modeling_pd.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, KBinsDiscretizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "books = pd.read_parquet(\"books_data/books.parquet\")\n",
    "users = pd.read_parquet(\"books_data/users.parquet\")\n",
    "ratings = pd.read_parquet(\"books_data/ratings.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                 int64\n",
       "location               object\n",
       "age                   float64\n",
       "city                   object\n",
       "state                  object\n",
       "country                object\n",
       "signup_date    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6447"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books[\"Category\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isbn</th>\n",
       "      <th>book_title</th>\n",
       "      <th>book_author</th>\n",
       "      <th>year_of_publication</th>\n",
       "      <th>publisher</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Language</th>\n",
       "      <th>Category</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>948518</th>\n",
       "      <td>0000913154</td>\n",
       "      <td>The Way Things Work: An Illustrated Encycloped...</td>\n",
       "      <td>C. van Amerongen (translator)</td>\n",
       "      <td>1967</td>\n",
       "      <td>Simon &amp; Schuster</td>\n",
       "      <td>Scientific principles, inventions, and chemica...</td>\n",
       "      <td>en</td>\n",
       "      <td>Technology &amp; Engineering</td>\n",
       "      <td>1967-01-01 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826650</th>\n",
       "      <td>0001055607</td>\n",
       "      <td>Cereus Blooms At Night</td>\n",
       "      <td>Shani Mootoo</td>\n",
       "      <td>1994</td>\n",
       "      <td>Fairmount Books Ltd Remainders</td>\n",
       "      <td>When Mala, old and notoriously crazy, arrives ...</td>\n",
       "      <td>en</td>\n",
       "      <td>Adult child abuse victims</td>\n",
       "      <td>1994-01-01 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908526</th>\n",
       "      <td>0001061127</td>\n",
       "      <td>CHESS FOR YOUNG BEGINNERS</td>\n",
       "      <td>William T. McLeod</td>\n",
       "      <td>1975</td>\n",
       "      <td>HarperCollins Publishers</td>\n",
       "      <td>A step by step guide to playing chess</td>\n",
       "      <td>en</td>\n",
       "      <td>Chess</td>\n",
       "      <td>1975-01-01 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025127</th>\n",
       "      <td>0001374362</td>\n",
       "      <td>When It's Time for Bed (Collins Baby &amp; Toddler...</td>\n",
       "      <td>Nick Butterworth</td>\n",
       "      <td>1994</td>\n",
       "      <td>Collins</td>\n",
       "      <td>Shows baby and his animal friends preparing fo...</td>\n",
       "      <td>en</td>\n",
       "      <td>Animals</td>\n",
       "      <td>1994-01-01 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935227</th>\n",
       "      <td>0001711253</td>\n",
       "      <td>The Big Honey Hunt</td>\n",
       "      <td>Stan Berenstein</td>\n",
       "      <td>1942</td>\n",
       "      <td>HarperCollins Publishers</td>\n",
       "      <td>Father Bear takes Small Bear on a honey hunt. ...</td>\n",
       "      <td>en</td>\n",
       "      <td>Bears</td>\n",
       "      <td>1942-01-01 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028471</th>\n",
       "      <td>000171421X</td>\n",
       "      <td>It's Not Easy Being a Bunny (A Beginner Book)</td>\n",
       "      <td>Marilyn Sadler</td>\n",
       "      <td>1984</td>\n",
       "      <td>HarperCollins Publishers</td>\n",
       "      <td>P.J. Funnybunny did not like being a bunny.</td>\n",
       "      <td>en</td>\n",
       "      <td>Animals</td>\n",
       "      <td>1984-01-01 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>907750</th>\n",
       "      <td>000184251X</td>\n",
       "      <td>February's Road</td>\n",
       "      <td>John Verney</td>\n",
       "      <td>1987</td>\n",
       "      <td>HarperCollins Publishers</td>\n",
       "      <td>The new road is to go right through the Callen...</td>\n",
       "      <td>en</td>\n",
       "      <td>Children's stories, English</td>\n",
       "      <td>1987-01-01 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917054</th>\n",
       "      <td>0001850121</td>\n",
       "      <td>A place called Lantern Light</td>\n",
       "      <td>Ellen Miller</td>\n",
       "      <td>1975</td>\n",
       "      <td>Collins</td>\n",
       "      <td>They were nearing the end of their long journey.</td>\n",
       "      <td>en</td>\n",
       "      <td>Children's stories</td>\n",
       "      <td>1975-01-01 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>907137</th>\n",
       "      <td>0001856367</td>\n",
       "      <td>The Collins Book of Ballet and Dance</td>\n",
       "      <td>Jean Ure</td>\n",
       "      <td>1996</td>\n",
       "      <td>HarperCollins Publishers</td>\n",
       "      <td>A collection of short stories about young peop...</td>\n",
       "      <td>en</td>\n",
       "      <td>Ballet</td>\n",
       "      <td>1996-01-01 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999997</th>\n",
       "      <td>0001935968</td>\n",
       "      <td>The Brambly Hedge Treasury</td>\n",
       "      <td>Jill Barklem</td>\n",
       "      <td>1991</td>\n",
       "      <td>HarperCollins Publishers</td>\n",
       "      <td>For this is the home of mice of Brambly Hills&amp;...</td>\n",
       "      <td>en</td>\n",
       "      <td>Country life</td>\n",
       "      <td>1991-01-01 12:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               isbn                                         book_title  \\\n",
       "948518   0000913154  The Way Things Work: An Illustrated Encycloped...   \n",
       "826650   0001055607                             Cereus Blooms At Night   \n",
       "908526   0001061127                          CHESS FOR YOUNG BEGINNERS   \n",
       "1025127  0001374362  When It's Time for Bed (Collins Baby & Toddler...   \n",
       "935227   0001711253                                 The Big Honey Hunt   \n",
       "1028471  000171421X      It's Not Easy Being a Bunny (A Beginner Book)   \n",
       "907750   000184251X                                    February's Road   \n",
       "917054   0001850121                       A place called Lantern Light   \n",
       "907137   0001856367               The Collins Book of Ballet and Dance   \n",
       "999997   0001935968                         The Brambly Hedge Treasury   \n",
       "\n",
       "                           book_author  year_of_publication  \\\n",
       "948518   C. van Amerongen (translator)                 1967   \n",
       "826650                    Shani Mootoo                 1994   \n",
       "908526               William T. McLeod                 1975   \n",
       "1025127               Nick Butterworth                 1994   \n",
       "935227                 Stan Berenstein                 1942   \n",
       "1028471                 Marilyn Sadler                 1984   \n",
       "907750                     John Verney                 1987   \n",
       "917054                    Ellen Miller                 1975   \n",
       "907137                        Jean Ure                 1996   \n",
       "999997                    Jill Barklem                 1991   \n",
       "\n",
       "                              publisher  \\\n",
       "948518                 Simon & Schuster   \n",
       "826650   Fairmount Books Ltd Remainders   \n",
       "908526         HarperCollins Publishers   \n",
       "1025127                         Collins   \n",
       "935227         HarperCollins Publishers   \n",
       "1028471        HarperCollins Publishers   \n",
       "907750         HarperCollins Publishers   \n",
       "917054                          Collins   \n",
       "907137         HarperCollins Publishers   \n",
       "999997         HarperCollins Publishers   \n",
       "\n",
       "                                                   Summary Language  \\\n",
       "948518   Scientific principles, inventions, and chemica...       en   \n",
       "826650   When Mala, old and notoriously crazy, arrives ...       en   \n",
       "908526               A step by step guide to playing chess       en   \n",
       "1025127  Shows baby and his animal friends preparing fo...       en   \n",
       "935227   Father Bear takes Small Bear on a honey hunt. ...       en   \n",
       "1028471        P.J. Funnybunny did not like being a bunny.       en   \n",
       "907750   The new road is to go right through the Callen...       en   \n",
       "917054    They were nearing the end of their long journey.       en   \n",
       "907137   A collection of short stories about young peop...       en   \n",
       "999997   For this is the home of mice of Brambly Hills&...       en   \n",
       "\n",
       "                            Category          created_at  \n",
       "948518      Technology & Engineering 1967-01-01 12:00:00  \n",
       "826650     Adult child abuse victims 1994-01-01 12:00:00  \n",
       "908526                         Chess 1975-01-01 12:00:00  \n",
       "1025127                      Animals 1994-01-01 12:00:00  \n",
       "935227                         Bears 1942-01-01 12:00:00  \n",
       "1028471                      Animals 1984-01-01 12:00:00  \n",
       "907750   Children's stories, English 1987-01-01 12:00:00  \n",
       "917054            Children's stories 1975-01-01 12:00:00  \n",
       "907137                        Ballet 1996-01-01 12:00:00  \n",
       "999997                  Country life 1991-01-01 12:00:00  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                      int64\n",
       "isbn                        object\n",
       "rating                       int64\n",
       "rating_timestamp    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from feast import FeatureStore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "store = FeatureStore(repo_path=\"feast_repo/feature_repo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isbn</th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_timestamp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>2</th>\n",
       "      <td>0195153448</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-08-04 16:03:16.862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0002005018</td>\n",
       "      <td>5</td>\n",
       "      <td>2020-08-15 16:00:36.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0440234743</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-09-02 01:44:06.535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1841721522</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-07-04 18:18:19.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1879384493</td>\n",
       "      <td>10</td>\n",
       "      <td>2020-07-23 04:19:17.666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278846</th>\n",
       "      <td>0060809833</td>\n",
       "      <td>8</td>\n",
       "      <td>2020-03-06 11:36:34.759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278849</th>\n",
       "      <td>0445210214</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-05-11 14:32:33.494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278851</th>\n",
       "      <td>1558531025</td>\n",
       "      <td>8</td>\n",
       "      <td>2020-07-28 17:31:12.181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278852</th>\n",
       "      <td>0449907597</td>\n",
       "      <td>8</td>\n",
       "      <td>2021-03-15 17:04:45.672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278854</th>\n",
       "      <td>0553579606</td>\n",
       "      <td>8</td>\n",
       "      <td>2021-05-22 20:58:30.992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92107 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               isbn  rating        rating_timestamp\n",
       "user_id                                            \n",
       "2        0195153448       0 2022-08-04 16:03:16.862\n",
       "8        0002005018       5 2020-08-15 16:00:36.000\n",
       "9        0440234743       0 2022-09-02 01:44:06.535\n",
       "10       1841721522       0 2020-07-04 18:18:19.970\n",
       "12       1879384493      10 2020-07-23 04:19:17.666\n",
       "...             ...     ...                     ...\n",
       "278846   0060809833       8 2020-03-06 11:36:34.759\n",
       "278849   0445210214       0 2020-05-11 14:32:33.494\n",
       "278851   1558531025       8 2020-07-28 17:31:12.181\n",
       "278852   0449907597       8 2021-03-15 17:04:45.672\n",
       "278854   0553579606       8 2021-05-22 20:58:30.992\n",
       "\n",
       "[92107 rows x 3 columns]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.groupby(\"user_id\").()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-01-01 23:59:07.561000')"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings[\"rating_timestamp\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-01-01 00:01:29.484000')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings[\"rating_timestamp\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using rating_timestamp as the event timestamp. To specify a column explicitly, please name it event_timestamp.\n"
     ]
    }
   ],
   "source": [
    "features_raw = store.get_historical_features(entity_df=ratings, features=store.get_feature_service(\"model_v1\")).to_df()\n",
    "features = features_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>isbn</th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_timestamp</th>\n",
       "      <th>book_title</th>\n",
       "      <th>book_author</th>\n",
       "      <th>year_of_publication</th>\n",
       "      <th>publisher</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Language</th>\n",
       "      <th>Category</th>\n",
       "      <th>location</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151265</td>\n",
       "      <td>0425045927</td>\n",
       "      <td>9</td>\n",
       "      <td>2022-08-24 07:44:02.092000+00:00</td>\n",
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       "      <td>las vegas, nevada, usa</td>\n",
       "      <td>67.0</td>\n",
       "      <td>las vegas</td>\n",
       "      <td>nevada</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>151265</td>\n",
       "      <td>067976402X</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-03-16 04:18:45.561000+00:00</td>\n",
       "      <td>Snow Falling on Cedars</td>\n",
       "      <td>David Guterson</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>Vintage Books USA</td>\n",
       "      <td>In 1954, Ishmael Chambers, a local reporter wh...</td>\n",
       "      <td>en</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>las vegas, nevada, usa</td>\n",
       "      <td>67.0</td>\n",
       "      <td>las vegas</td>\n",
       "      <td>nevada</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>265834</td>\n",
       "      <td>0802133908</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-12-14 00:00:56.926000+00:00</td>\n",
       "      <td>Pedro Paramo</td>\n",
       "      <td>Juan Rulfo</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>Grove Press</td>\n",
       "      <td>In one such village of the mind, Comala, he se...</td>\n",
       "      <td>en</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>madrid, madrid, spain</td>\n",
       "      <td>51.0</td>\n",
       "      <td>madrid</td>\n",
       "      <td>madrid</td>\n",
       "      <td>spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>265834</td>\n",
       "      <td>0140153195</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-09-12 03:21:35.054000+00:00</td>\n",
       "      <td>Justine (Alexandria Quartet)</td>\n",
       "      <td>Lawrence Durrell</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>Penguin Books</td>\n",
       "      <td>On the eve of World War II in the Egyptian cit...</td>\n",
       "      <td>en</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>madrid, madrid, spain</td>\n",
       "      <td>51.0</td>\n",
       "      <td>madrid</td>\n",
       "      <td>madrid</td>\n",
       "      <td>spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>265834</td>\n",
       "      <td>8483062461</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-08-18 15:15:56.585000+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>madrid, madrid, spain</td>\n",
       "      <td>51.0</td>\n",
       "      <td>madrid</td>\n",
       "      <td>madrid</td>\n",
       "      <td>spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>265834</td>\n",
       "      <td>8408022563</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-04-06 19:22:17.430000+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>madrid, madrid, spain</td>\n",
       "      <td>51.0</td>\n",
       "      <td>madrid</td>\n",
       "      <td>madrid</td>\n",
       "      <td>spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>265834</td>\n",
       "      <td>8420478725</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-04-02 12:49:01.442000+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>madrid, madrid, spain</td>\n",
       "      <td>51.0</td>\n",
       "      <td>madrid</td>\n",
       "      <td>madrid</td>\n",
       "      <td>spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>265834</td>\n",
       "      <td>0141180633</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-05-24 13:49:51.878000+00:00</td>\n",
       "      <td>Vineland</td>\n",
       "      <td>Thomas Pynchon</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Penguin Books</td>\n",
       "      <td>Zoyd Wheeler&amp;#39;s old nemesis arrives in Vine...</td>\n",
       "      <td>en</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>madrid, madrid, spain</td>\n",
       "      <td>51.0</td>\n",
       "      <td>madrid</td>\n",
       "      <td>madrid</td>\n",
       "      <td>spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>265834</td>\n",
       "      <td>0345354907</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-08-23 15:53:38.991000+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>madrid, madrid, spain</td>\n",
       "      <td>51.0</td>\n",
       "      <td>madrid</td>\n",
       "      <td>madrid</td>\n",
       "      <td>spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>265834</td>\n",
       "      <td>2070360342</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-06-24 22:38:04.729000+00:00</td>\n",
       "      <td>Les Caves Du Vatican</td>\n",
       "      <td>AndrÃ© Gide</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>Folio</td>\n",
       "      <td>Avec cette &amp;quot;sotie&amp;quot; de 1914 qui scand...</td>\n",
       "      <td>fr</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>madrid, madrid, spain</td>\n",
       "      <td>51.0</td>\n",
       "      <td>madrid</td>\n",
       "      <td>madrid</td>\n",
       "      <td>spain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id        isbn  rating                 rating_timestamp  \\\n",
       "0   151265  0425045927       9 2022-08-24 07:44:02.092000+00:00   \n",
       "1   151265  067976402X       0 2021-03-16 04:18:45.561000+00:00   \n",
       "2   265834  0802133908       0 2020-12-14 00:00:56.926000+00:00   \n",
       "3   265834  0140153195       0 2022-09-12 03:21:35.054000+00:00   \n",
       "4   265834  8483062461       0 2021-08-18 15:15:56.585000+00:00   \n",
       "5   265834  8408022563       0 2020-04-06 19:22:17.430000+00:00   \n",
       "6   265834  8420478725       0 2020-04-02 12:49:01.442000+00:00   \n",
       "7   265834  0141180633       0 2020-05-24 13:49:51.878000+00:00   \n",
       "8   265834  0345354907       0 2021-08-23 15:53:38.991000+00:00   \n",
       "9   265834  2070360342       0 2021-06-24 22:38:04.729000+00:00   \n",
       "\n",
       "                     book_title       book_author  year_of_publication  \\\n",
       "0                           NaN               NaN                  NaN   \n",
       "1        Snow Falling on Cedars    David Guterson               1995.0   \n",
       "2                  Pedro Paramo        Juan Rulfo               1994.0   \n",
       "3  Justine (Alexandria Quartet)  Lawrence Durrell               1991.0   \n",
       "4                           NaN               NaN                  NaN   \n",
       "5                           NaN               NaN                  NaN   \n",
       "6                           NaN               NaN                  NaN   \n",
       "7                      Vineland    Thomas Pynchon               1997.0   \n",
       "8                           NaN               NaN                  NaN   \n",
       "9          Les Caves Du Vatican       AndrÃ© Gide               1994.0   \n",
       "\n",
       "           publisher                                            Summary  \\\n",
       "0                NaN                                                NaN   \n",
       "1  Vintage Books USA  In 1954, Ishmael Chambers, a local reporter wh...   \n",
       "2        Grove Press  In one such village of the mind, Comala, he se...   \n",
       "3      Penguin Books  On the eve of World War II in the Egyptian cit...   \n",
       "4                NaN                                                NaN   \n",
       "5                NaN                                                NaN   \n",
       "6                NaN                                                NaN   \n",
       "7      Penguin Books  Zoyd Wheeler&#39;s old nemesis arrives in Vine...   \n",
       "8                NaN                                                NaN   \n",
       "9              Folio  Avec cette &quot;sotie&quot; de 1914 qui scand...   \n",
       "\n",
       "  Language Category                location   age       city   state country  \n",
       "0      NaN      NaN  las vegas, nevada, usa  67.0  las vegas  nevada     usa  \n",
       "1       en  Fiction  las vegas, nevada, usa  67.0  las vegas  nevada     usa  \n",
       "2       en  Fiction   madrid, madrid, spain  51.0     madrid  madrid   spain  \n",
       "3       en  Fiction   madrid, madrid, spain  51.0     madrid  madrid   spain  \n",
       "4      NaN      NaN   madrid, madrid, spain  51.0     madrid  madrid   spain  \n",
       "5      NaN      NaN   madrid, madrid, spain  51.0     madrid  madrid   spain  \n",
       "6      NaN      NaN   madrid, madrid, spain  51.0     madrid  madrid   spain  \n",
       "7       en  Fiction   madrid, madrid, spain  51.0     madrid  madrid   spain  \n",
       "8      NaN      NaN   madrid, madrid, spain  51.0     madrid  madrid   spain  \n",
       "9       fr  Fiction   madrid, madrid, spain  51.0     madrid  madrid   spain  "
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = OrdinalEncoder()\n",
    "# transform data\n",
    "features[\"Category\"] = encoder.fit_transform(features[[\"Category\"]])\n",
    "features[\"Language\"] = encoder.fit_transform(features[[\"Language\"]])\n",
    "features[\"state\"] = encoder.fit_transform(features[[\"state\"]])\n",
    "features[\"city\"] = encoder.fit_transform(features[[\"city\"]])\n",
    "features[\"country\"] = encoder.fit_transform(features[[\"country\"]])\n",
    "features[\"book_author\"] = encoder.fit_transform(features[[\"book_author\"]])\n",
    "features[\"publisher\"] = encoder.fit_transform(features[[\"publisher\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Category_1257.0</th>\n",
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       "      <th>state_9.0</th>\n",
       "      <th>state_67.0</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>5</th>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>7</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Category_1257.0  Category_3538.0  Category_4497.0  Category_nan  \\\n",
       "0                 0                0                0             1   \n",
       "1                 0                1                0             0   \n",
       "2                 0                1                0             0   \n",
       "3                 0                1                0             0   \n",
       "4                 0                0                0             1   \n",
       "5                 0                0                0             1   \n",
       "6                 0                0                0             1   \n",
       "7                 0                1                0             0   \n",
       "8                 0                0                0             1   \n",
       "9                 0                1                0             0   \n",
       "10                0                0                0             1   \n",
       "11                1                0                0             0   \n",
       "12                0                0                0             0   \n",
       "13                0                0                0             1   \n",
       "14                0                0                0             1   \n",
       "15                0                0                0             1   \n",
       "16                0                1                0             0   \n",
       "17                0                1                0             0   \n",
       "18                0                0                0             1   \n",
       "19                0                1                0             0   \n",
       "\n",
       "    Category_infrequent_sklearn  Language_6.0  Language_nan  \\\n",
       "0                             0             0             1   \n",
       "1                             0             1             0   \n",
       "2                             0             1             0   \n",
       "3                             0             1             0   \n",
       "4                             0             0             1   \n",
       "5                             0             0             1   \n",
       "6                             0             0             1   \n",
       "7                             0             1             0   \n",
       "8                             0             0             1   \n",
       "9                             0             0             0   \n",
       "10                            0             0             1   \n",
       "11                            0             1             0   \n",
       "12                            1             1             0   \n",
       "13                            0             0             1   \n",
       "14                            0             0             1   \n",
       "15                            0             0             1   \n",
       "16                            0             0             0   \n",
       "17                            0             1             0   \n",
       "18                            0             0             1   \n",
       "19                            0             1             0   \n",
       "\n",
       "    Language_infrequent_sklearn  state_9.0  state_67.0  ...  publisher_744.0  \\\n",
       "0                             0          0           0  ...                0   \n",
       "1                             0          0           0  ...                0   \n",
       "2                             0          0           0  ...                0   \n",
       "3                             0          0           0  ...                0   \n",
       "4                             0          0           0  ...                0   \n",
       "5                             0          0           0  ...                0   \n",
       "6                             0          0           0  ...                0   \n",
       "7                             0          0           0  ...                0   \n",
       "8                             0          0           0  ...                0   \n",
       "9                             1          0           0  ...                0   \n",
       "10                            0          0           0  ...                0   \n",
       "11                            0          0           0  ...                0   \n",
       "12                            0          0           0  ...                0   \n",
       "13                            0          0           0  ...                0   \n",
       "14                            0          0           0  ...                0   \n",
       "15                            0          0           0  ...                0   \n",
       "16                            1          0           0  ...                0   \n",
       "17                            0          0           0  ...                0   \n",
       "18                            0          0           0  ...                0   \n",
       "19                            0          0           0  ...                0   \n",
       "\n",
       "    publisher_747.0  publisher_917.0  publisher_3293.0  publisher_5715.0  \\\n",
       "0                 0                0                 0                 0   \n",
       "1                 0                0                 0                 0   \n",
       "2                 0                0                 0                 0   \n",
       "3                 0                0                 0                 1   \n",
       "4                 0                0                 0                 0   \n",
       "5                 0                0                 0                 0   \n",
       "6                 0                0                 0                 0   \n",
       "7                 0                0                 0                 1   \n",
       "8                 0                0                 0                 0   \n",
       "9                 0                0                 0                 0   \n",
       "10                0                0                 0                 0   \n",
       "11                0                0                 0                 0   \n",
       "12                0                0                 0                 0   \n",
       "13                0                0                 0                 0   \n",
       "14                0                0                 0                 0   \n",
       "15                0                0                 0                 0   \n",
       "16                0                0                 0                 0   \n",
       "17                0                0                 0                 0   \n",
       "18                0                0                 0                 0   \n",
       "19                0                0                 0                 0   \n",
       "\n",
       "    publisher_5920.0  publisher_6871.0  publisher_8038.0  publisher_nan  \\\n",
       "0                  0                 0                 0              1   \n",
       "1                  0                 0                 0              0   \n",
       "2                  0                 0                 0              0   \n",
       "3                  0                 0                 0              0   \n",
       "4                  0                 0                 0              1   \n",
       "5                  0                 0                 0              1   \n",
       "6                  0                 0                 0              1   \n",
       "7                  0                 0                 0              0   \n",
       "8                  0                 0                 0              1   \n",
       "9                  0                 0                 0              0   \n",
       "10                 0                 0                 0              1   \n",
       "11                 0                 0                 0              0   \n",
       "12                 0                 0                 0              0   \n",
       "13                 0                 0                 0              1   \n",
       "14                 0                 0                 0              1   \n",
       "15                 0                 0                 0              1   \n",
       "16                 0                 0                 0              0   \n",
       "17                 0                 0                 0              0   \n",
       "18                 0                 0                 0              1   \n",
       "19                 0                 0                 0              0   \n",
       "\n",
       "    publisher_infrequent_sklearn  \n",
       "0                              0  \n",
       "1                              1  \n",
       "2                              1  \n",
       "3                              0  \n",
       "4                              0  \n",
       "5                              0  \n",
       "6                              0  \n",
       "7                              0  \n",
       "8                              0  \n",
       "9                              1  \n",
       "10                             0  \n",
       "11                             1  \n",
       "12                             1  \n",
       "13                             0  \n",
       "14                             0  \n",
       "15                             0  \n",
       "16                             1  \n",
       "17                             1  \n",
       "18                             0  \n",
       "19                             1  \n",
       "\n",
       "[20 rows x 64 columns]"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot = OneHotEncoder(min_frequency=0.01, handle_unknown='ignore')\n",
    "transformed = one_hot.fit_transform(features[[\"Category\", \"Language\", \"state\", \"city\", \"country\", \"book_author\", \"publisher\"]]).astype(\"int64\").toarray()\n",
    "ohe_df = pd.DataFrame(transformed, columns=one_hot.get_feature_names_out())\n",
    "ohe_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = pd.concat([features, ohe_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "discretizer = KBinsDiscretizer(n_bins=10, encode='ordinal', strategy='uniform')\n",
    "features[\"age_binned\"] = discretizer.fit_transform(features[[\"age\"]]).astype(\"int64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = features.sort_values(['user_id', 'rating_timestamp'], ascending=[True, True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = features.set_index(keys=['user_id', 'rating_timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>last_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030477</th>\n",
       "      <td>1030477</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030478</th>\n",
       "      <td>1030478</td>\n",
       "      <td>3538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030479</th>\n",
       "      <td>1030479</td>\n",
       "      <td>3538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030480</th>\n",
       "      <td>1030480</td>\n",
       "      <td>3538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030481</th>\n",
       "      <td>1030481</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1030482 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           index  last_category\n",
       "0              0            NaN\n",
       "1              1            NaN\n",
       "2              2            NaN\n",
       "3              3         3538.0\n",
       "4              4          103.0\n",
       "...          ...            ...\n",
       "1030477  1030477            NaN\n",
       "1030478  1030478         3538.0\n",
       "1030479  1030479         3538.0\n",
       "1030480  1030480         3538.0\n",
       "1030481  1030481            NaN\n",
       "\n",
       "[1030482 rows x 2 columns]"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.groupby(by=\"user_id\", group_keys=True)[\"Category\"].shift(1).rolling(1).sum().rename(\"last_category\").reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "2",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m~/.pyenv/versions/3.9.9/envs/tecton/lib/python3.9/site-packages/pandas/core/indexes/range.py:391\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    390\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 391\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_range\u001b[39m.\u001b[39;49mindex(new_key)\n\u001b[1;32m    392\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mValueError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
      "\u001b[0;31mValueError\u001b[0m: 2 is not in range",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [362], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m features[\u001b[39m\"\u001b[39m\u001b[39mlast_category\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m features\u001b[39m.\u001b[39;49mgroupby(by\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39muser_id\u001b[39;49m\u001b[39m\"\u001b[39;49m, group_keys\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)[\u001b[39m\"\u001b[39;49m\u001b[39mCategory\u001b[39;49m\u001b[39m\"\u001b[39;49m]\u001b[39m.\u001b[39;49mshift(\u001b[39m1\u001b[39;49m)\u001b[39m.\u001b[39;49mrolling(\u001b[39m3\u001b[39;49m)\u001b[39m.\u001b[39;49magg(\u001b[39mlambda\u001b[39;49;00m rows: rows[\u001b[39m2\u001b[39;49m])\u001b[39m.\u001b[39mrename(\u001b[39m\"\u001b[39m\u001b[39mlast_category\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m      2\u001b[0m features[\u001b[39m\"\u001b[39m\u001b[39mlast_2_category\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m features\u001b[39m.\u001b[39mgroupby(by\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39muser_id\u001b[39m\u001b[39m\"\u001b[39m, group_keys\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)[\u001b[39m\"\u001b[39m\u001b[39mCategory\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mshift(\u001b[39m1\u001b[39m)\u001b[39m.\u001b[39mrolling(\u001b[39m3\u001b[39m)\u001b[39m.\u001b[39magg(\u001b[39mlambda\u001b[39;00m rows: rows[\u001b[39m1\u001b[39m])\u001b[39m.\u001b[39mrename(\u001b[39m\"\u001b[39m\u001b[39mlast_2_category\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m      3\u001b[0m features[\u001b[39m\"\u001b[39m\u001b[39mlast_3_category\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m features\u001b[39m.\u001b[39mgroupby(by\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39muser_id\u001b[39m\u001b[39m\"\u001b[39m, group_keys\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)[\u001b[39m\"\u001b[39m\u001b[39mCategory\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mshift(\u001b[39m1\u001b[39m)\u001b[39m.\u001b[39mrolling(\u001b[39m3\u001b[39m)\u001b[39m.\u001b[39magg(\u001b[39mlambda\u001b[39;00m rows: rows[\u001b[39m0\u001b[39m])\u001b[39m.\u001b[39mrename(\u001b[39m\"\u001b[39m\u001b[39mlast_3_category\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.9/envs/tecton/lib/python3.9/site-packages/pandas/core/window/rolling.py:1921\u001b[0m, in \u001b[0;36mRolling.aggregate\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1883\u001b[0m \u001b[39m@doc\u001b[39m(\n\u001b[1;32m   1884\u001b[0m     _shared_docs[\u001b[39m\"\u001b[39m\u001b[39maggregate\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[1;32m   1885\u001b[0m     see_also\u001b[39m=\u001b[39mdedent(\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1919\u001b[0m )\n\u001b[1;32m   1920\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39maggregate\u001b[39m(\u001b[39mself\u001b[39m, func, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m-> 1921\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49maggregate(func, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.9/envs/tecton/lib/python3.9/site-packages/pandas/core/window/rolling.py:711\u001b[0m, in \u001b[0;36mBaseWindow.aggregate\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    709\u001b[0m result \u001b[39m=\u001b[39m ResamplerWindowApply(\u001b[39mself\u001b[39m, func, args\u001b[39m=\u001b[39margs, kwargs\u001b[39m=\u001b[39mkwargs)\u001b[39m.\u001b[39magg()\n\u001b[1;32m    710\u001b[0m \u001b[39mif\u001b[39;00m result \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 711\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mapply(func, raw\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m, args\u001b[39m=\u001b[39;49margs, kwargs\u001b[39m=\u001b[39;49mkwargs)\n\u001b[1;32m    712\u001b[0m \u001b[39mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.9/envs/tecton/lib/python3.9/site-packages/pandas/core/window/rolling.py:2000\u001b[0m, in \u001b[0;36mRolling.apply\u001b[0;34m(self, func, raw, engine, engine_kwargs, args, kwargs)\u001b[0m\n\u001b[1;32m   1979\u001b[0m \u001b[39m@doc\u001b[39m(\n\u001b[1;32m   1980\u001b[0m     template_header,\n\u001b[1;32m   1981\u001b[0m     create_section_header(\u001b[39m\"\u001b[39m\u001b[39mParameters\u001b[39m\u001b[39m\"\u001b[39m),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1998\u001b[0m     kwargs: \u001b[39mdict\u001b[39m[\u001b[39mstr\u001b[39m, Any] \u001b[39m|\u001b[39m \u001b[39mNone\u001b[39;00m \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[1;32m   1999\u001b[0m ):\n\u001b[0;32m-> 2000\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mapply(\n\u001b[1;32m   2001\u001b[0m         func,\n\u001b[1;32m   2002\u001b[0m         raw\u001b[39m=\u001b[39;49mraw,\n\u001b[1;32m   2003\u001b[0m         engine\u001b[39m=\u001b[39;49mengine,\n\u001b[1;32m   2004\u001b[0m         engine_kwargs\u001b[39m=\u001b[39;49mengine_kwargs,\n\u001b[1;32m   2005\u001b[0m         args\u001b[39m=\u001b[39;49margs,\n\u001b[1;32m   2006\u001b[0m         kwargs\u001b[39m=\u001b[39;49mkwargs,\n\u001b[1;32m   2007\u001b[0m     )\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.9/envs/tecton/lib/python3.9/site-packages/pandas/core/window/rolling.py:1422\u001b[0m, in \u001b[0;36mRollingAndExpandingMixin.apply\u001b[0;34m(self, func, raw, engine, engine_kwargs, args, kwargs)\u001b[0m\n\u001b[1;32m   1419\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1420\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mengine must be either \u001b[39m\u001b[39m'\u001b[39m\u001b[39mnumba\u001b[39m\u001b[39m'\u001b[39m\u001b[39m or \u001b[39m\u001b[39m'\u001b[39m\u001b[39mcython\u001b[39m\u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m-> 1422\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_apply(\n\u001b[1;32m   1423\u001b[0m     apply_func,\n\u001b[1;32m   1424\u001b[0m     name\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mapply\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m   1425\u001b[0m     numba_args\u001b[39m=\u001b[39;49mnumba_args,\n\u001b[1;32m   1426\u001b[0m )\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.9/envs/tecton/lib/python3.9/site-packages/pandas/core/window/rolling.py:663\u001b[0m, in \u001b[0;36mBaseWindow._apply\u001b[0;34m(self, func, name, numeric_only, numba_args, **kwargs)\u001b[0m\n\u001b[1;32m    660\u001b[0m     \u001b[39mreturn\u001b[39;00m result\n\u001b[1;32m    662\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmethod \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39msingle\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m--> 663\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_apply_blockwise(homogeneous_func, name, numeric_only)\n\u001b[1;32m    664\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    665\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_apply_tablewise(homogeneous_func, name, numeric_only)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.9/envs/tecton/lib/python3.9/site-packages/pandas/core/window/rolling.py:503\u001b[0m, in \u001b[0;36mBaseWindow._apply_blockwise\u001b[0;34m(self, homogeneous_func, name, numeric_only)\u001b[0m\n\u001b[1;32m    501\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_validate_numeric_only(name, numeric_only)\n\u001b[1;32m    502\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_selected_obj\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m--> 503\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_apply_series(homogeneous_func, name)\n\u001b[1;32m    505\u001b[0m obj \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_create_data(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_selected_obj, numeric_only)\n\u001b[1;32m    506\u001b[0m \u001b[39mif\u001b[39;00m name \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mcount\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m    507\u001b[0m     \u001b[39m# GH 12541: Special case for count where we support date-like types\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.9/envs/tecton/lib/python3.9/site-packages/pandas/core/window/rolling.py:487\u001b[0m, in \u001b[0;36mBaseWindow._apply_series\u001b[0;34m(self, homogeneous_func, name)\u001b[0m\n\u001b[1;32m    484\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mTypeError\u001b[39;00m, \u001b[39mNotImplementedError\u001b[39;00m) \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m    485\u001b[0m     \u001b[39mraise\u001b[39;00m DataError(\u001b[39m\"\u001b[39m\u001b[39mNo numeric types to aggregate\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[0;32m--> 487\u001b[0m result \u001b[39m=\u001b[39m homogeneous_func(values)\n\u001b[1;32m    488\u001b[0m index \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_slice_axis_for_step(obj\u001b[39m.\u001b[39mindex, result)\n\u001b[1;32m    489\u001b[0m \u001b[39mreturn\u001b[39;00m obj\u001b[39m.\u001b[39m_constructor(result, index\u001b[39m=\u001b[39mindex, name\u001b[39m=\u001b[39mobj\u001b[39m.\u001b[39mname)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.9/envs/tecton/lib/python3.9/site-packages/pandas/core/window/rolling.py:658\u001b[0m, in \u001b[0;36mBaseWindow._apply.<locals>.homogeneous_func\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m    655\u001b[0m     \u001b[39mreturn\u001b[39;00m func(x, start, end, min_periods, \u001b[39m*\u001b[39mnumba_args)\n\u001b[1;32m    657\u001b[0m \u001b[39mwith\u001b[39;00m np\u001b[39m.\u001b[39merrstate(\u001b[39mall\u001b[39m\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mignore\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m--> 658\u001b[0m     result \u001b[39m=\u001b[39m calc(values)\n\u001b[1;32m    660\u001b[0m \u001b[39mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.9/envs/tecton/lib/python3.9/site-packages/pandas/core/window/rolling.py:655\u001b[0m, in \u001b[0;36mBaseWindow._apply.<locals>.homogeneous_func.<locals>.calc\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    646\u001b[0m start, end \u001b[39m=\u001b[39m window_indexer\u001b[39m.\u001b[39mget_window_bounds(\n\u001b[1;32m    647\u001b[0m     num_values\u001b[39m=\u001b[39m\u001b[39mlen\u001b[39m(x),\n\u001b[1;32m    648\u001b[0m     min_periods\u001b[39m=\u001b[39mmin_periods,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    651\u001b[0m     step\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstep,\n\u001b[1;32m    652\u001b[0m )\n\u001b[1;32m    653\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_window_bounds(start, end, \u001b[39mlen\u001b[39m(x))\n\u001b[0;32m--> 655\u001b[0m \u001b[39mreturn\u001b[39;00m func(x, start, end, min_periods, \u001b[39m*\u001b[39;49mnumba_args)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.9/envs/tecton/lib/python3.9/site-packages/pandas/core/window/rolling.py:1449\u001b[0m, in \u001b[0;36mRollingAndExpandingMixin._generate_cython_apply_func.<locals>.apply_func\u001b[0;34m(values, begin, end, min_periods, raw)\u001b[0m\n\u001b[1;32m   1446\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m raw:\n\u001b[1;32m   1447\u001b[0m     \u001b[39m# GH 45912\u001b[39;00m\n\u001b[1;32m   1448\u001b[0m     values \u001b[39m=\u001b[39m Series(values, index\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_on)\n\u001b[0;32m-> 1449\u001b[0m \u001b[39mreturn\u001b[39;00m window_func(values, begin, end, min_periods)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.9/envs/tecton/lib/python3.9/site-packages/pandas/_libs/window/aggregations.pyx:1410\u001b[0m, in \u001b[0;36mpandas._libs.window.aggregations.roll_apply\u001b[0;34m()\u001b[0m\n",
      "Cell \u001b[0;32mIn [362], line 1\u001b[0m, in \u001b[0;36m<lambda>\u001b[0;34m(rows)\u001b[0m\n\u001b[0;32m----> 1\u001b[0m features[\u001b[39m\"\u001b[39m\u001b[39mlast_category\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m features\u001b[39m.\u001b[39mgroupby(by\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39muser_id\u001b[39m\u001b[39m\"\u001b[39m, group_keys\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)[\u001b[39m\"\u001b[39m\u001b[39mCategory\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mshift(\u001b[39m1\u001b[39m)\u001b[39m.\u001b[39mrolling(\u001b[39m3\u001b[39m)\u001b[39m.\u001b[39magg(\u001b[39mlambda\u001b[39;00m rows: rows[\u001b[39m2\u001b[39;49m])\u001b[39m.\u001b[39mrename(\u001b[39m\"\u001b[39m\u001b[39mlast_category\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m      2\u001b[0m features[\u001b[39m\"\u001b[39m\u001b[39mlast_2_category\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m features\u001b[39m.\u001b[39mgroupby(by\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39muser_id\u001b[39m\u001b[39m\"\u001b[39m, group_keys\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)[\u001b[39m\"\u001b[39m\u001b[39mCategory\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mshift(\u001b[39m1\u001b[39m)\u001b[39m.\u001b[39mrolling(\u001b[39m3\u001b[39m)\u001b[39m.\u001b[39magg(\u001b[39mlambda\u001b[39;00m rows: rows[\u001b[39m1\u001b[39m])\u001b[39m.\u001b[39mrename(\u001b[39m\"\u001b[39m\u001b[39mlast_2_category\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m      3\u001b[0m features[\u001b[39m\"\u001b[39m\u001b[39mlast_3_category\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m features\u001b[39m.\u001b[39mgroupby(by\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39muser_id\u001b[39m\u001b[39m\"\u001b[39m, group_keys\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)[\u001b[39m\"\u001b[39m\u001b[39mCategory\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mshift(\u001b[39m1\u001b[39m)\u001b[39m.\u001b[39mrolling(\u001b[39m3\u001b[39m)\u001b[39m.\u001b[39magg(\u001b[39mlambda\u001b[39;00m rows: rows[\u001b[39m0\u001b[39m])\u001b[39m.\u001b[39mrename(\u001b[39m\"\u001b[39m\u001b[39mlast_3_category\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.9/envs/tecton/lib/python3.9/site-packages/pandas/core/series.py:981\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    978\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_values[key]\n\u001b[1;32m    980\u001b[0m \u001b[39melif\u001b[39;00m key_is_scalar:\n\u001b[0;32m--> 981\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_value(key)\n\u001b[1;32m    983\u001b[0m \u001b[39mif\u001b[39;00m is_hashable(key):\n\u001b[1;32m    984\u001b[0m     \u001b[39m# Otherwise index.get_value will raise InvalidIndexError\u001b[39;00m\n\u001b[1;32m    985\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    986\u001b[0m         \u001b[39m# For labels that don't resolve as scalars like tuples and frozensets\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.9/envs/tecton/lib/python3.9/site-packages/pandas/core/series.py:1089\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m   1086\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_values[label]\n\u001b[1;32m   1088\u001b[0m \u001b[39m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[0;32m-> 1089\u001b[0m loc \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mindex\u001b[39m.\u001b[39;49mget_loc(label)\n\u001b[1;32m   1090\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mindex\u001b[39m.\u001b[39m_get_values_for_loc(\u001b[39mself\u001b[39m, loc, label)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.9/envs/tecton/lib/python3.9/site-packages/pandas/core/indexes/range.py:393\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    391\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_range\u001b[39m.\u001b[39mindex(new_key)\n\u001b[1;32m    392\u001b[0m     \u001b[39mexcept\u001b[39;00m \u001b[39mValueError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m--> 393\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m    394\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n\u001b[1;32m    395\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 2"
     ]
    }
   ],
   "source": [
    "features[\"last_category\"] = features.groupby(by=\"user_id\", group_keys=True)[\"Category\"].shift(1).rolling(3).agg(lambda rows: rows[2]).rename(\"last_category\")\n",
    "features[\"last_2_category\"] = features.groupby(by=\"user_id\", group_keys=True)[\"Category\"].shift(1).rolling(3).agg(lambda rows: rows[1]).rename(\"last_2_category\")\n",
    "features[\"last_3_category\"] = features.groupby(by=\"user_id\", group_keys=True)[\"Category\"].shift(1).rolling(3).agg(lambda rows: rows[0]).rename(\"last_3_category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "        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></th>\n",
       "      <th>Category</th>\n",
       "      <th>last_category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th>rating_timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>2022-08-04 16:03:16.862000+00:00</th>\n",
       "      <td>5999.0</td>\n",
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       "      <th rowspan=\"17\" valign=\"top\">8</th>\n",
       "      <th>2020-08-04 07:22:59.858000+00:00</th>\n",
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       "      <th>2020-08-15 16:00:36+00:00</th>\n",
       "      <td>103.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-11-03 11:22:49.083000+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-04 10:12:55.667000+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-05 03:56:53.049000+00:00</th>\n",
       "      <td>4111.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-08 18:51:55.942000+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-15 02:44:56.907000+00:00</th>\n",
       "      <td>3538.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-16 16:01:37.436000+00:00</th>\n",
       "      <td>5090.0</td>\n",
       "      <td>3538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-29 12:19:41.604000+00:00</th>\n",
       "      <td>2837.0</td>\n",
       "      <td>5090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-21 13:10:34.477000+00:00</th>\n",
       "      <td>4843.0</td>\n",
       "      <td>2837.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-13 12:58:11.005000+00:00</th>\n",
       "      <td>3538.0</td>\n",
       "      <td>4843.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-08 21:25:25.406000+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-26 10:25:04.125000+00:00</th>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-04 07:26:55.243000+00:00</th>\n",
       "      <td>3538.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01 03:02:06.010000+00:00</th>\n",
       "      <td>3538.0</td>\n",
       "      <td>3538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28 04:19:48.895000+00:00</th>\n",
       "      <td>4111.0</td>\n",
       "      <td>3538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">9</th>\n",
       "      <th>2020-04-01 12:05:52.654000+00:00</th>\n",
       "      <td>3538.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-25 13:10:05.814000+00:00</th>\n",
       "      <td>4236.0</td>\n",
       "      <td>3538.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Category  last_category\n",
       "user_id rating_timestamp                                         \n",
       "2       2022-08-04 16:03:16.862000+00:00    5999.0            NaN\n",
       "8       2020-08-04 07:22:59.858000+00:00       NaN            NaN\n",
       "        2020-08-04 12:31:50.798000+00:00    3538.0            NaN\n",
       "        2020-08-15 16:00:36+00:00            103.0         3538.0\n",
       "        2020-11-03 11:22:49.083000+00:00       NaN          103.0\n",
       "        2020-12-04 10:12:55.667000+00:00       NaN            NaN\n",
       "        2020-12-05 03:56:53.049000+00:00    4111.0            NaN\n",
       "        2021-01-08 18:51:55.942000+00:00       NaN         4111.0\n",
       "        2021-07-15 02:44:56.907000+00:00    3538.0            NaN\n",
       "        2021-08-16 16:01:37.436000+00:00    5090.0         3538.0\n",
       "        2021-12-29 12:19:41.604000+00:00    2837.0         5090.0\n",
       "        2022-01-21 13:10:34.477000+00:00    4843.0         2837.0\n",
       "        2022-02-13 12:58:11.005000+00:00    3538.0         4843.0\n",
       "        2022-03-08 21:25:25.406000+00:00       NaN         3538.0\n",
       "        2022-03-26 10:25:04.125000+00:00      10.0            NaN\n",
       "        2022-06-04 07:26:55.243000+00:00    3538.0           10.0\n",
       "        2022-07-01 03:02:06.010000+00:00    3538.0         3538.0\n",
       "        2022-12-28 04:19:48.895000+00:00    4111.0         3538.0\n",
       "9       2020-04-01 12:05:52.654000+00:00    3538.0            NaN\n",
       "        2021-09-25 13:10:05.814000+00:00    4236.0         3538.0"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[[\"Category\", \"last_category\"]].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = features.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiation\n",
    "train_X_all = features[features[\"rating_timestamp\"] <= \"2022-01-01\"]\n",
    "train_Y = train_X_all[\"rating\"]\n",
    "train_X = train_X_all.drop(columns=[\"rating\", \"isbn\", \"rating_timestamp\", \"age\", \"state\", \"city\"])\n",
    "\n",
    "test_X_all = features[features[\"rating_timestamp\"] > \"2022-01-01\"]\n",
    "test_Y = test_X_all[\"rating\"]\n",
    "test_X = test_X_all.drop(columns=[\"rating\", \"isbn\", \"rating_timestamp\", \"age\", \"state\", \"city\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1901-01-01 12:00:00')"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books[\"created_at\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isbn</th>\n",
       "      <th>book_title</th>\n",
       "      <th>book_author</th>\n",
       "      <th>year_of_publication</th>\n",
       "      <th>publisher</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Language</th>\n",
       "      <th>Category</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [isbn, book_title, book_author, year_of_publication, publisher, Summary, Language, Category, created_at]\n",
       "Index: []"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books[books[\"isbn\"] == \"0887841740\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_features[\"Category\"] = train_features[\"Category\"].astype(\"category\")\n",
    "# train_features[\"Language\"] = train_features[\"Language\"].astype(\"category\")\n",
    "# train_features[\"state\"] = train_features[\"state\"].astype(\"category\")\n",
    "# train_features[\"city\"] = train_features[\"city\"].astype(\"category\")\n",
    "# train_features[\"country\"] = train_features[\"country\"].astype(\"category\")\n",
    "# train_features[\"book_author\"] = train_features[\"book_author\"].astype(\"category\")\n",
    "# train_features[\"publisher\"] = train_features[\"publisher\"].astype(\"category\")\n",
    "train_features = train_X.drop(columns=[\"book_title\", \"Summary\", \"location\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_features[\"Category\"] = test_features[\"Category\"].astype(\"category\")\n",
    "# test_features[\"Language\"] = test_features[\"Language\"].astype(\"category\")\n",
    "# test_features[\"state\"] = test_features[\"state\"].astype(\"category\")\n",
    "# test_features[\"city\"] = test_features[\"city\"].astype(\"category\")\n",
    "# test_features[\"country\"] = test_features[\"country\"].astype(\"category\")\n",
    "# test_features[\"book_author\"] = test_features[\"book_author\"].astype(\"category\")\n",
    "# test_features[\"publisher\"] = test_features[\"publisher\"].astype(\"category\")\n",
    "test_features = test_X.drop(columns=[\"book_title\", \"Summary\", \"location\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                           int64\n",
       "year_of_publication             float64\n",
       "Category_1257.0                   int64\n",
       "Category_3538.0                   int64\n",
       "Category_4497.0                   int64\n",
       "                                 ...   \n",
       "publisher_6871.0                  int64\n",
       "publisher_8038.0                  int64\n",
       "publisher_nan                     int64\n",
       "publisher_infrequent_sklearn      int64\n",
       "age_binned                        int64\n",
       "Length: 67, dtype: object"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_features.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>38792.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>5505.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5999.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>20401.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>2621.0</td>\n",
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       "      <td>4843.0</td>\n",
       "      <td>64.0</td>\n",
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       "      <th>14</th>\n",
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       "      <th>1030469</th>\n",
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       "    <tr>\n",
       "      <th>1030481</th>\n",
       "      <td>278854</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>383.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>343326 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id  book_author  year_of_publication  publisher  Language  \\\n",
       "0              2      38792.0               2002.0     5505.0       6.0   \n",
       "11             8      20401.0               1999.0     2621.0       6.0   \n",
       "12             8      11818.0               1988.0     2471.0       6.0   \n",
       "13             8          NaN                  NaN        NaN       NaN   \n",
       "14             8       7448.0               1991.0     3353.0       6.0   \n",
       "...          ...          ...                  ...        ...       ...   \n",
       "1030469   278851      24255.0               1986.0     8392.0       6.0   \n",
       "1030470   278851          NaN                  NaN        NaN       NaN   \n",
       "1030471   278851      38880.0               1994.0      747.0       6.0   \n",
       "1030472   278851          NaN                  NaN        NaN       NaN   \n",
       "1030481   278854          NaN                  NaN        NaN       NaN   \n",
       "\n",
       "         Category  country  Category_1257.0  Category_3538.0  Category_4497.0  \\\n",
       "0          5999.0    383.0                0                0                0   \n",
       "11         4843.0     64.0                0                0                0   \n",
       "12         3538.0     64.0                0                1                0   \n",
       "13            NaN     64.0                0                0                0   \n",
       "14           10.0     64.0                0                0                0   \n",
       "...           ...      ...              ...              ...              ...   \n",
       "1030469    4497.0    383.0                0                0                1   \n",
       "1030470       NaN    383.0                0                0                0   \n",
       "1030471    3538.0    383.0                0                1                0   \n",
       "1030472       NaN    383.0                0                0                0   \n",
       "1030481       NaN    383.0                0                0                0   \n",
       "\n",
       "         ...  publisher_917.0  publisher_3293.0  publisher_5715.0  \\\n",
       "0        ...                0                 0                 0   \n",
       "11       ...                0                 0                 0   \n",
       "12       ...                0                 0                 0   \n",
       "13       ...                0                 0                 0   \n",
       "14       ...                0                 0                 0   \n",
       "...      ...              ...               ...               ...   \n",
       "1030469  ...                0                 0                 0   \n",
       "1030470  ...                0                 0                 0   \n",
       "1030471  ...                0                 0                 0   \n",
       "1030472  ...                0                 0                 0   \n",
       "1030481  ...                0                 0                 0   \n",
       "\n",
       "         publisher_5920.0  publisher_6871.0  publisher_8038.0  publisher_nan  \\\n",
       "0                       0                 0                 0              0   \n",
       "11                      0                 0                 0              0   \n",
       "12                      0                 0                 0              0   \n",
       "13                      0                 0                 0              1   \n",
       "14                      0                 0                 0              0   \n",
       "...                   ...               ...               ...            ...   \n",
       "1030469                 0                 0                 0              0   \n",
       "1030470                 0                 0                 0              1   \n",
       "1030471                 0                 0                 0              0   \n",
       "1030472                 0                 0                 0              1   \n",
       "1030481                 0                 0                 0              1   \n",
       "\n",
       "         publisher_infrequent_sklearn  age_binned  last_category  \n",
       "0                                   1           1            NaN  \n",
       "11                                  1           3         2837.0  \n",
       "12                                  1           3         4843.0  \n",
       "13                                  0           3         3538.0  \n",
       "14                                  1           3            NaN  \n",
       "...                               ...         ...            ...  \n",
       "1030469                             1           2            NaN  \n",
       "1030470                             0           2         4497.0  \n",
       "1030471                             0           2            NaN  \n",
       "1030472                             0           2         3538.0  \n",
       "1030481                             0           3            NaN  \n",
       "\n",
       "[343326 rows x 73 columns]"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030476</th>\n",
       "      <td>278854</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>383.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030477</th>\n",
       "      <td>278854</td>\n",
       "      <td>49290.0</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>747.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3538.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030478</th>\n",
       "      <td>278854</td>\n",
       "      <td>56144.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>744.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3538.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030479</th>\n",
       "      <td>278854</td>\n",
       "      <td>35199.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>4032.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3538.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030480</th>\n",
       "      <td>278854</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>383.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3538.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>687156 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id  book_author  year_of_publication  publisher  Language  \\\n",
       "1              8          NaN                  NaN        NaN       NaN   \n",
       "2              8       1984.0               1991.0     6146.0       6.0   \n",
       "3              8      48571.0               2001.0     3350.0       6.0   \n",
       "4              8          NaN                  NaN        NaN       NaN   \n",
       "5              8          NaN                  NaN        NaN       NaN   \n",
       "...          ...          ...                  ...        ...       ...   \n",
       "1030476   278854          NaN                  NaN        NaN       NaN   \n",
       "1030477   278854      49290.0               1988.0      747.0       6.0   \n",
       "1030478   278854      56144.0               2000.0      744.0       6.0   \n",
       "1030479   278854      35199.0               1990.0     4032.0       6.0   \n",
       "1030480   278854          NaN                  NaN        NaN       NaN   \n",
       "\n",
       "         Category  country  Category_1257.0  Category_3538.0  Category_4497.0  \\\n",
       "1             NaN     64.0                0                0                0   \n",
       "2          3538.0     64.0                0                1                0   \n",
       "3           103.0     64.0                0                0                0   \n",
       "4             NaN     64.0                0                0                0   \n",
       "5             NaN     64.0                0                0                0   \n",
       "...           ...      ...              ...              ...              ...   \n",
       "1030476       NaN    383.0                0                0                0   \n",
       "1030477    3538.0    383.0                0                1                0   \n",
       "1030478    3538.0    383.0                0                1                0   \n",
       "1030479    3538.0    383.0                0                1                0   \n",
       "1030480       NaN    383.0                0                0                0   \n",
       "\n",
       "         ...  publisher_917.0  publisher_3293.0  publisher_5715.0  \\\n",
       "1        ...                0                 0                 0   \n",
       "2        ...                0                 0                 0   \n",
       "3        ...                0                 0                 0   \n",
       "4        ...                0                 0                 0   \n",
       "5        ...                0                 0                 0   \n",
       "...      ...              ...               ...               ...   \n",
       "1030476  ...                0                 0                 0   \n",
       "1030477  ...                0                 0                 0   \n",
       "1030478  ...                0                 0                 0   \n",
       "1030479  ...                0                 0                 0   \n",
       "1030480  ...                0                 0                 0   \n",
       "\n",
       "         publisher_5920.0  publisher_6871.0  publisher_8038.0  publisher_nan  \\\n",
       "1                       0                 0                 0              1   \n",
       "2                       0                 0                 0              0   \n",
       "3                       0                 0                 0              0   \n",
       "4                       0                 0                 0              1   \n",
       "5                       0                 0                 0              1   \n",
       "...                   ...               ...               ...            ...   \n",
       "1030476                 0                 0                 0              1   \n",
       "1030477                 0                 0                 0              0   \n",
       "1030478                 0                 0                 0              0   \n",
       "1030479                 0                 0                 0              0   \n",
       "1030480                 0                 0                 0              1   \n",
       "\n",
       "         publisher_infrequent_sklearn  age_binned  last_category  \n",
       "1                                   0           3            NaN  \n",
       "2                                   1           3            NaN  \n",
       "3                                   1           3         3538.0  \n",
       "4                                   0           3          103.0  \n",
       "5                                   0           3            NaN  \n",
       "...                               ...         ...            ...  \n",
       "1030476                             0           3         3538.0  \n",
       "1030477                             0           3            NaN  \n",
       "1030478                             0           3         3538.0  \n",
       "1030479                             1           3         3538.0  \n",
       "1030480                             0           3         3538.0  \n",
       "\n",
       "[687156 rows x 73 columns]"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "             grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "             interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012, max_bin=256,\n",
       "             max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "             max_depth=6, max_leaves=0, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=233, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "             grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "             interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012, max_bin=256,\n",
       "             max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "             max_depth=6, max_leaves=0, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=233, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "             grow_policy='depthwise', importance_type=None,\n",
       "             interaction_constraints='', learning_rate=0.300000012, max_bin=256,\n",
       "             max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "             max_depth=6, max_leaves=0, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints='()', n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=233, ...)"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_v1 = xgb.XGBRegressor(objective='reg:squarederror', random_state=233)\n",
    " \n",
    "# Fitting the model\n",
    "xgb_v1.fit(train_features[[\"user_id\", \"age_binned\"]], train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "             grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "             interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012, max_bin=256,\n",
       "             max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "             max_depth=6, max_leaves=0, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=233, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "             grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "             interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012, max_bin=256,\n",
       "             max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "             max_depth=6, max_leaves=0, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=233, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "             grow_policy='depthwise', importance_type=None,\n",
       "             interaction_constraints='', learning_rate=0.300000012, max_bin=256,\n",
       "             max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "             max_depth=6, max_leaves=0, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints='()', n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=233, ...)"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Full model\n",
    "xgb_r = xgb.XGBRegressor(objective='reg:squarederror', random_state=233)\n",
    " \n",
    "# Fitting the model\n",
    "xgb_r.fit(train_features, train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE :  3.540545\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "\n",
    "pred = xgb_r.predict(test_features)\n",
    "# RMSE Computation\n",
    "rmse = np.sqrt(MSE(test_Y, pred))\n",
    "print(\"RMSE : % f\" %(rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE :  3.578522\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "\n",
    "pred = xgb_v1.predict(test_features[[\"user_id\", \"age_binned\"]])\n",
    "# RMSE Computation\n",
    "rmse = np.sqrt(MSE(test_Y, pred))\n",
    "print(\"RMSE : % f\" %(rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE :  3.577680\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "\n",
    "pred = xgb_r.predict(test_features)\n",
    "# RMSE Computation\n",
    "rmse = np.sqrt(MSE(test_Y, pred))\n",
    "print(\"RMSE : % f\" %(rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.118087 , 1.8542459, 2.3469923, ..., 3.317025 , 3.2924807,\n",
       "       2.655191 ], dtype=float32)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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