Demo 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('delaware_anomaly.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FISCAL_YEAR</th>\n",
       "      <th>FISCAL_PERIOD</th>\n",
       "      <th>DEPT_NAME</th>\n",
       "      <th>DIV_NAME</th>\n",
       "      <th>MERCHANT</th>\n",
       "      <th>CAT_DESCR</th>\n",
       "      <th>TRANS_DT</th>\n",
       "      <th>MERCHANDISE_AMT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>DEPT OF EDUCATION</td>\n",
       "      <td>DEPARTMENT OF EDUCATION</td>\n",
       "      <td>DOUBLETREE HOTELS WASHING</td>\n",
       "      <td>LODGING</td>\n",
       "      <td>6/3/2014</td>\n",
       "      <td>-5.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>DEPT OF EDUCATION</td>\n",
       "      <td>DEPARTMENT OF EDUCATION</td>\n",
       "      <td>HYATT REGENCY NW ORLE F/B</td>\n",
       "      <td>EATING PLACES RESTAURANTS</td>\n",
       "      <td>6/26/2014</td>\n",
       "      <td>18.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>DEPT OF EDUCATION</td>\n",
       "      <td>DEPARTMENT OF EDUCATION</td>\n",
       "      <td>HYATT REGENCY NW ORLE F/B</td>\n",
       "      <td>EATING PLACES RESTAURANTS</td>\n",
       "      <td>6/26/2014</td>\n",
       "      <td>21.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>DEPT OF EDUCATION</td>\n",
       "      <td>DEPARTMENT OF EDUCATION</td>\n",
       "      <td>HYATT REGENCY NW ORLE F/B</td>\n",
       "      <td>EATING PLACES RESTAURANTS</td>\n",
       "      <td>6/26/2014</td>\n",
       "      <td>22.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>DEPT OF EDUCATION</td>\n",
       "      <td>DEPARTMENT OF EDUCATION</td>\n",
       "      <td>HYATT REGENCY NW ORLE F/B</td>\n",
       "      <td>EATING PLACES RESTAURANTS</td>\n",
       "      <td>6/26/2014</td>\n",
       "      <td>5.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FISCAL_YEAR  FISCAL_PERIOD          DEPT_NAME                 DIV_NAME  \\\n",
       "0         2015              1  DEPT OF EDUCATION  DEPARTMENT OF EDUCATION   \n",
       "1         2015              1  DEPT OF EDUCATION  DEPARTMENT OF EDUCATION   \n",
       "2         2015              1  DEPT OF EDUCATION  DEPARTMENT OF EDUCATION   \n",
       "3         2015              1  DEPT OF EDUCATION  DEPARTMENT OF EDUCATION   \n",
       "4         2015              1  DEPT OF EDUCATION  DEPARTMENT OF EDUCATION   \n",
       "\n",
       "                    MERCHANT                  CAT_DESCR   TRANS_DT  \\\n",
       "0  DOUBLETREE HOTELS WASHING                    LODGING   6/3/2014   \n",
       "1  HYATT REGENCY NW ORLE F/B  EATING PLACES RESTAURANTS  6/26/2014   \n",
       "2  HYATT REGENCY NW ORLE F/B  EATING PLACES RESTAURANTS  6/26/2014   \n",
       "3  HYATT REGENCY NW ORLE F/B  EATING PLACES RESTAURANTS  6/26/2014   \n",
       "4  HYATT REGENCY NW ORLE F/B  EATING PLACES RESTAURANTS  6/26/2014   \n",
       "\n",
       "   MERCHANDISE_AMT  \n",
       "0            -5.50  \n",
       "1            18.31  \n",
       "2            21.61  \n",
       "3            22.39  \n",
       "4             5.44  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WB MASON                     293\n",
       "AMAZON.COM                   211\n",
       "VZWRLSS*IVR VB               166\n",
       "AMAZON MKTPLACE PMTS         163\n",
       "USPS PO 0917600901           157\n",
       "                            ... \n",
       "SQ *UDUMAH TAXI GOSQ.COM       1\n",
       "MEARS LUXURY DIVISION          1\n",
       "STAPLS0154393874000001         1\n",
       "STAPLS0176291352000001         1\n",
       "AMTRAK .COM 0540725569664      1\n",
       "Name: MERCHANT, Length: 5802, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['MERCHANT'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15156, 8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FISCAL_YEAR          int64\n",
       "FISCAL_PERIOD        int64\n",
       "DEPT_NAME           object\n",
       "DIV_NAME            object\n",
       "MERCHANT            object\n",
       "CAT_DESCR           object\n",
       "TRANS_DT            object\n",
       "MERCHANDISE_AMT    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_b7267_row6_col1{\n",
       "            background-color:  lightgreen;\n",
       "        }</style><table id=\"T_b7267_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_b7267_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_b7267_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_b7267_row0_col1\" class=\"data row0 col1\" >123</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_b7267_row1_col0\" class=\"data row1 col0\" >Original Data</td>\n",
       "                        <td id=\"T_b7267_row1_col1\" class=\"data row1 col1\" >(15156, 8)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_b7267_row2_col0\" class=\"data row2 col0\" >Missing Values</td>\n",
       "                        <td id=\"T_b7267_row2_col1\" class=\"data row2 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_b7267_row3_col0\" class=\"data row3 col0\" >Numeric Features</td>\n",
       "                        <td id=\"T_b7267_row3_col1\" class=\"data row3 col1\" >3</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_b7267_row4_col0\" class=\"data row4 col0\" >Categorical Features</td>\n",
       "                        <td id=\"T_b7267_row4_col1\" class=\"data row4 col1\" >3</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_b7267_row5_col0\" class=\"data row5 col0\" >Ordinal Features</td>\n",
       "                        <td id=\"T_b7267_row5_col1\" class=\"data row5 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_b7267_row6_col0\" class=\"data row6 col0\" >High Cardinality Features</td>\n",
       "                        <td id=\"T_b7267_row6_col1\" class=\"data row6 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_b7267_row7_col0\" class=\"data row7 col0\" >High Cardinality Method</td>\n",
       "                        <td id=\"T_b7267_row7_col1\" class=\"data row7 col1\" >frequency</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_b7267_row8_col0\" class=\"data row8 col0\" >Transformed Data</td>\n",
       "                        <td id=\"T_b7267_row8_col1\" class=\"data row8 col1\" >(15156, 53)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_b7267_row9_col0\" class=\"data row9 col0\" >CPU Jobs</td>\n",
       "                        <td id=\"T_b7267_row9_col1\" class=\"data row9 col1\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_b7267_row10_col0\" class=\"data row10 col0\" >Use GPU</td>\n",
       "                        <td id=\"T_b7267_row10_col1\" class=\"data row10 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_b7267_row11_col0\" class=\"data row11 col0\" >Log Experiment</td>\n",
       "                        <td id=\"T_b7267_row11_col1\" class=\"data row11 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_b7267_row12_col0\" class=\"data row12 col0\" >Experiment Name</td>\n",
       "                        <td id=\"T_b7267_row12_col1\" class=\"data row12 col1\" >anomaly-default-name</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_b7267_row13_col0\" class=\"data row13 col0\" >USI</td>\n",
       "                        <td id=\"T_b7267_row13_col1\" class=\"data row13 col1\" >25da</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_b7267_row14_col0\" class=\"data row14 col0\" >Imputation Type</td>\n",
       "                        <td id=\"T_b7267_row14_col1\" class=\"data row14 col1\" >simple</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_b7267_row15_col0\" class=\"data row15 col0\" >Iterative Imputation Iteration</td>\n",
       "                        <td id=\"T_b7267_row15_col1\" class=\"data row15 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_b7267_row16_col0\" class=\"data row16 col0\" >Numeric Imputer</td>\n",
       "                        <td id=\"T_b7267_row16_col1\" class=\"data row16 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_b7267_row17_col0\" class=\"data row17 col0\" >Iterative Imputation Numeric Model</td>\n",
       "                        <td id=\"T_b7267_row17_col1\" class=\"data row17 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_b7267_row18_col0\" class=\"data row18 col0\" >Categorical Imputer</td>\n",
       "                        <td id=\"T_b7267_row18_col1\" class=\"data row18 col1\" >mode</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_b7267_row19_col0\" class=\"data row19 col0\" >Iterative Imputation Categorical Model</td>\n",
       "                        <td id=\"T_b7267_row19_col1\" class=\"data row19 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_b7267_row20_col0\" class=\"data row20 col0\" >Unknown Categoricals Handling</td>\n",
       "                        <td id=\"T_b7267_row20_col1\" class=\"data row20 col1\" >least_frequent</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_b7267_row21_col0\" class=\"data row21 col0\" >Normalize</td>\n",
       "                        <td id=\"T_b7267_row21_col1\" class=\"data row21 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_b7267_row22_col0\" class=\"data row22 col0\" >Normalize Method</td>\n",
       "                        <td id=\"T_b7267_row22_col1\" class=\"data row22 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_b7267_row23_col0\" class=\"data row23 col0\" >Transformation</td>\n",
       "                        <td id=\"T_b7267_row23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_b7267_row24_col0\" class=\"data row24 col0\" >Transformation Method</td>\n",
       "                        <td id=\"T_b7267_row24_col1\" class=\"data row24 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_b7267_row25_col0\" class=\"data row25 col0\" >PCA</td>\n",
       "                        <td id=\"T_b7267_row25_col1\" class=\"data row25 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_b7267_row26_col0\" class=\"data row26 col0\" >PCA Method</td>\n",
       "                        <td id=\"T_b7267_row26_col1\" class=\"data row26 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_b7267_row27_col0\" class=\"data row27 col0\" >PCA Components</td>\n",
       "                        <td id=\"T_b7267_row27_col1\" class=\"data row27 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_b7267_row28_col0\" class=\"data row28 col0\" >Ignore Low Variance</td>\n",
       "                        <td id=\"T_b7267_row28_col1\" class=\"data row28 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_b7267_row29_col0\" class=\"data row29 col0\" >Combine Rare Levels</td>\n",
       "                        <td id=\"T_b7267_row29_col1\" class=\"data row29 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_b7267_row30_col0\" class=\"data row30 col0\" >Rare Level Threshold</td>\n",
       "                        <td id=\"T_b7267_row30_col1\" class=\"data row30 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_b7267_row31_col0\" class=\"data row31 col0\" >Numeric Binning</td>\n",
       "                        <td id=\"T_b7267_row31_col1\" class=\"data row31 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_b7267_row32_col0\" class=\"data row32 col0\" >Remove Outliers</td>\n",
       "                        <td id=\"T_b7267_row32_col1\" class=\"data row32 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_b7267_row33_col0\" class=\"data row33 col0\" >Outliers Threshold</td>\n",
       "                        <td id=\"T_b7267_row33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_b7267_row34_col0\" class=\"data row34 col0\" >Remove Multicollinearity</td>\n",
       "                        <td id=\"T_b7267_row34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_b7267_row35_col0\" class=\"data row35 col0\" >Multicollinearity Threshold</td>\n",
       "                        <td id=\"T_b7267_row35_col1\" class=\"data row35 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_b7267_row36_col0\" class=\"data row36 col0\" >Clustering</td>\n",
       "                        <td id=\"T_b7267_row36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_b7267_row37_col0\" class=\"data row37 col0\" >Clustering Iteration</td>\n",
       "                        <td id=\"T_b7267_row37_col1\" class=\"data row37 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_b7267_row38_col0\" class=\"data row38 col0\" >Polynomial Features</td>\n",
       "                        <td id=\"T_b7267_row38_col1\" class=\"data row38 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_b7267_row39_col0\" class=\"data row39 col0\" >Polynomial Degree</td>\n",
       "                        <td id=\"T_b7267_row39_col1\" class=\"data row39 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_b7267_row40_col0\" class=\"data row40 col0\" >Trignometry Features</td>\n",
       "                        <td id=\"T_b7267_row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_b7267_row41_col0\" class=\"data row41 col0\" >Polynomial Threshold</td>\n",
       "                        <td id=\"T_b7267_row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "                        <td id=\"T_b7267_row42_col0\" class=\"data row42 col0\" >Group Features</td>\n",
       "                        <td id=\"T_b7267_row42_col1\" class=\"data row42 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "                        <td id=\"T_b7267_row43_col0\" class=\"data row43 col0\" >Feature Selection</td>\n",
       "                        <td id=\"T_b7267_row43_col1\" class=\"data row43 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "                        <td id=\"T_b7267_row44_col0\" class=\"data row44 col0\" >Features Selection Threshold</td>\n",
       "                        <td id=\"T_b7267_row44_col1\" class=\"data row44 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "                        <td id=\"T_b7267_row45_col0\" class=\"data row45 col0\" >Feature Interaction</td>\n",
       "                        <td id=\"T_b7267_row45_col1\" class=\"data row45 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "                        <td id=\"T_b7267_row46_col0\" class=\"data row46 col0\" >Feature Ratio</td>\n",
       "                        <td id=\"T_b7267_row46_col1\" class=\"data row46 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7267_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "                        <td id=\"T_b7267_row47_col0\" class=\"data row47 col0\" >Interaction Threshold</td>\n",
       "                        <td id=\"T_b7267_row47_col1\" class=\"data row47 col1\" >None</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x19b443cd048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.anomaly import *\n",
    "s = setup(data, session_id = 123,\n",
    "          ignore_features = ['CAT_DESCR'], high_cardinality_features= ['MERCHANT'],\n",
    "          numeric_features = ['FISCAL_YEAR', 'FISCAL_PERIOD' ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['FISCAL_YEAR', 'FISCAL_PERIOD', 'MERCHANT', 'MERCHANDISE_AMT',\n",
       "       'DEPT_NAME_DEPT OF EDUCATION', 'DIV_NAME_ADVISORY COUNCIL',\n",
       "       'DIV_NAME_Academic Support', 'DIV_NAME_Adult Education and Work Force',\n",
       "       'DIV_NAME_DEPARTMENT OF EDUCATION', 'DIV_NAME_DRIVER TRAINING',\n",
       "       'DIV_NAME_Department of Education', 'DIV_NAME_Driver Training',\n",
       "       'DIV_NAME_E Education Block Grants', 'DIV_NAME_E Transportation',\n",
       "       'DIV_NAME_EDUCATION BLOCK GRANTS', 'DIV_NAME_Education Block Grants',\n",
       "       'DIV_NAME_Educator Support', 'DIV_NAME_OTHER ITEMS',\n",
       "       'DIV_NAME_Office of Early Learning', 'DIV_NAME_Office of the Secretary',\n",
       "       'DIV_NAME_Operations Support', 'DIV_NAME_Other Items',\n",
       "       'DIV_NAME_Professional Standards Board',\n",
       "       'DIV_NAME_Public School Transportation',\n",
       "       'DIV_NAME_SPECIAL NEEDS PROGRAMS', 'DIV_NAME_Special Needs Programs',\n",
       "       'DIV_NAME_State Board of Education', 'DIV_NAME_Student Support',\n",
       "       'DIV_NAME_TRANSPORTATION', 'DIV_NAME_Transportation',\n",
       "       'TRANS_DT_month_1', 'TRANS_DT_month_10', 'TRANS_DT_month_11',\n",
       "       'TRANS_DT_month_12', 'TRANS_DT_month_2', 'TRANS_DT_month_3',\n",
       "       'TRANS_DT_month_4', 'TRANS_DT_month_5', 'TRANS_DT_month_6',\n",
       "       'TRANS_DT_month_7', 'TRANS_DT_month_8', 'TRANS_DT_month_9',\n",
       "       'TRANS_DT_weekday_0', 'TRANS_DT_weekday_1', 'TRANS_DT_weekday_2',\n",
       "       'TRANS_DT_weekday_3', 'TRANS_DT_weekday_4', 'TRANS_DT_weekday_5',\n",
       "       'TRANS_DT_weekday_6', 'TRANS_DT_is_month_end_0',\n",
       "       'TRANS_DT_is_month_end_1', 'TRANS_DT_is_month_start_0',\n",
       "       'TRANS_DT_is_month_start_1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_config('X').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "iforest = create_model('iforest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FISCAL_YEAR</th>\n",
       "      <th>FISCAL_PERIOD</th>\n",
       "      <th>DEPT_NAME</th>\n",
       "      <th>DIV_NAME</th>\n",
       "      <th>MERCHANT</th>\n",
       "      <th>CAT_DESCR</th>\n",
       "      <th>TRANS_DT</th>\n",
       "      <th>MERCHANDISE_AMT</th>\n",
       "      <th>Anomaly</th>\n",
       "      <th>Anomaly_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>DEPT OF EDUCATION</td>\n",
       "      <td>DEPARTMENT OF EDUCATION</td>\n",
       "      <td>DOUBLETREE HOTELS WASHING</td>\n",
       "      <td>LODGING</td>\n",
       "      <td>6/3/2014</td>\n",
       "      <td>-5.50</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.081450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>DEPT OF EDUCATION</td>\n",
       "      <td>DEPARTMENT OF EDUCATION</td>\n",
       "      <td>HYATT REGENCY NW ORLE F/B</td>\n",
       "      <td>EATING PLACES RESTAURANTS</td>\n",
       "      <td>6/26/2014</td>\n",
       "      <td>18.31</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.071574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>DEPT OF EDUCATION</td>\n",
       "      <td>DEPARTMENT OF EDUCATION</td>\n",
       "      <td>HYATT REGENCY NW ORLE F/B</td>\n",
       "      <td>EATING PLACES RESTAURANTS</td>\n",
       "      <td>6/26/2014</td>\n",
       "      <td>21.61</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.071574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>DEPT OF EDUCATION</td>\n",
       "      <td>DEPARTMENT OF EDUCATION</td>\n",
       "      <td>HYATT REGENCY NW ORLE F/B</td>\n",
       "      <td>EATING PLACES RESTAURANTS</td>\n",
       "      <td>6/26/2014</td>\n",
       "      <td>22.39</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.071574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>DEPT OF EDUCATION</td>\n",
       "      <td>DEPARTMENT OF EDUCATION</td>\n",
       "      <td>HYATT REGENCY NW ORLE F/B</td>\n",
       "      <td>EATING PLACES RESTAURANTS</td>\n",
       "      <td>6/26/2014</td>\n",
       "      <td>5.44</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.071310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FISCAL_YEAR  FISCAL_PERIOD          DEPT_NAME                 DIV_NAME  \\\n",
       "0         2015              1  DEPT OF EDUCATION  DEPARTMENT OF EDUCATION   \n",
       "1         2015              1  DEPT OF EDUCATION  DEPARTMENT OF EDUCATION   \n",
       "2         2015              1  DEPT OF EDUCATION  DEPARTMENT OF EDUCATION   \n",
       "3         2015              1  DEPT OF EDUCATION  DEPARTMENT OF EDUCATION   \n",
       "4         2015              1  DEPT OF EDUCATION  DEPARTMENT OF EDUCATION   \n",
       "\n",
       "                    MERCHANT                  CAT_DESCR   TRANS_DT  \\\n",
       "0  DOUBLETREE HOTELS WASHING                    LODGING   6/3/2014   \n",
       "1  HYATT REGENCY NW ORLE F/B  EATING PLACES RESTAURANTS  6/26/2014   \n",
       "2  HYATT REGENCY NW ORLE F/B  EATING PLACES RESTAURANTS  6/26/2014   \n",
       "3  HYATT REGENCY NW ORLE F/B  EATING PLACES RESTAURANTS  6/26/2014   \n",
       "4  HYATT REGENCY NW ORLE F/B  EATING PLACES RESTAURANTS  6/26/2014   \n",
       "\n",
       "   MERCHANDISE_AMT  Anomaly  Anomaly_Score  \n",
       "0            -5.50        0      -0.081450  \n",
       "1            18.31        0      -0.071574  \n",
       "2            21.61        0      -0.071574  \n",
       "3            22.39        0      -0.071574  \n",
       "4             5.44        0      -0.071310  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = assign_model(iforest)\n",
    "r.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r['Anomaly_Score'].hist(bins=100, figsize=(10,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}