examples/quickstart_fair.ipynb

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    "# FairPredictor Examples\n",
    "This file contains demo code for an extended version of the example in Readme.md (additionally handling more fairness over multiple groups),  and enforcing a range of fairness definition on COMPAS.\n",
    "\n",
    "FairPredictor is a postprocessing approach for enforcing fairness, with support for a wide range of performance metrics and fairness criteria, and support for inferred attributes, i.e. it does not require access to protected attributes at test time. \n",
    "Under the hood, FairPredictor works by adjusting the decision boundary for each group individually. Where groups are not available, it makes use of inferred group membership to adjust decision boundaries.\n",
    "\n",
    "The key idea underlying this toolkit is that for a wide range of use cases, the most suitable classifier should do more than maximize some form of accuracy.\n",
    "We offer a general toolkit that allows different measures to be optimized and additional constraints to be imposed by tuning the behavior of a binary predictor on validation data.\n",
    "\n",
    "For example, classifiers can be tuned to maximize performance for a wide range of metrics such as:\n",
    "\n",
    "* Accuracy\n",
    "* Balanced Accuracy\n",
    "* F1 score\n",
    "* MCC\n",
    "* Custom utility functions\n",
    "\n",
    "While also approximately satisfying a wide range of group constraints such as:\n",
    "\n",
    "* Demographic Parity (The idea that positive decisions should occur at the same rates for all protected groups, for example for men at the same rate as for women)\n",
    "* Equal Opportunity (The recall should be the same for all protected groups)\n",
    "* Minimum recall constraints (The recall should be above a particular level for all groups)\n",
    "* Minimum Precision constraints (The precision should be above a particular level for all groups)\n",
    "* Custom Fairness Metrics\n",
    "\n",
    "The full set of constraints and objectives can be seen in Readme.md "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/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",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "No path specified. Models will be saved in: \"AutogluonModels/ag-20221207_153238/\"\n",
      "Beginning AutoGluon training ...\n",
      "AutoGluon will save models to \"AutogluonModels/ag-20221207_153238/\"\n",
      "AutoGluon Version:  0.6.1b20221207\n",
      "Python Version:     3.9.15\n",
      "Operating System:   Darwin\n",
      "Platform Machine:   x86_64\n",
      "Platform Version:   Darwin Kernel Version 21.6.0: Thu Sep 29 20:12:57 PDT 2022; root:xnu-8020.240.7~1/RELEASE_X86_64\n",
      "Train Data Rows:    39073\n",
      "Train Data Columns: 14\n",
      "Label Column: class\n",
      "Preprocessing data ...\n",
      "AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).\n",
      "\t2 unique label values:  [' <=50K', ' >50K']\n",
      "\tIf 'binary' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])\n",
      "Selected class <--> label mapping:  class 1 =  >50K, class 0 =  <=50K\n",
      "\tNote: For your binary classification, AutoGluon arbitrarily selected which label-value represents positive ( >50K) vs negative ( <=50K) class.\n",
      "\tTo explicitly set the positive_class, either rename classes to 1 and 0, or specify positive_class in Predictor init.\n",
      "Using Feature Generators to preprocess the data ...\n",
      "Fitting AutoMLPipelineFeatureGenerator...\n",
      "\tAvailable Memory:                    5988.25 MB\n",
      "\tTrain Data (Original)  Memory Usage: 22.92 MB (0.4% of available memory)\n",
      "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
      "\tStage 1 Generators:\n",
      "\t\tFitting AsTypeFeatureGenerator...\n",
      "\t\t\tNote: Converting 1 features to boolean dtype as they only contain 2 unique values.\n",
      "\tStage 2 Generators:\n",
      "\t\tFitting FillNaFeatureGenerator...\n",
      "\tStage 3 Generators:\n",
      "\t\tFitting IdentityFeatureGenerator...\n",
      "\t\tFitting CategoryFeatureGenerator...\n",
      "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n",
      "\tStage 4 Generators:\n",
      "\t\tFitting DropUniqueFeatureGenerator...\n",
      "\tTypes of features in original data (raw dtype, special dtypes):\n",
      "\t\t('int', [])    : 6 | ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', ...]\n",
      "\t\t('object', []) : 8 | ['workclass', 'education', 'marital-status', 'occupation', 'relationship', ...]\n",
      "\tTypes of features in processed data (raw dtype, special dtypes):\n",
      "\t\t('category', [])  : 7 | ['workclass', 'education', 'marital-status', 'occupation', 'relationship', ...]\n",
      "\t\t('int', [])       : 6 | ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', ...]\n",
      "\t\t('int', ['bool']) : 1 | ['sex']\n",
      "\t0.2s = Fit runtime\n",
      "\t14 features in original data used to generate 14 features in processed data.\n",
      "\tTrain Data (Processed) Memory Usage: 2.19 MB (0.0% of available memory)\n",
      "Data preprocessing and feature engineering runtime = 0.27s ...\n",
      "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n",
      "\tTo change this, specify the eval_metric parameter of Predictor()\n",
      "Automatically generating train/validation split with holdout_frac=0.0639828014229775, Train Rows: 36573, Val Rows: 2500\n",
      "Fitting 13 L1 models ...\n",
      "Fitting model: KNeighborsUnif ...\n",
      "\t0.7752\t = Validation score   (accuracy)\n",
      "\t0.49s\t = Training   runtime\n",
      "\t0.03s\t = Validation runtime\n",
      "Fitting model: KNeighborsDist ...\n",
      "\t0.766\t = Validation score   (accuracy)\n",
      "\t0.03s\t = Training   runtime\n",
      "\t0.02s\t = Validation runtime\n",
      "Fitting model: LightGBMXT ...\n",
      "\t0.8792\t = Validation score   (accuracy)\n",
      "\t1.41s\t = Training   runtime\n",
      "\t0.03s\t = Validation runtime\n",
      "Fitting model: LightGBM ...\n",
      "\t0.8824\t = Validation score   (accuracy)\n",
      "\t0.64s\t = Training   runtime\n",
      "\t0.02s\t = Validation runtime\n",
      "Fitting model: RandomForestGini ...\n",
      "\t0.8588\t = Validation score   (accuracy)\n",
      "\t1.85s\t = Training   runtime\n",
      "\t0.09s\t = Validation runtime\n",
      "Fitting model: RandomForestEntr ...\n",
      "\t0.862\t = Validation score   (accuracy)\n",
      "\t1.94s\t = Training   runtime\n",
      "\t0.08s\t = Validation runtime\n",
      "Fitting model: CatBoost ...\n",
      "\t0.8824\t = Validation score   (accuracy)\n",
      "\t5.18s\t = Training   runtime\n",
      "\t0.02s\t = Validation runtime\n",
      "Fitting model: ExtraTreesGini ...\n",
      "\t0.85\t = Validation score   (accuracy)\n",
      "\t1.48s\t = Training   runtime\n",
      "\t0.08s\t = Validation runtime\n",
      "Fitting model: ExtraTreesEntr ...\n",
      "\t0.848\t = Validation score   (accuracy)\n",
      "\t1.43s\t = Training   runtime\n",
      "\t0.09s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI ...\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "\t0.8628\t = Validation score   (accuracy)\n",
      "\t35.19s\t = Training   runtime\n",
      "\t0.05s\t = Validation runtime\n",
      "Fitting model: XGBoost ...\n",
      "\tWarning: Exception caused XGBoost to fail during training... Skipping this model.\n",
      "\t\tCurrently, we only support \"xgboost>=1.6\". Installed version: \"xgboost==1.4.2\".\n",
      "Detailed Traceback:\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/core/src/autogluon/core/trainer/abstract_trainer.py\", line 1422, in _train_and_save\n",
      "    model = self._train_single(X, y, model, X_val, y_val, total_resources=total_resources, **model_fit_kwargs)\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/core/src/autogluon/core/trainer/abstract_trainer.py\", line 1367, in _train_single\n",
      "    model = model.fit(X=X, y=y, X_val=X_val, y_val=y_val, total_resources=total_resources, **model_fit_kwargs)\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/core/src/autogluon/core/models/abstract/abstract_model.py\", line 628, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/tabular/src/autogluon/tabular/models/xgboost/xgboost_model.py\", line 125, in _fit\n",
      "    try_import_xgboost()\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/core/src/autogluon/core/utils/try_import.py\", line 172, in try_import_xgboost\n",
      "    assert xgboost_version >= parse_version(min_version),\\\n",
      "AssertionError: Currently, we only support \"xgboost>=1.6\". Installed version: \"xgboost==1.4.2\".\n",
      "Fitting model: NeuralNetTorch ...\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "\t0.8564\t = Validation score   (accuracy)\n",
      "\t26.16s\t = Training   runtime\n",
      "\t0.04s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge ...\n",
      "\t0.8856\t = Validation score   (accuracy)\n",
      "\t10.34s\t = Training   runtime\n",
      "\t0.04s\t = Validation runtime\n",
      "Fitting model: WeightedEnsemble_L2 ...\n",
      "\t0.8872\t = Validation score   (accuracy)\n",
      "\t1.91s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "AutoGluon training complete, total runtime = 91.06s ... Best model: \"WeightedEnsemble_L2\"\n",
      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20221207_153238/\")\n"
     ]
    }
   ],
   "source": [
    "# Load and train a baseline classifier\n",
    "\n",
    "from autogluon.tabular import TabularDataset, TabularPredictor\n",
    "from autogluon.fair import FairPredictor \n",
    "from autogluon.fair.utils import group_metrics as gm\n",
    "train_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')\n",
    "test_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv')\n",
    "predictor = TabularPredictor(label='class').fit(train_data=train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n"
     ]
    }
   ],
   "source": [
    "# Modify predictor to enforce fairness over the train_data with respect to groups given by the column 'sex'\n",
    "fpredictor = FairPredictor(predictor,train_data,'sex')\n",
    "# Maximize accuracy while enforcing that the demographic parity (the difference in positive decision rates between men and women is at most 0.02)\n",
    "fpredictor.fit(gm.accuracy,gm.demographic_parity,0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0        <=50K\n",
       "1        <=50K\n",
       "2         >50K\n",
       "3        <=50K\n",
       "4        <=50K\n",
       "         ...  \n",
       "9764     <=50K\n",
       "9765     <=50K\n",
       "9766     <=50K\n",
       "9767     <=50K\n",
       "9768     <=50K\n",
       "Length: 9769, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate on test data\n",
    "fpredictor.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n"
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       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.874501</td>\n",
       "      <td>0.851059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balanced Accuracy</th>\n",
       "      <td>0.794690</td>\n",
       "      <td>0.757776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1 score</th>\n",
       "      <td>0.708512</td>\n",
       "      <td>0.648975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCC</th>\n",
       "      <td>0.634991</td>\n",
       "      <td>0.562474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.789195</td>\n",
       "      <td>0.736180</td>\n",
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       "      <th>Recall</th>\n",
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       "      <th>roc_auc</th>\n",
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       "      <td>0.817604</td>\n",
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       "                   original   updated\n",
       "Accuracy           0.874501  0.851059\n",
       "Balanced Accuracy  0.794690  0.757776\n",
       "F1 score           0.708512  0.648975\n",
       "MCC                0.634991  0.562474\n",
       "Precision          0.789195  0.736180\n",
       "Recall             0.642796  0.580242\n",
       "roc_auc            0.930278  0.817604"
      ]
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     "execution_count": 4,
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   "source": [
    "# Evaluate a range of performance measures, and compare against original classifier on test data\n",
    "fpredictor.evaluate(test_data, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n"
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       "      <th>Accuracy</th>\n",
       "      <td>0.874501</td>\n",
       "      <td>0.851059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balanced Accuracy</th>\n",
       "      <td>0.794690</td>\n",
       "      <td>0.757776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1 score</th>\n",
       "      <td>0.708512</td>\n",
       "      <td>0.648975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCC</th>\n",
       "      <td>0.634991</td>\n",
       "      <td>0.562474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.789195</td>\n",
       "      <td>0.736180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.642796</td>\n",
       "      <td>0.580242</td>\n",
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       "      <th>roc_auc</th>\n",
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       "                   original   updated\n",
       "Accuracy           0.874501  0.851059\n",
       "Balanced Accuracy  0.794690  0.757776\n",
       "F1 score           0.708512  0.648975\n",
       "MCC                0.634991  0.562474\n",
       "Precision          0.789195  0.736180\n",
       "Recall             0.642796  0.580242\n",
       "roc_auc            0.930278  0.817604"
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   ],
   "source": [
    "# Evaluate a range of performance measures, and compare against original classifier on test data\n",
    "fpredictor.evaluate(test_data, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n"
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       "      <th>Class Imbalance</th>\n",
       "      <td>0.195913</td>\n",
       "      <td>0.195913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Demographic Parity</th>\n",
       "      <td>0.169433</td>\n",
       "      <td>0.003617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Disparate Impact</th>\n",
       "      <td>0.323102</td>\n",
       "      <td>0.980901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Accuracy</th>\n",
       "      <td>0.098900</td>\n",
       "      <td>0.057692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Recall</th>\n",
       "      <td>0.053157</td>\n",
       "      <td>0.338102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Conditional Acceptance Rate</th>\n",
       "      <td>0.115600</td>\n",
       "      <td>1.065447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Acceptance Rate</th>\n",
       "      <td>0.004700</td>\n",
       "      <td>0.371785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Specificity</th>\n",
       "      <td>0.057236</td>\n",
       "      <td>0.071425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Conditional Rejectance Rate</th>\n",
       "      <td>0.041826</td>\n",
       "      <td>0.245513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Rejection Rate</th>\n",
       "      <td>0.094237</td>\n",
       "      <td>0.157882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Treatment Equality</th>\n",
       "      <td>0.112676</td>\n",
       "      <td>6.566495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generalized Entropy</th>\n",
       "      <td>0.067601</td>\n",
       "      <td>0.081161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         original   updated\n",
       "Class Imbalance                                          0.195913  0.195913\n",
       "Demographic Parity                                       0.169433  0.003617\n",
       "Disparate Impact                                         0.323102  0.980901\n",
       "Maximal Group Difference in Accuracy                     0.098900  0.057692\n",
       "Maximal Group Difference in Recall                       0.053157  0.338102\n",
       "Maximal Group Difference in Conditional Acceptance Rate  0.115600  1.065447\n",
       "Maximal Group Difference in Acceptance Rate              0.004700  0.371785\n",
       "Maximal Group Difference in Specificity                  0.057236  0.071425\n",
       "Maximal Group Difference in Conditional Rejectance Rate  0.041826  0.245513\n",
       "Maximal Group Difference in Rejection Rate               0.094237  0.157882\n",
       "Treatment Equality                                       0.112676  6.566495\n",
       "Generalized Entropy                                      0.067601  0.081161"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate against a range of standard fairness definitions and compare against original classifier on test data\n",
    "fpredictor.evaluate_fairness(test_data, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Balanced Accuracy</th>\n",
       "      <th>F1 score</th>\n",
       "      <th>MCC</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>Number of Datapoints</th>\n",
       "      <th>Positive Count</th>\n",
       "      <th>Negative Count</th>\n",
       "      <th>Positive Label Rate</th>\n",
       "      <th>Positive Prediction Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Groups</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">original</th>\n",
       "      <th>Overall</th>\n",
       "      <td>0.874501</td>\n",
       "      <td>0.794690</td>\n",
       "      <td>0.708512</td>\n",
       "      <td>0.634991</td>\n",
       "      <td>0.789195</td>\n",
       "      <td>0.642796</td>\n",
       "      <td>0.930278</td>\n",
       "      <td>9769.0</td>\n",
       "      <td>2318.0</td>\n",
       "      <td>7451.0</td>\n",
       "      <td>0.237281</td>\n",
       "      <td>0.193264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>0.940103</td>\n",
       "      <td>0.789500</td>\n",
       "      <td>0.681745</td>\n",
       "      <td>0.657332</td>\n",
       "      <td>0.793233</td>\n",
       "      <td>0.597734</td>\n",
       "      <td>0.948686</td>\n",
       "      <td>3289.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>2936.0</td>\n",
       "      <td>0.107327</td>\n",
       "      <td>0.080876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.841204</td>\n",
       "      <td>0.787461</td>\n",
       "      <td>0.713131</td>\n",
       "      <td>0.610049</td>\n",
       "      <td>0.788533</td>\n",
       "      <td>0.650891</td>\n",
       "      <td>0.911427</td>\n",
       "      <td>6480.0</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>4515.0</td>\n",
       "      <td>0.303241</td>\n",
       "      <td>0.250309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum difference</th>\n",
       "      <td>0.098900</td>\n",
       "      <td>0.002040</td>\n",
       "      <td>0.031386</td>\n",
       "      <td>0.047283</td>\n",
       "      <td>0.004700</td>\n",
       "      <td>0.053157</td>\n",
       "      <td>0.037259</td>\n",
       "      <td>3191.0</td>\n",
       "      <td>1612.0</td>\n",
       "      <td>1579.0</td>\n",
       "      <td>0.195913</td>\n",
       "      <td>0.169433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">updated</th>\n",
       "      <th>Overall</th>\n",
       "      <td>0.851059</td>\n",
       "      <td>0.757776</td>\n",
       "      <td>0.648975</td>\n",
       "      <td>0.562474</td>\n",
       "      <td>0.736180</td>\n",
       "      <td>0.580242</td>\n",
       "      <td>0.817604</td>\n",
       "      <td>9769.0</td>\n",
       "      <td>2318.0</td>\n",
       "      <td>7451.0</td>\n",
       "      <td>0.237281</td>\n",
       "      <td>0.187020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>0.889328</td>\n",
       "      <td>0.879443</td>\n",
       "      <td>0.627049</td>\n",
       "      <td>0.599470</td>\n",
       "      <td>0.491172</td>\n",
       "      <td>0.866856</td>\n",
       "      <td>0.948686</td>\n",
       "      <td>3289.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>2936.0</td>\n",
       "      <td>0.107327</td>\n",
       "      <td>0.189419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.831636</td>\n",
       "      <td>0.746104</td>\n",
       "      <td>0.655727</td>\n",
       "      <td>0.581693</td>\n",
       "      <td>0.862957</td>\n",
       "      <td>0.528753</td>\n",
       "      <td>0.911427</td>\n",
       "      <td>6480.0</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>4515.0</td>\n",
       "      <td>0.303241</td>\n",
       "      <td>0.185802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum difference</th>\n",
       "      <td>0.057692</td>\n",
       "      <td>0.133339</td>\n",
       "      <td>0.028678</td>\n",
       "      <td>0.017778</td>\n",
       "      <td>0.371785</td>\n",
       "      <td>0.338102</td>\n",
       "      <td>0.037259</td>\n",
       "      <td>3191.0</td>\n",
       "      <td>1612.0</td>\n",
       "      <td>1579.0</td>\n",
       "      <td>0.195913</td>\n",
       "      <td>0.003617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Accuracy  Balanced Accuracy  F1 score       MCC  \\\n",
       "         Groups                                                                \n",
       "original Overall             0.874501           0.794690  0.708512  0.634991   \n",
       "          Female             0.940103           0.789500  0.681745  0.657332   \n",
       "          Male               0.841204           0.787461  0.713131  0.610049   \n",
       "         Maximum difference  0.098900           0.002040  0.031386  0.047283   \n",
       "updated  Overall             0.851059           0.757776  0.648975  0.562474   \n",
       "          Female             0.889328           0.879443  0.627049  0.599470   \n",
       "          Male               0.831636           0.746104  0.655727  0.581693   \n",
       "         Maximum difference  0.057692           0.133339  0.028678  0.017778   \n",
       "\n",
       "                             Precision    Recall   roc_auc  \\\n",
       "         Groups                                              \n",
       "original Overall              0.789195  0.642796  0.930278   \n",
       "          Female              0.793233  0.597734  0.948686   \n",
       "          Male                0.788533  0.650891  0.911427   \n",
       "         Maximum difference   0.004700  0.053157  0.037259   \n",
       "updated  Overall              0.736180  0.580242  0.817604   \n",
       "          Female              0.491172  0.866856  0.948686   \n",
       "          Male                0.862957  0.528753  0.911427   \n",
       "         Maximum difference   0.371785  0.338102  0.037259   \n",
       "\n",
       "                             Number of Datapoints  Positive Count  \\\n",
       "         Groups                                                     \n",
       "original Overall                           9769.0          2318.0   \n",
       "          Female                           3289.0           353.0   \n",
       "          Male                             6480.0          1965.0   \n",
       "         Maximum difference                3191.0          1612.0   \n",
       "updated  Overall                           9769.0          2318.0   \n",
       "          Female                           3289.0           353.0   \n",
       "          Male                             6480.0          1965.0   \n",
       "         Maximum difference                3191.0          1612.0   \n",
       "\n",
       "                             Negative Count  Positive Label Rate  \\\n",
       "         Groups                                                    \n",
       "original Overall                     7451.0             0.237281   \n",
       "          Female                     2936.0             0.107327   \n",
       "          Male                       4515.0             0.303241   \n",
       "         Maximum difference          1579.0             0.195913   \n",
       "updated  Overall                     7451.0             0.237281   \n",
       "          Female                     2936.0             0.107327   \n",
       "          Male                       4515.0             0.303241   \n",
       "         Maximum difference          1579.0             0.195913   \n",
       "\n",
       "                             Positive Prediction Rate  \n",
       "         Groups                                        \n",
       "original Overall                             0.193264  \n",
       "          Female                             0.080876  \n",
       "          Male                               0.250309  \n",
       "         Maximum difference                  0.169433  \n",
       "updated  Overall                             0.187020  \n",
       "          Female                             0.189419  \n",
       "          Male                               0.185802  \n",
       "         Maximum difference                  0.003617  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate a range of performance measures per group, and compare against original classifier on test data\n",
    "fpredictor.evaluate_groups(test_data, verbose=True, return_original=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n"
     ]
    }
   ],
   "source": [
    "#We can repeat the same analysis using the feature 'race' instead of 'sex'\n",
    "fpredictor = FairPredictor(predictor,train_data, 'race')\n",
    "# Maximize accuracy while enforcing that the demographic parity (the difference in positive decision rates between men and women is at most 0.02)\n",
    "fpredictor.fit(gm.accuracy, gm.demographic_parity, .02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original</th>\n",
       "      <th>updated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Class Imbalance</th>\n",
       "      <td>0.163744</td>\n",
       "      <td>0.163744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Demographic Parity</th>\n",
       "      <td>0.151573</td>\n",
       "      <td>0.096357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Disparate Impact</th>\n",
       "      <td>0.272973</td>\n",
       "      <td>0.589214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Accuracy</th>\n",
       "      <td>0.091463</td>\n",
       "      <td>0.048780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Recall</th>\n",
       "      <td>0.103859</td>\n",
       "      <td>0.393506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Conditional Acceptance Rate</th>\n",
       "      <td>0.357061</td>\n",
       "      <td>1.045113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Acceptance Rate</th>\n",
       "      <td>0.103044</td>\n",
       "      <td>0.325330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Specificity</th>\n",
       "      <td>0.050832</td>\n",
       "      <td>0.101004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Conditional Rejectance Rate</th>\n",
       "      <td>0.025442</td>\n",
       "      <td>0.245520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Rejection Rate</th>\n",
       "      <td>0.073001</td>\n",
       "      <td>0.120072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Treatment Equality</th>\n",
       "      <td>0.296626</td>\n",
       "      <td>9.736832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generalized Entropy</th>\n",
       "      <td>0.067601</td>\n",
       "      <td>0.068875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         original   updated\n",
       "Class Imbalance                                          0.163744  0.163744\n",
       "Demographic Parity                                       0.151573  0.096357\n",
       "Disparate Impact                                         0.272973  0.589214\n",
       "Maximal Group Difference in Accuracy                     0.091463  0.048780\n",
       "Maximal Group Difference in Recall                       0.103859  0.393506\n",
       "Maximal Group Difference in Conditional Acceptance Rate  0.357061  1.045113\n",
       "Maximal Group Difference in Acceptance Rate              0.103044  0.325330\n",
       "Maximal Group Difference in Specificity                  0.050832  0.101004\n",
       "Maximal Group Difference in Conditional Rejectance Rate  0.025442  0.245520\n",
       "Maximal Group Difference in Rejection Rate               0.073001  0.120072\n",
       "Treatment Equality                                       0.296626  9.736832\n",
       "Generalized Entropy                                      0.067601  0.068875"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unlike the previous case, we find that demographic parity is still high on test data, although it is improved.\n",
    "fpredictor.evaluate_fairness(test_data, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#To debug this, we can look at the Pareto Frontier of solutions considered, on the training set\n",
    "fpredictor.plot_frontier() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
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     "metadata": {},
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    }
   ],
   "source": [
    "#and on the test set\n",
    "fpredictor.plot_frontier(test_data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n"
     ]
    },
    {
     "data": {
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       "      <th>Accuracy</th>\n",
       "      <th>Balanced Accuracy</th>\n",
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       "      <td>891.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>784.0</td>\n",
       "      <td>0.120090</td>\n",
       "      <td>0.196409</td>\n",
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       "      <th>Other</th>\n",
       "      <td>0.864198</td>\n",
       "      <td>0.879577</td>\n",
       "      <td>0.620690</td>\n",
       "      <td>0.589369</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.954930</td>\n",
       "      <td>81.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.123457</td>\n",
       "      <td>0.234568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>0.867362</td>\n",
       "      <td>0.797800</td>\n",
       "      <td>0.714911</td>\n",
       "      <td>0.633556</td>\n",
       "      <td>0.784181</td>\n",
       "      <td>0.656886</td>\n",
       "      <td>0.927211</td>\n",
       "      <td>8346.0</td>\n",
       "      <td>2113.0</td>\n",
       "      <td>6233.0</td>\n",
       "      <td>0.253175</td>\n",
       "      <td>0.212078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum difference</th>\n",
       "      <td>0.048780</td>\n",
       "      <td>0.146251</td>\n",
       "      <td>0.143483</td>\n",
       "      <td>0.098652</td>\n",
       "      <td>0.325330</td>\n",
       "      <td>0.393506</td>\n",
       "      <td>0.050081</td>\n",
       "      <td>8265.0</td>\n",
       "      <td>2103.0</td>\n",
       "      <td>6162.0</td>\n",
       "      <td>0.163744</td>\n",
       "      <td>0.096357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Accuracy  Balanced Accuracy  F1 score       MCC  \\\n",
       "Groups                                                                 \n",
       "Overall              0.869383           0.798170  0.706532  0.625270   \n",
       " Amer-Indian-Eskimo  0.902439           0.823458  0.571429  0.534904   \n",
       " Asian-Pac-Islander  0.853659           0.733326  0.619048  0.554851   \n",
       " Black               0.890011           0.876973  0.652482  0.616900   \n",
       " Other               0.864198           0.879577  0.620690  0.589369   \n",
       " White               0.867362           0.797800  0.714911  0.633556   \n",
       "Maximum difference   0.048780           0.146251  0.143483  0.098652   \n",
       "\n",
       "                     Precision    Recall   roc_auc  Number of Datapoints  \\\n",
       "Groups                                                                     \n",
       "Overall               0.756650  0.662640  0.904007                9769.0   \n",
       " Amer-Indian-Eskimo   0.470588  0.727273  0.945617                 123.0   \n",
       " Asian-Pac-Islander   0.795918  0.506493  0.904848                 328.0   \n",
       " Black                0.525714  0.859813  0.953176                 891.0   \n",
       " Other                0.473684  0.900000  0.954930                  81.0   \n",
       " White                0.784181  0.656886  0.927211                8346.0   \n",
       "Maximum difference    0.325330  0.393506  0.050081                8265.0   \n",
       "\n",
       "                     Positive Count  Negative Count  Positive Label Rate  \\\n",
       "Groups                                                                     \n",
       "Overall                      2318.0          7451.0             0.237281   \n",
       " Amer-Indian-Eskimo            11.0           112.0             0.089431   \n",
       " Asian-Pac-Islander            77.0           251.0             0.234756   \n",
       " Black                        107.0           784.0             0.120090   \n",
       " Other                         10.0            71.0             0.123457   \n",
       " White                       2113.0          6233.0             0.253175   \n",
       "Maximum difference           2103.0          6162.0             0.163744   \n",
       "\n",
       "                     Positive Prediction Rate  \n",
       "Groups                                         \n",
       "Overall                              0.207800  \n",
       " Amer-Indian-Eskimo                  0.138211  \n",
       " Asian-Pac-Islander                  0.149390  \n",
       " Black                               0.196409  \n",
       " Other                               0.234568  \n",
       " White                               0.212078  \n",
       "Maximum difference                   0.096357  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We find that the demographic parity is very different on test data for a range of solutions found.\n",
    "#By looking at the per group decomposition, we can find out why.\n",
    "fpredictor.evaluate_groups(test_data, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Balanced Accuracy</th>\n",
       "      <th>F1 score</th>\n",
       "      <th>MCC</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>Number of Datapoints</th>\n",
       "      <th>Positive Count</th>\n",
       "      <th>Negative Count</th>\n",
       "      <th>Positive Label Rate</th>\n",
       "      <th>Positive Prediction Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Groups</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Overall</th>\n",
       "      <td>0.911166</td>\n",
       "      <td>0.862549</td>\n",
       "      <td>0.805905</td>\n",
       "      <td>0.749919</td>\n",
       "      <td>0.846371</td>\n",
       "      <td>0.769132</td>\n",
       "      <td>0.958577</td>\n",
       "      <td>39073.0</td>\n",
       "      <td>9369.0</td>\n",
       "      <td>29704.0</td>\n",
       "      <td>0.239782</td>\n",
       "      <td>0.217900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amer-Indian-Eskimo</th>\n",
       "      <td>0.925072</td>\n",
       "      <td>0.957096</td>\n",
       "      <td>0.771930</td>\n",
       "      <td>0.758047</td>\n",
       "      <td>0.628571</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994674</td>\n",
       "      <td>347.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>0.126801</td>\n",
       "      <td>0.201729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian-Pac-Islander</th>\n",
       "      <td>0.924433</td>\n",
       "      <td>0.873697</td>\n",
       "      <td>0.848485</td>\n",
       "      <td>0.809016</td>\n",
       "      <td>0.961832</td>\n",
       "      <td>0.759036</td>\n",
       "      <td>0.982152</td>\n",
       "      <td>1191.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>859.0</td>\n",
       "      <td>0.278757</td>\n",
       "      <td>0.219983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>0.919610</td>\n",
       "      <td>0.952394</td>\n",
       "      <td>0.749795</td>\n",
       "      <td>0.737202</td>\n",
       "      <td>0.601316</td>\n",
       "      <td>0.995643</td>\n",
       "      <td>0.990321</td>\n",
       "      <td>3794.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>3335.0</td>\n",
       "      <td>0.120980</td>\n",
       "      <td>0.200316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.954386</td>\n",
       "      <td>0.754717</td>\n",
       "      <td>0.742139</td>\n",
       "      <td>0.606061</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994825</td>\n",
       "      <td>325.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>0.123077</td>\n",
       "      <td>0.203077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>0.909504</td>\n",
       "      <td>0.858583</td>\n",
       "      <td>0.809211</td>\n",
       "      <td>0.753631</td>\n",
       "      <td>0.871805</td>\n",
       "      <td>0.755004</td>\n",
       "      <td>0.971215</td>\n",
       "      <td>33416.0</td>\n",
       "      <td>8494.0</td>\n",
       "      <td>24922.0</td>\n",
       "      <td>0.254190</td>\n",
       "      <td>0.220134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum difference</th>\n",
       "      <td>0.015568</td>\n",
       "      <td>0.098513</td>\n",
       "      <td>0.098690</td>\n",
       "      <td>0.071814</td>\n",
       "      <td>0.360516</td>\n",
       "      <td>0.244996</td>\n",
       "      <td>0.023609</td>\n",
       "      <td>33091.0</td>\n",
       "      <td>8454.0</td>\n",
       "      <td>24637.0</td>\n",
       "      <td>0.157777</td>\n",
       "      <td>0.019818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Accuracy  Balanced Accuracy  F1 score       MCC  \\\n",
       "Groups                                                                 \n",
       "Overall              0.911166           0.862549  0.805905  0.749919   \n",
       " Amer-Indian-Eskimo  0.925072           0.957096  0.771930  0.758047   \n",
       " Asian-Pac-Islander  0.924433           0.873697  0.848485  0.809016   \n",
       " Black               0.919610           0.952394  0.749795  0.737202   \n",
       " Other               0.920000           0.954386  0.754717  0.742139   \n",
       " White               0.909504           0.858583  0.809211  0.753631   \n",
       "Maximum difference   0.015568           0.098513  0.098690  0.071814   \n",
       "\n",
       "                     Precision    Recall   roc_auc  Number of Datapoints  \\\n",
       "Groups                                                                     \n",
       "Overall               0.846371  0.769132  0.958577               39073.0   \n",
       " Amer-Indian-Eskimo   0.628571  1.000000  0.994674                 347.0   \n",
       " Asian-Pac-Islander   0.961832  0.759036  0.982152                1191.0   \n",
       " Black                0.601316  0.995643  0.990321                3794.0   \n",
       " Other                0.606061  1.000000  0.994825                 325.0   \n",
       " White                0.871805  0.755004  0.971215               33416.0   \n",
       "Maximum difference    0.360516  0.244996  0.023609               33091.0   \n",
       "\n",
       "                     Positive Count  Negative Count  Positive Label Rate  \\\n",
       "Groups                                                                     \n",
       "Overall                      9369.0         29704.0             0.239782   \n",
       " Amer-Indian-Eskimo            44.0           303.0             0.126801   \n",
       " Asian-Pac-Islander           332.0           859.0             0.278757   \n",
       " Black                        459.0          3335.0             0.120980   \n",
       " Other                         40.0           285.0             0.123077   \n",
       " White                       8494.0         24922.0             0.254190   \n",
       "Maximum difference           8454.0         24637.0             0.157777   \n",
       "\n",
       "                     Positive Prediction Rate  \n",
       "Groups                                         \n",
       "Overall                              0.217900  \n",
       " Amer-Indian-Eskimo                  0.201729  \n",
       " Asian-Pac-Islander                  0.219983  \n",
       " Black                               0.200316  \n",
       " Other                               0.203077  \n",
       " White                               0.220134  \n",
       "Maximum difference                   0.019818  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The groups with the smallest Positive Prediction Rate (American-Indian-Eskimo, and Asian-Pacific-Islander) \n",
    "# only have around 100-300 samples making it impossible to accurately evaluate demographic parity, while the\n",
    "# group labelled 'Other' has even less data.\n",
    "# Moreover, on training data we find that there are less than 350 people identified as American-Indian-Eskimo, \n",
    "# meaning that it is not possible to predict if the positive prediction rate will hold on new data. \n",
    "fpredictor.evaluate_groups(verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fairness on COMPAS using Inferred Attributes\n",
    "\n",
    "We demonstrate how to enforce a wide range of fairness definitions on the COMPAS dataset. This dataset records paroles caught violating the terms of parole. As it measures who was caught, it is strongly influenced by policing and environmental biases, and should not be confused with a measurement of who actually violated their terms of parole. See [this paper](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/92cc227532d17e56e07902b254dfad10-Paper-round1.pdf) for a discussion of its limitations and caveats. \n",
    "We use it because it is a standard fairness dataset that captures such strong differences in outcome between people identified as African-American and everyone else, that classifiers trained on this dataset violate most definitions of fairness.\n",
    "\n",
    "As many of the ethnic groups are too small for reliable statistical estimation, we only consider differences is in outcomes between African-Americans vs. everyone else (labeled as other).\n",
    "We load and preprocess the COMPAS dataset, splitting it into three roughly equal partitions of train, validation, and test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from autogluon.fair import inferred_attribute_builder\n",
    "all_data = pd.read_csv('https://github.com/propublica/compas-analysis/raw/master/compas-scores-two-years.csv')\n",
    "condensed_data=all_data[['sex','race','age', 'juv_fel_count', 'juv_misd_count', 'juv_other_count', 'priors_count', 'age_cat', 'c_charge_degree','two_year_recid']].copy()\n",
    "condensed_data.replace({'Caucasian':'Other', 'Hispanic':'Other', 'Native American':'Other', 'Asian':'Other'},inplace=True)\n",
    "train=condensed_data.sample(frac=0.3, random_state=0)\n",
    "val_and_test=condensed_data.drop(train.index)\n",
    "val=val_and_test.sample(frac=0.5, random_state=0)\n",
    "test=val_and_test.drop(val.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No path specified. Models will be saved in: \"AutogluonModels/ag-20221207_153607/\"\n",
      "Beginning AutoGluon training ...\n",
      "AutoGluon will save models to \"AutogluonModels/ag-20221207_153607/\"\n",
      "AutoGluon Version:  0.6.1b20221207\n",
      "Python Version:     3.9.15\n",
      "Operating System:   Darwin\n",
      "Platform Machine:   x86_64\n",
      "Platform Version:   Darwin Kernel Version 21.6.0: Thu Sep 29 20:12:57 PDT 2022; root:xnu-8020.240.7~1/RELEASE_X86_64\n",
      "Train Data Rows:    2164\n",
      "Train Data Columns: 8\n",
      "Label Column: two_year_recid\n",
      "Preprocessing data ...\n",
      "AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).\n",
      "\t2 unique label values:  [0, 1]\n",
      "\tIf 'binary' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])\n",
      "Selected class <--> label mapping:  class 1 = 1, class 0 = 0\n",
      "Using Feature Generators to preprocess the data ...\n",
      "Fitting AutoMLPipelineFeatureGenerator...\n",
      "\tAvailable Memory:                    10375.67 MB\n",
      "\tTrain Data (Original)  Memory Usage: 0.49 MB (0.0% of available memory)\n",
      "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
      "\tStage 1 Generators:\n",
      "\t\tFitting AsTypeFeatureGenerator...\n",
      "\t\t\tNote: Converting 2 features to boolean dtype as they only contain 2 unique values.\n",
      "\tStage 2 Generators:\n",
      "\t\tFitting FillNaFeatureGenerator...\n",
      "\tStage 3 Generators:\n",
      "\t\tFitting IdentityFeatureGenerator...\n",
      "\t\tFitting CategoryFeatureGenerator...\n",
      "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n",
      "\tStage 4 Generators:\n",
      "\t\tFitting DropUniqueFeatureGenerator...\n",
      "\tTypes of features in original data (raw dtype, special dtypes):\n",
      "\t\t('int', [])    : 5 | ['age', 'juv_fel_count', 'juv_misd_count', 'juv_other_count', 'priors_count']\n",
      "\t\t('object', []) : 3 | ['sex', 'age_cat', 'c_charge_degree']\n",
      "\tTypes of features in processed data (raw dtype, special dtypes):\n",
      "\t\t('category', [])  : 1 | ['age_cat']\n",
      "\t\t('int', [])       : 5 | ['age', 'juv_fel_count', 'juv_misd_count', 'juv_other_count', 'priors_count']\n",
      "\t\t('int', ['bool']) : 2 | ['sex', 'c_charge_degree']\n",
      "\t0.1s = Fit runtime\n",
      "\t8 features in original data used to generate 8 features in processed data.\n",
      "\tTrain Data (Processed) Memory Usage: 0.09 MB (0.0% of available memory)\n",
      "Data preprocessing and feature engineering runtime = 0.07s ...\n",
      "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n",
      "\tTo change this, specify the eval_metric parameter of Predictor()\n",
      "Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 1731, Val Rows: 433\n",
      "Fitting 13 L1 models ...\n",
      "Fitting model: KNeighborsUnif ...\n",
      "\t0.6351\t = Validation score   (accuracy)\n",
      "\t0.01s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: KNeighborsDist ...\n",
      "\t0.6166\t = Validation score   (accuracy)\n",
      "\t0.01s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: LightGBMXT ...\n",
      "\t0.6882\t = Validation score   (accuracy)\n",
      "\t0.49s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: LightGBM ...\n",
      "\t0.6767\t = Validation score   (accuracy)\n",
      "\t0.24s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: RandomForestGini ...\n",
      "\t0.612\t = Validation score   (accuracy)\n",
      "\t0.42s\t = Training   runtime\n",
      "\t0.05s\t = Validation runtime\n",
      "Fitting model: RandomForestEntr ...\n",
      "\t0.6074\t = Validation score   (accuracy)\n",
      "\t0.36s\t = Training   runtime\n",
      "\t0.05s\t = Validation runtime\n",
      "Fitting model: CatBoost ...\n",
      "\t0.6998\t = Validation score   (accuracy)\n",
      "\t0.57s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: ExtraTreesGini ...\n",
      "\t0.6028\t = Validation score   (accuracy)\n",
      "\t0.38s\t = Training   runtime\n",
      "\t0.06s\t = Validation runtime\n",
      "Fitting model: ExtraTreesEntr ...\n",
      "\t0.6051\t = Validation score   (accuracy)\n",
      "\t0.37s\t = Training   runtime\n",
      "\t0.05s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI ...\n",
      "No improvement since epoch 5: early stopping\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "\t0.6882\t = Validation score   (accuracy)\n",
      "\t1.3s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: XGBoost ...\n",
      "\tWarning: Exception caused XGBoost to fail during training... Skipping this model.\n",
      "\t\tCurrently, we only support \"xgboost>=1.6\". Installed version: \"xgboost==1.4.2\".\n",
      "Detailed Traceback:\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/core/src/autogluon/core/trainer/abstract_trainer.py\", line 1422, in _train_and_save\n",
      "    model = self._train_single(X, y, model, X_val, y_val, total_resources=total_resources, **model_fit_kwargs)\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/core/src/autogluon/core/trainer/abstract_trainer.py\", line 1367, in _train_single\n",
      "    model = model.fit(X=X, y=y, X_val=X_val, y_val=y_val, total_resources=total_resources, **model_fit_kwargs)\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/core/src/autogluon/core/models/abstract/abstract_model.py\", line 628, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/tabular/src/autogluon/tabular/models/xgboost/xgboost_model.py\", line 125, in _fit\n",
      "    try_import_xgboost()\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/core/src/autogluon/core/utils/try_import.py\", line 172, in try_import_xgboost\n",
      "    assert xgboost_version >= parse_version(min_version),\\\n",
      "AssertionError: Currently, we only support \"xgboost>=1.6\". Installed version: \"xgboost==1.4.2\".\n",
      "Fitting model: NeuralNetTorch ...\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "\t0.7044\t = Validation score   (accuracy)\n",
      "\t2.56s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge ...\n",
      "\t0.6605\t = Validation score   (accuracy)\n",
      "\t0.47s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: WeightedEnsemble_L2 ...\n",
      "\t0.709\t = Validation score   (accuracy)\n",
      "\t0.41s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "AutoGluon training complete, total runtime = 8.18s ... Best model: \"WeightedEnsemble_L2\"\n",
      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20221207_153607/\")\n",
      "No path specified. Models will be saved in: \"AutogluonModels/ag-20221207_153615/\"\n",
      "Beginning AutoGluon training ...\n",
      "AutoGluon will save models to \"AutogluonModels/ag-20221207_153615/\"\n",
      "AutoGluon Version:  0.6.1b20221207\n",
      "Python Version:     3.9.15\n",
      "Operating System:   Darwin\n",
      "Platform Machine:   x86_64\n",
      "Platform Version:   Darwin Kernel Version 21.6.0: Thu Sep 29 20:12:57 PDT 2022; root:xnu-8020.240.7~1/RELEASE_X86_64\n",
      "Train Data Rows:    2164\n",
      "Train Data Columns: 8\n",
      "Label Column: race\n",
      "Preprocessing data ...\n",
      "AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).\n",
      "\t2 unique label values:  ['African-American', 'Other']\n",
      "\tIf 'binary' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])\n",
      "Selected class <--> label mapping:  class 1 = Other, class 0 = African-American\n",
      "\tNote: For your binary classification, AutoGluon arbitrarily selected which label-value represents positive (Other) vs negative (African-American) class.\n",
      "\tTo explicitly set the positive_class, either rename classes to 1 and 0, or specify positive_class in Predictor init.\n",
      "Using Feature Generators to preprocess the data ...\n",
      "Fitting AutoMLPipelineFeatureGenerator...\n",
      "\tAvailable Memory:                    10112.6 MB\n",
      "\tTrain Data (Original)  Memory Usage: 0.49 MB (0.0% of available memory)\n",
      "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
      "\tStage 1 Generators:\n",
      "\t\tFitting AsTypeFeatureGenerator...\n",
      "\t\t\tNote: Converting 2 features to boolean dtype as they only contain 2 unique values.\n",
      "\tStage 2 Generators:\n",
      "\t\tFitting FillNaFeatureGenerator...\n",
      "\tStage 3 Generators:\n",
      "\t\tFitting IdentityFeatureGenerator...\n",
      "\t\tFitting CategoryFeatureGenerator...\n",
      "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n",
      "\tStage 4 Generators:\n",
      "\t\tFitting DropUniqueFeatureGenerator...\n",
      "\tTypes of features in original data (raw dtype, special dtypes):\n",
      "\t\t('int', [])    : 5 | ['age', 'juv_fel_count', 'juv_misd_count', 'juv_other_count', 'priors_count']\n",
      "\t\t('object', []) : 3 | ['sex', 'age_cat', 'c_charge_degree']\n",
      "\tTypes of features in processed data (raw dtype, special dtypes):\n",
      "\t\t('category', [])  : 1 | ['age_cat']\n",
      "\t\t('int', [])       : 5 | ['age', 'juv_fel_count', 'juv_misd_count', 'juv_other_count', 'priors_count']\n",
      "\t\t('int', ['bool']) : 2 | ['sex', 'c_charge_degree']\n",
      "\t0.0s = Fit runtime\n",
      "\t8 features in original data used to generate 8 features in processed data.\n",
      "\tTrain Data (Processed) Memory Usage: 0.09 MB (0.0% of available memory)\n",
      "Data preprocessing and feature engineering runtime = 0.07s ...\n",
      "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n",
      "\tTo change this, specify the eval_metric parameter of Predictor()\n",
      "Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 1731, Val Rows: 433\n",
      "Fitting 13 L1 models ...\n",
      "Fitting model: KNeighborsUnif ...\n",
      "\t0.6051\t = Validation score   (accuracy)\n",
      "\t0.01s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: KNeighborsDist ...\n",
      "\t0.5843\t = Validation score   (accuracy)\n",
      "\t0.01s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: LightGBMXT ...\n",
      "\t0.6559\t = Validation score   (accuracy)\n",
      "\t0.3s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: LightGBM ...\n",
      "\t0.6351\t = Validation score   (accuracy)\n",
      "\t0.23s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: RandomForestGini ...\n",
      "\t0.5751\t = Validation score   (accuracy)\n",
      "\t0.41s\t = Training   runtime\n",
      "\t0.04s\t = Validation runtime\n",
      "Fitting model: RandomForestEntr ...\n",
      "\t0.5704\t = Validation score   (accuracy)\n",
      "\t0.36s\t = Training   runtime\n",
      "\t0.05s\t = Validation runtime\n",
      "Fitting model: CatBoost ...\n",
      "\t0.6697\t = Validation score   (accuracy)\n",
      "\t0.41s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: ExtraTreesGini ...\n",
      "\t0.5843\t = Validation score   (accuracy)\n",
      "\t0.35s\t = Training   runtime\n",
      "\t0.04s\t = Validation runtime\n",
      "Fitting model: ExtraTreesEntr ...\n",
      "\t0.5797\t = Validation score   (accuracy)\n",
      "\t0.36s\t = Training   runtime\n",
      "\t0.05s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI ...\n",
      "No improvement since epoch 5: early stopping\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "\t0.6813\t = Validation score   (accuracy)\n",
      "\t1.28s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: XGBoost ...\n",
      "\tWarning: Exception caused XGBoost to fail during training... Skipping this model.\n",
      "\t\tCurrently, we only support \"xgboost>=1.6\". Installed version: \"xgboost==1.4.2\".\n",
      "Detailed Traceback:\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/core/src/autogluon/core/trainer/abstract_trainer.py\", line 1422, in _train_and_save\n",
      "    model = self._train_single(X, y, model, X_val, y_val, total_resources=total_resources, **model_fit_kwargs)\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/core/src/autogluon/core/trainer/abstract_trainer.py\", line 1367, in _train_single\n",
      "    model = model.fit(X=X, y=y, X_val=X_val, y_val=y_val, total_resources=total_resources, **model_fit_kwargs)\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/core/src/autogluon/core/models/abstract/abstract_model.py\", line 628, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/tabular/src/autogluon/tabular/models/xgboost/xgboost_model.py\", line 125, in _fit\n",
      "    try_import_xgboost()\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/core/src/autogluon/core/utils/try_import.py\", line 172, in try_import_xgboost\n",
      "    assert xgboost_version >= parse_version(min_version),\\\n",
      "AssertionError: Currently, we only support \"xgboost>=1.6\". Installed version: \"xgboost==1.4.2\".\n",
      "Fitting model: NeuralNetTorch ...\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "\t0.6651\t = Validation score   (accuracy)\n",
      "\t1.45s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge ...\n",
      "\t0.5958\t = Validation score   (accuracy)\n",
      "\t0.43s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: WeightedEnsemble_L2 ...\n",
      "\t0.6952\t = Validation score   (accuracy)\n",
      "\t0.39s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "AutoGluon training complete, total runtime = 6.56s ... Best model: \"WeightedEnsemble_L2\"\n",
      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20221207_153615/\")\n"
     ]
    }
   ],
   "source": [
    "# To enforce fairness constraints without access to protected attributes at test time, we train two classifiers to infer the 2-year recidivism rate, and ethnicity.\n",
    "  \n",
    "predictor2, protected = inferred_attribute_builder(train, 'two_year_recid', 'race')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n"
     ]
    }
   ],
   "source": [
    "# From these a single predictor that maximizes acuracy while reducing the demographic parity violation to less than 2.5% can be trained by running:\n",
    "\n",
    "fpredictor=FairPredictor(predictor2, val, 'race', inferred_groups=protected)\n",
    "fpredictor.fit(gm.accuracy, gm.demographic_parity, 0.025)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#However, instead we will show how a family of fairness measures can be individually optimized. \n",
    "# The following code plots a table showing the change in accuracy and the fairness measure on a held-out test set as we decrease\n",
    "# the fairness measure to less than 0.025 for all measures except for disparate impact which we raise to above 0.975.\n",
    "# We define a helper function for evaluation:\n",
    "\n",
    "def evaluate(fpredictor,use_metrics):\n",
    "    \"Print a table showing the accuracy drop that comes with enforcing fairness\"\n",
    "    extra_metrics= {**use_metrics, 'accuracy':gm.accuracy}\n",
    "    collect=pd.DataFrame(columns=['Measure (original)', 'Measure (updated)', 'Accuracy (original)', 'Accuracy (updated)'])\n",
    "    for d in use_metrics.items():\n",
    "        if d[1].greater_is_better is False:\n",
    "            fpredictor.fit(gm.accuracy,d[1], 0.025)\n",
    "        else:\n",
    "            fpredictor.fit(gm.accuracy,d[1], 1-0.025)\n",
    "        tmp=fpredictor.evaluate_fairness(test,metrics=extra_metrics)\n",
    "        collect.loc[d[1].name]=np.concatenate((np.asarray(tmp.loc[d[0]]), np.asarray(tmp.loc['accuracy'])), 0)\n",
    "    return collect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No path specified. Models will be saved in: \"AutogluonModels/ag-20221207_153622/\"\n",
      "Beginning AutoGluon training ...\n",
      "AutoGluon will save models to \"AutogluonModels/ag-20221207_153622/\"\n",
      "AutoGluon Version:  0.6.1b20221207\n",
      "Python Version:     3.9.15\n",
      "Operating System:   Darwin\n",
      "Platform Machine:   x86_64\n",
      "Platform Version:   Darwin Kernel Version 21.6.0: Thu Sep 29 20:12:57 PDT 2022; root:xnu-8020.240.7~1/RELEASE_X86_64\n",
      "Train Data Rows:    2164\n",
      "Train Data Columns: 9\n",
      "Label Column: two_year_recid\n",
      "Preprocessing data ...\n",
      "AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).\n",
      "\t2 unique label values:  [0, 1]\n",
      "\tIf 'binary' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])\n",
      "Selected class <--> label mapping:  class 1 = 1, class 0 = 0\n",
      "Using Feature Generators to preprocess the data ...\n",
      "Fitting AutoMLPipelineFeatureGenerator...\n",
      "\tAvailable Memory:                    10050.53 MB\n",
      "\tTrain Data (Original)  Memory Usage: 0.64 MB (0.0% of available memory)\n",
      "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
      "\tStage 1 Generators:\n",
      "\t\tFitting AsTypeFeatureGenerator...\n",
      "\t\t\tNote: Converting 3 features to boolean dtype as they only contain 2 unique values.\n",
      "\tStage 2 Generators:\n",
      "\t\tFitting FillNaFeatureGenerator...\n",
      "\tStage 3 Generators:\n",
      "\t\tFitting IdentityFeatureGenerator...\n",
      "\t\tFitting CategoryFeatureGenerator...\n",
      "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n",
      "\tStage 4 Generators:\n",
      "\t\tFitting DropUniqueFeatureGenerator...\n",
      "\tTypes of features in original data (raw dtype, special dtypes):\n",
      "\t\t('int', [])    : 5 | ['age', 'juv_fel_count', 'juv_misd_count', 'juv_other_count', 'priors_count']\n",
      "\t\t('object', []) : 4 | ['sex', 'race', 'age_cat', 'c_charge_degree']\n",
      "\tTypes of features in processed data (raw dtype, special dtypes):\n",
      "\t\t('category', [])  : 1 | ['age_cat']\n",
      "\t\t('int', [])       : 5 | ['age', 'juv_fel_count', 'juv_misd_count', 'juv_other_count', 'priors_count']\n",
      "\t\t('int', ['bool']) : 3 | ['sex', 'race', 'c_charge_degree']\n",
      "\t0.1s = Fit runtime\n",
      "\t9 features in original data used to generate 9 features in processed data.\n",
      "\tTrain Data (Processed) Memory Usage: 0.1 MB (0.0% of available memory)\n",
      "Data preprocessing and feature engineering runtime = 0.07s ...\n",
      "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n",
      "\tTo change this, specify the eval_metric parameter of Predictor()\n",
      "Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 1731, Val Rows: 433\n",
      "Fitting 13 L1 models ...\n",
      "Fitting model: KNeighborsUnif ...\n",
      "\t0.6351\t = Validation score   (accuracy)\n",
      "\t0.01s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: KNeighborsDist ...\n",
      "\t0.6166\t = Validation score   (accuracy)\n",
      "\t0.01s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: LightGBMXT ...\n",
      "\t0.6975\t = Validation score   (accuracy)\n",
      "\t0.25s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: LightGBM ...\n",
      "\t0.6744\t = Validation score   (accuracy)\n",
      "\t0.24s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: RandomForestGini ...\n",
      "\t0.6212\t = Validation score   (accuracy)\n",
      "\t0.41s\t = Training   runtime\n",
      "\t0.05s\t = Validation runtime\n",
      "Fitting model: RandomForestEntr ...\n",
      "\t0.6305\t = Validation score   (accuracy)\n",
      "\t0.37s\t = Training   runtime\n",
      "\t0.05s\t = Validation runtime\n",
      "Fitting model: CatBoost ...\n",
      "\t0.7067\t = Validation score   (accuracy)\n",
      "\t0.47s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: ExtraTreesGini ...\n",
      "\t0.6259\t = Validation score   (accuracy)\n",
      "\t0.37s\t = Training   runtime\n",
      "\t0.05s\t = Validation runtime\n",
      "Fitting model: ExtraTreesEntr ...\n",
      "\t0.6236\t = Validation score   (accuracy)\n",
      "\t0.38s\t = Training   runtime\n",
      "\t0.05s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI ...\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "\t0.7067\t = Validation score   (accuracy)\n",
      "\t1.47s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: XGBoost ...\n",
      "\tWarning: Exception caused XGBoost to fail during training... Skipping this model.\n",
      "\t\tCurrently, we only support \"xgboost>=1.6\". Installed version: \"xgboost==1.4.2\".\n",
      "Detailed Traceback:\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/core/src/autogluon/core/trainer/abstract_trainer.py\", line 1422, in _train_and_save\n",
      "    model = self._train_single(X, y, model, X_val, y_val, total_resources=total_resources, **model_fit_kwargs)\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/core/src/autogluon/core/trainer/abstract_trainer.py\", line 1367, in _train_single\n",
      "    model = model.fit(X=X, y=y, X_val=X_val, y_val=y_val, total_resources=total_resources, **model_fit_kwargs)\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/core/src/autogluon/core/models/abstract/abstract_model.py\", line 628, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/tabular/src/autogluon/tabular/models/xgboost/xgboost_model.py\", line 125, in _fit\n",
      "    try_import_xgboost()\n",
      "  File \"/Users/cmruss/Documents/Code/autogluon/core/src/autogluon/core/utils/try_import.py\", line 172, in try_import_xgboost\n",
      "    assert xgboost_version >= parse_version(min_version),\\\n",
      "AssertionError: Currently, we only support \"xgboost>=1.6\". Installed version: \"xgboost==1.4.2\".\n",
      "Fitting model: NeuralNetTorch ...\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "\t0.6952\t = Validation score   (accuracy)\n",
      "\t1.92s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge ...\n",
      "\t0.6628\t = Validation score   (accuracy)\n",
      "\t0.47s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: WeightedEnsemble_L2 ...\n",
      "\t0.7206\t = Validation score   (accuracy)\n",
      "\t0.41s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "AutoGluon training complete, total runtime = 7.37s ... Best model: \"WeightedEnsemble_L2\"\n",
      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20221207_153622/\")\n"
     ]
    }
   ],
   "source": [
    "#We can now contrast the behavior of a fair classifier that relies on access to the protected\n",
    "# attribtute at test time with one that infers it.\n",
    "\n",
    "# we first create a classifer using the protected attribute\n",
    "predictor=TabularPredictor(label='two_year_recid').fit(train_data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "No solutions satisfy the constraint found, selecting the closest solution\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
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      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
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      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "No solutions satisfy the constraint found, selecting the closest solution\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Measure (original)</th>\n",
       "      <th>Measure (updated)</th>\n",
       "      <th>Accuracy (original)</th>\n",
       "      <th>Accuracy (updated)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Class Imbalance</th>\n",
       "      <td>0.132203</td>\n",
       "      <td>0.132203</td>\n",
       "      <td>0.669307</td>\n",
       "      <td>0.666139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Demographic Parity</th>\n",
       "      <td>0.325463</td>\n",
       "      <td>0.013379</td>\n",
       "      <td>0.669307</td>\n",
       "      <td>0.670099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Disparate Impact</th>\n",
       "      <td>0.438751</td>\n",
       "      <td>0.986853</td>\n",
       "      <td>0.669307</td>\n",
       "      <td>0.645149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Accuracy</th>\n",
       "      <td>0.051849</td>\n",
       "      <td>0.035223</td>\n",
       "      <td>0.669307</td>\n",
       "      <td>0.669703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Recall</th>\n",
       "      <td>0.286954</td>\n",
       "      <td>0.043479</td>\n",
       "      <td>0.669307</td>\n",
       "      <td>0.665743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Conditional Acceptance Rate</th>\n",
       "      <td>0.599684</td>\n",
       "      <td>0.051112</td>\n",
       "      <td>0.669307</td>\n",
       "      <td>0.681584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Acceptance Rate</th>\n",
       "      <td>0.008864</td>\n",
       "      <td>0.054906</td>\n",
       "      <td>0.669307</td>\n",
       "      <td>0.662178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Specificity</th>\n",
       "      <td>0.288219</td>\n",
       "      <td>0.049785</td>\n",
       "      <td>0.669307</td>\n",
       "      <td>0.678812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Conditional Rejectance Rate</th>\n",
       "      <td>0.334529</td>\n",
       "      <td>0.046178</td>\n",
       "      <td>0.669307</td>\n",
       "      <td>0.678416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Rejection Rate</th>\n",
       "      <td>0.052565</td>\n",
       "      <td>0.038439</td>\n",
       "      <td>0.669307</td>\n",
       "      <td>0.671287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Treatment Equality</th>\n",
       "      <td>1.079171</td>\n",
       "      <td>0.004870</td>\n",
       "      <td>0.669307</td>\n",
       "      <td>0.680396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generalized Entropy</th>\n",
       "      <td>0.172806</td>\n",
       "      <td>0.050907</td>\n",
       "      <td>0.669307</td>\n",
       "      <td>0.443168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         Measure (original)  \\\n",
       "Class Imbalance                                                    0.132203   \n",
       "Demographic Parity                                                 0.325463   \n",
       "Disparate Impact                                                   0.438751   \n",
       "Maximal Group Difference in Accuracy                               0.051849   \n",
       "Maximal Group Difference in Recall                                 0.286954   \n",
       "Maximal Group Difference in Conditional Acceptance Rate            0.599684   \n",
       "Maximal Group Difference in Acceptance Rate                        0.008864   \n",
       "Maximal Group Difference in Specificity                            0.288219   \n",
       "Maximal Group Difference in Conditional Rejectance Rate            0.334529   \n",
       "Maximal Group Difference in Rejection Rate                         0.052565   \n",
       "Treatment Equality                                                 1.079171   \n",
       "Generalized Entropy                                                0.172806   \n",
       "\n",
       "                                                         Measure (updated)  \\\n",
       "Class Imbalance                                                   0.132203   \n",
       "Demographic Parity                                                0.013379   \n",
       "Disparate Impact                                                  0.986853   \n",
       "Maximal Group Difference in Accuracy                              0.035223   \n",
       "Maximal Group Difference in Recall                                0.043479   \n",
       "Maximal Group Difference in Conditional Acceptance Rate           0.051112   \n",
       "Maximal Group Difference in Acceptance Rate                       0.054906   \n",
       "Maximal Group Difference in Specificity                           0.049785   \n",
       "Maximal Group Difference in Conditional Rejectance Rate           0.046178   \n",
       "Maximal Group Difference in Rejection Rate                        0.038439   \n",
       "Treatment Equality                                                0.004870   \n",
       "Generalized Entropy                                               0.050907   \n",
       "\n",
       "                                                         Accuracy (original)  \\\n",
       "Class Imbalance                                                     0.669307   \n",
       "Demographic Parity                                                  0.669307   \n",
       "Disparate Impact                                                    0.669307   \n",
       "Maximal Group Difference in Accuracy                                0.669307   \n",
       "Maximal Group Difference in Recall                                  0.669307   \n",
       "Maximal Group Difference in Conditional Acceptance Rate             0.669307   \n",
       "Maximal Group Difference in Acceptance Rate                         0.669307   \n",
       "Maximal Group Difference in Specificity                             0.669307   \n",
       "Maximal Group Difference in Conditional Rejectance Rate             0.669307   \n",
       "Maximal Group Difference in Rejection Rate                          0.669307   \n",
       "Treatment Equality                                                  0.669307   \n",
       "Generalized Entropy                                                 0.669307   \n",
       "\n",
       "                                                         Accuracy (updated)  \n",
       "Class Imbalance                                                    0.666139  \n",
       "Demographic Parity                                                 0.670099  \n",
       "Disparate Impact                                                   0.645149  \n",
       "Maximal Group Difference in Accuracy                               0.669703  \n",
       "Maximal Group Difference in Recall                                 0.665743  \n",
       "Maximal Group Difference in Conditional Acceptance Rate            0.681584  \n",
       "Maximal Group Difference in Acceptance Rate                        0.662178  \n",
       "Maximal Group Difference in Specificity                            0.678812  \n",
       "Maximal Group Difference in Conditional Rejectance Rate            0.678416  \n",
       "Maximal Group Difference in Rejection Rate                         0.671287  \n",
       "Treatment Equality                                                 0.680396  \n",
       "Generalized Entropy                                                0.443168  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a fair predictor object, using the attribute 'race' on validation data\n",
    "fpredictor = FairPredictor(predictor, val, 'race')\n",
    "#and then evaluate it\n",
    "evaluate(fpredictor, gm.clarify_metrics)\n",
    "#Note that Class Imbalance is a property of the dataset and can not be altered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
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      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "No solutions satisfy the constraint found, selecting the closest solution\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
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      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "No solutions satisfy the constraint found, selecting the closest solution\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n",
      "[W ParallelNative.cpp:214] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)\n"
     ]
    },
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       "      <td>0.132203</td>\n",
       "      <td>0.680792</td>\n",
       "      <td>0.677228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Demographic Parity</th>\n",
       "      <td>0.217807</td>\n",
       "      <td>0.033052</td>\n",
       "      <td>0.680792</td>\n",
       "      <td>0.581782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Disparate Impact</th>\n",
       "      <td>0.564046</td>\n",
       "      <td>0.602585</td>\n",
       "      <td>0.680792</td>\n",
       "      <td>0.564752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Accuracy</th>\n",
       "      <td>0.013400</td>\n",
       "      <td>0.014385</td>\n",
       "      <td>0.680792</td>\n",
       "      <td>0.686733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Recall</th>\n",
       "      <td>0.212047</td>\n",
       "      <td>0.057136</td>\n",
       "      <td>0.680792</td>\n",
       "      <td>0.571881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Conditional Acceptance Rate</th>\n",
       "      <td>0.315828</td>\n",
       "      <td>0.021545</td>\n",
       "      <td>0.680792</td>\n",
       "      <td>0.602772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Acceptance Rate</th>\n",
       "      <td>0.070319</td>\n",
       "      <td>0.082540</td>\n",
       "      <td>0.680792</td>\n",
       "      <td>0.573465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Specificity</th>\n",
       "      <td>0.147527</td>\n",
       "      <td>0.051987</td>\n",
       "      <td>0.680792</td>\n",
       "      <td>0.623366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Conditional Rejectance Rate</th>\n",
       "      <td>0.114469</td>\n",
       "      <td>0.000478</td>\n",
       "      <td>0.680792</td>\n",
       "      <td>0.670099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximal Group Difference in Rejection Rate</th>\n",
       "      <td>0.050822</td>\n",
       "      <td>0.064287</td>\n",
       "      <td>0.680792</td>\n",
       "      <td>0.677228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Treatment Equality</th>\n",
       "      <td>0.413588</td>\n",
       "      <td>0.082096</td>\n",
       "      <td>0.680792</td>\n",
       "      <td>0.623762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generalized Entropy</th>\n",
       "      <td>0.175430</td>\n",
       "      <td>0.050907</td>\n",
       "      <td>0.680792</td>\n",
       "      <td>0.443168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         Measure (original)  \\\n",
       "Class Imbalance                                                    0.132203   \n",
       "Demographic Parity                                                 0.217807   \n",
       "Disparate Impact                                                   0.564046   \n",
       "Maximal Group Difference in Accuracy                               0.013400   \n",
       "Maximal Group Difference in Recall                                 0.212047   \n",
       "Maximal Group Difference in Conditional Acceptance Rate            0.315828   \n",
       "Maximal Group Difference in Acceptance Rate                        0.070319   \n",
       "Maximal Group Difference in Specificity                            0.147527   \n",
       "Maximal Group Difference in Conditional Rejectance Rate            0.114469   \n",
       "Maximal Group Difference in Rejection Rate                         0.050822   \n",
       "Treatment Equality                                                 0.413588   \n",
       "Generalized Entropy                                                0.175430   \n",
       "\n",
       "                                                         Measure (updated)  \\\n",
       "Class Imbalance                                                   0.132203   \n",
       "Demographic Parity                                                0.033052   \n",
       "Disparate Impact                                                  0.602585   \n",
       "Maximal Group Difference in Accuracy                              0.014385   \n",
       "Maximal Group Difference in Recall                                0.057136   \n",
       "Maximal Group Difference in Conditional Acceptance Rate           0.021545   \n",
       "Maximal Group Difference in Acceptance Rate                       0.082540   \n",
       "Maximal Group Difference in Specificity                           0.051987   \n",
       "Maximal Group Difference in Conditional Rejectance Rate           0.000478   \n",
       "Maximal Group Difference in Rejection Rate                        0.064287   \n",
       "Treatment Equality                                                0.082096   \n",
       "Generalized Entropy                                               0.050907   \n",
       "\n",
       "                                                         Accuracy (original)  \\\n",
       "Class Imbalance                                                     0.680792   \n",
       "Demographic Parity                                                  0.680792   \n",
       "Disparate Impact                                                    0.680792   \n",
       "Maximal Group Difference in Accuracy                                0.680792   \n",
       "Maximal Group Difference in Recall                                  0.680792   \n",
       "Maximal Group Difference in Conditional Acceptance Rate             0.680792   \n",
       "Maximal Group Difference in Acceptance Rate                         0.680792   \n",
       "Maximal Group Difference in Specificity                             0.680792   \n",
       "Maximal Group Difference in Conditional Rejectance Rate             0.680792   \n",
       "Maximal Group Difference in Rejection Rate                          0.680792   \n",
       "Treatment Equality                                                  0.680792   \n",
       "Generalized Entropy                                                 0.680792   \n",
       "\n",
       "                                                         Accuracy (updated)  \n",
       "Class Imbalance                                                    0.677228  \n",
       "Demographic Parity                                                 0.581782  \n",
       "Disparate Impact                                                   0.564752  \n",
       "Maximal Group Difference in Accuracy                               0.686733  \n",
       "Maximal Group Difference in Recall                                 0.571881  \n",
       "Maximal Group Difference in Conditional Acceptance Rate            0.602772  \n",
       "Maximal Group Difference in Acceptance Rate                        0.573465  \n",
       "Maximal Group Difference in Specificity                            0.623366  \n",
       "Maximal Group Difference in Conditional Rejectance Rate            0.670099  \n",
       "Maximal Group Difference in Rejection Rate                         0.677228  \n",
       "Treatment Equality                                                 0.623762  \n",
       "Generalized Entropy                                                0.443168  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Now using infered attributes\n",
    "fpredictor2 = FairPredictor(predictor2, val, 'race', inferred_groups=protected)\n",
    "evaluate(fpredictor2, gm.clarify_metrics)"
   ]
  }
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