examples/pzmm_classification_assess_model_bias.ipynb

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   "source": [
    "Copyright © 2023, SAS Institute Inc., Cary, NC, USA.  All Rights Reserved.\n",
    "SPDX-License-Identifier: Apache-2.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic Dataset : Assess Bias of Classification Models\n",
    "\n",
    "This notebook provides an example of how to assess model bias for python models using the titanic dataset. The `assess_model_bias()` function featured in this notebook produces JSON files that can be uploaded into Model Manager on SAS Viya 4. Lines of code that must be modified by the user, such as directory paths or the host server are noted with the comment \"_Changes required by user._\".\n",
    "\n",
    "_**Note:** If you download only this notebook and not the rest of the repository, you must also download the titanic.csv file from the data folder in the examples directory. These files are used when executing this notebook example._\n",
    "\n",
    "Here are the steps shown in this notebook:\n",
    "\n",
    "1. [Import Modules](#section_0)\n",
    "2. [Import, review, and preprocess data for model training.](#section_1)\n",
    "3. [Build, train, and assess a scikit-learn decision tree, random forest, and gradient boosting model.](#section_2)\n",
    "4. [Create score tables for each model using the test data.](#section_3)\n",
    "5. [Call `assess_model_bias()` to create JSON files and return relevant data frames.](#section_4)\n",
    "6. [Review data frames from assess bias function and generate graphics similar to Model Studio.](#section_5)\n",
    "7. [Run through steps 1-5 again but with a multi-classification model.](#section_6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<a id='section_0'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:09:58.420212400Z",
     "start_time": "2023-07-17T17:09:58.305397800Z"
    }
   },
   "outputs": [],
   "source": [
    "from sasctl import Session\n",
    "import sasctl.pzmm as pzmm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import getpass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<a id='section_1'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load, Assess, and Pre-process Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:09:58.751871600Z",
     "start_time": "2023-07-17T17:09:58.695111600Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Lname</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund</td>\n",
       "      <td>Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings</td>\n",
       "      <td>Mrs. John Bradley (Florence Briggs Thayer)</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen</td>\n",
       "      <td>Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle</td>\n",
       "      <td>Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass      Lname  \\\n",
       "0            1         0       3     Braund   \n",
       "1            2         1       1    Cumings   \n",
       "2            3         1       3  Heikkinen   \n",
       "3            4         1       1   Futrelle   \n",
       "4            5         0       3      Allen   \n",
       "\n",
       "                                          Name     Sex   Age  SibSp  Parch  \\\n",
       "0                              Mr. Owen Harris    male  22.0      1      0   \n",
       "1   Mrs. John Bradley (Florence Briggs Thayer)  female  38.0      1      0   \n",
       "2                                  Miss. Laina  female  26.0      0      0   \n",
       "3           Mrs. Jacques Heath (Lily May Peel)  female  35.0      1      0   \n",
       "4                            Mr. William Henry    male  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  \n",
       "0         A/5 21171   7.2500   NaN        S  \n",
       "1          PC 17599  71.2833   C85        C  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3            113803  53.1000  C123        S  \n",
       "4            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw = pd.read_csv('data/titanic.csv')\n",
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2023-07-17T17:09:58.937071300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "male      100\n",
       "female     56\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw['Sex'].value_counts()"
   ]
  },
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   "execution_count": 51,
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       "      <th>proportion</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
       "      <th>1</th>\n",
       "      <td>0.714286</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
       "      <th>0</th>\n",
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      "text/plain": [
       "                 proportion\n",
       "Sex    Survived            \n",
       "female 1           0.714286\n",
       "       0           0.285714\n",
       "male   0           0.860000\n",
       "       1           0.140000"
      ]
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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summary statistics for survival by gender\n",
    "sns.histplot(x=df_raw['Sex'], hue=df_raw['Survived'].astype(str), multiple=\"stack\", stat=\"percent\")\n",
    "df_raw[['Sex', 'Survived']].groupby('Sex').value_counts(normalize=True).to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:00.128013800Z",
     "start_time": "2023-07-17T17:09:59.730492200Z"
    }
   },
   "outputs": [],
   "source": [
    "# pre-processing data\n",
    "columns = ['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']\n",
    "df = df_raw[columns].dropna()\n",
    "df = pd.get_dummies(df, columns=['Sex', 'Pclass', 'Embarked'])\n",
    "df = df.drop(['Sex_male', 'Pclass_3', 'Embarked_S'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:00.130016100Z",
     "start_time": "2023-07-17T17:09:59.752633200Z"
    }
   },
   "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>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age  SibSp  Parch     Fare  Sex_female  Pclass_1  Pclass_2  \\\n",
       "0         0  22.0      1      0   7.2500       False     False     False   \n",
       "1         1  38.0      1      0  71.2833        True      True     False   \n",
       "2         1  26.0      0      0   7.9250        True     False     False   \n",
       "3         1  35.0      1      0  53.1000        True      True     False   \n",
       "4         0  35.0      0      0   8.0500       False     False     False   \n",
       "\n",
       "   Embarked_C  Embarked_Q  \n",
       "0       False       False  \n",
       "1        True       False  \n",
       "2       False       False  \n",
       "3       False       False  \n",
       "4       False       False  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<a id='section_2'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and Assess Python Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:00.444010500Z",
     "start_time": "2023-07-17T17:09:59.855070Z"
    }
   },
   "outputs": [],
   "source": [
    "features = df.drop('Survived', axis=1).columns\n",
    "target = 'Survived'\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(df[features], df[target], train_size=0.6, test_size=0.4,\n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:01.280096600Z",
     "start_time": "2023-07-17T17:09:59.977522900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(random_state=42)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(random_state=42)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc = DecisionTreeClassifier(random_state=42)\n",
    "rfc = RandomForestClassifier(random_state=42)\n",
    "gbc = GradientBoostingClassifier(random_state=42)\n",
    "\n",
    "dtc.fit(X_train, Y_train)\n",
    "rfc.fit(X_train, Y_train)\n",
    "gbc.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:01.303693700Z",
     "start_time": "2023-07-17T17:10:00.371212400Z"
    }
   },
   "outputs": [],
   "source": [
    "# Calculate the importance of a predictor\n",
    "def sort_feature_importance(model, data):\n",
    "    features = {}\n",
    "    for importance, name in sorted(zip(model.feature_importances_, data.columns), reverse=True):\n",
    "        features[name] = str(np.round(importance*100, 2)) + \"%\"\n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:01.304705200Z",
     "start_time": "2023-07-17T17:10:00.376741700Z"
    }
   },
   "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>DecisionTree</th>\n",
       "      <th>RandomForest</th>\n",
       "      <th>GradientBoosting</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sex_female</th>\n",
       "      <td>30.81%</td>\n",
       "      <td>23.55%</td>\n",
       "      <td>32.54%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>22.01%</td>\n",
       "      <td>25.76%</td>\n",
       "      <td>22.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>21.69%</td>\n",
       "      <td>24.34%</td>\n",
       "      <td>20.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>13.67%</td>\n",
       "      <td>9.8%</td>\n",
       "      <td>17.94%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass_2</th>\n",
       "      <td>8.93%</td>\n",
       "      <td>5.11%</td>\n",
       "      <td>5.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass_1</th>\n",
       "      <td>2.89%</td>\n",
       "      <td>3.89%</td>\n",
       "      <td>0.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.0%</td>\n",
       "      <td>3.29%</td>\n",
       "      <td>0.13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_Q</th>\n",
       "      <td>0.0%</td>\n",
       "      <td>0.63%</td>\n",
       "      <td>0.23%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_C</th>\n",
       "      <td>0.0%</td>\n",
       "      <td>3.63%</td>\n",
       "      <td>0.38%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           DecisionTree RandomForest GradientBoosting\n",
       "Sex_female       30.81%       23.55%           32.54%\n",
       "Age              22.01%       25.76%           22.19%\n",
       "Fare             21.69%       24.34%           20.77%\n",
       "SibSp            13.67%         9.8%           17.94%\n",
       "Pclass_2          8.93%        5.11%            5.01%\n",
       "Pclass_1          2.89%        3.89%             0.8%\n",
       "Parch              0.0%        3.29%            0.13%\n",
       "Embarked_Q         0.0%        0.63%            0.23%\n",
       "Embarked_C         0.0%        3.63%            0.38%"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances = pd.DataFrame.from_dict(sort_feature_importance(dtc, X_train), orient=\"index\").rename(columns={0: \"DecisionTree\"})\n",
    "importances[\"RandomForest\"] = pd.DataFrame.from_dict(sort_feature_importance(rfc, X_train), orient=\"index\")\n",
    "importances[\"GradientBoosting\"] = pd.DataFrame.from_dict(sort_feature_importance(gbc, X_train), orient=\"index\")\n",
    "importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:01.305694900Z",
     "start_time": "2023-07-17T17:10:00.427940300Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[31  6]\n",
      " [ 8  5]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.84      0.82        37\n",
      "           1       0.45      0.38      0.42        13\n",
      "\n",
      "    accuracy                           0.72        50\n",
      "   macro avg       0.62      0.61      0.62        50\n",
      "weighted avg       0.71      0.72      0.71        50\n",
      "\n",
      "Decision Tree Model Accuracy = 72.0%\n"
     ]
    }
   ],
   "source": [
    "# assess model fit\n",
    "y_dtc_predict = dtc.predict(X_test)\n",
    "y_dtc_proba = dtc.predict_proba(X_test)\n",
    "print(confusion_matrix(Y_test, y_dtc_predict))\n",
    "print(classification_report(Y_test, y_dtc_predict))\n",
    "print(\"Decision Tree Model Accuracy = \" + str(np.round(dtc.score(X_test, Y_test)*100,2)) + \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:01.520039600Z",
     "start_time": "2023-07-17T17:10:00.628310300Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[35  2]\n",
      " [10  3]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.95      0.85        37\n",
      "           1       0.60      0.23      0.33        13\n",
      "\n",
      "    accuracy                           0.76        50\n",
      "   macro avg       0.69      0.59      0.59        50\n",
      "weighted avg       0.73      0.76      0.72        50\n",
      "\n",
      "Random Forest Model Accuracy = 76.0%\n"
     ]
    }
   ],
   "source": [
    "y_rfc_predict = rfc.predict(X_test)\n",
    "y_rfc_proba = rfc.predict_proba(X_test)\n",
    "print(confusion_matrix(Y_test, y_rfc_predict))\n",
    "print(classification_report(Y_test, y_rfc_predict))\n",
    "print(\"Random Forest Model Accuracy = \" + str(np.round(rfc.score(X_test, Y_test)*100,2)) + \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:01.653003800Z",
     "start_time": "2023-07-17T17:10:00.775266Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[33  4]\n",
      " [ 9  4]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.89      0.84        37\n",
      "           1       0.50      0.31      0.38        13\n",
      "\n",
      "    accuracy                           0.74        50\n",
      "   macro avg       0.64      0.60      0.61        50\n",
      "weighted avg       0.71      0.74      0.72        50\n",
      "\n",
      "Gradient Boosting Model Accuracy = 74.0%\n"
     ]
    }
   ],
   "source": [
    "y_gbc_predict = gbc.predict(X_test)\n",
    "y_gbc_proba = gbc.predict_proba(X_test)\n",
    "print(confusion_matrix(Y_test, y_gbc_predict))\n",
    "print(classification_report(Y_test, y_gbc_predict))\n",
    "print(\"Gradient Boosting Model Accuracy = \" + str(np.round(gbc.score(X_test, Y_test)*100,2)) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<a id='section_3'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Score Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:01.784534700Z",
     "start_time": "2023-07-17T17:10:01.078350500Z"
    }
   },
   "outputs": [],
   "source": [
    "# recreating sex variable\n",
    "sex = X_test['Sex_female'].apply(lambda x: 'female' if x else 'male')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:01.956622500Z",
     "start_time": "2023-07-17T17:10:01.248853200Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:02.190659900Z",
     "start_time": "2023-07-17T17:10:01.388896800Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def build_score_table(model):\n",
    "    score_data = {'P_Survived1': model.predict_proba(X_test)[:,1],\n",
    "                  'P_Survived0': model.predict_proba(X_test)[:,0],\n",
    "                  'Survived': Y_test.to_numpy(),\n",
    "                  'Sex': sex}\n",
    "    data = pd.DataFrame(score_data)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:02.456158100Z",
     "start_time": "2023-07-17T17:10:01.579980400Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "score_tables = {\"DecisionTree\": build_score_table(dtc),\n",
    "                \"RandomForest\": build_score_table(rfc),\n",
    "                \"GradientBoost\": build_score_table(gbc)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:02.632127400Z",
     "start_time": "2023-07-17T17:10:01.716828400Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTree \n",
      "     P_Survived1  P_Survived0  Survived     Sex\n",
      "21          0.0          1.0         1    male\n",
      "57          0.0          1.0         0    male\n",
      "50          0.0          1.0         0    male\n",
      "99          0.0          1.0         0    male\n",
      "71          1.0          0.0         0  female\n",
      "RandomForest \n",
      "     P_Survived1  P_Survived0  Survived     Sex\n",
      "21         0.06         0.94         1    male\n",
      "57         0.38         0.62         0    male\n",
      "50         0.23         0.77         0    male\n",
      "99         0.11         0.89         0    male\n",
      "71         0.41         0.59         0  female\n",
      "GradientBoost \n",
      "     P_Survived1  P_Survived0  Survived     Sex\n",
      "21     0.004533     0.995467         1    male\n",
      "57     0.092341     0.907659         0    male\n",
      "50     0.477515     0.522485         0    male\n",
      "99     0.009657     0.990343         0    male\n",
      "71     0.900963     0.099037         0  female\n"
     ]
    }
   ],
   "source": [
    "for k,v in score_tables.items():\n",
    "    print(f'{k} \\n {v.head()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<a id='section_4'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using `assess_model_bias()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:12.432946300Z",
     "start_time": "2023-07-17T17:10:02.136004200Z"
    }
   },
   "outputs": [],
   "source": [
    "# setting up environment\n",
    "hostname = ''\n",
    "username = input(\"Username: \")\n",
    "password = getpass.getpass(\"Password: \") # changes required by user\n",
    "\n",
    "sess = Session(hostname, username, password, protocol='http')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:12.443348600Z",
     "start_time": "2023-07-17T17:10:12.431930300Z"
    }
   },
   "outputs": [],
   "source": [
    "# Model names\n",
    "model_prefix = [\"DecisionTree\", \"RandomForest\", \"GradientBoost\"]\n",
    "# Directory location for the model files\n",
    "zip_folder = [Path.cwd() / \"data/BiasMetrics/titanicModels/DecisionTree/\",\n",
    "             Path.cwd() / \"data/BiasMetrics/titanicModels/RandomForest/\",\n",
    "             Path.cwd() / \"data/BiasMetrics/titanicModels/GradientBoost/\"] # Changes required by user\n",
    "model = [dtc, rfc, gbc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:27.012742900Z",
     "start_time": "2023-07-17T17:10:12.448320500Z"
    }
   },
   "outputs": [],
   "source": [
    "# creating assess bias json files and getting dataframes\n",
    "assess_bias_dfs = {}\n",
    "\n",
    "for (model, path) in zip(model_prefix, zip_folder):\n",
    "    dfs = pzmm.JSONFiles.assess_model_bias(\n",
    "        score_table=score_tables[model],\n",
    "        actual_values='Survived', # actual classes\n",
    "        sensitive_values='Sex', # variable to assess for bias, can add more as a list\n",
    "        prob_values=['P_Survived1', 'P_Survived0'], # variables for predicted probabilities (classification), first class will be the target class\n",
    "        levels=['1', '0'], # same order as prob_values\n",
    "        json_path=path, # directory for json files, optional\n",
    "        return_dataframes=True # returns group metrics, bias metrics, and max differences data frames, default is False\n",
    "    )\n",
    "\n",
    "    assess_bias_dfs[model] = dfs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<a id='section_5'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assess Model Bias Output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examples of the Data Frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:27.035137Z",
     "start_time": "2023-07-17T17:10:27.012742900Z"
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       "      <th></th>\n",
       "      <th>INTO_EVENT</th>\n",
       "      <th>LEVEL</th>\n",
       "      <th>PREDICTED_EVENT</th>\n",
       "      <th>P_Survived0</th>\n",
       "      <th>P_Survived1</th>\n",
       "      <th>VLABEL</th>\n",
       "      <th>_DATAROLE_</th>\n",
       "      <th>_VARIABLE_</th>\n",
       "      <th>_acc_</th>\n",
       "      <th>_ase_</th>\n",
       "      <th>...</th>\n",
       "      <th>_mcll_</th>\n",
       "      <th>_misccutoff_</th>\n",
       "      <th>_miscks_</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.266667</td>\n",
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       "      <td>0.468000</td>\n",
       "      <td>0.532000</td>\n",
       "      <td>0.468000</td>\n",
       "      <td></td>\n",
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       "      <td>Sex</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.295813</td>\n",
       "      <td>...</td>\n",
       "      <td>0.827098</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.543887</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.80000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.028571</td>\n",
       "      <td>male</td>\n",
       "      <td>0.180286</td>\n",
       "      <td>0.819714</td>\n",
       "      <td>0.180286</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Sex</td>\n",
       "      <td>0.885714</td>\n",
       "      <td>0.108351</td>\n",
       "      <td>...</td>\n",
       "      <td>0.377885</td>\n",
       "      <td>0.114286</td>\n",
       "      <td>0.828571</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.329168</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.96875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "<p>2 rows × 35 columns</p>\n",
       "</div>"
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      "text/plain": [
       "   INTO_EVENT   LEVEL  PREDICTED_EVENT  P_Survived0  P_Survived1 VLABEL  \\\n",
       "0    0.266667  female         0.468000     0.532000     0.468000          \n",
       "1    0.028571    male         0.180286     0.819714     0.180286          \n",
       "\n",
       "  _DATAROLE_ _VARIABLE_     _acc_     _ase_  ...    _mcll_  _misccutoff_  \\\n",
       "0       TEST        Sex  0.466667  0.295813  ...  0.827098      0.533333   \n",
       "1       TEST        Sex  0.885714  0.108351  ...  0.377885      0.114286   \n",
       "\n",
       "   _miscks_  _nobs_    _rase_  _resp_  _tn_    _tnr_  _tp_  _tpr_  \n",
       "0  0.266667    15.0  0.543887     0.0   4.0  0.80000   3.0    0.3  \n",
       "1  0.828571    35.0  0.329168     0.0  31.0  0.96875   0.0    0.0  \n",
       "\n",
       "[2 rows x 35 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assess_bias_dfs['RandomForest']['groupMetricsData'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:27.114699800Z",
     "start_time": "2023-07-17T17:10:27.036147100Z"
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    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>BASE</th>\n",
       "      <th>COMPARE</th>\n",
       "      <th>Metric</th>\n",
       "      <th>MetricLabel</th>\n",
       "      <th>VLABEL</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>male</td>\n",
       "      <td>P_Survived1</td>\n",
       "      <td>Average P_Survived1</td>\n",
       "      <td></td>\n",
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       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>female</td>\n",
       "      <td>P_Survived0</td>\n",
       "      <td>Average P_Survived0</td>\n",
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       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>male</td>\n",
       "      <td>TPR</td>\n",
       "      <td>True Positive Rate</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Sex</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>male</td>\n",
       "      <td>FPR</td>\n",
       "      <td>False Positive Rate</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Sex</td>\n",
       "      <td>0.168750</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>female</td>\n",
       "      <td>TNR</td>\n",
       "      <td>True Negative Rate</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Sex</td>\n",
       "      <td>0.168750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     BASE COMPARE       Metric          MetricLabel VLABEL _DATAROLE_  \\\n",
       "0  female    male  P_Survived1  Average P_Survived1              TEST   \n",
       "1    male  female  P_Survived0  Average P_Survived0              TEST   \n",
       "2  female    male          TPR   True Positive Rate              TEST   \n",
       "3  female    male          FPR  False Positive Rate              TEST   \n",
       "4    male  female          TNR   True Negative Rate              TEST   \n",
       "\n",
       "  _VARIABLE_   maxdiff  \n",
       "0        Sex  0.287714  \n",
       "1        Sex  0.287714  \n",
       "2        Sex  0.300000  \n",
       "3        Sex  0.168750  \n",
       "4        Sex  0.168750  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assess_bias_dfs['RandomForest']['maxDifferencesData'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:27.115706300Z",
     "start_time": "2023-07-17T17:10:27.055734500Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metric</th>\n",
       "      <th>MetricLabel</th>\n",
       "      <th>Value</th>\n",
       "      <th>Base</th>\n",
       "      <th>Compare</th>\n",
       "      <th>Note</th>\n",
       "      <th>_VARIABLE_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DemographicParity</td>\n",
       "      <td>Demographic Parity (Statistical Parity)</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>female</td>\n",
       "      <td>male</td>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PredictiveParity</td>\n",
       "      <td>Predictive Parity</td>\n",
       "      <td>0.287714</td>\n",
       "      <td>female</td>\n",
       "      <td>male</td>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EqualAccuracy</td>\n",
       "      <td>Equal Accuracy</td>\n",
       "      <td>0.419048</td>\n",
       "      <td>male</td>\n",
       "      <td>female</td>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EqualizedOdds</td>\n",
       "      <td>Equalized Odds</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>female</td>\n",
       "      <td>male</td>\n",
       "      <td>The maximum TPR difference is greater than the...</td>\n",
       "      <td>Sex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EqualOpportunity</td>\n",
       "      <td>Equal Opportunity</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>female</td>\n",
       "      <td>male</td>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Metric                              MetricLabel     Value  \\\n",
       "0  DemographicParity  Demographic Parity (Statistical Parity)  0.238095   \n",
       "1   PredictiveParity                        Predictive Parity  0.287714   \n",
       "2      EqualAccuracy                           Equal Accuracy  0.419048   \n",
       "3      EqualizedOdds                           Equalized Odds  0.300000   \n",
       "4   EqualOpportunity                        Equal Opportunity  0.300000   \n",
       "\n",
       "     Base Compare                                               Note  \\\n",
       "0  female    male                                                      \n",
       "1  female    male                                                      \n",
       "2    male  female                                                      \n",
       "3  female    male  The maximum TPR difference is greater than the...   \n",
       "4  female    male                                                      \n",
       "\n",
       "  _VARIABLE_  \n",
       "0        Sex  \n",
       "1        Sex  \n",
       "2        Sex  \n",
       "3        Sex  \n",
       "4        Sex  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assess_bias_dfs['RandomForest']['biasMetricsData'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:27.115706300Z",
     "start_time": "2023-07-17T17:10:27.068458100Z"
    }
   },
   "outputs": [],
   "source": [
    "gm_dtc = assess_bias_dfs['DecisionTree']['groupMetricsData']\n",
    "gm_rfc = assess_bias_dfs['RandomForest']['groupMetricsData']\n",
    "gm_gbc = assess_bias_dfs['GradientBoost']['groupMetricsData']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:27.159808400Z",
     "start_time": "2023-07-17T17:10:27.082412700Z"
    }
   },
   "outputs": [],
   "source": [
    "md_dtc = assess_bias_dfs['DecisionTree']['maxDifferencesData']\n",
    "md_rfc = assess_bias_dfs['RandomForest']['maxDifferencesData']\n",
    "md_gbc = assess_bias_dfs['GradientBoost']['maxDifferencesData']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance Bias\n",
    "\n",
    "The following are examples of graphs created by Model Studio using the `assess_model_bias()` output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:27.160313600Z",
     "start_time": "2023-07-17T17:10:27.096116200Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [7, 4]\n",
    "plt.rcParams[\"figure.autolayout\"] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Log Loss: $$L_{log}(y|p) = -(ylog(p) + (1-y)log(1-p))$$\n",
    "Log loss measures accuracy and uncertainty. Lower values indicate either higher accuracy and or higher certainty. Here, the male category has better log loss values across the board. The random forest model minimizes this difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:27.969971300Z",
     "start_time": "2023-07-17T17:10:27.108177100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3)\n",
    "fig.suptitle(\"Log Loss by Model\")\n",
    "\n",
    "sns.barplot(data=gm_dtc, x=\"LEVEL\", y='_mcll_', ax=axes[0]).set(title=\"Decision Tree\")\n",
    "sns.barplot(data=gm_rfc, x=\"LEVEL\", y='_mcll_', ax=axes[1]).set(title=\"Random Forest\")\n",
    "sns.barplot(data=gm_gbc, x=\"LEVEL\", y='_mcll_', ax=axes[2]).set(title=\"Gradient Boosting\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:28.285897Z",
     "start_time": "2023-07-17T17:10:27.954861300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "md_mcll = [md_dtc[md_dtc['Metric'] == 'MCLL']['maxdiff'], md_rfc[md_rfc['Metric'] == 'MCLL']['maxdiff'], md_gbc[md_gbc['Metric'] == 'MCLL']['maxdiff']]\n",
    "md_mcll = [x.values[0] for x in md_mcll]\n",
    "sns.barplot(x=['Decision Tree', 'Random Forest', 'Gradient Boosting'], y = md_mcll).set(title=\"Maximum Log Loss Difference by Model\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "True Positive Rate: $$\\frac{TP} {TP+FN}$$\n",
    "Higher true positive rates, or sensitivity, indicate that a higher portion of observations in the positive class were correctly classified. This metric coincides with the concept of equal opportunity. Equal opportunity states that groups of people who qualify for something are accepted at equal rates to other groups. No males were predicted to survive in this example, so the true positive rate for this category is 0. The random forest model has the smallest true positive rate difference, but it also has the lowest overall true positive rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:29.828697900Z",
     "start_time": "2023-07-17T17:10:28.288399700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3)\n",
    "fig.suptitle(\"True Positive Rate by Model\")\n",
    "\n",
    "sns.barplot(data=gm_dtc, x=\"LEVEL\", y='_tpr_', ax=axes[0]).set(title=\"Decision Tree\")\n",
    "sns.barplot(data=gm_rfc, x=\"LEVEL\", y='_tpr_', ax=axes[1]).set(title=\"Random Forest\")\n",
    "sns.barplot(data=gm_gbc, x=\"LEVEL\", y='_tpr_', ax=axes[2]).set(title=\"Gradient Boosting\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:30.207870200Z",
     "start_time": "2023-07-17T17:10:29.854310600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "md_tpr = [md_dtc[md_dtc['Metric'] == 'TPR']['maxdiff'], md_rfc[md_rfc['Metric'] == 'TPR']['maxdiff'], md_gbc[md_gbc['Metric'] == 'TPR']['maxdiff']]\n",
    "md_tpr = [x.values[0] for x in md_tpr]\n",
    "sns.barplot(x=['Decision Tree', 'Random Forest', 'Gradient Boosting'], y = md_tpr).set(title=\"Maximum True Positive Rate Difference by Model\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Max Kolmogorov-Smirnov: $$max(\\mid{F_{Survive=1}(x) - F_{Survive=0}(x)\\mid)$$\n",
    "\n",
    "The maximum Kolmogorov-Smirnov statistic for binary classification problem is the maximum CDF difference between the predicted probability distributions of each level of the target variable. Higher values indicate that a binary classification model is more capable of distinguishing between the two classes. Except for the decision tree model, the models are better at distinguishing survival and death for females compared to males. The decision tree classifier minimizes the max ks difference between the two groups, but it also has the worst possible ks score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:30.723978200Z",
     "start_time": "2023-07-17T17:10:30.208874300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3)\n",
    "fig.suptitle(\"Best Kolmogorov-Smirnov along ROC by Model\")\n",
    "\n",
    "sns.barplot(data=assess_bias_dfs['DecisionTree']['groupMetricsData'], x=\"LEVEL\", y='_ks_', ax=axes[0]).set(title=\"Decision Tree\")\n",
    "sns.barplot(data=assess_bias_dfs['RandomForest']['groupMetricsData'], x=\"LEVEL\", y='_ks_', ax=axes[1]).set(title=\"Random Forest\")\n",
    "sns.barplot(data=assess_bias_dfs['GradientBoost']['groupMetricsData'], x=\"LEVEL\", y='_ks_', ax=axes[2]).set(title=\"Gradient Boosting\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:31.026041400Z",
     "start_time": "2023-07-17T17:10:30.725687200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "md_ks = [md_dtc[md_dtc['Metric'] == 'maxKS']['maxdiff'], md_rfc[md_rfc['Metric'] == 'maxKS']['maxdiff'], md_gbc[md_gbc['Metric'] == 'maxKS']['maxdiff']]\n",
    "md_ks = [x.values[0] for x in md_ks]\n",
    "sns.barplot(x=['Decision Tree', 'Random Forest', 'Gradient Boosting'], y = md_ks).set(title=\"Maximum Best KS Difference by Model\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Prediction for Event"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predicted event metric is the average predicted probability of the target class. The metric coincides with the concept of predictive parity where ideally each group has the same probability of an event. In this case, the event of interest is survival and the female category has a much higher predicted probability of surviving. The random forest model produces the lowest difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:31.527868100Z",
     "start_time": "2023-07-17T17:10:31.027042600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3)\n",
    "fig.suptitle(\"Predicted Probability of Survival by Model\")\n",
    "\n",
    "sns.barplot(data=assess_bias_dfs['DecisionTree']['groupMetricsData'], x=\"LEVEL\", y='PREDICTED_EVENT', ax=axes[0]).set(title=\"Decision Tree\")\n",
    "sns.barplot(data=assess_bias_dfs['RandomForest']['groupMetricsData'], x=\"LEVEL\", y='PREDICTED_EVENT', ax=axes[1]).set(title=\"Random Forest\")\n",
    "sns.barplot(data=assess_bias_dfs['GradientBoost']['groupMetricsData'], x=\"LEVEL\", y='PREDICTED_EVENT', ax=axes[2]).set(title=\"Gradient Boosting\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:31.840691900Z",
     "start_time": "2023-07-17T17:10:31.526869300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "md_pe = [md_dtc[md_dtc['Metric'] == 'PREDICTED_EVENT']['maxdiff'], md_rfc[md_rfc['Metric'] == 'PREDICTED_EVENT']['maxdiff'], md_gbc[md_gbc['Metric'] == 'PREDICTED_EVENT']['maxdiff']]\n",
    "md_pe = [x.values[0] for x in md_pe]\n",
    "sns.barplot(x=['Decision Tree', 'Random Forest', 'Gradient Boosting'], y = md_pe).set(title=\"Maximum Predicted Probability of Survival Difference by Model\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<a id='section_6'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-classification Example\n",
    "This section provides a quick overview for how to assess bias on a multi-classification model and the corresponding outputs. This example will also use the titanic dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and Pre-process Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:31.854558200Z",
     "start_time": "2023-07-17T17:10:31.850034200Z"
    }
   },
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Lname</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund</td>\n",
       "      <td>Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings</td>\n",
       "      <td>Mrs. John Bradley (Florence Briggs Thayer)</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen</td>\n",
       "      <td>Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle</td>\n",
       "      <td>Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass      Lname  \\\n",
       "0            1         0       3     Braund   \n",
       "1            2         1       1    Cumings   \n",
       "2            3         1       3  Heikkinen   \n",
       "3            4         1       1   Futrelle   \n",
       "4            5         0       3      Allen   \n",
       "\n",
       "                                          Name     Sex   Age  SibSp  Parch  \\\n",
       "0                              Mr. Owen Harris    male  22.0      1      0   \n",
       "1   Mrs. John Bradley (Florence Briggs Thayer)  female  38.0      1      0   \n",
       "2                                  Miss. Laina  female  26.0      0      0   \n",
       "3           Mrs. Jacques Heath (Lily May Peel)  female  35.0      1      0   \n",
       "4                            Mr. William Henry    male  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  \n",
       "0         A/5 21171   7.2500   NaN        S  \n",
       "1          PC 17599  71.2833   C85        C  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3            113803  53.1000  C123        S  \n",
       "4            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:31.867741100Z",
     "start_time": "2023-07-17T17:10:31.857554700Z"
    }
   },
   "outputs": [],
   "source": [
    "columns = ['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']\n",
    "df = df_raw[columns].dropna()\n",
    "df = pd.get_dummies(df, columns=['Sex', 'Embarked'])\n",
    "df['Survived'] = df['Survived'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:31.948617600Z",
     "start_time": "2023-07-17T17:10:31.873301100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Survived  Pclass   Age  SibSp  Parch     Fare  Sex_female  Embarked_C  \\\n",
       "0        0       3  22.0      1      0   7.2500       False       False   \n",
       "1        1       1  38.0      1      0  71.2833        True        True   \n",
       "2        1       3  26.0      0      0   7.9250        True       False   \n",
       "3        1       1  35.0      1      0  53.1000        True       False   \n",
       "4        0       3  35.0      0      0   8.0500       False       False   \n",
       "\n",
       "   Embarked_Q  \n",
       "0       False  \n",
       "1       False  \n",
       "2       False  \n",
       "3       False  \n",
       "4       False  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['Sex_male', 'Embarked_S'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Python Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:31.948617600Z",
     "start_time": "2023-07-17T17:10:31.891340400Z"
    }
   },
   "outputs": [],
   "source": [
    "features = df.drop('Pclass', axis=1).columns\n",
    "target = 'Pclass'\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(df[features], df[target], train_size=0.6, test_size=0.4,\n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:32.222662500Z",
     "start_time": "2023-07-17T17:10:31.907710300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(random_state=42)\n",
    "rfc.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assess Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:32.223674200Z",
     "start_time": "2023-07-17T17:10:32.085640Z"
    }
   },
   "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>RandomForest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>53.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>21.58%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>6.94%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>4.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>4.75%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_C</th>\n",
       "      <td>4.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_female</th>\n",
       "      <td>3.65%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_Q</th>\n",
       "      <td>0.88%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RandomForest\n",
       "Fare             53.18%\n",
       "Age              21.58%\n",
       "SibSp             6.94%\n",
       "Parch             4.84%\n",
       "Survived          4.75%\n",
       "Embarked_C        4.18%\n",
       "Sex_female        3.65%\n",
       "Embarked_Q        0.88%"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict(sort_feature_importance(rfc, X_train), orient=\"index\").rename(columns={0: \"RandomForest\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Score Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:32.223674200Z",
     "start_time": "2023-07-17T17:10:32.113693900Z"
    }
   },
   "outputs": [],
   "source": [
    "# recreating sex variable\n",
    "sex = X_test['Sex_female'].apply(lambda x: 'female' if x else 'male')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:32.223674200Z",
     "start_time": "2023-07-17T17:10:32.122874800Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3], dtype=int64)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:32.275704100Z",
     "start_time": "2023-07-17T17:10:32.137247Z"
    }
   },
   "outputs": [],
   "source": [
    "score_data = {'P_Pclass1': rfc.predict_proba(X_test)[:,0],\n",
    "              'P_Pclass2': rfc.predict_proba(X_test)[:,1],\n",
    "              'P_Pclass3': rfc.predict_proba(X_test)[:,2],\n",
    "              'Pclass': Y_test.to_numpy(),\n",
    "              'Sex': sex}\n",
    "score_table = pd.DataFrame(score_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:10:32.290364100Z",
     "start_time": "2023-07-17T17:10:32.188771800Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P_Pclass1</th>\n",
       "      <th>P_Pclass2</th>\n",
       "      <th>P_Pclass3</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.69</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.57</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.50</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    P_Pclass1  P_Pclass2  P_Pclass3  Pclass     Sex\n",
       "21       0.10       0.45       0.45       2    male\n",
       "57       0.09       0.22       0.69       3    male\n",
       "50       0.22       0.21       0.57       3    male\n",
       "99       0.07       0.79       0.14       2    male\n",
       "71       0.35       0.15       0.50       3  female"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using `assess_model_bias()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:11:01.828497600Z",
     "start_time": "2023-07-17T17:10:56.827664500Z"
    }
   },
   "outputs": [],
   "source": [
    "# creating assess bias json files and getting dataframes\n",
    "dfs = pzmm.JSONFiles.assess_model_bias(\n",
    "        score_table=score_table,\n",
    "        actual_values='Pclass',\n",
    "        sensitive_values='Sex',\n",
    "        prob_values=['P_Pclass1', 'P_Pclass2', 'P_Pclass3'],\n",
    "        levels=['1', '2', '3'],\n",
    "        json_path=Path.cwd() / \"data/BiasMetrics/titanicModels/MultiRandomForest/\",\n",
    "        return_dataframes=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assess Model Bias Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:11:01.881499200Z",
     "start_time": "2023-07-17T17:11:01.852019400Z"
    }
   },
   "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>INTO_EVENT</th>\n",
       "      <th>LEVEL</th>\n",
       "      <th>PREDICTED_EVENT</th>\n",
       "      <th>P_Pclass1</th>\n",
       "      <th>P_Pclass2</th>\n",
       "      <th>P_Pclass3</th>\n",
       "      <th>VLABEL</th>\n",
       "      <th>_DATAROLE_</th>\n",
       "      <th>_VARIABLE_</th>\n",
       "      <th>_acc_</th>\n",
       "      <th>...</th>\n",
       "      <th>_mcll_</th>\n",
       "      <th>_misccutoff_</th>\n",
       "      <th>_miscks_</th>\n",
       "      <th>_nobs_</th>\n",
       "      <th>_rase_</th>\n",
       "      <th>_resp_</th>\n",
       "      <th>_tn_</th>\n",
       "      <th>_tnr_</th>\n",
       "      <th>_tp_</th>\n",
       "      <th>_tpr_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.066667</td>\n",
       "      <td>female</td>\n",
       "      <td>0.149333</td>\n",
       "      <td>0.149333</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.510667</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Sex</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.643379</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.363563</td>\n",
       "      <td>50.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.171429</td>\n",
       "      <td>male</td>\n",
       "      <td>0.225714</td>\n",
       "      <td>0.225714</td>\n",
       "      <td>0.272286</td>\n",
       "      <td>0.502000</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Sex</td>\n",
       "      <td>0.885714</td>\n",
       "      <td>...</td>\n",
       "      <td>0.527678</td>\n",
       "      <td>0.114286</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.318224</td>\n",
       "      <td>12.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   INTO_EVENT   LEVEL  PREDICTED_EVENT  P_Pclass1  P_Pclass2  P_Pclass3  \\\n",
       "0    0.066667  female         0.149333   0.149333   0.340000   0.510667   \n",
       "1    0.171429    male         0.225714   0.225714   0.272286   0.502000   \n",
       "\n",
       "  VLABEL _DATAROLE_ _VARIABLE_     _acc_  ...    _mcll_  _misccutoff_  \\\n",
       "0              TEST        Sex  0.933333  ...  0.643379      0.066667   \n",
       "1              TEST        Sex  0.885714  ...  0.527678      0.114286   \n",
       "\n",
       "   _miscks_  _nobs_    _rase_  _resp_  _tn_     _tnr_  _tp_  _tpr_  \n",
       "0  0.200000    15.0  0.363563    50.0  13.0  1.000000   1.0  0.500  \n",
       "1  0.142857    35.0  0.318224    12.5  26.0  0.962963   5.0  0.625  \n",
       "\n",
       "[2 rows x 36 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs['groupMetricsData'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:11:01.883530900Z",
     "start_time": "2023-07-17T17:11:01.862648Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>BASE</th>\n",
       "      <th>COMPARE</th>\n",
       "      <th>Metric</th>\n",
       "      <th>MetricLabel</th>\n",
       "      <th>VLABEL</th>\n",
       "      <th>_DATAROLE_</th>\n",
       "      <th>_VARIABLE_</th>\n",
       "      <th>maxdiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>female</td>\n",
       "      <td>P_Pclass1</td>\n",
       "      <td>Average P_Pclass1</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Sex</td>\n",
       "      <td>0.076381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>male</td>\n",
       "      <td>P_Pclass2</td>\n",
       "      <td>Average P_Pclass2</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Sex</td>\n",
       "      <td>0.067714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>male</td>\n",
       "      <td>P_Pclass3</td>\n",
       "      <td>Average P_Pclass3</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Sex</td>\n",
       "      <td>0.008667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>female</td>\n",
       "      <td>TPR</td>\n",
       "      <td>True Positive Rate</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Sex</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>female</td>\n",
       "      <td>FPR</td>\n",
       "      <td>False Positive Rate</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Sex</td>\n",
       "      <td>0.037037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     BASE COMPARE     Metric          MetricLabel VLABEL _DATAROLE_  \\\n",
       "0    male  female  P_Pclass1    Average P_Pclass1              TEST   \n",
       "1  female    male  P_Pclass2    Average P_Pclass2              TEST   \n",
       "2  female    male  P_Pclass3    Average P_Pclass3              TEST   \n",
       "3    male  female        TPR   True Positive Rate              TEST   \n",
       "4    male  female        FPR  False Positive Rate              TEST   \n",
       "\n",
       "  _VARIABLE_   maxdiff  \n",
       "0        Sex  0.076381  \n",
       "1        Sex  0.067714  \n",
       "2        Sex  0.008667  \n",
       "3        Sex  0.125000  \n",
       "4        Sex  0.037037  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs['maxDifferencesData'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:11:01.907035600Z",
     "start_time": "2023-07-17T17:11:01.882517200Z"
    }
   },
   "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>Metric</th>\n",
       "      <th>MetricLabel</th>\n",
       "      <th>Value</th>\n",
       "      <th>Base</th>\n",
       "      <th>Compare</th>\n",
       "      <th>Note</th>\n",
       "      <th>_VARIABLE_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DemographicParity</td>\n",
       "      <td>Demographic Parity (Statistical Parity)</td>\n",
       "      <td>0.104762</td>\n",
       "      <td>male</td>\n",
       "      <td>female</td>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PredictiveParity</td>\n",
       "      <td>Predictive Parity</td>\n",
       "      <td>0.076381</td>\n",
       "      <td>male</td>\n",
       "      <td>female</td>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EqualAccuracy</td>\n",
       "      <td>Equal Accuracy</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>female</td>\n",
       "      <td>male</td>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EqualizedOdds</td>\n",
       "      <td>Equalized Odds</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>male</td>\n",
       "      <td>female</td>\n",
       "      <td>The maximum TPR difference is greater than the...</td>\n",
       "      <td>Sex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EqualOpportunity</td>\n",
       "      <td>Equal Opportunity</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>male</td>\n",
       "      <td>female</td>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Metric                              MetricLabel     Value  \\\n",
       "0  DemographicParity  Demographic Parity (Statistical Parity)  0.104762   \n",
       "1   PredictiveParity                        Predictive Parity  0.076381   \n",
       "2      EqualAccuracy                           Equal Accuracy  0.047619   \n",
       "3      EqualizedOdds                           Equalized Odds  0.125000   \n",
       "4   EqualOpportunity                        Equal Opportunity  0.125000   \n",
       "\n",
       "     Base Compare                                               Note  \\\n",
       "0    male  female                                                      \n",
       "1    male  female                                                      \n",
       "2  female    male                                                      \n",
       "3    male  female  The maximum TPR difference is greater than the...   \n",
       "4    male  female                                                      \n",
       "\n",
       "  _VARIABLE_  \n",
       "0        Sex  \n",
       "1        Sex  \n",
       "2        Sex  \n",
       "3        Sex  \n",
       "4        Sex  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs['biasMetricsData'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Performance Bias*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:11:02.657117500Z",
     "start_time": "2023-07-17T17:11:01.901011700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lC9WoUQPvv/8+PD091SOQuihM2P99LOTn5yMjI0MrMSypSZMmoXXr1li3bh1+/fVXdbmu+/RVCuP89/Hw8rGg699dRStNXIUXz82aNQt79+7V+ikc5TU1NcWkSZNw6NAhnDhxAgsWLEB6errWKDARMZEleqXmzZujQYMG+O677zTK09PTkZSUhPbt2wN4cXrwzJkzGlde//LLL2Vyg/4OHTrg8uXL+OOPPzTK9+3bhwYNGsDW1hbbt29H9+7dkZeXB1NTU3Tp0gWLFy8GANy/fx+XL1/GW2+9heTkZBgZGcHBwQHTpk2DnZ0d7t+/X2S7KSkpyM3Nxfjx4/H666+rR1nPnDkDQL8nC3Xu3BmXL1/WuLjr5s2bSE9P12tf9O3bF5mZmYiKisKTJ0/UV3D/W2JiIry9vdGpUyd1wll4F4WXL9xZtWoVHjx4gPXr1+Ojjz7CunXrNO70oAszMzOsWLECeXl5mDZtGvLz8wG8GIXNyMjAl19+qbWOUqlEeno67OzsdG6n8AK0f199f+DAASiVSri6uuoVd3FMTEwQEhKCgoICLFmyRF2u6z59lc6dOwOA1oV7hVNWAN3/7ipaaeJq0aIFrKyscO/ePTg7O6t/bGxssGrVKvzyyy/IyclB79691V+oGjdujOHDh6Nfv37qv9P/unCPqLrh1AIiAA8ePNC6YhoA7Ozs8NZbb2H69OkIDg7GjBkz8M477yAjIwORkZGoV68eRo8eDQCYOHEiDh48iLFjx8Lf3x9Pnz7F2rVrYWxsrHH7ouLExsZqnSI3NjaGn58fRo8ejX379mHUqFGYMmUK6tevj/j4eJw/fx7Lli2DsbExOnfujJUrVyIgIAAfffQRJBIJdu3aBVNTU3Tv3h1NmjSBmZkZZs2ahcDAQFhbW+PHH3/E9evX4efnV2RMMpkMJiYmWLFiBfz9/ZGXl4e4uDicPHkSADRuGfQqI0eOxN69ezFmzBgEBgZCqVRizZo1xT4MoDjNmjWDs7MzNm/ejF69emmcun1ZmzZtsH//fshkMjRq1Ag///wztmzZAiMjI/UI6cWLF7Fz505MmzYNb7zxBj755BMcOXIEs2fPxq5du/RKGJycnDB58mSsXbsWq1evRlBQENzd3dG/f3+sXr0aaWlp6N27NywtLfHgwQP1HMjiRsOL0qpVKwwaNAjr1q1DdnY2OnTogOvXryMyMhKdOnXC22+/rfO2XsXFxQXvvPMOvv32Wxw6dAh9+/bVaZ/qwtbWFu+//z7WrFmDgoICODg44Ntvv0VaWpq6jrGxsU5/dxWtNHFJJBJMmzYNCxYsgEQiQffu3fH06VNs3LgRDx8+hEwmg5mZGWQyGSIjI1GjRg3Y29vjzp07+Oabb9C7d28AQN26dQEA586dQ8uWLTXmAhNVN0xkifDiFHpRT8kZMmQI3nrrLQwePBi1a9fG5s2bERAQgDp16uDtt9/G9OnT1fP6bG1tER0djYiICEydOhVWVlaYMGECNm3apNN8xY0bN2qVSSQS+Pn5oUGDBvjqq6+watUqLFmyBPn5+WjdujU2btyoHpFs3bo1oqKisGHDBkyfPh1KpRJOTk6Qy+XqJ0LJ5XKsWrUKS5cuxdOnT/HGG29g0aJFGDx4cJEx2draYtWqVYiMjMSkSZNQr149tGvXDl9++SVGjBiBS5cu6fxIUQsLC3z11VdYunQpZs+ejdq1a2Ps2LEleiqXj48Prl69Wuy0AgBYvny5en4hALzxxhsIDQ3Fvn37cOnSJWRlZSE4OBh2dnYYM2YMgBfzShcsWIBJkyZh27ZtmDBhgl5xTZgwAadPn8bnn3+Ot956C2+//bb6EbP79u3DvHnzkJWVBUtLS7i7uyMsLKzY+8gWZ+nSpbC1tUVsbCy2bt2Khg0bws/PD5MnT4axcdmeZJs5cyaOHj2KiIgIdOvW7ZX7VB8LFy6EtbU1du7ciSdPnuDtt9/GxIkTNRJ7Xf7uxFCauIYOHYratWtj27Zt2L17N2rVqoX27dtj5cqV6mNh0aJF+OyzzyCXy6FQKGBlZYUhQ4bg448/BvBiPvDo0aOxe/dunDp1CmfPntX7CyFRVWEk6HNukIiKde7cOdSoUUPjCUxPnz7FW2+9hVmzZhU76klEREQlwxFZojJy7do1rFu3DtOnT4dMJkNmZiY+//xz1K1bF/379xc7PCIioiqHiSxRGSmcQ/rVV1/hzz//RK1atdCxY0eEhYVp3DuViIiIyganFhARERGRQeLtt4iIiIjIIDGRJSIiIiKDxESWiIiIiAwSE1kiIiIiMkhMZImIiIjIIDGRJSIiIiKDxESWiIiIiAwSE1kiIiIiMkhMZImIiIjIIDGRJSIiIiKDxESWiIiIiAwSE1kiIiIiMkhMZImIiIjIIDGRJSIiIiKDxESWiIiIiAwSE1kiIiIiMkhMZImIiIjIIDGRJSIiIiKDxESWiIiIiAwSE1kiIiIiMkhMZKnCCIIgdghERERUhTCRrcZGjBgBe3t7DBs2rNg606ZNg729PWbPnq3Xtr28vDTW2bhxI6Kjo9Wv169fD3t7e523N3v2bHh5eekVQ3mIi4uDvb097t27J1oM9vb2WL9+vWjtE1HVUZ7/B0qiuL4+Pz8fkyZNQuvWrRETE6Mu/+OPPzB37lx4enrCyckJnTt3xsSJE3Hx4sVyj5UqByay1ZyxsTGSkpLw4MEDrWVZWVk4ceJEmbSzdu1aZGdnq18PHToUu3fvLpNtExFRyVXU/4GSys/PxyeffIKTJ09i8eLFGD58OABAoVDg/fffR2pqKqZPnw65XI6QkBDk5+dj5MiROHz4sKhxU8VgIlvNOTo6ombNmkhISNBaduLECUilUtjY2JR5u40aNUK7du3KfLtERKQfsf4P6KKgoADTp0/HiRMnEBYWhqFDh6qXff3113j69Cl27NiBgQMHomPHjujTpw+2bNkCBwcHrF27VpSYqWIxka3matWqBU9PzyI7sIMHD6J3794wMTFRl927dw/29vaIi4vTqPtfp/4LpxBERkaqfy9qakF8fDwGDRqEtm3bolu3bli1ahXy8vKK3GZOTg5WrVoFb29vODk5oX379hg9ejSuX7+urvP3339jxowZcHd3h7OzMwYOHIj4+Hj1cpVKhTVr1sDLywtOTk7w8vLCqlWrkJ+f/x97TDePHj1CcHAwPD090aZNGwwZMgTHjh3TqPP8+XMsWLAAXbp0gYuLC6ZNm4bt27frNeWiOEqlEjExMRgwYADatGmDbt26YeXKlcjNzVXXEXP/EFHloe//AeBF/xEaGoru3bvDyckJHTt2REBAgHraVUpKCmQymcZ0hL/++gtdunTB6NGjdbpmojCJPX78OCIiIuDr66ux/PHjxzAyMoJSqdQol0gkmDFjBt5//31ddwEZMCayBB8fH63TSs+fP8fp06fRv3//Um+/cArBkCFDip1OEBMTg6CgIMhkMkRGRmL8+PH48ssvsWTJkiLrz5o1C7GxsRg/fjzkcjmCg4Nx48YNzJgxQ91Bfvrpp7h16xZCQ0OxdetWODo6IigoCOfPnwcAbN26FV999RUCAgIgl8vxwQcfIDo6Gps2bSrV+338+DGGDBmCS5cuYdq0aVi/fj2aNGmCgIAA7Nu3T11v8uTJOHToEAIDA7FmzRr8888/WLVqVanaLrRgwQKEhYWhZ8+e2LRpE4YPH46dO3di8uTJou8fIqp89Pk/IAgCJkyYgLNnz2LmzJmIjo7GlClTcO7cOSxcuBAA4OTkhHHjxuGbb77BuXPnALzol1QqFZYvXw4jI6P/jKegoAAzZszA4cOHsXr16iL/F3Xr1g05OTl47733EB0djV9++UWd1Lq7u8PPz69U+4QMg8mrq1BV161bN0ilUiQkJGDUqFEAgCNHjsDKygqurq6l3n7hFILiphOoVCps2LABPXv21Ehcs7OzceDAAa0RwLy8PPzzzz+YN28efHx8AAAdO3bE8+fPsXz5cjx+/BgNGjTAxYsXERAQgJ49e6rr1K9fH6ampgCAixcvwsnJCe+++656uVQqRd26dUv1fj///HP8/fff+P7779GkSRMAgKenJ0aNGoWIiAj0798fFy5cwIULF7B+/Xp4e3sDALp27Yr+/fvj1q1bpWr/5s2b2Lt3L2bMmIHx48cDeNGpN2zYELNmzcLp06fh6ekp2v4hospHn/8Djx49glQqRVBQENzc3AAAnTp1wu+//64xWBEQEIDjx48jNDQU48ePx9GjR7F27dpXTlNQKpWYOXMmvv/+ewiCgIyMjCLreXp6YsGCBVi9ejUiIiIAAHXq1EGXLl3wwQcfwN3dvaS7gwwIR2QJZmZm8PLy0jitdODAAfTt2/eV35rLwp07d/DXX3+hV69eGuVjxoxBXFwcatSooVFuamqK6Oho+Pj44OHDhzh//jx27dqlviChcDpCp06dsH79ekydOhV79uzB48ePERQUhPbt26uXnz17Fh9++CG2bduGmzdv4qOPPsLAgQNL9X4uXrwIFxcXdRJb6J133oFCocDt27dx/vx51KhRQ51EAi8uuChMzEvbPgD069dPo7xfv36QSCS4cOECAPH2DxFVPvr8H7CxscEXX3wBV1dX3Lt3D2fPnsWXX36Jn3/+WWM6WI0aNRAeHo579+5h7ty5GDRoEPr06fPKWB48eIATJ05g8+bNcHV1RVhYGG7evFlk3eHDh+OHH35AZGQkhg8fjtdeew1HjhyBv78/li9fXsK9QYaEiSwBAPr27as+rZSRkYFz585pJULlJTMzEwBgZWWl8zpnzpxB37590bVrV0yePBn79u1TjyQWnjpfs2YNRo0ahZSUFMybNw+enp4YM2YM/vjjDwDA2LFjsWDBAuTk5GDlypXo168f+vfvrz61XlJPnjxBgwYNtMqtra0BAE+fPkVGRgbq168PY2PNP0F99sF/tQ9AKwYTExNYWFjg2bNnAMTbP0RUOenzf2Dfvn3o3r07evTogenTp+PYsWMwMzPTqufg4AB7e3uoVCp0795dpzhq1KiBqKgoeHp6IiIiAhKJBNOmTdOY4/8yqVSKXr16YcGCBfjuu+9w+PBhuLm54fPPP8evv/6q+w4gg8RElgC8OK1du3ZtJCQk4MiRI2jatCmcnJy06hV+M//35PqsrKwSt21ubg7gxcUDL8vIyMDZs2e1tv37778jICAADg4OOHLkCBITE/G///1Pq5OsW7cuPv30Uxw/fhyHDh3C9OnT8fPPPyM0NBTAixHQ4cOHIy4uDmfPnkVYWBjy8vIQGBhY7EVmuqhXrx4UCoVWeWGZhYUFbGxskJGRAZVKpVHnr7/+KnG7L7f/cnuF8vPzkZGRAQsLCwDi7R8iqpx0/T9w6dIlBAUFwdvbG6dPn8aFCxewffv2IqeO7d69GykpKWjdujWWLl2Kp0+fvjKOhg0bokuXLgCApk2bYu7cufj1118RFhamrqNUKuHl5YV169ZprW9ra4t58+YBQLEjuVR1MJElAC9O1/fs2RPff/89Dh06VOy38Dp16gAAHj58qC7Lz89HcnLyf27/3yOPL2vRogUsLCy07lX47bffYvz48VpzZFNSUpCbm4vx48fj9ddfVyfXZ86cAfBiRPaPP/7QuAq3RYsWGDduHN566y3cv38fADBs2DD1nFwrKysMHjwYw4cPx9OnT/H8+fP/fD//pUOHDrh8+bJ6ZLPQvn370KBBA9ja2qJjx44oKCjA8ePH1csFQcDRo0dL3G6hjh07AnhxWvBlBw4cgFKphKurq6j7h4gqJ13/D1y+fBkqlQqBgYHq+a5KpRI//vgjAKi/oP/xxx8IDw/HkCFDEBUVhWfPnmHp0qV6x/Xuu++iV69e+Oqrr3DkyBEAL+5M0LBhQ8TGxhY5h/bOnTsAADs7O73bI8PCi71IzcfHBxMmTICxsbH62+y/1atXDy4uLvjyyy9ha2uLevXq4YsvvkBOTg5q1apV7LbNzc3x888/46efflJfHFBIIpEgMDAQixYtgpWVFby8vHDnzh2sW7cOw4cPV48wFpLJZDAxMcGKFSvg7++PvLw8xMXF4eTJkwBejA7b29ujUaNGWLJkCZ4/f47XX38dKSkpOHXqFCZMmADgRcIpl8thbW0NFxcXPHz4EJ9//jk6duwIS0vL/9xXsbGxWnEZGxvDz88Po0ePxr59+zBq1ChMmTIF9evXR3x8PM6fP49ly5bB2NgYHTp0gLu7O+bOnYvHjx+jcePG2Lt3L9LS0nSal5yUlITt27drlb/99tto1aoVBg0ahHXr1iE7OxsdOnTA9evXERkZiU6dOuHtt9+GsbFxue4fIjJMuvwfaNOmDQBg0aJFePfdd/HkyRPExMQgNTUVwIs+uHbt2pg7dy6kUilmzZqFevXq4ZNPPsGyZcvQu3dvvZ/UuGjRIiQlJWHevHlwcnLCa6+9hnnz5mHEiBEYPHgw/Pz84ODgAJVKhZ9++gnbt2/HsGHD0KpVq9LtEKr0mMiS2ltvvQVzc3O89tpraNmyZbH1li9fjsWLF2PevHmoU6cOhgwZAldXV+zZs6fYdSZOnIiNGzdi3LhxOHjwoNby4cOHo1atWoiOjsbu3bvRqFEjjBs3DuPGjdOqa2tri1WrViEyMhKTJk1CvXr10K5dO3z55ZcYMWIELl26BHt7e0RGRmL16tVYu3YtMjIy8Nprr2HKlCnqK/k//vhjmJqaIjY2Fhs2bEDdunXh5eWFGTNmvHJfbdy4UatMIpHAz88PDRo0wFdffYVVq1ZhyZIlyM/PR+vWrbFx40b06NFDXX/NmjVYvnw5Vq1ahYKCAvTo0QMffPCBxr1ci/PDDz/ghx9+0Cq3sLBAy5YtsXTpUtja2iI2NhZbt25Fw4YN4efnh8mTJ6tHx8tz/xCRYdLl/0CnTp2wYMECfP7550hISIC1tTU6deqEyMhIBAQEIDExEffu3cO5c+fw2Wefqb/0jxgxAvv378eCBQvQvn171K9fX+e4LC0tsXTpUowfPx4zZ87EF198AScnJ8THx2Pz5s3YuXMnFAoFJBIJWrVqhTlz5mDIkCFlsUuokjMSdLkrMRGVqT/++ANJSUno0aOHxgUSU6dORXp6Or755hsRoyMiIjIMHJElEoGxsTFmz56NHj16YMiQIZBIJDhz5gwOHz6scUEDERERFY8jskQiOX/+PDZs2IDr16+joKAALVu2xOjRo8vkaWpERETVARNZIiIiIjJIvP0WERERERkkJrJEREREZJCqzcVeKpUKBQUFMDY21uk+nUREuhAEASqVCiYmJv/54I+qiP0qEZUHffrVapPIFhQU4OrVq2KHQURVlLOzM0xNTcUOo0KxXyWi8qRLv1ptEtnCjN7Z2RkSiUTkaIioqlAqlbh69Wq1G40F2K8SUfnQp1+tNols4WkviUTCDpeIylx1PLXOfpWIypMu/Wr1G0IgIiIioiqBiSwRERERGSQmskRERERkkJjIEhEREZFBYiJLRERERAaJiSwRERERGSQmskRERERkkJjIEhEREZFBYiJLRERERAaJiSwRERERGSQmskRERERkkJjI6kCpUokdApUQPzuqjnJzczFnzhy4ubnBw8MDcrm82LpHjhxB37594eLigg8++ADXrl2rwEiJdCOolGKHQCVU3p+dSbluvYqQGBtj3v/O4M6jJ2KHQnpo3rAelnz4tthhEFW4iIgIpKSkYMeOHbh//z6CgoLQuHFj9OnTR6PejRs3MGPGDCxatAjt27fH9u3bMWHCBBw5cgRSqVSk6Im0GRlL8DhuNvIf3xY7FNJDDesWsB68vFzbYCKrozuPniD1j7/FDoOI6D9lZWVhz5492Lp1K2QyGWQyGW7cuIGYmBitRPbs2bNo1aoVfH19AQDTp09HTEwMbt68CWdnZxGiJype/uPbyH9wXewwqJJhIktEVIWkpqaioKAALi4u6jJXV1dERUVBpVLB2Pj/ZpTVr18fN2/eRGJiIlxcXBAXF4c6derg9ddf16tNpZKnfal8SSQSsUOgUtC3j9CnPhNZIqIqRKFQwMLCAqampuoya2tr5ObmIjMzE5aWlupyHx8fHD9+HB9++CEkEgmMjY2xefNm1KtXT682r169WmbxE/2bVCqFo6Oj2GFQKaSlpSE7O7tcts1EloioCsnOztZIYgGoX+fl5WmUZ2RkQKFQYMGCBWjbti2++uorBAcH45tvvoGVlZXObTo7O3PEjIiKZW9vr1d9pVKp8xdkJrJERFVIzZo1tRLWwtdmZmYa5StXroSdnR2GDx8OAFi8eDH69u2L2NhYjB8/Xuc2JRIJE1kiKlZ59g+8/RYRURViY2ODjIwMFBQUqMsUCgXMzMxgbm6uUffatWto3bq1+rWxsTFat26N+/fvV1i8RESlwUSWiKgKcXBwgImJCZKSktRliYmJcHZ21rjQCwAaNmyIW7duaZTduXMHTZs2rYhQiYhKjYksEVEVIpVK4evri5CQECQnJ+Po0aOQy+Xw8/MD8GJ0NicnBwDw3nvv4euvv0Z8fDzu3r2LlStX4v79+xg0aJCYb4GISGecI0tEVMUEBwcjJCQEI0eORJ06dRAYGAhvb28AgIeHB8LCwjB48GD4+Pjgn3/+webNm/HgwQM4ODhgx44del3oRUQkJiayRERVjFQqRXh4OMLDw7WWpaWlabweOnQohg4dWlGhERGVKU4tICIiIiKDJGoim5ubizlz5sDNzQ0eHh6Qy+XF1k1LS8MHH3yANm3aYMCAATh//nwFRkpERERElY2oiWxERARSUlKwY8cOLFy4EJGRkUhISNCq9+zZM/j7+6NVq1bYv38/evXqhSlTpuCvv/4SIWoiIiotpUoldghUCvz8qLIQbY5sVlYW9uzZg61bt0Imk0Emk+HGjRuIiYlBnz59NOp+8803qFWrFkJCQiCRSDB16lScOnUKKSkp8PT0FOkdEBFRSUmMjTHvf2dw59ETsUMhPTVvWA9LPnxb7DCIAIiYyKampqKgoAAuLi7qMldXV0RFRUGlUmnc7/DixYvo0aOHxpMhYmNjS9SuUqnUex0+scawleQzJ9IVj6+Su/PoCVL/+FvsMIjIgImWyCoUClhYWGg8E9za2hq5ubnIzMyEpaWlujw9PR1t2rTB/Pnzcfz4cTRp0gRBQUFwdXXVu11dn91bSCqVwtHRUe92qPJIS0tDdna22GEQERFRGRMtkc3OztZIYgGoX//7OeFZWVnYsmUL/Pz8sHXrVhw4cABjxozBoUOH8Nprr+nVrrOzM0dYqxl7e3uxQ6AqTKlU6v0FmYiIyoZoiWzNmjW1EtbC12ZmZhrlEokEDg4OmDp1KgDA0dERZ8+exbfffouJEyfq1a5EImEiW83w8yYiIqqaRLtrgY2NDTIyMlBQUKAuUygUMDMzg7m5uUbdBg0aoEWLFhplb7zxBv78888KiZWIiIiIKh/RElkHBweYmJggKSlJXZaYmAhnZ2eNC70AoF27dlpPo7l9+zaaNGlSEaESERERUSUkWiIrlUrh6+uLkJAQJCcn4+jRo5DL5fDz8wPwYnQ2JycHADBs2DCkpaVh/fr1uHv3LtauXYv09HQMHDhQrPCJiIiISGSiPhAhODgYMpkMI0eORGhoKAIDA+Ht7Q0A8PDwwMGDBwEATZo0wbZt23DixAn0798fJ06cwJYtW2BjYyNm+EREREQkItEu9gJejMqGh4cjPDxca9m/pxK4uroiLi6uokIjIiIiokpO1BFZIiIiIqKSYiJLRERERAaJiSwRERERGSQmskRERERkkJjIEhEREZFBYiJLRERERAaJiSwRERERGSQmskREVUxubi7mzJkDNzc3eHh4QC6XF1lvxIgRsLe31/oJDg6u4IiJiEpG1AciEBFR2YuIiEBKSgp27NiB+/fvIygoCI0bN0afPn006q1fvx75+fnq11euXMEnn3yCDz/8sKJDJiIqESayRERVSFZWFvbs2YOtW7dCJpNBJpPhxo0biImJ0Upk69evr/5dqVRizZo1GDt2LJydnSs4aiKikuHUAiKiKiQ1NRUFBQVwcXFRl7m6uuLKlStQqVTFrhcXF4cnT55g3LhxFREmEVGZ4IgsEVEVolAoYGFhAVNTU3WZtbU1cnNzkZmZCUtLS611BEHAtm3b4Ofnh9q1a+vdplKp1HsdiUSi9zpUuZTkcy8pHi+GTd9jRZ/6TGSJiKqQ7OxsjSQWgPp1Xl5eketcuHABDx48wHvvvVeiNq9evapXfalUCkdHxxK1RZVHWloasrOzy70dHi+GrzyPFSayRERVSM2aNbUS1sLXZmZmRa7z/fffo2vXrhpzZvXh7OzMEbNqyN7eXuwQyEDoe6wolUqdvyAzkSUiqkJsbGyQkZGBgoICmJi86OIVCgXMzMxgbm5e5DpnzpzBlClTStymRCJhIlsN8TMnXZXnscKLvYiIqhAHBweYmJggKSlJXZaYmAhnZ2cYG2t3+X///TfS09Ph6upagVESEZUNJrJERFWIVCqFr68vQkJCkJycjKNHj0Iul8PPzw/Ai9HZnJwcdf0bN26gZs2aaNq0qVghExGVGBNZIqIqJjg4GDKZDCNHjkRoaCgCAwPh7e0NAPDw8MDBgwfVdf/66y+Ym5vDyMhIrHCJiEqMc2SJiKoYqVSK8PBwhIeHay1LS0vTeO3j4wMfH5+KCo2IqExxRJaIiIiIDBITWSIiIiIySExkiYiIiMggMZElKkOCquIe2Uhlj58fEZFh4cVeRGXIyFiCx3Gzkf/4ttihkJ5qWLeA9eDlYodBRER6YCJLVMbyH99G/oPrYodBRERU5XFqAREREREZJCayRERERGSQmMgSERERkUFiIktEREREBknURDY3Nxdz5syBm5sbPDw8IJfLi607adIk2Nvba/ycOHGiAqMlIiIiospE1LsWREREICUlBTt27MD9+/cRFBSExo0bo0+fPlp1b926hRUrVqBLly7qsnr16lVkuERERERUiYiWyGZlZWHPnj3YunUrZDIZZDIZbty4gZiYGK1ENi8vD/fu3YOzszMaNGggUsREREREVJmIlsimpqaioKAALi4u6jJXV1dERUVBpVLB2Pj/Zj3cvn0bRkZGaNasWanbVSr1f3KPRCIpdbsknpJ85iXFY8Xw6Xu8VOTxRUREmkRLZBUKBSwsLGBqaqous7a2Rm5uLjIzM2Fpaakuv337NurUqYNZs2bh4sWLaNSoEQIDA+Hp6al3u1evXtWrvlQqhaOjo97tUOWRlpaG7Ozscm+Hx0rVUFHHCxERlZ5oiWx2drZGEgtA/TovL0+j/Pbt28jJyYGHhwfGjx+PI0eOYNKkSdi9ezecnZ31atfZ2ZmjZtWMvb292CGQAdH3eFEqlXp/QSYiorIhWiJbs2ZNrYS18LWZmZlG+eTJkzFixAj1xV2tW7fGtWvX8PXXX+udyEokEiay1Qw/b9IHjxciIsMh2u23bGxskJGRgYKCAnWZQqGAmZkZzM3NNeoaGxtr3aGgRYsWePjwYYXESkRERESVj2iJrIODA0xMTJCUlKQuS0xMhLOzs8aFXgAwe/ZsBAcHa5SlpqaiRYsWFREqEREREVVCoiWyUqkUvr6+CAkJQXJyMo4ePQq5XA4/Pz8AL0Znc3JyAABeXl7Yv38/4uPjcffuXURGRiIxMREfffSRWOETERERkchEfbJXcHAwZDIZRo4cidDQUAQGBsLb2xsA4OHhgYMHDwIAvL29sXDhQmzatAn9+/fH8ePHsW3bNjRt2lTM8ImIiIhIRKI+2UsqlSI8PBzh4eFay9LS0jReDx06FEOHDq2o0IiIDFZubi5CQ0Nx+PBhmJmZwd/fH/7+/kXWTUtLQ0hICK5duwZbW1vMnTsXnTt3ruCIiYhKRtQRWSIiKnsvP/574cKFiIyMREJCgla9Z8+ewd/fH61atcL+/fvRq1cvTJkyBX/99ZcIURMR6Y+JLBFRFVL4+O+5c+dCJpOhV69eGDt2LGJiYrTqfvPNN6hVqxZCQkJga2uLqVOnwtbWFikpKSJETkSkP1GnFhARUdnS5/HfFy9eRI8ePTTunRsbG6t3m3z0d/XEx3+Trsrz0d9MZImIqhB9Hv+dnp6ONm3aYP78+Th+/DiaNGmCoKAguLq66tUmH/1dPfHx36Sr8jxWmMgSEVUh+jz+OysrC1u2bIGfnx+2bt2KAwcOYMyYMTh06BBee+01ndvko7+rJz7+m3RVno/+ZiJLRFSF6PP4b4lEAgcHB0ydOhUA4OjoiLNnz+Lbb7/FxIkTdW6Tj/6unviZk67K81jhxV5ERFWIPo//btCggdYTEt944w38+eefFRIrEVFpMZElIqpC9Hn8d7t27bTu2X379m00adKkIkIlIio1JrJERFWIPo//HjZsGNLS0rB+/XrcvXsXa9euRXp6OgYOHCjmWyAi0hkTWSKiKkbXx383adIE27Ztw4kTJ9C/f3+cOHECW7ZsgY2NjZjhExHpjBd7ERFVMfo8/tvV1RVxcXEVFRoRUZniiCwRERERGSQmskRERERkkJjIEhEREZFBYiJLRERERAaJiSwRERERGSQmskRERERkkJjIEhEREZFBYiJLRERERAaJiSwRERERGSQmskRERERkkJjIEhEREZFBYiJLRERERAaJiSwRERERGSQmskRERERkkJjIEhEREZFBYiJLRERERAaJiSwRERERGSQmskRERERkkERNZHNzczFnzhy4ubnBw8MDcrn8levcu3cPLi4uuHDhQgVESERERESVlYmYjUdERCAlJQU7duzA/fv3ERQUhMaNG6NPnz7FrhMSEoKsrKwKjJKIiIiIKiPRRmSzsrKwZ88ezJ07FzKZDL169cLYsWMRExNT7Dr79u3DP//8U4FREhEZHn3Odk2aNAn29vYaPydOnKjAaImISk60EdnU1FQUFBTAxcVFXebq6oqoqCioVCoYG2vm2BkZGVixYgXkcjn69+9f4naVSqXe60gkkhK3R+IryWdeUjxWDJ++x0tFHl+60uds161bt7BixQp06dJFXVavXr2KDJeIqMRES2QVCgUsLCxgamqqLrO2tkZubi4yMzNhaWmpUX/58uUYNGgQ3nzzzVK1e/XqVb3qS6VSODo6lqpNEldaWhqys7PLvR0eK1VDRR0v5aXwbNfWrVshk8kgk8lw48YNxMTEaCWyeXl5uHfvHpydndGgQQORIiYiKjnREtns7GyNJBaA+nVeXp5G+Y8//ojExER89913pW7X2dmZo2bVjL29vdghkAHR93hRKpV6f0EuT/qc7bp9+zaMjIzQrFmzUrXJM13VE892ka7K80yXaIlszZo1tRLWwtdmZmbqspycHCxYsAALFy7UKC8piUTCP4hqhp836cPQjxd9znbdvn0bderUwaxZs3Dx4kU0atQIgYGB8PT01KtNnumqnni2i3RVnseKaImsjY0NMjIyUFBQABOTF2EoFAqYmZnB3NxcXS85ORnp6emYOnWqxvrjxo2Dr68vFi1aVKFxExFVZvqc7bp9+zZycnLg4eGB8ePH48iRI5g0aRJ2794NZ2dnndvkma7qiWe7SFfleaZLtETWwcEBJiYmSEpKgpubGwAgMTERzs7OGqe+2rRpg8OHD2us6+3tjSVLlsDd3b1CYyYiqux0PdsFAJMnT8aIESPUF3e1bt0a165dw9dff61XIsszXdUTP3PSVXkeK6IlslKpFL6+vggJCcGyZcvw6NEjyOVyhIWFAXgxOlu3bl2YmZnB1tZWa30bGxtYWVlVdNhERJWarme7AMDY2FjrDgUtWrTAzZs3KyxeIqLSEPXJXsHBwZDJZBg5ciRCQ0MRGBgIb29vAICHhwcOHjwoZnhERAbn5bNdhYo62wUAs2fPRnBwsEZZamoqWrRoURGhEhGVWrmNyBZ2nP+eq/UyqVSK8PBwhIeHay1LS0srdr3/WkZEVJ3pc7bLy8sL06dPR6dOneDi4oL9+/cjMTGR1x4QkcEotxHZcePG4eHDh+W1eSIiKoauZ7u8vb2xcOFCbNq0Cf3798fx48exbds2NG3aVMzwiYh0Vm4jsoIglNemiYjoP+hztmvo0KEYOnRoRYVGRFSmRJ0jS0RERERUUkxkiYiIiMggMZElIjIQ3333HbKyssQOg4io0mAiS0RkIBYsWIC//vpL7DCIiCoNJrJERAaCF9ESEWkqt0S2efPmqFGjRnltnoiIiIiqOZ1uvxUfH6/zBn19fQEAcXFxJYmHiIiIiEgnOiWy69at02ljRkZG6kSWiIiIiKg86ZTIHj9+vLzjICIiIiLSi06J7E8//aTTxoyMjODm5laqgIiIiIiIdKFTIjtixAidNmZkZITr16+XKiAiIiqau7s7pFKp2GEQEVUaOiWyqamp5R0HEVG19fz5c0RFRWHw4MF44403MHv2bBw+fBiOjo5YsWIFmjRpAgCIjIwUOVIiospF79tv5eXlISIiAjExMeqywYMHY+XKlcjPzy/T4IiIqoPQ0FCcOnUKRkZG2L9/Pw4fPoxly5bB2toaoaGhYodHRFRp6Z3ILlmyBKdOnULr1q3VZZMnT8bJkycRHh5epsEREVUHp06dwooVK9C8eXN8//336N69O3x8fDB9+nSdr1EgIqqO9E5kDx8+jJUrV8LV1VVd1rNnT4SFheHgwYNlGhwRUXUgCAJq1KiBnJwcnDt3Dp6engCAJ0+eoFatWiJHR0RUeek0R/ZlgiAgNze3yHJOLSAi0l/nzp0xf/581KpVC8bGxujZsyfOnTuHxYsXw8vLS+zwiIgqLb1HZHv37o358+fj0qVLyMrKQlZWFn7++WeEhISgV69e5REjEVGVtmzZMjg6OsLU1BQbNmxAnTp1kJaWBk9PT8ybN0/s8IiIKi29R2SDg4Mxd+5cjBw5EiqVCgBgbGwMX19fzJkzp8wDJCKq6oyMjLQS1lGjRgEAjh49ip49e4oQFRFR5ad3IiuVSrF69Wo8ffoUd+/eRY0aNdC0aVPUqVOnPOIjIqryRowYgejoaFhaWqrL0tPTsWjRIpw7dw4pKSkiRkdEVHnpncgCL+4re/v2beTl5alfF/L19S2TwIiIqotmzZrhgw8+wOeffw5ra2ts2rQJ0dHRaNu2LWJjY8UOj4io0tI7kV25ciW2bdsGKysr1KxZU2OZkZERE1kiIj2tXbsWS5cuxbBhw2BqagqVSoXw8HD07du3RNvLzc1FaGgoDh8+DDMzM/j7+8Pf3/8/17l37x4GDBiAqKgodOrUqUTtEhFVNL0T2d27d2Pp0qV49913yyMeIqJqp3CObKNGjfDZZ59h27Zt6Ny5c4m3FxERgZSUFOzYsQP3799HUFAQGjdujD59+hS7TkhICLKyskrcJhGRGPROZOvWrQtnZ+fyiIWIqNrw8vKCkZFRkcsmTJgAa2tr9etjx47pvN2srCzs2bMHW7duhUwmg0wmw40bNxATE1NsIrtv3z78888/+r0BIqJKQO9ENigoCIsWLcLUqVPRuHFjGBtr3sGrcePGZRYcEVFVFRgYqPc6AwYMwJYtW/Daa68VWyc1NRUFBQVwcXFRl7m6uiIqKgoqlUqrz87IyMCKFSsgl8vRv39/vWMiIhKT3olsTk4Orl27Bj8/P43RBEEQYGRkhOvXr5dpgEREVdGgQYP0XufevXsoKCj4zzoKhQIWFhYwNTVVl1lbWyM3NxeZmZkad0YAgOXLl2PQoEF488039Y6nkFKp1HsdiURS4vaocijJ515SPF4Mm77Hij719U5kV6xYgffeew/vvfcezMzM9F2diIjKUXZ2tkYSC0D9uvBOM4V+/PFHJCYm4rvvvitVm1evXtWrvlQqhaOjY6naJPGlpaUhOzu73Nvh8WL4yvNY0TuRzcvLw0cffYRmzZqVRzxERFQKNWvW1EpYC1+/PPiQk5ODBQsWYOHChaUelHB2duaIWTVkb28vdghkIPQ9VpRKpc5fkPV+RK2/vz82b96M3NxcfVfVkpubizlz5sDNzQ0eHh6Qy+XF1t23bx969+6NNm3aYNiwYUhOTi51+0REVY2NjQ0yMjI0piAoFAqYmZnB3NxcXZacnIz09HRMnToVLi4u6jm148aNw4IFC/RqUyKR6P1Dhq8kn3tJf8iwlednrveI7NmzZ5GUlIT4+HhYW1trNabP1bW63iLm0qVLmDt3LpYsWYL27dvjf//7H8aNG4fjx4+jdu3a+r4FIqIqy8HBASYmJkhKSoKbmxsAIDExEc7OzhoXerVp0waHDx/WWNfb2xtLliyBu7t7hcZMRFRSeieygwcPxuDBg19Z71VX1+pzixiFQoHJkydj4MCBAICAgADI5XLcunULbdq00fctEBEZpOJu1/UyqVQKX19fhISEYNmyZXj06BHkcjnCwsIAvOhP69atCzMzM9ja2mqtb2NjAysrqzKPnYioPOidyOp6pe2rrq7V5xYxLz/dJicnB9u3b4eVlRVatmypb/i8urYa4pW1pI/yvLq2tARB0KlecHAwQkJCMHLkSNSpUweBgYHw9vYGAHh4eCAsLEynAQkiospO70S2rOh7ixgAOHfuHPz9/SEIAlauXFmiaQW8urb64ZW1pI+KOl5K4osvvkCjRo1eWU8qlSI8PBzh4eFay9LS0opd77+WERFVRqIlsvrcIqbQm2++ibi4OJw4cQKzZ89G06ZN0a5dO73a5dW11Q+vrCV9lOfVtaXFpyoSEWkSLZHV9RYxL7O2toa1tTUcHBxw5coV7Nq1S+9ElldAVj/8vEkfPF6IiAyH3rffKiu63iIGeHGbmGvXrmmUtWzZEhkZGRUSKxERERFVPuWWyL7q6tqXbxFTqKhbxADA3r17sXr1ao2ya9euoUWLFmUWLxEREREZlnJLZF91de3Lt4hJTk7G0aNHIZfL4efnB+DF6GxOTg4A4P3338f58+exY8cO/Pbbb1i3bh2Sk5MxatSo8gqfiIiIiCq5cktkdbm6Njg4GDKZDCNHjkRoaKjWLWIOHjwIAJDJZIiMjMTevXvxzjvv4NSpU4iOjoaNjU15hU9ERERElVy5Xeyly9W1+twipnv37ujevXuZxUdEREREhk20i72IiIiIiEqDiSwRERERGSQmskRERERkkJjIEhEREZFBYiJLRERERAaJiSwRERERGSQmskRERERkkJjIEhEREZFBYiJLRERERAaJiSwRERERGSQmskRERERkkJjIEhEREZFBYiJLRERERAaJiSwRURWTm5uLOXPmwM3NDR4eHpDL5cXW3bdvH3r37o02bdpg2LBhSE5OrsBIiYhKh4ksEVEVExERgZSUFOzYsQMLFy5EZGQkEhIStOpdunQJc+fOxeTJk3HgwAG4uLhg3Lhx+Oeff0SImohIf0xkiYiqkKysLOzZswdz586FTCZDr169MHbsWMTExGjVVSgUmDx5MgYOHIhmzZohICAAmZmZuHXrlgiRExHpz0TsAIiIqOykpqaioKAALi4u6jJXV1dERUVBpVLB2Pj/xi/69u2r/j0nJwfbt2+HlZUVWrZsWaExExGVFBNZIqIqRKFQwMLCAqampuoya2tr5ObmIjMzE5aWllrrnDt3Dv7+/hAEAStXrkTt2rX1alOpVOodp0Qi0XsdqlxK8rmXFI8Xw6bvsaJPfSayRERVSHZ2tkYSC0D9Oi8vr8h13nzzTcTFxeHEiROYPXs2mjZtinbt2unc5tWrV/WKUSqVwtHRUa91qPJJS0tDdnZ2ubfD48XwleexwkSWiKgKqVmzplbCWvjazMysyHWsra1hbW0NBwcHXLlyBbt27dIrkXV2duaIWTVkb28vdghkIPQ9VpRKpc5fkJnIEhFVITY2NsjIyEBBQQFMTF508QqFAmZmZjA3N9eom5ycDIlEAplMpi5r2bKl3hd7SSQSJrLVED9z0lV5Hiu8awERURXi4OAAExMTJCUlqcsSExPh7OyscaEXAOzduxerV6/WKLt27RpatGhREaESEZUaE1kioipEKpXC19cXISEhSE5OxtGjRyGXy+Hn5wfgxehsTk4OAOD999/H+fPnsWPHDvz2229Yt24dkpOTMWrUKBHfARGR7pjIEhFVMcHBwZDJZBg5ciRCQ0MRGBgIb29vAICHhwcOHjwIAJDJZIiMjMTevXvxzjvv4NSpU4iOjoaNjY2Y4RMR6YxzZImIqhipVIrw8HCEh4drLUtLS9N43b17d3Tv3r2iQiMiKlMckSUiIiIig8REloiIiIgMEhNZIiIiIjJIoiayubm5mDNnDtzc3ODh4QG5XF5s3ZMnT2LgwIFwcXHBgAEDcOzYsQqMlIiIiIgqG1ET2YiICKSkpGDHjh1YuHAhIiMjkZCQoFUvNTUVU6ZMwbvvvov4+HgMGzYMH3/8MVJTU0WImoiIiIgqA9HuWpCVlYU9e/Zg69atkMlkkMlkuHHjBmJiYtCnTx+Nut999x06d+6svg+ira0tjh8/jkOHDqF169ZihE9EREREIhMtkU1NTUVBQQFcXFzUZa6uroiKioJKpdJ4As2gQYOQn5+vtY1nz55VSKxEREREVPmIlsgqFApYWFjA1NRUXWZtbY3c3FxkZmbC0tJSXd6yZUuNdW/cuIFz585h2LBhererVCr1XofPkzZsJfnMS4rHiuHT93ipyOOLiIg0iZbIZmdnaySxANSv8/Lyil3v77//RmBgINq3b48ePXro3e7Vq1f1qi+VSuHo6Kh3O1R5pKWlITs7u9zb4bFSNVTU8UJERKUnWiJbs2ZNrYS18LWZmVmR6zx+/BijR4+GIAhYt26dxvQDXTk7O3PUrJqxt7cXOwQyIPoeL0qlUu8vyEREVDZES2RtbGyQkZGBgoICmJi8CEOhUMDMzAzm5uZa9R8+fKi+2OuLL77QmHqgD4lEwkS2muHnTfrg8UJEZDhEu/2Wg4MDTExMkJSUpC5LTEyEs7Oz1khrVlYWxo4dC2NjY+zcuRM2NjYVHC0RERERVTaiJbJSqRS+vr4ICQlBcnIyjh49Crlcrh51VSgUyMnJAQBs3rwZv//+O8LDw9XLFAoF71pAREREVI2JNrUAAIKDgxESEoKRI0eiTp06CAwMhLe3NwDAw8MDYWFhGDx4ML7//nvk5ORg6NChGusPGjQIy5cvFyN0IiIiIhKZqImsVCpFeHi4eqT1ZWlpaerfi3raFxERERFVb6I+opaIiIiIqKSYyBIRERGRQWIiS0REREQGiYksERERERkkJrJEREREZJCYyBIRERGRQWIiS0RUxeTm5mLOnDlwc3ODh4cH5HJ5sXVPnjyJgQMHwsXFBQMGDMCxY8cqMFIiotJhIktEVMVEREQgJSUFO3bswMKFCxEZGVnk/bhTU1MxZcoUvPvuu4iPj8ewYcPw8ccfIzU1VYSoiYj0J+oDEYiIqGxlZWVhz5492Lp1K2QyGWQyGW7cuIGYmBj06dNHo+53332Hzp07qx8Nbmtri+PHj+PQoUNo3bq1GOETEemFiSwRURWSmpqKgoICuLi4qMtcXV0RFRUFlUoFY+P/OxE3aNAg5Ofna23j2bNnerWpVCr1jlMikei9DlUuJfncS4rHi2HT91jRpz4TWSKiKkShUMDCwgKmpqbqMmtra+Tm5iIzMxOWlpbq8pYtW2qse+PGDZw7dw7Dhg3Tq82rV6/qVV8qlcLR0VGvdajySUtLQ3Z2drm3w+PF8JXnscJEloioCsnOztZIYgGoX+fl5RW73t9//43AwEC0b98ePXr00KtNZ2dnjphVQ/b29mKHQAZC32NFqVTq/AWZiSwRURVSs2ZNrYS18LWZmVmR6zx+/BijR4+GIAhYt26dxvQDXUgkEiay1RA/c9JVeR4rvGsBEVEVYmNjg4yMDBQUFKjLFAoFzMzMYG5urlX/4cOHGD58OPLy8vDFF19oTD0gIqrsmMgSEVUhDg4OMDExQVJSkrosMTERzs7OWiOtWVlZGDt2LIyNjbFz507Y2NhUcLRERKXDRJaIqAqRSqXw9fVFSEgIkpOTcfToUcjlcvUtthQKBXJycgAAmzdvxu+//47w8HD1MoVCofddC4iIxMI5skREVUxwcDBCQkIwcuRI1KlTB4GBgfD29gYAeHh4ICwsDIMHD8b333+PnJwcDB06VGP9QYMGYfny5WKETkSkFyayRERVjFQqRXh4uHqk9WVpaWnq34t62hcRkSHh1AIiIiIiMkhMZImIiIjIIDGRJSIiIiKDxESWiIiIiAwSE1kiIiIiMkhMZImIiIjIIDGRJSIiIiKDxESWiIiIiAwSE1kiIiIiMkhMZImIiIjIIDGRJSIiIiKDJGoim5ubizlz5sDNzQ0eHh6Qy+WvXOfSpUvo0aNHBURHRERERJWZiZiNR0REICUlBTt27MD9+/cRFBSExo0bo0+fPkXWT0tLw8cff4yaNWtWcKREREREVNmINiKblZWFPXv2YO7cuZDJZOjVqxfGjh2LmJiYIuvv2rULw4YNg5WVVQVHSkRERESVkWiJbGpqKgoKCuDi4qIuc3V1xZUrV6BSqbTqnz59GuHh4Rg1alQFRklERERElZVoUwsUCgUsLCxgamqqLrO2tkZubi4yMzNhaWmpUX/jxo0AgLi4uFK1q1Qq9V5HIpGUqk0SV0k+85LisWL49D1eKvL4IiIiTaIlstnZ2RpJLAD167y8vHJr9+rVq3rVl0qlcHR0LKdoqCKkpaUhOzu73NvhsVI1VNTxQkREpSdaIluzZk2thLXwtZmZWbm16+zszFGzasbe3l7sEMiA6Hu8KJVKvb8gExFR2RAtkbWxsUFGRgYKCgpgYvIiDIVCATMzM5ibm5dbuxKJhIlsNcPPm/TB44WIyHCIdrGXg4MDTExMkJSUpC5LTEyEs7MzjI35nAYiIiIi+m+iZYxSqRS+vr4ICQlBcnIyjh49CrlcDj8/PwAvRmdzcnLECo+IyGDxYTNEVF2IOvQZHBwMmUyGkSNHIjQ0FIGBgfD29gYAeHh44ODBg2KGR0RkkF5+2MzChQsRGRmJhISEYusXPmxGEIQKjJKIqPREfbKXVCpFeHg4wsPDtZalpaUVuc7gwYMxePDg8g6NiMggFT5sZuvWrZDJZJDJZLhx4wZiYmKKfGrirl27EB4ejmbNmuH58+ciRExEVHKiJrJERFS2invYTFRUFFQqldY1CIUPm3n+/DkiIyNL1Cbvz1098R7dpKvyvD83E1kioipEjIfN8P7c1RPv0U26Ks9jhYksEVEVIsbDZnh/7uqJ9+gmXZXn/bmZyBIRVSFiPGyG9+eunviZk67K81jhDVuJiKqQlx82U6giHjZDRCQGJrJERFUIHzZDRNUJezUioiqED5shouqEiSwRURXDh80QUXXBi72IiKoYPmyGiKoLjsgSERERkUFiIktEREREBomJLBEREREZJCayRERERGSQmMgSERERkUFiIktEREREBomJLBEREREZJCayRERERGSQmMgSERERkUFiIktEREREBomJLBEREREZJCayRERERGSQmMgSERERkUFiIktEREREBomJLBEREREZJCayRERERGSQmMgSERERkUFiIktEREREBomJLBEREREZJCayRERERGSQRE1kc3NzMWfOHLi5ucHDwwNyubzYur/88guGDh2Ktm3b4t1330VKSkoFRkpEZDjYtxJRdSFqIhsREYGUlBTs2LEDCxcuRGRkJBISErTqZWVlYfz48XBzc0NcXBxcXFwwYcIEZGVliRA1EVHlxr6ViKoL0RLZrKws7NmzB3PnzoVMJkOvXr0wduxYxMTEaNU9ePAgatasiVmzZqFly5aYO3cuateuXWTHTERUnbFvJaLqRLRENjU1FQUFBXBxcVGXubq64sqVK1CpVBp1r1y5AldXVxgZGQEAjIyM0L59eyQlJVVkyERElR77ViKqTkzEalihUMDCwgKmpqbqMmtra+Tm5iIzMxOWlpYadVu1aqWxvpWVFW7cuKFze4IgAADy8vIgkUj0ilUikeDNRvVgKjHSaz0Sl20DcyiVSiiVygprUyKRQNLADipj01dXpkpFYvVGiY6XwvqFfYzYKrJvZb9aPbFvJV1VRL8qWiKbnZ2t0dECUL/Oy8vTqe6/6/2XwpGIX375pSThYsCbtYA3a5VoXRKPKCNLrw8CXq/4Zqn00ktxvPx7tFMsFdm3sl+tvti3kq7Ku18VLZGtWbOmVmdZ+NrMzEynuv+u919MTEzg7OwMY2Nj9Wk0IqLSEgQBKpUKJiaidacaKrJvZb9KROVBn35VtJ7XxsYGGRkZKCgoUAeqUChgZmYGc3NzrbqPHz/WKHv8+DEaNmyoc3vGxsZaIw9ERFVNRfat7FeJSGyiXezl4OAAExMTjdMTiYmJ6m/3L2vbti0uX76snishCAJ+/vlntG3btiJDJiKq9Ni3ElF1IloiK5VK4evri5CQECQnJ+Po0aOQy+Xw8/MD8GIEIScnBwDQp08fPH36FEuXLsXNmzexdOlSZGdno2/fvmKFT0RUKbFvJaLqxEgQ8VLb7OxshISE4PDhw6hTpw7GjBmDUaNGAQDs7e0RFhaGwYMHAwCSk5OxcOFC3Lp1C/b29ggNDYWjo6NYoRMRVVrsW4mouhA1kSUiIiIiKilRH1FLRERERFRSTGSJiIiIyCAxkSUiIiIig8RE1gAdO3YMXbt2Rdu2bXHmzJkKafPevXuwt7fHvXv3KqQ9MhxeXl6Ii4sTOwyiUmG/SpUJ+1XdVY5H0ZBe1q1bBw8PDwQEBMDKykrscIiIDB77VSLDxETWAD179gyurq5o0qSJ2KEQEVUJ7FeJDBOnFhgYLy8v/PHHH5gzZw68vLzw559/YuLEiWjbti28vLwQGRkJpVIJAIiLi8OIESOwadMmdOjQAe7u7oiPj0dCQgK6d+8ONzc3rFixQr3thw8fYurUqejQoQOcnJwwaNAgJCYmFhnH06dP8emnn6J9+/bw8PDA4sWL1TdZp8qv8JTmyZMn4eXlBRcXFyxZsgS//vorBg8ejHbt2mHChAl4/vw58vLyEBYWhrfffhsymQxeXl7YvXt3kdsVBAEbNmyAh4cH3NzcMHHiRNy/f7+C3x2RftivUllgvyoOJrIGZu/evWjUqBHmzJmDvXv3YsqUKbCyssI333yDsLAw7N+/H1FRUer6ly9fRnp6Ovbu3Yt+/fohJCQEX3zxBTZt2oTZs2dj27Zt+OWXXwAAM2fOhFKpxK5duxAfHw8bGxuEhIQUGcfcuXPx7NkzfPXVV9i4cSOuXr2KRYsWVcQuoDK0ZcsWbNy4EYsXL8aXX36JKVOmYMaMGYiOjkZSUhL27t2LLVu24OTJk1i/fj0SEhLg6+uLxYsX4/Hjx1rb27lzJ/bv349Vq1Zh9+7dsLKygr+/P/Lz80V4d0S6Yb9KZYn9agUTyOB0795diI2NFX788Uehc+fOglKpVC87duyY0LFjR0EQBCE2NlZwdHQU/vnnH0EQBOHmzZuCnZ2d8OOPP6rrd+nSRdi/f7+gUqmE7du3C3/++ad62enTp4XWrVsLgiAI6enpgp2dnZCeni7cvXtXaN26tfD06VN13dTUVK0yqrwKP88zZ86oy7p06SJ89tln6tcff/yxMH/+fOHIkSPCTz/9pC7Pzc0V7Ozs1GWFx6MgCELXrl2FY8eOqesWFBQInTt31igjqozYr1JpsV8VB+fIGrBbt24hMzMTrq6u6jKVSoWcnBxkZGQAAKysrFCrVi0AQM2aNQEATZs2Vdc3MzNDXl4ejIyM8MEHH+DgwYP4+eefcefOHaSkpEClUhXZrkqlQteuXTXKVSoV7t69CycnpzJ/r1Q+mjVrpv7dzMxMY35g4bHRs2dPnD17FsuXL8ft27fVI02Fp1oL/fPPP3jw4AGmTZsGY+P/O9mTk5OD3377rXzfCFEZYb9KpcV+tWIxkTVgBQUFaNGiBTZu3Ki1rG7dugAAExPtj9jIyEirTKVSwd/fH0+fPoWPjw+8vLyQn5+PKVOmaNVVKpWoW7cuYmNjtZbZ2NiU5K2QSCQSicbrlzvKQmvWrMGePXswePBg+Pr6YuHChfDy8tKqV9gBr127Fs2bN9dYVq9evTKMmqj8sF+l0mK/WrE4R9aANW/eHPfv34elpSVsbW1ha2uLe/fuYd26dUV2qv/l5s2b+Omnn7B9+3ZMnDgR3bp1w6NHjwC8mGj+73afPXsGIyMjdbs5OTmIiIhAXl5emb0/qhx27dqF+fPnY+bMmfDx8UF2djYA7ePC3NwcVlZWUCgU6uPitddew4oVK3Dnzh0xQifSG/tVqgjsV8sOE1kD5uHhgSZNmuDTTz9FWloaLl26hPnz50MqlWp9I3wVc3NzGBsb48CBA/jjjz+QkJCA9evXA4BWJ9qyZUu8/fbbmDlzJpKTk3Ht2jUEBwcjKysL5ubmZfb+qHKoX78+Tpw4gfT0dFy6dAmzZs0CoH1cAMCoUaPw2Wef4fjx4/jtt98wb948/Pzzz2jRokVFh01UIuxXqSKwXy07TGQNmEQiwaZNm6BSqfDee+8hMDAQnp6emDdvnt7batSoEUJCQrB161b0798fW7Zswbx582BiYqKeu/OyiIgING3aFKNGjcLo0aPRvHlzrF69uizeFlUyy5Ytw/Xr19GvXz8EBwejT58+aNOmDa5fv65Vd8yYMRgyZAgWLFgAX19f3L9/H9HR0TwFRgaD/SpVBParZcdI+Pc4NhERERGRAeCILBEREREZJCayRERERGSQmMgSERERkUFiIktEREREBomJLBEREREZJCayRERERGSQmMgSERERkUFiIktEREREBomJLFUJ9vb2uHDhQpHLLly4AHt7+yJ/vLy8IAgCunXrhjVr1hS5/qZNm+Dj4wMAGDFiRLHbiouLAwDMnj0bs2fPLp83SkRUQdivkiEwETsAooryww8/aJVJJBIYGRnBx8cHhw8fxrRp07TqHDp0CP3791e/9vf3h7+/v1a9unXrlm3ARESVHPtVEhsTWao2GjRoUOyy/v37Izo6Gjdv3kSrVq3U5bdv30ZaWho2bNigLqtVq9Z/bouIqLpgv0pi49QCIgCOjo5o0aIFDh8+rFF+6NAhtG3bFs2aNRMpMiIiw8R+lSoCE1mi/69fv35aHW5CQoLG6S8iItId+1Uqb5xaQNWGi4uLVtmECRMwceJEAMCAAQOwfv16pKeno1mzZrh16xZu3bqlviCh0ObNmyGXy7W2dfny5fIJnIiokmK/SmJjIkvVRnx8vFZZvXr11L/b2trCyckJhw8fxpgxY3Do0CF06dIF1tbWGusMGzYMI0aMKO9wiYgqPfarJDYmslRt2NravrLOgAEDkJCQoO5wx44dq1WnXr16Om2LiKiqY79KYuMcWaKX9O3bF1evXsWlS5eQnp6OXr16iR0SEZFBY79K5YkjslRlJCcnIzc3V6OsQ4cO6t8VCkWR61lZWcHY+MV3OhsbG7i6umLJkiXo1q0b6tSpo1U/KyuryG1JpVJ1/YcPH+L06dMay19//XW88cYber0nIiIxsV+lyo6JLFUZK1eu1Cp7+WpZDw+PItc7deoUGjVqpH7dv39/zJ8/HwEBAUXWl8vlRV6UMGTIECxduhQA8OOPP+LHH3/UWD5x4sQibwxORFRZsV+lys5IEARB7CCIiIiIiPTFObJEREREZJCYyBIRERGRQWIiS0REREQGiYksERERERkkJrJEREREZJCYyBIRERGRQWIiS0REREQGiYksERERERkkJrJEREREZJCYyBIRERGRQWIiS0REREQG6f8B6EI1pf5fd18AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2)\n",
    "fig.suptitle(\"Log Loss and Max KS for Random Forest\")\n",
    "\n",
    "sns.barplot(data=dfs['groupMetricsData'], x=\"LEVEL\", y='_mcll_', ax=axes[0]).set(title=\"Multiclass Log Loss\")\n",
    "sns.barplot(data=dfs['groupMetricsData'], x=\"LEVEL\", y='_ks_', ax=axes[1]).set(title=\"Max KS\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:11:02.657117500Z",
     "start_time": "2023-07-17T17:11:02.527245Z"
    }
   },
   "outputs": [],
   "source": [
    "md_mcll = dfs['maxDifferencesData'][dfs['maxDifferencesData']['Metric'] == 'MCLL']['maxdiff'].values[0]\n",
    "md_ks = dfs['maxDifferencesData'][dfs['maxDifferencesData']['Metric'] == 'maxKS']['maxdiff'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:11:03.038467200Z",
     "start_time": "2023-07-17T17:11:02.542609700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y=[md_mcll, md_ks], x=['Multiclass Log Los', 'Max KS']).set(title=\"Maximum Difference for Random Forest\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Average Prediction for Event*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:11:03.462232Z",
     "start_time": "2023-07-17T17:11:03.037461400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=dfs['groupMetricsData'], x=\"LEVEL\", y='PREDICTED_EVENT').set(title=\"Predicted Probability of Pclass 1\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T17:11:03.896059200Z",
     "start_time": "2023-07-17T17:11:03.482102500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "md_pe = dfs['maxDifferencesData'][dfs['maxDifferencesData']['Metric'] == 'PREDICTED_EVENT']['maxdiff'].values[0]\n",
    "sns.barplot(y=[md_pe], x=['Predicted Probability of Pclass 1']).set(title=\"Maximum Difference\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "***"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}