Demo 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# install pycaret\n",
    "# pip install pycaret\n",
    "\n",
    "# pip install pycaret[full]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.3'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pycaret.utils import version\n",
    "version()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>Col3</th>\n",
       "      <th>Col4</th>\n",
       "      <th>Col5</th>\n",
       "      <th>Col6</th>\n",
       "      <th>Col7</th>\n",
       "      <th>Col8</th>\n",
       "      <th>Col9</th>\n",
       "      <th>Col10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.263995</td>\n",
       "      <td>0.764929</td>\n",
       "      <td>0.138424</td>\n",
       "      <td>0.935242</td>\n",
       "      <td>0.605867</td>\n",
       "      <td>0.518790</td>\n",
       "      <td>0.912225</td>\n",
       "      <td>0.608234</td>\n",
       "      <td>0.723782</td>\n",
       "      <td>0.733591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.546092</td>\n",
       "      <td>0.653975</td>\n",
       "      <td>0.065575</td>\n",
       "      <td>0.227772</td>\n",
       "      <td>0.845269</td>\n",
       "      <td>0.837066</td>\n",
       "      <td>0.272379</td>\n",
       "      <td>0.331679</td>\n",
       "      <td>0.429297</td>\n",
       "      <td>0.367422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.336714</td>\n",
       "      <td>0.538842</td>\n",
       "      <td>0.192801</td>\n",
       "      <td>0.553563</td>\n",
       "      <td>0.074515</td>\n",
       "      <td>0.332993</td>\n",
       "      <td>0.365792</td>\n",
       "      <td>0.861309</td>\n",
       "      <td>0.899017</td>\n",
       "      <td>0.088600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.092108</td>\n",
       "      <td>0.995017</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>0.176371</td>\n",
       "      <td>0.241530</td>\n",
       "      <td>0.514724</td>\n",
       "      <td>0.562208</td>\n",
       "      <td>0.158963</td>\n",
       "      <td>0.073715</td>\n",
       "      <td>0.208463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.325261</td>\n",
       "      <td>0.805968</td>\n",
       "      <td>0.957033</td>\n",
       "      <td>0.331665</td>\n",
       "      <td>0.307923</td>\n",
       "      <td>0.355315</td>\n",
       "      <td>0.501899</td>\n",
       "      <td>0.558449</td>\n",
       "      <td>0.885169</td>\n",
       "      <td>0.182754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Col1      Col2      Col3      Col4      Col5      Col6      Col7  \\\n",
       "0  0.263995  0.764929  0.138424  0.935242  0.605867  0.518790  0.912225   \n",
       "1  0.546092  0.653975  0.065575  0.227772  0.845269  0.837066  0.272379   \n",
       "2  0.336714  0.538842  0.192801  0.553563  0.074515  0.332993  0.365792   \n",
       "3  0.092108  0.995017  0.014465  0.176371  0.241530  0.514724  0.562208   \n",
       "4  0.325261  0.805968  0.957033  0.331665  0.307923  0.355315  0.501899   \n",
       "\n",
       "       Col8      Col9     Col10  \n",
       "0  0.608234  0.723782  0.733591  \n",
       "1  0.331679  0.429297  0.367422  \n",
       "2  0.861309  0.899017  0.088600  \n",
       "3  0.158963  0.073715  0.208463  \n",
       "4  0.558449  0.885169  0.182754  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.datasets import get_data\n",
    "data = get_data('anomaly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Col1</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.491362</td>\n",
       "      <td>0.259138</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.287458</td>\n",
       "      <td>0.492070</td>\n",
       "      <td>0.694192</td>\n",
       "      <td>0.994431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col2</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.490200</td>\n",
       "      <td>0.251931</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.291449</td>\n",
       "      <td>0.488656</td>\n",
       "      <td>0.686531</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col3</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.509077</td>\n",
       "      <td>0.256606</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.337802</td>\n",
       "      <td>0.510077</td>\n",
       "      <td>0.686914</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col4</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.497362</td>\n",
       "      <td>0.263562</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.256147</td>\n",
       "      <td>0.497537</td>\n",
       "      <td>0.731949</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col5</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.586120</td>\n",
       "      <td>0.334658</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.169680</td>\n",
       "      <td>0.782019</td>\n",
       "      <td>0.847956</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col6</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.514636</td>\n",
       "      <td>0.317470</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142587</td>\n",
       "      <td>0.537953</td>\n",
       "      <td>0.856512</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col7</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.508270</td>\n",
       "      <td>0.278483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.246021</td>\n",
       "      <td>0.465679</td>\n",
       "      <td>0.804935</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col8</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.457541</td>\n",
       "      <td>0.220129</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.245539</td>\n",
       "      <td>0.515619</td>\n",
       "      <td>0.626757</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col9</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.477685</td>\n",
       "      <td>0.241432</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.291452</td>\n",
       "      <td>0.387753</td>\n",
       "      <td>0.723674</td>\n",
       "      <td>0.988732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col10</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.495760</td>\n",
       "      <td>0.211677</td>\n",
       "      <td>0.014495</td>\n",
       "      <td>0.329904</td>\n",
       "      <td>0.488891</td>\n",
       "      <td>0.659528</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count      mean       std       min       25%       50%       75%  \\\n",
       "Col1   1000.0  0.491362  0.259138  0.000000  0.287458  0.492070  0.694192   \n",
       "Col2   1000.0  0.490200  0.251931  0.000000  0.291449  0.488656  0.686531   \n",
       "Col3   1000.0  0.509077  0.256606  0.000000  0.337802  0.510077  0.686914   \n",
       "Col4   1000.0  0.497362  0.263562  0.000000  0.256147  0.497537  0.731949   \n",
       "Col5   1000.0  0.586120  0.334658  0.000000  0.169680  0.782019  0.847956   \n",
       "Col6   1000.0  0.514636  0.317470  0.000000  0.142587  0.537953  0.856512   \n",
       "Col7   1000.0  0.508270  0.278483  0.000000  0.246021  0.465679  0.804935   \n",
       "Col8   1000.0  0.457541  0.220129  0.000000  0.245539  0.515619  0.626757   \n",
       "Col9   1000.0  0.477685  0.241432  0.000000  0.291452  0.387753  0.723674   \n",
       "Col10  1000.0  0.495760  0.211677  0.014495  0.329904  0.488891  0.659528   \n",
       "\n",
       "            max  \n",
       "Col1   0.994431  \n",
       "Col2   1.000000  \n",
       "Col3   1.000000  \n",
       "Col4   1.000000  \n",
       "Col5   1.000000  \n",
       "Col6   1.000000  \n",
       "Col7   1.000000  \n",
       "Col8   1.000000  \n",
       "Col9   0.988732  \n",
       "Col10  1.000000  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 10)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_59ff6_row11_col1{\n",
       "            background-color:  lightgreen;\n",
       "        }</style><table id=\"T_59ff6_\" ><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_59ff6_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_59ff6_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_59ff6_row0_col1\" class=\"data row0 col1\" >123</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_59ff6_row1_col0\" class=\"data row1 col0\" >Original Data</td>\n",
       "                        <td id=\"T_59ff6_row1_col1\" class=\"data row1 col1\" >(1000, 10)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_59ff6_row2_col0\" class=\"data row2 col0\" >Missing Values</td>\n",
       "                        <td id=\"T_59ff6_row2_col1\" class=\"data row2 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_59ff6_row3_col0\" class=\"data row3 col0\" >Numeric Features</td>\n",
       "                        <td id=\"T_59ff6_row3_col1\" class=\"data row3 col1\" >10</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_59ff6_row4_col0\" class=\"data row4 col0\" >Categorical Features</td>\n",
       "                        <td id=\"T_59ff6_row4_col1\" class=\"data row4 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_59ff6_row5_col0\" class=\"data row5 col0\" >Ordinal Features</td>\n",
       "                        <td id=\"T_59ff6_row5_col1\" class=\"data row5 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_59ff6_row6_col0\" class=\"data row6 col0\" >High Cardinality Features</td>\n",
       "                        <td id=\"T_59ff6_row6_col1\" class=\"data row6 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_59ff6_row7_col0\" class=\"data row7 col0\" >High Cardinality Method</td>\n",
       "                        <td id=\"T_59ff6_row7_col1\" class=\"data row7 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_59ff6_row8_col0\" class=\"data row8 col0\" >Transformed Data</td>\n",
       "                        <td id=\"T_59ff6_row8_col1\" class=\"data row8 col1\" >(1000, 10)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_59ff6_row9_col0\" class=\"data row9 col0\" >CPU Jobs</td>\n",
       "                        <td id=\"T_59ff6_row9_col1\" class=\"data row9 col1\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_59ff6_row10_col0\" class=\"data row10 col0\" >Use GPU</td>\n",
       "                        <td id=\"T_59ff6_row10_col1\" class=\"data row10 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_59ff6_row11_col0\" class=\"data row11 col0\" >Log Experiment</td>\n",
       "                        <td id=\"T_59ff6_row11_col1\" class=\"data row11 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_59ff6_row12_col0\" class=\"data row12 col0\" >Experiment Name</td>\n",
       "                        <td id=\"T_59ff6_row12_col1\" class=\"data row12 col1\" >anomaly-demo</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_59ff6_row13_col0\" class=\"data row13 col0\" >USI</td>\n",
       "                        <td id=\"T_59ff6_row13_col1\" class=\"data row13 col1\" >5118</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_59ff6_row14_col0\" class=\"data row14 col0\" >Imputation Type</td>\n",
       "                        <td id=\"T_59ff6_row14_col1\" class=\"data row14 col1\" >simple</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_59ff6_row15_col0\" class=\"data row15 col0\" >Iterative Imputation Iteration</td>\n",
       "                        <td id=\"T_59ff6_row15_col1\" class=\"data row15 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_59ff6_row16_col0\" class=\"data row16 col0\" >Numeric Imputer</td>\n",
       "                        <td id=\"T_59ff6_row16_col1\" class=\"data row16 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_59ff6_row17_col0\" class=\"data row17 col0\" >Iterative Imputation Numeric Model</td>\n",
       "                        <td id=\"T_59ff6_row17_col1\" class=\"data row17 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_59ff6_row18_col0\" class=\"data row18 col0\" >Categorical Imputer</td>\n",
       "                        <td id=\"T_59ff6_row18_col1\" class=\"data row18 col1\" >mode</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_59ff6_row19_col0\" class=\"data row19 col0\" >Iterative Imputation Categorical Model</td>\n",
       "                        <td id=\"T_59ff6_row19_col1\" class=\"data row19 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_59ff6_row20_col0\" class=\"data row20 col0\" >Unknown Categoricals Handling</td>\n",
       "                        <td id=\"T_59ff6_row20_col1\" class=\"data row20 col1\" >least_frequent</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_59ff6_row21_col0\" class=\"data row21 col0\" >Normalize</td>\n",
       "                        <td id=\"T_59ff6_row21_col1\" class=\"data row21 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_59ff6_row22_col0\" class=\"data row22 col0\" >Normalize Method</td>\n",
       "                        <td id=\"T_59ff6_row22_col1\" class=\"data row22 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_59ff6_row23_col0\" class=\"data row23 col0\" >Transformation</td>\n",
       "                        <td id=\"T_59ff6_row23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_59ff6_row24_col0\" class=\"data row24 col0\" >Transformation Method</td>\n",
       "                        <td id=\"T_59ff6_row24_col1\" class=\"data row24 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_59ff6_row25_col0\" class=\"data row25 col0\" >PCA</td>\n",
       "                        <td id=\"T_59ff6_row25_col1\" class=\"data row25 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_59ff6_row26_col0\" class=\"data row26 col0\" >PCA Method</td>\n",
       "                        <td id=\"T_59ff6_row26_col1\" class=\"data row26 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_59ff6_row27_col0\" class=\"data row27 col0\" >PCA Components</td>\n",
       "                        <td id=\"T_59ff6_row27_col1\" class=\"data row27 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_59ff6_row28_col0\" class=\"data row28 col0\" >Ignore Low Variance</td>\n",
       "                        <td id=\"T_59ff6_row28_col1\" class=\"data row28 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_59ff6_row29_col0\" class=\"data row29 col0\" >Combine Rare Levels</td>\n",
       "                        <td id=\"T_59ff6_row29_col1\" class=\"data row29 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_59ff6_row30_col0\" class=\"data row30 col0\" >Rare Level Threshold</td>\n",
       "                        <td id=\"T_59ff6_row30_col1\" class=\"data row30 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_59ff6_row31_col0\" class=\"data row31 col0\" >Numeric Binning</td>\n",
       "                        <td id=\"T_59ff6_row31_col1\" class=\"data row31 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_59ff6_row32_col0\" class=\"data row32 col0\" >Remove Outliers</td>\n",
       "                        <td id=\"T_59ff6_row32_col1\" class=\"data row32 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_59ff6_row33_col0\" class=\"data row33 col0\" >Outliers Threshold</td>\n",
       "                        <td id=\"T_59ff6_row33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_59ff6_row34_col0\" class=\"data row34 col0\" >Remove Multicollinearity</td>\n",
       "                        <td id=\"T_59ff6_row34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_59ff6_row35_col0\" class=\"data row35 col0\" >Multicollinearity Threshold</td>\n",
       "                        <td id=\"T_59ff6_row35_col1\" class=\"data row35 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_59ff6_row36_col0\" class=\"data row36 col0\" >Clustering</td>\n",
       "                        <td id=\"T_59ff6_row36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_59ff6_row37_col0\" class=\"data row37 col0\" >Clustering Iteration</td>\n",
       "                        <td id=\"T_59ff6_row37_col1\" class=\"data row37 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_59ff6_row38_col0\" class=\"data row38 col0\" >Polynomial Features</td>\n",
       "                        <td id=\"T_59ff6_row38_col1\" class=\"data row38 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_59ff6_row39_col0\" class=\"data row39 col0\" >Polynomial Degree</td>\n",
       "                        <td id=\"T_59ff6_row39_col1\" class=\"data row39 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_59ff6_row40_col0\" class=\"data row40 col0\" >Trignometry Features</td>\n",
       "                        <td id=\"T_59ff6_row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_59ff6_row41_col0\" class=\"data row41 col0\" >Polynomial Threshold</td>\n",
       "                        <td id=\"T_59ff6_row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "                        <td id=\"T_59ff6_row42_col0\" class=\"data row42 col0\" >Group Features</td>\n",
       "                        <td id=\"T_59ff6_row42_col1\" class=\"data row42 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "                        <td id=\"T_59ff6_row43_col0\" class=\"data row43 col0\" >Feature Selection</td>\n",
       "                        <td id=\"T_59ff6_row43_col1\" class=\"data row43 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "                        <td id=\"T_59ff6_row44_col0\" class=\"data row44 col0\" >Features Selection Threshold</td>\n",
       "                        <td id=\"T_59ff6_row44_col1\" class=\"data row44 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "                        <td id=\"T_59ff6_row45_col0\" class=\"data row45 col0\" >Feature Interaction</td>\n",
       "                        <td id=\"T_59ff6_row45_col1\" class=\"data row45 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "                        <td id=\"T_59ff6_row46_col0\" class=\"data row46 col0\" >Feature Ratio</td>\n",
       "                        <td id=\"T_59ff6_row46_col1\" class=\"data row46 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_59ff6_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "                        <td id=\"T_59ff6_row47_col0\" class=\"data row47 col0\" >Interaction Threshold</td>\n",
       "                        <td id=\"T_59ff6_row47_col1\" class=\"data row47 col1\" >None</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x26a502c5a48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.anomaly import *\n",
    "s = setup(data, session_id = 123, log_experiment = True, experiment_name = 'anomaly-demo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Name</th>\n",
       "      <th>Reference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>abod</th>\n",
       "      <td>Angle-base Outlier Detection</td>\n",
       "      <td>pyod.models.abod.ABOD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <td>Clustering-Based Local Outlier</td>\n",
       "      <td>pyod.models.cblof.CBLOF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cof</th>\n",
       "      <td>Connectivity-Based Local Outlier</td>\n",
       "      <td>pyod.models.cof.COF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iforest</th>\n",
       "      <td>Isolation Forest</td>\n",
       "      <td>pyod.models.iforest.IForest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>histogram</th>\n",
       "      <td>Histogram-based Outlier Detection</td>\n",
       "      <td>pyod.models.hbos.HBOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knn</th>\n",
       "      <td>K-Nearest Neighbors Detector</td>\n",
       "      <td>pyod.models.knn.KNN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lof</th>\n",
       "      <td>Local Outlier Factor</td>\n",
       "      <td>pyod.models.lof.LOF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>svm</th>\n",
       "      <td>One-class SVM detector</td>\n",
       "      <td>pyod.models.ocsvm.OCSVM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pca</th>\n",
       "      <td>Principal Component Analysis</td>\n",
       "      <td>pyod.models.pca.PCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcd</th>\n",
       "      <td>Minimum Covariance Determinant</td>\n",
       "      <td>pyod.models.mcd.MCD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sod</th>\n",
       "      <td>Subspace Outlier Detection</td>\n",
       "      <td>pyod.models.sod.SOD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sos</th>\n",
       "      <td>Stochastic Outlier Selection</td>\n",
       "      <td>pyod.models.sos.SOS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Name                    Reference\n",
       "ID                                                                       \n",
       "abod            Angle-base Outlier Detection        pyod.models.abod.ABOD\n",
       "cluster       Clustering-Based Local Outlier      pyod.models.cblof.CBLOF\n",
       "cof         Connectivity-Based Local Outlier          pyod.models.cof.COF\n",
       "iforest                     Isolation Forest  pyod.models.iforest.IForest\n",
       "histogram  Histogram-based Outlier Detection        pyod.models.hbos.HBOS\n",
       "knn             K-Nearest Neighbors Detector          pyod.models.knn.KNN\n",
       "lof                     Local Outlier Factor          pyod.models.lof.LOF\n",
       "svm                   One-class SVM detector      pyod.models.ocsvm.OCSVM\n",
       "pca             Principal Component Analysis          pyod.models.pca.PCA\n",
       "mcd           Minimum Covariance Determinant          pyod.models.mcd.MCD\n",
       "sod               Subspace Outlier Detection          pyod.models.sod.SOD\n",
       "sos             Stochastic Outlier Selection          pyod.models.sos.SOS"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "iforest = create_model('iforest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
       "    max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=-1,\n",
       "    random_state=123, verbose=0)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iforest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
       "    max_features=1.0, max_samples='auto', n_estimators=200, n_jobs=-1,\n",
       "    random_state=123, verbose=0)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iforest = create_model('iforest', n_estimators=200)\n",
    "iforest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN(algorithm='auto', contamination=0.05, leaf_size=30, method='largest',\n",
      "  metric='minkowski', metric_params=None, n_jobs=-1, n_neighbors=5, p=2,\n",
      "  radius=1.0)\n"
     ]
    }
   ],
   "source": [
    "knn = create_model('knn')\n",
    "print(knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(iforest, plot = 'umap')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Pipeline(memory=None,\n",
       "          steps=[('dtypes',\n",
       "                  DataTypes_Auto_infer(categorical_features=[],\n",
       "                                       display_types=True, features_todrop=[],\n",
       "                                       id_columns=[], ml_usecase='regression',\n",
       "                                       numerical_features=[],\n",
       "                                       target='UNSUPERVISED_DUMMY_TARGET',\n",
       "                                       time_features=[])),\n",
       "                 ('imputer',\n",
       "                  Simple_Imputer(categorical_strategy='most frequent',\n",
       "                                 fill_value_categorical=None,\n",
       "                                 fill_value_numerical=None...\n",
       "                 ('fix_perfect', 'passthrough'),\n",
       "                 ('clean_names', Clean_Colum_Names()),\n",
       "                 ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
       "                 ('dfs', 'passthrough'), ('pca', 'passthrough'),\n",
       "                 ['trained_model',\n",
       "                  IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
       "     max_features=1.0, max_samples='auto', n_estimators=200, n_jobs=-1,\n",
       "     random_state=123, verbose=0)]],\n",
       "          verbose=False), 'abc.pkl')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_model(iforest, model_name = 'abc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Successfully Loaded\n"
     ]
    }
   ],
   "source": [
    "l = load_model('abc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Successfully Loaded\n"
     ]
    },
    {
     "data": {
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       "      <th>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>Col3</th>\n",
       "      <th>Col4</th>\n",
       "      <th>Col5</th>\n",
       "      <th>Col6</th>\n",
       "      <th>Col7</th>\n",
       "      <th>Col8</th>\n",
       "      <th>Col9</th>\n",
       "      <th>Col10</th>\n",
       "      <th>Anomaly</th>\n",
       "      <th>Anomaly_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.263995</td>\n",
       "      <td>0.764929</td>\n",
       "      <td>0.138424</td>\n",
       "      <td>0.935242</td>\n",
       "      <td>0.605867</td>\n",
       "      <td>0.518790</td>\n",
       "      <td>0.912225</td>\n",
       "      <td>0.608234</td>\n",
       "      <td>0.723782</td>\n",
       "      <td>0.733591</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.030361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.546092</td>\n",
       "      <td>0.653975</td>\n",
       "      <td>0.065575</td>\n",
       "      <td>0.227772</td>\n",
       "      <td>0.845269</td>\n",
       "      <td>0.837066</td>\n",
       "      <td>0.272379</td>\n",
       "      <td>0.331679</td>\n",
       "      <td>0.429297</td>\n",
       "      <td>0.367422</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.078290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.336714</td>\n",
       "      <td>0.538842</td>\n",
       "      <td>0.192801</td>\n",
       "      <td>0.553563</td>\n",
       "      <td>0.074515</td>\n",
       "      <td>0.332993</td>\n",
       "      <td>0.365792</td>\n",
       "      <td>0.861309</td>\n",
       "      <td>0.899017</td>\n",
       "      <td>0.088600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.026938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.092108</td>\n",
       "      <td>0.995017</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>0.176371</td>\n",
       "      <td>0.241530</td>\n",
       "      <td>0.514724</td>\n",
       "      <td>0.562208</td>\n",
       "      <td>0.158963</td>\n",
       "      <td>0.073715</td>\n",
       "      <td>0.208463</td>\n",
       "      <td>1</td>\n",
       "      <td>0.053551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.325261</td>\n",
       "      <td>0.805968</td>\n",
       "      <td>0.957033</td>\n",
       "      <td>0.331665</td>\n",
       "      <td>0.307923</td>\n",
       "      <td>0.355315</td>\n",
       "      <td>0.501899</td>\n",
       "      <td>0.558449</td>\n",
       "      <td>0.885169</td>\n",
       "      <td>0.182754</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.015639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Col1      Col2      Col3      Col4      Col5      Col6      Col7  \\\n",
       "0  0.263995  0.764929  0.138424  0.935242  0.605867  0.518790  0.912225   \n",
       "1  0.546092  0.653975  0.065575  0.227772  0.845269  0.837066  0.272379   \n",
       "2  0.336714  0.538842  0.192801  0.553563  0.074515  0.332993  0.365792   \n",
       "3  0.092108  0.995017  0.014465  0.176371  0.241530  0.514724  0.562208   \n",
       "4  0.325261  0.805968  0.957033  0.331665  0.307923  0.355315  0.501899   \n",
       "\n",
       "       Col8      Col9     Col10  Anomaly  Anomaly_Score  \n",
       "0  0.608234  0.723782  0.733591        0      -0.030361  \n",
       "1  0.331679  0.429297  0.367422        0      -0.078290  \n",
       "2  0.861309  0.899017  0.088600        1       0.026938  \n",
       "3  0.158963  0.073715  0.208463        1       0.053551  \n",
       "4  0.558449  0.885169  0.182754        0      -0.015639  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pycaret.anomaly import load_model, predict_model\n",
    "l = load_model('iforest-abc', platform = 'aws', authentication = .......)\n",
    "p = predict_model(l, data=data)\n",
    "p.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Col10</th>\n",
       "      <th>Anomaly</th>\n",
       "      <th>Anomaly_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.263995</td>\n",
       "      <td>0.764929</td>\n",
       "      <td>0.138424</td>\n",
       "      <td>0.935242</td>\n",
       "      <td>0.605867</td>\n",
       "      <td>0.518790</td>\n",
       "      <td>0.912225</td>\n",
       "      <td>0.608234</td>\n",
       "      <td>0.723782</td>\n",
       "      <td>0.733591</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.030361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.546092</td>\n",
       "      <td>0.653975</td>\n",
       "      <td>0.065575</td>\n",
       "      <td>0.227772</td>\n",
       "      <td>0.845269</td>\n",
       "      <td>0.837066</td>\n",
       "      <td>0.272379</td>\n",
       "      <td>0.331679</td>\n",
       "      <td>0.429297</td>\n",
       "      <td>0.367422</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.078290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.336714</td>\n",
       "      <td>0.538842</td>\n",
       "      <td>0.192801</td>\n",
       "      <td>0.553563</td>\n",
       "      <td>0.074515</td>\n",
       "      <td>0.332993</td>\n",
       "      <td>0.365792</td>\n",
       "      <td>0.861309</td>\n",
       "      <td>0.899017</td>\n",
       "      <td>0.088600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.026938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.092108</td>\n",
       "      <td>0.995017</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>0.176371</td>\n",
       "      <td>0.241530</td>\n",
       "      <td>0.514724</td>\n",
       "      <td>0.562208</td>\n",
       "      <td>0.158963</td>\n",
       "      <td>0.073715</td>\n",
       "      <td>0.208463</td>\n",
       "      <td>1</td>\n",
       "      <td>0.053551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.325261</td>\n",
       "      <td>0.805968</td>\n",
       "      <td>0.957033</td>\n",
       "      <td>0.331665</td>\n",
       "      <td>0.307923</td>\n",
       "      <td>0.355315</td>\n",
       "      <td>0.501899</td>\n",
       "      <td>0.558449</td>\n",
       "      <td>0.885169</td>\n",
       "      <td>0.182754</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.015639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0.305055</td>\n",
       "      <td>0.656837</td>\n",
       "      <td>0.331665</td>\n",
       "      <td>0.822525</td>\n",
       "      <td>0.907127</td>\n",
       "      <td>0.882276</td>\n",
       "      <td>0.855732</td>\n",
       "      <td>0.584786</td>\n",
       "      <td>0.808640</td>\n",
       "      <td>0.242762</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.082756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0.812627</td>\n",
       "      <td>0.864258</td>\n",
       "      <td>0.616604</td>\n",
       "      <td>0.167966</td>\n",
       "      <td>0.811223</td>\n",
       "      <td>0.938071</td>\n",
       "      <td>0.418462</td>\n",
       "      <td>0.472306</td>\n",
       "      <td>0.348347</td>\n",
       "      <td>0.671129</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.065453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0.250967</td>\n",
       "      <td>0.138627</td>\n",
       "      <td>0.919703</td>\n",
       "      <td>0.461234</td>\n",
       "      <td>0.886555</td>\n",
       "      <td>0.869888</td>\n",
       "      <td>0.800908</td>\n",
       "      <td>0.530324</td>\n",
       "      <td>0.779433</td>\n",
       "      <td>0.234952</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.055405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.502436</td>\n",
       "      <td>0.936820</td>\n",
       "      <td>0.580062</td>\n",
       "      <td>0.540773</td>\n",
       "      <td>0.151995</td>\n",
       "      <td>0.059452</td>\n",
       "      <td>0.225220</td>\n",
       "      <td>0.242755</td>\n",
       "      <td>0.279385</td>\n",
       "      <td>0.538755</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.068005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.457991</td>\n",
       "      <td>0.017755</td>\n",
       "      <td>0.714113</td>\n",
       "      <td>0.125992</td>\n",
       "      <td>0.063316</td>\n",
       "      <td>0.154739</td>\n",
       "      <td>0.922974</td>\n",
       "      <td>0.692299</td>\n",
       "      <td>0.816777</td>\n",
       "      <td>0.307592</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.012268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Col1      Col2      Col3      Col4      Col5      Col6      Col7  \\\n",
       "0    0.263995  0.764929  0.138424  0.935242  0.605867  0.518790  0.912225   \n",
       "1    0.546092  0.653975  0.065575  0.227772  0.845269  0.837066  0.272379   \n",
       "2    0.336714  0.538842  0.192801  0.553563  0.074515  0.332993  0.365792   \n",
       "3    0.092108  0.995017  0.014465  0.176371  0.241530  0.514724  0.562208   \n",
       "4    0.325261  0.805968  0.957033  0.331665  0.307923  0.355315  0.501899   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "995  0.305055  0.656837  0.331665  0.822525  0.907127  0.882276  0.855732   \n",
       "996  0.812627  0.864258  0.616604  0.167966  0.811223  0.938071  0.418462   \n",
       "997  0.250967  0.138627  0.919703  0.461234  0.886555  0.869888  0.800908   \n",
       "998  0.502436  0.936820  0.580062  0.540773  0.151995  0.059452  0.225220   \n",
       "999  0.457991  0.017755  0.714113  0.125992  0.063316  0.154739  0.922974   \n",
       "\n",
       "         Col8      Col9     Col10  Anomaly  Anomaly_Score  \n",
       "0    0.608234  0.723782  0.733591        0      -0.030361  \n",
       "1    0.331679  0.429297  0.367422        0      -0.078290  \n",
       "2    0.861309  0.899017  0.088600        1       0.026938  \n",
       "3    0.158963  0.073715  0.208463        1       0.053551  \n",
       "4    0.558449  0.885169  0.182754        0      -0.015639  \n",
       "..        ...       ...       ...      ...            ...  \n",
       "995  0.584786  0.808640  0.242762        0      -0.082756  \n",
       "996  0.472306  0.348347  0.671129        0      -0.065453  \n",
       "997  0.530324  0.779433  0.234952        0      -0.055405  \n",
       "998  0.242755  0.279385  0.538755        0      -0.068005  \n",
       "999  0.692299  0.816777  0.307592        0      -0.012268  \n",
       "\n",
       "[1000 rows x 12 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>Col3</th>\n",
       "      <th>Col4</th>\n",
       "      <th>Col5</th>\n",
       "      <th>Col6</th>\n",
       "      <th>Col7</th>\n",
       "      <th>Col8</th>\n",
       "      <th>Col9</th>\n",
       "      <th>Col10</th>\n",
       "      <th>Anomaly</th>\n",
       "      <th>Anomaly_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.263995</td>\n",
       "      <td>0.764929</td>\n",
       "      <td>0.138424</td>\n",
       "      <td>0.935242</td>\n",
       "      <td>0.605867</td>\n",
       "      <td>0.518790</td>\n",
       "      <td>0.912225</td>\n",
       "      <td>0.608234</td>\n",
       "      <td>0.723782</td>\n",
       "      <td>0.733591</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.035865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.546092</td>\n",
       "      <td>0.653975</td>\n",
       "      <td>0.065575</td>\n",
       "      <td>0.227772</td>\n",
       "      <td>0.845269</td>\n",
       "      <td>0.837066</td>\n",
       "      <td>0.272379</td>\n",
       "      <td>0.331679</td>\n",
       "      <td>0.429297</td>\n",
       "      <td>0.367422</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.084927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.336714</td>\n",
       "      <td>0.538842</td>\n",
       "      <td>0.192801</td>\n",
       "      <td>0.553563</td>\n",
       "      <td>0.074515</td>\n",
       "      <td>0.332993</td>\n",
       "      <td>0.365792</td>\n",
       "      <td>0.861309</td>\n",
       "      <td>0.899017</td>\n",
       "      <td>0.088600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.025356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.092108</td>\n",
       "      <td>0.995017</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>0.176371</td>\n",
       "      <td>0.241530</td>\n",
       "      <td>0.514724</td>\n",
       "      <td>0.562208</td>\n",
       "      <td>0.158963</td>\n",
       "      <td>0.073715</td>\n",
       "      <td>0.208463</td>\n",
       "      <td>1</td>\n",
       "      <td>0.042415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.325261</td>\n",
       "      <td>0.805968</td>\n",
       "      <td>0.957033</td>\n",
       "      <td>0.331665</td>\n",
       "      <td>0.307923</td>\n",
       "      <td>0.355315</td>\n",
       "      <td>0.501899</td>\n",
       "      <td>0.558449</td>\n",
       "      <td>0.885169</td>\n",
       "      <td>0.182754</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.023408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Col1      Col2      Col3      Col4      Col5      Col6      Col7  \\\n",
       "0  0.263995  0.764929  0.138424  0.935242  0.605867  0.518790  0.912225   \n",
       "1  0.546092  0.653975  0.065575  0.227772  0.845269  0.837066  0.272379   \n",
       "2  0.336714  0.538842  0.192801  0.553563  0.074515  0.332993  0.365792   \n",
       "3  0.092108  0.995017  0.014465  0.176371  0.241530  0.514724  0.562208   \n",
       "4  0.325261  0.805968  0.957033  0.331665  0.307923  0.355315  0.501899   \n",
       "\n",
       "       Col8      Col9     Col10  Anomaly  Anomaly_Score  \n",
       "0  0.608234  0.723782  0.733591        0      -0.035865  \n",
       "1  0.331679  0.429297  0.367422        0      -0.084927  \n",
       "2  0.861309  0.899017  0.088600        1       0.025356  \n",
       "3  0.158963  0.073715  0.208463        1       0.042415  \n",
       "4  0.558449  0.885169  0.182754        0      -0.023408  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iforest = create_model('iforest', fraction = 0.05)\n",
    "results = assign_model(iforest)\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results['Anomaly_Score'].hist(bins=100, figsize=(10,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import set_config\n",
    "set_config(display=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"121e7376-e50f-4812-917c-7ee857530086\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"121e7376-e50f-4812-917c-7ee857530086\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(memory=None,\n",
       "         steps=[('dtypes',\n",
       "                 DataTypes_Auto_infer(categorical_features=[],\n",
       "                                      display_types=True, features_todrop=[],\n",
       "                                      id_columns=[], ml_usecase='regression',\n",
       "                                      numerical_features=[],\n",
       "                                      target='UNSUPERVISED_DUMMY_TARGET',\n",
       "                                      time_features=[])),\n",
       "                ('imputer',\n",
       "                 Simple_Imputer(categorical_strategy='most frequent',\n",
       "                                fill_value_categorical=None,\n",
       "                                fill_value_numerical=None...\n",
       "                ('fix_perfect', 'passthrough'),\n",
       "                ('clean_names', Clean_Colum_Names()),\n",
       "                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
       "                ('dfs', 'passthrough'), ('pca', 'passthrough'),\n",
       "                ['trained_model',\n",
       "                 IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
       "    max_features=1.0, max_samples='auto', n_estimators=200, n_jobs=-1,\n",
       "    random_state=123, verbose=0)]],\n",
       "         verbose=False)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"1203b7c0-0088-41eb-b7aa-7bf344f25f82\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"1203b7c0-0088-41eb-b7aa-7bf344f25f82\">DataTypes_Auto_infer</label><div class=\"sk-toggleable__content\"><pre>DataTypes_Auto_infer(ml_usecase='regression',\n",
       "                     target='UNSUPERVISED_DUMMY_TARGET')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f0ba02a9-eee9-4f16-b5ee-4e8fe9c39b43\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"f0ba02a9-eee9-4f16-b5ee-4e8fe9c39b43\">Simple_Imputer</label><div class=\"sk-toggleable__content\"><pre>Simple_Imputer(categorical_strategy='most frequent',\n",
       "               fill_value_categorical=None, fill_value_numerical=None,\n",
       "               numeric_strategy='mean', target_variable=None)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"7caad950-dc7f-40ea-9cd9-07df1a650f95\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"7caad950-dc7f-40ea-9cd9-07df1a650f95\">New_Catagorical_Levels_in_TestData</label><div class=\"sk-toggleable__content\"><pre>New_Catagorical_Levels_in_TestData(replacement_strategy='least frequent',\n",
       "                                   target='UNSUPERVISED_DUMMY_TARGET')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f7b81f8c-bdda-4e1d-ad88-a83402c299f2\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"f7b81f8c-bdda-4e1d-ad88-a83402c299f2\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"98cf3825-94d8-43c7-a84c-c07818055bff\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"98cf3825-94d8-43c7-a84c-c07818055bff\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"599cd2c2-394b-44a2-bc53-2037b11193e2\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"599cd2c2-394b-44a2-bc53-2037b11193e2\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"6af731e7-fee5-4d5f-ab21-dc14ab79d663\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"6af731e7-fee5-4d5f-ab21-dc14ab79d663\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"c335f609-02d4-4f40-9b5c-6a656b4ba2d2\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"c335f609-02d4-4f40-9b5c-6a656b4ba2d2\">New_Catagorical_Levels_in_TestData</label><div class=\"sk-toggleable__content\"><pre>New_Catagorical_Levels_in_TestData(replacement_strategy='least frequent',\n",
       "                                   target='UNSUPERVISED_DUMMY_TARGET')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"07e17ef2-ca8d-4f64-8236-952a16f03cef\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"07e17ef2-ca8d-4f64-8236-952a16f03cef\">Make_Time_Features</label><div class=\"sk-toggleable__content\"><pre>Make_Time_Features(list_of_features=None,\n",
       "                   time_feature=Index([], dtype='object'))</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"76842f45-c1cb-4703-b18f-364b265c52bd\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"76842f45-c1cb-4703-b18f-364b265c52bd\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"15a642d1-ca46-478f-9ec3-4a3a10807f34\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"15a642d1-ca46-478f-9ec3-4a3a10807f34\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f4598183-ac38-44e0-8447-0f2fa7a12d48\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"f4598183-ac38-44e0-8447-0f2fa7a12d48\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"4d52fe16-faf0-4def-8998-667e2d7ec754\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"4d52fe16-faf0-4def-8998-667e2d7ec754\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"e23c0278-5f23-4cb1-b4ab-4c819572bf2f\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"e23c0278-5f23-4cb1-b4ab-4c819572bf2f\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"e28376f6-2531-42a0-9919-481f23892958\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"e28376f6-2531-42a0-9919-481f23892958\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"75ccb5a3-4b37-4d52-9889-91343412e7f8\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"75ccb5a3-4b37-4d52-9889-91343412e7f8\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"87b0860c-f3fc-40a5-bdf3-61fda737e07c\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"87b0860c-f3fc-40a5-bdf3-61fda737e07c\">Dummify</label><div class=\"sk-toggleable__content\"><pre>Dummify(target='UNSUPERVISED_DUMMY_TARGET')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"beb8d54b-8a29-4de2-b0d8-a945c66f4a5d\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"beb8d54b-8a29-4de2-b0d8-a945c66f4a5d\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a6541815-b2db-46d3-a237-756a4807bd15\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"a6541815-b2db-46d3-a237-756a4807bd15\">Clean_Colum_Names</label><div class=\"sk-toggleable__content\"><pre>Clean_Colum_Names()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"450a8d59-7155-4496-85d2-119d834c4d62\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"450a8d59-7155-4496-85d2-119d834c4d62\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"30edd21e-0382-4ec5-8844-550dbd7b8f92\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"30edd21e-0382-4ec5-8844-550dbd7b8f92\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"4a574de8-4abd-4aa2-9032-f3827d1b6601\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"4a574de8-4abd-4aa2-9032-f3827d1b6601\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"3b448f78-d49a-4068-ae79-5f5f6e92f7a4\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"3b448f78-d49a-4068-ae79-5f5f6e92f7a4\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"4f7e9d78-544e-4354-ad7b-f71106553aac\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"4f7e9d78-544e-4354-ad7b-f71106553aac\">IForest</label><div class=\"sk-toggleable__content\"><pre>IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
       "    max_features=1.0, max_samples='auto', n_estimators=200, n_jobs=-1,\n",
       "    random_state=123, verbose=0)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('dtypes',\n",
       "                 DataTypes_Auto_infer(categorical_features=[],\n",
       "                                      display_types=True, features_todrop=[],\n",
       "                                      id_columns=[], ml_usecase='regression',\n",
       "                                      numerical_features=[],\n",
       "                                      target='UNSUPERVISED_DUMMY_TARGET',\n",
       "                                      time_features=[])),\n",
       "                ('imputer',\n",
       "                 Simple_Imputer(categorical_strategy='most frequent',\n",
       "                                fill_value_categorical=None,\n",
       "                                fill_value_numerical=None...\n",
       "                ('fix_perfect', 'passthrough'),\n",
       "                ('clean_names', Clean_Colum_Names()),\n",
       "                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
       "                ('dfs', 'passthrough'), ('pca', 'passthrough'),\n",
       "                ['trained_model',\n",
       "                 IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
       "    max_features=1.0, max_samples='auto', n_estimators=200, n_jobs=-1,\n",
       "    random_state=123, verbose=0)]],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Succesfully Deployed on AWS S3\n"
     ]
    }
   ],
   "source": [
    "deploy_model(iforest, 'iforest-abc', platform = 'aws', \n",
    "             authentication = {'bucket' : 'pycaret-test'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
       "    max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=-1,\n",
       "    random_state=123, verbose=0)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iforest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pyod.models.iforest.IForest"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iforest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>Col3</th>\n",
       "      <th>Col4</th>\n",
       "      <th>Col5</th>\n",
       "      <th>Col6</th>\n",
       "      <th>Col7</th>\n",
       "      <th>Col8</th>\n",
       "      <th>Col9</th>\n",
       "      <th>Col10</th>\n",
       "      <th>Anomaly</th>\n",
       "      <th>Anomaly_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.263995</td>\n",
       "      <td>0.764929</td>\n",
       "      <td>0.138424</td>\n",
       "      <td>0.935242</td>\n",
       "      <td>0.605867</td>\n",
       "      <td>0.518790</td>\n",
       "      <td>0.912225</td>\n",
       "      <td>0.608234</td>\n",
       "      <td>0.723782</td>\n",
       "      <td>0.733591</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.035865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.546092</td>\n",
       "      <td>0.653975</td>\n",
       "      <td>0.065575</td>\n",
       "      <td>0.227772</td>\n",
       "      <td>0.845269</td>\n",
       "      <td>0.837066</td>\n",
       "      <td>0.272379</td>\n",
       "      <td>0.331679</td>\n",
       "      <td>0.429297</td>\n",
       "      <td>0.367422</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.084927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.336714</td>\n",
       "      <td>0.538842</td>\n",
       "      <td>0.192801</td>\n",
       "      <td>0.553563</td>\n",
       "      <td>0.074515</td>\n",
       "      <td>0.332993</td>\n",
       "      <td>0.365792</td>\n",
       "      <td>0.861309</td>\n",
       "      <td>0.899017</td>\n",
       "      <td>0.088600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.025356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.092108</td>\n",
       "      <td>0.995017</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>0.176371</td>\n",
       "      <td>0.241530</td>\n",
       "      <td>0.514724</td>\n",
       "      <td>0.562208</td>\n",
       "      <td>0.158963</td>\n",
       "      <td>0.073715</td>\n",
       "      <td>0.208463</td>\n",
       "      <td>1</td>\n",
       "      <td>0.042415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.325261</td>\n",
       "      <td>0.805968</td>\n",
       "      <td>0.957033</td>\n",
       "      <td>0.331665</td>\n",
       "      <td>0.307923</td>\n",
       "      <td>0.355315</td>\n",
       "      <td>0.501899</td>\n",
       "      <td>0.558449</td>\n",
       "      <td>0.885169</td>\n",
       "      <td>0.182754</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.023408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Col1      Col2      Col3      Col4      Col5      Col6      Col7  \\\n",
       "0  0.263995  0.764929  0.138424  0.935242  0.605867  0.518790  0.912225   \n",
       "1  0.546092  0.653975  0.065575  0.227772  0.845269  0.837066  0.272379   \n",
       "2  0.336714  0.538842  0.192801  0.553563  0.074515  0.332993  0.365792   \n",
       "3  0.092108  0.995017  0.014465  0.176371  0.241530  0.514724  0.562208   \n",
       "4  0.325261  0.805968  0.957033  0.331665  0.307923  0.355315  0.501899   \n",
       "\n",
       "       Col8      Col9     Col10  Anomaly  Anomaly_Score  \n",
       "0  0.608234  0.723782  0.733591        0      -0.035865  \n",
       "1  0.331679  0.429297  0.367422        0      -0.084927  \n",
       "2  0.861309  0.899017  0.088600        1       0.025356  \n",
       "3  0.158963  0.073715  0.208463        1       0.042415  \n",
       "4  0.558449  0.885169  0.182754        0      -0.023408  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = assign_model(iforest)\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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