mirror of
https://github.com/IBM/ai-privacy-toolkit.git
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Support 1-hot encoded features in anonymization + fixes related to encoding in minimization (#86)
* 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>
This commit is contained in:
abigailgold
GitHub
parent
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5dce961092
7 changed files with 670 additions and 255 deletions
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load data"
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]
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/abigailt/Library/Python/3.9/lib/python/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[ 39 13 2174 0 40]\n",
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" [ 50 13 0 0 13]\n",
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" [ 38 9 0 0 40]\n",
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" ...\n",
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" [ 27 13 0 0 40]\n",
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" [ 26 11 0 0 48]\n",
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" [ 27 9 0 0 40]]\n"
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"import os\n",
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"import sys\n",
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"sys.path.insert(0, os.path.abspath('..'))\n",
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"from apt.utils.dataset_utils import get_adult_dataset_pd\n",
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"\n",
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"# requires a folder called 'datasets' in the current directory\n",
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"(x_train, y_train), (x_test, y_test) = get_adult_dataset_pd()\n",
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"x_train = x_train.to_numpy()\n",
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"y_train = y_train.to_numpy().astype(int)\n",
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"x_test = x_test.to_numpy()\n",
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"y_test = y_test.to_numpy().astype(int)\n",
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"\n",
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"# Use only numeric features (age, education-num, capital-gain, capital-loss, hours-per-week)\n",
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"x_train = x_train[:, [0, 2, 8, 9, 10]].astype(int)\n",
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"x_test = x_test[:, [0, 2, 8, 9, 10]].astype(int)\n",
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"\n",
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"# get balanced dataset\n",
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"x_train = x_train[:x_test.shape[0]]\n",
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"y_train = y_train[:y_test.shape[0]]\n",
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"\n",
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"print(x_train)"
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]
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Train decision tree model"
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]
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"cell_type": "code",
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"execution_count": 3,
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[ 39. 13. 2174. 0. 40.]\n",
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" [ 50. 13. 0. 0. 13.]\n",
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" [ 38. 9. 0. 0. 40.]\n",
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" ...\n",
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" [ 27. 13. 0. 0. 40.]\n",
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" [ 26. 11. 0. 0. 48.]\n",
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" [ 27. 9. 0. 0. 40.]]\n"
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}
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"source": [
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"import numpy as np\n",
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"\n",
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"# Use only numeric features (age, education-num, capital-gain, capital-loss, hours-per-week)\n",
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"x_train = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
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" usecols=(0, 4, 10, 11, 12), delimiter=\", \")\n",
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"\n",
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"y_train = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
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" usecols=14, dtype=str, delimiter=\", \")\n",
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"\n",
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"\n",
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"x_test = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
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" usecols=(0, 4, 10, 11, 12), delimiter=\", \", skiprows=1)\n",
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"\n",
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"y_test = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
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" usecols=14, dtype=str, delimiter=\", \", skiprows=1)\n",
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"\n",
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"# Trim trailing period \".\" from label\n",
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"y_test = np.array([a[:-1] for a in y_test])\n",
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"\n",
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"y_train[y_train == '<=50K'] = 0\n",
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"y_train[y_train == '>50K'] = 1\n",
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"y_train = y_train.astype(int)\n",
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"\n",
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"y_test[y_test == '<=50K'] = 0\n",
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"y_test[y_test == '>50K'] = 1\n",
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"y_test = y_test.astype(int)\n",
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"\n",
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"# get balanced dataset\n",
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"x_train = x_train[:x_test.shape[0]]\n",
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"y_train = y_train[:y_test.shape[0]]\n",
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"\n",
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"print(x_train)"
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]
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Train decision tree model"
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]
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Base model accuracy: 0.8076285240464345\n"
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"Base model accuracy: 0.8087341072415699\n"
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]
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"name": "stderr",
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"text": [
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"/home/mayaa/Development/GitHub/aiprivacy/ai-privacy-toolkit/venv1/lib/python3.8/site-packages/sklearn/utils/deprecation.py:103: FutureWarning: The attribute `n_features_` is deprecated in 1.0 and will be removed in 1.2. Use `n_features_in_` instead.\n",
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"/Users/abigailt/Library/Python/3.9/lib/python/site-packages/sklearn/utils/deprecation.py:103: FutureWarning: The attribute `n_features_` is deprecated in 1.0 and will be removed in 1.2. Use `n_features_in_` instead.\n",
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" warnings.warn(msg, category=FutureWarning)\n"
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]
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}
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"\n",
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"art_classifier = ScikitlearnDecisionTreeClassifier(model)\n",
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"\n",
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"print('Base model accuracy: ', model.score(x_test, y_test))\n",
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"\n",
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"x_train_predictions = np.array([np.argmax(arr) for arr in art_classifier.predict(x_train)]).reshape(-1,1)"
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"print('Base model accuracy: ', model.score(x_test, y_test))"
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]
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"output_type": "stream",
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"text": [
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"0.5460017196904557\n"
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"0.5434836015231544\n"
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"text": [
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"[[38. 13. 0. 0. 40.]\n",
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" ...\n",
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" [28 9 0 0 40]]\n"
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}
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],
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"source": [
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"import os\n",
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"import sys\n",
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"sys.path.insert(0, os.path.abspath('..'))\n",
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"from apt.utils.datasets import ArrayDataset\n",
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"from apt.anonymization import Anonymize\n",
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"\n",
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"x_train_predictions = np.array([np.argmax(arr) for arr in art_classifier.predict(x_train)])\n",
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"\n",
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"# QI = (age, education-num, capital-gain, hours-per-week)\n",
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"QI = [0, 1, 2, 4]\n",
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"anonymizer = Anonymize(100, QI)\n",
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"6739"
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"execution_count": 7,
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"output_type": "execute_result"
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"401"
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"execution_count": 8,
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"output_type": "execute_result"
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"execution_count": 10,
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"execution_count": 9,
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"metadata": {},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Anonymized model accuracy: 0.826914808672686\n"
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"Anonymized model accuracy: 0.8308457711442786\n"
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]
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},
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{
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"name": "stderr",
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"text": [
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"/home/mayaa/Development/GitHub/aiprivacy/ai-privacy-toolkit/venv1/lib/python3.8/site-packages/sklearn/utils/deprecation.py:103: FutureWarning: The attribute `n_features_` is deprecated in 1.0 and will be removed in 1.2. Use `n_features_in_` instead.\n",
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"/Users/abigailt/Library/Python/3.9/lib/python/site-packages/sklearn/utils/deprecation.py:103: FutureWarning: The attribute `n_features_` is deprecated in 1.0 and will be removed in 1.2. Use `n_features_in_` instead.\n",
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" warnings.warn(msg, category=FutureWarning)\n"
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]
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"execution_count": 11,
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.49692912418621793\n"
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"0.4944724235351923\n"
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]
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}
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],
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"output_type": "stream",
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"text": [
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"(0.5316007088009451, 0.7738607050730868)\n",
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"(0.4971184877823882, 0.5297874953936863)\n"
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"without anonymization: (0.5303914835164835, 0.7588748311018303)\n",
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"with anonymization: (0.49255952380952384, 0.3659255619702739)\n"
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]
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}
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],
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" return precision, recall\n",
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"\n",
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"# regular\n",
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"print(calc_precision_recall(np.concatenate((inferred_train_bb, inferred_test_bb)), \n",
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"print('without anonymization:', calc_precision_recall(np.concatenate((inferred_train_bb, inferred_test_bb)), \n",
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" np.concatenate((np.ones(len(inferred_train_bb)), np.zeros(len(inferred_test_bb))))))\n",
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"# anon\n",
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"print(calc_precision_recall(np.concatenate((anon_inferred_train_bb, anon_inferred_test_bb)), \n",
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"print('with anonymization:', calc_precision_recall(np.concatenate((anon_inferred_train_bb, anon_inferred_test_bb)), \n",
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" np.concatenate((np.ones(len(anon_inferred_train_bb)), np.zeros(len(anon_inferred_test_bb))))))"
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]
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},
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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"version": "3.9.6"
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}
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},
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"nbformat": 4,
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