhousingfinancesocialhealth
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" }, - "execution_count": 136, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -63,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -104,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -178,14 +178,14 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.5076210688790276\n" + "0.5122515917422342\n" ] } ], @@ -225,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -233,7 +233,7 @@ "text/plain": " parents has_nurs form children housing finance \\\n0 pretentious very_crit foster 1 less_conv convenient \n1 great_pret very_crit complete 1 critical inconv \n2 usual critical complete 4 less_conv convenient \n3 great_pret critical foster 1 critical convenient \n4 usual proper complete 2 convenient convenient \n... ... ... ... ... ... ... \n10361 pretentious less_proper complete 1 convenient inconv \n10362 usual less_proper incomplete 2 less_conv convenient \n10363 great_pret less_proper foster 4 convenient convenient \n10364 pretentious improper completed 3 less_conv convenient \n10365 usual proper incomplete 1 critical convenient \n\n social health \n0 0 not_recom \n1 1 recommended \n2 0 not_recom \n3 0 not_recom \n4 0 not_recom \n... ... ... \n10361 0 recommended \n10362 1 priority \n10363 0 priority \n10364 1 recommended \n10365 0 not_recom \n\n[10366 rows x 8 columns]", "text/html": "
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10361pretentiousless_propercomplete1convenientinconv0recommended
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" }, - "execution_count": 141, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -254,14 +254,14 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": "7585" }, - "execution_count": 142, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -273,14 +273,14 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": "5766" }, - "execution_count": 143, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -299,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -332,7 +332,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -368,14 +368,14 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.5218985143739148\n" + "0.5245996527107852\n" ] } ], @@ -399,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -444,15 +444,15 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(0.9322033898305084, 0.01066925315227934)\n", - "(0.9806763285024155, 0.03937924345295829)\n" + "(1.0, 0.019204655674102813)\n", + "(0.9829787234042553, 0.04481086323957323)\n" ] } ], @@ -483,7 +483,7 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -493,14 +493,14 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": "4226" }, - "execution_count": 150, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -519,7 +519,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -552,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -588,14 +588,14 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.5184256222265098\n" + "0.515820953115956\n" ] } ], @@ -612,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -621,8 +621,8 @@ "text": [ "(0.49415432579890883, 0.48976438779451525)\n", "(0.49415432579890883, 0.48976438779451525)\n", - "(0.9322033898305084, 0.01066925315227934)\n", - "(1.0, 0.03161978661493695)\n" + "(1.0, 0.019204655674102813)\n", + "(1.0, 0.026382153249272552)\n" ] } ], @@ -655,34 +655,9 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 20, "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "argument must be a string or number", - "output_type": "error", - "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mTypeError\u001B[0m Traceback (most recent call last)", - "File \u001B[0;32m~/PycharmProjects/ai-privacy-toolkit-internal/venv/lib/python3.8/site-packages/sklearn/preprocessing/_label.py:112\u001B[0m, in \u001B[0;36m_encode\u001B[0;34m(values, uniques, encode, check_unknown)\u001B[0m\n\u001B[1;32m 111\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 112\u001B[0m res \u001B[38;5;241m=\u001B[39m \u001B[43m_encode_python\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalues\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43muniques\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mencode\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 113\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n", - "File \u001B[0;32m~/PycharmProjects/ai-privacy-toolkit-internal/venv/lib/python3.8/site-packages/sklearn/preprocessing/_label.py:60\u001B[0m, in \u001B[0;36m_encode_python\u001B[0;34m(values, uniques, encode)\u001B[0m\n\u001B[1;32m 59\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m uniques \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m---> 60\u001B[0m uniques \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43msorted\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mset\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mvalues\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 61\u001B[0m uniques \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray(uniques, dtype\u001B[38;5;241m=\u001B[39mvalues\u001B[38;5;241m.\u001B[39mdtype)\n", - "\u001B[0;31mTypeError\u001B[0m: '<' not supported between instances of 'int' and 'str'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001B[0;31mTypeError\u001B[0m Traceback (most recent call last)", - "Input \u001B[0;32mIn [155]\u001B[0m, in \u001B[0;36m\u001B[0;34m()\u001B[0m\n\u001B[1;32m 2\u001B[0m QI2_indexes \u001B[38;5;241m=\u001B[39m [i \u001B[38;5;28;01mfor\u001B[39;00m i, v \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(features) \u001B[38;5;28;01mif\u001B[39;00m v \u001B[38;5;129;01min\u001B[39;00m QI2]\n\u001B[1;32m 3\u001B[0m anonymizer3 \u001B[38;5;241m=\u001B[39m Anonymize(\u001B[38;5;241m100\u001B[39m, QI2_indexes, categorical_features\u001B[38;5;241m=\u001B[39mcategorical_features_indexes)\n\u001B[0;32m----> 4\u001B[0m anon3 \u001B[38;5;241m=\u001B[39m \u001B[43manonymizer3\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43manonymize\u001B[49m\u001B[43m(\u001B[49m\u001B[43mArrayDataset\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx_train\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mx_train_predictions\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[0;32m~/PycharmProjects/ai-privacy-toolkit-internal/apt/anonymization/anonymizer.py:55\u001B[0m, in \u001B[0;36mAnonymize.anonymize\u001B[0;34m(self, dataset)\u001B[0m\n\u001B[1;32m 52\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 53\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mNo data provided\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m---> 55\u001B[0m transformed \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_anonymize\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdataset\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_samples\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcopy\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdataset\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_labels\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 56\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m dataset\u001B[38;5;241m.\u001B[39mis_pandas:\n\u001B[1;32m 57\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m pd\u001B[38;5;241m.\u001B[39mDataFrame(transformed, columns\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_features)\n", - "File \u001B[0;32m~/PycharmProjects/ai-privacy-toolkit-internal/apt/anonymization/anonymizer.py:68\u001B[0m, in \u001B[0;36mAnonymize._anonymize\u001B[0;34m(self, x, y)\u001B[0m\n\u001B[1;32m 66\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcategorical_features:\n\u001B[1;32m 67\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mwhen supplying an array with non-numeric data, categorical_features must be defined\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m---> 68\u001B[0m x_prepared \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_modify_categorical_features\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx_anonymizer_train\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 69\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 70\u001B[0m x_prepared \u001B[38;5;241m=\u001B[39m x_anonymizer_train\n", - "File \u001B[0;32m~/PycharmProjects/ai-privacy-toolkit-internal/apt/anonymization/anonymizer.py:144\u001B[0m, in \u001B[0;36mAnonymize._modify_categorical_features\u001B[0;34m(self, x)\u001B[0m\n\u001B[1;32m 142\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_modify_categorical_features\u001B[39m(\u001B[38;5;28mself\u001B[39m, x):\n\u001B[1;32m 143\u001B[0m encoder \u001B[38;5;241m=\u001B[39m OneHotEncoder()\n\u001B[0;32m--> 144\u001B[0m one_hot_encoded \u001B[38;5;241m=\u001B[39m \u001B[43mencoder\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit_transform\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 145\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m one_hot_encoded\n", - "File \u001B[0;32m~/PycharmProjects/ai-privacy-toolkit-internal/venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:372\u001B[0m, in \u001B[0;36mOneHotEncoder.fit_transform\u001B[0;34m(self, X, y)\u001B[0m\n\u001B[1;32m 352\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m 353\u001B[0m \u001B[38;5;124;03mFit OneHotEncoder to X, then transform X.\u001B[39;00m\n\u001B[1;32m 354\u001B[0m \n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 369\u001B[0m \u001B[38;5;124;03m Transformed input.\u001B[39;00m\n\u001B[1;32m 370\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m 371\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_validate_keywords()\n\u001B[0;32m--> 372\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit_transform\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[0;32m~/PycharmProjects/ai-privacy-toolkit-internal/venv/lib/python3.8/site-packages/sklearn/base.py:571\u001B[0m, in \u001B[0;36mTransformerMixin.fit_transform\u001B[0;34m(self, X, y, **fit_params)\u001B[0m\n\u001B[1;32m 567\u001B[0m \u001B[38;5;66;03m# non-optimized default implementation; override when a better\u001B[39;00m\n\u001B[1;32m 568\u001B[0m \u001B[38;5;66;03m# method is possible for a given clustering algorithm\u001B[39;00m\n\u001B[1;32m 569\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m y \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m 570\u001B[0m \u001B[38;5;66;03m# fit method of arity 1 (unsupervised transformation)\u001B[39;00m\n\u001B[0;32m--> 571\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mfit_params\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241m.\u001B[39mtransform(X)\n\u001B[1;32m 572\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 573\u001B[0m \u001B[38;5;66;03m# fit method of arity 2 (supervised transformation)\u001B[39;00m\n\u001B[1;32m 574\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfit(X, y, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mfit_params)\u001B[38;5;241m.\u001B[39mtransform(X)\n", - "File \u001B[0;32m~/PycharmProjects/ai-privacy-toolkit-internal/venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:347\u001B[0m, in \u001B[0;36mOneHotEncoder.fit\u001B[0;34m(self, X, y)\u001B[0m\n\u001B[1;32m 330\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m 331\u001B[0m \u001B[38;5;124;03mFit OneHotEncoder to X.\u001B[39;00m\n\u001B[1;32m 332\u001B[0m \n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 344\u001B[0m \u001B[38;5;124;03mself\u001B[39;00m\n\u001B[1;32m 345\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m 346\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_validate_keywords()\n\u001B[0;32m--> 347\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_fit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mhandle_unknown\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mhandle_unknown\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 348\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdrop_idx_ \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_compute_drop_idx()\n\u001B[1;32m 349\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\n", - "File \u001B[0;32m~/PycharmProjects/ai-privacy-toolkit-internal/venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:86\u001B[0m, in \u001B[0;36m_BaseEncoder._fit\u001B[0;34m(self, X, handle_unknown)\u001B[0m\n\u001B[1;32m 84\u001B[0m Xi \u001B[38;5;241m=\u001B[39m X_list[i]\n\u001B[1;32m 85\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcategories \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mauto\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[0;32m---> 86\u001B[0m cats \u001B[38;5;241m=\u001B[39m \u001B[43m_encode\u001B[49m\u001B[43m(\u001B[49m\u001B[43mXi\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 87\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 88\u001B[0m cats \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcategories[i], dtype\u001B[38;5;241m=\u001B[39mXi\u001B[38;5;241m.\u001B[39mdtype)\n", - "File \u001B[0;32m~/PycharmProjects/ai-privacy-toolkit-internal/venv/lib/python3.8/site-packages/sklearn/preprocessing/_label.py:114\u001B[0m, in \u001B[0;36m_encode\u001B[0;34m(values, uniques, encode, check_unknown)\u001B[0m\n\u001B[1;32m 112\u001B[0m res \u001B[38;5;241m=\u001B[39m _encode_python(values, uniques, encode)\n\u001B[1;32m 113\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[0;32m--> 114\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124margument must be a string or number\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 115\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m res\n\u001B[1;32m 116\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n", - "\u001B[0;31mTypeError\u001B[0m: argument must be a string or number" - ] - } - ], + "outputs": [], "source": [ "QI2 = [\"parents\", \"has_nurs\", \"form\", \"children\", \"housing\", \"finance\", \"social\", \"health\"]\n", "QI2_indexes = [i for i, v in enumerate(features) if v in QI2]\n", @@ -692,9 +667,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": "39" + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# number of distinct rows in anonymized data\n", "len(anon3.drop_duplicates())" @@ -702,9 +686,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Anonymized model accuracy: 0.751929012345679\n", + "BB attack accuracy: 1.0\n", + "WB attack accuracy: 0.5187150299054601\n" + ] + } + ], "source": [ "anon3_str = anon3.astype(str)\n", "anon3_encoded = OneHotEncoder(sparse=False).fit_transform(anon3_str)\n", @@ -742,9 +736,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.49415432579890883, 0.48976438779451525)\n", + "(0.49415432579890883, 0.48976438779451525)\n", + "(1.0, 0.019204655674102813)\n", + "(1.0, 0.032201745877788554)\n" + ] + } + ], "source": [ "# black-box regular\n", "print(calc_precision_recall(inferred_train_bb, x_train_feature))\n", diff --git a/tests/test_anonymizer.py b/tests/test_anonymizer.py index d7072e4..83710cd 100644 --- a/tests/test_anonymizer.py +++ b/tests/test_anonymizer.py @@ -44,7 +44,7 @@ def test_anonymize_pandas_adult(): QI_indexes = [i for i, v in enumerate(features) if v in QI] categorical_features_indexes = [i for i, v in enumerate(features) if v in categorical_features] anonymizer = Anonymize(k, QI_indexes, categorical_features=categorical_features_indexes) - anon = anonymizer.anonymize(ArrayDataset(x_train, pred)) + anon = anonymizer.anonymize(ArrayDataset(x_train, pred, features)) assert(anon.loc[:, QI].drop_duplicates().shape[0] < x_train.loc[:, QI].drop_duplicates().shape[0]) assert (anon.loc[:, QI].value_counts().min() >= k)