efx_make_thin_files.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "252abb39",
   "metadata": {},
   "source": [
    "<h1 align=\"center\"> The Framework to calculate Richness v2 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32eaa678",
   "metadata": {},
   "source": [
    "<center>\n",
    "<img src=\"./static/framework0.png\">\n",
    "</center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a154ff9",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "028b3b27",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math,os,sys\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set_style(style= 'darkgrid')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "142d7b9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "CSV_PATH = r\"\"\"C:\\Users\\M1049231\\Downloads\\UCI_Credit_Card.csv\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b6ab157b",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw = pd.read_csv(CSV_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e43811e9",
   "metadata": {},
   "source": [
    "<p style=\"color:red;\">Columns in the dataset </p>\n",
    "<hr>\n",
    "<p style=\"color:gray;\">'ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', </p> \n",
    "<p style=\"color:gray;\">'PAY_0',\n",
    "       'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6',  </p>\n",
    "<p style=\"color:gray;\">'BILL_AMT1',\n",
    "       'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', </p>\n",
    "<p style=\"color:gray;\">'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5',\n",
    "       'PAY_AMT6',  </p>\n",
    "<p style=\"color:gray;\">'default.payment.next.month'</p>\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "c4e63457",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "819aae62",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"DEPRICATED: 23/ 25 columns will be used as features in an idea scenario\"\"\"\n",
    "# FEAT_COLUMNS = list(raw.columns.values)[1:-1] \n",
    "\n",
    "\"\"\"the Target variable\"\"\"\n",
    "TARGET_COLUMN = list(raw.columns.values)[-1]\n",
    "\n",
    "\"\"\"Financial data\"\"\"\n",
    "FEAT_COLUMN = list(raw.columns.values)[12:-1] \n",
    "\n",
    "\"\"\"Initialize feature importance of various features in FEAT_COLUMN\"\"\"\n",
    "FEAT_WEIGHT_MAP = {col:1/len(FEAT_COLUMN) for col in FEAT_COLUMN}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e3c07715",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import wraps\n",
    "from time import time\n",
    "\n",
    "def timing(f):\n",
    "    @wraps(f)\n",
    "    def wrap(*args, **kw):\n",
    "        ts = time()\n",
    "        result = f(*args, **kw)\n",
    "        te = time()\n",
    "        print(f'func: {f.__name__} took: {te-ts} sec')\n",
    "        return result\n",
    "    return wrap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f598c198",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4ce7f90e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<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>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>default.payment.next.month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3272.0</td>\n",
       "      <td>3455.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14331.0</td>\n",
       "      <td>14948.0</td>\n",
       "      <td>15549.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>28314.0</td>\n",
       "      <td>28959.0</td>\n",
       "      <td>29547.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20940.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>19131.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "0   1    20000.0    2          2         1   24      2      2     -1     -1   \n",
       "1   2   120000.0    2          2         2   26     -1      2      0      0   \n",
       "2   3    90000.0    2          2         2   34      0      0      0      0   \n",
       "3   4    50000.0    2          2         1   37      0      0      0      0   \n",
       "4   5    50000.0    1          2         1   57     -1      0     -1      0   \n",
       "\n",
       "   ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  \\\n",
       "0  ...        0.0        0.0        0.0       0.0     689.0       0.0   \n",
       "1  ...     3272.0     3455.0     3261.0       0.0    1000.0    1000.0   \n",
       "2  ...    14331.0    14948.0    15549.0    1518.0    1500.0    1000.0   \n",
       "3  ...    28314.0    28959.0    29547.0    2000.0    2019.0    1200.0   \n",
       "4  ...    20940.0    19146.0    19131.0    2000.0   36681.0   10000.0   \n",
       "\n",
       "   PAY_AMT4  PAY_AMT5  PAY_AMT6  default.payment.next.month  \n",
       "0       0.0       0.0       0.0                           1  \n",
       "1    1000.0       0.0    2000.0                           1  \n",
       "2    1000.0    1000.0    5000.0                           0  \n",
       "3    1100.0    1069.0    1000.0                           0  \n",
       "4    9000.0     689.0     679.0                           0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a1f833eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ssn(n:int, indv:int=10000):\n",
    "    \"\"\"Random generation of SSN in the range 1 to indv individuals \"\"\"\n",
    "    \n",
    "    print(f\"the ratio of indv to population is : {indv/n}\")\n",
    "    print(f\"the ratio of population to  indv is : {n/indv}\")\n",
    "    return [np.random.randint(1,(indv+1)) for i in range(n)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5a8b0220",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the ratio of indv to population is : 0.43333333333333335\n",
      "the ratio of population to  indv is : 2.3076923076923075\n"
     ]
    }
   ],
   "source": [
    "if 'SSN' in raw.columns:\n",
    "    if input('do you want to delete SSN columns (y/n): ') == 'y':\n",
    "        raw.drop('SSN',axis = 1, inplace = True)\n",
    "\n",
    "raw.insert(0,'SSN', get_ssn(n= len(raw), indv = 13000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "80f725d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b89fbb0df0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"having a look at the new distribution of SSIDs. Now we have duplicate ssids which means one-to-many \n",
    "mapping between individual and no. active credit cards. we also notice that the there are 2466 individuals with \n",
    "1 card and ~3k with 2 cards. these categories represent the thin files in our dataset \"\"\"\n",
    "\n",
    "counsdf = raw.groupby('SSN').size()\n",
    "g = sns.displot(counsdf , kind = 'hist', height = 5, aspect = 2)\n",
    "g.set_axis_labels(\"No of Credit cards\", \"No. of individuals\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3291ff8f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a8688d80",
   "metadata": {},
   "source": [
    "<h3> Calculating the richness of each datapoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e359257",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "09a776ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _row_richness(df, FEAT_COLUMN):\n",
    "    \"\"\"This funtion helps us assign richness to each data point in the a dataset FEAT_COLUMN \n",
    "    represents the Columns that impact our score the most, Future updates can include weights \n",
    "    illustrating the  importance of the \"\"\"\n",
    "    \n",
    "    full_health = len(FEAT_COLUMN)\n",
    "    health = sum(df[FEAT_COLUMN].values != 0)\n",
    "    return(health/full_health)                          \n",
    "    \n",
    "def new_row_richness(df, FEAT_WEIGHT_MAP:dict):\n",
    "    \"\"\"This funtion helps us assign richness to each data point in the a dataset FEAT_COLUMN \n",
    "    represents the Columns that impact our score the most, Future updates can include weights \n",
    "    illustrating the  importance of the \"\"\"\n",
    "    \n",
    "    magnitude = 0\n",
    "    for col in list(FEAT_WEIGHT_MAP.keys()):\n",
    "        if df[col]!=0:\n",
    "            magnitude += FEAT_WEIGHT_MAP[col]\n",
    "\n",
    "    return magnitude.__round__(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7f620cf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10483aa2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec22e9b5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "151eeded",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf2aea32",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "82032065",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "removed richness column, populating now...\n"
     ]
    }
   ],
   "source": [
    "if 'richness' in raw.columns:\n",
    "    print(\"removed richness column, populating now...\")\n",
    "    raw.drop('richness',axis = 1, inplace = True)\n",
    "# raw.insert(1,'richness', raw.apply(_row_richness,FEAT_WEIGHT_MAP=FEAT_WEIGHT_MAP ,axis = 1))\n",
    "raw.insert(1,'richness', raw.apply(_row_richness,FEAT_COLUMN=FEAT_COLUMN ,axis = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "525444e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of slim file SSN's : 2919\n",
      "number of slim files found : 2919\n"
     ]
    }
   ],
   "source": [
    "def _ssn_slim_file(counsdf, slimness:int = 1):\n",
    "    li = (counsdf[counsdf.values==slimness]).index.tolist()\n",
    "    return li\n",
    "\n",
    "SSN_SLIM_FILE = _ssn_slim_file(counsdf)\n",
    "print(f\"number of slim file SSN\\'s : {len(SSN_SLIM_FILE)}\")\n",
    "slim_file_df = raw[raw['SSN'].isin(SSN_SLIM_FILE)]\n",
    "print(f\"number of slim files found : {len(slim_file_df)}\")\n",
    "assert len(SSN_SLIM_FILE) == len(slim_file_df), \"check the value counts\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f5c13e19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b8a0068850>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"Having a look at the richness of out slim files to identify a cut of that might help\n",
    "in identifying any thin files in the dataset !! ['hist', 'kde', 'ecdf'] \"\"\"\n",
    "\n",
    "g = sns.displot(slim_file_df['richness'] , kind = 'hist', height = 5, aspect = 2, kde = True)\n",
    "g.set_axis_labels(\"Richness\", \"Population\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cd64f8ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files with richness less than 50% :264\n"
     ]
    }
   ],
   "source": [
    "print(f\"Files with richness less than 50% :{slim_file_df[slim_file_df['richness'] <0.5].shape[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8445d26",
   "metadata": {},
   "source": [
    "<h3 style=\"color:Green\"> Having a look at the richness of the whole population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0b798eff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SSN', 'richness', 'ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE',\n",
       "       'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6',\n",
       "       'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5',\n",
       "       'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5',\n",
       "       'PAY_AMT6', 'default.payment.next.month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ade37d3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b89fc7d640>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jJEnS6HKOK9/oAiRJktScHt83wqO7h3j6wBiTubirNceZKzs5b103HS25xhZ4DAZkSZIkzalKJeGe7ft5aNcgXa05tqzr5vQVHRwYK7Jt7wg/eeYgT+4f4epz19BWWHgh2YAsSZKkOTNeqvC7n32Ah3YNcu7aLi45fTnZTAaANd2tnN3Xxa4DY/zzw7u5I+7htS9c3eCKn8+ALEmStMT09HbQMouR24limQMDI8dtN16q8F++/CD3P32Aizf2smVdN5laOE5b19PGy89axTcf3cu3HtvHDeXKrOqvNwOyJEnSEtNSyHHzVx+ccfsbrtly3DZJkvCBbz7G/U8f4INvPp+fPbV/2vabVnYwesZyvvfEfv7+gZ28fGPvjOupN1exkCRJ0kn7+5/s4msPPcdbL9nAGy48dUaPOXdtN1e9sI9XvXBNnaubHQOyJEmSTsq/7TzALd/axkvPWMG1L9k4q8ee2tvO8s6WOlV2YgzIkiRJOmEDo0X+6B8fZt2yVv6f176AXPb5c44Xm7rNQQ4hXAz8aYzxihDCZuBTQAI8BFwXY6yEEN4HvA4oAe+MMd57rLb1qlOSJEkn7gPf3MrAaJEPvfFCutua4/K2uowghxD+EPg40FY7dStwY4zxMiADXBNCeBFwOXAx8BbgI8dqW48aJUmSdHLueGQ3/xL3cO2lGwmruxpdzpypV8zfBrwR+HTt9kXAnbXj24BXAxG4I8aYAE+FEPIhhL5jtP3KdE+Wy2Xo7e2Y2+9gBnK5bEOeV/Vn3zY3+7d52bfNayn2bVtbYVbtZ/vzOdmvv3twjD/71jbOP7WH333l2eRzR4671rv+eqpLQI4xfjmEcHrqVKYWhAEGgR5gGbAv1Wby/FRtp1UuJwzMYG2+udbb29GQ51X92bfNzf5tXvZt81pqfdvX183YWHFWj5nNz+dkv36SJFz/Dz9ltFjmxledxdDg2Jx+/fnS19c95fn5ukgvPYe4GxgADtaOjz4/VVtJkiQtEF976Fm++3g/1112BqevWDgjv3NlvgLyAyGEK2rHVwF3AXcDV4YQsiGEDUA2xrj3GG0lSZK0ADxzYIxb//VxLjqthzdfuL7R5dTFfF1q+C7gYyGEFuBh4EsxxnII4S7gHqpB/bpjtZ2nGiVJkjSNSpLwf38jksnAe68MZKfYRroZ1C0gxxi3A5fUjh+lumLF0W1uAm466tyUbSVJktRYX3jgGe7bcYAbX30W63vajv+ARcqNQiRJknRcj+8b5sN3PcFLz1jBL5+3ttHl1JUBWZIkSdMaK5a58euP0FHIceOVZ5Np0qkVk5pjuxNJkiTVzQdujzy2Z5gPveE8VnW2NLqcujMgS5Ik6Zh27B/l9kf28OYL1/PSTSsaXc68cIqFJEmSpjQ0XuLObfsIa7r53V/Y1Ohy5o0jyJIkSXqeUrnCHXEPpUrCR37jQlqbe9rxERxBliRJ0hGSJOHObf3sGy7yi2etYvPqqbdkblYGZEmSJB3hxzsP8vi+EX5+Qy8blrc3upx5Z0CWJEnSIT97dpAf7TjAmas6OH/90ho5nmRAliRJEgAPPzfE3U/sZ8Pydi4/c2XTr3d8LAZkSZIk8chzQ3z38X5O623jlWevIpddmuEYXMVCkiRpSaskCT98aoCfPDPIab1tvCr0LelwDAZkSZKkJWusWOZbj+1j54ExXrimi5ecvnzJh2MwIEuSJC1JO/aP8t3H+xkplrls0wpesKar0SUtGAZkSZKkJWTv0Dh/8i+Pcfsje+htz3P12WtY3d3a6LIWFAOyJEnSEnBgtMhn7nuaz9//DKUk4aLTejh//TKnVEzBgCxJkppOT28HLYXcjNtPFMscGBipY0WNs294gi88sJPPP/AMwxNlXnl2H++++hw++70nGl3agmVAliRJTaelkOPmrz444/Y3XLOljtU0xhP7RvjMfU9z28+eo1hO+MWzV/G2l2xk86pO+vqcbzwdA7IkSVKTSJKEH+0Y4DM/2sndT/TTms9y9Xlr+bUXncLGFR2NLm/RMCBLkiQtckmScNe2fXzsnid5+LkhlrcXuPbSjbzp/HUs72hpdHmLjgFZkiRpEds5MMYbPvo9frxjgFN62nj3q87iteesoTXvhsknyoAsSZK0CA2Ol/j+9v1s7x/llN52bnjVWfzSuWvI5wzGJ8uALEmStIhUkoSfPHOQ+58+CMDPb+jhb699CQf3N+cqHI1gQJYkSVokDo6V+PbWvTw3OMHpK9p5yenL6WrN05qf+ZJ2Oj4DsiRJ0iLw2J5h7n6iH4CXb17J5r7OBlfUvAzIkiRJC1ilknDP9v387Lkh1nS38vLNK+luM8LVkz9dSZKkBWq0WOZ/x708OzjOlnXdvHhjL9mMW0PXmwFZkiRpAdo/UuT2h3czWqw4pWKeGZAlSZIWmB9u7+drDz1LLpvh6vPW0NflZh/zyYAsSZK0gHzrsb28558fob2Q4zUvXM0y5xvPO3/ikiRJC8QXHniGW761lQs29HLe6k7aCi7f1ghutSJJktRgSZLwkbue4M++tZWXbVrB373tEsNxAzmCLEmSNEs9vR20zCLAThTLHBiYeqe7UrnC++94lK//bDev37KW6195Fu0thuNGMiBLkiTNUkshx81ffXDG7W+4ZsuU54cnSvzRPz7M97fv5+2XbuQ/XbKBjMu4NZwBWZIkqQH2DU/we195iEd3D3Hjq8/imi3rGl2SagzIkiRJ8+zxfcP83ld+Sv/wBLe8/lxetmllo0tSigFZkiRpHn1/ez9/9I8P05rP8j9+9ec4d92yRpekoxiQJUmS5kGSJHzxx7u49V+3csbKTm59w7msW9bW6LI0BQOyJElSnQ2Pl3jvbZHbH97Nyzat4P2vewGdLcawhcqekSRJqqP9I0Wu+cjdbNs9xNsv3chvXbyBXHZxrVRRKlfo6+tudBnzxoAsSZJUB5Uk4aFdg/zoqQMs7yzw4Tdt4cUblze6rBOSz2XnZFm7xWJeA3II4X7gYO3mE8BfA/8dKAF3xBj/JISQBT4KnA+MA2+LMW6dzzolSZJORv/IBN/Z1s+eoQk2LG/ni79zKdnxYqPL0gzNW0AOIbQBmRjjFalzPwb+PfA48PUQwoXAGUBbjPElIYRLgD8HrpmvOiVJUtVc7ha3VAxPlLh/x0Hi7iFa81leftZKzlzZwZplbezZY0BeLOZzBPl8oCOEcEfteW8CWmOM2wBCCN8AXgmsA24HiDF+P4Tw7+axRkmSVDNXu8UtBUPjJX767CA/fXaIJEk4Z20XF57aQ/ssXmBo4ZjPgDwC3AJ8HDgLuA0YSN0/CGwClgEHUufLIYR8jLF0rC+cy2Xo7e2Y84KPJ5fLNuR5VX/2bXOzf5uXfTv32toKs2pfr5//ifRtvWtvac2zc2CUh3YeZNveYUjgrDVdXHLGCnran//cC63+2X79erdfSH+78xmQHwW2xhgT4NEQwgFgRer+bqqBuaN2PCk7XTgGKJcTBhrwlk5vb0dDnlf1Z982N/u3edm3c6uvr5uxsdlNC6jXz3+2fVuv2kcmyvzkmQP8YOdBvnzf04wWK7TmsmxZ1805a7rpbqtGq6meeyHUfzJfv97tG/G3e6yVOeYzIL8V2AL85xDCeqpBeDiEcCbVOchXAn8CnApcDXyhNgd55u/tSJIkzaE9Q+P8eOdB/m3nAf5t50Ee2zNEOYH2Qo61y9o4Y0U7G5e3k89lG12q5tB8BuT/CXwqhPBdIKEamCvAZ4Ac1VUsfhBC+CHwqhDC94AM8FvzWKMkSVqiKknC4/tG+MnOA9VQ/MxBnjkwBkBbPst567r5zYs3cP76ZbzqglP44G0PN7hi1cu8BeQY4wTw61PcdclR7SrAb89LUZIkaclKkoR9I0WeHhjjtz55Lz/avp/B8eqszhUdBS44pYc3X7ie80/pIfR1HjFK3OEueE3N3pUkSUtGuZLw9MAoT/SP8vTAKKPFCgCbV3fxirNXcf4py7jglB5O6Wkjk1lcu91p7hiQJUlSU0uShGcPjhP3DLO9f4RiOaEll+G03nZO7W3j1N52bv6VC9izZ7DRpWqBMCBLkqSmNFYs88juYeLuIQ6OlSjkMpyxooNNKztY39NGLusIsaZmQJYkSU1le/8IH/rudj53/zOUKwlru1u58NRlbFrR4WoTmhEDsiRJWvSSJOHepwb47H07ufuJflryWc5c2cGWdd2s6GxpdHlaZAzIkiRp0RovVfjGI7v57H072bp3mBUdBa59yUaufcVZ/PU3H210eVqkDMiSJGnR6R+Z4Ms/3sWX/u0Z+keKbF7VyXuuPJsrX7Ca1nyWVV2tjS5Ri5gBWZIkLRpb9wzzuft3ctvDzzFRTnjpGSv49YtO4ec39Losm+aMAVmSJC1oQ+Mlbv/hDj5371P89NlBWvNZrj5vLW+58BROX9nR6PJmpFSu0NfX3egyNEMGZEmStOAMjpW46/F9fOvRvdyzvZ+JcsKmlR383hWbeO0L19DbUWh0ibOSz2W5+asPzrj9DddsqWM1Oh4DsiRJddLT20FLITfj9hPFMgcGRupY0cI1MFokPjfEfU8P8MOnBnj42UHKCazuauENP7eON/38BjZ2FZxGoXlhQJYkqU5aCjlHDWvGimUGRov0jxTZP1pk14ExdgyM8vTAGI/tGWLXwXEAchk4Z+0yfvPiDVx6+nK2rF9GNpOht7eDgSX64kHzz4AsSZJOSJIkTJQThsZLDE+U+dvvP8m2XQfYNzxB/0jxUCAeGCkyUiw/7/Gt+Syn9raxZd0yfuWCLsLqLs5Z201Xq/FEjeVvoCRJOq6JUoXdQ+PsGy7SP1INwAfHSpQqyaE233hkD7kMrOhsYXl7gRUdLZza286KjgK97YXa5xaWdxRYt6yVM0/ppbVlZlGkWK7U61uTnseALEmSnqdUrnD31r1874l+dh0cp3+keOi+zpYcKzoKrF/WRmdrjq7WPJ0tOd599XkwNkEuO7N5wq0t+RlPQWnm6SdaeAzIkiQJgJGJMvds7+fbW/dx9+P9DI6XyGUzrO1u5UWndrC2u5VVXS205rNTPn5tTxt7JopT3ictJgZkSZKWsH3DE3xn2z6+s20f9z65n4lyQk9bnss3r+SXX3Qq98TnyOemDsRSszIgS5K0xDy1f5Q7t+7l21v38eAzB0mA9T1tvOmC9Vy+eSU/t76HfDZDX183P9y6p9HlSvPOgCxJUpOrJAkPPzvIt7fu486t+3iiv7pc2gtWd3HtpRu5YvMqzlzVsaDXGHYnOs0nA7IkSU1oz9A4P3hyP/c+OcC9Tw2wb3iCXAZedFovb7pgHb9w5krWLmtrdJkz5k50mk8GZEmSmsDe4Ql+umuQH+0Y4AdP7ueJfdVR4uXtBV68sZdLz1jByzatYFnb4tqiWWoEA7IkacGa7VbNS2Gt3AOjRbb3j/Dk/lGe7B/lyf4RHtk9xHOD1Z3oWvNZLjhlGVefu4YXb1zOWX2dZBfw1AlpITIgS1ITm23AnCiWObCAtvNdals1j0yUeGr/KHuGxtk9NM6ewYnq56EJ9gyNs2NgjIHRw8uo5bMZTlvezs+tX8Z567o5Z003L1jTRdss+nwuOU9YzcKALElNbKkFzIVurFhmcLzE4HiZofESg2MlhiZKDI6VGZ4o8bF7nnreYzpbcvR1tdDX1coVm1eycUUHG5e3c/qKDtb1tJGf4aYc88F5wmoWBmRJ0pJVrxH2sWKZbftG2P3kAPds38/+kSL7R4qMFMtHtGvJZehqzbOsLc+6Za287oJT6MxAX1cLq7ta6etuoXOGWzFraXP0fm75VydJWrLmYoS9XEl4on+En+46yE+fHeShXYM8vneYclK9P5fNsLy9wCm9bazoKLCsLU93a56u1vzzdqS77uWb2bNncMb1nMgc7YKbfjQlR+/nlgFZkqRZ6B+Z4MFnBnlo10Ee2nWQnz07dGhkuLs1z7lru7ns4pW8YHUXLz57NZ/+7ra6XSR3IgHfECUdnwFZkqRjKFcS9g1PsHtogt2D49z+gW+xo38UqI4Mn93XyWvPWc1565Zx7rpuNixvPyIM962a3QoSvk0uLQwGZEnSkpckCaPFCv0jE+wfKdJfmzPcPzJxaKpEZ0uOXzi7jzect5Yt65bVZbUI3yaXFgYDsiSp6VWShKHxEruHJnju4Di7Do7x7OA4+8fL/ODxvQyMlhgvHV5Dub2QZXl7gXPWdrO6u5XVXS10tea54Zots5ojLGlxMiBLkhaVSpIwXqowWiwzVqwwXqowVqowXipz89d/xq7+EQ6MFjkwVjr0+eBYkUpy5NfJZzOs761OiThjRTvLO1pY0VFgeUeB9gatI6zGcXqL0gzIkqQTNtfLpJUrCXuGxtl5YIxnDowxUNrFt7fuY2SizGixfCgUJ8d4/IO7BlnWmqenvUBPe4Gz+jqrx23Vc6s6W1jT3cq6ZW2s7Gxh7Zpls5rSoObl9BalGZAlSSdstqsovPuXz2Pf8ATP1ALwMwfHjjh+9uA4pdRQbyYDHYUcnS05ulvzrO5qoa2Qo72Qo72Qpa2Qoy2fpTWfpS2f5X1vPN8pEJJOmgFZknTSkiShXEmYKCeMFsuMTJQZnqjuDjc8Ub09OF7mb3+0k9GjNstY0VFg3bI2Xrimm1ec3cf6njZOWdbG+p42zt20klu+/rMGfVeSlioDsiQ1idFimWcPjrN3eJy9wxPsHZpgpAL/+theJkoVyklCuVKdxlCpBdokATIwuRDZv27rp1yukM1AhgyZ2n3ZTO04k6ndpjr3t5zw3MExiuXK8+b4TmovZOlsydPTlueV565leSHL+p62Qx/TzfdtzTsXWNL8MyBL0iJTKld4cv8o2/YO1z5G2LZvmJ0DY8+bm9tWyFLIZmnNZ8hlM+QyGVoKWbKZ6u1MBiYflABhTTejY0USqqPCSVK9KK56GxISKkn1vt72AiuXtfHoroMUclla8hlactXpDl2teTpbcnQUcmSzh9cBvuGXzqnrFAgvtJI0FwzIkpaebGZWIep4F5bVSyVJ2HVwrBqAU2F4e//IoXm6uQxsWN7BC1Z38dpz1nBabzurOluqH10tnH5KL//1aw/N+Dlnu4xZX1/3grrIzQutJM0FA/LJWCT/yUo6UmGBhahSucLOA2M8tX+UHQOjPL6vGogf3ztyaAtjgHXLWjlzVSeXnrGCzX0dnLmyk40rOmjNZ4/5tTN12uJYkpqZAfkkLLT/ZLV4zPXSWPNtsddfb5M/n6S2Xu/ASJE9g+PsHhyrfR5nz+A4O/aPsH3vMDv2j1JOTeDtbS+weVUHV5+3hk2rOtm8qpNNKzvoavWfbEmaD/5rKzXAbJfGWmgvrhZ7/UdLr8AwUa4wUaowXq5QLCeUKhU+/f0n2bt/pLYGb3Ud3uqavBXGStVzk8ejxQoT5YQDIxOUKskx1+ttzWfpasnR017gdy4/k1WtOTYsb+e05e30thfm9fuXJB3JgHyCkqR65fbIRJlspnqFd7Z2hffk54XOUUDVQ1K7oKtSSagAWWC8VKZUrpDNZsjO4d9GkiQUa6F2rFhmaLzM0ESJ4aM+D41XlxobGi8xOF5mIkmIuwYPheGJaVZgALhza/+h40IuQ3tt7d30erzdrXn6ulpoL+To7W7jwR37KWQz5HPVC9c6ahesdbRUH5NLXbj2B1cG1+5dJLwIUFoaFmRADiFkgY8C5wPjwNtijFsbW9WRPnv/Tj747cenvC8Dh/5TLOSytOQyFHJZnh68j1yS0NlSXfS+oyVPe20B/PaWHJ2F6ufJ/0jbCllytSvN87WP6lXnGSpJQqWSUK5dYV69DeVDx9X70ss5VRJqyzxVzy0fr/DJ72ytLeN0VMDn8PHkle83vn5hjwI2QqlcYaQ2ejg6UWa0VE7t+FU9N1I8vAPY5LlyNsNPduyvjlCWE8q11QISJj9XVwyA6nJa33l8PyTJEb8Lk78P+WyGfC575O0j7s+Szx0+l8lUfy/KlYRShUO/H+VKtY5SpfZRG0EtTn6uHL6dZDLsGhitPbb2NVK/d0f75L1PH3F78kVlLltdMixXC86Z1HJjk8uJVY8Pn6tUaqO8tbpmqqOQo6s1R2drnhVdrbQXsvS05WnJZ2lJrcBQPc7W/n4zFHIZ3nXVOQwfHKWtkCOfPX7AX2gXrs2GAXB6XgQoLQ0LMiADrwfaYowvCSFcAvw5cE1jSzrSa89Zw9qVnXzt/qepTC6DlBwOG5OjWsVy9e3WYrnC1t1DHBiZYKRYZni8fMy3XheqT9y7g5ZaEJsMDy356ouAQvbw8eQLgiOPD79gyGUzJEly6OcGHFo2avJzwmTwByZfBHDkslP5Qo5MJnPo9uE+mPxaR769Xa4klGoXPB3xs0/Sh9Ub+XyO8YkS46Vq343XRhqL5QrjtRHHiXJyxLzR48lloK02gtjdVmB4okwhm6WttuRWtvqqpPbi5HBQrCRw+qoORkaLh8NrJWG0WF3XtlSuHDpXrhwZcKv3Hz53dD3Z2oufXCpQTwbXdD8Wasdt+epIaWd7gYli+YgXUNXRYQ4d52qBt5LAFS9cw+DQGJXK4b4qJ5NTG47svwQotOTJ1q47m+oFQ2s+Vw2xtR3UshnIlMp0teTpbM3R1ZKvLjNWO+5oOXLEdrYBdvWyNvaMF2fcfrZmG0rr+W5OvQOgAVzSYrBQA/LLgNsBYozfDyH8uwbX8zy97QXe/PMb2PrMgRk/Jr180uTFOyO1HacOfRQPfx4rVg6N+FaDT+XQSHA1hNRG4VLhpBq0MuSyRwaVbO32ZADKZjL09LTz+e9vn3K903TILNdGpi89q4+Bg2MUK9XQOPnWdKmS1MJj9UXByESFYrn0vBcIk/eXK8mhkcNM5vAGBNlD4fDwaHa6zdEj2/l8jv6h8UMj4HA4WFZHHGvnaj//9cvbKZUqh/oj/U7/kWOCGfJJNSj0tOZoTQWxyWDWms/SWqje7mzN05LLkCmWawE4W3sH4PDb6e2FXLVN7UlnG9Bmu/TWVNNn0i9K8tnMEdOAZhu4Zlv/dS/ffOJLh001YFspU54oMzoBo8D1v3Qu+dyxV3I4WfUOdUtpVHIpfa+SFq9Mksx8BGy+hBA+Dnw5xnhb7fZTwKYYY+kYD9kDPDlf9UmSJKkp7AVec/TJhTqCfBBID9dkpwnHAH11rkeSJElLRP3ekzw5dwOvBajNQV6cV7tIkiRp0VmoI8hfAV4VQvge1RmIv9XgeiRJkrRELMg5yJIkSVKjLNQpFpIkSVJDGJAlSZKkFAOyJEmSlLJQL9JbUI639XUI4f8C3g6UgPfHGP+pIYVq1mbQt78HvKV2859jjH8y/1XqRMxky/pam68DX40x/o/5r1InagZ/u1cB76N6ofd9wHUxRi+6WQRm0LfvAn4dqAD/Ncb4lYYUqhMWQrgY+NMY4xVHnb8aeC/VPPWJGOPHGlAe4AjyTL2e2tbXwB9R3foagBDCWuC/AC8FrgT+WwihtRFF6oS8nmP37SbgN4BLgUuAV4cQfq4RReqEvJ5j9G3K+4Hl81mU5szrOfbfbjfwZ8AvxRgvBrYDqxpQo07M6zl23/YC7wBeArwa+ND8l6eTEUL4Q+DjQNtR5wvAB6n26+XAtSGENfNfYZUBeWaO2PoaSG99/WLg7hjjeIzxALAVMEQtHtP17Q7gNTHGcm3kqQCMzX+JOkHT9S0hhDdRHYG6ff5L0xyYrn8vpbp+/p+HEO4Cnosx7pn/EnWCpuvbYao753bWPirzXp1O1jbgjVOcfyGwNca4P8Y4AXwX+IV5rSzFgDwzy4ADqdvlEEL+GPcNAj3zVZhO2jH7NsZYjDHuDSFkQgi3AA/EGB9tSJU6Ecfs2xDCeVTfon1vIwrTnJju3+VVwMuB64GrgHeGEM6e5/p04qbrW6gOXvwMuB/4i/ksTCcvxvhloDjFXQsqTxmQZ2a6ra+Pvq8bGJinunTypt3WPITQBnym1uY/z3NtOjnT9e3/CZwCfAv4j8DvhxBeM7/l6SRN17/7gB/GGJ+NMQ4B3wEumOf6dOKm69urgHXAGcAG4PUhhBfPc32qjwWVpwzIMzPd1tf3ApeFENpCCD1U3yJ4aP5L1Ak6Zt+GEDLAV4F/izG+PcZYbkyJOkHH7NsY4x/GGC+uXSDyKeDWGKNTLRaX6f5dvh84L4SwqjbyeAnVEUctDtP17X5gFBiPMY5RDVC981yf6uNh4KwQwooQQgvV6RX3NKoYV7GYmedtfR1C+H2qc2W+FkL4C+Auqi84bqj90WpxOGbfAjmqFwq01q6IB/jjGGPD/mA1K9P+3Ta2NM2B4/27/MfAN2ptvxBjdOBi8The374S+H4IoUJ1nuq/NLBWnaQQwq8DXTHGv6n18zeo5qlPxBh3Nqout5qWJEmSUpxiIUmSJKUYkCVJkqQUA7IkSZKUYkCWJEmSUgzIkiRJUooBWZIWuBDC309z37dDCC+Yz3okqdkZkCVpgYsxvrHRNUjSUuI6yJK0AIUQ/iPwVqoDGSHG2BdCuBj4UO3cTuA3gNuAXcAaoBP4Napb8F4PTACbgM/FGG8OIZwG/A3QTnU3smuBPcAXgB6gg+pmR3eEED4JbK61/e8xxk/Pw7ctSQuCI8iStHDtjzG+DJjc5vyvgbfGGC8Gvk51a3uAr8cYf5FqWH5T7dxG4N9T3Wb5D2vnbgH+orbF9i3A/wucCawCrqYarvMhhG6q27y+EXhN6vklaUkwIEvSwhWPur02xvgwQIzxf8YY76+dv6/2+Vmqo8AAD8YYSzHGYaqjxQBbgHeHEL4NvBdYE2P8KdXg/Vngo0A2xjgIvJPqaPPngda5/sYkaSEzIEvSwlU56vYzIYSzAEII14cQ3lA7P9VcuanOPQJcXxtBfjvwxRDCFqA7xvg64DeBvwwhrAMuijG+AXgd8IEQQv7kvx1JWhz8B0+SFo+3A58IIVSozjv+EPCOWTz+D4C/CiG0UZ1b/A7gMeB9IYRfpTpo8l6qI9FrQwjfozq94pYYY2nOvgtJWuC8SE+SJElKcYqFJEmSlGJAliRJklIMyJIkSVKKAVmSJElKMSBLkiRJKQZkSZIkKcWALEmSJKX8/2dxZcvQw1fxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" Individuals with only one credit line is considered for this experiment, but we can also consider individuals for 2 > too\n",
    "which shows that the inactive nature of the multiple lines of credit.\"\"\"\n",
    "\n",
    "raw_richness_map = raw.groupby('SSN')['richness'].mean()\n",
    "sns.displot(raw_richness_map, kind=\"hist\", kde=True, height = 5, aspect = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d9b0159e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files with richness less than 50% :  462\n"
     ]
    }
   ],
   "source": [
    "print(f\"Files with richness less than 50% :  {raw_richness_map[raw_richness_map<0.5].shape[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f49fd30",
   "metadata": {},
   "source": [
    "<h3 style=\"color:green;\">The alternate data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bcf10f68",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_data_quad(rows):\n",
    "    x_array = []\n",
    "    id = 1\n",
    "    while len(x_array) < rows:\n",
    "        a = float(np.random.randint(-10, 10))\n",
    "        b = float(np.random.randint(-10, 10))\n",
    "        c = float(np.random.randint(-10, 10))\n",
    "        y = [0, 0]\n",
    "\n",
    "        try:\n",
    "            y = [\n",
    "                (-b + math.sqrt((b * b) - (4 * a * c))) / (2 * a),\n",
    "                (-b - math.sqrt((b * b) - (4 * a * c))) / (2 * a)]\n",
    "        except (ValueError, ZeroDivisionError):\n",
    "            pass\n",
    "\n",
    "        x_array.append([id, a, b, c, abs(y[0] - y[1])])\n",
    "        id += 1\n",
    "    return np.array(x_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c7deb02a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _add_list_to_df(df:pd.DataFrame,li:list):\n",
    "    \n",
    "    df_ix = len(df)\n",
    "    for row in li:\n",
    "        df.loc[df_ix] = row\n",
    "        df_ix += 1\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ce94d29",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "05534f54",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = generate_data_quad(13000)\n",
    "alt_df_raw = pd.DataFrame(columns=['SSN', 'x1', 'x2', 'x3', 'x4'])\n",
    "alt_df_raw = _add_list_to_df(alt_df_raw, x)\n",
    "alt_df_raw.SSN = alt_df_raw.SSN.astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ee4dd8b5",
   "metadata": {},
   "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>SSN</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>3.480102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SSN   x1   x2    x3        x4\n",
       "0    1  8.0 -8.0  -6.0  2.000000\n",
       "1    2  8.0 -1.0   2.0  0.000000\n",
       "2    3 -9.0  6.0 -10.0  0.000000\n",
       "3    4  3.0  1.0  -9.0  3.480102\n",
       "4    5 -1.0 -2.0   0.0  2.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt_df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ced13142",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bf22d730",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='x4', ylabel='Density'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,2,figsize=(13, 7))\n",
    "for i in [ 'x1', 'x2', 'x3']:\n",
    "    sns.kdeplot(alt_df_raw[i], ax = ax[0])\n",
    "sns.kdeplot(alt_df_raw.x4, ax = ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a6aafea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bb12a9c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _induce_null(col:list, df:pd.DataFrame, prob = 10):\n",
    "    for i in range(len(col)):\n",
    "        rand = np.random.randint(0,100)\n",
    "        if rand>prob:\n",
    "            df[col[i]] = 0\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d64566d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "alt_df_raw.insert(1,'alt_richness', alt_df_raw.apply(_row_richness,FEAT_COLUMN=['x1', 'x2', 'x3', 'x4'] ,axis = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "706d8282",
   "metadata": {},
   "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>SSN</th>\n",
       "      <th>alt_richness</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.75</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.75</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>3.480102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SSN  alt_richness   x1   x2    x3        x4\n",
       "0    1          1.00  8.0 -8.0  -6.0  2.000000\n",
       "1    2          0.75  8.0 -1.0   2.0  0.000000\n",
       "2    3          0.75 -9.0  6.0 -10.0  0.000000\n",
       "3    4          1.00  3.0  1.0  -9.0  3.480102\n",
       "4    5          0.75 -1.0 -2.0   0.0  2.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt_df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7f387ca9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b8a01d2d30>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alt_ssn_richness = alt_df_raw.groupby('SSN')['alt_richness'].mean()\n",
    "sns.displot(alt_ssn_richness, kind=\"hist\", kde=True,height = 5, aspect = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9c889add",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files with richness less than 50% :  87\n"
     ]
    }
   ],
   "source": [
    "print(f\"Files with richness less than 50% :  {alt_ssn_richness[alt_ssn_richness<0.5].shape[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ab2cc30f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(alt_ssn_richness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f53a0368",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "de832f2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _generate_weighted_richness(df_raw, alt_ssn_map:dict, alt_col:str = \"alt_richness\", raw_col:str = \"richness\", weightage:dict={\"raw\":0.5,\"alt\":0.5}):\n",
    "    # print(df_raw)\n",
    "    # import pdb;pdb.set_trace()\n",
    "    res = alt_ssn_map[df_raw['SSN']]*weightage['alt'] + df_raw[raw_col]*weightage['raw']\n",
    "    return [df_raw['SSN'], df_raw[raw_col], res] #['SSN',\"baseline_richness\",\"new_richness\"]\n",
    "\n",
    "def generate_weighted_richness_wrapper(raw,alt_ssn_richness):\n",
    "    res = []\n",
    "    generate = partial(_generate_weighted_richness, alt_ssn_map=alt_ssn_richness.to_dict())\n",
    "    for i in raw.index:\n",
    "        res.append(generate({'SSN':i,'richness':raw[i]}))\n",
    "    return res\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b9b46467",
   "metadata": {},
   "outputs": [],
   "source": [
    "# raw_richness_map.apply(_generate_weighted_richness, alt_ssn_map = alt_ssn_richness.to_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6f66fcc2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "_ = generate_weighted_richness_wrapper(raw = raw_richness_map, alt_ssn_richness=alt_ssn_richness)\n",
    "merged_richness = pd.DataFrame(_, columns= ['SSN',\"baseline_richness\",\"new_richness\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b86de7d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='new_richness', ylabel='Density'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(13, 7))\n",
    "sns.kdeplot(merged_richness[\"new_richness\"], ax = ax)\n",
    "sns.kdeplot(merged_richness[\"baseline_richness\"], ax = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8639f30f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files with BASELINE richness less than 50% :  462\n",
      "Files in MERGED dataset with richness less than 50% :114\n"
     ]
    }
   ],
   "source": [
    "print(f\"Files with BASELINE richness less than 50% :  {raw_richness_map[raw_richness_map<0.5].shape[0]}\")\n",
    "print(f\"Files in MERGED dataset with richness less than 50% :{merged_richness[merged_richness['new_richness'] <0.5].shape[0]}\")"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "db954a3dfd3e2d6d0f27bd3bcb2986fe8f335d9177c092fd9f93a298a3366a1b"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}