Updated 12 example notebooks to run on Colab. Fixes Trusted-AI#385

Signed-off-by: Clayton O'Dell <codell2@nd.edu>
codell2 · Dec 6, 2024 · 22a79db · 22a79db
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diff --git a/examples/demo_FACTS.ipynb b/examples/demo_FACTS.ipynb
diff --git a/examples/demo_gerryfair.ipynb b/examples/demo_gerryfair.ipynb
diff --git a/examples/demo_json_explainers.ipynb b/examples/demo_json_explainers.ipynb
diff --git a/examples/demo_lfr.ipynb b/examples/demo_lfr.ipynb
diff --git a/examples/demo_mdss_classifier_metric.ipynb b/examples/demo_mdss_classifier_metric.ipynb
diff --git a/examples/demo_mdss_detector.ipynb b/examples/demo_mdss_detector.ipynb
diff --git a/examples/demo_meta_classifier.ipynb b/examples/demo_meta_classifier.ipynb
diff --git a/examples/demo_ot_metric.ipynb b/examples/demo_ot_metric.ipynb
diff --git a/examples/demo_reject_option_classification.ipynb b/examples/demo_reject_option_classification.ipynb
diff --git a/examples/demo_reweighing_preproc.ipynb b/examples/demo_reweighing_preproc.ipynb
diff --git a/examples/demo_short_gerryfair_test.ipynb b/examples/demo_short_gerryfair_test.ipynb
@@ -1,183 +1,202 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iteration: 1, error: 0.229, fairness violation: 0.05428400000000001, violated group size: 0.249\n",
-      "iteration: 2, error: 0.3645, fairness violation: 0.027142000000000006, violated group size: 0.249\n",
-      "iteration: 3, error: 0.4096666666666666, fairness violation: 0.01809466666666667, violated group size: 0.251\n",
-      "iteration: 4, error: 0.43225, fairness violation: 0.013571000000000003, violated group size: 0.249\n",
-      "iteration: 5, error: 0.44580000000000014, fairness violation: 0.0108568, violated group size: 0.251\n",
-      "iteration: 6, error: 0.4548333333333334, fairness violation: 0.009047333333333338, violated group size: 0.251\n",
-      "iteration: 7, error: 0.46128571428571435, fairness violation: 0.007754857142857144, violated group size: 0.251\n",
-      "iteration: 8, error: 0.466125, fairness violation: 0.006785500000000003, violated group size: 0.251\n",
-      "iteration: 9, error: 0.469888888888889, fairness violation: 0.006031555555555558, violated group size: 0.249\n"
-     ]
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "import sys\n",
-    "sys.path.append(\"../\")\n",
-    "from aif360.algorithms.inprocessing import GerryFairClassifier\n",
-    "from aif360.algorithms.inprocessing.gerryfair.clean import array_to_tuple\n",
-    "from aif360.algorithms.preprocessing.optim_preproc_helpers.data_preproc_functions import load_preproc_data_adult\n",
-    "from sklearn import svm\n",
-    "from sklearn import tree\n",
-    "from sklearn.kernel_ridge import KernelRidge\n",
-    "from sklearn import linear_model\n",
-    "from aif360.metrics import BinaryLabelDatasetMetric\n",
-    "from IPython.display import Image\n",
-    "import pickle\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# load data set\n",
-    "data_set = load_preproc_data_adult(sub_samp=1000, balance=True)\n",
-    "max_iterations = 10\n",
-    "C = 100\n",
-    "print_flag = True\n",
-    "gamma = .005\n",
-    "\n",
-    "fair_model = GerryFairClassifier(C=C, printflag=print_flag, gamma=gamma, fairness_def='FP',\n",
-    "             max_iters=max_iterations, heatmapflag=False)\n",
-    "# fit method\n",
-    "fair_model.fit(data_set, early_termination=True)\n",
-    "\n",
-    "# predict method. If threshold in (0, 1) produces binary predictions\n",
-    "dataset_yhat = fair_model.predict(data_set, threshold=False)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "source": [
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Trusted-AI/AIF360/blob/main/examples/demo_short_gerryfair_test.ipynb)"
+      ],
+      "metadata": {
+        "id": "qsaaqV7jGtYV"
+      }
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0060315555555555565\n"
-     ]
-    }
-   ],
-   "source": [
-    "# auditing \n",
-    "\n",
-    "gerry_metric = BinaryLabelDatasetMetric(data_set)\n",
-    "gamma_disparity = gerry_metric.rich_subgroup(array_to_tuple(dataset_yhat.labels), 'FP')\n",
-    "print(gamma_disparity)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "pycharm": {
+          "is_executing": false
+        },
+        "id": "9Cb8bfPXGUa0",
+        "outputId": "165bfd3e-1281-4b34-d21d-137182075fab"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "iteration: 1, error: 0.229, fairness violation: 0.05428400000000001, violated group size: 0.249\n",
+            "iteration: 2, error: 0.3645, fairness violation: 0.027142000000000006, violated group size: 0.249\n",
+            "iteration: 3, error: 0.4096666666666666, fairness violation: 0.01809466666666667, violated group size: 0.251\n",
+            "iteration: 4, error: 0.43225, fairness violation: 0.013571000000000003, violated group size: 0.249\n",
+            "iteration: 5, error: 0.44580000000000014, fairness violation: 0.0108568, violated group size: 0.251\n",
+            "iteration: 6, error: 0.4548333333333334, fairness violation: 0.009047333333333338, violated group size: 0.251\n",
+            "iteration: 7, error: 0.46128571428571435, fairness violation: 0.007754857142857144, violated group size: 0.251\n",
+            "iteration: 8, error: 0.466125, fairness violation: 0.006785500000000003, violated group size: 0.251\n",
+            "iteration: 9, error: 0.469888888888889, fairness violation: 0.006031555555555558, violated group size: 0.249\n"
+          ]
+        }
+      ],
+      "source": [
+        "%matplotlib inline\n",
+        "import warnings\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "import sys\n",
+        "sys.path.append(\"../\")\n",
+        "from aif360.algorithms.inprocessing import GerryFairClassifier\n",
+        "from aif360.algorithms.inprocessing.gerryfair.clean import array_to_tuple\n",
+        "from aif360.algorithms.preprocessing.optim_preproc_helpers.data_preproc_functions import load_preproc_data_adult\n",
+        "from sklearn import svm\n",
+        "from sklearn import tree\n",
+        "from sklearn.kernel_ridge import KernelRidge\n",
+        "from sklearn import linear_model\n",
+        "from aif360.metrics import BinaryLabelDatasetMetric\n",
+        "from IPython.display import Image\n",
+        "import pickle\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# load data set\n",
+        "data_set = load_preproc_data_adult(sub_samp=1000, balance=True)\n",
+        "max_iterations = 10\n",
+        "C = 100\n",
+        "print_flag = True\n",
+        "gamma = .005\n",
+        "\n",
+        "fair_model = GerryFairClassifier(C=C, printflag=print_flag, gamma=gamma, fairness_def='FP',\n",
+        "             max_iters=max_iterations, heatmapflag=False)\n",
+        "# fit method\n",
+        "fair_model.fit(data_set, early_termination=True)\n",
+        "\n",
+        "# predict method. If threshold in (0, 1) produces binary predictions\n",
+        "dataset_yhat = fair_model.predict(data_set, threshold=False)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "pycharm": {
+          "is_executing": false
+        },
+        "id": "cCdltbMWGUa2",
+        "outputId": "0ab1dd98-6cae-45a1-e51c-41ef9b7193df"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "0.0060315555555555565\n"
+          ]
+        }
+      ],
+      "source": [
+        "# auditing\n",
+        "\n",
+        "gerry_metric = BinaryLabelDatasetMetric(data_set)\n",
+        "gamma_disparity = gerry_metric.rich_subgroup(array_to_tuple(dataset_yhat.labels), 'FP')\n",
+        "print(gamma_disparity)\n",
+        "\n"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Curr Predictor: Linear\n",
-      "Curr Predictor: SVR\n",
-      "Curr Predictor: Tree\n",
-      "Curr Predictor: Kernel\n"
-     ]
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "pycharm": {
+          "is_executing": false
+        },
+        "id": "on2jaBgsGUa3",
+        "outputId": "ed58df2b-2288-491b-bf07-ad5d783baa06"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Curr Predictor: Linear\n",
+            "Curr Predictor: SVR\n",
+            "Curr Predictor: Tree\n",
+            "Curr Predictor: Kernel\n"
+          ]
+        }
+      ],
+      "source": [
+        "# set to 10 iterations for fast running of notebook - set >= 1000 when running real experiments\n",
+        "# tests learning with different hypothesis classes\n",
+        "pareto_iters = 10\n",
+        "def multiple_classifiers_pareto(dataset, gamma_list=[0.002, 0.005, 0.01], save_results=False, iters=pareto_iters):\n",
+        "\n",
+        "    ln_predictor = linear_model.LinearRegression()\n",
+        "    svm_predictor = svm.LinearSVR()\n",
+        "    tree_predictor = tree.DecisionTreeRegressor(max_depth=3)\n",
+        "    kernel_predictor = KernelRidge(alpha=1.0, gamma=1.0, kernel='rbf')\n",
+        "    predictor_dict = {'Linear': {'predictor': ln_predictor, 'iters': iters},\n",
+        "                      'SVR': {'predictor': svm_predictor, 'iters': iters},\n",
+        "                      'Tree': {'predictor': tree_predictor, 'iters': iters},\n",
+        "                      'Kernel': {'predictor': kernel_predictor, 'iters': iters}}\n",
+        "\n",
+        "    results_dict = {}\n",
+        "\n",
+        "    for pred in predictor_dict:\n",
+        "        print('Curr Predictor: {}'.format(pred))\n",
+        "        predictor = predictor_dict[pred]['predictor']\n",
+        "        max_iters = predictor_dict[pred]['iters']\n",
+        "        fair_clf = GerryFairClassifier(C=100, printflag=True, gamma=1, predictor=predictor, max_iters=max_iters)\n",
+        "        fair_clf.printflag = False\n",
+        "        fair_clf.max_iters=max_iters\n",
+        "        errors, fp_violations, fn_violations = fair_clf.pareto(dataset, gamma_list)\n",
+        "        results_dict[pred] = {'errors': errors, 'fp_violations': fp_violations, 'fn_violations': fn_violations}\n",
+        "    if save_results:\n",
+        "        pickle.dump(results_dict, open('results_dict_' + str(gamma_list) + '_gammas' + str(gamma_list) + '.pkl', 'wb'))\n",
+        "\n",
+        "multiple_classifiers_pareto(data_set)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "pycharm": {
+          "is_executing": false
+        },
+        "id": "tgGHGbQZGUa3"
+      },
+      "outputs": [],
+      "source": []
     }
-   ],
-   "source": [
-    "# set to 10 iterations for fast running of notebook - set >= 1000 when running real experiments\n",
-    "# tests learning with different hypothesis classes\n",
-    "pareto_iters = 10\n",
-    "def multiple_classifiers_pareto(dataset, gamma_list=[0.002, 0.005, 0.01], save_results=False, iters=pareto_iters):\n",
-    "\n",
-    "    ln_predictor = linear_model.LinearRegression()\n",
-    "    svm_predictor = svm.LinearSVR()\n",
-    "    tree_predictor = tree.DecisionTreeRegressor(max_depth=3)\n",
-    "    kernel_predictor = KernelRidge(alpha=1.0, gamma=1.0, kernel='rbf')\n",
-    "    predictor_dict = {'Linear': {'predictor': ln_predictor, 'iters': iters},\n",
-    "                      'SVR': {'predictor': svm_predictor, 'iters': iters},\n",
-    "                      'Tree': {'predictor': tree_predictor, 'iters': iters},\n",
-    "                      'Kernel': {'predictor': kernel_predictor, 'iters': iters}}\n",
-    "\n",
-    "    results_dict = {}\n",
-    "\n",
-    "    for pred in predictor_dict:\n",
-    "        print('Curr Predictor: {}'.format(pred))\n",
-    "        predictor = predictor_dict[pred]['predictor']\n",
-    "        max_iters = predictor_dict[pred]['iters']\n",
-    "        fair_clf = GerryFairClassifier(C=100, printflag=True, gamma=1, predictor=predictor, max_iters=max_iters)\n",
-    "        fair_clf.printflag = False\n",
-    "        fair_clf.max_iters=max_iters\n",
-    "        errors, fp_violations, fn_violations = fair_clf.pareto(dataset, gamma_list)\n",
-    "        results_dict[pred] = {'errors': errors, 'fp_violations': fp_violations, 'fn_violations': fn_violations}\n",
-    "    if save_results:\n",
-    "        pickle.dump(results_dict, open('results_dict_' + str(gamma_list) + '_gammas' + str(gamma_list) + '.pkl', 'wb'))\n",
-    "\n",
-    "multiple_classifiers_pareto(data_set)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.9"
+    },
     "pycharm": {
-     "is_executing": false
+      "stem_cell": {
+        "cell_type": "raw",
+        "metadata": {
+          "collapsed": false
+        },
+        "source": []
+      }
+    },
+    "colab": {
+      "provenance": []
     }
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  },
-  "pycharm": {
-   "stem_cell": {
-    "cell_type": "raw",
-    "metadata": {
-     "collapsed": false
-    },
-    "source": []
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
+  "nbformat": 4,
+  "nbformat_minor": 0
+}