User_based.ipynb

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     "text": [
      "Requirement already satisfied: cupy-cuda11x in c:\\users\\gandl\\anaconda3\\lib\\site-packages (13.2.0)\n",
      "Requirement already satisfied: numpy<2.3,>=1.22 in c:\\users\\gandl\\anaconda3\\lib\\site-packages (from cupy-cuda11x) (1.26.4)\n",
      "Requirement already satisfied: fastrlock>=0.5 in c:\\users\\gandl\\anaconda3\\lib\\site-packages (from cupy-cuda11x) (0.8.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install cupy-cuda11x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "30c5f753-cbe9-4a0d-963e-276cc0280d0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import ast\n",
    "import numpy as np\n",
    "import pickle\n",
    "import cupy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5e97aae0-30a5-4b23-ac44-9243cbed7694",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the Popularity datset to recommend the user\n",
    "# We recommend them by the movie which they havnet seen, movies seen by the similar users \n",
    "# and order will be according to the popularity of the movie\n",
    "Popularity_data = pd.read_csv(\"popular_movies_by_id.csv.\")\n",
    "movie_order = Popularity_data['movieId'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "019e5bc2-187c-4175-bc71-752d49357da2",
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    {
     "data": {
      "text/plain": [
       "(45115,)"
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     "execution_count": 10,
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   ],
   "source": [
    "Popularity_data\n",
    "movie_order.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8caf6990-ab23-4cff-8f3a-32050a21b04e",
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       "          userId  movieId  rating\n",
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     "execution_count": 14,
     "metadata": {},
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   ],
   "source": [
    "ratings = pd.read_csv(\"data/ratings.csv\")\n",
    "ratings.drop(\"timestamp\",axis = 1,inplace=True)\n",
    "ratings"
   ]
  },
  {
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       "   adult                              belongs_to_collection    budget  \\\n",
       "0  False  {'id': 10194, 'name': 'Toy Story Collection', ...  30000000   \n",
       "1  False                                                NaN  65000000   \n",
       "2  False  {'id': 119050, 'name': 'Grumpy Old Men Collect...         0   \n",
       "3  False                                                NaN  16000000   \n",
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       "\n",
       "                                              genres  \\\n",
       "0  [{'id': 16, 'name': 'Animation'}, {'id': 35, '...   \n",
       "1  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   \n",
       "2  [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...   \n",
       "3  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...   \n",
       "4                     [{'id': 35, 'name': 'Comedy'}]   \n",
       "\n",
       "                               homepage     id    imdb_id original_language  \\\n",
       "0  http://toystory.disney.com/toy-story    862  tt0114709                en   \n",
       "1                                   NaN   8844  tt0113497                en   \n",
       "2                                   NaN  15602  tt0113228                en   \n",
       "3                                   NaN  31357  tt0114885                en   \n",
       "4                                   NaN  11862  tt0113041                en   \n",
       "\n",
       "                original_title  \\\n",
       "0                    Toy Story   \n",
       "1                      Jumanji   \n",
       "2             Grumpier Old Men   \n",
       "3            Waiting to Exhale   \n",
       "4  Father of the Bride Part II   \n",
       "\n",
       "                                            overview  ... release_date  \\\n",
       "0  Led by Woody, Andy's toys live happily in his ...  ...   1995-10-30   \n",
       "1  When siblings Judy and Peter discover an encha...  ...   1995-12-15   \n",
       "2  A family wedding reignites the ancient feud be...  ...   1995-12-22   \n",
       "3  Cheated on, mistreated and stepped on, the wom...  ...   1995-12-22   \n",
       "4  Just when George Banks has recovered from his ...  ...   1995-02-10   \n",
       "\n",
       "       revenue runtime                                   spoken_languages  \\\n",
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       "\n",
       "     status                                            tagline  \\\n",
       "0  Released                                                NaN   \n",
       "1  Released          Roll the dice and unleash the excitement!   \n",
       "2  Released  Still Yelling. Still Fighting. Still Ready for...   \n",
       "3  Released  Friends are the people who let you be yourself...   \n",
       "4  Released  Just When His World Is Back To Normal... He's ...   \n",
       "\n",
       "                         title  video vote_average vote_count  \n",
       "0                    Toy Story  False          7.7     5415.0  \n",
       "1                      Jumanji  False          6.9     2413.0  \n",
       "2             Grumpier Old Men  False          6.5       92.0  \n",
       "3            Waiting to Exhale  False          6.1       34.0  \n",
       "4  Father of the Bride Part II  False          5.7      173.0  \n",
       "\n",
       "[5 rows x 24 columns]"
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "movies_info =  pd.read_csv(\"data/movies_metadata.csv\",low_memory=False)\n",
    "movies_info.head()\n"
   ]
  },
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   "execution_count": 25,
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       "          userId  movieId  rating\n",
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       "26024286  270896    63082     4.5\n",
       "26024287  270896    64957     4.5\n",
       "26024288  270896    71878     2.0\n",
       "\n",
       "[26024289 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_rating_data = ratings\n",
    "user_rating_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a3281810-95c6-469b-8092-ffdaef605caf",
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>858</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1221</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1246</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26024284</th>\n",
       "      <td>270896</td>\n",
       "      <td>58559</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26024285</th>\n",
       "      <td>270896</td>\n",
       "      <td>60069</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26024286</th>\n",
       "      <td>270896</td>\n",
       "      <td>63082</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26024287</th>\n",
       "      <td>270896</td>\n",
       "      <td>64957</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26024288</th>\n",
       "      <td>270896</td>\n",
       "      <td>71878</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26024289 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          userId  movieId  rating\n",
       "0              1      110     1.0\n",
       "1              1      147     4.5\n",
       "2              1      858     5.0\n",
       "3              1     1221     5.0\n",
       "4              1     1246     5.0\n",
       "...          ...      ...     ...\n",
       "26024284  270896    58559     5.0\n",
       "26024285  270896    60069     5.0\n",
       "26024286  270896    63082     4.5\n",
       "26024287  270896    64957     4.5\n",
       "26024288  270896    71878     2.0\n",
       "\n",
       "[26024289 rows x 3 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_rating_data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4571f34b-a47d-488e-ba09-ea8f976b301a",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_rating_data.sha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d49e1f90-b250-4bd6-9899-d818c79f3e7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data = user_rating_data.sample(10000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9de21b1a-ffbb-4d35-82af-1cfcf7aceeed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gandl\\anaconda3\\Lib\\site-packages\\pandas\\core\\reshape\\reshape.py:143: RuntimeWarning: overflow encountered in scalar multiply\n",
      "  num_cells = num_rows * num_columns\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 1196752560 is out of bounds for axis 0 with size 1196742495",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[40], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m pt \u001b[38;5;241m=\u001b[39m sample_data\u001b[38;5;241m.\u001b[39mpivot_table(values\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrating\u001b[39m\u001b[38;5;124m\"\u001b[39m,index \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muserId\u001b[39m\u001b[38;5;124m\"\u001b[39m,columns\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmovieId\u001b[39m\u001b[38;5;124m\"\u001b[39m,fill_value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m      2\u001b[0m pt\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:9509\u001b[0m, in \u001b[0;36mDataFrame.pivot_table\u001b[1;34m(self, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)\u001b[0m\n\u001b[0;32m   9492\u001b[0m \u001b[38;5;129m@Substitution\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   9493\u001b[0m \u001b[38;5;129m@Appender\u001b[39m(_shared_docs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpivot_table\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m   9494\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpivot_table\u001b[39m(\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   9505\u001b[0m     sort: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m   9506\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame:\n\u001b[0;32m   9507\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mreshape\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpivot\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pivot_table\n\u001b[1;32m-> 9509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m pivot_table(\n\u001b[0;32m   9510\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   9511\u001b[0m         values\u001b[38;5;241m=\u001b[39mvalues,\n\u001b[0;32m   9512\u001b[0m         index\u001b[38;5;241m=\u001b[39mindex,\n\u001b[0;32m   9513\u001b[0m         columns\u001b[38;5;241m=\u001b[39mcolumns,\n\u001b[0;32m   9514\u001b[0m         aggfunc\u001b[38;5;241m=\u001b[39maggfunc,\n\u001b[0;32m   9515\u001b[0m         fill_value\u001b[38;5;241m=\u001b[39mfill_value,\n\u001b[0;32m   9516\u001b[0m         margins\u001b[38;5;241m=\u001b[39mmargins,\n\u001b[0;32m   9517\u001b[0m         dropna\u001b[38;5;241m=\u001b[39mdropna,\n\u001b[0;32m   9518\u001b[0m         margins_name\u001b[38;5;241m=\u001b[39mmargins_name,\n\u001b[0;32m   9519\u001b[0m         observed\u001b[38;5;241m=\u001b[39mobserved,\n\u001b[0;32m   9520\u001b[0m         sort\u001b[38;5;241m=\u001b[39msort,\n\u001b[0;32m   9521\u001b[0m     )\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\reshape\\pivot.py:102\u001b[0m, in \u001b[0;36mpivot_table\u001b[1;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)\u001b[0m\n\u001b[0;32m     99\u001b[0m     table \u001b[38;5;241m=\u001b[39m concat(pieces, keys\u001b[38;5;241m=\u001b[39mkeys, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m    100\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m table\u001b[38;5;241m.\u001b[39m__finalize__(data, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpivot_table\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 102\u001b[0m table \u001b[38;5;241m=\u001b[39m __internal_pivot_table(\n\u001b[0;32m    103\u001b[0m     data,\n\u001b[0;32m    104\u001b[0m     values,\n\u001b[0;32m    105\u001b[0m     index,\n\u001b[0;32m    106\u001b[0m     columns,\n\u001b[0;32m    107\u001b[0m     aggfunc,\n\u001b[0;32m    108\u001b[0m     fill_value,\n\u001b[0;32m    109\u001b[0m     margins,\n\u001b[0;32m    110\u001b[0m     dropna,\n\u001b[0;32m    111\u001b[0m     margins_name,\n\u001b[0;32m    112\u001b[0m     observed,\n\u001b[0;32m    113\u001b[0m     sort,\n\u001b[0;32m    114\u001b[0m )\n\u001b[0;32m    115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m table\u001b[38;5;241m.\u001b[39m__finalize__(data, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpivot_table\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\reshape\\pivot.py:203\u001b[0m, in \u001b[0;36m__internal_pivot_table\u001b[1;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)\u001b[0m\n\u001b[0;32m    201\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    202\u001b[0m             to_unstack\u001b[38;5;241m.\u001b[39mappend(name)\n\u001b[1;32m--> 203\u001b[0m     table \u001b[38;5;241m=\u001b[39m agged\u001b[38;5;241m.\u001b[39munstack(to_unstack, fill_value\u001b[38;5;241m=\u001b[39mfill_value)\n\u001b[0;32m    205\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m dropna:\n\u001b[0;32m    206\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(table\u001b[38;5;241m.\u001b[39mindex, MultiIndex):\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:9928\u001b[0m, in \u001b[0;36mDataFrame.unstack\u001b[1;34m(self, level, fill_value, sort)\u001b[0m\n\u001b[0;32m   9864\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   9865\u001b[0m \u001b[38;5;124;03mPivot a level of the (necessarily hierarchical) index labels.\u001b[39;00m\n\u001b[0;32m   9866\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   9924\u001b[0m \u001b[38;5;124;03mdtype: float64\u001b[39;00m\n\u001b[0;32m   9925\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   9926\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mreshape\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mreshape\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m unstack\n\u001b[1;32m-> 9928\u001b[0m result \u001b[38;5;241m=\u001b[39m unstack(\u001b[38;5;28mself\u001b[39m, level, fill_value, sort)\n\u001b[0;32m   9930\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munstack\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\reshape\\reshape.py:504\u001b[0m, in \u001b[0;36munstack\u001b[1;34m(obj, level, fill_value, sort)\u001b[0m\n\u001b[0;32m    502\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, DataFrame):\n\u001b[0;32m    503\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj\u001b[38;5;241m.\u001b[39mindex, MultiIndex):\n\u001b[1;32m--> 504\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m _unstack_frame(obj, level, fill_value\u001b[38;5;241m=\u001b[39mfill_value, sort\u001b[38;5;241m=\u001b[39msort)\n\u001b[0;32m    505\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    506\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39mT\u001b[38;5;241m.\u001b[39mstack(future_stack\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\reshape\\reshape.py:529\u001b[0m, in \u001b[0;36m_unstack_frame\u001b[1;34m(obj, level, fill_value, sort)\u001b[0m\n\u001b[0;32m    525\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_unstack_frame\u001b[39m(\n\u001b[0;32m    526\u001b[0m     obj: DataFrame, level, fill_value\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, sort: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m    527\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame:\n\u001b[0;32m    528\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj\u001b[38;5;241m.\u001b[39mindex, MultiIndex)  \u001b[38;5;66;03m# checked by caller\u001b[39;00m\n\u001b[1;32m--> 529\u001b[0m     unstacker \u001b[38;5;241m=\u001b[39m _Unstacker(\n\u001b[0;32m    530\u001b[0m         obj\u001b[38;5;241m.\u001b[39mindex, level\u001b[38;5;241m=\u001b[39mlevel, constructor\u001b[38;5;241m=\u001b[39mobj\u001b[38;5;241m.\u001b[39m_constructor, sort\u001b[38;5;241m=\u001b[39msort\n\u001b[0;32m    531\u001b[0m     )\n\u001b[0;32m    533\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m obj\u001b[38;5;241m.\u001b[39m_can_fast_transpose:\n\u001b[0;32m    534\u001b[0m         mgr \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_mgr\u001b[38;5;241m.\u001b[39munstack(unstacker, fill_value\u001b[38;5;241m=\u001b[39mfill_value)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\reshape\\reshape.py:154\u001b[0m, in \u001b[0;36m_Unstacker.__init__\u001b[1;34m(self, index, level, constructor, sort)\u001b[0m\n\u001b[0;32m    146\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_cells \u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39miinfo(np\u001b[38;5;241m.\u001b[39mint32)\u001b[38;5;241m.\u001b[39mmax:\n\u001b[0;32m    147\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    148\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe following operation may generate \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_cells\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m cells \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    149\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124min the resulting pandas object.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    150\u001b[0m         PerformanceWarning,\n\u001b[0;32m    151\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m    152\u001b[0m     )\n\u001b[1;32m--> 154\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_selectors()\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\reshape\\reshape.py:207\u001b[0m, in \u001b[0;36m_Unstacker._make_selectors\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    205\u001b[0m selector \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msorted_labels[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m stride \u001b[38;5;241m*\u001b[39m comp_index \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlift\n\u001b[0;32m    206\u001b[0m mask \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros(np\u001b[38;5;241m.\u001b[39mprod(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfull_shape), dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mbool\u001b[39m)\n\u001b[1;32m--> 207\u001b[0m mask\u001b[38;5;241m.\u001b[39mput(selector, \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    209\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39msum() \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex):\n\u001b[0;32m    210\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIndex contains duplicate entries, cannot reshape\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mIndexError\u001b[0m: index 1196752560 is out of bounds for axis 0 with size 1196742495"
     ]
    }
   ],
   "source": [
    "pt = user_rating_data.pivot_table(values=\"rating\",index = \"userId\",columns=\"movie\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab81a8d5-9653-469c-85bf-08cc2336a9b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2aabef83-1976-4b8e-b503-a760a2e9d2ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1c3dfb97-eba0-403a-9392-81fd40310c67",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.        , 0.        , ..., 0.06291708, 0.        ,\n",
       "        0.01746565],\n",
       "       [0.        , 1.        , 0.12429498, ..., 0.02413984, 0.17059464,\n",
       "        0.1131753 ],\n",
       "       [0.        , 0.12429498, 1.        , ..., 0.08098382, 0.13660585,\n",
       "        0.17019275],\n",
       "       ...,\n",
       "       [0.06291708, 0.02413984, 0.08098382, ..., 1.        , 0.04260878,\n",
       "        0.08520194],\n",
       "       [0.        , 0.17059464, 0.13660585, ..., 0.04260878, 1.        ,\n",
       "        0.22867673],\n",
       "       [0.01746565, 0.1131753 , 0.17019275, ..., 0.08520194, 0.22867673,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simiraity_score = cosine_similarity(pt)\n",
    "simiraity_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3386a8f7-e476-4356-a1ee-3ac28e4e076b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "          0         1         2         3         4         5         6    \\\n",
       "0    1.000000  0.000000  0.000000  0.074482  0.016818  0.000000  0.083884   \n",
       "1    0.000000  1.000000  0.124295  0.118821  0.103646  0.000000  0.212985   \n",
       "2    0.000000  0.124295  1.000000  0.081640  0.151531  0.060691  0.154714   \n",
       "3    0.074482  0.118821  0.081640  1.000000  0.130649  0.079648  0.319745   \n",
       "4    0.016818  0.103646  0.151531  0.130649  1.000000  0.063796  0.095888   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "666  0.000000  0.425462  0.124562  0.088735  0.058252  0.000000  0.232051   \n",
       "667  0.000000  0.084646  0.124911  0.068483  0.042926  0.019563  0.058773   \n",
       "668  0.062917  0.024140  0.080984  0.104309  0.038358  0.024583  0.073151   \n",
       "669  0.000000  0.170595  0.136606  0.054512  0.062642  0.019465  0.096240   \n",
       "670  0.017466  0.113175  0.170193  0.211609  0.225086  0.087705  0.268672   \n",
       "\n",
       "          7         8         9    ...       661       662       663  \\\n",
       "0    0.000000  0.012843  0.000000  ...  0.000000  0.000000  0.014474   \n",
       "1    0.113190  0.113333  0.043213  ...  0.477306  0.063202  0.077745   \n",
       "2    0.249781  0.134475  0.114672  ...  0.161205  0.064198  0.176134   \n",
       "3    0.191013  0.030417  0.137186  ...  0.114319  0.047228  0.136579   \n",
       "4    0.165712  0.086616  0.032370  ...  0.191029  0.021142  0.146173   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "666  0.069005  0.066412  0.032653  ...  0.342283  0.050754  0.074080   \n",
       "667  0.112366  0.194493  0.098561  ...  0.074089  0.059010  0.093021   \n",
       "668  0.055143  0.029291  0.060549  ...  0.015960  0.025953  0.077927   \n",
       "669  0.247687  0.384429  0.158650  ...  0.183662  0.122126  0.123407   \n",
       "670  0.406414  0.168497  0.189703  ...  0.096704  0.070749  0.219728   \n",
       "\n",
       "          664       665       666       667       668       669       670  \n",
       "0    0.043719  0.000000  0.000000  0.000000  0.062917  0.000000  0.017466  \n",
       "1    0.164162  0.466281  0.425462  0.084646  0.024140  0.170595  0.113175  \n",
       "2    0.158357  0.177098  0.124562  0.124911  0.080984  0.136606  0.170193  \n",
       "3    0.254030  0.121905  0.088735  0.068483  0.104309  0.054512  0.211609  \n",
       "4    0.224245  0.139721  0.058252  0.042926  0.038358  0.062642  0.225086  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "666  0.124214  0.351207  1.000000  0.091597  0.018416  0.153111  0.127995  \n",
       "667  0.082525  0.114487  0.091597  1.000000  0.000000  0.178017  0.135387  \n",
       "668  0.101707  0.028773  0.018416  0.000000  1.000000  0.042609  0.085202  \n",
       "669  0.143380  0.159479  0.153111  0.178017  0.042609  1.000000  0.228677  \n",
       "670  0.239318  0.127204  0.127995  0.135387  0.085202  0.228677  1.000000  \n",
       "\n",
       "[671 rows x 671 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simiraity_score_df = pd.DataFrame(simiraity_score)\n",
    "simiraity_score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9bb3de0d-19af-4b35-a606-a248e558b412",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gandl\\anaconda3\\Lib\\site-packages\\pandas\\core\\reshape\\reshape.py:143: RuntimeWarning: overflow encountered in scalar multiply\n",
      "  num_cells = num_rows * num_columns\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "negative dimensions are not allowed",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[36], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# Create a pivot table as a sparse DataFrame\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m pt \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mpivot_table(\n\u001b[0;32m      5\u001b[0m     user_rating_data,\n\u001b[0;32m      6\u001b[0m     values\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrating\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m      7\u001b[0m     index\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muserId\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m      8\u001b[0m     columns\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmovieId\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m      9\u001b[0m     fill_value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[0;32m     10\u001b[0m     aggfunc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m     11\u001b[0m )\u001b[38;5;241m.\u001b[39mastype(pd\u001b[38;5;241m.\u001b[39mSparseDtype(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfloat\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m0\u001b[39m))\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\reshape\\pivot.py:102\u001b[0m, in \u001b[0;36mpivot_table\u001b[1;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)\u001b[0m\n\u001b[0;32m     99\u001b[0m     table \u001b[38;5;241m=\u001b[39m concat(pieces, keys\u001b[38;5;241m=\u001b[39mkeys, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m    100\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m table\u001b[38;5;241m.\u001b[39m__finalize__(data, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpivot_table\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 102\u001b[0m table \u001b[38;5;241m=\u001b[39m __internal_pivot_table(\n\u001b[0;32m    103\u001b[0m     data,\n\u001b[0;32m    104\u001b[0m     values,\n\u001b[0;32m    105\u001b[0m     index,\n\u001b[0;32m    106\u001b[0m     columns,\n\u001b[0;32m    107\u001b[0m     aggfunc,\n\u001b[0;32m    108\u001b[0m     fill_value,\n\u001b[0;32m    109\u001b[0m     margins,\n\u001b[0;32m    110\u001b[0m     dropna,\n\u001b[0;32m    111\u001b[0m     margins_name,\n\u001b[0;32m    112\u001b[0m     observed,\n\u001b[0;32m    113\u001b[0m     sort,\n\u001b[0;32m    114\u001b[0m )\n\u001b[0;32m    115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m table\u001b[38;5;241m.\u001b[39m__finalize__(data, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpivot_table\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\reshape\\pivot.py:203\u001b[0m, in \u001b[0;36m__internal_pivot_table\u001b[1;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)\u001b[0m\n\u001b[0;32m    201\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    202\u001b[0m             to_unstack\u001b[38;5;241m.\u001b[39mappend(name)\n\u001b[1;32m--> 203\u001b[0m     table \u001b[38;5;241m=\u001b[39m agged\u001b[38;5;241m.\u001b[39munstack(to_unstack, fill_value\u001b[38;5;241m=\u001b[39mfill_value)\n\u001b[0;32m    205\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m dropna:\n\u001b[0;32m    206\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(table\u001b[38;5;241m.\u001b[39mindex, MultiIndex):\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:9928\u001b[0m, in \u001b[0;36mDataFrame.unstack\u001b[1;34m(self, level, fill_value, sort)\u001b[0m\n\u001b[0;32m   9864\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   9865\u001b[0m \u001b[38;5;124;03mPivot a level of the (necessarily hierarchical) index labels.\u001b[39;00m\n\u001b[0;32m   9866\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   9924\u001b[0m \u001b[38;5;124;03mdtype: float64\u001b[39;00m\n\u001b[0;32m   9925\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   9926\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mreshape\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mreshape\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m unstack\n\u001b[1;32m-> 9928\u001b[0m result \u001b[38;5;241m=\u001b[39m unstack(\u001b[38;5;28mself\u001b[39m, level, fill_value, sort)\n\u001b[0;32m   9930\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munstack\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\reshape\\reshape.py:504\u001b[0m, in \u001b[0;36munstack\u001b[1;34m(obj, level, fill_value, sort)\u001b[0m\n\u001b[0;32m    502\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, DataFrame):\n\u001b[0;32m    503\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj\u001b[38;5;241m.\u001b[39mindex, MultiIndex):\n\u001b[1;32m--> 504\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m _unstack_frame(obj, level, fill_value\u001b[38;5;241m=\u001b[39mfill_value, sort\u001b[38;5;241m=\u001b[39msort)\n\u001b[0;32m    505\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    506\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39mT\u001b[38;5;241m.\u001b[39mstack(future_stack\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\reshape\\reshape.py:529\u001b[0m, in \u001b[0;36m_unstack_frame\u001b[1;34m(obj, level, fill_value, sort)\u001b[0m\n\u001b[0;32m    525\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_unstack_frame\u001b[39m(\n\u001b[0;32m    526\u001b[0m     obj: DataFrame, level, fill_value\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, sort: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m    527\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame:\n\u001b[0;32m    528\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj\u001b[38;5;241m.\u001b[39mindex, MultiIndex)  \u001b[38;5;66;03m# checked by caller\u001b[39;00m\n\u001b[1;32m--> 529\u001b[0m     unstacker \u001b[38;5;241m=\u001b[39m _Unstacker(\n\u001b[0;32m    530\u001b[0m         obj\u001b[38;5;241m.\u001b[39mindex, level\u001b[38;5;241m=\u001b[39mlevel, constructor\u001b[38;5;241m=\u001b[39mobj\u001b[38;5;241m.\u001b[39m_constructor, sort\u001b[38;5;241m=\u001b[39msort\n\u001b[0;32m    531\u001b[0m     )\n\u001b[0;32m    533\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m obj\u001b[38;5;241m.\u001b[39m_can_fast_transpose:\n\u001b[0;32m    534\u001b[0m         mgr \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_mgr\u001b[38;5;241m.\u001b[39munstack(unstacker, fill_value\u001b[38;5;241m=\u001b[39mfill_value)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\reshape\\reshape.py:154\u001b[0m, in \u001b[0;36m_Unstacker.__init__\u001b[1;34m(self, index, level, constructor, sort)\u001b[0m\n\u001b[0;32m    146\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_cells \u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39miinfo(np\u001b[38;5;241m.\u001b[39mint32)\u001b[38;5;241m.\u001b[39mmax:\n\u001b[0;32m    147\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    148\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe following operation may generate \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_cells\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m cells \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    149\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124min the resulting pandas object.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    150\u001b[0m         PerformanceWarning,\n\u001b[0;32m    151\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m    152\u001b[0m     )\n\u001b[1;32m--> 154\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_selectors()\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\reshape\\reshape.py:206\u001b[0m, in \u001b[0;36m_Unstacker._make_selectors\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    203\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfull_shape \u001b[38;5;241m=\u001b[39m ngroups, stride\n\u001b[0;32m    205\u001b[0m selector \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msorted_labels[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m stride \u001b[38;5;241m*\u001b[39m comp_index \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlift\n\u001b[1;32m--> 206\u001b[0m mask \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros(np\u001b[38;5;241m.\u001b[39mprod(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfull_shape), dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mbool\u001b[39m)\n\u001b[0;32m    207\u001b[0m mask\u001b[38;5;241m.\u001b[39mput(selector, \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    209\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39msum() \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex):\n",
      "\u001b[1;31mValueError\u001b[0m: negative dimensions are not allowed"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Create a pivot table as a sparse DataFrame\n",
    "pt = pd.pivot_table(\n",
    "    user_rating_data,\n",
    "    values=\"rating\",\n",
    "    index=\"userId\",\n",
    "    columns=\"movieId\",\n",
    "    fill_value=0,\n",
    "    aggfunc='mean'\n",
    ").astype(pd.SparseDtype(\"float\", 0))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d1bd3a5c-14c3-4bd7-81bb-7b18dec7ecf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# treshold value of the similarity between users. If treshold > 0.5, similar users\n",
    "# returns the index of the similar user\n",
    "def similar_users(userid,simiraity_treshold = 0.5):\n",
    "    index = userid - 1\n",
    "    frame = simiraity_score_df[index]\n",
    "    a = frame[frame >= simiraity_treshold]\n",
    "    similar_users = a.index.values + 1 #Because index starts from 0 and our userid starts from 1\n",
    "    return similar_users\n",
    "\n",
    "# returns the movies list seen by the user\n",
    "def user_seen_movies(userid):\n",
    "    movie_list_index =  ratings[ratings[\"userId\"] == userid][\"movieId\"].values\n",
    "    return set(movie_list_index)\n",
    "\n",
    "def seen_movies_by_similar_users(userid):\n",
    "    similar_user_list = similar_users(userid=userid)\n",
    "    movie_list_index = []\n",
    "    for i in similar_user_list:\n",
    "        movie_list_index.extend(ratings[ratings[\"userId\"] == i][\"movieId\"].values)\n",
    "    return set(movie_list_index)\n",
    "\n",
    "\n",
    "def recommendations_based_upon_user(user_id):\n",
    "    user_set = user_seen_movies(userid = user_id)\n",
    "    all_set = seen_movies_by_similar_users(userid=user_id)\n",
    "    set_difference = all_set.difference(user_set)\n",
    "    rec_mov = []\n",
    "    for i in movie_order:\n",
    "        if (i in set_difference):\n",
    "            rec_mov.append(i)\n",
    "    return rec_mov\n",
    "recommendations_based_upon_user(2)"
   ]
  },
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