class_01b_first_encounter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "exciting-specific",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from IPython.display import Markdown, display, HTML\n",
    "from collections import defaultdict\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "# Fix the dying kernel problem (only a problem in some installations - you can remove it, if it works without it)\n",
    "import os\n",
    "os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'\n",
    "\n",
    "pd.set_option('display.max_rows', 300)\n",
    "pd.set_option('display.max_columns', 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "administrative-charleston",
   "metadata": {},
   "source": [
    "# Movielens data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "architectural-andrews",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of movies: 9742\n",
      "Number of users: 610\n",
      "Number of interactions: 100836\n",
      "\n",
      "Movies\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Heat (1995)</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Sabrina (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Tom and Huck (1995)</td>\n",
       "      <td>Adventure|Children</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Sudden Death (1995)</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_id                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "5        6                         Heat (1995)   \n",
       "6        7                      Sabrina (1995)   \n",
       "7        8                 Tom and Huck (1995)   \n",
       "8        9                 Sudden Death (1995)   \n",
       "9       10                    GoldenEye (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  \n",
       "5                        Action|Crime|Thriller  \n",
       "6                               Comedy|Romance  \n",
       "7                           Adventure|Children  \n",
       "8                                       Action  \n",
       "9                    Action|Adventure|Thriller  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interactions\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
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       "      <th>3</th>\n",
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       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>3.0</td>\n",
       "      <td>964982400</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964980868</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>151</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964984041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964984100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>216</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964981208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>223</td>\n",
       "      <td>3.0</td>\n",
       "      <td>964980985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>231</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964981179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>235</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964980908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>260</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964981680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>3.0</td>\n",
       "      <td>964982967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>316</td>\n",
       "      <td>3.0</td>\n",
       "      <td>964982310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964981179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>349</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>356</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964980962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>362</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>367</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981710</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>423</td>\n",
       "      <td>3.0</td>\n",
       "      <td>964982363</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>441</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964980868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>457</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964981909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>480</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>3.0</td>\n",
       "      <td>964981208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>527</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964984002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>543</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  item_id  rating  timestamp\n",
       "0         1        1     4.0  964982703\n",
       "1         1        3     4.0  964981247\n",
       "2         1        6     4.0  964982224\n",
       "3         1       47     5.0  964983815\n",
       "4         1       50     5.0  964982931\n",
       "5         1       70     3.0  964982400\n",
       "6         1      101     5.0  964980868\n",
       "7         1      110     4.0  964982176\n",
       "8         1      151     5.0  964984041\n",
       "9         1      157     5.0  964984100\n",
       "10        1      163     5.0  964983650\n",
       "11        1      216     5.0  964981208\n",
       "12        1      223     3.0  964980985\n",
       "13        1      231     5.0  964981179\n",
       "14        1      235     4.0  964980908\n",
       "15        1      260     5.0  964981680\n",
       "16        1      296     3.0  964982967\n",
       "17        1      316     3.0  964982310\n",
       "18        1      333     5.0  964981179\n",
       "19        1      349     4.0  964982563\n",
       "20        1      356     4.0  964980962\n",
       "21        1      362     5.0  964982588\n",
       "22        1      367     4.0  964981710\n",
       "23        1      423     3.0  964982363\n",
       "24        1      441     4.0  964980868\n",
       "25        1      457     5.0  964981909\n",
       "26        1      480     4.0  964982346\n",
       "27        1      500     3.0  964981208\n",
       "28        1      527     5.0  964984002\n",
       "29        1      543     4.0  964981179"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml_ratings_df = pd.read_csv(os.path.join(\"data\", \"movielens_small\", \"ratings.csv\")).rename(columns={'userId': 'user_id', 'movieId': 'item_id'})\n",
    "ml_movies_df = pd.read_csv(os.path.join(\"data\", \"movielens_small\", \"movies.csv\")).rename(columns={'movieId': 'item_id'})\n",
    "ml_df = pd.merge(ml_ratings_df, ml_movies_df, on='item_id')\n",
    "print(\"Number of movies: {}\".format(len(ml_movies_df)))\n",
    "print(\"Number of users: {}\".format(len(ml_ratings_df.user_id.unique())))\n",
    "print(\"Number of interactions: {}\".format(len(ml_ratings_df)))\n",
    "print()\n",
    "print(\"Movies\")\n",
    "\n",
    "display(ml_movies_df.head(10))\n",
    "print(\"Interactions\")\n",
    "display(ml_ratings_df.head(30))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alike-surfing",
   "metadata": {},
   "source": [
    "# Steam data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "sought-advancement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of records: 200000\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>game_title</th>\n",
       "      <th>behavior_name</th>\n",
       "      <th>value</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151603712</td>\n",
       "      <td>The Elder Scrolls V Skyrim</td>\n",
       "      <td>purchase</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>151603712</td>\n",
       "      <td>The Elder Scrolls V Skyrim</td>\n",
       "      <td>play</td>\n",
       "      <td>273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151603712</td>\n",
       "      <td>Fallout 4</td>\n",
       "      <td>purchase</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151603712</td>\n",
       "      <td>Fallout 4</td>\n",
       "      <td>play</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151603712</td>\n",
       "      <td>Spore</td>\n",
       "      <td>purchase</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>151603712</td>\n",
       "      <td>Spore</td>\n",
       "      <td>play</td>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>151603712</td>\n",
       "      <td>Fallout New Vegas</td>\n",
       "      <td>purchase</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>151603712</td>\n",
       "      <td>Fallout New Vegas</td>\n",
       "      <td>play</td>\n",
       "      <td>12.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>151603712</td>\n",
       "      <td>Left 4 Dead 2</td>\n",
       "      <td>purchase</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>151603712</td>\n",
       "      <td>Left 4 Dead 2</td>\n",
       "      <td>play</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id                  game_title behavior_name  value\n",
       "0  151603712  The Elder Scrolls V Skyrim      purchase    1.0\n",
       "1  151603712  The Elder Scrolls V Skyrim          play  273.0\n",
       "2  151603712                   Fallout 4      purchase    1.0\n",
       "3  151603712                   Fallout 4          play   87.0\n",
       "4  151603712                       Spore      purchase    1.0\n",
       "5  151603712                       Spore          play   14.9\n",
       "6  151603712           Fallout New Vegas      purchase    1.0\n",
       "7  151603712           Fallout New Vegas          play   12.1\n",
       "8  151603712               Left 4 Dead 2      purchase    1.0\n",
       "9  151603712               Left 4 Dead 2          play    8.9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "steam_df = pd.read_csv(os.path.join(\"data\", \"steam\", \"steam-200k.csv\"), header=None, \n",
    "                       names=['user_id', 'game_title', 'behavior_name', 'value', 'zero']).drop(columns='zero')\n",
    "print(\"Number of records: {}\".format(len(steam_df)))\n",
    "display(steam_df.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "automotive-florence",
   "metadata": {},
   "source": [
    "# Hotel data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cosmetic-belle",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of records: 17250\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>reservation_id</th>\n",
       "      <th>group_id</th>\n",
       "      <th>room_id</th>\n",
       "      <th>room_group_id</th>\n",
       "      <th>date_from</th>\n",
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       "      <th>n_children_1</th>\n",
       "      <th>n_children_2</th>\n",
       "      <th>n_children_3</th>\n",
       "      <th>discount</th>\n",
       "      <th>accomodation_price</th>\n",
       "      <th>meal_price</th>\n",
       "      <th>service_price</th>\n",
       "      <th>paid</th>\n",
       "      <th>rate_plan</th>\n",
       "      <th>client_id</th>\n",
       "      <th>client_name</th>\n",
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       "      <th>phone</th>\n",
       "      <th>is_company</th>\n",
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       "      <td>800.00</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>120165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162</td>\n",
       "      <td>162</td>\n",
       "      <td>2018-11-16</td>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
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       "      <td>402.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>402.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>63419</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>120183</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>2018-08-16</td>\n",
       "      <td>2018-08-18</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>1</td>\n",
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       "      <td>660.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>660.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>61777</td>\n",
       "      <td>f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>120184</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>2018-08-17</td>\n",
       "      <td>2018-08-18</td>\n",
       "      <td>2018-02-19</td>\n",
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       "      <td>320.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>320.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>61778</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>120185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "      <td>2018-08-16</td>\n",
       "      <td>2018-08-18</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>1</td>\n",
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       "      <td>720.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>720.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>61777</td>\n",
       "      <td>f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "      <td>11</td>\n",
       "      <td>120186</td>\n",
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       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>2018-08-16</td>\n",
       "      <td>2018-08-18</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>480.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>480.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>61777</td>\n",
       "      <td>f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>120194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>267</td>\n",
       "      <td>267</td>\n",
       "      <td>2018-08-10</td>\n",
       "      <td>2018-08-11</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>595.98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>595.98</td>\n",
       "      <td>Standard</td>\n",
       "      <td>61378</td>\n",
       "      <td>f929533e542ff1302069567400b5ba584dc27dcbd63aae...</td>\n",
       "      <td>a90206b6164e13331d087034d9d9a963a9a4bcf8b2969f...</td>\n",
       "      <td>446e3878681ff4dac3067bf75b8ec3b7e5be2cf8a02c75...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>120209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370</td>\n",
       "      <td>362</td>\n",
       "      <td>2018-08-03</td>\n",
       "      <td>2018-08-05</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>915.00</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>915.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>61105</td>\n",
       "      <td>8373dfd3bf2b44222dce774ee032a32b74a495f6ddd355...</td>\n",
       "      <td>37827cbb5aec4ed83d13193d766324efb93251cd44b28b...</td>\n",
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       "      <td>120232</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3579.40</td>\n",
       "      <td>Standard</td>\n",
       "      <td>61807</td>\n",
       "      <td>cdd642820a12bb4a8f2407ed1d02b67435991afef52d9c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5aeffc70468d19eba135a79c74024c206abe83c29fc6f0...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>120256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>2018-07-15</td>\n",
       "      <td>2018-08-14</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1031</td>\n",
       "      <td>d2c7d67c462db4ac6637597e7843b0c080cd41baa5dae0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>120264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>2018-07-13</td>\n",
       "      <td>2018-07-15</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>61391</td>\n",
       "      <td>4d9a25ef49b785020f71d87e7202cd209bdc4197b2649c...</td>\n",
       "      <td>25dec48fdcf1d2e7dbe97aff20d52cae0b2660f5eae1d8...</td>\n",
       "      <td>97edf16b92140283616026230d86f5ded26d8e09863a76...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>120279</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>2018-07-07</td>\n",
       "      <td>2018-07-08</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>640.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>640.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>60668</td>\n",
       "      <td>37a1b20c8796e967f521788e5f6dc03fe769784dcc4e9b...</td>\n",
       "      <td>a9ba8f12a4509f4c598578fa743236beda2b791c938954...</td>\n",
       "      <td>7ec6cc25066b8b7008b78d25027a2159d2116522611223...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>120285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>2018-07-04</td>\n",
       "      <td>2018-07-07</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>793.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>793.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>61508</td>\n",
       "      <td>1f277c7e23db63f3d3b99a1c91959d2d87cf65dd150c16...</td>\n",
       "      <td>4d70747ed821eca5c6fb0ec95f2a8e005369e53440a99c...</td>\n",
       "      <td>1fda0af8c40fa82143ccbeac216fe4e1b6f82b5b57bd00...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>120292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>2018-07-03</td>\n",
       "      <td>2018-07-09</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>61536</td>\n",
       "      <td>d2125403e9a982b702f1e2ae3d961f6a2b1113cac9b4ab...</td>\n",
       "      <td>b068c4964a0082cea54c0c6b5b31907f8b5009cc5c29a9...</td>\n",
       "      <td>18796a14e703d952770ff333e440744e5645f36395ab23...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>120314</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>2018-06-23</td>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2970.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2970.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>59251</td>\n",
       "      <td>1fc1fc3c2f76244d172dce793478abe25bfa262bf84d88...</td>\n",
       "      <td>03f8d9987cae994de741857336b1d5bfa6e13567691336...</td>\n",
       "      <td>fbf6f729bdbc5c399fda4952800159bad22b606de316dc...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>120368</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270</td>\n",
       "      <td>270</td>\n",
       "      <td>2018-05-28</td>\n",
       "      <td>2018-06-02</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1799.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1799.20</td>\n",
       "      <td>Standard</td>\n",
       "      <td>61237</td>\n",
       "      <td>90c2ec1072904a2ba8b88c66c17930e8b85f8d0f10b4e2...</td>\n",
       "      <td>02f2e085320d0930db4c9d722d315e7e77235dac96303f...</td>\n",
       "      <td>7e2f2677699ae3b5b58365cf109eac9fc4803d83b900d4...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>120371</td>\n",
       "      <td>NaN</td>\n",
       "      <td>227</td>\n",
       "      <td>227</td>\n",
       "      <td>2018-05-26</td>\n",
       "      <td>2018-06-09</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5891.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5891.20</td>\n",
       "      <td>Standard</td>\n",
       "      <td>61428</td>\n",
       "      <td>d96743d064f148fd88ea765513663700c2873765710c90...</td>\n",
       "      <td>4bc6914285bb73a34da5fc07ea6ed16eb3e08b0e6a5230...</td>\n",
       "      <td>00d2c14014162af4c5c454800a93dfdcb4ea9ee96d109c...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>120372</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162</td>\n",
       "      <td>162</td>\n",
       "      <td>2018-05-26</td>\n",
       "      <td>2018-05-30</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2172.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2172.40</td>\n",
       "      <td>Standard</td>\n",
       "      <td>61522</td>\n",
       "      <td>2f31fadcb70beff6db9b09d6f62463beaa005569998791...</td>\n",
       "      <td>22893f17c5a4cd6e0913a85ecdbc597ff2ff891e347570...</td>\n",
       "      <td>b29bb8fee8266f9f23b7200a0979430bdd101cc2dc2e58...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>120373</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>2018-05-30</td>\n",
       "      <td>2018-06-02</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1031</td>\n",
       "      <td>d2c7d67c462db4ac6637597e7843b0c080cd41baa5dae0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>120375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>247</td>\n",
       "      <td>247</td>\n",
       "      <td>2018-05-26</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>54132</td>\n",
       "      <td>4255913c4a150c7f6171ba2807857992e91b34f1b2862e...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>120377</td>\n",
       "      <td>NaN</td>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>2018-05-26</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>480.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>480.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>61739</td>\n",
       "      <td>b486b37b72db13811533a126c556cc8867eb2bbc432b8d...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>120378</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>2018-05-22</td>\n",
       "      <td>2018-05-30</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1031</td>\n",
       "      <td>d2c7d67c462db4ac6637597e7843b0c080cd41baa5dae0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>120379</td>\n",
       "      <td>NaN</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "      <td>2018-05-22</td>\n",
       "      <td>2018-05-24</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1031</td>\n",
       "      <td>d2c7d67c462db4ac6637597e7843b0c080cd41baa5dae0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>120399</td>\n",
       "      <td>NaN</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>2018-04-30</td>\n",
       "      <td>2018-07-14</td>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>2018-02-19 17:44:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>61851</td>\n",
       "      <td>fa1b8b5a01558c675885bdd7b844e74c7b061079f9623b...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  reservation_id  group_id  room_id  room_group_id   date_from  \\\n",
       "0            0           14160       NaN      135            135  2017-09-01   \n",
       "1            1           16075       NaN      118            118  2018-02-10   \n",
       "2            2           16076       NaN      270            270  2018-02-28   \n",
       "3            3           16635       NaN      294            294  2018-02-14   \n",
       "4            4           16964       NaN      183            183  2018-02-03   \n",
       "5            5           17173       NaN       64             64  2018-01-29   \n",
       "6            6           17308       NaN      111            111  2018-03-28   \n",
       "7            7          120165       NaN      162            162  2018-11-16   \n",
       "8            8          120183       NaN       45             45  2018-08-16   \n",
       "9            9          120184       NaN       64             64  2018-08-17   \n",
       "10          10          120185       NaN      126            126  2018-08-16   \n",
       "11          11          120186       NaN       65             65  2018-08-16   \n",
       "12          12          120194       NaN      267            267  2018-08-10   \n",
       "13          13          120209       NaN      370            362  2018-08-03   \n",
       "14          14          120232       NaN      321            321  2018-07-22   \n",
       "15          15          120256       NaN      158            158  2018-07-15   \n",
       "16          16          120264       NaN      213            213  2018-07-13   \n",
       "17          17          120279       NaN      131            131  2018-07-07   \n",
       "18          18          120285       NaN       20             20  2018-07-04   \n",
       "19          19          120292       NaN      213            213  2018-07-03   \n",
       "20          20          120314       NaN       36             36  2018-06-23   \n",
       "21          21          120368       NaN      270            270  2018-05-28   \n",
       "22          22          120371       NaN      227            227  2018-05-26   \n",
       "23          23          120372       NaN      162            162  2018-05-26   \n",
       "24          24          120373       NaN      131            131  2018-05-30   \n",
       "25          25          120375       NaN      247            247  2018-05-26   \n",
       "26          26          120377       NaN      126            126  2018-05-25   \n",
       "27          27          120378       NaN       51             51  2018-05-22   \n",
       "28          28          120379       NaN      325            325  2018-05-22   \n",
       "29          29          120399       NaN      158            158  2018-04-30   \n",
       "\n",
       "       date_to booking_date         booking_time  n_people  n_children_1  \\\n",
       "0   2018-03-30   2017-07-04  2017-07-04 10:52:00         1             0   \n",
       "1   2018-02-12   2017-08-17  2017-08-17 15:01:00         5             0   \n",
       "2   2018-03-02   2017-08-17  2017-08-17 15:08:00         4             0   \n",
       "3   2018-02-15   2017-08-29  2017-08-29 13:58:00         2             0   \n",
       "4   2018-02-09   2017-09-04  2017-09-04 15:52:00         4             0   \n",
       "5   2018-02-02   2017-09-07  2017-09-07 13:21:00         2             0   \n",
       "6   2018-03-31   2017-09-11  2017-09-11 10:31:00         5             0   \n",
       "7   2018-11-17   2018-02-19  2018-02-19 17:44:00         5             0   \n",
       "8   2018-08-18   2018-02-19  2018-02-19 17:44:00         1             0   \n",
       "9   2018-08-18   2018-02-19  2018-02-19 17:44:00         1             0   \n",
       "10  2018-08-18   2018-02-19  2018-02-19 17:44:00         1             0   \n",
       "11  2018-08-18   2018-02-19  2018-02-19 17:44:00         1             0   \n",
       "12  2018-08-11   2018-02-19  2018-02-19 17:44:00         3             0   \n",
       "13  2018-08-05   2018-02-19  2018-02-19 17:44:00         4             0   \n",
       "14  2018-07-30   2018-02-19  2018-02-19 17:44:00         3             0   \n",
       "15  2018-08-14   2018-02-19  2018-02-19 17:44:00         1             0   \n",
       "16  2018-07-15   2018-02-19  2018-02-19 17:44:00         4             0   \n",
       "17  2018-07-08   2018-02-19  2018-02-19 17:44:00         6             0   \n",
       "18  2018-07-07   2018-02-19  2018-02-19 17:44:00         3             0   \n",
       "19  2018-07-09   2018-02-19  2018-02-19 17:44:00         4             0   \n",
       "20  2018-07-01   2018-02-19  2018-02-19 17:44:00         3             0   \n",
       "21  2018-06-02   2018-02-19  2018-02-19 17:44:00         4             0   \n",
       "22  2018-06-09   2018-02-19  2018-02-19 17:44:00         6             0   \n",
       "23  2018-05-30   2018-02-19  2018-02-19 17:44:00         7             0   \n",
       "24  2018-06-02   2018-02-19  2018-02-19 17:44:00         1             0   \n",
       "25  2018-08-31   2018-02-19  2018-02-19 17:44:00         1             0   \n",
       "26  2018-05-26   2018-02-19  2018-02-19 17:44:00         1             0   \n",
       "27  2018-05-30   2018-02-19  2018-02-19 17:44:00         1             0   \n",
       "28  2018-05-24   2018-02-19  2018-02-19 17:44:00         1             0   \n",
       "29  2018-07-14   2018-02-19  2018-02-19 17:44:00         1             0   \n",
       "\n",
       "    n_children_2  n_children_3  discount  accomodation_price  meal_price  \\\n",
       "0              0             0       NaN                0.00         0.0   \n",
       "1              0             0       NaN              992.29         0.0   \n",
       "2              0             0       0.0              693.40         0.0   \n",
       "3              0             0       NaN              366.80         0.0   \n",
       "4              0             0       NaN             1064.60         0.0   \n",
       "5              0             0       NaN              713.00         0.0   \n",
       "6              0             0       0.0              800.00         0.0   \n",
       "7              0             0       0.0              402.00         0.0   \n",
       "8              0             0       0.0              660.00         0.0   \n",
       "9              0             0       0.0              320.00         0.0   \n",
       "10             0             0       0.0              720.00         0.0   \n",
       "11             0             0       0.0              480.00         0.0   \n",
       "12             0             0       0.0              595.98         0.0   \n",
       "13             0             0       0.0              915.00         0.0   \n",
       "14             0             0       0.0             3579.40         0.0   \n",
       "15             0             0       0.0                0.00         NaN   \n",
       "16             0             0       0.0                0.00         NaN   \n",
       "17             0             0       0.0              640.00         0.0   \n",
       "18             0             0       0.0              793.00         0.0   \n",
       "19             0             0       0.0                0.00         NaN   \n",
       "20             0             0       0.0             2970.00         0.0   \n",
       "21             0             0       0.0             1799.20         0.0   \n",
       "22             0             0       0.0             5891.20         0.0   \n",
       "23             0             0       0.0             2172.40         0.0   \n",
       "24             0             0       0.0                0.00         0.0   \n",
       "25             0             0       0.0                0.00         NaN   \n",
       "26             0             0       0.0              480.00         0.0   \n",
       "27             0             0       0.0                0.00         0.0   \n",
       "28             0             0       0.0                0.00         NaN   \n",
       "29             0             0       0.0                0.00         NaN   \n",
       "\n",
       "    service_price     paid rate_plan  client_id  \\\n",
       "0             0.0     0.00  Standard      51665   \n",
       "1             0.0     1.00  Standard      54117   \n",
       "2             0.0   693.40  Standard      54118   \n",
       "3             0.0     1.00  Standard      54790   \n",
       "4             0.0     1.00  Standard      55177   \n",
       "5             0.0     1.00  Standard      55412   \n",
       "6             0.0   800.00  Standard      55560   \n",
       "7             0.0   402.00  Standard      63419   \n",
       "8             0.0   660.00  Standard      61777   \n",
       "9             0.0   320.00  Standard      61778   \n",
       "10            0.0   720.00  Standard      61777   \n",
       "11            0.0   480.00  Standard      61777   \n",
       "12            0.0   595.98  Standard      61378   \n",
       "13            0.0   915.00  Standard      61105   \n",
       "14            0.0  3579.40  Standard      61807   \n",
       "15            NaN     0.00  Standard       1031   \n",
       "16            NaN     1.00  Standard      61391   \n",
       "17            0.0   640.00  Standard      60668   \n",
       "18            0.0   793.00  Standard      61508   \n",
       "19            NaN     1.00  Standard      61536   \n",
       "20            0.0  2970.00  Standard      59251   \n",
       "21            0.0  1799.20  Standard      61237   \n",
       "22            0.0  5891.20  Standard      61428   \n",
       "23            0.0  2172.40  Standard      61522   \n",
       "24            0.0     0.00  Standard       1031   \n",
       "25            NaN     0.00  Standard      54132   \n",
       "26            0.0   480.00  Standard      61739   \n",
       "27            0.0     0.00  Standard       1031   \n",
       "28            NaN     0.00  Standard       1031   \n",
       "29            NaN     0.00  Standard      61851   \n",
       "\n",
       "                                          client_name  \\\n",
       "0   86bd787ca115281ad9642c5fd6e79e6f2d87841c2fd9c6...   \n",
       "1   ca83ddae9b7d15212b5391c815a689b8acfd8ef31d0d80...   \n",
       "2   4db36724fc28085e053a3003dce55368ee207cce37d355...   \n",
       "3   e41ecdb28a96d0b3e294aea6e854d8dc39a1d61bb3dfe4...   \n",
       "4   5380adccf08ea3000791aad3ccc478e3b6a8de440910aa...   \n",
       "5   4aebfe125cf6c059588792b9fb871afe282a8806299dfe...   \n",
       "6   1f4b60816f6efcb45dfa67da7a6adab42d4a05b90a9278...   \n",
       "7   d47bcb623e5031df97cd9faf472e28d9fe40f1386bbd92...   \n",
       "8   f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...   \n",
       "9   364e80d6c0608116ff8808b339ff25dc2e3f8f2211ba38...   \n",
       "10  f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...   \n",
       "11  f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...   \n",
       "12  f929533e542ff1302069567400b5ba584dc27dcbd63aae...   \n",
       "13  8373dfd3bf2b44222dce774ee032a32b74a495f6ddd355...   \n",
       "14  cdd642820a12bb4a8f2407ed1d02b67435991afef52d9c...   \n",
       "15  d2c7d67c462db4ac6637597e7843b0c080cd41baa5dae0...   \n",
       "16  4d9a25ef49b785020f71d87e7202cd209bdc4197b2649c...   \n",
       "17  37a1b20c8796e967f521788e5f6dc03fe769784dcc4e9b...   \n",
       "18  1f277c7e23db63f3d3b99a1c91959d2d87cf65dd150c16...   \n",
       "19  d2125403e9a982b702f1e2ae3d961f6a2b1113cac9b4ab...   \n",
       "20  1fc1fc3c2f76244d172dce793478abe25bfa262bf84d88...   \n",
       "21  90c2ec1072904a2ba8b88c66c17930e8b85f8d0f10b4e2...   \n",
       "22  d96743d064f148fd88ea765513663700c2873765710c90...   \n",
       "23  2f31fadcb70beff6db9b09d6f62463beaa005569998791...   \n",
       "24  d2c7d67c462db4ac6637597e7843b0c080cd41baa5dae0...   \n",
       "25  4255913c4a150c7f6171ba2807857992e91b34f1b2862e...   \n",
       "26  b486b37b72db13811533a126c556cc8867eb2bbc432b8d...   \n",
       "27  d2c7d67c462db4ac6637597e7843b0c080cd41baa5dae0...   \n",
       "28  d2c7d67c462db4ac6637597e7843b0c080cd41baa5dae0...   \n",
       "29  fa1b8b5a01558c675885bdd7b844e74c7b061079f9623b...   \n",
       "\n",
       "                                                email  \\\n",
       "0                                                 NaN   \n",
       "1                                                 NaN   \n",
       "2   f9c0564c66d6a830c4964a30ac261038dd7cf762b0641c...   \n",
       "3   f6a8c77530865b7e437eb746c3564c4cbdc522c10d35f6...   \n",
       "4   6d08a7230580a09f1fde268bb7c1a5d74a55bdcc9183f8...   \n",
       "5   0d6aafda88cc3d5844da8c60ca9d1b6682f1ce1a4dfe12...   \n",
       "6   6163ca5013b2bc940219a59d0e30ec401ecd01bb498e03...   \n",
       "7   5213ac7a6db98631330ac74a241ffdf840e1857481a0b5...   \n",
       "8                                                 NaN   \n",
       "9                                                 NaN   \n",
       "10                                                NaN   \n",
       "11                                                NaN   \n",
       "12  a90206b6164e13331d087034d9d9a963a9a4bcf8b2969f...   \n",
       "13  37827cbb5aec4ed83d13193d766324efb93251cd44b28b...   \n",
       "14                                                NaN   \n",
       "15                                                NaN   \n",
       "16  25dec48fdcf1d2e7dbe97aff20d52cae0b2660f5eae1d8...   \n",
       "17  a9ba8f12a4509f4c598578fa743236beda2b791c938954...   \n",
       "18  4d70747ed821eca5c6fb0ec95f2a8e005369e53440a99c...   \n",
       "19  b068c4964a0082cea54c0c6b5b31907f8b5009cc5c29a9...   \n",
       "20  03f8d9987cae994de741857336b1d5bfa6e13567691336...   \n",
       "21  02f2e085320d0930db4c9d722d315e7e77235dac96303f...   \n",
       "22  4bc6914285bb73a34da5fc07ea6ed16eb3e08b0e6a5230...   \n",
       "23  22893f17c5a4cd6e0913a85ecdbc597ff2ff891e347570...   \n",
       "24                                                NaN   \n",
       "25                                                NaN   \n",
       "26                                                NaN   \n",
       "27                                                NaN   \n",
       "28                                                NaN   \n",
       "29                                                NaN   \n",
       "\n",
       "                                                phone  is_company  \\\n",
       "0                                                 NaN           0   \n",
       "1   318faec979ecaf8adaee0c8e5d7531a67f309b7247d30b...           0   \n",
       "2   cb550ba6d303bf230379073bcbdd55c37229eab3f173dc...           0   \n",
       "3   1c56315c10c9d8153ca7820648900befbd9109fb6cfb81...           0   \n",
       "4   3aff5ce689580e51de899de8ec75e8a8eaa470e4e99df4...           0   \n",
       "5   ea16c664798581a9d93a3128d772b8b89e05743edbbfae...           0   \n",
       "6   516b31d7892e1b5f4b6078ea0fc4c63a06bb9ceceb885d...           0   \n",
       "7   6416a3bc7ea31b09ae63628a143b160d7976978cdbd298...           0   \n",
       "8                                                 NaN           0   \n",
       "9                                                 NaN           0   \n",
       "10                                                NaN           0   \n",
       "11                                                NaN           0   \n",
       "12  446e3878681ff4dac3067bf75b8ec3b7e5be2cf8a02c75...           0   \n",
       "13  27673c0003a9f52f8e73269e2e5799dd8d4c2b0bc0dc07...           0   \n",
       "14  5aeffc70468d19eba135a79c74024c206abe83c29fc6f0...           0   \n",
       "15                                                NaN           0   \n",
       "16  97edf16b92140283616026230d86f5ded26d8e09863a76...           0   \n",
       "17  7ec6cc25066b8b7008b78d25027a2159d2116522611223...           0   \n",
       "18  1fda0af8c40fa82143ccbeac216fe4e1b6f82b5b57bd00...           0   \n",
       "19  18796a14e703d952770ff333e440744e5645f36395ab23...           0   \n",
       "20  fbf6f729bdbc5c399fda4952800159bad22b606de316dc...           0   \n",
       "21  7e2f2677699ae3b5b58365cf109eac9fc4803d83b900d4...           0   \n",
       "22  00d2c14014162af4c5c454800a93dfdcb4ea9ee96d109c...           0   \n",
       "23  b29bb8fee8266f9f23b7200a0979430bdd101cc2dc2e58...           0   \n",
       "24                                                NaN           0   \n",
       "25                                                NaN           0   \n",
       "26                                                NaN           0   \n",
       "27                                                NaN           0   \n",
       "28                                                NaN           0   \n",
       "29                                                NaN           0   \n",
       "\n",
       "    reservation_status  \n",
       "0                    1  \n",
       "1                    1  \n",
       "2                    2  \n",
       "3                    1  \n",
       "4                    1  \n",
       "5                    1  \n",
       "6                    4  \n",
       "7                    4  \n",
       "8                    4  \n",
       "9                    4  \n",
       "10                   4  \n",
       "11                   4  \n",
       "12                   4  \n",
       "13                   4  \n",
       "14                   3  \n",
       "15                   0  \n",
       "16                   0  \n",
       "17                   4  \n",
       "18                   4  \n",
       "19                   0  \n",
       "20                   3  \n",
       "21                   3  \n",
       "22                   3  \n",
       "23                   4  \n",
       "24                   2  \n",
       "25                   0  \n",
       "26                   4  \n",
       "27                   2  \n",
       "28                   0  \n",
       "29                   0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hotel_original_data = pd.read_csv(os.path.join(\"data\", \"hotel_data\", \"hotel_data_original.csv\"))\n",
    "print(\"Number of records: {}\".format(len(hotel_original_data)))\n",
    "display(hotel_original_data.head(30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "colonial-convert",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of records: 16102\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>term</th>\n",
       "      <th>length_of_stay_bucket</th>\n",
       "      <th>rate_plan</th>\n",
       "      <th>room_segment</th>\n",
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       "      <th>weekend_stay</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>Standard</td>\n",
       "      <td>[160-260]</td>\n",
       "      <td>[1-1]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>HighSeason</td>\n",
       "      <td>[2-3]</td>\n",
       "      <td>Standard</td>\n",
       "      <td>[160-260]</td>\n",
       "      <td>[3-4]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>HighSeason</td>\n",
       "      <td>[2-3]</td>\n",
       "      <td>Standard</td>\n",
       "      <td>[260-360]</td>\n",
       "      <td>[3-4]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>HighSeason</td>\n",
       "      <td>[8-inf]</td>\n",
       "      <td>Standard</td>\n",
       "      <td>[160-260]</td>\n",
       "      <td>[3-4]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>HighSeason</td>\n",
       "      <td>[2-3]</td>\n",
       "      <td>Standard</td>\n",
       "      <td>[0-160]</td>\n",
       "      <td>[3-4]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>LowSeason</td>\n",
       "      <td>[2-3]</td>\n",
       "      <td>Standard</td>\n",
       "      <td>[160-260]</td>\n",
       "      <td>[5-inf]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>LowSeason</td>\n",
       "      <td>[4-7]</td>\n",
       "      <td>Standard</td>\n",
       "      <td>[160-260]</td>\n",
       "      <td>[3-4]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>LowSeason</td>\n",
       "      <td>[4-7]</td>\n",
       "      <td>Standard</td>\n",
       "      <td>[0-160]</td>\n",
       "      <td>[3-4]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>LowSeason</td>\n",
       "      <td>[8-inf]</td>\n",
       "      <td>Standard</td>\n",
       "      <td>[160-260]</td>\n",
       "      <td>[3-4]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>OffSeason</td>\n",
       "      <td>[4-7]</td>\n",
       "      <td>Standard</td>\n",
       "      <td>[160-260]</td>\n",
       "      <td>[3-4]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>OffSeason</td>\n",
       "      <td>[8-inf]</td>\n",
       "      <td>Standard</td>\n",
       "      <td>[260-360]</td>\n",
       "      <td>[5-inf]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>OffSeason</td>\n",
       "      <td>[4-7]</td>\n",
       "      <td>Standard</td>\n",
       "      <td>[260-360]</td>\n",
       "      <td>[5-inf]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>OffSeason</td>\n",
       "      <td>[4-7]</td>\n",
       "      <td>Standard</td>\n",
       "      <td>[160-260]</td>\n",
       "      <td>[1-1]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "      <td>OffSeason</td>\n",
       "      <td>[2-3]</td>\n",
       "      <td>Standard</td>\n",
       "      <td>[160-260]</td>\n",
       "      <td>[1-1]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>OffSeason</td>\n",
       "      <td>[8-inf]</td>\n",
       "      <td>Standard</td>\n",
       "      <td>[160-260]</td>\n",
       "      <td>[1-1]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>OffSeason</td>\n",
       "      <td>[2-3]</td>\n",
       "      <td>Standard</td>\n",
       "      <td>[160-260]</td>\n",
       "      <td>[1-1]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>MayLongWeekend</td>\n",
       "      <td>[4-7]</td>\n",
       "      <td>Standard</td>\n",
       "      <td>[160-260]</td>\n",
       "      <td>[3-4]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "      <td>MayLongWeekend</td>\n",
       "      <td>[2-3]</td>\n",
       "      <td>Standard</td>\n",
       "      <td>[260-360]</td>\n",
       "      <td>[1-1]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>OffSeason</td>\n",
       "      <td>[2-3]</td>\n",
       "      <td>Standard</td>\n",
       "      <td>[160-260]</td>\n",
       "      <td>[5-inf]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>28</td>\n",
       "      <td>27</td>\n",
       "      <td>OffSeason</td>\n",
       "      <td>[2-3]</td>\n",
       "      <td>Standard</td>\n",
       "      <td>[160-260]</td>\n",
       "      <td>[2-2]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  item_id            term length_of_stay_bucket rate_plan  \\\n",
       "0         1        0  WinterVacation                 [2-3]  Standard   \n",
       "1         2        1  WinterVacation                 [2-3]  Standard   \n",
       "2         3        2  WinterVacation                 [2-3]  Standard   \n",
       "3         4        3  WinterVacation                 [4-7]  Standard   \n",
       "4         5        4  WinterVacation                 [4-7]  Standard   \n",
       "5         6        5          Easter                 [4-7]  Standard   \n",
       "6         7        6       OffSeason                 [2-3]  Standard   \n",
       "7         8        7      HighSeason                 [2-3]  Standard   \n",
       "8         9        8      HighSeason                 [2-3]  Standard   \n",
       "9         8        7      HighSeason                 [2-3]  Standard   \n",
       "10        8        7      HighSeason                 [2-3]  Standard   \n",
       "11       10        9      HighSeason                 [2-3]  Standard   \n",
       "12       11       10      HighSeason                 [2-3]  Standard   \n",
       "13       12       11      HighSeason               [8-inf]  Standard   \n",
       "14       14       12      HighSeason                 [2-3]  Standard   \n",
       "15       15       13       LowSeason                 [2-3]  Standard   \n",
       "16       16       14       LowSeason                 [4-7]  Standard   \n",
       "17       17       15       LowSeason                 [4-7]  Standard   \n",
       "18       18       16       LowSeason               [8-inf]  Standard   \n",
       "19       19       17       OffSeason                 [4-7]  Standard   \n",
       "20       20       18       OffSeason               [8-inf]  Standard   \n",
       "21       21       19       OffSeason                 [4-7]  Standard   \n",
       "22       13       20       OffSeason                 [4-7]  Standard   \n",
       "23       23       21       OffSeason                 [2-3]  Standard   \n",
       "24       13       22       OffSeason               [8-inf]  Standard   \n",
       "25       13       23       OffSeason                 [2-3]  Standard   \n",
       "26       25       24  MayLongWeekend                 [4-7]  Standard   \n",
       "27       26       25  MayLongWeekend                 [2-3]  Standard   \n",
       "28       27       26       OffSeason                 [2-3]  Standard   \n",
       "29       28       27       OffSeason                 [2-3]  Standard   \n",
       "\n",
       "   room_segment n_people_bucket  weekend_stay  \n",
       "0     [260-360]         [5-inf]          True  \n",
       "1     [160-260]           [3-4]          True  \n",
       "2     [160-260]           [2-2]         False  \n",
       "3     [160-260]           [3-4]          True  \n",
       "4       [0-160]           [2-2]          True  \n",
       "5     [260-360]         [5-inf]          True  \n",
       "6     [260-360]         [5-inf]          True  \n",
       "7     [160-260]           [1-1]          True  \n",
       "8       [0-160]           [1-1]          True  \n",
       "9     [160-260]           [1-1]          True  \n",
       "10    [160-260]           [1-1]          True  \n",
       "11    [160-260]           [3-4]          True  \n",
       "12    [260-360]           [3-4]          True  \n",
       "13    [160-260]           [3-4]          True  \n",
       "14      [0-160]           [3-4]          True  \n",
       "15    [160-260]         [5-inf]          True  \n",
       "16    [160-260]           [3-4]          True  \n",
       "17      [0-160]           [3-4]          True  \n",
       "18    [160-260]           [3-4]          True  \n",
       "19    [160-260]           [3-4]          True  \n",
       "20    [260-360]         [5-inf]          True  \n",
       "21    [260-360]         [5-inf]          True  \n",
       "22    [160-260]           [1-1]          True  \n",
       "23    [160-260]           [1-1]          True  \n",
       "24    [160-260]           [1-1]          True  \n",
       "25    [160-260]           [1-1]         False  \n",
       "26    [160-260]           [3-4]          True  \n",
       "27    [260-360]           [1-1]          True  \n",
       "28    [160-260]         [5-inf]         False  \n",
       "29    [160-260]           [2-2]         False  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hotel_data_interactions_df = pd.read_csv(os.path.join(\"data\", \"hotel_data\", \"hotel_data_interactions_df.csv\"))\n",
    "print(\"Number of records: {}\".format(len(hotel_data_interactions_df)))\n",
    "display(hotel_data_interactions_df.head(30))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "whole-buffer",
   "metadata": {},
   "source": [
    "# Test recommenders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "coastal-kernel",
   "metadata": {},
   "source": [
    "## Load a sample of Movielens data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "absent-uncle",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of left interactions: 9692\n"
     ]
    }
   ],
   "source": [
    "# Filter the data to reduce the number of movies\n",
    "\n",
    "ml_ratings_df = pd.read_csv(os.path.join(\"data\", \"movielens_small\", \"ratings.csv\")).rename(columns={'userId': 'user_id', 'movieId': 'item_id'})\n",
    "ml_movies_df = pd.read_csv(os.path.join(\"data\", \"movielens_small\", \"movies.csv\")).rename(columns={'movieId': 'item_id'})\n",
    "ml_df = pd.merge(ml_ratings_df, ml_movies_df, on='item_id')\n",
    "\n",
    "seed = 6789\n",
    "rng = np.random.RandomState(seed=seed)\n",
    "left_ids = rng.choice(ml_movies_df['item_id'], size=1000, replace=False)\n",
    "\n",
    "ml_ratings_df = ml_ratings_df.loc[ml_ratings_df['item_id'].isin(left_ids)]\n",
    "ml_movies_df = ml_movies_df.loc[ml_movies_df['item_id'].isin(left_ids)]\n",
    "ml_df = ml_df.loc[ml_df['item_id'].isin(left_ids)]\n",
    "\n",
    "print(\"Number of left interactions: {}\".format(len(ml_ratings_df)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "registered-membrane",
   "metadata": {},
   "source": [
    "## Train several recommenders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "capable-rally",
   "metadata": {},
   "outputs": [],
   "source": [
    "from recommenders.basic_recommenders import RandomRecommender\n",
    "from recommenders.basic_recommenders import MostPopularRecommender\n",
    "from recommenders.basic_recommenders import HighestRatedRecommender\n",
    "from recommenders.tfidf_recommender import TFIDFRecommender\n",
    "from recommenders.nearest_neighbors_recommender import ItemBasedCosineNearestNeighborsRecommender\n",
    "from recommenders.amazon_recommender import AmazonRecommender\n",
    "from recommenders.netflix_recommender import NetflixRecommender\n",
    "from recommenders.gmf_recommenders import GMFRecommender\n",
    "\n",
    "random_recommender = RandomRecommender()\n",
    "most_popular_recommender = MostPopularRecommender()\n",
    "highest_rated_recommender = HighestRatedRecommender()\n",
    "tfidf_recommender = TFIDFRecommender()\n",
    "ibcnn_recommender = ItemBasedCosineNearestNeighborsRecommender(n_neighbors=30)\n",
    "amazon_recommender = AmazonRecommender()\n",
    "netflix_recommender = NetflixRecommender(print_type='live', embedding_dim=8, n_epochs=20)\n",
    "gmf_recommender = GMFRecommender(print_type='live', n_neg_per_pos=10, batch_size=16, \n",
    "                                 embedding_dim=6, lr=0.001, weight_decay=0.0001, n_epochs=5, seed=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "chronic-darkness",
   "metadata": {},
   "outputs": [],
   "source": [
    "random_recommender.fit(ml_ratings_df, None, ml_movies_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "wrapped-democracy",
   "metadata": {},
   "outputs": [],
   "source": [
    "most_popular_recommender = MostPopularRecommender()\n",
    "highest_rated_recommender = HighestRatedRecommender()\n",
    "most_popular_recommender.fit(ml_ratings_df, None, ml_movies_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "digital-forty",
   "metadata": {},
   "outputs": [],
   "source": [
    "highest_rated_recommender.fit(ml_ratings_df, None, ml_movies_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "incorrect-newcastle",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_recommender.fit(ml_ratings_df, None, ml_movies_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "global-rocket",
   "metadata": {},
   "outputs": [],
   "source": [
    "ibcnn_recommender.fit(ml_ratings_df, None, ml_movies_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "arctic-ladder",
   "metadata": {},
   "outputs": [],
   "source": [
    "amazon_recommender.fit(ml_ratings_df, None, ml_movies_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "romantic-prisoner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss\n",
      "\ttraining         \t (min:    0.099, max:    0.270, cur:    0.099)\n",
      "\tvalidation       \t (min:    0.110, max:    0.318, cur:    0.110)\n"
     ]
    }
   ],
   "source": [
    "netflix_recommender.fit(ml_ratings_df, None, ml_movies_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "immune-fleet",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss\n",
      "\tloss             \t (min:    0.171, max:    0.694, cur:    0.171)\n"
     ]
    }
   ],
   "source": [
    "gmf_recommender.fit(ml_ratings_df, None, ml_movies_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "simple-first",
   "metadata": {},
   "outputs": [],
   "source": [
    "recommenders = [random_recommender, most_popular_recommender, highest_rated_recommender, tfidf_recommender, \n",
    "                ibcnn_recommender, amazon_recommender, netflix_recommender, gmf_recommender]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "neural-trademark",
   "metadata": {},
   "source": [
    "## Take a look on user 6 preferences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "sorted-alcohol",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active user history\n"
     ]
    },
    {
     "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>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30965</th>\n",
       "      <td>6</td>\n",
       "      <td>337</td>\n",
       "      <td>5.0</td>\n",
       "      <td>845553586</td>\n",
       "      <td>What's Eating Gilbert Grape (1993)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20124</th>\n",
       "      <td>6</td>\n",
       "      <td>595</td>\n",
       "      <td>5.0</td>\n",
       "      <td>845553174</td>\n",
       "      <td>Beauty and the Beast (1991)</td>\n",
       "      <td>Animation|Children|Fantasy|Musical|Romance|IMAX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29952</th>\n",
       "      <td>6</td>\n",
       "      <td>248</td>\n",
       "      <td>5.0</td>\n",
       "      <td>845554263</td>\n",
       "      <td>Houseguest (1994)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30057</th>\n",
       "      <td>6</td>\n",
       "      <td>257</td>\n",
       "      <td>5.0</td>\n",
       "      <td>845554176</td>\n",
       "      <td>Just Cause (1995)</td>\n",
       "      <td>Mystery|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5339</th>\n",
       "      <td>6</td>\n",
       "      <td>780</td>\n",
       "      <td>5.0</td>\n",
       "      <td>845556915</td>\n",
       "      <td>Independence Day (a.k.a. ID4) (1996)</td>\n",
       "      <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19723</th>\n",
       "      <td>6</td>\n",
       "      <td>475</td>\n",
       "      <td>4.0</td>\n",
       "      <td>845554099</td>\n",
       "      <td>In the Name of the Father (1993)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31285</th>\n",
       "      <td>6</td>\n",
       "      <td>355</td>\n",
       "      <td>4.0</td>\n",
       "      <td>845553819</td>\n",
       "      <td>Flintstones, The (1994)</td>\n",
       "      <td>Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31374</th>\n",
       "      <td>6</td>\n",
       "      <td>370</td>\n",
       "      <td>4.0</td>\n",
       "      <td>845553794</td>\n",
       "      <td>Naked Gun 33 1/3: The Final Insult (1994)</td>\n",
       "      <td>Action|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30653</th>\n",
       "      <td>6</td>\n",
       "      <td>304</td>\n",
       "      <td>4.0</td>\n",
       "      <td>845555546</td>\n",
       "      <td>Roommates (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28298</th>\n",
       "      <td>6</td>\n",
       "      <td>95</td>\n",
       "      <td>4.0</td>\n",
       "      <td>845553559</td>\n",
       "      <td>Broken Arrow (1996)</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30156</th>\n",
       "      <td>6</td>\n",
       "      <td>274</td>\n",
       "      <td>4.0</td>\n",
       "      <td>845555151</td>\n",
       "      <td>Man of the House (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32802</th>\n",
       "      <td>6</td>\n",
       "      <td>542</td>\n",
       "      <td>4.0</td>\n",
       "      <td>845554524</td>\n",
       "      <td>Son in Law (1993)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32337</th>\n",
       "      <td>6</td>\n",
       "      <td>477</td>\n",
       "      <td>4.0</td>\n",
       "      <td>845554141</td>\n",
       "      <td>What's Love Got to Do with It? (1993)</td>\n",
       "      <td>Drama|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29563</th>\n",
       "      <td>6</td>\n",
       "      <td>207</td>\n",
       "      <td>4.0</td>\n",
       "      <td>845554024</td>\n",
       "      <td>Walk in the Clouds, A (1995)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29706</th>\n",
       "      <td>6</td>\n",
       "      <td>212</td>\n",
       "      <td>4.0</td>\n",
       "      <td>845556181</td>\n",
       "      <td>Bushwhacked (1995)</td>\n",
       "      <td>Adventure|Comedy|Crime|Mystery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28705</th>\n",
       "      <td>6</td>\n",
       "      <td>145</td>\n",
       "      <td>4.0</td>\n",
       "      <td>845553966</td>\n",
       "      <td>Bad Boys (1995)</td>\n",
       "      <td>Action|Comedy|Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31704</th>\n",
       "      <td>6</td>\n",
       "      <td>405</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845554603</td>\n",
       "      <td>Highlander III: The Sorcerer (a.k.a. Highlande...</td>\n",
       "      <td>Action|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31775</th>\n",
       "      <td>6</td>\n",
       "      <td>426</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845554551</td>\n",
       "      <td>Body Snatchers (1993)</td>\n",
       "      <td>Horror|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30223</th>\n",
       "      <td>6</td>\n",
       "      <td>279</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845555436</td>\n",
       "      <td>My Family (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31938</th>\n",
       "      <td>6</td>\n",
       "      <td>435</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845553489</td>\n",
       "      <td>Coneheads (1993)</td>\n",
       "      <td>Comedy|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32241</th>\n",
       "      <td>6</td>\n",
       "      <td>460</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845555281</td>\n",
       "      <td>Getting Even with Dad (1994)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31145</th>\n",
       "      <td>6</td>\n",
       "      <td>343</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845555172</td>\n",
       "      <td>Baby-Sitters Club, The (1995)</td>\n",
       "      <td>Children</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32980</th>\n",
       "      <td>6</td>\n",
       "      <td>606</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845555024</td>\n",
       "      <td>Candyman: Farewell to the Flesh (1995)</td>\n",
       "      <td>Fantasy|Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33469</th>\n",
       "      <td>6</td>\n",
       "      <td>837</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845556241</td>\n",
       "      <td>Matilda (1996)</td>\n",
       "      <td>Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33547</th>\n",
       "      <td>6</td>\n",
       "      <td>842</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845555675</td>\n",
       "      <td>Tales from the Crypt Presents: Bordello of Blo...</td>\n",
       "      <td>Comedy|Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32331</th>\n",
       "      <td>6</td>\n",
       "      <td>472</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845555632</td>\n",
       "      <td>I'll Do Anything (1994)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33651</th>\n",
       "      <td>6</td>\n",
       "      <td>979</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845556808</td>\n",
       "      <td>Nothing Personal (1995)</td>\n",
       "      <td>Drama|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30666</th>\n",
       "      <td>6</td>\n",
       "      <td>313</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845555546</td>\n",
       "      <td>Swan Princess, The (1994)</td>\n",
       "      <td>Animation|Children</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30371</th>\n",
       "      <td>6</td>\n",
       "      <td>289</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845554455</td>\n",
       "      <td>Only You (1994)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3791</th>\n",
       "      <td>6</td>\n",
       "      <td>543</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845554099</td>\n",
       "      <td>So I Married an Axe Murderer (1993)</td>\n",
       "      <td>Comedy|Romance|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30102</th>\n",
       "      <td>6</td>\n",
       "      <td>267</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845554376</td>\n",
       "      <td>Major Payne (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29729</th>\n",
       "      <td>6</td>\n",
       "      <td>219</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845556131</td>\n",
       "      <td>Cure, The (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29584</th>\n",
       "      <td>6</td>\n",
       "      <td>208</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845553253</td>\n",
       "      <td>Waterworld (1995)</td>\n",
       "      <td>Action|Adventure|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29193</th>\n",
       "      <td>6</td>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845554263</td>\n",
       "      <td>Hackers (1995)</td>\n",
       "      <td>Action|Adventure|Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27260</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845554349</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25825</th>\n",
       "      <td>6</td>\n",
       "      <td>266</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845553457</td>\n",
       "      <td>Legends of the Fall (1994)</td>\n",
       "      <td>Drama|Romance|War|Western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19257</th>\n",
       "      <td>6</td>\n",
       "      <td>171</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845555402</td>\n",
       "      <td>Jeffrey (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29248</th>\n",
       "      <td>6</td>\n",
       "      <td>180</td>\n",
       "      <td>2.0</td>\n",
       "      <td>845555006</td>\n",
       "      <td>Mallrats (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30804</th>\n",
       "      <td>6</td>\n",
       "      <td>327</td>\n",
       "      <td>1.0</td>\n",
       "      <td>845554062</td>\n",
       "      <td>Tank Girl (1995)</td>\n",
       "      <td>Action|Comedy|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>845553381</td>\n",
       "      <td>Usual Suspects, The (1995)</td>\n",
       "      <td>Crime|Mystery|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  item_id  rating  timestamp  \\\n",
       "30965        6      337     5.0  845553586   \n",
       "20124        6      595     5.0  845553174   \n",
       "29952        6      248     5.0  845554263   \n",
       "30057        6      257     5.0  845554176   \n",
       "5339         6      780     5.0  845556915   \n",
       "19723        6      475     4.0  845554099   \n",
       "31285        6      355     4.0  845553819   \n",
       "31374        6      370     4.0  845553794   \n",
       "30653        6      304     4.0  845555546   \n",
       "28298        6       95     4.0  845553559   \n",
       "30156        6      274     4.0  845555151   \n",
       "32802        6      542     4.0  845554524   \n",
       "32337        6      477     4.0  845554141   \n",
       "29563        6      207     4.0  845554024   \n",
       "29706        6      212     4.0  845556181   \n",
       "28705        6      145     4.0  845553966   \n",
       "31704        6      405     3.0  845554603   \n",
       "31775        6      426     3.0  845554551   \n",
       "30223        6      279     3.0  845555436   \n",
       "31938        6      435     3.0  845553489   \n",
       "32241        6      460     3.0  845555281   \n",
       "31145        6      343     3.0  845555172   \n",
       "32980        6      606     3.0  845555024   \n",
       "33469        6      837     3.0  845556241   \n",
       "33547        6      842     3.0  845555675   \n",
       "32331        6      472     3.0  845555632   \n",
       "33651        6      979     3.0  845556808   \n",
       "30666        6      313     3.0  845555546   \n",
       "30371        6      289     3.0  845554455   \n",
       "3791         6      543     3.0  845554099   \n",
       "30102        6      267     3.0  845554376   \n",
       "29729        6      219     3.0  845556131   \n",
       "29584        6      208     3.0  845553253   \n",
       "29193        6      170     3.0  845554263   \n",
       "27260        6        4     3.0  845554349   \n",
       "25825        6      266     3.0  845553457   \n",
       "19257        6      171     3.0  845555402   \n",
       "29248        6      180     2.0  845555006   \n",
       "30804        6      327     1.0  845554062   \n",
       "574          6       50     1.0  845553381   \n",
       "\n",
       "                                                   title  \\\n",
       "30965                 What's Eating Gilbert Grape (1993)   \n",
       "20124                        Beauty and the Beast (1991)   \n",
       "29952                                  Houseguest (1994)   \n",
       "30057                                  Just Cause (1995)   \n",
       "5339                Independence Day (a.k.a. ID4) (1996)   \n",
       "19723                   In the Name of the Father (1993)   \n",
       "31285                            Flintstones, The (1994)   \n",
       "31374          Naked Gun 33 1/3: The Final Insult (1994)   \n",
       "30653                                   Roommates (1995)   \n",
       "28298                                Broken Arrow (1996)   \n",
       "30156                            Man of the House (1995)   \n",
       "32802                                  Son in Law (1993)   \n",
       "32337              What's Love Got to Do with It? (1993)   \n",
       "29563                       Walk in the Clouds, A (1995)   \n",
       "29706                                 Bushwhacked (1995)   \n",
       "28705                                    Bad Boys (1995)   \n",
       "31704  Highlander III: The Sorcerer (a.k.a. Highlande...   \n",
       "31775                              Body Snatchers (1993)   \n",
       "30223                                   My Family (1995)   \n",
       "31938                                   Coneheads (1993)   \n",
       "32241                       Getting Even with Dad (1994)   \n",
       "31145                      Baby-Sitters Club, The (1995)   \n",
       "32980             Candyman: Farewell to the Flesh (1995)   \n",
       "33469                                     Matilda (1996)   \n",
       "33547  Tales from the Crypt Presents: Bordello of Blo...   \n",
       "32331                            I'll Do Anything (1994)   \n",
       "33651                            Nothing Personal (1995)   \n",
       "30666                          Swan Princess, The (1994)   \n",
       "30371                                    Only You (1994)   \n",
       "3791                 So I Married an Axe Murderer (1993)   \n",
       "30102                                 Major Payne (1995)   \n",
       "29729                                   Cure, The (1995)   \n",
       "29584                                  Waterworld (1995)   \n",
       "29193                                     Hackers (1995)   \n",
       "27260                           Waiting to Exhale (1995)   \n",
       "25825                         Legends of the Fall (1994)   \n",
       "19257                                     Jeffrey (1995)   \n",
       "29248                                    Mallrats (1995)   \n",
       "30804                                   Tank Girl (1995)   \n",
       "574                           Usual Suspects, The (1995)   \n",
       "\n",
       "                                                genres  \n",
       "30965                                            Drama  \n",
       "20124  Animation|Children|Fantasy|Musical|Romance|IMAX  \n",
       "29952                                           Comedy  \n",
       "30057                                 Mystery|Thriller  \n",
       "5339                  Action|Adventure|Sci-Fi|Thriller  \n",
       "19723                                            Drama  \n",
       "31285                          Children|Comedy|Fantasy  \n",
       "31374                                    Action|Comedy  \n",
       "30653                                     Comedy|Drama  \n",
       "28298                        Action|Adventure|Thriller  \n",
       "30156                                           Comedy  \n",
       "32802                             Comedy|Drama|Romance  \n",
       "32337                                    Drama|Musical  \n",
       "29563                                    Drama|Romance  \n",
       "29706                   Adventure|Comedy|Crime|Mystery  \n",
       "28705               Action|Comedy|Crime|Drama|Thriller  \n",
       "31704                                   Action|Fantasy  \n",
       "31775                           Horror|Sci-Fi|Thriller  \n",
       "30223                                            Drama  \n",
       "31938                                    Comedy|Sci-Fi  \n",
       "32241                                           Comedy  \n",
       "31145                                         Children  \n",
       "32980                                   Fantasy|Horror  \n",
       "33469                          Children|Comedy|Fantasy  \n",
       "33547                                    Comedy|Horror  \n",
       "32331                                     Comedy|Drama  \n",
       "33651                                        Drama|War  \n",
       "30666                               Animation|Children  \n",
       "30371                                   Comedy|Romance  \n",
       "3791                           Comedy|Romance|Thriller  \n",
       "30102                                           Comedy  \n",
       "29729                                            Drama  \n",
       "29584                          Action|Adventure|Sci-Fi  \n",
       "29193                  Action|Adventure|Crime|Thriller  \n",
       "27260                             Comedy|Drama|Romance  \n",
       "25825                        Drama|Romance|War|Western  \n",
       "19257                                     Comedy|Drama  \n",
       "29248                                   Comedy|Romance  \n",
       "30804                             Action|Comedy|Sci-Fi  \n",
       "574                             Crime|Mystery|Thriller  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print movies watched by user 6\n",
    "\n",
    "active_user_movies = ml_df.loc[ml_df['user_id'] == 6]\n",
    "print(\"Active user history\")\n",
    "display(active_user_movies.sort_values('rating', ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "attractive-divorce",
   "metadata": {},
   "source": [
    "## Generate recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "oriented-service",
   "metadata": {
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>score</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>952</td>\n",
       "      <td>0.549181</td>\n",
       "      <td>Around the World in 80 Days (1956)</td>\n",
       "      <td>Adventure|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>85394</td>\n",
       "      <td>0.542451</td>\n",
       "      <td>Cave of Forgotten Dreams (2010)</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>2818</td>\n",
       "      <td>0.530260</td>\n",
       "      <td>Iron Eagle IV (1995)</td>\n",
       "      <td>Action|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.528602</td>\n",
       "      <td>Grosse Pointe Blank (1997)</td>\n",
       "      <td>Comedy|Crime|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>4239</td>\n",
       "      <td>0.510292</td>\n",
       "      <td>Blow (2001)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  user_id item_id     score                               title  \\\n",
       "0       6     952  0.549181  Around the World in 80 Days (1956)   \n",
       "1       6   85394  0.542451     Cave of Forgotten Dreams (2010)   \n",
       "2       6    2818  0.530260                Iron Eagle IV (1995)   \n",
       "3       6    1500  0.528602          Grosse Pointe Blank (1997)   \n",
       "4       6    4239  0.510292                         Blow (2001)   \n",
       "\n",
       "                 genres  \n",
       "0      Adventure|Comedy  \n",
       "1           Documentary  \n",
       "2            Action|War  \n",
       "3  Comedy|Crime|Romance  \n",
       "4           Crime|Drama  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendations for GMFRecommender\n"
     ]
    },
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>5502</td>\n",
       "      <td>0.688946</td>\n",
       "      <td>Signs (2002)</td>\n",
       "      <td>Horror|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>858</td>\n",
       "      <td>0.665873</td>\n",
       "      <td>Godfather, The (1972)</td>\n",
       "      <td>Crime|Drama</td>\n",
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       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>788</td>\n",
       "      <td>0.663187</td>\n",
       "      <td>Nutty Professor, The (1996)</td>\n",
       "      <td>Comedy|Fantasy|Romance|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2716</td>\n",
       "      <td>0.654819</td>\n",
       "      <td>Ghostbusters (a.k.a. Ghost Busters) (1984)</td>\n",
       "      <td>Action|Comedy|Sci-Fi</td>\n",
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       "  user_id item_id     score                                       title  \\\n",
       "0       6    1079  0.746184                 Fish Called Wanda, A (1988)   \n",
       "1       6    5502  0.688946                                Signs (2002)   \n",
       "2       6     858  0.665873                       Godfather, The (1972)   \n",
       "3       6     788  0.663187                 Nutty Professor, The (1996)   \n",
       "4       6    2716  0.654819  Ghostbusters (a.k.a. Ghost Busters) (1984)   \n",
       "\n",
       "                          genres  \n",
       "0                   Comedy|Crime  \n",
       "1         Horror|Sci-Fi|Thriller  \n",
       "2                    Crime|Drama  \n",
       "3  Comedy|Fantasy|Romance|Sci-Fi  \n",
       "4           Action|Comedy|Sci-Fi  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for recommender in recommenders:\n",
    "    recommendations = recommender.recommend(pd.DataFrame([[6]], columns=['user_id']), ml_movies_df, 5)\n",
    "\n",
    "    recommendations = pd.merge(recommendations, ml_movies_df, on='item_id', how='left')\n",
    "    print(\"Recommendations for {}\".format(type(recommender).__name__))\n",
    "    display(recommendations)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sealed-liberty",
   "metadata": {},
   "source": [
    "### Train-test split test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "identified-destination",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Recommender</th>\n",
       "      <th>HR@1</th>\n",
       "      <th>HR@3</th>\n",
       "      <th>HR@5</th>\n",
       "      <th>HR@10</th>\n",
       "      <th>NDCG@1</th>\n",
       "      <th>NDCG@3</th>\n",
       "      <th>NDCG@5</th>\n",
       "      <th>NDCG@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomRecommender</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008949</td>\n",
       "      <td>0.01566</td>\n",
       "      <td>0.044743</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004767</td>\n",
       "      <td>0.00756</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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       "         Recommender  HR@1      HR@3     HR@5     HR@10  NDCG@1    NDCG@3  \\\n",
       "0  RandomRecommender   0.0  0.008949  0.01566  0.044743     0.0  0.004767   \n",
       "\n",
       "    NDCG@5   NDCG@10  \n",
       "0  0.00756  0.016974  "
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       "      <th>HR@1</th>\n",
       "      <th>HR@3</th>\n",
       "      <th>HR@5</th>\n",
       "      <th>HR@10</th>\n",
       "      <th>NDCG@1</th>\n",
       "      <th>NDCG@3</th>\n",
       "      <th>NDCG@5</th>\n",
       "      <th>NDCG@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MostPopularRecommender</td>\n",
       "      <td>0.076063</td>\n",
       "      <td>0.284116</td>\n",
       "      <td>0.440716</td>\n",
       "      <td>0.680089</td>\n",
       "      <td>0.076063</td>\n",
       "      <td>0.192392</td>\n",
       "      <td>0.256306</td>\n",
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       "              Recommender      HR@1      HR@3      HR@5     HR@10    NDCG@1  \\\n",
       "0  MostPopularRecommender  0.076063  0.284116  0.440716  0.680089  0.076063   \n",
       "\n",
       "     NDCG@3    NDCG@5   NDCG@10  \n",
       "0  0.192392  0.256306  0.332798  "
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       "      <th>0</th>\n",
       "      <td>HighestRatedRecommender</td>\n",
       "      <td>0.114094</td>\n",
       "      <td>0.239374</td>\n",
       "      <td>0.331096</td>\n",
       "      <td>0.568233</td>\n",
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       "               Recommender      HR@1      HR@3      HR@5     HR@10    NDCG@1  \\\n",
       "0  HighestRatedRecommender  0.114094  0.239374  0.331096  0.568233  0.114094   \n",
       "\n",
       "     NDCG@3    NDCG@5   NDCG@10  \n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TFIDFRecommender</td>\n",
       "      <td>0.017897</td>\n",
       "      <td>0.044743</td>\n",
       "      <td>0.058166</td>\n",
       "      <td>0.09396</td>\n",
       "      <td>0.017897</td>\n",
       "      <td>0.033077</td>\n",
       "      <td>0.038368</td>\n",
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       "        Recommender      HR@1      HR@3      HR@5    HR@10    NDCG@1  \\\n",
       "0  TFIDFRecommender  0.017897  0.044743  0.058166  0.09396  0.017897   \n",
       "\n",
       "     NDCG@3    NDCG@5   NDCG@10  \n",
       "0  0.033077  0.038368  0.049966  "
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ItemBasedCosineNearestNeighborsRecommender</td>\n",
       "      <td>0.14094</td>\n",
       "      <td>0.404922</td>\n",
       "      <td>0.610738</td>\n",
       "      <td>0.955257</td>\n",
       "      <td>0.14094</td>\n",
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       "                                  Recommender     HR@1      HR@3      HR@5  \\\n",
       "0  ItemBasedCosineNearestNeighborsRecommender  0.14094  0.404922  0.610738   \n",
       "\n",
       "      HR@10   NDCG@1    NDCG@3    NDCG@5   NDCG@10  \n",
       "0  0.955257  0.14094  0.291677  0.376199  0.488492  "
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       "      <td>AmazonRecommender</td>\n",
       "      <td>0.060403</td>\n",
       "      <td>0.129754</td>\n",
       "      <td>0.192394</td>\n",
       "      <td>0.331096</td>\n",
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       "      <th>0</th>\n",
       "      <td>NetflixRecommender</td>\n",
       "      <td>0.008949</td>\n",
       "      <td>0.049217</td>\n",
       "      <td>0.100671</td>\n",
       "      <td>0.228188</td>\n",
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       "          Recommender      HR@1      HR@3      HR@5     HR@10    NDCG@1  \\\n",
       "0  NetflixRecommender  0.008949  0.049217  0.100671  0.228188  0.008949   \n",
       "\n",
       "    NDCG@3    NDCG@5   NDCG@10  \n",
       "0  0.03084  0.051824  0.092152  "
      ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recommender</th>\n",
       "      <th>HR@1</th>\n",
       "      <th>HR@3</th>\n",
       "      <th>HR@5</th>\n",
       "      <th>HR@10</th>\n",
       "      <th>NDCG@1</th>\n",
       "      <th>NDCG@3</th>\n",
       "      <th>NDCG@5</th>\n",
       "      <th>NDCG@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GMFRecommender</td>\n",
       "      <td>0.147651</td>\n",
       "      <td>0.317673</td>\n",
       "      <td>0.469799</td>\n",
       "      <td>0.767338</td>\n",
       "      <td>0.147651</td>\n",
       "      <td>0.239985</td>\n",
       "      <td>0.301972</td>\n",
       "      <td>0.397954</td>\n",
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      ],
      "text/plain": [
       "      Recommender      HR@1      HR@3      HR@5     HR@10    NDCG@1    NDCG@3  \\\n",
       "0  GMFRecommender  0.147651  0.317673  0.469799  0.767338  0.147651  0.239985   \n",
       "\n",
       "     NDCG@5   NDCG@10  \n",
       "0  0.301972  0.397954  "
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recommender</th>\n",
       "      <th>HR@1</th>\n",
       "      <th>HR@3</th>\n",
       "      <th>HR@5</th>\n",
       "      <th>HR@10</th>\n",
       "      <th>NDCG@1</th>\n",
       "      <th>NDCG@3</th>\n",
       "      <th>NDCG@5</th>\n",
       "      <th>NDCG@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomRecommender</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008949</td>\n",
       "      <td>0.015660</td>\n",
       "      <td>0.044743</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004767</td>\n",
       "      <td>0.007560</td>\n",
       "      <td>0.016974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MostPopularRecommender</td>\n",
       "      <td>0.076063</td>\n",
       "      <td>0.284116</td>\n",
       "      <td>0.440716</td>\n",
       "      <td>0.680089</td>\n",
       "      <td>0.076063</td>\n",
       "      <td>0.192392</td>\n",
       "      <td>0.256306</td>\n",
       "      <td>0.332798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HighestRatedRecommender</td>\n",
       "      <td>0.114094</td>\n",
       "      <td>0.239374</td>\n",
       "      <td>0.331096</td>\n",
       "      <td>0.568233</td>\n",
       "      <td>0.114094</td>\n",
       "      <td>0.186693</td>\n",
       "      <td>0.224823</td>\n",
       "      <td>0.302199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TFIDFRecommender</td>\n",
       "      <td>0.017897</td>\n",
       "      <td>0.044743</td>\n",
       "      <td>0.058166</td>\n",
       "      <td>0.093960</td>\n",
       "      <td>0.017897</td>\n",
       "      <td>0.033077</td>\n",
       "      <td>0.038368</td>\n",
       "      <td>0.049966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ItemBasedCosineNearestNeighborsRecommender</td>\n",
       "      <td>0.140940</td>\n",
       "      <td>0.404922</td>\n",
       "      <td>0.610738</td>\n",
       "      <td>0.955257</td>\n",
       "      <td>0.140940</td>\n",
       "      <td>0.291677</td>\n",
       "      <td>0.376199</td>\n",
       "      <td>0.488492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AmazonRecommender</td>\n",
       "      <td>0.060403</td>\n",
       "      <td>0.129754</td>\n",
       "      <td>0.192394</td>\n",
       "      <td>0.331096</td>\n",
       "      <td>0.060403</td>\n",
       "      <td>0.099765</td>\n",
       "      <td>0.125370</td>\n",
       "      <td>0.169699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NetflixRecommender</td>\n",
       "      <td>0.008949</td>\n",
       "      <td>0.049217</td>\n",
       "      <td>0.100671</td>\n",
       "      <td>0.228188</td>\n",
       "      <td>0.008949</td>\n",
       "      <td>0.030840</td>\n",
       "      <td>0.051824</td>\n",
       "      <td>0.092152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GMFRecommender</td>\n",
       "      <td>0.147651</td>\n",
       "      <td>0.317673</td>\n",
       "      <td>0.469799</td>\n",
       "      <td>0.767338</td>\n",
       "      <td>0.147651</td>\n",
       "      <td>0.239985</td>\n",
       "      <td>0.301972</td>\n",
       "      <td>0.397954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Recommender      HR@1      HR@3      HR@5  \\\n",
       "0                           RandomRecommender  0.000000  0.008949  0.015660   \n",
       "1                      MostPopularRecommender  0.076063  0.284116  0.440716   \n",
       "2                     HighestRatedRecommender  0.114094  0.239374  0.331096   \n",
       "3                            TFIDFRecommender  0.017897  0.044743  0.058166   \n",
       "4  ItemBasedCosineNearestNeighborsRecommender  0.140940  0.404922  0.610738   \n",
       "5                           AmazonRecommender  0.060403  0.129754  0.192394   \n",
       "6                          NetflixRecommender  0.008949  0.049217  0.100671   \n",
       "7                              GMFRecommender  0.147651  0.317673  0.469799   \n",
       "\n",
       "      HR@10    NDCG@1    NDCG@3    NDCG@5   NDCG@10  \n",
       "0  0.044743  0.000000  0.004767  0.007560  0.016974  \n",
       "1  0.680089  0.076063  0.192392  0.256306  0.332798  \n",
       "2  0.568233  0.114094  0.186693  0.224823  0.302199  \n",
       "3  0.093960  0.017897  0.033077  0.038368  0.049966  \n",
       "4  0.955257  0.140940  0.291677  0.376199  0.488492  \n",
       "5  0.331096  0.060403  0.099765  0.125370  0.169699  \n",
       "6  0.228188  0.008949  0.030840  0.051824  0.092152  \n",
       "7  0.767338  0.147651  0.239985  0.301972  0.397954  "
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "from evaluation_and_testing.testing import evaluate_train_test_split_implicit\n",
    "\n",
    "random_recommender = RandomRecommender()\n",
    "most_popular_recommender = MostPopularRecommender()\n",
    "highest_rated_recommender = HighestRatedRecommender()\n",
    "tfidf_recommender = TFIDFRecommender()\n",
    "ibcnn_recommender = ItemBasedCosineNearestNeighborsRecommender(n_neighbors=30)\n",
    "amazon_recommender = AmazonRecommender()\n",
    "netflix_recommender = NetflixRecommender(print_type=None, embedding_dim=8, n_epochs=20)\n",
    "gmf_recommender = GMFRecommender(print_type=None, n_neg_per_pos=10, batch_size=16, \n",
    "                                 embedding_dim=6, lr=0.001, weight_decay=0.0001, n_epochs=5, seed=1)\n",
    "\n",
    "recommenders = [random_recommender, most_popular_recommender, highest_rated_recommender, tfidf_recommender, \n",
    "                ibcnn_recommender, amazon_recommender, netflix_recommender, gmf_recommender]\n",
    "\n",
    "all_results = []\n",
    "\n",
    "for recommender in recommenders:\n",
    "    results = [[type(recommender).__name__] + list(evaluate_train_test_split_implicit(\n",
    "        recommender, ml_ratings_df, ml_movies_df))]\n",
    "\n",
    "    results = pd.DataFrame(results, \n",
    "                           columns=['Recommender', 'HR@1', 'HR@3', 'HR@5', 'HR@10', 'NDCG@1', 'NDCG@3', 'NDCG@5', 'NDCG@10'])\n",
    "    all_results.append(results)\n",
    "\n",
    "    display(results)\n",
    "    \n",
    "all_results = pd.concat(all_results).reset_index(drop=True)\n",
    "display(all_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "split-insertion",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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