inspect_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "1f65f367",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Get the packages we need to manipulate the dataset \n",
    "\"\"\"\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import os \n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn import metrics\n",
    "from tqdm import tqdm\n",
    "from datatools import make_clean_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "94c93ca3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATA_PATH is a valid path\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Specify the directories for data. These are universal for our repository-\n",
    "this is the only thing you should not change.\n",
    "\"\"\"\n",
    "\n",
    "#rewrite this in the preferred way using os.join and os.path \n",
    "\n",
    "DATA_DIR = './' + 'raw_data'\n",
    "\n",
    "DATA_NAME = '/CreditCard_data.csv'\n",
    "\n",
    "\"\"\"\n",
    "The sample Dataset summarizes the usage behavior of about\n",
    "9000 active credit card holders during the last 6 months\n",
    "\"\"\"\n",
    "\n",
    "DATA_PATH = DATA_DIR + DATA_NAME\n",
    "\n",
    "os.listdir(DATA_DIR)\n",
    "\n",
    "if os.path.isfile(DATA_PATH): \n",
    "    print('DATA_PATH is a valid path')\n",
    "else:\n",
    "    raise ValueError('DATA_PATH is not valid path')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5101c310",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the data is : (8950, 18) \n",
      "\n",
      "The features of the data:\n",
      " Index(['CUST_ID', 'BALANCE', 'BALANCE_FREQUENCY', 'PURCHASES',\n",
      "       'ONEOFF_PURCHASES', 'INSTALLMENTS_PURCHASES', 'CASH_ADVANCE',\n",
      "       'PURCHASES_FREQUENCY', 'ONEOFF_PURCHASES_FREQUENCY',\n",
      "       'PURCHASES_INSTALLMENTS_FREQUENCY', 'CASH_ADVANCE_FREQUENCY',\n",
      "       'CASH_ADVANCE_TRX', 'PURCHASES_TRX', 'CREDIT_LIMIT', 'PAYMENTS',\n",
      "       'MINIMUM_PAYMENTS', 'PRC_FULL_PAYMENT', 'TENURE'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Define size of data sample to read-in. Use to_skip to define the logic. \n",
    "\n",
    "Import data as pandas dataframe for that format \n",
    "\"\"\"\n",
    "\n",
    "SAMPLE_SIZE = 10000\n",
    "\n",
    "def to_skip(index): \n",
    "    \"\"\"\n",
    "    Function to be used with pandas skiprows\n",
    "    \"\"\"\n",
    "    keeprow = False\n",
    "    \n",
    "    if index <= SAMPLE_SIZE:\n",
    "        keeprow = True\n",
    "    else:\n",
    "        keeprow = False\n",
    "    \n",
    "    return keeprow\n",
    "\n",
    "data_raw = pd.read_csv(DATA_PATH,nrows = SAMPLE_SIZE)\n",
    "                                              \n",
    "print('Shape of the data is :', data_raw.shape, '\\n')\n",
    "print('The features of the data:\\n', data_raw.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c738b437",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "  CUST_ID      BALANCE  BALANCE_FREQUENCY  PURCHASES  ONEOFF_PURCHASES  \\\n0  C10001    40.900749           0.818182      95.40              0.00   \n1  C10002  3202.467416           0.909091       0.00              0.00   \n2  C10003  2495.148862           1.000000     773.17            773.17   \n3  C10004  1666.670542           0.636364    1499.00           1499.00   \n4  C10005   817.714335           1.000000      16.00             16.00   \n5  C10006  1809.828751           1.000000    1333.28              0.00   \n6  C10007   627.260806           1.000000    7091.01           6402.63   \n7  C10008  1823.652743           1.000000     436.20              0.00   \n8  C10009  1014.926473           1.000000     861.49            661.49   \n9  C10010   152.225975           0.545455    1281.60           1281.60   \n\n   INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n0                   95.40      0.000000             0.166667   \n1                    0.00   6442.945483             0.000000   \n2                    0.00      0.000000             1.000000   \n3                    0.00    205.788017             0.083333   \n4                    0.00      0.000000             0.083333   \n5                 1333.28      0.000000             0.666667   \n6                  688.38      0.000000             1.000000   \n7                  436.20      0.000000             1.000000   \n8                  200.00      0.000000             0.333333   \n9                    0.00      0.000000             0.166667   \n\n   ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n0                    0.000000                          0.083333   \n1                    0.000000                          0.000000   \n2                    1.000000                          0.000000   \n3                    0.083333                          0.000000   \n4                    0.083333                          0.000000   \n5                    0.000000                          0.583333   \n6                    1.000000                          1.000000   \n7                    0.000000                          1.000000   \n8                    0.083333                          0.250000   \n9                    0.166667                          0.000000   \n\n   CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  PURCHASES_TRX  CREDIT_LIMIT  \\\n0                0.000000                 0              2        1000.0   \n1                0.250000                 4              0        7000.0   \n2                0.000000                 0             12        7500.0   \n3                0.083333                 1              1        7500.0   \n4                0.000000                 0              1        1200.0   \n5                0.000000                 0              8        1800.0   \n6                0.000000                 0             64       13500.0   \n7                0.000000                 0             12        2300.0   \n8                0.000000                 0              5        7000.0   \n9                0.000000                 0              3       11000.0   \n\n      PAYMENTS  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT  TENURE  \n0   201.802084        139.509787          0.000000      12  \n1  4103.032597       1072.340217          0.222222      12  \n2   622.066742        627.284787          0.000000      12  \n3     0.000000               NaN          0.000000      12  \n4   678.334763        244.791237          0.000000      12  \n5  1400.057770       2407.246035          0.000000      12  \n6  6354.314328        198.065894          1.000000      12  \n7   679.065082        532.033990          0.000000      12  \n8   688.278568        311.963409          0.000000      12  \n9  1164.770591        100.302262          0.000000      12  ",
      "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>CUST_ID</th>\n      <th>BALANCE</th>\n      <th>BALANCE_FREQUENCY</th>\n      <th>PURCHASES</th>\n      <th>ONEOFF_PURCHASES</th>\n      <th>INSTALLMENTS_PURCHASES</th>\n      <th>CASH_ADVANCE</th>\n      <th>PURCHASES_FREQUENCY</th>\n      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n      <th>CASH_ADVANCE_FREQUENCY</th>\n      <th>CASH_ADVANCE_TRX</th>\n      <th>PURCHASES_TRX</th>\n      <th>CREDIT_LIMIT</th>\n      <th>PAYMENTS</th>\n      <th>MINIMUM_PAYMENTS</th>\n      <th>PRC_FULL_PAYMENT</th>\n      <th>TENURE</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>C10001</td>\n      <td>40.900749</td>\n      <td>0.818182</td>\n      <td>95.40</td>\n      <td>0.00</td>\n      <td>95.40</td>\n      <td>0.000000</td>\n      <td>0.166667</td>\n      <td>0.000000</td>\n      <td>0.083333</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1000.0</td>\n      <td>201.802084</td>\n      <td>139.509787</td>\n      <td>0.000000</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>C10002</td>\n      <td>3202.467416</td>\n      <td>0.909091</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>6442.945483</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.250000</td>\n      <td>4</td>\n      <td>0</td>\n      <td>7000.0</td>\n      <td>4103.032597</td>\n      <td>1072.340217</td>\n      <td>0.222222</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>C10003</td>\n      <td>2495.148862</td>\n      <td>1.000000</td>\n      <td>773.17</td>\n      <td>773.17</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>12</td>\n      <td>7500.0</td>\n      <td>622.066742</td>\n      <td>627.284787</td>\n      <td>0.000000</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>C10004</td>\n      <td>1666.670542</td>\n      <td>0.636364</td>\n      <td>1499.00</td>\n      <td>1499.00</td>\n      <td>0.00</td>\n      <td>205.788017</td>\n      <td>0.083333</td>\n      <td>0.083333</td>\n      <td>0.000000</td>\n      <td>0.083333</td>\n      <td>1</td>\n      <td>1</td>\n      <td>7500.0</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>C10005</td>\n      <td>817.714335</td>\n      <td>1.000000</td>\n      <td>16.00</td>\n      <td>16.00</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.083333</td>\n      <td>0.083333</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1200.0</td>\n      <td>678.334763</td>\n      <td>244.791237</td>\n      <td>0.000000</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>C10006</td>\n      <td>1809.828751</td>\n      <td>1.000000</td>\n      <td>1333.28</td>\n      <td>0.00</td>\n      <td>1333.28</td>\n      <td>0.000000</td>\n      <td>0.666667</td>\n      <td>0.000000</td>\n      <td>0.583333</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>8</td>\n      <td>1800.0</td>\n      <td>1400.057770</td>\n      <td>2407.246035</td>\n      <td>0.000000</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>C10007</td>\n      <td>627.260806</td>\n      <td>1.000000</td>\n      <td>7091.01</td>\n      <td>6402.63</td>\n      <td>688.38</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>64</td>\n      <td>13500.0</td>\n      <td>6354.314328</td>\n      <td>198.065894</td>\n      <td>1.000000</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>C10008</td>\n      <td>1823.652743</td>\n      <td>1.000000</td>\n      <td>436.20</td>\n      <td>0.00</td>\n      <td>436.20</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>12</td>\n      <td>2300.0</td>\n      <td>679.065082</td>\n      <td>532.033990</td>\n      <td>0.000000</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>C10009</td>\n      <td>1014.926473</td>\n      <td>1.000000</td>\n      <td>861.49</td>\n      <td>661.49</td>\n      <td>200.00</td>\n      <td>0.000000</td>\n      <td>0.333333</td>\n      <td>0.083333</td>\n      <td>0.250000</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>5</td>\n      <td>7000.0</td>\n      <td>688.278568</td>\n      <td>311.963409</td>\n      <td>0.000000</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>C10010</td>\n      <td>152.225975</td>\n      <td>0.545455</td>\n      <td>1281.60</td>\n      <td>1281.60</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.166667</td>\n      <td>0.166667</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>3</td>\n      <td>11000.0</td>\n      <td>1164.770591</td>\n      <td>100.302262</td>\n      <td>0.000000</td>\n      <td>12</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Examine a sample of the data\n",
    "\"\"\"\n",
    "data_raw.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8788e366",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the features related to $$:\n",
      " ['BALANCE', 'PURCHASES', 'ONEOFF_PURCHASES', 'INSTALLMENTS_PURCHASES', 'CASH_ADVANCE', 'CREDIT_LIMIT', 'PAYMENTS', 'MINIMUM_PAYMENTS', 'PRC_FULL_PAYMENT'] \n",
      "\n",
      "Here are the features related to frequency: \n",
      " ['BALANCE_FREQUENCY', 'PURCHASES_FREQUENCY', 'ONEOFF_PURCHASES_FREQUENCY', 'PURCHASES_INSTALLMENTS_FREQUENCY', 'CASH_ADVANCE_FREQUENCY'] \n",
      "\n",
      "Here are the features not used: \n",
      " {'TENURE', 'CUST_ID', 'CASH_ADVANCE_TRX', 'PURCHASES_TRX'}\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Define the features desired to keep, two group of features (in different scale) available:\n",
    "1. Absolute amount in dollar\n",
    "2. Rating of frequency \\in R([0,1])\n",
    "\"\"\" \n",
    "\n",
    "features = data_raw.columns\n",
    "\n",
    "#Here are the features related to the absolute amount of money\n",
    "#Some of them can be used as LABEL\n",
    "ft_basic =['BALANCE', 'PURCHASES',\n",
    "       'ONEOFF_PURCHASES', 'INSTALLMENTS_PURCHASES', 'CASH_ADVANCE',\n",
    "       'CREDIT_LIMIT', 'PAYMENTS',\n",
    "       'MINIMUM_PAYMENTS', 'PRC_FULL_PAYMENT']\n",
    "# the last one is the percent of full payment being paid\n",
    "# ft_basic_keep = ft_basic[:]\n",
    "\n",
    "#Here are the features related to the frequency\n",
    "ft_freq= ['BALANCE_FREQUENCY','PURCHASES_FREQUENCY', 'ONEOFF_PURCHASES_FREQUENCY',\n",
    "       'PURCHASES_INSTALLMENTS_FREQUENCY', 'CASH_ADVANCE_FREQUENCY']\n",
    "\n",
    "#Here are the features haven't been used\n",
    "ft_unused = set(list(features))-set(ft_basic)-set(ft_freq)\n",
    "\n",
    "print('Here are the features related to $$:\\n', ft_basic,'\\n')\n",
    "print('Here are the features related to frequency: \\n',ft_freq,'\\n')\n",
    "print('Here are the features not used: \\n', ft_unused)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "70560f24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "       BALANCE  PURCHASES  ONEOFF_PURCHASES  INSTALLMENTS_PURCHASES  \\\n0    40.900749      95.40              0.00                   95.40   \n1  3202.467416       0.00              0.00                    0.00   \n2  2495.148862     773.17            773.17                    0.00   \n3  1666.670542    1499.00           1499.00                    0.00   \n4   817.714335      16.00             16.00                    0.00   \n5  1809.828751    1333.28              0.00                 1333.28   \n6   627.260806    7091.01           6402.63                  688.38   \n7  1823.652743     436.20              0.00                  436.20   \n8  1014.926473     861.49            661.49                  200.00   \n9   152.225975    1281.60           1281.60                    0.00   \n\n   CASH_ADVANCE  CREDIT_LIMIT     PAYMENTS  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT  \n0      0.000000        1000.0   201.802084        139.509787          0.000000  \n1   6442.945483        7000.0  4103.032597       1072.340217          0.222222  \n2      0.000000        7500.0   622.066742        627.284787          0.000000  \n3    205.788017        7500.0     0.000000               NaN          0.000000  \n4      0.000000        1200.0   678.334763        244.791237          0.000000  \n5      0.000000        1800.0  1400.057770       2407.246035          0.000000  \n6      0.000000       13500.0  6354.314328        198.065894          1.000000  \n7      0.000000        2300.0   679.065082        532.033990          0.000000  \n8      0.000000        7000.0   688.278568        311.963409          0.000000  \n9      0.000000       11000.0  1164.770591        100.302262          0.000000  ",
      "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>BALANCE</th>\n      <th>PURCHASES</th>\n      <th>ONEOFF_PURCHASES</th>\n      <th>INSTALLMENTS_PURCHASES</th>\n      <th>CASH_ADVANCE</th>\n      <th>CREDIT_LIMIT</th>\n      <th>PAYMENTS</th>\n      <th>MINIMUM_PAYMENTS</th>\n      <th>PRC_FULL_PAYMENT</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>40.900749</td>\n      <td>95.40</td>\n      <td>0.00</td>\n      <td>95.40</td>\n      <td>0.000000</td>\n      <td>1000.0</td>\n      <td>201.802084</td>\n      <td>139.509787</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3202.467416</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>6442.945483</td>\n      <td>7000.0</td>\n      <td>4103.032597</td>\n      <td>1072.340217</td>\n      <td>0.222222</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2495.148862</td>\n      <td>773.17</td>\n      <td>773.17</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>7500.0</td>\n      <td>622.066742</td>\n      <td>627.284787</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1666.670542</td>\n      <td>1499.00</td>\n      <td>1499.00</td>\n      <td>0.00</td>\n      <td>205.788017</td>\n      <td>7500.0</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>817.714335</td>\n      <td>16.00</td>\n      <td>16.00</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>1200.0</td>\n      <td>678.334763</td>\n      <td>244.791237</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1809.828751</td>\n      <td>1333.28</td>\n      <td>0.00</td>\n      <td>1333.28</td>\n      <td>0.000000</td>\n      <td>1800.0</td>\n      <td>1400.057770</td>\n      <td>2407.246035</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>627.260806</td>\n      <td>7091.01</td>\n      <td>6402.63</td>\n      <td>688.38</td>\n      <td>0.000000</td>\n      <td>13500.0</td>\n      <td>6354.314328</td>\n      <td>198.065894</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1823.652743</td>\n      <td>436.20</td>\n      <td>0.00</td>\n      <td>436.20</td>\n      <td>0.000000</td>\n      <td>2300.0</td>\n      <td>679.065082</td>\n      <td>532.033990</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1014.926473</td>\n      <td>861.49</td>\n      <td>661.49</td>\n      <td>200.00</td>\n      <td>0.000000</td>\n      <td>7000.0</td>\n      <td>688.278568</td>\n      <td>311.963409</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>152.225975</td>\n      <td>1281.60</td>\n      <td>1281.60</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>11000.0</td>\n      <td>1164.770591</td>\n      <td>100.302262</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Subset the Raw Data: Here, we refine data according to the desired features to keep defined in the previous cell. Then\n",
    "we type convert the columns in order to actually apply clustering to them. \n",
    "\"\"\"\n",
    "\n",
    "data = data_raw[ft_basic]\n",
    "ft_kept = data.columns\n",
    "data.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c44af57",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ft_kept' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/c_/y_0x4rsd6tb4chd_y83jnpzr0000gn/T/ipykernel_9333/1788693932.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m     11\u001B[0m \u001B[0;31m#each feature column- this can likely be done across the whole array simultaneously\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     12\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 13\u001B[0;31m \u001B[0;32mfor\u001B[0m \u001B[0mfeature\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mft_kept\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     14\u001B[0m     \u001B[0mfeature_series\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdata\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mfeature\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     15\u001B[0m     \u001B[0mmissing_bool\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mfeature_series\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0misnull\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mNameError\u001B[0m: name 'ft_kept' is not defined"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Clean the Data: Here we identify rows with missing fields in the desired categories. First, nan/None type is filtered and \n",
    "the row number is added to a list of rows which will be skipped when reading in the data for processing\n",
    "\"\"\"\n",
    "\n",
    "#Iterate over the list of desired features to keep. \n",
    "\n",
    "rows_nan = list()\n",
    "\n",
    "#This is an iterative approach by collecting drop-indices from \n",
    "#each feature column- this can likely be done across the whole array simultaneously \n",
    "\n",
    "for feature in ft_kept:\n",
    "    feature_series = data[feature]\n",
    "    missing_bool = feature_series.isnull()\n",
    "    nan_indices = feature_series.index[missing_bool]\n",
    "    if not nan_indices.empty:\n",
    "        print(\"Issue Feature:\\n\", feature,'\\n', nan_indices, '\\n Num of null=', len(nan_indices), '\\n\\n')\n",
    "        rows_nan += list(nan_indices)\n",
    "        print('Here are Nan Indices:', nan_indices)\n",
    "\n",
    "#Eliminate duplicates and sort \n",
    "rows_nan = list(set(rows_nan))\n",
    "rows_nan.sort()\n",
    "\n",
    "# Get rid of rows containing null or empty\n",
    "data_clean = data.drop(rows_nan)\n",
    "\n",
    "#Check the number of resulting data points \n",
    "print('Here is shape of original data:',data.shape,'\\n\\n')\n",
    "print('Here is shape of the clean data:', data_clean.shape,'\\n Number of Removed Instances =',len(rows_nan))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2cf78c74",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "CLEAN_DATA_DIR = './clean_data'\n",
    "\n",
    "CLEAN_DATA_FILE = '/clean_credit_data.csv'\n",
    "\n",
    "CLEAN_DATA_PATH = CLEAN_DATA_DIR + CLEAN_DATA_FILE\n",
    "\n",
    "data_clean.to_csv(CLEAN_DATA_PATH) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "caa024c0",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Here we create a clusterable dataset by converting into Numpy array.\n",
    "We also additionally extract one feature to be used as label\n",
    "Return: X: (NxD) data ready to be clustered\n",
    "        y: (Nx1) categorical label to examine clusters\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"\n",
    "?????????????????? Will Question: why are we removing any features? We can leave them in for the clustering. \n",
    "\"\"\"\n",
    "\n",
    "# specify one label from clean data feature\n",
    "ft_label = 'CREDIT_LIMIT'\n",
    "assert ft_label in ft_kept, \"Label feature not in features being used\"\n",
    "\n",
    "# the remaining are the real data feature\n",
    "ft_data = list(ft_kept).copy()\n",
    "ft_data.remove(ft_label)\n",
    "X = data_clean[ft_data].values.astype(np.float64)\n",
    "# label\n",
    "y = data_clean[ft_label].values\n",
    "\"\"\"\n",
    "Further grouping needs to be done to convert into categorical label\n",
    "\"\"\"\n",
    "\n",
    "assert not np.any(np.isnan(X)), \"Data contains NaN\"\n",
    "assert np.all(np.isfinite(X)), \"Data contains +/-Inf\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [],
   "source": [
    "# hyperparameter\n",
    "N = X.shape[0] #number of data\n",
    "d = X.shape[1] #dimension\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4a22d136",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Pipeline for Kmeans clustering given input data X\n",
    "\"\"\"\n",
    "k = 10 # number of clusters\n",
    "kmeans = KMeans(n_clusters=k, random_state=0).fit(X)\n",
    "center = kmeans.cluster_centers_\n",
    "kmeans_label = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "10fe1fea",
   "metadata": {
    "pycharm": {
     "name": "#%% plotting the result (separated from  doing clustering)\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature correspondence is \n",
      " [(0, 'BALANCE'), (1, 'PURCHASES'), (2, 'ONEOFF_PURCHASES'), (3, 'INSTALLMENTS_PURCHASES'), (4, 'CASH_ADVANCE'), (5, 'PAYMENTS'), (6, 'MINIMUM_PAYMENTS'), (7, 'PRC_FULL_PAYMENT')]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Plotting clusters for KMeans\n",
    "\"\"\"\n",
    "%matplotlib inline\n",
    "plt.figure(0)\n",
    "plt.plot(np.tile(np.arange(d),(k,1)).T,center.T, linewidth = 2)\n",
    "plt.xlabel('Feature Dimension', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('Amount of money',fontsize=14, fontweight='bold')\n",
    "plt.title('KMeans-Clustering using {} centers'.format(k),fontsize=16, fontweight='bold')\n",
    "\n",
    "print('feature correspondence is \\n', [(i, feature) for (i, feature) in enumerate(ft_data)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c5537f25",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Pipeline for GMM clustering given input data X\n",
    "\"\"\"\n",
    "gm = GaussianMixture(n_components=k, random_state=0).fit(X)\n",
    "gm_center = gm.means_\n",
    "gm_label = gm.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1e43ebe7",
   "metadata": {
    "pycharm": {
     "name": "#%% plotting the result (separated from doing clustering)\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature correspondence is \n",
      " [(0, 'BALANCE'), (1, 'PURCHASES'), (2, 'ONEOFF_PURCHASES'), (3, 'INSTALLMENTS_PURCHASES'), (4, 'CASH_ADVANCE'), (5, 'PAYMENTS'), (6, 'MINIMUM_PAYMENTS'), (7, 'PRC_FULL_PAYMENT')]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Plotting clusters for GMM\n",
    "\"\"\"\n",
    "plt.figure(0)\n",
    "plt.plot(np.tile(np.arange(d),(k,1)).T,gm_center.T, linewidth = 2)\n",
    "plt.xlabel('Feature Dimension', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('Amount of money',fontsize=14, fontweight='bold')\n",
    "plt.title('GMM-Clustering using {} centers'.format(k),fontsize=16, fontweight='bold')\n",
    "print('feature correspondence is \\n', [(i, feature) for (i, feature) in enumerate(ft_data)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0fbdaf70",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number in each bin is\n",
      " [4402 2006 1300  569  210  107   32    6    1    3]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y, bins=10)\n",
    "plt.title('Credit Limit (Label) Distribution')\n",
    "plt.xlabel('Credit Limit')\n",
    "plt.ylabel('Number of Customers')\n",
    "print('number in each bin is\\n', np.histogram(y,bins=10)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Credit Limit')"
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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R8VRErI+IV4GLgYPS5suA3cp2nwA8keITqsQH7CNpc2Bb4JnhPIY81js2M+tkrRy1JeDbwAMRcUFZfJeyzd4L/DpdnwfMTCOx9gQmAXdFxJPAakmHpMc8AbiubJ8T0/X3A7emfpRhM31KL7NmTKZ3XA8Cesf1MGvGZHe0m5klrRy1NRX4S2CRpHtS7DPAsZIOIGuCegz4MEBE3CfpGuB+shFfp6YRWwAfAS4BeshGa92Y4t8GLpO0mKwmMrMVB9Lq9Y7NzDqZhvkHfOH19fVFf39/u4thZtZRJC2IiL5q9/nMdjMza4oTiZmZNcWJxMzMmuJEYmZmTWnlqK0RY+7C5Zx/00M8sWoNu47r4bSj9vEoLjPrGK3+DnMiGYQnbTSzTpbHd5ibtgbhSRvNrJPl8R3mRDIIT9poZp0sj+8wN20NYtdxPSyv8oJ70kYzG0wR+lfz+A5zjWQQnrTRzDZFqW9i+ao1BBv6JvJeYTWP7zAnkkF40kYz2xRF6V/N4zvMTVsN8KSNZo0pQlNOURSpf7XV32GukZjZsChKU05RdNOieE4kZjYsitKUUxTd1L/qpi0zGxZFasopglJTUhGa+nxmu5l1hHFbjubZF1+pGu9WRehf9ZntZtYxaq2R12Vr5xWOz2w3s47x3JqNayP14paPPJocnUjMbFh00yilTpLH++JEYmbD4m37jh9S3PKRx/viRGJmw+K2B1cOKW75yON9cSIxs2Hh4b/F5D4SM+sYtYb5dvPw3yJwH4mZdQwP/y2mPM6wb9kJiZJ2Ay4FXg+8ClwUEV+StD1wNTAReAz4QEQ8m/Y5AzgJWA98LCJuSvEDgUuAHuAG4OMREZLGpOc4EHga+GBEPDbcx/LmM3/E82s3jMMeO2YU9549bbifxqyjFWn472fnLuLKO5eyPoJREscevBvnTJ+cezmKYPqUXvoff2bA6/G+A4f3RMlW1kjWAZ+KiD8ADgFOlbQfcDpwS0RMAm5Jt0n3zQT2B6YBX5dUSqMXAqcAk9Kl9C1+EvBsROwNfAH43HAfRGUSAXh+7XrefOaPhvupzDratj3Vm7BqxVvls3MXcfkdS1ifqkLrI7j8jiV8du6iXMtRFHMXLufaBcsHvB7XLlg+rJNptiyRRMSTEXF3ur4aeADoBY4GZqfNZgPT0/WjgasiYm1EPAosBg6StAswNiLmR0SQ1UDK9yk91hzgCEkazuOoTCKDxc26Va3/vOH9jxzclXcuHVJ8pMvjzPZc5tqSNBGYAtwJ7BwRT0KWbCTtlDbrBe4o221Zir2SrlfGS/ssTY+1TtJzwA7A7yqe/xSyGg277777sB2XWVEUYR2QVVXm2aoXb5X1NTplasVHuhExakvS1sC1wCci4vl6m1aJRZ14vX0GBiIuioi+iOgbP94nR9nIUpR1QIpyZvuoGlWgWvGRruNHbUkaTZZEroiI76fwU6m5ivR3RYovA3Yr230C8ESKT6gSH7CPpM2BbYFnhvMYNq/x2asVN8tbUdYBKcr6G8cevNuQ4iPdxB2qJ4xa8U3RskSS+iq+DTwQEReU3TUPODFdPxG4riw+U9IYSXuSdarflZrBVks6JD3mCRX7lB7r/cCtqR9l2ETVSk/tuFneinIiYB5rgzfinOmTOf6Q3V+rgYySOP6Q3bt21NYdjzw7pPimaGUfyVTgL4FFku5Jsc8A5wHXSDoJWAIcAxAR90m6BrifbMTXqRFR+pn1ETYM/70xXSBLVJdJWkxWE5k53Afh9tbiKkK/QBHsOq6H5VWSRjdPlnjO9Mldmzgq5fEd1rJEEhH/Q/U+DIAjauxzLnBulXg/8KYq8ZdIiahVRklVX/BubW8tijwW6+kUpx21z4DXAtrTpOT3pHsN2rQlaaNzM6rFRiq3txZTUfoFiqAoTUp+T7pXIzWSI4FPV8TeWSU2Ip0zfTI33/dbnlr98muxnbd5navNbVaUfoGiKMKSrn5PulfNGomkj0haBOwj6d6yy6PAvfkVsb2Ou3j+gCQC8NTqlznu4vltKpFBcYaa2ga1GnvdCDzy1Wva+i7wHrKRUe8puxwYEcfnULZC+Plvqo8mrhW3fBRlqKlt8OoQ4zZy1Gvaioh4TNKplXdI2j4i/E3apYowWmr6lF6+dtvDPLzihddiE7bbou3NO1YMRZm0sSjlaLXBaiQAC4D+9HdB2W3rQkU5i/q4i+cPSCIAD694wU2OVphJG4tSjjzUTCQR8e70d8+IeEP6W7q8Ib8iWpEUZWSOmxytlqJM2liUcuShofNIJL2ZbP2Q17Yvm/LEuohH5ljRFeUk4qKUIw+DJhJJ3wHeDNzHhn6zAJxIupDPoraiK8pJxEUpRx4amWvrkDRz7okR8Vfp8tctL1lBeNLGgd62b/XZk2vFW2XsmFFDilvr7bzN64YUb5VD3rDdkOKtUpSTmfP4DmskkcxPqxd2JU/aONBtD64cUrxVvOBY8Ww+qnoSrxVvlceert7MWiveKkWZPDKP77BG+khmkyWT3wJryc4vioh487CVosC6qZ2zEe4jKaYiDDOt1uRZL94qRfqM9u2xPbc9uJInVq3h9dtuQd8e2+dehqJM2vgd0iy+dOG5Rd3UztkI95EUT2mYaUlpmCkwIs9ZGMy4LUfzbJVVGcdtme/a8d00iWUjTVtLImJeRDwaEY+XLi0vWUEUpZ2zKIpyRvnoGp/cWvGRrJuGmTai1g/tvBsRijJUPg+N/Ns9KOm7ko6VNKN0aXnJCqLWL7pu/KUH2S+psVsMTCRjtxiV+y+sdTXqxrXiI5mbXwd6bk31NeJrxVulSE1srdZIIukh6xv5MzbMt/XuVhaqSCaefv2Q4iPdkRfcXnUSyyMvuD3XctRqpsi7+aIIvEb5QJvX+FarFW+VbppYdNA+koj4qzwKYp2hclqSweKtUpTmiyI49uDdBvSRlMfzJLITzKrF8/RKjVpprXirFGXBsTzUTCSS/iki/l3SV6jy+YiIj7W0ZGZ1FKX5oghKzaztHrVlA5Wae9s9wWke6tVIHkh/q03Q2IW/+6xIPHpsoCKsUe73ZGNFWHAsD/Umbfyv9Hd25YVsyhTrQkU5078oZ9jbBhN3qJ4wasVbZepe1c/VqBUf6fJYcGxTu58+MIxlsA6yrkZdtFa8VYpyhr1tcMcjzw4p3ipXnHzoRklj6l7bc8XJh+ZajqKo9a85nP+yDc3+W0V3DgexwuimoZWdokjDkLs1abRLvc72WvVA4URibbbF6M1YU2UYzhbdeEaiWZvVq5EsIKv9VEsaL1eJWRcYJVhf5QfmqJx/WqytceZhrbiZtU7NRBIRezbzwGkdk3cDKyLiTSl2FnAyUGrI/kxE3JDuOwM4CVgPfCwibkrxA4FLyE6MvAH4eESEpDHApcCBwNPAByPisWbKXHRFWCv91RqtFLXiI70cVkxFmMSym7SyHeASYFqV+Bci4oB0KSWR/YCZwP5pn69LKs3DcSFwCjApXUqPeRLwbETsDXwB+FyrDqQIirJW+utqnB5cK94qm9WoAdWKW/foprXSi6Jl//0R8TOg0QW0jwauioi1EfEosBg4SNIuwNiImB8RQVYDmV62z+x0fQ5whDRy54QoygRwRWlSGlMjcdWKW/fwJJb5a8d/3Ucl3SvpO5JKS5b1AuXv8rIU603XK+MD9omIdcBzwA7VnlDSKZL6JfWvXNmZw0M9Smmgl2rMd1Erbt2jSKPHusWgiUTSZY3EGnQhsBdwAPAk8PnSQ1bZtlZHf+nTUO++gcGIi9JywX3jx3fmCWvdNAFcI/x6WC2exDJ/jdRI9i+/kfouDtyUJ4uIpyJifUS8ClwMHJTuWgaUzzA3AXgixSdUiQ/YR9LmwLY03pTWcYpyJveknbYaUrxVnl790pDi1j1erVHzqBW35tVMJJLOkLQaeLOk59NlNbACuG5Tniz1eZS8F/h1uj4PmClpjKQ9yTrV74qIJ4HVkg5J/R8nlD33PODEdP39wK2pH2VEKsqZ3C++XL3pqFa8VV6qNga5Tty6Rx5ncttA9Yb/zgJmSZoVEWcM9YElXQkcDuwoaRlwJnC4pAPI3tPHgA+n57pP0jXA/cA64NSIKPUsf4QNw39vTBeAbwOXSVpMVhOZOdQydpKi9JEUpRw2UBGGhlv3qndm+74R8SDwPUl/WHl/RNxd74Ej4tgq4W/X2f5c4Nwq8X7gTVXiLwHH1CvDSFKUmVWLUg7boJvWBrdiqtdH8qn09/NVLv/R4nJZhaKslV6UctgGRRkabt2rXtPWyenv2/IrjtUyfUovX7vt4QErEU7Ybovcf3FOn9JL/+PPDDhr+H0HdseaC0Xl5kZrt3pNWzPq7RgR3x/+4lgtx108f6PlbB9e8QLHXTw/15lO5y5czrULlg84a/jaBcvp22N7J5M2cXOjtVu9pq33pMtJZH0bx6XLt4DjW180K/fz31Qf2Vwr3ipuRikeNzdau9Vr2vorAEk/BPZLQ3FLQ3i/lk/xrGjcjLKxdo+YcnOjtVsjJyROLCWR5CngjS0qjxWczygfqAiTadZqbsx7Qk/rXo0kktsl3STpQ5JOBK4HbmtxuaxCUdahdjPKQEVo6itCGay7DZpIIuKjwDeAt5DNkXVRRPx9i8tlFYqyDvX0Kb3MmjGZ3nE9COgd18OsGZO7thmlCE19RSiDdbdG12y/G1gdET+RtKWkbSJidSsLZhs7pm93Hnt6zWtt8cf07d6Wckyf4vb3kiKMmCpCGay7NTL778lk6318M4V6gbktLJNVUYS2eNtYEZr6ilAG626N9JGcCkwFngeIiIeBnVpZKNuY28GLqQhNfUUog3W3Rpq21kbEy6XFB9OU7Z5IM2dFagc/7uL5A85faUdfTZG4qc9qaffQ8Lw0UiP5qaTPAD2SjgS+B/xXa4tllcZtOXpI8VapTCKQnRR53MXzcy2HbeBmz2LqpvelkUTyaWAlsIhs2vcbgM+2slC2sVorreS9AktRzrC3DdzsWUzd9L7UbdqStBlwb0S8iWxFQ2uT59a8MqS4dY8iNXvaBt30vtStkaQlcX8lqT3jTO01PqPcaukZXf3fuFbc8tFN/7ONfNJ2Ae6TdIukeaVLqwtmA5121D6M3kwDYqM3U+5DPItyhr1tsGZd9WWOa8UtH900LLuRUVtnt7wU1hgNcjsHV5x8qEdtFUxR+s9soNLorG4YtVVvPZK9gZ0j4qcV8bcCI2/YQcGdf9NDvLJ+4DfDK+uD8296KPcPppNGsYySXpuwsTJu7dUtQ8PrNW19Eag2DcqL6T7LUbUpMOrFrXu8YfyWQ4qbDbd6iWRiRNxbGYyIfmBiy0pkZkPyyMoXhxQ3G271+ki2qHPfyBt2YA078oLbByz7O2mnrbj5k4e3r0BdrlqzVr242XCrVyP5ZZqwcQBJJwELWlckK7LKJALZ2vFHXnB7ewpk1OoKcReJ5aVejeQTwA8kHceGxNEHvA54b4vLZQVVmUQGi1vr9Wy+GS++svFQ357NfR6J5aPmJy0inoqIw8iG/z6WLmdHxKER8dvBHljSdyStkPTrstj2km6W9HD6u13ZfWdIWizpIUlHlcUPlLQo3fdlpdkjJY2RdHWK3ylp4iYcv1nHW1MlidSLmw23RlZIvC0ivpIutw7hsS8BplXETgduiYhJwC3pNpL2A2YC+6d9vi6pdCbPhcApwKR0KT3mScCzEbE38AXgc0Mom9mI8boaNY9acbPh1rJPWkT8DKicye9oYHa6PhuYXha/KiLWRsSjwGLgIEm7AGMjYn5EBHBpxT6lx5oDHFGqrZh1k7U1zmCvFTcbbnn/ZNk5Ip4ESH9LC2T1AkvLtluWYr3pemV8wD4RsQ54Dtih2pNKOkVSv6T+lStXDtOhmJkZ5J9IaqlWk4g68Xr7bByMuCgi+iKib/z48ZtYRDMzqybvRPJUaq4i/V2R4suA3cq2mwA8keITqsQH7JNWbdyWjZvSbJjVajt0m2L7eCJNa7e8E8k84MR0/UTgurL4zDQSa0+yTvW7UvPXakmHpP6PEyr2KT3W+4FbUz+KtVCtF9gvfPtccfKhGyUNT6RpeWpk9t9NIulK4HBgR0nLgDOB84Br0kmNS4BjACLiPknXAPcD64BTI6K0tNhHyEaA9QA3pgvAt4HLJC0mq4nMbNWxmNVThHW59xy/NXc88izrIxglsef4rXN9futuLUskEXFsjbuOqLH9ucC5VeL9wJuqxF8iJSKzdimty11aUrW0LjeQWzL57NxFXH7Hktdur4947fY50yfnUgbrbkXpbDfrSEVYl/vKO5cOKW423JxIzJpQhHW5PWmjtZsTiVkTirAud60FrLywleXFicSsCUVYl/vYg3cbUtxsuDmRmDVh+pReZs2YTO+4HgT0juth1ozJuY7aOmf65KrDf93Rbnlp2agts27R7nW55y5czt1LnhsQu3vJc8xduLwr1gu39nONxKzDFWHkmHU3JxKzDleEkWPW3ZxIzDpcEUaOWXdzIjHrcEUYOWbdzZ3tZh2u1KHe7vm+rHs5kZiNAO0eOWbdzU1bZmbWFCcSMzNripu2zJpUhPVIzNrJicSsCUVYj8Ss3dy0ZdYEn1Vu5kRi1hSfVW7mpi2zpuw6roflVZJG3meVu5/G2sk1ErMmvG3f8UOKt0Kpn2b5qjUEG/pp5i5cnlsZrLs5kZg14bYHVw4p3grup7F2cyIxa0IR+kiKUAbrbk4kZk0owsy7RSiDdTcnErMmFGHm3SKUwbpbWxKJpMckLZJ0j6T+FNte0s2SHk5/tyvb/gxJiyU9JOmosviB6XEWS/qyJLXjeKx7TZ/Sy/sO7GVU+uiNknjfgflOoFiEMlh3a2eN5G0RcUBE9KXbpwO3RMQk4JZ0G0n7ATOB/YFpwNcllX5+XQicAkxKl2k5lt+MuQuXc+2C5ayPAGB9BNcuWJ7riKkilMG6W5Gato4GZqfrs4HpZfGrImJtRDwKLAYOkrQLMDYi5kdEAJeW7WOWiyKMmCpCGay7tSuRBPBjSQsknZJiO0fEkwDp704p3gssLdt3WYr1puuV8Y1IOkVSv6T+lSvzG5ZpI18RRkwVoQzW3dqVSKZGxB8C7wROlfTWOttW6/eIOvGNgxEXRURfRPSNH5/fiWI28hVhxFQRymDdrS2JJCKeSH9XAD8ADgKeSs1VpL8r0ubLgN3Kdp8APJHiE6rEzXIzcYfqX9a14q3gUVvWbrknEklbSdqmdB34M+DXwDzgxLTZicB16fo8YKakMZL2JOtUvys1f62WdEgarXVC2T5mubjjkWeHFG+F6VN6mTVjMr3jehDQO66HWTMme9SW5aYdkzbuDPwgjdTdHPhuRPxI0i+BaySdBCwBjgGIiPskXQPcD6wDTo2IUs/iR4BLgB7gxnQxy01ppFSj8Vbxmu3WTrknkoh4BHhLlfjTwBE19jkXOLdKvB9403CX0czMGlek4b9mZtaBnEjMmtBbY2RUrbjZSOREYtaEIqxHYtZuTiRmTSjCeiRm7ealds2aUJSzyr3UrrWTE4lZE8ZtOZpnX3ylajwvpaV2S/NtlZbaBZxMLBdu2jJrQq3TRfI8jcSTNlq7OZGYNeG5NRvXRurFW6EozWvWvZxIzJpQhAkTi1AG625OJGZNKMLw3yJMHGndzYnErAlFGP5bhIkjrbs5kZg1oQj9E0WZONK6lxOJWRNqDfPNc/jvKFVb46123Gy4OZGYNaEIw3+PPXi3IcXNhptPSDRrQhGG/54zfTIAV965lPURjJI49uDdXoubtZoTiVkTinBmO2TJxInD2sWJxKwJRWjaAs+1Ze3lRGLWhFU1mrBqxVvBc21Zu7mz3azDea4tazcnErMOt7zGOSu14tZdthxd/Wu+VnxTOJGYdTifR2L1rFn36pDim8KJxKzD+cx2qyePASFOJGYdrrfGLL+14mbDzYnErAlT99p+SPFWOO2ofegZPWpArGf0KE47ap/cymDdreMTiaRpkh6StFjS6e0uj3WXK04+dKOkMXWv7bni5ENzK8P0Kb3MmjGZ3nE9iKwmMmvGZA/9tdx09HkkkkYBXwOOBJYBv5Q0LyLub2/JrJvkmTRqmT6l14nD2qbTayQHAYsj4pGIeBm4Cji6zWUa0caOGTWkuJm113Y1puupFd8UnZ5IeoGlZbeXpdgAkk6R1C+pf+XK/BYcGk5F6VC99+xpGyWNsWNGce/Z03IthxXPFz94wJDilo8z37M/o0cNHAo+epQ48z37D9tzdHTTFlBtoPxGg9oi4iLgIoC+vr4hDXo7/pDdufyOJVXjeTrtqH0GTIMB7etQLULSmLTTVjy84oWqcWuPUtNau+f82q7GRJrD+Qu8k+TxvnR6IlkGlC+6MAF4YjifoChTdBfln7Qobv7k4Rx5we0Dksmknbbi5k8e3r5CWSH6as58z/6cNudXvLJ+w2/G4f4F3mla/b4oOvikJUmbA/8LHAEsB34J/EVE3Fdrn76+vujv78+phGbWDp4NefhJWhARfdXu6+gaSUSsk/RR4CZgFPCdeknEzLpDEWpG3aSjEwlARNwA3NDucpiZdatOH7VlZmZt5kRiZmZNcSIxM7OmOJGYmVlTOnr476aQtBJ4fBN33xH43TAWp9P59RjIr8cGfi0GGgmvxx4RMb7aHV2XSJohqb/WOOpu5NdjIL8eG/i1GGikvx5u2jIzs6Y4kZiZWVOcSIbmonYXoGD8egzk12MDvxYDjejXw30kZmbWFNdIzMysKU4kZmbWFCeSBkmaJukhSYslnd7u8rSLpN0k3SbpAUn3Sfp4u8tUBJJGSVoo6YftLku7SRonaY6kB9PnpP2L2reJpH9I/ye/lnSlpC3aXaZWcCJpgKRRwNeAdwL7AcdK2q+9pWqbdcCnIuIPgEOAU7v4tSj3ceCBdheiIL4E/Cgi9gXeQpe+LpJ6gY8BfRHxJrKlLma2t1St4UTSmIOAxRHxSES8DFwFHN3mMrVFRDwZEXen66vJviS6euEHSROA/wN8q91laTdJY4G3At8GiIiXI2JVWwvVXpsDPWkRvi0Z5hVci8KJpDG9wNKy28vo8i9PAEkTgSnAnW0uSrt9Efgn4NU2l6MI3gCsBP4zNfV9S9JW7S5UO0TEcuA/gCXAk8BzEfHj9paqNZxIGqMqsa4eNy1pa+Ba4BMR8Xy7y9Mukt4NrIiIBe0uS0FsDvwhcGFETAFeALqyT1HSdmQtF3sCuwJbSTq+vaVqDSeSxiwDdiu7PYERWkVthKTRZEnkioj4frvL02ZTgT+X9BhZk+fbJV3e3iK11TJgWUSUaqlzyBJLN3oH8GhErIyIV4DvA4e1uUwt4UTSmF8CkyTtKel1ZB1m89pcpraQJLL27wci4oJ2l6fdIuKMiJgQERPJPhe3RsSI/NXZiIj4LbBU0j4pdARwfxuL1E5LgEMkbZn+b45ghA486Pg12/MQEeskfRS4iWzkxXci4r42F6tdpgJ/CSySdE+KfSYibmhfkaxg/h64Iv3oegT4qzaXpy0i4k5Jc4C7yUY7LmSETpXiKVLMzKwpbtoyM7OmOJGYmVlTnEjMzKwpTiRmZtYUJxIzM2uKE4mNOJJ2lvRdSY9IWiBpvqT3pvsOlxSSTirbfkqK/WO6fYmkFyVtU7bNl9I2O1Z5vq0lfVPSb9JMrz+TdHC67/ebeAzTm50MMx3rkGYjlnRW6XUwa5QTiY0o6cSvucDPIuINEXEg2YmCE8o2WwR8sOz2TOBXFQ+1mDQxp6TNgLcBy2s87beAZ4BJEbE/8CFgo4QzRNPJZppuWJoY0Cx3TiQ20rwdeDkivlEKRMTjEfGVsm2WAFukmouAacCNFY9zJRuSzeHAz8lOKhtA0l7AwcBnI+LV9HyPRMT1FdsNqB1I+qqkD6Xr50m6X9K9kv5D0mHAnwPnS7pH0l7p8qNUw/pvSfumfS+RdIGk24DP1XpRUk3jO5JuTzW1j5Xd989prZ2fAPuUxTd6TkmbS/qlpMPTNrMknVvrea07+BeMjTT7k51JPJg5wDFkZxvfDaytuP9h4Og08d6xwOVk69FUe757ImL9phRW0vbAe4F9IyIkjYuIVZLmAT+MiDlpu1uAv42Ih1Oz2dfJkibAG4F3NFCGfclqVtsAD0m6EHgzWY1sCtn3wd1AaQLKiyqfMyLenhLgnJSMppElUutiTiQ2okn6GvDHZLWUPyq76xrgarIv1yupPpne98m+ZA8GPtyiIj4PvAR8S9L1wEZ9Gmmm5cOA72UVKADGlG3yvQYT2fURsRZYK2kFsDPwJ8APIuLF9FzzBnvOiLhP0mXAfwGHpjV6rIs5kdhIcx/wvtKNiDg1dZD3l28UEb+V9ApwJNnqhtUSyVVkv9BnR8SrZV+olc/3FkmblZq2aljHwKbkLVI51kk6iGxCv5nAR9lQ0yjZDFgVEQfUeOwX6jxvufJa13o2/P9XmydpsOecDKwiS0bW5dxHYiPNrWT9Hx8pi21ZY9v/B3y61q/5iFgC/DNZM1JVEfEbsiR1dupvQdIkSZUraD4O7CdpjKRtyRJH6Zf/tmnSy08AB6TtV5M1QZHWe3lU0jFpH0l6S60yDdHPgPdK6kmj1N4z2HNKmgHsQLYS4pcljRumsliHciKxESWyWUinA38q6VFJdwGzgU9X2fYXETF3kMf7ZkoW9fwN8HpgsaRFwMVUrFcTEUvJmtPuBa4g65uBLFn8UNK9wE+Bf0jxq4DTlK0yuBdwHHCSpF+R1YKGZanntGzy1cA9ZGvM/HfZ3Rs9Z6rdnQecFBH/C3yVbI1262Ke/dfMzJriGomZmTXFicTMzJriRGJmZk1xIjEzs6Y4kZiZWVOcSMzMrClOJGZm1pT/Dx93LQWl0E9vAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "The ideal case should be the Credit Limit is well-separated\n",
    "according to the clustering indices.\n",
    "This is in essence a visualization of the homogeneity&completeness metric\n",
    "\"\"\"\n",
    "\n",
    "plt.figure(1)\n",
    "plt.scatter(kmeans_label, y)\n",
    "plt.title('Kmeans Clustering Label v.s. Credit Limit')\n",
    "plt.xlabel('Kmeans Cluster Index')\n",
    "plt.ylabel('Credit Limit')\n",
    "\n",
    "plt.figure(2)\n",
    "plt.scatter(gm_label, y)\n",
    "plt.title('GMM Clustering Label v.s. Credit Limit')\n",
    "plt.xlabel('GMM Cluster Index')\n",
    "plt.ylabel('Credit Limit')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Here we implement the elbow method with different metric to find\n",
    "the best number of clusters\n",
    "\"\"\"\n",
    "\n",
    "k_search = np.linspace(start=5, stop=100, num=20)\n",
    "silh_score_kmeans = np.zeros(len(k_search))\n",
    "CHindex_score_kmeans = silh_score_kmeans.copy()\n",
    "DBindex_score_kmeans = silh_score_kmeans.copy()\n",
    "SoS = silh_score_kmeans.copy()\n",
    "for (i,k) in tqdm(enumerate(k_search), total=len(k_search)):\n",
    "    kmeans = KMeans(n_clusters=int(k), random_state=0).fit(X)\n",
    "    kmeans_label = kmeans.labels_\n",
    "    SoS[i] = kmeans.inertia_\n",
    "    silh_score_kmeans[i] = metrics.silhouette_score(X, kmeans_label, metric='euclidean')\n",
    "    CHindex_score_kmeans[i] = metrics.calinski_harabasz_score(X,kmeans_label)\n",
    "    DBindex_score_kmeans[i] = metrics.davies_bouldin_score(X,kmeans_label)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Davies-Bouldin Index¶')"
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 0 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Visualization of Elbow Methods for KMeans\n",
    "\"\"\"\n",
    "# SoS\n",
    "plt.figure(0)\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.plot(k_search, SoS, '-o')\n",
    "plt.xlabel(r'Number of clusters $k$')\n",
    "plt.ylabel('Sum of squared distance')\n",
    "\n",
    "# Silhouette\n",
    "plt.figure(1)\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.plot(k_search, silh_score_kmeans, '-o')\n",
    "plt.xlabel(r'Number of clusters $k$')\n",
    "plt.ylabel('Silhouette Coefficients')\n",
    "\n",
    "# CH index\n",
    "plt.figure(2)\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.plot(k_search, CHindex_score_kmeans, '-o')\n",
    "plt.xlabel(r'Number of clusters $k$')\n",
    "plt.ylabel('Calinski-Harabasz Index')\n",
    "\n",
    "# DB index\n",
    "plt.figure(3)\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.plot(k_search, DBindex_score_kmeans, '-o')\n",
    "plt.xlabel(r'Number of clusters $k$')\n",
    "plt.ylabel('Davies-Bouldin Index')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "20it [01:24,  4.22s/it]\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Here we implement the elbow method with different metric to find\n",
    "the best number of clusters\n",
    "\"\"\"\n",
    "\n",
    "k_search = np.linspace(start=5, stop=100, num=20)\n",
    "silh_score_gm = np.zeros(len(k_search))\n",
    "CHindex_score_gm = silh_score_gm.copy()\n",
    "DBindex_score_gm = silh_score_gm.copy()\n",
    "# with tqdm(total=total) as pbar:\n",
    "#     for item in train_iter:\n",
    "for (i,k) in tqdm(enumerate(k_search), total=len(k_search)):\n",
    "    gm = GaussianMixture(n_components=int(k), random_state=0).fit(X)\n",
    "    gm_label = gm.predict(X)\n",
    "\n",
    "    silh_score_gm[i] = metrics.silhouette_score(X, gm_label, metric='euclidean')\n",
    "    CHindex_score_gm[i] = metrics.calinski_harabasz_score(X,gm_label)\n",
    "    DBindex_score_gm[i] = metrics.davies_bouldin_score(X,gm_label)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Davies-Bouldin Index¶')"
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 0 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Visualization of Elbow Methods for GMM\n",
    "\"\"\"\n",
    "# Silhouette\n",
    "plt.figure(1)\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.plot(k_search, silh_score_gm, '-o')\n",
    "plt.xlabel(r'Number of clusters $k$')\n",
    "plt.ylabel('Silhouette Coefficients')\n",
    "\n",
    "# CH index\n",
    "plt.figure(2)\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.plot(k_search, CHindex_score_gm, '-o')\n",
    "plt.xlabel(r'Number of clusters $k$')\n",
    "plt.ylabel('Calinski-Harabasz Index')\n",
    "\n",
    "# DB index\n",
    "plt.figure(3)\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.plot(k_search, DBindex_score_gm, '-o')\n",
    "plt.xlabel(r'Number of clusters $k$')\n",
    "plt.ylabel('Davies-Bouldin Index')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "#Clear all cache\n",
    "%reset -f\n",
    "#Autoload module update\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Issue Feature:\n",
      " CREDIT_LIMIT \n",
      " \n",
      " Num of null= 1 \n",
      "\n",
      "\n",
      "Issue Feature:\n",
      " MINIMUM_PAYMENTS \n",
      " \n",
      " Num of null= 313 \n",
      "\n",
      "\n",
      "Total Number of Removed Row Instances =  314 \n",
      " \n",
      "Percentage of Removed Features: \n",
      " {'CREDIT_LIMIT': 0.000111731843575419, 'MINIMUM_PAYMENTS': 0.034972067039106144}\n",
      "Here are the selected features (related to dollars):\n",
      " ['BALANCE', 'PURCHASES', 'ONEOFF_PURCHASES', 'INSTALLMENTS_PURCHASES', 'CASH_ADVANCE', 'CREDIT_LIMIT', 'PAYMENTS', 'MINIMUM_PAYMENTS', 'PRC_FULL_PAYMENT'] \n",
      "\n",
      "Features retained are: basic\n",
      "\n",
      "Here are the features not used: \n",
      " ['PURCHASES_FREQUENCY', 'CASH_ADVANCE_TRX', 'BALANCE_FREQUENCY', 'CASH_ADVANCE_FREQUENCY', 'PURCHASES_TRX', 'CUST_ID', 'TENURE', 'ONEOFF_PURCHASES_FREQUENCY', 'PURCHASES_INSTALLMENTS_FREQUENCY']\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Examine the datatools script\n",
    "\"\"\"\n",
    "from datatools import make_clean_data, select_features, remove_quantiles,\\\n",
    "    get_elbow_index, elbow_method\n",
    "\n",
    "\n",
    "data_clean, missing_dict, bad_row_index = make_clean_data(data_raw, verbose=True)\n",
    "data_kept, ft_kept, ft_unused = select_features(data_clean, which='basic')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent of Data Removed Across These Quantiles Is:  8.603520148216766\n"
     ]
    }
   ],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       29.093764] BALANCE\n",
      "[29.093764 89.579737] BALANCE\n",
      "[ 89.579737 233.394616] BALANCE\n",
      "[233.394616 512.011287] BALANCE\n",
      "[512.011287 916.855459] BALANCE\n",
      "[ 916.855459 1247.423771] BALANCE\n",
      "[1247.423771 1736.263934] BALANCE\n",
      "[1736.263934 2613.123168] BALANCE\n",
      "[2613.123168  4382.8418065] BALANCE\n",
      "[ 4382.8418065 19043.13856  ] BALANCE\n",
      "[0. 0.] PURCHASES\n",
      "[0. 0.] PURCHASES\n",
      "[ 0.    94.425] PURCHASES\n",
      "[ 94.425 223.34 ] PURCHASES\n",
      "[223.34  375.405] PURCHASES\n",
      "[375.405 577.98 ] PURCHASES\n",
      "[577.98  915.925] PURCHASES\n",
      "[ 915.925 1451.5  ] PURCHASES\n",
      "[1451.5  2591.36] PURCHASES\n",
      "[ 2591.36 49039.57] PURCHASES\n",
      "[0. 0.] ONEOFF_PURCHASES\n",
      "[0. 0.] ONEOFF_PURCHASES\n",
      "[0. 0.] ONEOFF_PURCHASES\n",
      "[0. 0.] ONEOFF_PURCHASES\n",
      "[ 0.    44.995] ONEOFF_PURCHASES\n",
      "[ 44.995 177.   ] ONEOFF_PURCHASES\n",
      "[177.  420.1] ONEOFF_PURCHASES\n",
      "[420.1 834.8] ONEOFF_PURCHASES\n",
      "[ 834.8   1627.535] ONEOFF_PURCHASES\n",
      "[ 1627.535 40761.25 ] ONEOFF_PURCHASES\n",
      "[0. 0.] INSTALLMENTS_PURCHASES\n",
      "[0. 0.] INSTALLMENTS_PURCHASES\n",
      "[0. 0.] INSTALLMENTS_PURCHASES\n",
      "[0. 0.] INSTALLMENTS_PURCHASES\n",
      "[ 0.    94.785] INSTALLMENTS_PURCHASES\n",
      "[ 94.785 224.54 ] INSTALLMENTS_PURCHASES\n",
      "[224.54 384.52] INSTALLMENTS_PURCHASES\n",
      "[384.52 620.84] INSTALLMENTS_PURCHASES\n",
      "[ 620.84 1169.5 ] INSTALLMENTS_PURCHASES\n",
      "[ 1169.5 22500. ] INSTALLMENTS_PURCHASES\n",
      "[0. 0.] CASH_ADVANCE\n",
      "[0. 0.] CASH_ADVANCE\n",
      "[0. 0.] CASH_ADVANCE\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/c_/y_0x4rsd6tb4chd_y83jnpzr0000gn/T/ipykernel_54802/1155998711.py:16: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  data_reduced_qunatile = data_reduced_qunatile.drop(ft, 1)\n",
      "/var/folders/c_/y_0x4rsd6tb4chd_y83jnpzr0000gn/T/ipykernel_54802/1155998711.py:16: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  data_reduced_qunatile = data_reduced_qunatile.drop(ft, 1)\n",
      "/var/folders/c_/y_0x4rsd6tb4chd_y83jnpzr0000gn/T/ipykernel_54802/1155998711.py:16: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  data_reduced_qunatile = data_reduced_qunatile.drop(ft, 1)\n",
      "/var/folders/c_/y_0x4rsd6tb4chd_y83jnpzr0000gn/T/ipykernel_54802/1155998711.py:16: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  data_reduced_qunatile = data_reduced_qunatile.drop(ft, 1)\n",
      "/var/folders/c_/y_0x4rsd6tb4chd_y83jnpzr0000gn/T/ipykernel_54802/1155998711.py:16: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  data_reduced_qunatile = data_reduced_qunatile.drop(ft, 1)\n",
      "/var/folders/c_/y_0x4rsd6tb4chd_y83jnpzr0000gn/T/ipykernel_54802/1155998711.py:16: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  data_reduced_qunatile = data_reduced_qunatile.drop(ft, 1)\n",
      "/var/folders/c_/y_0x4rsd6tb4chd_y83jnpzr0000gn/T/ipykernel_54802/1155998711.py:16: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  data_reduced_qunatile = data_reduced_qunatile.drop(ft, 1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0.] CASH_ADVANCE\n",
      "[0. 0.] CASH_ADVANCE\n",
      "[  0.       254.450638] CASH_ADVANCE\n",
      "[254.450638 825.344843] CASH_ADVANCE\n",
      "[ 825.344843 1600.758078] CASH_ADVANCE\n",
      "[1600.758078 3126.9753  ] CASH_ADVANCE\n",
      "[ 3126.9753  47137.21176] CASH_ADVANCE\n",
      "[  50. 1200.] CREDIT_LIMIT\n",
      "[1200. 1500.] CREDIT_LIMIT\n",
      "[1500. 2000.] CREDIT_LIMIT\n",
      "[2000. 2500.] CREDIT_LIMIT\n",
      "[2500. 3000.] CREDIT_LIMIT\n",
      "[3000. 4500.] CREDIT_LIMIT\n",
      "[4500. 6000.] CREDIT_LIMIT\n",
      "[6000. 7000.] CREDIT_LIMIT\n",
      "[7000. 9500.] CREDIT_LIMIT\n",
      "[ 9500. 30000.] CREDIT_LIMIT\n",
      "[4.9513000e-02 2.1477765e+02] PAYMENTS\n",
      "[214.77765  346.833439] PAYMENTS\n",
      "[346.833439 497.195284] PAYMENTS\n",
      "[497.195284 665.239171] PAYMENTS\n",
      "[665.239171 896.675701] PAYMENTS\n",
      "[ 896.675701 1233.301038] PAYMENTS\n",
      "[1233.301038  1653.7194825] PAYMENTS\n",
      "[1653.7194825 2373.896899 ] PAYMENTS\n",
      "[2373.896899 4002.988707] PAYMENTS\n",
      "[ 4002.988707 50721.48336 ] PAYMENTS\n",
      "[1.91630000e-02 1.06113465e+02] MINIMUM_PAYMENTS\n",
      "[106.113465 154.583752] MINIMUM_PAYMENTS\n",
      "[154.583752  178.8627335] MINIMUM_PAYMENTS\n",
      "[178.8627335 216.74072  ] MINIMUM_PAYMENTS\n",
      "[216.74072   312.4522915] MINIMUM_PAYMENTS\n",
      "[312.4522915 462.345834 ] MINIMUM_PAYMENTS\n",
      "[462.345834 671.296955] MINIMUM_PAYMENTS\n",
      "[ 671.296955 1026.321014] MINIMUM_PAYMENTS\n",
      "[1026.321014 1780.726297] MINIMUM_PAYMENTS\n",
      "[ 1780.726297 76406.20752 ] MINIMUM_PAYMENTS\n",
      "[0. 0.] PRC_FULL_PAYMENT\n",
      "[0. 0.] PRC_FULL_PAYMENT\n",
      "[0. 0.] PRC_FULL_PAYMENT\n",
      "[0. 0.] PRC_FULL_PAYMENT\n",
      "[0. 0.] PRC_FULL_PAYMENT\n",
      "[0. 0.] PRC_FULL_PAYMENT\n",
      "[0.       0.090909] PRC_FULL_PAYMENT\n",
      "[0.090909 0.25    ] PRC_FULL_PAYMENT\n",
      "[0.25      0.7207795] PRC_FULL_PAYMENT\n",
      "[0.7207795 1.       ] PRC_FULL_PAYMENT\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/c_/y_0x4rsd6tb4chd_y83jnpzr0000gn/T/ipykernel_54802/1155998711.py:16: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  data_reduced_qunatile = data_reduced_qunatile.drop(ft, 1)\n",
      "/var/folders/c_/y_0x4rsd6tb4chd_y83jnpzr0000gn/T/ipykernel_54802/1155998711.py:16: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  data_reduced_qunatile = data_reduced_qunatile.drop(ft, 1)\n"
     ]
    }
   ],
   "source": [
    "p = np.linspace(0,10,11)*0.1\n",
    "data_reduced_qunatile = data_kept.copy()\n",
    "percent_of_zero = np.zeros(len(ft_kept))\n",
    "eps = 1e-5\n",
    "for (j,ft) in enumerate(ft_kept):\n",
    "    ft_new = ft + '_QUANTILE'\n",
    "    data_reduced_qunatile[ft_new] = 0\n",
    "    for (i, quantile) in enumerate(p[:-1]):\n",
    "        quantile_filter = np.quantile(data_kept[ft], [quantile, p[i+1]])\n",
    "        data_reduced_qunatile.loc[((data_kept[ft]>quantile_filter[0])&\n",
    "                                   (data_kept[ft]<=quantile_filter[1])), ft_new]=i+1\n",
    "        if i == 0 and quantile_filter[0]>0: # deal with 0-quantile being non-zero\n",
    "            data_reduced_qunatile.loc[((data_kept[ft]>=quantile_filter[0])&\n",
    "                                   (data_kept[ft]<=quantile_filter[1])), ft_new]=i+1\n",
    "\n",
    "        print(quantile_filter, ft)\n",
    "        if quantile_filter[0]<= eps and quantile_filter[1] >= eps:\n",
    "            percent_of_zero[j] = quantile\n",
    "\n",
    "    data_reduced_qunatile = data_reduced_qunatile.drop(ft, 1)\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "outputs": [],
   "source": [
    "percent_of_zero = np.zeros(len(ft_kept))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "2\n",
      "0\n",
      "3\n",
      "0\n",
      "4\n",
      "0\n",
      "5\n",
      "205\n",
      "6\n",
      "1070\n",
      "7\n",
      "1932\n",
      "8\n",
      "2796\n",
      "9\n",
      "3659\n"
     ]
    }
   ],
   "source": [
    "test = 0\n",
    "ft = 'ONEOFF_PURCHASES'\n",
    "ft_new = ft + '_QUANTILE'\n",
    "data_reduced_qunatile[ft_new] = 0\n",
    "for [i, quantile] in enumerate(p[:-1]):\n",
    "    quantile_filter = np.quantile(data_kept[ft], [quantile, p[i+1]])\n",
    "    data_reduced_qunatile.loc[((data_kept[ft]>=quantile_filter[0])&\n",
    "                               (data_kept[ft]<quantile_filter[1])), ft_new]=i+1\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "outputs": [],
   "source": [
    "quantile_filter = np.quantile(data_kept[ft], [0.2, 0.3])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "outputs": [
    {
     "data": {
      "text/plain": "array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])"
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p[:-1]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "X_clean = data_kept.values.astype(np.float64)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Elbow Method...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:13<00:00,  1.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The elbow (num of clusters) of SoS given by Method 1 is 6.0, by Method 2 is 6.0\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1080x360 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for -: 'str' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/c_/y_0x4rsd6tb4chd_y83jnpzr0000gn/T/ipykernel_9333/1137655566.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      5\u001B[0m \u001B[0mk_search\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mlinspace\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mstart\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m2\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mstop\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m11\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mnum\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m10\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      6\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 7\u001B[0;31m \u001B[0mmetric_list\u001B[0m\u001B[0;34m=\u001B[0m \u001B[0melbow_method\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mX_clean\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mk_search\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m'KMeans'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      8\u001B[0m \u001B[0;31m# print(k_opt)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      9\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/Desktop/Study/Graduate_Study_Austin/Math/Courses/2021-2022/2022Spring/M393C_ML/m393c_project/datatools.py\u001B[0m in \u001B[0;36melbow_method\u001B[0;34m(X, k_search, method, plot)\u001B[0m\n\u001B[1;32m    331\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    332\u001B[0m         \u001B[0;31m# Compute the Silhouette and Plot\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 333\u001B[0;31m         \u001B[0mprint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m'Now computing silhouette over each cluster number from {} to {}'\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mformat\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mcenter\u001B[0m\u001B[0;34m-\u001B[0m\u001B[0;36m4\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcenter\u001B[0m\u001B[0;34m+\u001B[0m\u001B[0;36m4\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    334\u001B[0m         \u001B[0msilh_interval\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0marange\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mcenter\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m-\u001B[0m\u001B[0;36m4\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mcenter\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m+\u001B[0m\u001B[0;36m5\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    335\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mTypeError\u001B[0m: unsupported operand type(s) for -: 'str' and 'int'"
     ]
    }
   ],
   "source": [
    "from datatools import data_quantization\n",
    "a, b = data_quantization(data_kept)\n",
    "\n",
    "\n",
    "k_search = np.linspace(start=2, stop=11, num=10)\n",
    "\n",
    "metric_list= elbow_method(X_clean, k_search, 'KMeans')\n",
    "# print(k_opt)\n",
    "\n",
    "#low DB, high CH"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [
    "k_diff = k_search[1:len(k_search)]-k_search[0:len(k_search)-1]\n",
    "assert min(k_diff) == max(k_diff)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [],
   "source": [
    "scores = metric_list[-1]\n",
    "diff1 = np.zeros(len(scores))\n",
    "diff2 = diff1.copy()\n",
    "# strength = diff1.copy()\n",
    "diff1[1:] = (1/k_diff[0])*(scores[1:len(scores)]-scores[0:len(scores)-1])\n",
    "diff2[2:] = (1/k_diff[0])*(diff1[2:len(diff1)]-diff1[1:len(diff1)-1])\n",
    "strength[1:-1] = diff2[2:] - diff1[2:]\n",
    "# res = next(x for (x, val) in enumerate(diff1) if val < 0)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "array([-1.45945675e-02,  9.15718553e-03,  2.37906991e-03,  9.49343209e-04,\n        8.04327562e-04,  2.50365378e-04,  2.72314104e-04,  8.27466399e-05,\n       -3.16276401e-05,  2.52292203e-04, -5.09280121e-05,  6.91306402e-05,\n        1.18136893e-04,  1.48650812e-05, -1.03980645e-06,  6.11945367e-05,\n       -6.43555879e-05,  1.50193719e-04, -5.39561323e-05])"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1/k_diff[0])*(diff1[1:len(diff1)]-diff1[0:len(diff1)-1])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7fecced3e580>]"
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(strength[1:])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7fecce5999d0>]"
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(diff1[1:])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7feccd71e100>]"
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(diff2[2:])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'numpy' has no attribute 'repmat'",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mAttributeError\u001B[0m                            Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/c_/y_0x4rsd6tb4chd_y83jnpzr0000gn/T/ipykernel_9333/1568774067.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      8\u001B[0m \u001B[0mlineVecNorm\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mlineVec\u001B[0m \u001B[0;34m/\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msqrt\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msum\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mlineVec\u001B[0m\u001B[0;34m**\u001B[0m\u001B[0;36m2\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      9\u001B[0m \u001B[0mvecFromFirst\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mallCoord\u001B[0m \u001B[0;34m-\u001B[0m \u001B[0mfirstPoint\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 10\u001B[0;31m \u001B[0mscalarProduct\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msum\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mvecFromFirst\u001B[0m \u001B[0;34m*\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mrepmat\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mlineVecNorm\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mnPoints\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m1\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0maxis\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     11\u001B[0m \u001B[0mvecFromFirstParallel\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mouter\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mscalarProduct\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mlineVecNorm\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     12\u001B[0m \u001B[0mvecToLine\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mvecFromFirst\u001B[0m \u001B[0;34m-\u001B[0m \u001B[0mvecFromFirstParallel\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/opt/anaconda3/envs/DataScience/lib/python3.9/site-packages/numpy/__init__.py\u001B[0m in \u001B[0;36m__getattr__\u001B[0;34m(attr)\u001B[0m\n\u001B[1;32m    311\u001B[0m                 \u001B[0;32mreturn\u001B[0m \u001B[0mTester\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    312\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 313\u001B[0;31m             raise AttributeError(\"module {!r} has no attribute \"\n\u001B[0m\u001B[1;32m    314\u001B[0m                                  \"{!r}\".format(__name__, attr))\n\u001B[1;32m    315\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mAttributeError\u001B[0m: module 'numpy' has no attribute 'repmat'"
     ]
    }
   ],
   "source": [
    "import numpy.matlib\n",
    "curve = scores\n",
    "nPoints = len(curve)\n",
    "allCoord = np.vstack((range(nPoints), curve)).T\n",
    "np.array([range(nPoints), curve])\n",
    "firstPoint = allCoord[0]\n",
    "lineVec = allCoord[-1] - allCoord[0]\n",
    "lineVecNorm = lineVec / np.sqrt(np.sum(lineVec**2))\n",
    "vecFromFirst = allCoord - firstPoint\n",
    "scalarProduct = np.sum(vecFromFirst * np.matlib.repmat(lineVecNorm, nPoints, 1), axis=1)\n",
    "vecFromFirstParallel = np.outer(scalarProduct, lineVecNorm)\n",
    "vecToLine = vecFromFirst - vecFromFirstParallel\n",
    "distToLine = np.sqrt(np.sum(vecToLine ** 2, axis=1))\n",
    "idxOfBestPoint = np.argmax(distToLine)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7feccd4f83d0>]"
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff2 = scores[2:]+scores[:-2]-2*scores[1:-1]\n",
    "plt.plot(diff2[6:])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "outputs": [],
   "source": [
    "from kneed import KneeLocator\n",
    "kn = KneeLocator(\n",
    "    np.array(range(99)),\n",
    "    scores,\n",
    "    curve='convex',\n",
    "    direction='decreasing',\n",
    "    interp_method='polynomial',\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "outputs": [
    {
     "data": {
      "text/plain": "3"
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmin(abs(metric_list[2][2:] - metric_list[1][2:]))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7fecce5c9ca0>]"
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(metric_list[2][2:] - metric_list[1][2:])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "outputs": [
    {
     "data": {
      "text/plain": "15"
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kn.elbow\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATA_PATH is a valid path\n",
      "Here are the selected features (all of them): \n",
      " ['BALANCE', 'PURCHASES', 'ONEOFF_PURCHASES', 'INSTALLMENTS_PURCHASES', 'CASH_ADVANCE', 'CREDIT_LIMIT', 'PAYMENTS', 'MINIMUM_PAYMENTS', 'PRC_FULL_PAYMENT', 'BALANCE_FREQUENCY', 'PURCHASES_FREQUENCY', 'ONEOFF_PURCHASES_FREQUENCY', 'PURCHASES_INSTALLMENTS_FREQUENCY', 'CASH_ADVANCE_FREQUENCY'] \n",
      "\n",
      "Features retained are: all\n",
      "\n",
      "Here are the features not used: \n",
      " ['PURCHASES_TRX', 'CUST_ID', 'CASH_ADVANCE_TRX', 'TENURE']\n"
     ]
    }
   ],
   "source": [
    "METHOD = 'KMeans' #options are 'GM' or 'KMeans'\n",
    "\n",
    "dataset = 'all'  #which dataset; options are 'basic', 'all', or 'freq'\n",
    "no_change = False  #Run clustering on cleaned (NaN-removed data). No touching the outliers.\n",
    "\n",
    "#Choose only one of these; run clustering on quantized data, or outlier-removed data.\n",
    "do_quantize = True\n",
    "remove_outliers = not do_quantize\n",
    "\n",
    "#Apply SVD on the data (after quantizing / removing outliers)?\n",
    "reduce_dim = False\n",
    "\n",
    "if remove_outliers:\n",
    "    rescale = True  #If we remove outliers, default behavior is to rescale the data to [0,1] after cleaning\n",
    "else:\n",
    "    rescale = False\n",
    "\n",
    "\n",
    "feature_plot = ['CREDIT_LIMIT', 'BALANCE', 'PURCHASES_FREQUENCY', 'PRC_FULL_PAYMENT', 'CASH_ADVANCE_FREQUENCY',\\\n",
    "                'MINIMUM_PAYMENTS', 'CASH_ADVANCE', 'PURCHASES',  'PURCHASES_INSTALLMENTS_FREQUENCY']\n",
    "\"\"\"\n",
    "2. Read In Data\n",
    "\"\"\"\n",
    "DATA_PATH = './raw_data/CreditCard_data.csv'\n",
    "if os.path.isfile(DATA_PATH):\n",
    "    print('DATA_PATH is a valid path')\n",
    "else:\n",
    "    raise ValueError('DATA_PATH is not valid path')\n",
    "\n",
    "#SAMPLE_SIZE = 10000\n",
    "data_raw = pd.read_csv(DATA_PATH)\n",
    "\n",
    "\"\"\"\n",
    "3. Remove bad data values (NaN) ato obtain valid data. Slice features chosen to obtain desired, valid data.\n",
    "\"\"\"\n",
    "data_valid, _, _ = make_clean_data(data_raw, verbose=False)\n",
    "data_kept, feature_kept, _ = select_features(data_valid, which= dataset)\n",
    "X = data_kept.values.astype(np.float64) # numpy array ready to be clustered\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features have at least the following percentage of being zero:\n",
      " {'BALANCE': 0.0, 'PURCHASES': 0.2, 'ONEOFF_PURCHASES': 0.4, 'INSTALLMENTS_PURCHASES': 0.4, 'CASH_ADVANCE': 0.5, 'CREDIT_LIMIT': 0, 'PAYMENTS': 0, 'MINIMUM_PAYMENTS': 0, 'PRC_FULL_PAYMENT': 0.6000000000000001, 'BALANCE_FREQUENCY': 0.0, 'PURCHASES_FREQUENCY': 0.2, 'ONEOFF_PURCHASES_FREQUENCY': 0.4, 'PURCHASES_INSTALLMENTS_FREQUENCY': 0.4, 'CASH_ADVANCE_FREQUENCY': 0.5}\n",
      "Running Elbow Method...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:00<00:00, 12.46it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The elbow (num of clusters) of SoS given by Method 1 is 5, by Method 2 is 6\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lewisliu/Desktop/Study/Graduate_Study_Austin/Math/Courses/2021-2022/2022Spring/M393C_ML/m393c_project/datatools.py:428: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  return\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now computing silhouette over each cluster number from 2 to 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9/9 [00:00<00:00, 15.80it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1080x360 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1080x720 with 9 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this number of cluster, the CH score is 7.726516946421749, the DB score is 0.8156080653408626\n",
      "Optimal input was 5\n",
      "() [ 1  3 10 12]\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "too many indices for array: array is 0-dimensional, but 1 were indexed",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mIndexError\u001B[0m                                Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/c_/y_0x4rsd6tb4chd_y83jnpzr0000gn/T/ipykernel_9333/1563553008.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      5\u001B[0m \u001B[0moptimal_cluster\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0marange\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;36m2\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;36m12\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      6\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 7\u001B[0;31m \u001B[0melbow_method\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mX_transpose\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0moptimal_cluster\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmethod\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m'KMeans'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      8\u001B[0m \u001B[0;31m# for optimal_num in optimal_cluster:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      9\u001B[0m \u001B[0;31m#     if METHOD == 'KMeans':\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/Desktop/Study/Graduate_Study_Austin/Math/Courses/2021-2022/2022Spring/M393C_ML/m393c_project/datatools.py\u001B[0m in \u001B[0;36melbow_method\u001B[0;34m(X, k_search, method, plot, savedir, do_transpose, feature_select)\u001B[0m\n\u001B[1;32m    542\u001B[0m         \u001B[0mKmeansOpt\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mKMeans\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mn_clusters\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0moptimal_num\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mrandom_state\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mX\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    543\u001B[0m         \u001B[0moptimal_label_kmeans\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mKmeansOpt\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mlabels_\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 544\u001B[0;31m         plot_optimal(Xin = Xpca, labels= optimal_label_kmeans, num_comps= num_comps,\\\n\u001B[0m\u001B[1;32m    545\u001B[0m                      method = method, savepath = savedir+'/optimal_kmeans.eps', annotation = do_transpose, ft_select = feature_select)\n\u001B[1;32m    546\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/Desktop/Study/Graduate_Study_Austin/Math/Courses/2021-2022/2022Spring/M393C_ML/m393c_project/datatools.py\u001B[0m in \u001B[0;36mplot_optimal\u001B[0;34m(Xin, labels, num_comps, method, savepath, annotation, ft_select)\u001B[0m\n\u001B[1;32m    310\u001B[0m         \u001B[0mprint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0marray\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mft_select\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mshape\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mwhere\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mcluster\u001B[0m \u001B[0;34m==\u001B[0m \u001B[0mlabels\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    311\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 312\u001B[0;31m         \u001B[0mXname\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0marray\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mft_select\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mwhere\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mcluster\u001B[0m \u001B[0;34m==\u001B[0m \u001B[0mlabels\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    313\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    314\u001B[0m         \u001B[0;32mfor\u001B[0m \u001B[0mcomp\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mrange\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mnum_comps\u001B[0m\u001B[0;34m-\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mIndexError\u001B[0m: too many indices for array: array is 0-dimensional, but 1 were indexed"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1224x576 with 4 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_quant, percent_zero = data_quantization(data_kept)\n",
    "print('Features have at least the following percentage of being zero:\\n', percent_zero)\n",
    "X_clean = data_quant.values.astype(np.float64)\n",
    "X_transpose = X_clean.transpose()\n",
    "optimal_cluster = np.arange(2,12)\n",
    "\n",
    "elbow_method(X_transpose, optimal_cluster, method='KMeans')\n",
    "# for optimal_num in optimal_cluster:\n",
    "#     if METHOD == 'KMeans':\n",
    "#         cluster = KMeans(n_clusters=int(optimal_num), random_state=0).fit(X_clean)\n",
    "#         cluster_labels = cluster.labels_\n",
    "#\n",
    "#     elif METHOD == 'GM':\n",
    "#         cluster = GaussianMixture(n_components=int(optimal_num), random_state=0).fit(X_clean)\n",
    "#         cluster_labels = cluster.labels_"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 13)\n",
      "(4, 13)\n",
      "(4, 13)\n",
      "(7, 13)\n",
      "(7, 13)\n",
      "(3, 13)\n",
      "(3, 13)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1224x576 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import TruncatedSVD, PCA\n",
    "from datatools import plot_optimal\n",
    "makePCA = PCA(n_components=np.min((X_transpose.shape[0]-1, X_transpose.shape[1]-1)))\n",
    "makePCA.fit(X_transpose)\n",
    "Xpca = makePCA.transform(X_transpose)\n",
    "# Re-run kmeans for optimal number\n",
    "\n",
    "#assert (type(optimal_K_1) == int), 'First Optimal KMeans Cluster Value is Not Integer'\n",
    "#assert (type(optimal_K_2) == int), 'Second Optimal KMeans Cluster Value is Not Integer'\n",
    "optimal_num = 3\n",
    "if METHOD == 'GM':\n",
    "    GMMOpt = GaussianMixture(n_components = optimal_num, random_state=0).fit(X_transpose)\n",
    "    optimal_label_gmm = GMMOpt.predict(X_transpose)\n",
    "    plot_optimal(Xin = Xpca, labels= optimal_label_gmm, num_comps= 3,\n",
    "                 method =METHOD, annotation=True, ft_select=feature_kept)\n",
    "elif METHOD == 'KMeans':\n",
    "    KmeansOpt = KMeans(n_clusters=optimal_num, random_state=0).fit(X_transpose)\n",
    "    optimal_label_kmeans = KmeansOpt.labels_\n",
    "    plot_optimal(Xin = Xpca, labels= optimal_label_kmeans, num_comps= 3,\n",
    "                 method = METHOD, annotation=True, ft_select=feature_kept)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "outputs": [
    {
     "data": {
      "text/plain": "7"
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "feature_kept.index(feature_plot[j])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "outputs": [],
   "source": [
    "makePCA = PCA(n_components = 13)\n",
    "makePCA.fit(X_transpose)\n",
    "Xpca = makePCA.transform(X_transpose)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "outputs": [
    {
     "ename": "AxisError",
     "evalue": "axis 8635 is out of bounds for array of dimension 0",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mAxisError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/c_/y_0x4rsd6tb4chd_y83jnpzr0000gn/T/ipykernel_9333/1870547998.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mmin\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mX_transpose\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mshape\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m-\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mX_transpose\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mshape\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m-\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[0;32m<__array_function__ internals>\u001B[0m in \u001B[0;36mamin\u001B[0;34m(*args, **kwargs)\u001B[0m\n",
      "\u001B[0;32m~/opt/anaconda3/envs/DataScience/lib/python3.9/site-packages/numpy/core/fromnumeric.py\u001B[0m in \u001B[0;36mamin\u001B[0;34m(a, axis, out, keepdims, initial, where)\u001B[0m\n\u001B[1;32m   2877\u001B[0m     \u001B[0;36m6\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   2878\u001B[0m     \"\"\"\n\u001B[0;32m-> 2879\u001B[0;31m     return _wrapreduction(a, np.minimum, 'min', axis, None, out,\n\u001B[0m\u001B[1;32m   2880\u001B[0m                           keepdims=keepdims, initial=initial, where=where)\n\u001B[1;32m   2881\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/opt/anaconda3/envs/DataScience/lib/python3.9/site-packages/numpy/core/fromnumeric.py\u001B[0m in \u001B[0;36m_wrapreduction\u001B[0;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001B[0m\n\u001B[1;32m     84\u001B[0m                 \u001B[0;32mreturn\u001B[0m \u001B[0mreduction\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0maxis\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0maxis\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mout\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mout\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mpasskwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     85\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 86\u001B[0;31m     \u001B[0;32mreturn\u001B[0m \u001B[0mufunc\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mreduce\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mobj\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0maxis\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdtype\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mout\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mpasskwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     87\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     88\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mAxisError\u001B[0m: axis 8635 is out of bounds for array of dimension 0"
     ]
    }
   ],
   "source": [
    "np.min(X_transpose.shape[0]-1, X_transpose.shape[1]-1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "outputs": [
    {
     "data": {
      "text/plain": "array(['INSTALLMENTS_PURCHASES', 'PRC_FULL_PAYMENT',\n       'PURCHASES_FREQUENCY', 'PURCHASES_INSTALLMENTS_FREQUENCY'],\n      dtype='<U32')"
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters = list(set(optimal_label_kmeans.tolist()))\n",
    "clusters.sort()\n",
    "ft_kept_np = np.array(feature_kept)\n",
    "ft_kept_np[np.where(clusters[0] == optimal_label_kmeans)[0]]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "outputs": [
    {
     "data": {
      "text/plain": "array([ 3,  8, 10, 12])"
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(clusters[0] == optimal_label_kmeans)[0]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[autoreload of datatools failed: Traceback (most recent call last):\n",
      "  File \"/Users/lewisliu/.local/lib/python3.9/site-packages/IPython/extensions/autoreload.py\", line 245, in check\n",
      "    superreload(m, reload, self.old_objects)\n",
      "  File \"/Users/lewisliu/.local/lib/python3.9/site-packages/IPython/extensions/autoreload.py\", line 394, in superreload\n",
      "    module = reload(module)\n",
      "  File \"/Users/lewisliu/opt/anaconda3/envs/DataScience/lib/python3.9/imp.py\", line 314, in reload\n",
      "    return importlib.reload(module)\n",
      "  File \"/Users/lewisliu/opt/anaconda3/envs/DataScience/lib/python3.9/importlib/__init__.py\", line 169, in reload\n",
      "    _bootstrap._exec(spec, module)\n",
      "  File \"<frozen importlib._bootstrap>\", line 613, in _exec\n",
      "  File \"<frozen importlib._bootstrap_external>\", line 846, in exec_module\n",
      "  File \"<frozen importlib._bootstrap_external>\", line 983, in get_code\n",
      "  File \"<frozen importlib._bootstrap_external>\", line 913, in source_to_code\n",
      "  File \"<frozen importlib._bootstrap>\", line 228, in _call_with_frames_removed\n",
      "  File \"/Users/lewisliu/Desktop/Study/Graduate_Study_Austin/Math/Courses/2021-2022/2022Spring/M393C_ML/m393c_project/datatools.py\", line 322\n",
      "    plt.show()\n",
      "IndentationError: expected an indented block\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "print(X_clean.max())\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "outputs": [],
   "source": [
    "\n",
    "X_test = X.copy()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "outputs": [],
   "source": [
    "X_test = X_test.transpose()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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