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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>
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notebooks/anonymization_one_hot_adult.ipynb
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notebooks/anonymization_one_hot_adult.ipynb
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{
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"cells": [
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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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"# Using ML anonymization on one-hot encoded data"
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]
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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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"In this tutorial we will show how to anonymize models using the ML anonymization module, specifically when the inout data is already one-hot encoded. \n",
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"\n",
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"This will be demonstarted using the Adult dataset (original dataset can be found here: https://archive.ics.uci.edu/ml/datasets/adult). "
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]
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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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"## Load data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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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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"[['State-gov' 'Never-married' 'Adm-clerical' ... 'White' 'Male'\n",
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" 'UnitedStates']\n",
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" ['Self-emp-not-inc' 'Married-civ-spouse' 'Exec-managerial' ... 'White'\n",
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" 'Male' 'UnitedStates']\n",
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" ['Private' 'Divorced' 'Handlers-cleaners' ... 'White' 'Male'\n",
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" 'UnitedStates']\n",
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" ...\n",
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" ['Private' 'Never-married' 'Sales' ... 'White' 'Female' 'UnitedStates']\n",
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" ['Private' 'Never-married' 'Craft-repair' ... 'White' 'Male'\n",
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" 'UnitedStates']\n",
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" ['Private' 'Never-married' 'Handlers-cleaners' ... 'White' 'Male'\n",
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" 'UnitedStates']]\n"
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]
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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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"# 'workclass', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'\n",
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"categorical_features = [1, 3, 4, 5, 6, 7, 11]\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()[:, [1, 3, 4, 5, 6, 7, 11]]\n",
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"y_train = y_train.to_numpy().astype(int)\n",
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"x_test = x_test.to_numpy()[:, [1, 3, 4, 5, 6, 7, 11]]\n",
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"y_test = y_test.to_numpy().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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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Encode data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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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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"[[0 0 0 ... 0 1 0]\n",
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" [0 0 0 ... 0 1 0]\n",
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" [0 0 0 ... 0 1 0]\n",
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" ...\n",
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" [0 0 0 ... 0 1 0]\n",
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" [0 0 0 ... 0 1 0]\n",
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" [0 0 0 ... 0 1 0]]\n"
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]
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}
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],
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"source": [
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"from sklearn.preprocessing import OneHotEncoder\n",
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"import scipy\n",
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"\n",
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"preprocessor = OneHotEncoder(handle_unknown=\"ignore\")\n",
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"\n",
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"x_train = preprocessor.fit_transform(x_train)\n",
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"x_test = preprocessor.transform(x_test)\n",
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"if scipy.sparse.issparse(x_train):\n",
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" x_train = x_train.toarray().astype(int)\n",
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"if scipy.sparse.issparse(x_test):\n",
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" x_test = x_test.toarray().astype(int)\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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{
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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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{
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"cell_type": "code",
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"execution_count": 23,
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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: 0.814446287083103\n"
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]
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},
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{
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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/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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],
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"source": [
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"from sklearn.tree import DecisionTreeClassifier\n",
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"from art.estimators.classification.scikitlearn import ScikitlearnDecisionTreeClassifier\n",
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"\n",
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"model = DecisionTreeClassifier()\n",
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"model.fit(x_train, y_train)\n",
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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))"
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]
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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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"# Anonymize data\n",
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"## k=100\n",
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"\n",
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"The data is anonymized on the quasi-identifiers: age, education-num, capital-gain, hours-per-week and with a privact parameter k=100.\n",
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"\n",
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"This means that each record in the anonymized dataset is identical to 99 others on the quasi-identifier values (i.e., when looking only at those features, the records are indistinguishable)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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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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"[[0 0 0 ... 0 1 0]\n",
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" [0 0 0 ... 0 1 0]\n",
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" [0 0 0 ... 0 1 0]\n",
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" ...\n",
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" [0 0 0 ... 0 1 0]\n",
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" [0 0 0 ... 0 1 0]\n",
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" [0 0 0 ... 0 1 0]]\n"
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]
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}
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],
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"source": [
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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 = (race, sex)\n",
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"QI = [53, 52, 51, 50, 49, 48, 47]\n",
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"QI_slices = [[47, 48, 49, 50, 51], [52, 53]]\n",
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"anonymizer = Anonymize(100, QI)\n",
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"anon = anonymizer.anonymize(ArrayDataset(x_train, x_train_predictions))\n",
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"print(anon)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2711"
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]
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},
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"execution_count": 26,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# number of distinct rows in original data\n",
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"len(np.unique(x_train, axis=0))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2476"
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]
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},
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"execution_count": 27,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# number of distinct rows in anonymized data\n",
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"len(np.unique(anon, axis=0))"
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]
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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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},
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{
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"cell_type": "code",
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"execution_count": 28,
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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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"Anonymized model accuracy: 0.8135863890424421\n"
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]
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},
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{
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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/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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],
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"source": [
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"anon_model = DecisionTreeClassifier()\n",
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"anon_model.fit(anon, y_train)\n",
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"\n",
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"anon_art_classifier = ScikitlearnDecisionTreeClassifier(anon_model)\n",
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"\n",
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"print('Anonymized model accuracy: ', anon_model.score(x_test, y_test))"
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]
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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 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.9.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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