machine_learning/example_recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning - Recommendation System example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Links: [MovieLens (GroupLens)](https://grouplens.org/datasets/movielens/latest/)  |  [Article (TowardsDataScience)](https://towardsdatascience.com/modern-recommendation-systems-with-neural-networks-3cc06a6ded2c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0 - Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Import pckgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "## for data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "from datetime import datetime\n",
    "\n",
    "## for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "## for machine learning\n",
    "from sklearn import metrics, preprocessing\n",
    "\n",
    "## for deep learning\n",
    "from tensorflow.keras import models, layers, utils  #(2.6.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>product</th>\n",
       "      <th>name</th>\n",
       "      <th>date</th>\n",
       "      <th>old</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>1</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>2</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>3</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9737</th>\n",
       "      <td>193581</td>\n",
       "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
       "      <td>Action|Animation|Comedy|Fantasy</td>\n",
       "      <td>9736</td>\n",
       "      <td>Black Butler: Book of the Atlantic</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9738</th>\n",
       "      <td>193583</td>\n",
       "      <td>No Game No Life: Zero (2017)</td>\n",
       "      <td>Animation|Comedy|Fantasy</td>\n",
       "      <td>9737</td>\n",
       "      <td>No Game No Life: Zero</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9739</th>\n",
       "      <td>193585</td>\n",
       "      <td>Flint (2017)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>9738</td>\n",
       "      <td>Flint</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9740</th>\n",
       "      <td>193587</td>\n",
       "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
       "      <td>Action|Animation</td>\n",
       "      <td>9739</td>\n",
       "      <td>Bungo Stray Dogs: Dead Apple</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9741</th>\n",
       "      <td>193609</td>\n",
       "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>9740</td>\n",
       "      <td>Andrew Dice Clay: Dice Rules</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9741 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                                      title  \\\n",
       "0           1                           Toy Story (1995)   \n",
       "1           2                             Jumanji (1995)   \n",
       "2           3                    Grumpier Old Men (1995)   \n",
       "3           4                   Waiting to Exhale (1995)   \n",
       "4           5         Father of the Bride Part II (1995)   \n",
       "...       ...                                        ...   \n",
       "9737   193581  Black Butler: Book of the Atlantic (2017)   \n",
       "9738   193583               No Game No Life: Zero (2017)   \n",
       "9739   193585                               Flint (2017)   \n",
       "9740   193587        Bungo Stray Dogs: Dead Apple (2018)   \n",
       "9741   193609        Andrew Dice Clay: Dice Rules (1991)   \n",
       "\n",
       "                                           genres  product  \\\n",
       "0     Adventure|Animation|Children|Comedy|Fantasy        0   \n",
       "1                      Adventure|Children|Fantasy        1   \n",
       "2                                  Comedy|Romance        2   \n",
       "3                            Comedy|Drama|Romance        3   \n",
       "4                                          Comedy        4   \n",
       "...                                           ...      ...   \n",
       "9737              Action|Animation|Comedy|Fantasy     9736   \n",
       "9738                     Animation|Comedy|Fantasy     9737   \n",
       "9739                                        Drama     9738   \n",
       "9740                             Action|Animation     9739   \n",
       "9741                                       Comedy     9740   \n",
       "\n",
       "                                    name    date  old  \n",
       "0                              Toy Story  1995.0    1  \n",
       "1                                Jumanji  1995.0    1  \n",
       "2                       Grumpier Old Men  1995.0    1  \n",
       "3                      Waiting to Exhale  1995.0    1  \n",
       "4            Father of the Bride Part II  1995.0    1  \n",
       "...                                  ...     ...  ...  \n",
       "9737  Black Butler: Book of the Atlantic  2017.0    0  \n",
       "9738               No Game No Life: Zero  2017.0    0  \n",
       "9739                               Flint  2017.0    0  \n",
       "9740        Bungo Stray Dogs: Dead Apple  2018.0    0  \n",
       "9741        Andrew Dice Clay: Dice Rules  1991.0    1  \n",
       "\n",
       "[9741 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Products\n",
    "dtf_products = pd.read_excel(\"data_movies.xlsx\", sheet_name=\"products\")\n",
    "\n",
    "dtf_products = dtf_products[~dtf_products[\"genres\"].isna()]\n",
    "dtf_products[\"product\"] = range(0,len(dtf_products))\n",
    "dtf_products[\"name\"] = dtf_products[\"title\"].apply(lambda x: re.sub(\"[\\(\\[].*?[\\)\\]]\", \"\", x).strip())\n",
    "dtf_products[\"date\"] = dtf_products[\"title\"].apply(lambda x: int(x.split(\"(\")[-1].replace(\")\",\"\").strip()) \n",
    "                                                             if \"(\" in x else np.nan)\n",
    "\n",
    "## add features\n",
    "dtf_products[\"date\"] = dtf_products[\"date\"].fillna(9999)\n",
    "dtf_products[\"old\"] = dtf_products[\"date\"].apply(lambda x: 1 if x < 2000 else 0)\n",
    "\n",
    "dtf_products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>y</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>user</th>\n",
       "      <th>daytime</th>\n",
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       "      <td>1</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>2000-07-30 20:20:47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2000-07-30 20:37:04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2000-07-30 21:03:35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2000-07-30 20:48:51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>66</td>\n",
       "      <td>248</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2005-04-11 05:41:32</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>66</td>\n",
       "      <td>255</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2005-04-11 05:07:20</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>66</td>\n",
       "      <td>260</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2004-08-29 04:45:50</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>66</td>\n",
       "      <td>272</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2005-04-11 05:31:59</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>66</td>\n",
       "      <td>273</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2005-04-11 05:31:55</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      userId  movieId    y           timestamp  user  daytime  weekend  \\\n",
       "0          1        1  4.0 2000-07-30 20:45:03     0        0        1   \n",
       "1          1        3  4.0 2000-07-30 20:20:47     0        0        1   \n",
       "2          1        6  4.0 2000-07-30 20:37:04     0        0        1   \n",
       "3          1       47  5.0 2000-07-30 21:03:35     0        0        1   \n",
       "4          1       50  5.0 2000-07-30 20:48:51     0        0        1   \n",
       "...      ...      ...  ...                 ...   ...      ...      ...   \n",
       "9995      66      248  3.0 2005-04-11 05:41:32    65        0        0   \n",
       "9996      66      255  0.5 2005-04-11 05:07:20    65        0        0   \n",
       "9997      66      260  2.5 2004-08-29 04:45:50    65        0        1   \n",
       "9998      66      272  3.5 2005-04-11 05:31:59    65        0        0   \n",
       "9999      66      273  3.5 2005-04-11 05:31:55    65        0        0   \n",
       "\n",
       "      product  \n",
       "0           0  \n",
       "1           2  \n",
       "2           5  \n",
       "3          43  \n",
       "4          46  \n",
       "...       ...  \n",
       "9995      212  \n",
       "9996      219  \n",
       "9997      224  \n",
       "9998      234  \n",
       "9999      235  \n",
       "\n",
       "[10000 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Users\n",
    "dtf_users = pd.read_excel(\"data_movies.xlsx\", sheet_name=\"users\").head(10000)\n",
    "\n",
    "dtf_users[\"user\"] = dtf_users[\"userId\"].apply(lambda x: x-1)\n",
    "\n",
    "dtf_users[\"timestamp\"] = dtf_users[\"timestamp\"].apply(lambda x: datetime.fromtimestamp(x))\n",
    "dtf_users[\"daytime\"] = dtf_users[\"timestamp\"].apply(lambda x: 1 if 6<int(x.strftime(\"%H\"))<20 else 0)\n",
    "dtf_users[\"weekend\"] = dtf_users[\"timestamp\"].apply(lambda x: 1 if x.weekday() in [5,6] else 0)\n",
    "\n",
    "dtf_users = dtf_users.merge(dtf_products[[\"movieId\",\"product\"]], how=\"left\")\n",
    "dtf_users = dtf_users.rename(columns={\"rating\":\"y\"})\n",
    "\n",
    "dtf_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>name</th>\n",
       "      <th>old</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jumanji</td>\n",
       "      <td>1</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>1</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>1</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>1</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                name  old  \\\n",
       "product                                     \n",
       "0                          Toy Story    1   \n",
       "1                            Jumanji    1   \n",
       "2                   Grumpier Old Men    1   \n",
       "3                  Waiting to Exhale    1   \n",
       "4        Father of the Bride Part II    1   \n",
       "\n",
       "                                              genres  \n",
       "product                                               \n",
       "0        Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                         Adventure|Children|Fantasy  \n",
       "2                                     Comedy|Romance  \n",
       "3                               Comedy|Drama|Romance  \n",
       "4                                             Comedy  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select only useful columns\n",
    "dtf_products = dtf_products[[\"product\",\"name\",\"old\",\"genres\"]].set_index(\"product\")\n",
    "dtf_products.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>product</th>\n",
       "      <th>daytime</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user  product  daytime  weekend\n",
       "0     0        0        0        1\n",
       "1     0        2        0        1\n",
       "2     0        5        0        1\n",
       "3     0       43        0        1\n",
       "4     0       46        0        1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract context\n",
    "dtf_context = dtf_users[[\"user\",\"product\",\"daytime\",\"weekend\"]]\n",
    "dtf_context.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>product</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user  product    y\n",
       "0     0        0  4.0\n",
       "1     0        2  4.0\n",
       "2     0        5  4.0\n",
       "3     0       43  5.0\n",
       "4     0       46  5.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtf_users = dtf_users[[\"user\",\"product\",\"y\"]]\n",
    "dtf_users.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 - Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Mystery', 'Children', 'Comedy', 'Adventure', 'Thriller', 'Drama', 'Horror', 'Action', 'IMAX', 'Fantasy', 'Western', 'War', 'Sci-Fi', 'Film-Noir', 'Romance', 'Animation', 'Crime', 'Musical', 'Documentary']\n"
     ]
    }
   ],
   "source": [
    "tags = [i.split(\"|\") for i in dtf_products[\"genres\"].unique()]\n",
    "columns = list(set([i for lst in tags for i in lst]))\n",
    "columns.remove('(no genres listed)')\n",
    "print(columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\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>name</th>\n",
       "      <th>old</th>\n",
       "      <th>genres</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Children</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>Drama</th>\n",
       "      <th>Horror</th>\n",
       "      <th>...</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Western</th>\n",
       "      <th>War</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Documentary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jumanji</td>\n",
       "      <td>1</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>1</td>\n",
       "      <td>Comedy|Romance</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>1</td>\n",
       "      <td>Comedy|Drama|Romance</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>1</td>\n",
       "      <td>Comedy</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                name  old  \\\n",
       "product                                     \n",
       "0                          Toy Story    1   \n",
       "1                            Jumanji    1   \n",
       "2                   Grumpier Old Men    1   \n",
       "3                  Waiting to Exhale    1   \n",
       "4        Father of the Bride Part II    1   \n",
       "\n",
       "                                              genres  Mystery  Children  \\\n",
       "product                                                                   \n",
       "0        Adventure|Animation|Children|Comedy|Fantasy        0         1   \n",
       "1                         Adventure|Children|Fantasy        0         1   \n",
       "2                                     Comedy|Romance        0         0   \n",
       "3                               Comedy|Drama|Romance        0         0   \n",
       "4                                             Comedy        0         0   \n",
       "\n",
       "         Comedy  Adventure  Thriller  Drama  Horror  ...  Fantasy  Western  \\\n",
       "product                                              ...                     \n",
       "0             1          1         0      0       0  ...        1        0   \n",
       "1             0          1         0      0       0  ...        1        0   \n",
       "2             1          0         0      0       0  ...        0        0   \n",
       "3             1          0         0      1       0  ...        0        0   \n",
       "4             1          0         0      0       0  ...        0        0   \n",
       "\n",
       "         War  Sci-Fi  Film-Noir  Romance  Animation  Crime  Musical  \\\n",
       "product                                                               \n",
       "0          0       0          0        0          1      0        0   \n",
       "1          0       0          0        0          0      0        0   \n",
       "2          0       0          0        1          0      0        0   \n",
       "3          0       0          0        1          0      0        0   \n",
       "4          0       0          0        0          0      0        0   \n",
       "\n",
       "         Documentary  \n",
       "product               \n",
       "0                  0  \n",
       "1                  0  \n",
       "2                  0  \n",
       "3                  0  \n",
       "4                  0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in columns:\n",
    "    dtf_products[col] = dtf_products[\"genres\"].apply(lambda x: 1 if col in x else 0)\n",
    "dtf_products.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,5))\n",
    "sns.heatmap(dtf_products==0, vmin=0, vmax=1, cbar=False, ax=ax).set_title(\"Products x Features\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dtf_users.groupby(\"user\").count()[\"product\"].sort_values(ascending=False).plot(\n",
    "    kind=\"bar\", color=\"green\", title=\"Y by user\", figsize=(20,5)).grid(axis='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dtf_users[\"y\"].value_counts().sort_index().plot(kind=\"bar\", color=\"green\", title=\"Y disribution\", \n",
    "                                                figsize=(20,5)).grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>product</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>9731</th>\n",
       "      <th>9732</th>\n",
       "      <th>9733</th>\n",
       "      <th>9734</th>\n",
       "      <th>9735</th>\n",
       "      <th>9736</th>\n",
       "      <th>9737</th>\n",
       "      <th>9738</th>\n",
       "      <th>9739</th>\n",
       "      <th>9740</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>61</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>5.0</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>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 9741 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "product  0     1     2     3     4     5     6     7     8     9     ...  \\\n",
       "user                                                                 ...   \n",
       "0         4.0   NaN   4.0   NaN   NaN   4.0   NaN   NaN   NaN   NaN  ...   \n",
       "1         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "2         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "3         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "4         4.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   \n",
       "61        NaN   4.0   NaN   NaN   NaN   4.5   NaN   NaN   NaN   NaN  ...   \n",
       "62        5.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   3.0  ...   \n",
       "63        4.0   NaN   3.5   NaN   NaN   4.5   NaN   NaN   NaN   NaN  ...   \n",
       "64        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "65        4.0   NaN   NaN   NaN   4.0   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "\n",
       "product  9731  9732  9733  9734  9735  9736  9737  9738  9739  9740  \n",
       "user                                                                 \n",
       "0         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "1         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "4         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "61        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "62        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "63        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "64        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "65        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[66 rows x 9741 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = dtf_users.copy()\n",
    "dtf_users = tmp.pivot_table(index=\"user\", columns=\"product\", values=\"y\")\n",
    "missing_cols = list(set(dtf_products.index) - set(dtf_users.columns))\n",
    "for col in missing_cols:\n",
    "    dtf_users[col] = np.nan\n",
    "dtf_users = dtf_users[sorted(dtf_users.columns)]\n",
    "dtf_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,5))\n",
    "sns.heatmap(dtf_users.isnull(), vmin=0, vmax=1, cbar=False, ax=ax).set_title(\"Users x Products\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dtf_context[\"weekend\"].value_counts().plot(kind=\"barh\", color=\"green\", title=\"Weekend (count)\", \n",
    "                                         figsize=(5,3)).grid(axis='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dtf_context[\"daytime\"].value_counts().plot(kind=\"barh\", color=\"green\", title=\"Daytime (count)\", \n",
    "                                         figsize=(5,3)).grid(axis='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 - Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Scaling\n",
    "- Partitioning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>product</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>9731</th>\n",
       "      <th>9732</th>\n",
       "      <th>9733</th>\n",
       "      <th>9734</th>\n",
       "      <th>9735</th>\n",
       "      <th>9736</th>\n",
       "      <th>9737</th>\n",
       "      <th>9738</th>\n",
       "      <th>9739</th>\n",
       "      <th>9740</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.8</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>61</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1.0</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>NaN</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 9741 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "product  0         1      2     3     4      5     6     7     8         9     \\\n",
       "user                                                                            \n",
       "0         0.8       NaN  0.750   NaN   NaN  0.750   NaN   NaN   NaN       NaN   \n",
       "1         NaN       NaN    NaN   NaN   NaN    NaN   NaN   NaN   NaN       NaN   \n",
       "2         NaN       NaN    NaN   NaN   NaN    NaN   NaN   NaN   NaN       NaN   \n",
       "3         NaN       NaN    NaN   NaN   NaN    NaN   NaN   NaN   NaN       NaN   \n",
       "4         0.8       NaN    NaN   NaN   NaN    NaN   NaN   NaN   NaN       NaN   \n",
       "...       ...       ...    ...   ...   ...    ...   ...   ...   ...       ...   \n",
       "61        NaN  0.833333    NaN   NaN   NaN  0.875   NaN   NaN   NaN       NaN   \n",
       "62        1.0       NaN    NaN   NaN   NaN    NaN   NaN   NaN   NaN  0.666667   \n",
       "63        0.8       NaN  0.625   NaN   NaN  0.875   NaN   NaN   NaN       NaN   \n",
       "64        NaN       NaN    NaN   NaN   NaN    NaN   NaN   NaN   NaN       NaN   \n",
       "65        0.8       NaN    NaN   NaN  0.75    NaN   NaN   NaN   NaN       NaN   \n",
       "\n",
       "product  ...  9731  9732  9733  9734  9735  9736  9737  9738  9739  9740  \n",
       "user     ...                                                              \n",
       "0        ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "1        ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2        ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3        ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "4        ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "...      ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "61       ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "62       ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "63       ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "64       ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "65       ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[66 rows x 9741 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtf_users = pd.DataFrame(preprocessing.MinMaxScaler(feature_range=(0.5,1)).fit_transform(dtf_users.values), \n",
    "                         columns=dtf_users.columns, index=dtf_users.index)\n",
    "dtf_users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Partitioning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7792"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split = int(0.8*dtf_users.shape[1])\n",
    "split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "non-null data: 9329\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>product</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>7782</th>\n",
       "      <th>7783</th>\n",
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       "      <th>7789</th>\n",
       "      <th>7790</th>\n",
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       "      <th>user</th>\n",
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       "      <td>NaN</td>\n",
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       "      <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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.8</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1.0</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>NaN</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 7792 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "product  0         1      2     3     4      5     6     7     8         9     \\\n",
       "user                                                                            \n",
       "0         0.8       NaN  0.750   NaN   NaN  0.750   NaN   NaN   NaN       NaN   \n",
       "1         NaN       NaN    NaN   NaN   NaN    NaN   NaN   NaN   NaN       NaN   \n",
       "2         NaN       NaN    NaN   NaN   NaN    NaN   NaN   NaN   NaN       NaN   \n",
       "3         NaN       NaN    NaN   NaN   NaN    NaN   NaN   NaN   NaN       NaN   \n",
       "4         0.8       NaN    NaN   NaN   NaN    NaN   NaN   NaN   NaN       NaN   \n",
       "...       ...       ...    ...   ...   ...    ...   ...   ...   ...       ...   \n",
       "61        NaN  0.833333    NaN   NaN   NaN  0.875   NaN   NaN   NaN       NaN   \n",
       "62        1.0       NaN    NaN   NaN   NaN    NaN   NaN   NaN   NaN  0.666667   \n",
       "63        0.8       NaN  0.625   NaN   NaN  0.875   NaN   NaN   NaN       NaN   \n",
       "64        NaN       NaN    NaN   NaN   NaN    NaN   NaN   NaN   NaN       NaN   \n",
       "65        0.8       NaN    NaN   NaN  0.75    NaN   NaN   NaN   NaN       NaN   \n",
       "\n",
       "product  ...  7782  7783  7784  7785  7786  7787  7788  7789  7790  7791  \n",
       "user     ...                                                              \n",
       "0        ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "1        ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2        ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3        ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "4        ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "...      ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "61       ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "62       ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "63       ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "64       ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "65       ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[66 rows x 7792 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train\n",
    "dtf_train = dtf_users.loc[:, :split-1]\n",
    "print(\"non-null data:\", dtf_train[dtf_train>0].count().sum())\n",
    "dtf_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "non-null data: 671\n"
     ]
    },
    {
     "data": {
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       "      <th>product</th>\n",
       "      <th>7792</th>\n",
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       "    </tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 1949 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "product  7792  7793  7794  7795  7796  7797  7798  7799  7800  7801  ...  \\\n",
       "user                                                                 ...   \n",
       "0         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "1         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "2         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "3         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "4         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   \n",
       "61        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "62        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "63        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "64        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "65        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "\n",
       "product  9731  9732  9733  9734  9735  9736  9737  9738  9739  9740  \n",
       "user                                                                 \n",
       "0         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "1         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "4         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "61        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "62        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "63        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "64        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "65        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[66 rows x 1949 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test\n",
    "dtf_test = dtf_users.loc[:, split:]\n",
    "print(\"non-null data:\", dtf_test[dtf_test>0].count().sum())\n",
    "dtf_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 - Baseline (Content-Based)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: 21 | test: 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7792</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7793</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7794</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7795</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7796</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          y\n",
       "product    \n",
       "7792    NaN\n",
       "7793    NaN\n",
       "7794    NaN\n",
       "7795    NaN\n",
       "7796    NaN"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select a user\n",
    "i = 1\n",
    "train = dtf_train.iloc[i].to_frame(name=\"y\")\n",
    "test = dtf_test.iloc[i].to_frame(name=\"y\")\n",
    "\n",
    "print(\"train:\", len(train[~train[\"y\"].isna()]), \"| test:\", len(test[~test[\"y\"].isna()]))\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9736</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9737</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9738</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9739</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9740</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          y\n",
       "product    \n",
       "9736    NaN\n",
       "9737    NaN\n",
       "9738    NaN\n",
       "9739    NaN\n",
       "9740    NaN"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add all products in test but empty\n",
    "tmp = test.copy()\n",
    "tmp[\"y\"] = np.nan\n",
    "train = train.append(tmp)\n",
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Users (1, 9741)  x  Products (9741, 20)\n"
     ]
    }
   ],
   "source": [
    "# Model\n",
    "usr = train[[\"y\"]].fillna(0).values.T\n",
    "prd = dtf_products.drop([\"name\",\"genres\"],axis=1).values\n",
    "print(\"Users\", usr.shape, \" x  Products\", prd.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>product</th>\n",
       "      <th>y</th>\n",
       "      <th>yhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>8063</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.364068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>8305</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.441084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>8376</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.077697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>8466</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.215465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>8509</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.477922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>8550</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.356925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>8681</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.327055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1036</th>\n",
       "      <td>8828</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.033233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      product         y      yhat\n",
       "271      8063  0.812500  0.364068\n",
       "513      8305  1.000000  0.441084\n",
       "584      8376  0.777778  0.077697\n",
       "674      8466  0.800000  0.215465\n",
       "717      8509  0.500000  0.477922\n",
       "758      8550  0.875000  0.356925\n",
       "889      8681  1.000000  0.327055\n",
       "1036     8828  0.500000  0.033233"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numpy\n",
    "import numpy as np\n",
    "\n",
    "## usr_ft(users,fatures) = usr(users,products) x prd(products,features)\n",
    "usr_ft = np.dot(usr, prd)\n",
    "## normalize\n",
    "weights = usr_ft / usr_ft.sum()\n",
    "## predicted rating(users,products) = weights(users,fatures) x prd.T(features,products)\n",
    "pred = np.dot(weights, prd.T)\n",
    "\n",
    "test = test.merge(pd.DataFrame(pred[0], columns=[\"yhat\"]), how=\"left\", left_index=True, right_index=True).reset_index()\n",
    "test = test[~test[\"y\"].isna()]\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tensorflow\n",
    "#import tensorflow as tf\n",
    "\n",
    "## usr_ft(users,fatures) = usr(users,products) x prd(products,features)\n",
    "#usr_ft = tf.matmul(usr, prd)\n",
    "## normalize\n",
    "#weights = usr_ft / tf.reduce_sum(usr_ft, axis=1, keepdims=True)\n",
    "## rating(users,products) = weights(users,fatures) x prd.T(features,products)\n",
    "#pred = tf.matmul(weights, prd.T)\n",
    "\n",
    "#test = test.merge(pd.DataFrame(pred[0], columns=[\"yhat\"]), how=\"left\", left_index=True, right_index=True).reset_index()\n",
    "#test = test[~test[\"y\"].isna()]\n",
    "#test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate\n",
    "def mean_reciprocal_rank(y_test, predicted):\n",
    "    score = []\n",
    "    for product in y_test:\n",
    "        mrr = 1 / (list(predicted).index(product) + 1) if product in predicted else 0\n",
    "        score.append(mrr)\n",
    "    return np.mean(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- user 1 ---\n",
      "y_test: [8305 8681 8550 8063 8466]\n",
      "predicted: [8509 8305 8063 8550 8681]\n",
      "true positive: 4 (80.0%)\n",
      "accuracy: 0.0%\n",
      "mrr: 0.26\n"
     ]
    }
   ],
   "source": [
    "print(\"--- user\", i, \"---\")\n",
    "\n",
    "top = 5\n",
    "y_test = test.sort_values(\"y\", ascending=False)[\"product\"].values[:top]\n",
    "print(\"y_test:\", y_test)\n",
    "\n",
    "predicted = test.sort_values(\"yhat\", ascending=False)[\"product\"].values[:top]\n",
    "print(\"predicted:\", predicted)\n",
    "\n",
    "true_positive = len(list(set(y_test) & set(predicted)))\n",
    "print(\"true positive:\", true_positive, \"(\"+str(round(true_positive/top*100,1))+\"%)\")\n",
    "print(\"accuracy:\", str(round(metrics.accuracy_score(y_test,predicted)*100,1))+\"%\")\n",
    "print(\"mrr:\", round(mean_reciprocal_rank(y_test, predicted),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>product</th>\n",
       "      <th>y</th>\n",
       "      <th>yhat</th>\n",
       "      <th>name</th>\n",
       "      <th>old</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>8509</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.477922</td>\n",
       "      <td>The Drop</td>\n",
       "      <td>0</td>\n",
       "      <td>Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>8305</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.441084</td>\n",
       "      <td>Wolf of Wall Street, The</td>\n",
       "      <td>0</td>\n",
       "      <td>Comedy|Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>8063</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.364068</td>\n",
       "      <td>Django Unchained</td>\n",
       "      <td>0</td>\n",
       "      <td>Action|Drama|Western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>8550</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.356925</td>\n",
       "      <td>Ex Machina</td>\n",
       "      <td>0</td>\n",
       "      <td>Drama|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>8681</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.327055</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>0</td>\n",
       "      <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>8466</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.215465</td>\n",
       "      <td>Whiplash</td>\n",
       "      <td>0</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>8376</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.077697</td>\n",
       "      <td>Interstellar</td>\n",
       "      <td>0</td>\n",
       "      <td>Sci-Fi|IMAX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1036</th>\n",
       "      <td>8828</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.033233</td>\n",
       "      <td>The Jinx: The Life and Deaths of Robert Durst</td>\n",
       "      <td>0</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      product         y      yhat  \\\n",
       "717      8509  0.500000  0.477922   \n",
       "513      8305  1.000000  0.441084   \n",
       "271      8063  0.812500  0.364068   \n",
       "758      8550  0.875000  0.356925   \n",
       "889      8681  1.000000  0.327055   \n",
       "674      8466  0.800000  0.215465   \n",
       "584      8376  0.777778  0.077697   \n",
       "1036     8828  0.500000  0.033233   \n",
       "\n",
       "                                               name  old  \\\n",
       "717                                        The Drop    0   \n",
       "513                        Wolf of Wall Street, The    0   \n",
       "271                                Django Unchained    0   \n",
       "758                                      Ex Machina    0   \n",
       "889                              Mad Max: Fury Road    0   \n",
       "674                                        Whiplash    0   \n",
       "584                                    Interstellar    0   \n",
       "1036  The Jinx: The Life and Deaths of Robert Durst    0   \n",
       "\n",
       "                                genres  \n",
       "717               Crime|Drama|Thriller  \n",
       "513                 Comedy|Crime|Drama  \n",
       "271               Action|Drama|Western  \n",
       "758              Drama|Sci-Fi|Thriller  \n",
       "889   Action|Adventure|Sci-Fi|Thriller  \n",
       "674                              Drama  \n",
       "584                        Sci-Fi|IMAX  \n",
       "1036                       Documentary  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.merge(\n",
    "        dtf_products[[\"name\",\"old\",\"genres\"]], left_on=\"product\", right_index=True\n",
    "    ).sort_values(\"yhat\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4 - Model Desing & Testing (Collaborative Filtering)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>user</th>\n",
       "      <th>product</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user  product     y\n",
       "0     0        0  0.80\n",
       "1     0        2  0.75\n",
       "2     0        5  0.75\n",
       "3     0       43  1.00\n",
       "4     0       46  1.00"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = dtf_train.stack(dropna=True).reset_index().rename(columns={0:\"y\"})\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>user</th>\n",
       "      <th>product</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8063</td>\n",
       "      <td>0.812500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>8305</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>8376</td>\n",
       "      <td>0.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>8466</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>8509</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user  product         y\n",
       "0     1     8063  0.812500\n",
       "1     1     8305  1.000000\n",
       "2     1     8376  0.777778\n",
       "3     1     8466  0.800000\n",
       "4     1     8509  0.500000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = dtf_test.stack(dropna=True).reset_index().rename(columns={0:\"y\"})\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Model Design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"CollaborativeFiltering\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "xusers_in (InputLayer)          [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "xproducts_in (InputLayer)       [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "xusers_emb (Embedding)          (None, 1, 50)        3300        xusers_in[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "xproducts_emb (Embedding)       (None, 1, 50)        487050      xproducts_in[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "xusers (Reshape)                (None, 50)           0           xusers_emb[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "xproducts (Reshape)             (None, 50)           0           xproducts_emb[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "xx (Dot)                        (None, 1)            0           xusers[0][0]                     \n",
      "                                                                 xproducts[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "y_out (Dense)                   (None, 1)            2           xx[0][0]                         \n",
      "==================================================================================================\n",
      "Total params: 490,352\n",
      "Trainable params: 490,352\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "embeddings_size = 50\n",
    "usr, prd = dtf_users.shape[0], dtf_users.shape[1]\n",
    "\n",
    "# Users (,1,embedding_size) and Reshape (,embedding_size)\n",
    "xusers_in = layers.Input(name=\"xusers_in\", shape=(1,))\n",
    "xusers_emb = layers.Embedding(name=\"xusers_emb\", input_dim=usr, output_dim=embeddings_size)(xusers_in)\n",
    "xusers = layers.Reshape(name='xusers', target_shape=(embeddings_size,))(xusers_emb)\n",
    "\n",
    "# Products (,1,embedding_size) and Reshape (,embedding_size)\n",
    "xproducts_in = layers.Input(name=\"xproducts_in\", shape=(1,))\n",
    "xproducts_emb = layers.Embedding(name=\"xproducts_emb\", input_dim=prd, output_dim=embeddings_size)(xproducts_in)\n",
    "xproducts = layers.Reshape(name='xproducts', target_shape=(embeddings_size,))(xproducts_emb)\n",
    "\n",
    "# Product (,1) values[-1,1]\n",
    "xx = layers.Dot(name='xx', normalize=True, axes=1)([xusers, xproducts])\n",
    "\n",
    "# Predict ratings (,1)\n",
    "y_out = layers.Dense(name=\"y_out\", units=1, activation='linear')(xx)\n",
    "\n",
    "# Compile\n",
    "model = models.Model(inputs=[xusers_in,xproducts_in], outputs=y_out, name=\"CollaborativeFiltering\")\n",
    "model.compile(optimizer='adam', loss='mean_absolute_error', metrics=['mean_absolute_percentage_error'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Train / Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Plot loss and metrics of keras training.\n",
    "'''\n",
    "def utils_plot_keras_training(training):\n",
    "    metrics = [k for k in training.history.keys() if (\"loss\" not in k) and (\"val\" not in k)]\n",
    "    fig, ax = plt.subplots(nrows=1, ncols=2, sharey=True, figsize=(15,3))\n",
    "    \n",
    "    ## training\n",
    "    ax[0].set(title=\"Training\")\n",
    "    ax11 = ax[0].twinx()\n",
    "    ax[0].plot(training.history['loss'], color='black')\n",
    "    ax[0].set_xlabel('Epochs')\n",
    "    ax[0].set_ylabel('Loss', color='black')\n",
    "    for metric in metrics:\n",
    "        ax11.plot(training.history[metric], label=metric)\n",
    "    ax11.set_ylabel(\"Score\", color='steelblue')\n",
    "    ax11.legend()\n",
    "    \n",
    "    ## validation\n",
    "    ax[1].set(title=\"Validation\")\n",
    "    ax22 = ax[1].twinx()\n",
    "    ax[1].plot(training.history['val_loss'], color='black')\n",
    "    ax[1].set_xlabel('Epochs')\n",
    "    ax[1].set_ylabel('Loss', color='black')\n",
    "    for metric in metrics:\n",
    "        ax22.plot(training.history['val_'+metric], label=metric)\n",
    "    ax22.set_ylabel(\"Score\", color=\"steelblue\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train\n",
    "training = model.fit(x=[train[\"user\"], train[\"product\"]], y=train[\"y\"], \n",
    "                     epochs=100, batch_size=128, shuffle=True, verbose=0, validation_split=0.3)\n",
    "model = training.model\n",
    "utils_plot_keras_training(training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>product</th>\n",
       "      <th>y</th>\n",
       "      <th>yhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8063</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.565522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>8305</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.453738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>8376</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.316190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>8466</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.489369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>8509</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.456186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>64</td>\n",
       "      <td>8023</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.269087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>64</td>\n",
       "      <td>8376</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.555192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>64</td>\n",
       "      <td>8438</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.299151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>64</td>\n",
       "      <td>8569</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.536587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>64</td>\n",
       "      <td>8691</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.655016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>671 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user  product         y      yhat\n",
       "0       1     8063  0.812500  0.565522\n",
       "1       1     8305  1.000000  0.453738\n",
       "2       1     8376  0.777778  0.316190\n",
       "3       1     8466  0.800000  0.489369\n",
       "4       1     8509  0.500000  0.456186\n",
       "..    ...      ...       ...       ...\n",
       "666    64     8023  0.500000  0.269087\n",
       "667    64     8376  0.944444  0.555192\n",
       "668    64     8438  0.666667  0.299151\n",
       "669    64     8569  0.900000  0.536587\n",
       "670    64     8691  0.777778  0.655016\n",
       "\n",
       "[671 rows x 4 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test\n",
    "test[\"yhat\"] = model.predict([test[\"user\"], test[\"product\"]])\n",
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (Σ|y-pred|/n): 0\n",
      "Mean Absolute Perc Error (Σ(|y-pred|/y)/n): 0.66%\n"
     ]
    }
   ],
   "source": [
    "# overall\n",
    "y_test = test[\"y\"]\n",
    "predicted = test[\"yhat\"]\n",
    "\n",
    "print(\"Mean Absolute Error (Σ|y-pred|/n):\", \"{:,.0f}\".format(metrics.mean_absolute_error(y_test, predicted)))\n",
    "print(\"Mean Absolute Perc Error (Σ(|y-pred|/y)/n):\", str(round(np.mean(np.abs((y_test-predicted)/predicted)), 2))+\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- user 1 ---\n",
      "y_test: [8305 8681 8550 8063 8466]\n",
      "predicted: [8828 8063 8466 8509 8305]\n",
      "true positive: 3 (60.0%)\n",
      "accuracy: 0.0%\n",
      "mrr: 0.21\n"
     ]
    }
   ],
   "source": [
    "# single case\n",
    "i = 1\n",
    "print(\"--- user\", i, \"---\")\n",
    "\n",
    "top = 5\n",
    "y_test = test[test[\"user\"]==i].sort_values(\"y\", ascending=False)[\"product\"].values[:top]\n",
    "print(\"y_test:\", y_test)\n",
    "\n",
    "predicted = test[test[\"user\"]==i].sort_values(\"yhat\", ascending=False)[\"product\"].values[:top]\n",
    "print(\"predicted:\", predicted)\n",
    "\n",
    "true_positive = len(list(set(y_test) & set(predicted)))\n",
    "print(\"true positive:\", true_positive, \"(\"+str(round(true_positive/top*100,1))+\"%)\")\n",
    "print(\"accuracy:\", str(round(metrics.accuracy_score(y_test,predicted)*100,1))+\"%\")\n",
    "print(\"mrr:\", round(mean_reciprocal_rank(y_test, predicted),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>user</th>\n",
       "      <th>product</th>\n",
       "      <th>y</th>\n",
       "      <th>yhat</th>\n",
       "      <th>name</th>\n",
       "      <th>old</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>8828</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.603382</td>\n",
       "      <td>The Jinx: The Life and Deaths of Robert Durst</td>\n",
       "      <td>0</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8063</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.565522</td>\n",
       "      <td>Django Unchained</td>\n",
       "      <td>0</td>\n",
       "      <td>Action|Drama|Western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>8466</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.489369</td>\n",
       "      <td>Whiplash</td>\n",
       "      <td>0</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>8509</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.456186</td>\n",
       "      <td>The Drop</td>\n",
       "      <td>0</td>\n",
       "      <td>Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>8305</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.453738</td>\n",
       "      <td>Wolf of Wall Street, The</td>\n",
       "      <td>0</td>\n",
       "      <td>Comedy|Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>8681</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.442616</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>0</td>\n",
       "      <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>8550</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.423473</td>\n",
       "      <td>Ex Machina</td>\n",
       "      <td>0</td>\n",
       "      <td>Drama|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>8376</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.316190</td>\n",
       "      <td>Interstellar</td>\n",
       "      <td>0</td>\n",
       "      <td>Sci-Fi|IMAX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user  product         y      yhat  \\\n",
       "7     1     8828  0.500000  0.603382   \n",
       "0     1     8063  0.812500  0.565522   \n",
       "3     1     8466  0.800000  0.489369   \n",
       "4     1     8509  0.500000  0.456186   \n",
       "1     1     8305  1.000000  0.453738   \n",
       "6     1     8681  1.000000  0.442616   \n",
       "5     1     8550  0.875000  0.423473   \n",
       "2     1     8376  0.777778  0.316190   \n",
       "\n",
       "                                            name  old  \\\n",
       "7  The Jinx: The Life and Deaths of Robert Durst    0   \n",
       "0                               Django Unchained    0   \n",
       "3                                       Whiplash    0   \n",
       "4                                       The Drop    0   \n",
       "1                       Wolf of Wall Street, The    0   \n",
       "6                             Mad Max: Fury Road    0   \n",
       "5                                     Ex Machina    0   \n",
       "2                                   Interstellar    0   \n",
       "\n",
       "                             genres  \n",
       "7                       Documentary  \n",
       "0              Action|Drama|Western  \n",
       "3                             Drama  \n",
       "4              Crime|Drama|Thriller  \n",
       "1                Comedy|Crime|Drama  \n",
       "6  Action|Adventure|Sci-Fi|Thriller  \n",
       "5             Drama|Sci-Fi|Thriller  \n",
       "2                       Sci-Fi|IMAX  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[test[\"user\"]==i].merge(\n",
    "        dtf_products[[\"name\",\"old\",\"genres\"]], left_on=\"product\", right_index=True\n",
    "    ).sort_values(\"yhat\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5 - Model Desing & Testing (Neural Collaborative Filtering)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>user</th>\n",
       "      <th>product</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user  product     y\n",
       "0     0        0  0.80\n",
       "1     0        2  0.75\n",
       "2     0        5  0.75\n",
       "3     0       43  1.00\n",
       "4     0       46  1.00"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = dtf_train.stack(dropna=True).reset_index().rename(columns={0:\"y\"})\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>user</th>\n",
       "      <th>product</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8063</td>\n",
       "      <td>0.812500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>8305</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>8376</td>\n",
       "      <td>0.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>8466</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>8509</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user  product         y\n",
       "0     1     8063  0.812500\n",
       "1     1     8305  1.000000\n",
       "2     1     8376  0.777778\n",
       "3     1     8466  0.800000\n",
       "4     1     8509  0.500000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = dtf_test.stack(dropna=True).reset_index().rename(columns={0:\"y\"})\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Model Design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"Neural_CollaborativeFiltering\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "xusers_in (InputLayer)          [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "xproducts_in (InputLayer)       [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "nn_xusers_emb (Embedding)       (None, 1, 50)        3300        xusers_in[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "nn_xproducts_emb (Embedding)    (None, 1, 50)        487050      xproducts_in[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "cf_xusers_emb (Embedding)       (None, 1, 50)        3300        xusers_in[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "cf_xproducts_emb (Embedding)    (None, 1, 50)        487050      xproducts_in[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "nn_xusers (Reshape)             (None, 50)           0           nn_xusers_emb[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "nn_xproducts (Reshape)          (None, 50)           0           nn_xproducts_emb[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "cf_xusers (Reshape)             (None, 50)           0           cf_xusers_emb[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "cf_xproducts (Reshape)          (None, 50)           0           cf_xproducts_emb[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 100)          0           nn_xusers[0][0]                  \n",
      "                                                                 nn_xproducts[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "cf_xx (Dot)                     (None, 1)            0           cf_xusers[0][0]                  \n",
      "                                                                 cf_xproducts[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "nn_xx (Dense)                   (None, 25)           2525        concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 26)           0           cf_xx[0][0]                      \n",
      "                                                                 nn_xx[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "y_out (Dense)                   (None, 1)            27          concatenate_1[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 983,252\n",
      "Trainable params: 983,252\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "embeddings_size = 50\n",
    "usr, prd = dtf_users.shape[0], dtf_users.shape[1]\n",
    "\n",
    "# Input layer\n",
    "xusers_in = layers.Input(name=\"xusers_in\", shape=(1,))\n",
    "xproducts_in = layers.Input(name=\"xproducts_in\", shape=(1,))\n",
    "\n",
    "# A) Matrix Factorization\n",
    "## embeddings and reshape\n",
    "cf_xusers_emb = layers.Embedding(name=\"cf_xusers_emb\", input_dim=usr, output_dim=embeddings_size)(xusers_in)\n",
    "cf_xusers = layers.Reshape(name='cf_xusers', target_shape=(embeddings_size,))(cf_xusers_emb)\n",
    "## embeddings and reshape\n",
    "cf_xproducts_emb = layers.Embedding(name=\"cf_xproducts_emb\", input_dim=prd, output_dim=embeddings_size)(xproducts_in)\n",
    "cf_xproducts = layers.Reshape(name='cf_xproducts', target_shape=(embeddings_size,))(cf_xproducts_emb)\n",
    "## product\n",
    "cf_xx = layers.Dot(name='cf_xx', normalize=True, axes=1)([cf_xusers, cf_xproducts])\n",
    "\n",
    "# B) Neural Network\n",
    "## embeddings and reshape\n",
    "nn_xusers_emb = layers.Embedding(name=\"nn_xusers_emb\", input_dim=usr, output_dim=embeddings_size)(xusers_in)\n",
    "nn_xusers = layers.Reshape(name='nn_xusers', target_shape=(embeddings_size,))(nn_xusers_emb)\n",
    "## embeddings and reshape\n",
    "nn_xproducts_emb = layers.Embedding(name=\"nn_xproducts_emb\", input_dim=prd, output_dim=embeddings_size)(xproducts_in)\n",
    "nn_xproducts = layers.Reshape(name='nn_xproducts', target_shape=(embeddings_size,))(nn_xproducts_emb)\n",
    "## concat and dense\n",
    "nn_xx = layers.Concatenate()([nn_xusers, nn_xproducts])\n",
    "nn_xx = layers.Dense(name=\"nn_xx\", units=int(embeddings_size/2), activation='relu')(nn_xx)\n",
    "\n",
    "# Merge A & B\n",
    "y_out = layers.Concatenate()([cf_xx, nn_xx])\n",
    "y_out = layers.Dense(name=\"y_out\", units=1, activation='linear')(y_out)\n",
    "\n",
    "# Compile\n",
    "model = models.Model(inputs=[xusers_in,xproducts_in], outputs=y_out, name=\"Neural_CollaborativeFiltering\")\n",
    "model.compile(optimizer='adam', loss='mean_absolute_error', metrics=['mean_absolute_percentage_error'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Train / Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train\n",
    "training = model.fit(x=[train[\"user\"], train[\"product\"]], y=train[\"y\"], \n",
    "                     epochs=100, batch_size=128, shuffle=True, verbose=0, validation_split=0.3)\n",
    "model = training.model\n",
    "utils_plot_keras_training(training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>user</th>\n",
       "      <th>product</th>\n",
       "      <th>y</th>\n",
       "      <th>yhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8063</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.694346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>8305</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.717424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>8376</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.665485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>8466</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.725228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>8509</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.715174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>64</td>\n",
       "      <td>8023</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.640784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>64</td>\n",
       "      <td>8376</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.476182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>64</td>\n",
       "      <td>8438</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.570333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>64</td>\n",
       "      <td>8569</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.558572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>64</td>\n",
       "      <td>8691</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.629250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>671 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user  product         y      yhat\n",
       "0       1     8063  0.812500  0.694346\n",
       "1       1     8305  1.000000  0.717424\n",
       "2       1     8376  0.777778  0.665485\n",
       "3       1     8466  0.800000  0.725228\n",
       "4       1     8509  0.500000  0.715174\n",
       "..    ...      ...       ...       ...\n",
       "666    64     8023  0.500000  0.640784\n",
       "667    64     8376  0.944444  0.476182\n",
       "668    64     8438  0.666667  0.570333\n",
       "669    64     8569  0.900000  0.558572\n",
       "670    64     8691  0.777778  0.629250\n",
       "\n",
       "[671 rows x 4 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test\n",
    "test[\"yhat\"] = model.predict([test[\"user\"], test[\"product\"]])\n",
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (Σ|y-pred|/n): 0\n",
      "Mean Absolute Perc Error (Σ(|y-pred|/y)/n): 0.3%\n"
     ]
    }
   ],
   "source": [
    "# overall\n",
    "y_test = test[\"y\"]\n",
    "predicted = test[\"yhat\"]\n",
    "\n",
    "print(\"Mean Absolute Error (Σ|y-pred|/n):\", \"{:,.0f}\".format(metrics.mean_absolute_error(y_test, predicted)))\n",
    "print(\"Mean Absolute Perc Error (Σ(|y-pred|/y)/n):\", str(round(np.mean(np.abs((y_test-predicted)/predicted)), 2))+\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- user 1 ---\n",
      "y_test: [8305 8681 8550 8063 8466]\n",
      "predicted: [8828 8681 8550 8466 8305]\n",
      "true positive: 4 (80.0%)\n",
      "accuracy: 40.0%\n",
      "mrr: 0.26\n"
     ]
    }
   ],
   "source": [
    "# single case\n",
    "i = 1\n",
    "print(\"--- user\", i, \"---\")\n",
    "\n",
    "top = 5\n",
    "y_test = test[test[\"user\"]==i].sort_values(\"y\", ascending=False)[\"product\"].values[:top]\n",
    "print(\"y_test:\", y_test)\n",
    "\n",
    "predicted = test[test[\"user\"]==i].sort_values(\"yhat\", ascending=False)[\"product\"].values[:top]\n",
    "print(\"predicted:\", predicted)\n",
    "\n",
    "true_positive = len(list(set(y_test) & set(predicted)))\n",
    "print(\"true positive:\", true_positive, \"(\"+str(round(true_positive/top*100,1))+\"%)\")\n",
    "print(\"accuracy:\", str(round(metrics.accuracy_score(y_test,predicted)*100,1))+\"%\")\n",
    "print(\"mrr:\", round(mean_reciprocal_rank(y_test, predicted),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>user</th>\n",
       "      <th>product</th>\n",
       "      <th>y</th>\n",
       "      <th>yhat</th>\n",
       "      <th>name</th>\n",
       "      <th>old</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>8828</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.852103</td>\n",
       "      <td>The Jinx: The Life and Deaths of Robert Durst</td>\n",
       "      <td>0</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>8681</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.792930</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>0</td>\n",
       "      <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>8550</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.728708</td>\n",
       "      <td>Ex Machina</td>\n",
       "      <td>0</td>\n",
       "      <td>Drama|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>8466</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.725228</td>\n",
       "      <td>Whiplash</td>\n",
       "      <td>0</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>8305</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.717424</td>\n",
       "      <td>Wolf of Wall Street, The</td>\n",
       "      <td>0</td>\n",
       "      <td>Comedy|Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>8509</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.715174</td>\n",
       "      <td>The Drop</td>\n",
       "      <td>0</td>\n",
       "      <td>Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8063</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.694346</td>\n",
       "      <td>Django Unchained</td>\n",
       "      <td>0</td>\n",
       "      <td>Action|Drama|Western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>8376</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.665485</td>\n",
       "      <td>Interstellar</td>\n",
       "      <td>0</td>\n",
       "      <td>Sci-Fi|IMAX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user  product         y      yhat  \\\n",
       "7     1     8828  0.500000  0.852103   \n",
       "6     1     8681  1.000000  0.792930   \n",
       "5     1     8550  0.875000  0.728708   \n",
       "3     1     8466  0.800000  0.725228   \n",
       "1     1     8305  1.000000  0.717424   \n",
       "4     1     8509  0.500000  0.715174   \n",
       "0     1     8063  0.812500  0.694346   \n",
       "2     1     8376  0.777778  0.665485   \n",
       "\n",
       "                                            name  old  \\\n",
       "7  The Jinx: The Life and Deaths of Robert Durst    0   \n",
       "6                             Mad Max: Fury Road    0   \n",
       "5                                     Ex Machina    0   \n",
       "3                                       Whiplash    0   \n",
       "1                       Wolf of Wall Street, The    0   \n",
       "4                                       The Drop    0   \n",
       "0                               Django Unchained    0   \n",
       "2                                   Interstellar    0   \n",
       "\n",
       "                             genres  \n",
       "7                       Documentary  \n",
       "6  Action|Adventure|Sci-Fi|Thriller  \n",
       "5             Drama|Sci-Fi|Thriller  \n",
       "3                             Drama  \n",
       "1                Comedy|Crime|Drama  \n",
       "4              Crime|Drama|Thriller  \n",
       "0              Action|Drama|Western  \n",
       "2                       Sci-Fi|IMAX  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[test[\"user\"]==i].merge(\n",
    "        dtf_products[[\"name\",\"old\",\"genres\"]], left_on=\"product\", right_index=True\n",
    "    ).sort_values(\"yhat\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6 - Hybrid Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['old', 'Mystery', 'Children', 'Comedy', 'Adventure', 'Thriller',\n",
      "       'Drama', 'Horror', 'Action', 'IMAX', 'Fantasy', 'Western', 'War',\n",
      "       'Sci-Fi', 'Film-Noir', 'Romance', 'Animation', 'Crime', 'Musical',\n",
      "       'Documentary'],\n",
      "      dtype='object')\n",
      "Index(['daytime', 'weekend'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Extra data\n",
    "features = dtf_products.drop([\"genres\",\"name\"], axis=1).columns\n",
    "print(features)\n",
    "\n",
    "context = dtf_context.drop([\"user\",\"product\"], axis=1).columns\n",
    "print(context)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>product</th>\n",
       "      <th>y</th>\n",
       "      <th>old</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Children</th>\n",
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       "      <th>Drama</th>\n",
       "      <th>...</th>\n",
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       "      <th>Film-Noir</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Documentary</th>\n",
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       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1</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>...</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.75</td>\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>...</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</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>...</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>4</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</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>...</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   user  product     y  old  Mystery  Children  Comedy  Adventure  Thriller  \\\n",
       "0     0        0  0.80    1        0         1       1          1         0   \n",
       "1     0        2  0.75    1        0         0       1          0         0   \n",
       "2     0        5  0.75    1        0         0       0          0         1   \n",
       "3     0       43  1.00    1        1         0       0          0         1   \n",
       "4     0       46  1.00    1        1         0       0          0         1   \n",
       "\n",
       "   Drama  ...  War  Sci-Fi  Film-Noir  Romance  Animation  Crime  Musical  \\\n",
       "0      0  ...    0       0          0        0          1      0        0   \n",
       "1      0  ...    0       0          0        1          0      0        0   \n",
       "2      0  ...    0       0          0        0          0      1        0   \n",
       "3      0  ...    0       0          0        0          0      0        0   \n",
       "4      0  ...    0       0          0        0          0      1        0   \n",
       "\n",
       "   Documentary  daytime  weekend  \n",
       "0            0        0        1  \n",
       "1            0        0        1  \n",
       "2            0        0        1  \n",
       "3            0        0        1  \n",
       "4            0        0        1  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = dtf_train.stack(dropna=True).reset_index().rename(columns={0:\"y\"})\n",
    "\n",
    "## add features\n",
    "train = train.merge(dtf_products[features], how=\"left\", left_on=\"product\", right_index=True)\n",
    "\n",
    "## add context\n",
    "train = train.merge(dtf_context, how=\"left\")\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>user</th>\n",
       "      <th>product</th>\n",
       "      <th>y</th>\n",
       "      <th>old</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Children</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>Drama</th>\n",
       "      <th>...</th>\n",
       "      <th>War</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>daytime</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8063</td>\n",
       "      <td>0.812500</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>...</td>\n",
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       "      <td>0</td>\n",
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       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>8305</td>\n",
       "      <td>1.000000</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>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>8376</td>\n",
       "      <td>0.777778</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>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>8466</td>\n",
       "      <td>0.800000</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>...</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>8509</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   user  product         y  old  Mystery  Children  Comedy  Adventure  \\\n",
       "0     1     8063  0.812500    0        0         0       0          0   \n",
       "1     1     8305  1.000000    0        0         0       1          0   \n",
       "2     1     8376  0.777778    0        0         0       0          0   \n",
       "3     1     8466  0.800000    0        0         0       0          0   \n",
       "4     1     8509  0.500000    0        0         0       0          0   \n",
       "\n",
       "   Thriller  Drama  ...  War  Sci-Fi  Film-Noir  Romance  Animation  Crime  \\\n",
       "0         0      1  ...    0       0          0        0          0      0   \n",
       "1         0      1  ...    0       0          0        0          0      1   \n",
       "2         0      0  ...    0       1          0        0          0      0   \n",
       "3         0      1  ...    0       0          0        0          0      0   \n",
       "4         1      1  ...    0       0          0        0          0      1   \n",
       "\n",
       "   Musical  Documentary  daytime  weekend  \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",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = dtf_test.stack(dropna=True).reset_index().rename(columns={0:\"y\"})\n",
    "\n",
    "## add features\n",
    "test = test.merge(dtf_products.drop([\"genres\",\"name\"], axis=1), how=\"left\", left_on=\"product\", right_index=True)\n",
    "\n",
    "## add context\n",
    "test[context] = 0 #--> simulate production for a weekday night\n",
    "#test = test.merge(dtf_context, how=\"left\") #--> use test set\n",
    "\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Model Design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"Hybrid_Model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "xusers_in (InputLayer)          [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "xproducts_in (InputLayer)       [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "nn_xusers_emb (Embedding)       (None, 1, 50)        3300        xusers_in[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "nn_xproducts_emb (Embedding)    (None, 1, 50)        487050      xproducts_in[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "cf_xusers_emb (Embedding)       (None, 1, 50)        3300        xusers_in[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "cf_xproducts_emb (Embedding)    (None, 1, 50)        487050      xproducts_in[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "nn_xusers (Reshape)             (None, 50)           0           nn_xusers_emb[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "nn_xproducts (Reshape)          (None, 50)           0           nn_xproducts_emb[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "cf_xusers (Reshape)             (None, 50)           0           cf_xusers_emb[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "cf_xproducts (Reshape)          (None, 50)           0           cf_xproducts_emb[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 100)          0           nn_xusers[0][0]                  \n",
      "                                                                 nn_xproducts[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "features_in (InputLayer)        [(None, 20)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "contexts_in (InputLayer)        [(None, 2)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "cf_xx (Dot)                     (None, 1)            0           cf_xusers[0][0]                  \n",
      "                                                                 cf_xproducts[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "nn_xx (Dense)                   (None, 25)           2525        concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "features_x (Dense)              (None, 20)           420         features_in[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "context_x (Dense)               (None, 2)            6           contexts_in[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_5 (Concatenate)     (None, 48)           0           cf_xx[0][0]                      \n",
      "                                                                 nn_xx[0][0]                      \n",
      "                                                                 features_x[0][0]                 \n",
      "                                                                 context_x[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "y_out (Dense)                   (None, 1)            49          concatenate_5[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 983,700\n",
      "Trainable params: 983,700\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "embeddings_size = 50\n",
    "usr, prd = dtf_users.shape[0], dtf_users.shape[1]\n",
    "feat = len(features)\n",
    "ctx = len(context)\n",
    "\n",
    "########################################## COLLABORATIVE FILTERING ###################################################\n",
    "# Input layer\n",
    "xusers_in = layers.Input(name=\"xusers_in\", shape=(1,))\n",
    "xproducts_in = layers.Input(name=\"xproducts_in\", shape=(1,))\n",
    "\n",
    "# A) Matrix Factorization\n",
    "## embeddings and reshape\n",
    "cf_xusers_emb = layers.Embedding(name=\"cf_xusers_emb\", input_dim=usr, output_dim=embeddings_size)(xusers_in)\n",
    "cf_xusers = layers.Reshape(name='cf_xusers', target_shape=(embeddings_size,))(cf_xusers_emb)\n",
    "## embeddings and reshape\n",
    "cf_xproducts_emb = layers.Embedding(name=\"cf_xproducts_emb\", input_dim=prd, output_dim=embeddings_size)(xproducts_in)\n",
    "cf_xproducts = layers.Reshape(name='cf_xproducts', target_shape=(embeddings_size,))(cf_xproducts_emb)\n",
    "## product\n",
    "cf_xx = layers.Dot(name='cf_xx', normalize=True, axes=1)([cf_xusers, cf_xproducts])\n",
    "\n",
    "# B) Neural Network\n",
    "## embeddings and reshape\n",
    "nn_xusers_emb = layers.Embedding(name=\"nn_xusers_emb\", input_dim=usr, output_dim=embeddings_size)(xusers_in)\n",
    "nn_xusers = layers.Reshape(name='nn_xusers', target_shape=(embeddings_size,))(nn_xusers_emb)\n",
    "## embeddings and reshape\n",
    "nn_xproducts_emb = layers.Embedding(name=\"nn_xproducts_emb\", input_dim=prd, output_dim=embeddings_size)(xproducts_in)\n",
    "nn_xproducts = layers.Reshape(name='nn_xproducts', target_shape=(embeddings_size,))(nn_xproducts_emb)\n",
    "## concat and dense\n",
    "nn_xx = layers.Concatenate()([nn_xusers, nn_xproducts])\n",
    "nn_xx = layers.Dense(name=\"nn_xx\", units=int(embeddings_size/2), activation='relu')(nn_xx)\n",
    "\n",
    "########################################## CONTENT BASED ###################################################\n",
    "# Product Features\n",
    "features_in = layers.Input(name=\"features_in\", shape=(feat,))\n",
    "features_x = layers.Dense(name=\"features_x\", units=feat, activation='relu')(features_in)\n",
    "\n",
    "########################################## KNOWLEDGE BASED #################################################\n",
    "# Context\n",
    "contexts_in = layers.Input(name=\"contexts_in\", shape=(ctx,))\n",
    "context_x = layers.Dense(name=\"context_x\", units=ctx, activation='relu')(contexts_in)\n",
    "\n",
    "########################################## OUTPUT ###################################################\n",
    "# Merge all\n",
    "y_out = layers.Concatenate()([cf_xx, nn_xx, features_x, context_x])\n",
    "y_out = layers.Dense(name=\"y_out\", units=1, activation='linear')(y_out)\n",
    "\n",
    "# Compile\n",
    "model = models.Model(inputs=[xusers_in,xproducts_in, features_in, contexts_in], outputs=y_out, name=\"Hybrid_Model\")\n",
    "model.compile(optimizer='adam', loss='mean_absolute_error', metrics=['mean_absolute_percentage_error'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Train / Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train\n",
    "training = model.fit(x=[train[\"user\"], train[\"product\"], train[features], train[context]], y=train[\"y\"], \n",
    "                     epochs=100, batch_size=128, shuffle=True, verbose=0, validation_split=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = training.model\n",
    "utils_plot_keras_training(training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>user</th>\n",
       "      <th>product</th>\n",
       "      <th>y</th>\n",
       "      <th>old</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Children</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>Drama</th>\n",
       "      <th>...</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>daytime</th>\n",
       "      <th>weekend</th>\n",
       "      <th>yhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8063</td>\n",
       "      <td>0.812500</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>...</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>0.721495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>8305</td>\n",
       "      <td>1.000000</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>1</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>0.630752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>8376</td>\n",
       "      <td>0.777778</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.787670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>8466</td>\n",
       "      <td>0.800000</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>...</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>0.488056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>8509</td>\n",
       "      <td>0.500000</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>1</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>0.608305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>666</th>\n",
       "      <td>64</td>\n",
       "      <td>8023</td>\n",
       "      <td>0.500000</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>1</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>0.584866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>64</td>\n",
       "      <td>8376</td>\n",
       "      <td>0.944444</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.654225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>64</td>\n",
       "      <td>8438</td>\n",
       "      <td>0.666667</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>64</td>\n",
       "      <td>8569</td>\n",
       "      <td>0.900000</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>1</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>0.505993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>64</td>\n",
       "      <td>8691</td>\n",
       "      <td>0.777778</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.637153</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>671 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user  product         y  old  Mystery  Children  Comedy  Adventure  \\\n",
       "0       1     8063  0.812500    0        0         0       0          0   \n",
       "1       1     8305  1.000000    0        0         0       1          0   \n",
       "2       1     8376  0.777778    0        0         0       0          0   \n",
       "3       1     8466  0.800000    0        0         0       0          0   \n",
       "4       1     8509  0.500000    0        0         0       0          0   \n",
       "..    ...      ...       ...  ...      ...       ...     ...        ...   \n",
       "666    64     8023  0.500000    0        0         0       1          0   \n",
       "667    64     8376  0.944444    0        0         0       0          0   \n",
       "668    64     8438  0.666667    0        0         0       0          0   \n",
       "669    64     8569  0.900000    0        0         0       0          0   \n",
       "670    64     8691  0.777778    0        0         0       1          1   \n",
       "\n",
       "     Thriller  Drama  ...  Sci-Fi  Film-Noir  Romance  Animation  Crime  \\\n",
       "0           0      1  ...       0          0        0          0      0   \n",
       "1           0      1  ...       0          0        0          0      1   \n",
       "2           0      0  ...       1          0        0          0      0   \n",
       "3           0      1  ...       0          0        0          0      0   \n",
       "4           1      1  ...       0          0        0          0      1   \n",
       "..        ...    ...  ...     ...        ...      ...        ...    ...   \n",
       "666         0      1  ...       0          0        0          0      0   \n",
       "667         0      0  ...       1          0        0          0      0   \n",
       "668         0      0  ...       1          0        0          0      0   \n",
       "669         1      1  ...       0          0        0          0      0   \n",
       "670         0      0  ...       1          0        0          0      0   \n",
       "\n",
       "     Musical  Documentary  daytime  weekend      yhat  \n",
       "0          0            0        0        0  0.721495  \n",
       "1          0            0        0        0  0.630752  \n",
       "2          0            0        0        0  0.787670  \n",
       "3          0            0        0        0  0.488056  \n",
       "4          0            0        0        0  0.608305  \n",
       "..       ...          ...      ...      ...       ...  \n",
       "666        0            0        0        0  0.584866  \n",
       "667        0            0        0        0  0.654225  \n",
       "668        0            0        0        0  0.573649  \n",
       "669        0            0        0        0  0.505993  \n",
       "670        0            0        0        0  0.637153  \n",
       "\n",
       "[671 rows x 26 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test\n",
    "test[\"yhat\"] = model.predict([test[\"user\"], test[\"product\"], test[features], test[context]])\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (Σ|y-pred|/n): 0\n",
      "Mean Absolute Perc Error (Σ(|y-pred|/y)/n): 0.3%\n"
     ]
    }
   ],
   "source": [
    "# overall\n",
    "y_test = test[\"y\"]\n",
    "predicted = test[\"yhat\"]\n",
    "\n",
    "print(\"Mean Absolute Error (Σ|y-pred|/n):\", \"{:,.0f}\".format(metrics.mean_absolute_error(y_test, predicted)))\n",
    "print(\"Mean Absolute Perc Error (Σ(|y-pred|/y)/n):\", str(round(np.mean(np.abs((y_test-predicted)/predicted)), 2))+\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- user 1 ---\n",
      "y_test: [8305 8681 8550 8063 8466]\n",
      "predicted: [8376 8681 8550 8063 8305]\n",
      "true positive: 4 (80.0%)\n",
      "accuracy: 60.0%\n",
      "mrr: 0.26\n"
     ]
    }
   ],
   "source": [
    "# single case\n",
    "i = 1\n",
    "print(\"--- user\", i, \"---\")\n",
    "\n",
    "top = 5\n",
    "y_test = test[test[\"user\"]==i].sort_values(\"y\", ascending=False)[\"product\"].values[:top]\n",
    "print(\"y_test:\", y_test)\n",
    "\n",
    "predicted = test[test[\"user\"]==i].sort_values(\"yhat\", ascending=False)[\"product\"].values[:top]\n",
    "print(\"predicted:\", predicted)\n",
    "\n",
    "true_positive = len(list(set(y_test) & set(predicted)))\n",
    "print(\"true positive:\", true_positive, \"(\"+str(round(true_positive/top*100,1))+\"%)\")\n",
    "print(\"accuracy:\", str(round(metrics.accuracy_score(y_test,predicted)*100,1))+\"%\")\n",
    "print(\"mrr:\", round(mean_reciprocal_rank(y_test, predicted),2))"
   ]
  }
 ],
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