examples/high-dim_fairlearn_comparision.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "This notebook compares the overfitting of Fairlearn Vs OxonFair on a resampled version of the [myocardial infarction dataset](https://archive.ics.uci.edu/dataset/579/myocardial+infarction+complications).\n",
    "\n",
    "We use sex as the protected attribute.\n",
    "\n",
    "The initial dataset is balanced, and to induce unfairness in the downstream classifier, we drop half the datapoints that satisfy sex=1 and target_label=0.\n",
    "\n",
    "Because the dataset is relatively high-dimensional (dims ~= 100) with around 1,000 training points, xgboost overfits perfectly obtaining zero error on the train set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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     "shell.execute_reply": "2024-06-17T19:19:03.167948Z"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/envs/ag/lib/python3.10/site-packages/tqdm/auto.py:21: 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"
     ]
    }
   ],
   "source": [
    "from oxonfair import FairPredictor, performance, dataset_loader\n",
    "from oxonfair import group_metrics as gm\n",
    "import xgboost\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-06-17T19:19:07.882019Z"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chris.russell/code/oxon-fair/src/oxonfair/utils/dataset_loader.py:216: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X[X.isnull()] = -1\n",
      "/Users/chris.russell/code/oxon-fair/src/oxonfair/utils/dataset_loader.py:216: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X[X.isnull()] = -1\n"
     ]
    }
   ],
   "source": [
    "sampler=dataset_loader.resample(1,0,0.5)\n",
    "train,val,test = dataset_loader.myocardial_infarction(resample=sampler,seed=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now train XGBoost, and specify a fair predictor over the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-06-17T19:19:08.184122Z"
    }
   },
   "outputs": [],
   "source": [
    "classifier = xgboost.XGBClassifier().fit(X=train['data'], y=train['target'])\n",
    "fpred=FairPredictor(classifier,val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We call fit to enforce equal opportunity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-17T19:19:08.186753Z",
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     "shell.execute_reply": "2024-06-17T19:19:08.190779Z"
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   "outputs": [],
   "source": [
    "fpred.fit(gm.accuracy,gm.equal_opportunity,0.02)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And evaluate fairness on validation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-17T19:19:08.193364Z",
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     "data": {
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       "      <th>Statistical Parity</th>\n",
       "      <td>0.025708</td>\n",
       "      <td>0.084434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predictive Parity</th>\n",
       "      <td>0.031401</td>\n",
       "      <td>0.095238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equal Opportunity</th>\n",
       "      <td>0.150649</td>\n",
       "      <td>0.009524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average Group Difference in False Negative Rate</th>\n",
       "      <td>0.150649</td>\n",
       "      <td>0.009524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equalized Odds</th>\n",
       "      <td>0.075635</td>\n",
       "      <td>0.020452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conditional Use Accuracy</th>\n",
       "      <td>0.054287</td>\n",
       "      <td>0.070252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average Group Difference in Accuracy</th>\n",
       "      <td>0.065920</td>\n",
       "      <td>0.057193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Treatment Equality</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.234848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                                                 original   updated\n",
       "Statistical Parity                               0.025708  0.084434\n",
       "Predictive Parity                                0.031401  0.095238\n",
       "Equal Opportunity                                0.150649  0.009524\n",
       "Average Group Difference in False Negative Rate  0.150649  0.009524\n",
       "Equalized Odds                                   0.075635  0.020452\n",
       "Conditional Use Accuracy                         0.054287  0.070252\n",
       "Average Group Difference in Accuracy             0.065920  0.057193\n",
       "Treatment Equality                               0.111111  0.234848"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpred.evaluate_fairness()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
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    {
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       "<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>Statistical Parity</th>\n",
       "      <td>0.041601</td>\n",
       "      <td>0.101147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predictive Parity</th>\n",
       "      <td>0.185464</td>\n",
       "      <td>0.065831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equal Opportunity</th>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.205882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average Group Difference in False Negative Rate</th>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.205882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equalized Odds</th>\n",
       "      <td>0.036109</td>\n",
       "      <td>0.114259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conditional Use Accuracy</th>\n",
       "      <td>0.113700</td>\n",
       "      <td>0.038816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average Group Difference in Accuracy</th>\n",
       "      <td>0.021453</td>\n",
       "      <td>0.017707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Treatment Equality</th>\n",
       "      <td>0.215278</td>\n",
       "      <td>0.212121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 original   updated\n",
       "Statistical Parity                               0.041601  0.101147\n",
       "Predictive Parity                                0.185464  0.065831\n",
       "Equal Opportunity                                0.058824  0.205882\n",
       "Average Group Difference in False Negative Rate  0.058824  0.205882\n",
       "Equalized Odds                                   0.036109  0.114259\n",
       "Conditional Use Accuracy                         0.113700  0.038816\n",
       "Average Group Difference in Accuracy             0.021453  0.017707\n",
       "Treatment Equality                               0.215278  0.212121"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpred.evaluate_fairness(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now check validation performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
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   "outputs": [
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     "data": {
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       "      <th></th>\n",
       "      <th>original</th>\n",
       "      <th>updated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.922353</td>\n",
       "      <td>0.929412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balanced Accuracy</th>\n",
       "      <td>0.775210</td>\n",
       "      <td>0.821078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1 score</th>\n",
       "      <td>0.697248</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCC</th>\n",
       "      <td>0.683479</td>\n",
       "      <td>0.718412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.926829</td>\n",
       "      <td>0.865385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.558824</td>\n",
       "      <td>0.661765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROC AUC</th>\n",
       "      <td>0.933844</td>\n",
       "      <td>0.912300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   original   updated\n",
       "Accuracy           0.922353  0.929412\n",
       "Balanced Accuracy  0.775210  0.821078\n",
       "F1 score           0.697248  0.750000\n",
       "MCC                0.683479  0.718412\n",
       "Precision          0.926829  0.865385\n",
       "Recall             0.558824  0.661765\n",
       "ROC AUC            0.933844  0.912300"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpred.evaluate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
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       "      <th>original</th>\n",
       "      <th>updated</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.905882</td>\n",
       "      <td>0.903529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balanced Accuracy</th>\n",
       "      <td>0.741597</td>\n",
       "      <td>0.769958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1 score</th>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.655462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCC</th>\n",
       "      <td>0.606665</td>\n",
       "      <td>0.609107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.764706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.573529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROC AUC</th>\n",
       "      <td>0.900972</td>\n",
       "      <td>0.862539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   original   updated\n",
       "Accuracy           0.905882  0.903529\n",
       "Balanced Accuracy  0.741597  0.769958\n",
       "F1 score           0.629630  0.655462\n",
       "MCC                0.606665  0.609107\n",
       "Precision          0.850000  0.764706\n",
       "Recall             0.500000  0.573529\n",
       "ROC AUC            0.900972  0.862539"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpred.evaluate(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now run fairlearn on the same data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/envs/ag/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:214: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  self.pos_basis[i][\"+\", e, g] = 1\n",
      "/opt/miniconda3/envs/ag/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:215: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  self.neg_basis[i][\"-\", e, g] = 1\n"
     ]
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       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExponentiatedGradient(constraints=&lt;fairlearn.reductions._moments.utility_parity.TruePositiveRateParity object at 0x1659d0130&gt;,\n",
       "                      estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                              callbacks=None,\n",
       "                                              colsample_bylevel=None,\n",
       "                                              colsample_bynode=None,\n",
       "                                              colsample_bytree=None,\n",
       "                                              device=None,\n",
       "                                              early_stopping_rounds=None,\n",
       "                                              enable_categorical=False,\n",
       "                                              eval_metric=None,\n",
       "                                              feature_typ...\n",
       "                                              grow_policy=None,\n",
       "                                              importance_type=None,\n",
       "                                              interaction_constraints=None,\n",
       "                                              learning_rate=None, max_bin=None,\n",
       "                                              max_cat_threshold=None,\n",
       "                                              max_cat_to_onehot=None,\n",
       "                                              max_delta_step=None,\n",
       "                                              max_depth=None, max_leaves=None,\n",
       "                                              min_child_weight=None,\n",
       "                                              missing=nan,\n",
       "                                              monotone_constraints=None,\n",
       "                                              multi_strategy=None,\n",
       "                                              n_estimators=None, n_jobs=None,\n",
       "                                              num_parallel_tree=None,\n",
       "                                              random_state=None, ...),\n",
       "                      nu=0.0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ExponentiatedGradient<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>ExponentiatedGradient(constraints=&lt;fairlearn.reductions._moments.utility_parity.TruePositiveRateParity object at 0x1659d0130&gt;,\n",
       "                      estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                              callbacks=None,\n",
       "                                              colsample_bylevel=None,\n",
       "                                              colsample_bynode=None,\n",
       "                                              colsample_bytree=None,\n",
       "                                              device=None,\n",
       "                                              early_stopping_rounds=None,\n",
       "                                              enable_categorical=False,\n",
       "                                              eval_metric=None,\n",
       "                                              feature_typ...\n",
       "                                              grow_policy=None,\n",
       "                                              importance_type=None,\n",
       "                                              interaction_constraints=None,\n",
       "                                              learning_rate=None, max_bin=None,\n",
       "                                              max_cat_threshold=None,\n",
       "                                              max_cat_to_onehot=None,\n",
       "                                              max_delta_step=None,\n",
       "                                              max_depth=None, max_leaves=None,\n",
       "                                              min_child_weight=None,\n",
       "                                              missing=nan,\n",
       "                                              monotone_constraints=None,\n",
       "                                              multi_strategy=None,\n",
       "                                              n_estimators=None, n_jobs=None,\n",
       "                                              num_parallel_tree=None,\n",
       "                                              random_state=None, ...),\n",
       "                      nu=0.0)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ExponentiatedGradient(constraints=<fairlearn.reductions._moments.utility_parity.TruePositiveRateParity object at 0x1659d0130>,\n",
       "                      estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                              callbacks=None,\n",
       "                                              colsample_bylevel=None,\n",
       "                                              colsample_bynode=None,\n",
       "                                              colsample_bytree=None,\n",
       "                                              device=None,\n",
       "                                              early_stopping_rounds=None,\n",
       "                                              enable_categorical=False,\n",
       "                                              eval_metric=None,\n",
       "                                              feature_typ...\n",
       "                                              grow_policy=None,\n",
       "                                              importance_type=None,\n",
       "                                              interaction_constraints=None,\n",
       "                                              learning_rate=None, max_bin=None,\n",
       "                                              max_cat_threshold=None,\n",
       "                                              max_cat_to_onehot=None,\n",
       "                                              max_delta_step=None,\n",
       "                                              max_depth=None, max_leaves=None,\n",
       "                                              min_child_weight=None,\n",
       "                                              missing=nan,\n",
       "                                              monotone_constraints=None,\n",
       "                                              multi_strategy=None,\n",
       "                                              n_estimators=None, n_jobs=None,\n",
       "                                              num_parallel_tree=None,\n",
       "                                              random_state=None, ...),\n",
       "                      nu=0.0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from fairlearn.reductions import TruePositiveRateParity, ExponentiatedGradient\n",
    "mitagator = ExponentiatedGradient(xgboost.XGBClassifier(),TruePositiveRateParity())\n",
    "mitagator.fit(X=train['data'],y=train['target'],sensitive_features=train['data']['SEX'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate fairlearn, we write a helper function to evaluate performance and fairness on train or test, and concatenate the outputs together.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-17T19:20:20.927263Z",
     "iopub.status.busy": "2024-06-17T19:20:20.926575Z",
     "iopub.status.idle": "2024-06-17T19:20:21.014963Z",
     "shell.execute_reply": "2024-06-17T19:20:21.014580Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balanced Accuracy</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.741597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1 score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCC</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.606665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROC AUC</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.741597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Statistical Parity</th>\n",
       "      <td>0.088807</td>\n",
       "      <td>0.041601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predictive Parity</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.185464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equal Opportunity</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average Group Difference in False Negative Rate</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equalized Odds</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.036109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conditional Use Accuracy</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.113700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average Group Difference in Accuracy</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Treatment Equality</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.215278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    train      test\n",
       "Accuracy                                         1.000000  0.905882\n",
       "Balanced Accuracy                                1.000000  0.741597\n",
       "F1 score                                         1.000000  0.629630\n",
       "MCC                                              1.000000  0.606665\n",
       "Precision                                        1.000000  0.850000\n",
       "Recall                                           1.000000  0.500000\n",
       "ROC AUC                                          1.000000  0.741597\n",
       "Statistical Parity                               0.088807  0.041601\n",
       "Predictive Parity                                0.000000  0.185464\n",
       "Equal Opportunity                                0.000000  0.058824\n",
       "Average Group Difference in False Negative Rate  0.000000  0.058824\n",
       "Equalized Odds                                   0.000000  0.036109\n",
       "Conditional Use Accuracy                         0.000000  0.113700\n",
       "Average Group Difference in Accuracy             0.000000  0.021453\n",
       "Treatment Equality                               0.000000  0.215278"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def eval(train, classifier=mitagator):\n",
    "    return pd.concat((performance.evaluate(train['target'], classifier.predict(train['data'])),\n",
    "                      performance.evaluate_fairness(train['target'], classifier.predict(train['data']),\n",
    "                                                    train['data'][train['groups']])),axis=0)\n",
    "\n",
    "out = pd.concat((eval(train), eval(test)), axis=1)\n",
    "out.columns = ['train', 'test']\n",
    "out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating the initially trained baseline classifier we find that, as expected, fairlearn did not alter the performance or unfairness of the classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-17T19:20:21.019772Z",
     "iopub.status.busy": "2024-06-17T19:20:21.019621Z",
     "iopub.status.idle": "2024-06-17T19:20:21.049112Z",
     "shell.execute_reply": "2024-06-17T19:20:21.048579Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balanced Accuracy</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.741597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1 score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCC</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.606665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROC AUC</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.741597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Statistical Parity</th>\n",
       "      <td>0.088807</td>\n",
       "      <td>0.041601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predictive Parity</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.185464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equal Opportunity</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average Group Difference in False Negative Rate</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equalized Odds</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.036109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conditional Use Accuracy</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.113700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average Group Difference in Accuracy</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Treatment Equality</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.215278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    train      test\n",
       "Accuracy                                         1.000000  0.905882\n",
       "Balanced Accuracy                                1.000000  0.741597\n",
       "F1 score                                         1.000000  0.629630\n",
       "MCC                                              1.000000  0.606665\n",
       "Precision                                        1.000000  0.850000\n",
       "Recall                                           1.000000  0.500000\n",
       "ROC AUC                                          1.000000  0.741597\n",
       "Statistical Parity                               0.088807  0.041601\n",
       "Predictive Parity                                0.000000  0.185464\n",
       "Equal Opportunity                                0.000000  0.058824\n",
       "Average Group Difference in False Negative Rate  0.000000  0.058824\n",
       "Equalized Odds                                   0.000000  0.036109\n",
       "Conditional Use Accuracy                         0.000000  0.113700\n",
       "Average Group Difference in Accuracy             0.000000  0.021453\n",
       "Treatment Equality                               0.000000  0.215278"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = pd.concat((eval(train, classifier), eval(test, classifier)), axis=1)\n",
    "out.columns = ['train', 'test']\n",
    "out"
   ]
  }
 ],
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