{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple example of building recommendations with RecTools\n",
    "\n",
    "\n",
    "- Building simple model\n",
    "- Visual recommendations checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from implicit.nearest_neighbours import TFIDFRecommender\n",
    "\n",
    "from rectools import Columns\n",
    "from rectools.dataset import Dataset\n",
    "from rectools.models import ImplicitItemKNNWrapperModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  ml-1m.zip\n",
      "  inflating: ml-1m/movies.dat        \n",
      "  inflating: ml-1m/ratings.dat       \n",
      "  inflating: ml-1m/README            \n",
      "  inflating: ml-1m/users.dat         \n",
      "CPU times: user 134 ms, sys: 415 ms, total: 548 ms\n",
      "Wall time: 4.39 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "!wget -q https://files.grouplens.org/datasets/movielens/ml-1m.zip -O ml-1m.zip\n",
    "!unzip -o ml-1m.zip\n",
    "!rm ml-1m.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000209, 4)\n",
      "CPU times: user 5.76 s, sys: 409 ms, total: 6.17 s\n",
      "Wall time: 6.16 s\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>weight</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>661</td>\n",
       "      <td>3</td>\n",
       "      <td>978302109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>978301968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3408</td>\n",
       "      <td>4</td>\n",
       "      <td>978300275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>978824291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  item_id  weight   datetime\n",
       "0        1     1193       5  978300760\n",
       "1        1      661       3  978302109\n",
       "2        1      914       3  978301968\n",
       "3        1     3408       4  978300275\n",
       "4        1     2355       5  978824291"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "ratings = pd.read_csv(\n",
    "    \"ml-1m/ratings.dat\", \n",
    "    sep=\"::\",\n",
    "    engine=\"python\",  # Because of 2-chars separators\n",
    "    header=None,\n",
    "    names=[Columns.User, Columns.Item, Columns.Weight, Columns.Datetime],\n",
    ")\n",
    "print(ratings.shape)\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3883, 3)\n",
      "CPU times: user 9.55 ms, sys: 1.62 ms, total: 11.2 ms\n",
      "Wall time: 10.4 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_id                               title                        genres\n",
       "0        1                    Toy Story (1995)   Animation|Children's|Comedy\n",
       "1        2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2        3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3        4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4        5  Father of the Bride Part II (1995)                        Comedy"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "movies = pd.read_csv(\n",
    "    \"ml-1m/movies.dat\", \n",
    "    sep=\"::\",\n",
    "    engine=\"python\",  # Because of 2-chars separators\n",
    "    header=None,\n",
    "    names=[Columns.Item, \"title\", \"genres\"],\n",
    "    encoding_errors=\"ignore\",\n",
    ")\n",
    "print(movies.shape)\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare a dataset to build a model\n",
    "dataset = Dataset.construct(ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.05 s, sys: 274 ms, total: 6.32 s\n",
      "Wall time: 1.42 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Fit model and generate recommendations for all users\n",
    "model = ImplicitItemKNNWrapperModel(TFIDFRecommender(K=10))\n",
    "model.fit(dataset)\n",
    "recos = model.recommend(\n",
    "    users=ratings[Columns.User].unique(),\n",
    "    dataset=dataset,\n",
    "    k=10,\n",
    "    filter_viewed=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>score</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>364</td>\n",
       "      <td>20.436578</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1196</td>\n",
       "      <td>15.716834</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>318</td>\n",
       "      <td>15.625371</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2096</td>\n",
       "      <td>14.876911</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2571</td>\n",
       "      <td>12.718620</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  item_id      score  rank\n",
       "0        1      364  20.436578     1\n",
       "1        1     1196  15.716834     2\n",
       "2        1      318  15.625371     3\n",
       "3        1     2096  14.876911     4\n",
       "4        1     2571  12.718620     5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sample of recommendations - it's sorted by relevance (= rank) for each user\n",
    "recos.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select random user, see history of views and reco for this user\n",
    "user_id = 3883\n",
    "user_viewed = ratings.query(\"user_id == @user_id\").merge(movies, on=\"item_id\")\n",
    "user_recos = recos.query(\"user_id == @user_id\").merge(movies, on=\"item_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2997</td>\n",
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       "      <td>967134212</td>\n",
       "      <td>Being John Malkovich (1999)</td>\n",
       "      <td>Comedy</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3883</td>\n",
       "      <td>1265</td>\n",
       "      <td>5</td>\n",
       "      <td>967134285</td>\n",
       "      <td>Groundhog Day (1993)</td>\n",
       "      <td>Comedy|Romance</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3883</td>\n",
       "      <td>2858</td>\n",
       "      <td>5</td>\n",
       "      <td>965822230</td>\n",
       "      <td>American Beauty (1999)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3883</td>\n",
       "      <td>2369</td>\n",
       "      <td>4</td>\n",
       "      <td>965822136</td>\n",
       "      <td>Desperately Seeking Susan (1985)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3883</td>\n",
       "      <td>3189</td>\n",
       "      <td>4</td>\n",
       "      <td>965822296</td>\n",
       "      <td>My Dog Skip (1999)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3883</td>\n",
       "      <td>1784</td>\n",
       "      <td>4</td>\n",
       "      <td>965822136</td>\n",
       "      <td>As Good As It Gets (1997)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3883</td>\n",
       "      <td>2599</td>\n",
       "      <td>4</td>\n",
       "      <td>967134250</td>\n",
       "      <td>Election (1999)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3883</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>967134285</td>\n",
       "      <td>Babe (1995)</td>\n",
       "      <td>Children's|Comedy|Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  item_id  weight   datetime                             title  \\\n",
       "0      3883     2997       5  967134212       Being John Malkovich (1999)   \n",
       "2      3883     1265       5  967134285              Groundhog Day (1993)   \n",
       "4      3883     2858       5  965822230            American Beauty (1999)   \n",
       "10     3883     2369       4  965822136  Desperately Seeking Susan (1985)   \n",
       "14     3883     3189       4  965822296                My Dog Skip (1999)   \n",
       "16     3883     1784       4  965822136         As Good As It Gets (1997)   \n",
       "17     3883     2599       4  967134250                   Election (1999)   \n",
       "18     3883       34       4  967134285                       Babe (1995)   \n",
       "\n",
       "                     genres  \n",
       "0                    Comedy  \n",
       "2            Comedy|Romance  \n",
       "4              Comedy|Drama  \n",
       "10           Comedy|Romance  \n",
       "14                   Comedy  \n",
       "16             Comedy|Drama  \n",
       "17                   Comedy  \n",
       "18  Children's|Comedy|Drama  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# History, but only films that user likes\n",
    "user_viewed.query(\"weight > 3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3883</td>\n",
       "      <td>2396</td>\n",
       "      <td>13.991358</td>\n",
       "      <td>1</td>\n",
       "      <td>Shakespeare in Love (1998)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3883</td>\n",
       "      <td>2762</td>\n",
       "      <td>10.249648</td>\n",
       "      <td>2</td>\n",
       "      <td>Sixth Sense, The (1999)</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3883</td>\n",
       "      <td>318</td>\n",
       "      <td>7.728188</td>\n",
       "      <td>3</td>\n",
       "      <td>Shawshank Redemption, The (1994)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3883</td>\n",
       "      <td>608</td>\n",
       "      <td>7.617913</td>\n",
       "      <td>4</td>\n",
       "      <td>Fargo (1996)</td>\n",
       "      <td>Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3883</td>\n",
       "      <td>356</td>\n",
       "      <td>5.674010</td>\n",
       "      <td>5</td>\n",
       "      <td>Forrest Gump (1994)</td>\n",
       "      <td>Comedy|Romance|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3883</td>\n",
       "      <td>2395</td>\n",
       "      <td>5.508895</td>\n",
       "      <td>6</td>\n",
       "      <td>Rushmore (1998)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3883</td>\n",
       "      <td>223</td>\n",
       "      <td>5.398012</td>\n",
       "      <td>7</td>\n",
       "      <td>Clerks (1994)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3883</td>\n",
       "      <td>593</td>\n",
       "      <td>5.335058</td>\n",
       "      <td>8</td>\n",
       "      <td>Silence of the Lambs, The (1991)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3883</td>\n",
       "      <td>296</td>\n",
       "      <td>4.828189</td>\n",
       "      <td>9</td>\n",
       "      <td>Pulp Fiction (1994)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3883</td>\n",
       "      <td>2959</td>\n",
       "      <td>4.615653</td>\n",
       "      <td>10</td>\n",
       "      <td>Fight Club (1999)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  item_id      score  rank                             title  \\\n",
       "0     3883     2396  13.991358     1        Shakespeare in Love (1998)   \n",
       "1     3883     2762  10.249648     2           Sixth Sense, The (1999)   \n",
       "2     3883      318   7.728188     3  Shawshank Redemption, The (1994)   \n",
       "3     3883      608   7.617913     4                      Fargo (1996)   \n",
       "4     3883      356   5.674010     5               Forrest Gump (1994)   \n",
       "5     3883     2395   5.508895     6                   Rushmore (1998)   \n",
       "6     3883      223   5.398012     7                     Clerks (1994)   \n",
       "7     3883      593   5.335058     8  Silence of the Lambs, The (1991)   \n",
       "8     3883      296   4.828189     9               Pulp Fiction (1994)   \n",
       "9     3883     2959   4.615653    10                 Fight Club (1999)   \n",
       "\n",
       "                 genres  \n",
       "0        Comedy|Romance  \n",
       "1              Thriller  \n",
       "2                 Drama  \n",
       "3  Crime|Drama|Thriller  \n",
       "4    Comedy|Romance|War  \n",
       "5                Comedy  \n",
       "6                Comedy  \n",
       "7        Drama|Thriller  \n",
       "8           Crime|Drama  \n",
       "9                 Drama  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recommendations\n",
    "user_recos.sort_values(\"rank\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the simple example, we only used ratings to train the model and we only prepared recommendations for users who have rated movies before. But some models allow you to use explicit features, e.g. user age or item genre. And some models allow you to generate recommendations for users that have not rated any movies before. See [documentation](https://rectools.readthedocs.io/en/stable/features.html#models) for the details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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