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add minimization notebook (#22)
* add german credit notebook to showcase new features (minimize only some features and categorical features) * add notebook to show minimization data on a regression problem
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olasaadi
GitHub
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3 changed files with 648 additions and 1 deletions
262
notebooks/minimization_diabetes_reg.ipynb
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262
notebooks/minimization_diabetes_reg.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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"collapsed": true,
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"# Applying data minimization to a trained regression ML model"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"In this tutorial we will show how to perform data minimization for regression ML models using the minimization module.\n",
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"\n",
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"We will show you applying data minimization to a different trained regression models."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Load data\n",
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"QI parameter determines which features will be minimized."
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 54,
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"outputs": [],
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"source": [
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"from sklearn.datasets import load_diabetes\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"dataset = load_diabetes()\n",
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"X_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.5, random_state=14)\n",
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"\n",
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"features = ['age', 'sex', 'bmi', 'bp',\n",
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" 's1', 's2', 's3', 's4', 's5', 's6']\n",
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"QI = [0, 2, 5, 8, 9]"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Train DecisionTreeRegressor model"
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],
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"metadata": {
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"collapsed": false
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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": 55,
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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 (R2 score): 0.15014421352446072\n"
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]
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}
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],
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"source": [
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"from apt.minimization import GeneralizeToRepresentative\n",
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"from sklearn.tree import DecisionTreeRegressor\n",
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"\n",
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"model1 = DecisionTreeRegressor(random_state=10, min_samples_split=2)\n",
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"model1.fit(X_train, y_train)\n",
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"print('Base model accuracy (R2 score): ', model1.score(X_test, y_test))"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Run minimization\n",
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"We will try to run minimization with only a subset of the features."
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],
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"metadata": {
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"collapsed": false
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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": 56,
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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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"Initial accuracy of model on generalized data, relative to original model predictions (base generalization derived from tree, before improvements): 0.108922\n",
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"Improving accuracy\n",
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"feature to remove: s5\n",
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"Removed feature: s5, new relative accuracy: 0.505498\n",
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"feature to remove: s6\n",
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"Removed feature: s6, new relative accuracy: 0.404757\n",
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"feature to remove: bmi\n",
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"Removed feature: bmi, new relative accuracy: 0.718978\n",
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"Accuracy on minimized data: 0.11604533946025941\n",
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"generalizations: {'ranges': {'age': [-0.07090024650096893, -0.043656209483742714, -0.041839939542114735, -0.03639113181270659, -0.01459590089507401, -0.012779632292222232, -0.009147093165665865, -0.0036982858437113464, 0.03989217430353165, 0.039892176166176796, 0.05623859912157059, 0.06713621318340302], 's2': [-0.0550188384950161, -0.0285577941685915, -0.024643437936902046, -0.02135537937283516, -0.013683241792023182, -0.006480826530605555, 0.009176596067845821, 0.023111702874302864, 0.02420772146433592, 0.02655633445829153, 0.039082273840904236]}, 'categories': {}, 'untouched': ['s3', 'bmi', 's6', 'bp', 's4', 's5', 'sex', 's1']}\n"
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]
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}
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],
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"source": [
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"# note that is_regression param is True\n",
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"\n",
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"minimizer1 = GeneralizeToRepresentative(model1, target_accuracy=0.7, features=features, is_regression=True,\n",
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" features_to_minimize=QI)\n",
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"\n",
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"# Fitting the minimizar can be done either on training or test data. Doing it with test data is better as the\n",
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"# resulting accuracy on test data will be closer to the desired target accuracy (when working with training\n",
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"# data it could result in a larger gap)\n",
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"# Don't forget to leave a hold-out set for final validation!\n",
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"X_generalizer_train1, x_test1, y_generalizer_train1, y_test1 = train_test_split(X_test, y_test,\n",
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" test_size = 0.4, random_state = 38)\n",
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"\n",
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"x_train_predictions1 = model1.predict(X_generalizer_train1)\n",
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"minimizer1.fit(X_generalizer_train1, x_train_predictions1)\n",
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"transformed1 = minimizer1.transform(x_test1)\n",
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"print('Accuracy on minimized data: ', model1.score(transformed1, y_test1))\n",
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"print('generalizations: ',minimizer1.generalizations_)#%% md"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Train linear regression model"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "code",
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"source": [
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"from sklearn.linear_model import LinearRegression\n",
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"from apt.minimization import GeneralizeToRepresentative\n",
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"\n",
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"model2 = LinearRegression()\n",
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"model2.fit(X_train, y_train)\n",
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"print('Base model accuracy (R2 score): ', model2.score(X_test, y_test))"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Run minimization\n",
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"We will try to run minimization with only a subset of the features."
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],
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"metadata": {
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"collapsed": false
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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": 58,
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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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"Initial accuracy of model on generalized data, relative to original model predictions (base generalization derived from tree, before improvements): 0.225782\n",
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"Improving accuracy\n",
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"feature to remove: age\n",
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"Removed feature: age, new relative accuracy: 0.223565\n",
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"feature to remove: s2\n",
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"Removed feature: s2, new relative accuracy: 0.759788\n",
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"Accuracy on minimized data: 0.4414329261774286\n",
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"generalizations: {'ranges': {'bmi': [-0.0660245232284069, -0.06171327643096447, -0.048779530450701714, -0.036923596635460854, -0.022912041284143925, -0.015906263142824173, -0.009978296235203743, 0.007266696775332093, 0.022356065921485424, 0.028822937980294228, 0.04499012045562267, 0.053073709830641747, 0.10103634744882584], 's5': [-0.08940735459327698, -0.07823517918586731, -0.07310866191983223, -0.07022909820079803, -0.06740894541144371, -0.06558558344841003, -0.041897499933838844, -0.04049498960375786, -0.03781316243112087, -0.033939776942133904, -0.03263746201992035, -0.02538660168647766, -0.023219254799187183, -0.017585186287760735, -0.016525186598300934, -0.008522996446117759, 0.0015758189256303012, 0.012934560421854258, 0.014069339726120234, 0.015929921995848417, 0.01947084255516529, 0.028651678003370762, 0.03358383011072874, 0.03639278281480074, 0.041416410356760025, 0.06386702693998814], 's6': [-0.07356456853449345, -0.052854035049676895, -0.048711927607655525, -0.0383566590026021, -0.02800139266764745, -0.021788232028484344, -0.007290858076885343, -0.007290857844054699, 0.017561784014105797, 0.02377494378015399, 0.02791705122217536, 0.02998810407007113, 0.054840744473040104]}, 'categories': {}, 'untouched': ['s2', 's3', 'bp', 's4', 'age', 'sex', 's1']}\n"
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]
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}
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],
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"source": [
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"# note that is_regression param is True\n",
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"\n",
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"minimizer2 = GeneralizeToRepresentative(model2, target_accuracy=0.7, features=features, is_regression=True,\n",
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" features_to_minimize=QI)\n",
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"\n",
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"# Fitting the minimizar can be done either on training or test data. Doing it with test data is better as the\n",
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"# resulting accuracy on test data will be closer to the desired target accuracy (when working with training\n",
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"# data it could result in a larger gap)\n",
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"# Don't forget to leave a hold-out set for final validation!\n",
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"X_generalizer_train2, x_test2, y_generalizer_train2, y_test2 = train_test_split(X_test, y_test,\n",
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" test_size = 0.4, random_state = 38)\n",
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"\n",
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"x_train_predictions2 = model2.predict(X_generalizer_train2)\n",
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"minimizer2.fit(X_generalizer_train2, x_train_predictions2)\n",
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"transformed2 = minimizer2.transform(x_test2)\n",
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"print('Accuracy on minimized data: ', model2.score(transformed2, y_test2))\n",
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"print('generalizations: ',minimizer2.generalizations_)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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",
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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": 2
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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": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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385
notebooks/minimization_german_credit.ipynb
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385
notebooks/minimization_german_credit.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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"source": [
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"# Applying data minimization with categorical data and only a subset of the features to a trained ML model"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"In this tutorial we will show how to perform data minimization for ML models using the minimization module.\n",
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"\n",
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"This will be demonstarted using the German Credit dataset (original dataset can be found here: https://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/german.data)."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Load data\n",
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"QI parameter determines which features will be minimized."
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],
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"metadata": {
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"collapsed": false
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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": 1,
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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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" Existing_checking_account Duration_in_month Credit_history Purpose \\\n",
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"0 A14 24 A32 A41 \n",
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"1 A14 33 A33 A49 \n",
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"2 A11 9 A32 A42 \n",
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"3 A14 28 A34 A43 \n",
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"4 A11 24 A33 A43 \n",
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".. ... ... ... ... \n",
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"695 A14 12 A32 A43 \n",
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"696 A14 13 A32 A43 \n",
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"697 A11 48 A30 A41 \n",
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"698 A12 21 A34 A42 \n",
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"699 A13 15 A32 A46 \n",
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"\n",
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" Credit_amount Savings_account Present_employment_since Installment_rate \\\n",
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"0 7814 A61 A74 3 \n",
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"1 2764 A61 A73 2 \n",
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"2 2136 A61 A73 3 \n",
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"3 2743 A61 A75 4 \n",
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"4 1659 A61 A72 4 \n",
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".. ... ... ... ... \n",
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"695 1963 A61 A74 4 \n",
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"696 1409 A62 A71 2 \n",
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"697 4605 A61 A75 3 \n",
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"698 2745 A64 A74 3 \n",
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"699 1905 A61 A75 4 \n",
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"\n",
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" Personal_status_sex debtors Present_residence Property Age \\\n",
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"0 A93 A101 3 A123 38 \n",
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"1 A92 A101 2 A123 26 \n",
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"2 A93 A101 2 A121 25 \n",
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"3 A93 A101 2 A123 29 \n",
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"4 A92 A101 2 A123 29 \n",
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".. ... ... ... ... ... \n",
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"695 A93 A101 2 A123 31 \n",
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"696 A92 A101 4 A121 64 \n",
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"697 A93 A101 4 A124 24 \n",
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"698 A93 A101 2 A123 32 \n",
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"699 A93 A101 4 A123 40 \n",
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"\n",
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" Other_installment_plans Housing Number_of_existing_credits Job \\\n",
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"0 A143 A152 1 A174 \n",
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"1 A143 A152 2 A173 \n",
|
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"2 A143 A152 1 A173 \n",
|
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"3 A143 A152 2 A173 \n",
|
||||
"4 A143 A151 1 A172 \n",
|
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".. ... ... ... ... \n",
|
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"695 A143 A151 2 A174 \n",
|
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"696 A143 A152 1 A173 \n",
|
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"697 A143 A153 2 A173 \n",
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"698 A143 A152 2 A173 \n",
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"699 A143 A151 1 A174 \n",
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"\n",
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" N_people_being_liable_provide_maintenance Telephone Foreign_worker \n",
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"0 1 1 1 \n",
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"1 1 1 1 \n",
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"2 1 0 1 \n",
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"3 1 0 1 \n",
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||||
"4 1 1 1 \n",
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".. ... ... ... \n",
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"695 2 1 1 \n",
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"696 1 0 1 \n",
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"697 2 0 1 \n",
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"698 1 1 1 \n",
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"699 1 1 1 \n",
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"\n",
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"[700 rows x 20 columns]\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 import get_german_credit_dataset\n",
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"\n",
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"(x_train, y_train), (x_test, y_test) = get_german_credit_dataset()\n",
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"features = [\"Existing_checking_account\", \"Duration_in_month\", \"Credit_history\", \"Purpose\", \"Credit_amount\",\n",
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" \"Savings_account\", \"Present_employment_since\", \"Installment_rate\", \"Personal_status_sex\", \"debtors\",\n",
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" \"Present_residence\", \"Property\", \"Age\", \"Other_installment_plans\", \"Housing\",\n",
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" \"Number_of_existing_credits\", \"Job\", \"N_people_being_liable_provide_maintenance\", \"Telephone\",\n",
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" \"Foreign_worker\"]\n",
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"categorical_features = [\"Existing_checking_account\", \"Credit_history\", \"Purpose\", \"Savings_account\",\n",
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" \"Present_employment_since\", \"Personal_status_sex\", \"debtors\", \"Property\",\n",
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" \"Other_installment_plans\", \"Housing\", \"Job\"]\n",
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"QI = [\"Duration_in_month\", \"Credit_history\", \"Purpose\", \"debtors\", \"Property\", \"Other_installment_plans\",\n",
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" \"Housing\", \"Job\"]\n",
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"\n",
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"print(x_train)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## Train decision tree model\n",
|
||||
"we use OneHotEncoder to handle categorical features."
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Base model accuracy: 0.7033333333333334\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.compose import ColumnTransformer\n",
|
||||
"from sklearn.preprocessing import OneHotEncoder\n",
|
||||
"from sklearn.impute import SimpleImputer\n",
|
||||
"from sklearn.pipeline import Pipeline\n",
|
||||
"from sklearn.tree import DecisionTreeClassifier\n",
|
||||
"numeric_features = [f for f in features if f not in categorical_features]\n",
|
||||
"numeric_transformer = Pipeline(\n",
|
||||
" steps=[('imputer', SimpleImputer(strategy='constant', fill_value=0))]\n",
|
||||
")\n",
|
||||
"categorical_transformer = OneHotEncoder(handle_unknown=\"ignore\", sparse=False)\n",
|
||||
"preprocessor = ColumnTransformer(\n",
|
||||
" transformers=[\n",
|
||||
" (\"num\", numeric_transformer, numeric_features),\n",
|
||||
" (\"cat\", categorical_transformer, categorical_features),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"encoded_train = preprocessor.fit_transform(x_train)\n",
|
||||
"model = DecisionTreeClassifier()\n",
|
||||
"model.fit(encoded_train, y_train)\n",
|
||||
"\n",
|
||||
"encoded_test = preprocessor.transform(x_test)\n",
|
||||
"print('Base model accuracy: ', model.score(encoded_test, y_test))"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## Run minimization\n",
|
||||
"We will try to run minimization with categorical features and only a subset of the features with different possible values of target accuracy (how close to the original model's accuracy we want to get, 1 being same accuracy as for original data)."
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Initial accuracy of model on generalized data, relative to original model predictions (base generalization derived from tree, before improvements): 0.791667\n",
|
||||
"Improving accuracy\n",
|
||||
"feature to remove: Property\n",
|
||||
"Removed feature: Property, new relative accuracy: 0.819444\n",
|
||||
"feature to remove: Other_installment_plans\n",
|
||||
"Removed feature: Other_installment_plans, new relative accuracy: 0.833333\n",
|
||||
"feature to remove: Job\n",
|
||||
"Removed feature: Job, new relative accuracy: 0.833333\n",
|
||||
"feature to remove: Housing\n",
|
||||
"Removed feature: Housing, new relative accuracy: 0.833333\n",
|
||||
"feature to remove: Purpose\n",
|
||||
"Removed feature: Purpose, new relative accuracy: 0.916667\n",
|
||||
"feature to remove: Credit_history\n",
|
||||
"Removed feature: Credit_history, new relative accuracy: 0.930556\n",
|
||||
"feature to remove: debtors\n",
|
||||
"Removed feature: debtors, new relative accuracy: 0.944444\n",
|
||||
"feature to remove: Duration_in_month\n",
|
||||
"Removed feature: Duration_in_month, new relative accuracy: 1.000000\n",
|
||||
"Accuracy on minimized data: 0.6666666666666666\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import sys\n",
|
||||
"import os\n",
|
||||
"sys.path.insert(0, os.path.abspath('..'))\n",
|
||||
"\n",
|
||||
"from apt.minimization import GeneralizeToRepresentative\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"\n",
|
||||
"# default target_accuracy is 0.998\n",
|
||||
"minimizer = GeneralizeToRepresentative(model, features=features,\n",
|
||||
" categorical_features=categorical_features, features_to_minimize=QI)\n",
|
||||
"\n",
|
||||
"# Fitting the minimizar can be done either on training or test data. Doing it with test data is better as the\n",
|
||||
"# resulting accuracy on test data will be closer to the desired target accuracy (when working with training\n",
|
||||
"# data it could result in a larger gap)\n",
|
||||
"# Don't forget to leave a hold-out set for final validation!\n",
|
||||
"X_generalizer_train, x_test, y_generalizer_train, y_test = train_test_split(x_test, y_test, stratify=y_test,\n",
|
||||
" test_size = 0.4, random_state = 38)\n",
|
||||
"X_generalizer_train.reset_index(drop=True, inplace=True)\n",
|
||||
"y_generalizer_train.reset_index(drop=True, inplace=True)\n",
|
||||
"x_test.reset_index(drop=True, inplace=True)\n",
|
||||
"y_test.reset_index(drop=True, inplace=True)\n",
|
||||
"encoded_generalizer_train = preprocessor.transform(X_generalizer_train)\n",
|
||||
"x_train_predictions = model.predict(encoded_generalizer_train)\n",
|
||||
"minimizer.fit(X_generalizer_train, x_train_predictions)\n",
|
||||
"transformed = minimizer.transform(x_test)\n",
|
||||
"\n",
|
||||
"encoded_transformed = preprocessor.transform(transformed)\n",
|
||||
"print('Accuracy on minimized data: ', model.score(encoded_transformed, y_test))"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"#### Let's see what features were generalized"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'ranges': {}, 'categories': {}, 'untouched': ['Purpose', 'Present_residence', 'Credit_history', 'Telephone', 'Job', 'Housing', 'Installment_rate', 'Number_of_existing_credits', 'Foreign_worker', 'Existing_checking_account', 'Other_installment_plans', 'N_people_being_liable_provide_maintenance', 'Property', 'Savings_account', 'Present_employment_since', 'Personal_status_sex', 'Duration_in_month', 'debtors', 'Credit_amount', 'Age']}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"generalizations = minimizer.generalizations\n",
|
||||
"print(generalizations)"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"We can see that for the default target accuracy of 0.998 of the original accuracy, no generalizations are possible (all features are left untouched, i.e., not generalized).\n",
|
||||
"\n",
|
||||
"Let's change to a slightly lower target accuracy."
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Initial accuracy of model on generalized data, relative to original model predictions (base generalization derived from tree, before improvements): 0.791667\n",
|
||||
"Improving accuracy\n",
|
||||
"feature to remove: Property\n",
|
||||
"Removed feature: Property, new relative accuracy: 0.819444\n",
|
||||
"feature to remove: Other_installment_plans\n",
|
||||
"Removed feature: Other_installment_plans, new relative accuracy: 0.833333\n",
|
||||
"feature to remove: Job\n",
|
||||
"Removed feature: Job, new relative accuracy: 0.833333\n",
|
||||
"feature to remove: Housing\n",
|
||||
"Removed feature: Housing, new relative accuracy: 0.833333\n",
|
||||
"feature to remove: Purpose\n",
|
||||
"Removed feature: Purpose, new relative accuracy: 0.916667\n",
|
||||
"feature to remove: Credit_history\n",
|
||||
"Removed feature: Credit_history, new relative accuracy: 0.930556\n",
|
||||
"Accuracy on minimized data: 0.6416666666666667\n",
|
||||
"{'ranges': {'Duration_in_month': [7.0, 8.5, 11.0, 13.0, 14.0, 18.0, 23.0, 25.5, 34.5, 47.5]}, 'categories': {'debtors': [['A101', 'A102'], ['A103']]}, 'untouched': ['Existing_checking_account', 'Savings_account', 'Present_employment_since', 'Property', 'Housing', 'Purpose', 'Personal_status_sex', 'Present_residence', 'Credit_history', 'Telephone', 'Installment_rate', 'Other_installment_plans', 'Number_of_existing_credits', 'Credit_amount', 'N_people_being_liable_provide_maintenance', 'Foreign_worker', 'Age', 'Job']}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# We allow a 1% deviation in accuracy from the original model accuracy\n",
|
||||
"minimizer2 = GeneralizeToRepresentative(model, target_accuracy=0.92, features=features,\n",
|
||||
" categorical_features=categorical_features, features_to_minimize=QI)\n",
|
||||
"\n",
|
||||
"minimizer2.fit(X_generalizer_train, x_train_predictions)\n",
|
||||
"transformed2 = minimizer2.transform(x_test)\n",
|
||||
"\n",
|
||||
"encoded_transformed2 = preprocessor.transform(transformed2)\n",
|
||||
"print('Accuracy on minimized data: ', model.score(encoded_transformed2, y_test))\n",
|
||||
"generalizations2 = minimizer2.generalizations\n",
|
||||
"print(generalizations2)"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"This time we were able to generalize two features (Duration_in_month and debtors)."
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 2
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2",
|
||||
"version": "2.7.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
|
|
@ -510,7 +510,7 @@ def test_regression():
|
|||
transformed = gen.transform(x_train)
|
||||
print('Base model accuracy (R2 score): ', model.score(x_test, y_test))
|
||||
model.fit(transformed, y_train)
|
||||
print('Base model accuracy (R2 score) after anonymization: ', model.score(x_test, y_test))
|
||||
print('Base model accuracy (R2 score) after minimization: ', model.score(x_test, y_test))
|
||||
gener = gen.generalizations_
|
||||
expexted_generalizations = {'ranges': {
|
||||
'age': [-0.07816532626748085, -0.07090024650096893, -0.05637009255588055, -0.05092128552496433,
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue