{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "muEXbZ25b2wI"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import io\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Wip56al9mH4X"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "TZedhHlzcYhP"
   },
   "outputs": [],
   "source": [
    "#you can also find the csv \n",
    "#url = 'https://raw.githubusercontent.com/mcelikkaya/medium_articles2/main/user_movie_rating.csv'\n",
    "#df_user_movie_rating = pd.read_csv(url)\n",
    "\n",
    "\n",
    "df_user_movie_rating = pd.read_csv(\"D:/user_movie_rating.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105339"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_user_movie_rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 201
    },
    "id": "itMoB5lxdGD7",
    "outputId": "86ea6c8c-674e-4a47-c92e-4d0eebf8256b"
   },
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</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>16</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Casino (1995)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Casino (1995)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Casino (1995)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Casino (1995)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>16</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Casino (1995)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating          title       genres\n",
       "0       1       16     4.0  Casino (1995)  Crime|Drama\n",
       "1       9       16     4.0  Casino (1995)  Crime|Drama\n",
       "2      12       16     1.5  Casino (1995)  Crime|Drama\n",
       "3      24       16     4.0  Casino (1995)  Crime|Drama\n",
       "4      29       16     3.0  Casino (1995)  Crime|Drama"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user_movie_rating.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId       668\n",
       "movieId    10325\n",
       "rating        10\n",
       "title      10323\n",
       "genres       938\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checks unique values\n",
    "df_user_movie_rating.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_id : 668\n",
      "my_id : 669\n"
     ]
    }
   ],
   "source": [
    "#get the max id in set, and add yourself as next user\n",
    "max_id = max(df_user_movie_rating[\"userId\"])\n",
    "print(\"max_id :\",max_id)\n",
    "my_id = max_id + 1\n",
    "print(\"my_id :\",my_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a map of movieId -> title\n",
    "title_movieId_map = { y:x for x,y in df_user_movie_rating[ [\"movieId\",\"title\"] ].to_numpy() }\n",
    "#'Casino (1995)': 16,\n",
    "# 'Powder (1995)': 24,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#defines movies that you want to rate 5 or whatever u want (change the 5 at 1st loop )\n",
    "my_5_list = ['Braveheart (1995)','Seven (a.k.a. Se7en) (1995)','Twelve Monkeys (a.k.a. 12 Monkeys) (1995)',\n",
    "             'Star Wars: Episode VI - Return of the Jedi (1983)','Seven Samurai (Shichinin no samurai) (1954)',\n",
    "             'Shrek 2 (2004)','Truman Show, The (1998)','Terminator, The (1984)','Rocky (1976)','Mummy, The (1999)'\n",
    "             ]\n",
    "\n",
    "#defines movies that you want to rate 2 or whatever u want (change the 2 at 2nd loop )\n",
    "my_2_list = [ 'Dumb & Dumber (Dumb and Dumber) (1994)','Kill Bill: Vol. 2 (2004)','Space Jam (1996)' ]\n",
    "\n",
    "for my_5 in my_5_list:\n",
    "    movide_id = title_movieId_map[my_5] \n",
    "    df_user_movie_rating.loc[len(df_user_movie_rating)] = [my_id, movide_id, 5,my_5,\"nogenre\"] \n",
    "\n",
    "for my_2 in my_2_list:\n",
    "    movide_id = title_movieId_map[my_2] \n",
    "    df_user_movie_rating.loc[len(df_user_movie_rating)] = [my_id, movide_id, 2,my_2,\"nogenre\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>105339</th>\n",
       "      <td>669</td>\n",
       "      <td>110</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Braveheart (1995)</td>\n",
       "      <td>nogenre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105340</th>\n",
       "      <td>669</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Seven (a.k.a. Se7en) (1995)</td>\n",
       "      <td>nogenre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>32</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Twelve Monkeys (a.k.a. 12 Monkeys) (1995)</td>\n",
       "      <td>nogenre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105342</th>\n",
       "      <td>669</td>\n",
       "      <td>1210</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Star Wars: Episode VI - Return of the Jedi (1983)</td>\n",
       "      <td>nogenre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105343</th>\n",
       "      <td>669</td>\n",
       "      <td>2019</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Seven Samurai (Shichinin no samurai) (1954)</td>\n",
       "      <td>nogenre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105344</th>\n",
       "      <td>669</td>\n",
       "      <td>8360</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Shrek 2 (2004)</td>\n",
       "      <td>nogenre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105345</th>\n",
       "      <td>669</td>\n",
       "      <td>1682</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Truman Show, The (1998)</td>\n",
       "      <td>nogenre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105346</th>\n",
       "      <td>669</td>\n",
       "      <td>1240</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Terminator, The (1984)</td>\n",
       "      <td>nogenre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105347</th>\n",
       "      <td>669</td>\n",
       "      <td>1954</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Rocky (1976)</td>\n",
       "      <td>nogenre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105348</th>\n",
       "      <td>669</td>\n",
       "      <td>2617</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Mummy, The (1999)</td>\n",
       "      <td>nogenre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105349</th>\n",
       "      <td>669</td>\n",
       "      <td>231</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Dumb &amp; Dumber (Dumb and Dumber) (1994)</td>\n",
       "      <td>nogenre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105350</th>\n",
       "      <td>669</td>\n",
       "      <td>7438</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Kill Bill: Vol. 2 (2004)</td>\n",
       "      <td>nogenre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105351</th>\n",
       "      <td>669</td>\n",
       "      <td>673</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Space Jam (1996)</td>\n",
       "      <td>nogenre</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId  movieId  rating  \\\n",
       "105339     669      110     5.0   \n",
       "105340     669       47     5.0   \n",
       "105341     669       32     5.0   \n",
       "105342     669     1210     5.0   \n",
       "105343     669     2019     5.0   \n",
       "105344     669     8360     5.0   \n",
       "105345     669     1682     5.0   \n",
       "105346     669     1240     5.0   \n",
       "105347     669     1954     5.0   \n",
       "105348     669     2617     5.0   \n",
       "105349     669      231     2.0   \n",
       "105350     669     7438     2.0   \n",
       "105351     669      673     2.0   \n",
       "\n",
       "                                                    title   genres  \n",
       "105339                                  Braveheart (1995)  nogenre  \n",
       "105340                        Seven (a.k.a. Se7en) (1995)  nogenre  \n",
       "105341          Twelve Monkeys (a.k.a. 12 Monkeys) (1995)  nogenre  \n",
       "105342  Star Wars: Episode VI - Return of the Jedi (1983)  nogenre  \n",
       "105343        Seven Samurai (Shichinin no samurai) (1954)  nogenre  \n",
       "105344                                     Shrek 2 (2004)  nogenre  \n",
       "105345                            Truman Show, The (1998)  nogenre  \n",
       "105346                             Terminator, The (1984)  nogenre  \n",
       "105347                                       Rocky (1976)  nogenre  \n",
       "105348                                  Mummy, The (1999)  nogenre  \n",
       "105349             Dumb & Dumber (Dumb and Dumber) (1994)  nogenre  \n",
       "105350                           Kill Bill: Vol. 2 (2004)  nogenre  \n",
       "105351                                   Space Jam (1996)  nogenre  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check your ratings\n",
    "df_user_movie_rating[df_user_movie_rating[\"userId\"]==my_id ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 414
    },
    "id": "A_iRWXkJftcG",
    "outputId": "fc123a28-82f7-42e1-f816-f31d16907393"
   },
   "outputs": [],
   "source": [
    "#check ratings of a movie\n",
    "#df_user_movie_rating[ df_user_movie_rating[\"movieId\"] == 1073  ] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 48
    },
    "id": "OU17hSbVSl0D",
    "outputId": "c45dca26-5481-41e7-d338-6cecdf70681e"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "P4Tj3QOnK6V8",
    "outputId": "82cc336d-25a0-4216-cf4e-682044ff674d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10325\n",
      "['Toy Story (1995)', 'Jumanji (1995)', 'Grumpier Old Men (1995)', 'Waiting to Exhale (1995)']\n",
      "Toy Story (1995)\n",
      "Jumanji (1995)\n"
     ]
    }
   ],
   "source": [
    "#create utility maps\n",
    "movieId_genres_map = { x:y for x,y in df_user_movie_rating[ [\"movieId\",\"genres\"] ].to_numpy() }\n",
    "movieId_title_map = { x:y for x,y in df_user_movie_rating[ [\"movieId\",\"title\"] ].to_numpy() }\n",
    "\n",
    "genres_sorted = [movieId_genres_map[k] for k in sorted(movieId_genres_map.keys())]\n",
    "title_sorted = [movieId_title_map[k] for k in sorted(movieId_title_map.keys())]\n",
    "\n",
    "index_movieId_map = { index:k for index,k in enumerate(sorted(movieId_genres_map.keys())) }\n",
    "movieId_index_map = { index_movieId_map[k]:k for k in index_movieId_map.keys() }\n",
    "\n",
    "print( len(movieId_title_map) )\n",
    "print(title_sorted[0:4])\n",
    "print(movieId_title_map[1] )\n",
    "print(movieId_title_map[2] )\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "SOjPxFLJfaPG"
   },
   "outputs": [],
   "source": [
    "#index_movieId_map[0]\n",
    "#title_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9eCoSuSVcw2s",
    "outputId": "8c55e8b9-92bc-4401-f79e-fa50bfe86e64"
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "iRY1JIRscx03"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(669, 10325)\n"
     ]
    }
   ],
   "source": [
    "#group by userid,movieid and fill empty values with 0\n",
    "#we can also fill with avg\n",
    "user_item = df_user_movie_rating.groupby(['userId', 'movieId'])['rating'].first().unstack(fill_value=0.0)\n",
    "print(user_item.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get user ids \n",
    "user_ids = user_item.index.values.flatten()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2fHaK0kiczSD",
    "outputId": "bf0d77eb-993a-4200-d606-094bec0348e0"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>movieId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>148626</th>\n",
       "      <th>149532</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th></th>\n",
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       "      <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>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 10325 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "movieId  1       2       3       4       5       6       7       8       \\\n",
       "userId                                                                    \n",
       "1           0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2           5.0     0.0     2.0     0.0     3.0     0.0     0.0     0.0   \n",
       "3           0.0     0.0     0.0     0.0     3.0     0.0     3.0     0.0   \n",
       "4           0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "movieId  9       10      ...  144482  144656  144976  146344  146656  146684  \\\n",
       "userId                   ...                                                   \n",
       "1           0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2           0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "3           0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "4           0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "movieId  146878  148238  148626  149532  \n",
       "userId                                   \n",
       "1           0.0     0.0     0.0     0.0  \n",
       "2           0.0     0.0     0.0     0.0  \n",
       "3           0.0     0.0     0.0     0.0  \n",
       "4           0.0     0.0     0.0     0.0  \n",
       "\n",
       "[4 rows x 10325 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_item.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 250
    },
    "id": "kagBc-OGc02A",
    "outputId": "213ee945-af8a-4f92-eb6d-de98f9f02cd7"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "eSU8H4MTc6t9"
   },
   "outputs": [],
   "source": [
    "from scipy.sparse.linalg import svds\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "sySPt3WydQLG"
   },
   "outputs": [],
   "source": [
    "#apply svd for different latent dims\n",
    "def apply_svd_with_dim( latent_dim ):\n",
    "  user_f, features, f_movie = svds(user_item, k = latent_dim)\n",
    "  feature_diag_matrix=np.diag(features)\n",
    "  return user_f,feature_diag_matrix,f_movie,features\n",
    "\n",
    "#for the generated matrixes generate predictions\n",
    "def generate_predictions(user_f,feature_diag_matrix,f_movie):\n",
    "  predicted_ratings = np.dot(np.dot(user_f, feature_diag_matrix), f_movie)    \n",
    "  preds_df = pd.DataFrame(predicted_ratings, columns = user_item.columns, index=user_item.index)\n",
    "  return preds_df\n",
    "\n",
    "#utility method to apply svd and generate a dataframe with it\n",
    "def get_svd(dim_latent):\n",
    "  user_feature,feature_diag_matrix,movie_feature,features = apply_svd_with_dim(dim_latent)\n",
    "  df_predictions = generate_predictions(user_feature,feature_diag_matrix,movie_feature)\n",
    "  df_movie_feature = pd.DataFrame( )\n",
    "  #print(features)\n",
    "  df_movie_feature[ \"genres\"] = genres_sorted\n",
    "  df_movie_feature[ \"title\"] = title_sorted\n",
    "  df_movie_feature[ \"movie_feature\"] = movie_feature.T.tolist() \n",
    "  return df_movie_feature,features,df_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "N-_r1scopRD7"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EuHuMUg3pi1t",
    "outputId": "effc071b-6541-4135-fd55-7fa398582558"
   },
   "outputs": [],
   "source": [
    "#generate data for different dimensions\n",
    "\n",
    "df_movie_feature5 ,features_5,df_predictions5 = get_svd(5)\n",
    "df_movie_feature20,features_20,df_predictions20 = get_svd(20)\n",
    "df_movie_feature50,features_50,df_predictions50 = get_svd(50)\n",
    "\n",
    "df_movie_feature100,features_100,df_predictions100 = get_svd(100)\n",
    "df_movie_feature200,features_200,df_predictions200 = get_svd(200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OACJQv9Pp2cW",
    "outputId": "147a3512-38df-4400-f983-57c89afcf645"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features_5 [160.90105453 176.50354004 220.42877044 230.86015596 541.57372104]\n",
      "\n",
      "features_20 [ 93.48631205  94.38514462  95.68863921  97.44113044 101.13641678\n",
      " 104.0215854  107.16268388 110.28730529 113.45205702 118.47315723\n",
      " 120.30630581 123.04858606 130.20027085 138.34731952 143.71249278\n",
      " 160.90105453 176.50354004 220.42877044 230.86015596 541.57372104]\n",
      "\n",
      "features_50 [ 67.77497152  68.11315447  68.42066932  69.48851931  69.72274153\n",
      "  70.15760936  70.64460945  71.35687876  72.04342246  72.49456943\n",
      "  72.73573032  73.72084696  74.05905354  75.09842337  75.75286308\n",
      "  76.53708969  78.38528137  79.1801502   80.15156481  80.36982411\n",
      "  81.50654181  81.70485319  83.48856827  84.10398337  85.47397733\n",
      "  87.99522526  88.34491791  89.15460291  89.73142791  92.29512081\n",
      "  93.48631205  94.38514462  95.68863921  97.44113044 101.13641678\n",
      " 104.0215854  107.16268388 110.28730529 113.45205702 118.47315723\n",
      " 120.30630581 123.04858606 130.20027085 138.34731952 143.71249278\n",
      " 160.90105453 176.50354004 220.42877044 230.86015596 541.57372104]\n",
      "\n",
      "features_100 [ 52.69425056  52.76840533  52.94281001  53.21414761  53.31575762\n",
      "  53.39577559  53.7267482   53.98011808  54.32111119  54.35524292\n",
      "  54.59639328  54.98063042  55.05097458  55.51226821  55.69719222\n",
      "  55.8865868   56.3477966   56.62801585  56.81985439  56.97175431\n",
      "  57.58629435  57.74778955  57.93089512  58.28448441  58.66473638\n",
      "  58.84327377  59.10674391  59.43868964  59.78380836  59.92936929\n",
      "  60.42384553  60.69511022  61.15958413  61.33930668  61.39079433\n",
      "  61.86338991  62.36199269  62.80356054  62.98353834  63.26301138\n",
      "  63.6240788   63.9432263   64.08840148  64.83960669  65.00704297\n",
      "  65.47664442  65.64238739  65.8842913   66.42703218  66.97551648\n",
      "  67.77497152  68.11315447  68.42066932  69.48851931  69.72274153\n",
      "  70.15760936  70.64460945  71.35687876  72.04342246  72.49456943\n",
      "  72.73573032  73.72084696  74.05905354  75.09842337  75.75286308\n",
      "  76.53708969  78.38528137  79.1801502   80.15156481  80.36982411\n",
      "  81.50654181  81.70485319  83.48856827  84.10398337  85.47397733\n",
      "  87.99522526  88.34491791  89.15460291  89.73142791  92.29512081\n",
      "  93.48631205  94.38514462  95.68863921  97.44113044 101.13641678\n",
      " 104.0215854  107.16268388 110.28730529 113.45205702 118.47315723\n",
      " 120.30630581 123.04858606 130.20027085 138.34731952 143.71249278\n",
      " 160.90105453 176.50354004 220.42877044 230.86015596 541.57372104]\n"
     ]
    }
   ],
   "source": [
    "#check features matrixes, u can think these as weights\n",
    "#and if u see a number which is much less than others you can remove\n",
    "#probably it is noise\n",
    "print(\"features_5\",features_5)\n",
    "print(\"\")\n",
    "print(\"features_20\",features_20)\n",
    "print(\"\")\n",
    "print(\"features_50\",features_50)\n",
    "print(\"\")\n",
    "print(\"features_100\",features_100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jDQ3HCNZdcsm",
    "outputId": "da21f218-5bd2-4f07-a7c1-58adc623f256"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(669, 5)\n",
      "(5, 5)\n",
      "(5, 10325)\n",
      "[160.90105453 176.50354004 220.42877044 230.86015596 541.57372104]\n"
     ]
    }
   ],
   "source": [
    "#TODO remove\n",
    "user_feature,feature_diag_matrix,movie_feature,features = apply_svd_with_dim(5)\n",
    "print( user_feature.shape )\n",
    "print( feature_diag_matrix.shape )\n",
    "print( movie_feature.shape )\n",
    "print(features)\n",
    "\n",
    "df_predictions = generate_predictions(user_feature,feature_diag_matrix,movie_feature)\n",
    "df_movie_feature = pd.DataFrame( )\n",
    "\n",
    "\n",
    "\n",
    "df_movie_feature[ \"genres\"] = genres_sorted\n",
    "df_movie_feature[ \"title\"] = title_sorted\n",
    "df_movie_feature[ \"movie_feature\"] = movie_feature.T.tolist() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pCawEmQ9dedP",
    "outputId": "2fe229e0-2ce4-4f31-927a-322ed9fc66d3"
   },
   "outputs": [],
   "source": [
    "#df_predictions = generate_predictions(user_feature,feature_diag_matrix,movie_feature)\n",
    "#df_predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 220
    },
    "id": "OCxJ6UJidojY",
    "outputId": "b11957c5-8000-4f3e-ab12-132848babd9b"
   },
   "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>movieId</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>10</th>\n",
       "      <th>...</th>\n",
       "      <th>144482</th>\n",
       "      <th>144656</th>\n",
       "      <th>144976</th>\n",
       "      <th>146344</th>\n",
       "      <th>146656</th>\n",
       "      <th>146684</th>\n",
       "      <th>146878</th>\n",
       "      <th>148238</th>\n",
       "      <th>148626</th>\n",
       "      <th>149532</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</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>1</th>\n",
       "      <td>2.244121</td>\n",
       "      <td>0.781555</td>\n",
       "      <td>-0.019930</td>\n",
       "      <td>0.023924</td>\n",
       "      <td>0.108648</td>\n",
       "      <td>0.819317</td>\n",
       "      <td>0.047663</td>\n",
       "      <td>0.008983</td>\n",
       "      <td>-0.035280</td>\n",
       "      <td>1.053225</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021298</td>\n",
       "      <td>-0.007142</td>\n",
       "      <td>-0.054427</td>\n",
       "      <td>-0.004081</td>\n",
       "      <td>0.022276</td>\n",
       "      <td>0.013699</td>\n",
       "      <td>0.008562</td>\n",
       "      <td>-0.006122</td>\n",
       "      <td>-0.086983</td>\n",
       "      <td>-0.008163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.417457</td>\n",
       "      <td>0.228670</td>\n",
       "      <td>0.155480</td>\n",
       "      <td>0.053551</td>\n",
       "      <td>0.159560</td>\n",
       "      <td>0.287265</td>\n",
       "      <td>0.194862</td>\n",
       "      <td>0.018741</td>\n",
       "      <td>0.066500</td>\n",
       "      <td>0.456863</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004044</td>\n",
       "      <td>-0.001901</td>\n",
       "      <td>0.000255</td>\n",
       "      <td>-0.001086</td>\n",
       "      <td>-0.007021</td>\n",
       "      <td>-0.003007</td>\n",
       "      <td>-0.001880</td>\n",
       "      <td>-0.001630</td>\n",
       "      <td>0.001294</td>\n",
       "      <td>-0.002173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.376954</td>\n",
       "      <td>0.864621</td>\n",
       "      <td>0.532816</td>\n",
       "      <td>0.190015</td>\n",
       "      <td>0.598891</td>\n",
       "      <td>0.951395</td>\n",
       "      <td>0.685031</td>\n",
       "      <td>0.080138</td>\n",
       "      <td>0.254902</td>\n",
       "      <td>1.869014</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010661</td>\n",
       "      <td>-0.010658</td>\n",
       "      <td>0.013479</td>\n",
       "      <td>-0.006090</td>\n",
       "      <td>-0.023350</td>\n",
       "      <td>-0.002002</td>\n",
       "      <td>-0.001251</td>\n",
       "      <td>-0.009135</td>\n",
       "      <td>0.041111</td>\n",
       "      <td>-0.012180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 10325 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "movieId    1         2         3         4         5         6         7       \\\n",
       "userId                                                                          \n",
       "1        2.244121  0.781555 -0.019930  0.023924  0.108648  0.819317  0.047663   \n",
       "2        0.417457  0.228670  0.155480  0.053551  0.159560  0.287265  0.194862   \n",
       "3        1.376954  0.864621  0.532816  0.190015  0.598891  0.951395  0.685031   \n",
       "\n",
       "movieId    8         9         10      ...    144482    144656    144976  \\\n",
       "userId                                 ...                                 \n",
       "1        0.008983 -0.035280  1.053225  ...  0.021298 -0.007142 -0.054427   \n",
       "2        0.018741  0.066500  0.456863  ... -0.004044 -0.001901  0.000255   \n",
       "3        0.080138  0.254902  1.869014  ... -0.010661 -0.010658  0.013479   \n",
       "\n",
       "movieId    146344    146656    146684    146878    148238    148626    149532  \n",
       "userId                                                                         \n",
       "1       -0.004081  0.022276  0.013699  0.008562 -0.006122 -0.086983 -0.008163  \n",
       "2       -0.001086 -0.007021 -0.003007 -0.001880 -0.001630  0.001294 -0.002173  \n",
       "3       -0.006090 -0.023350 -0.002002 -0.001251 -0.009135  0.041111 -0.012180  \n",
       "\n",
       "[3 rows x 10325 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predictions.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#user_ids.tolist()  669"
   ]
  },
  {
   "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",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>[0.06382285500333078, -0.014155681199677516, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>[0.0015994635519848777, 0.01367331704126093, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.033567701410516695, 0.07147524576436985, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>[0.05153228288548356, -0.04589555038793302, -0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>[0.000724424135900615, 0.002951478419209708, 0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id                                       user_feature\n",
       "0        1  [0.06382285500333078, -0.014155681199677516, 0...\n",
       "1        2  [0.0015994635519848777, 0.01367331704126093, 0...\n",
       "2        3  [0.033567701410516695, 0.07147524576436985, 0....\n",
       "3        4  [0.05153228288548356, -0.04589555038793302, -0...\n",
       "4        5  [0.000724424135900615, 0.002951478419209708, 0..."
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a user feature dataframe\n",
    "\n",
    "df_user_features = pd.DataFrame( )\n",
    "\n",
    "df_user_features[ \"user_id\"] = user_ids.tolist() \n",
    "df_user_features[ \"user_feature\"] = user_feature.tolist() \n",
    "df_user_features.head( 5 )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "iw5VZycrgfUt"
   },
   "outputs": [],
   "source": [
    "#create a user movie dataframe\n",
    "df_movie_feature = pd.DataFrame( )\n",
    "df_movie_feature[ \"genres\"] = genres_sorted\n",
    "df_movie_feature[ \"title\"] = title_sorted\n",
    "df_movie_feature[ \"movie_feature\"] = movie_feature.T.tolist() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "id": "eRjSuEfwjRbf",
    "outputId": "585b7eca-e000-4329-dc57-a2ece3b48236"
   },
   "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>genres</th>\n",
       "      <th>title</th>\n",
       "      <th>movie_feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>[0.03023206340790512, 0.004140776526474332, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>[0.0029633795935668636, 0.03420429912517415, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>[-0.01999921508513389, 0.03217626962055229, -0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>[-0.001016369036543841, 0.007298146514648678, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        genres                     title  \\\n",
       "0  Adventure|Animation|Children|Comedy|Fantasy          Toy Story (1995)   \n",
       "1                   Adventure|Children|Fantasy            Jumanji (1995)   \n",
       "2                               Comedy|Romance   Grumpier Old Men (1995)   \n",
       "3                         Comedy|Drama|Romance  Waiting to Exhale (1995)   \n",
       "\n",
       "                                       movie_feature  \n",
       "0  [0.03023206340790512, 0.004140776526474332, 0....  \n",
       "1  [0.0029633795935668636, 0.03420429912517415, 0...  \n",
       "2  [-0.01999921508513389, 0.03217626962055229, -0...  \n",
       "3  [-0.001016369036543841, 0.007298146514648678, ...  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#len(df_movie_feature.iloc[0].to_numpy()[0])\n",
    "df_movie_feature.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kqZ_XYi9gfRl",
    "outputId": "939ef069-d9d6-4d1b-c8f2-c69094862da2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10325, 5)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_numpy = np.array( [ np.array(x) for x in  df_movie_feature[ \"movie_feature\"].to_numpy() ])\n",
    "features_numpy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vhy1Nk9mgfOy",
    "outputId": "f33d39cd-993e-4b5b-f63a-8edca96a7068"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.03023206,  0.00414078,  0.04535296, -0.0388622 , -0.07300348])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_numpy[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UxpSLC8egdiI",
    "outputId": "aa8118d6-ae8b-405c-9ad2-c75bd13dc7e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index 230 -> movieId260 \n"
     ]
    }
   ],
   "source": [
    "movieId = df_user_movie_rating[ df_user_movie_rating[\"title\"] == \"Star Wars: Episode IV - A New Hope (1977)\"][\"movieId\"].iloc[0]\n",
    "#movieId = 260\n",
    "index = movieId_index_map[movieId]\n",
    "print(f\"index {index} -> movieId{movieId} \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "hu8fCo7Mid8w"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "def get_movie_id_byname(title):\n",
    "  movieId = df_user_movie_rating[ df_user_movie_rating[\"title\"] == title][\"movieId\"].iloc[0]\n",
    "  index = movieId_index_map[movieId]\n",
    "  return index ,movieId\n",
    "\n",
    "def get_similarity_for_movie(df_movie_features,title,top_n=10,dump_results=False):\n",
    "\n",
    "  features_numpy = np.array( [ np.array(x) for x in  df_movie_features[ \"movie_feature\"].to_numpy() ])\n",
    "\n",
    "  index ,movieId = get_movie_id_byname( title )\n",
    "  all_cosines = cosine_similarity(features_numpy[index].reshape(1,-1),features_numpy)\n",
    "  all_cosines = all_cosines.flatten()\n",
    "  top_indexes = all_cosines.argsort()[-1 * top_n:][::-1]\n",
    "  #print(top_indexes)\n",
    "  similar_results = []\n",
    "  for top_index in top_indexes:\n",
    "    #top_index+1 because movie ids begin from 0\n",
    "    if dump_results: \n",
    "      print( f\"{index_movieId_map[top_index]} ){all_cosines[top_index]} -> {movieId_title_map[index_movieId_map[top_index]]}\")\n",
    "    movie_name = movieId_title_map[index_movieId_map[top_index]]\n",
    "    similar_results.append( [ top_index,index_movieId_map[top_index],all_cosines[top_index],movie_name]  )\n",
    "  return similar_results   \n",
    "\n",
    "def get_similarity_for_user(df_user_features_,user_index,top_n=4,dump_results=False):\n",
    "  index = user_index - 1\n",
    "  features_numpy = np.array( [ np.array(x) for x in  df_user_features_[ \"user_feature\"].to_numpy() ])\n",
    "\n",
    "  all_cosines = cosine_similarity(features_numpy[index].reshape(1,-1),features_numpy)\n",
    "  all_cosines = all_cosines.flatten()\n",
    "  top_indexes = all_cosines.argsort()[-1 * top_n:][::-1]\n",
    "  print(top_indexes)\n",
    "  similar_results = []\n",
    "  for top_index in top_indexes[1:]:\n",
    "        similar_user = top_index + 1\n",
    "        df_sub = df_user_movie_rating[ df_user_movie_rating[\"userId\"] == similar_user ]\n",
    "        df_sub = df_sub.sort_values(by=['rating'], ascending=False)\n",
    "        similar_results.extend( df_sub[[\"userId\",\"rating\",\"title\"]].head(5).to_numpy() )\n",
    "  \n",
    "  return similar_results   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[668 666 167 492]\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>userId</th>\n",
       "      <th>rating</th>\n",
       "      <th>Movie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>667</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Hours, The (2002)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>667</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lord of the Rings: The Fellowship of the Ring,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>667</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Twelve Monkeys (a.k.a. 12 Monkeys) (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>667</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Being John Malkovich (1999)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>667</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Seven (a.k.a. Se7en) (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>168</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lion King, The (1994)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>168</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Shawshank Redemption, The (1994)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>168</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Jerry Maguire (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>168</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Star Wars: Episode V - The Empire Strikes Back...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>168</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Forrest Gump (1994)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>493</td>\n",
       "      <td>5.0</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>493</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Schindler's List (1993)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>493</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Psycho (1960)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>493</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Exorcist, The (1973)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>493</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Dead Man Walking (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId rating                                              Movie\n",
       "0     667    5.0                                  Hours, The (2002)\n",
       "1     667    5.0  Lord of the Rings: The Fellowship of the Ring,...\n",
       "2     667    4.5          Twelve Monkeys (a.k.a. 12 Monkeys) (1995)\n",
       "3     667    4.5                        Being John Malkovich (1999)\n",
       "4     667    4.5                        Seven (a.k.a. Se7en) (1995)\n",
       "5     168    5.0                              Lion King, The (1994)\n",
       "6     168    5.0                   Shawshank Redemption, The (1994)\n",
       "7     168    5.0                               Jerry Maguire (1996)\n",
       "8     168    5.0  Star Wars: Episode V - The Empire Strikes Back...\n",
       "9     168    5.0                                Forrest Gump (1994)\n",
       "10    493    5.0             One Flew Over the Cuckoo's Nest (1975)\n",
       "11    493    5.0                            Schindler's List (1993)\n",
       "12    493    5.0                                      Psycho (1960)\n",
       "13    493    5.0                               Exorcist, The (1973)\n",
       "14    493    5.0                            Dead Man Walking (1995)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get similar users with you and show their top rated movies\n",
    "#Are results good?\n",
    "pd.DataFrame( np.array(get_similarity_for_user(df_user_features,my_id)) ,columns=[\"userId\",\"rating\",\"Movie\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "oLYIYmKKorRI"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_movie_feature5\n",
      "260 )1.0000000000000002 -> Star Wars: Episode IV - A New Hope (1977)\n",
      "1198 )0.9907068054182815 -> Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981)\n",
      "1270 )0.9879725725429758 -> Back to the Future (1985)\n",
      "1196 )0.9801329176833984 -> Star Wars: Episode V - The Empire Strikes Back (1980)\n",
      "2028 )0.9689809951432004 -> Saving Private Ryan (1998)\n",
      "2799 )0.9609155371642213 -> Problem Child 2 (1991)\n",
      "1089 )0.949603646801789 -> Reservoir Dogs (1992)\n",
      "1210 )0.9445297114803557 -> Star Wars: Episode VI - Return of the Jedi (1983)\n",
      "1136 )0.9423509725778274 -> Monty Python and the Holy Grail (1975)\n",
      "1265 )0.9368680097768936 -> Groundhog Day (1993)\n",
      "df_movie_feature20\n",
      "260 )0.9999999999999998 -> Star Wars: Episode IV - A New Hope (1977)\n",
      "1210 )0.9571210743716894 -> Star Wars: Episode VI - Return of the Jedi (1983)\n",
      "1 )0.8106156519992324 -> Toy Story (1995)\n",
      "780 )0.8105105328344275 -> Independence Day (a.k.a. ID4) (1996)\n",
      "1196 )0.7539218449003093 -> Star Wars: Episode V - The Empire Strikes Back (1980)\n",
      "648 )0.7443504622819895 -> Mission: Impossible (1996)\n",
      "733 )0.7303890562054596 -> Rock, The (1996)\n",
      "1580 )0.6937960920541835 -> Men in Black (a.k.a. MIB) (1997)\n",
      "1198 )0.6888810684670407 -> Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981)\n",
      "618 )0.6771938019650895 -> Two Much (1995)\n",
      "df_movie_feature50\n",
      "260 )0.9999999999999998 -> Star Wars: Episode IV - A New Hope (1977)\n",
      "1210 )0.826970742438492 -> Star Wars: Episode VI - Return of the Jedi (1983)\n",
      "1196 )0.7158343138144241 -> Star Wars: Episode V - The Empire Strikes Back (1980)\n",
      "780 )0.5168164649215975 -> Independence Day (a.k.a. ID4) (1996)\n",
      "1198 )0.5041614825580734 -> Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981)\n",
      "993 )0.49372676421338585 -> Infinity (1996)\n",
      "618 )0.4936635007362338 -> Two Much (1995)\n",
      "1 )0.4897486351742783 -> Toy Story (1995)\n",
      "1390 )0.46785828118754785 -> My Fellow Americans (1996)\n",
      "1471 )0.45888719710529663 -> Boys Life 2 (1997)\n",
      "df_movie_feature100\n",
      "260 )1.0 -> Star Wars: Episode IV - A New Hope (1977)\n",
      "1196 )0.6946486507887075 -> Star Wars: Episode V - The Empire Strikes Back (1980)\n",
      "1210 )0.6136180237539699 -> Star Wars: Episode VI - Return of the Jedi (1983)\n",
      "226 )0.3144639372870211 -> Dream Man (1995)\n",
      "2882 )0.3095792885783813 -> Jakob the Liar (1999)\n",
      "71550 )0.2998285009818546 -> Assassination of a High School President (2008)\n",
      "68392 )0.29982850098185454 -> Happy Campers (2001)\n",
      "8643 )0.2977792028641997 -> Cinderella Story, A (2004)\n",
      "33493 )0.2942752137605703 -> Star Wars: Episode III - Revenge of the Sith (2005)\n",
      "1232 )0.28667895569105806 -> Stalker (1979)\n"
     ]
    }
   ],
   "source": [
    "#dump suggestions for different dimensions\n",
    "#Are results getting better when dimension is higher?\n",
    "models = [df_movie_feature5,df_movie_feature20,df_movie_feature50,df_movie_feature100]\n",
    "names  = [\"df_movie_feature5\",\"df_movie_feature20\",\"df_movie_feature50\",\"df_movie_feature100\"]\n",
    "movie_name = \"Star Wars: Episode IV - A New Hope (1977)\"\n",
    "for model_,name in zip( models,names) :\n",
    "    print(name)\n",
    "    similar_results = get_similarity_for_movie(model_,movie_name ,top_n=10,dump_results=True)\n",
    "  \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "ihja22Mfrjs7"
   },
   "outputs": [],
   "source": [
    "def get_2d_map(df_movie_feature_,title,item_count):\n",
    "    similar_results = get_similarity_for_movie(df_movie_feature_, title ,item_count,False)\n",
    "\n",
    "    features_numpy = np.array( [ np.array(x) for x in  df_movie_feature_[ \"movie_feature\"].to_numpy() ])\n",
    "    pca = PCA(2)\n",
    "    features_numpy_2d = pca.fit_transform( features_numpy )\n",
    "    indexes = np.array(similar_results)[:,0]\n",
    "    movie_names = np.array(similar_results)[:,3]\n",
    "    \n",
    "    feature_indexes = indexes.tolist()\n",
    "    data_2d = features_numpy_2d[np.asarray(indexes).astype(\"int8\") ,:]\n",
    "    return data_2d[:,0:1],data_2d[:,1],movie_names\n",
    "\n",
    "\n",
    "def draw_on_2d(x,y,labels):\n",
    "    fig, ax = plt.subplots()\n",
    "    fig.set_size_inches(10,10)\n",
    "    ax.scatter(x, y)\n",
    "\n",
    "    for i, txt in enumerate(labels):\n",
    "        txt = txt[0:txt.find(\"(\")]\n",
    "        ax.annotate(txt, (x[i], y[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Find 10 most similar movie to Terminator for 50 features\n",
    "x,y,movie_names = get_2d_map(df_movie_feature50,'Terminator, The (1984)',10)\n",
    "_ = draw_on_2d( x,y,movie_names  )\n",
    "#Find 100 most similar movie to Terminator\n",
    "x,y,movie_names = get_2d_map(df_movie_feature50,'Terminator, The (1984)',40)\n",
    "_ = draw_on_2d( x,y,movie_names  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Find 10 most similar movie to Terminator\n",
    "x,y,movie_names = get_2d_map(df_movie_feature200,'Terminator, The (1984)',10)\n",
    "_ = draw_on_2d( x,y,movie_names  )\n",
    "#Find 100 most similar movie to Terminator\n",
    "x,y,movie_names = get_2d_map(df_movie_feature200,'Terminator, The (1984)',40)\n",
    "_ = draw_on_2d( x,y,movie_names  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "suprise_svd_v2.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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