RSNMF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading ratings file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aman Sharif\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  \n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>ratings</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>661</td>\n",
       "      <td>3</td>\n",
       "      <td>978302109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>978301968</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3408</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>978824291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   user_id  movie_id  ratings  timestamp\n",
       "0        1      1193        5  978300760\n",
       "1        1       661        3  978302109\n",
       "2        1       914        3  978301968\n",
       "3        1      3408        4  978300275\n",
       "4        1      2355        5  978824291"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_headers = ['user_id', 'movie_id', 'ratings', 'timestamp']\n",
    "ratings = pd.read_table('ml-1m/ratings.dat', sep='::', header=None, names=ratings_headers)\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading users file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aman Sharif\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  \n"
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip-code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
       "      <td>16</td>\n",
       "      <td>70072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <th>3</th>\n",
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       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>7</td>\n",
       "      <td>02460</td>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "   user_id gender  age  occupation zip-code\n",
       "0        1      F    1          10    48067\n",
       "1        2      M   56          16    70072\n",
       "2        3      M   25          15    55117\n",
       "3        4      M   45           7    02460\n",
       "4        5      M   25          20    55455"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_headers = ['user_id','gender','age','occupation','zip-code']\n",
    "users = pd.read_table('ml-1m/users.dat', sep='::', header=None, names=users_headers)\n",
    "users.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading movies file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aman Sharif\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  \n"
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   movie_id                               title                        genres\n",
       "0         1                    Toy Story (1995)   Animation|Children's|Comedy\n",
       "1         2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2         3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3         4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4         5  Father of the Bride Part II (1995)                        Comedy"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_headers = ['movie_id', 'title', 'genres']\n",
    "movies = pd.read_table('ml-1m/movies.dat',sep='::', header=None, names=movies_headers)\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joining three dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = users.join(ratings.set_index('user_id'), on=['user_id']).join(movies.set_index('movie_id'), on=['movie_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>user_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip-code</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>ratings</th>\n",
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       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
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       "      <td>978302109</td>\n",
       "      <td>James and the Giant Peach (1996)</td>\n",
       "      <td>Animation|Children's|Musical</td>\n",
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       "      <th>0</th>\n",
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       "      <td>My Fair Lady (1964)</td>\n",
       "      <td>Musical|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>3408</td>\n",
       "      <td>4</td>\n",
       "      <td>978300275</td>\n",
       "      <td>Erin Brockovich (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>978824291</td>\n",
       "      <td>Bug's Life, A (1998)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id gender  age  occupation zip-code  movie_id  ratings  timestamp  \\\n",
       "0        1      F    1          10    48067      1193        5  978300760   \n",
       "0        1      F    1          10    48067       661        3  978302109   \n",
       "0        1      F    1          10    48067       914        3  978301968   \n",
       "0        1      F    1          10    48067      3408        4  978300275   \n",
       "0        1      F    1          10    48067      2355        5  978824291   \n",
       "\n",
       "                                    title                        genres  \n",
       "0  One Flew Over the Cuckoo's Nest (1975)                         Drama  \n",
       "0        James and the Giant Peach (1996)  Animation|Children's|Musical  \n",
       "0                     My Fair Lady (1964)               Musical|Romance  \n",
       "0                  Erin Brockovich (2000)                         Drama  \n",
       "0                    Bug's Life, A (1998)   Animation|Children's|Comedy  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000209, 10)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating spreadsheet-style pivot table as a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>user_id</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3706 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "movie_id  1     2     3     4     5     6     7     8     9     10    ...   \\\n",
       "user_id                                                               ...    \n",
       "1          5.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "2          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "3          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "4          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "5          NaN   NaN   NaN   NaN   NaN   2.0   NaN   NaN   NaN   NaN  ...    \n",
       "\n",
       "movie_id  3943  3944  3945  3946  3947  3948  3949  3950  3951  3952  \n",
       "user_id                                                               \n",
       "1          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "4          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "5          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[5 rows x 3706 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rp = df.pivot_table(columns=['movie_id'],index=['user_id'],values='ratings')\n",
    "rp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>movie_id</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>3943</th>\n",
       "      <th>3944</th>\n",
       "      <th>3945</th>\n",
       "      <th>3946</th>\n",
       "      <th>3947</th>\n",
       "      <th>3948</th>\n",
       "      <th>3949</th>\n",
       "      <th>3950</th>\n",
       "      <th>3951</th>\n",
       "      <th>3952</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</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>6036</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6037</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6038</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6039</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6040</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3706 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "movie_id  1     2     3     4     5     6     7     8     9     10    ...   \\\n",
       "user_id                                                               ...    \n",
       "6036       NaN   NaN   NaN   2.0   NaN   3.0   NaN   NaN   NaN   NaN  ...    \n",
       "6037       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "6038       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "6039       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "6040       3.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "\n",
       "movie_id  3943  3944  3945  3946  3947  3948  3949  3950  3951  3952  \n",
       "user_id                                                               \n",
       "6036       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "6037       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "6038       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "6039       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "6040       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[5 rows x 3706 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rp.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6040, 3706)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rp.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filling NaN with 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>movie_id</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>3943</th>\n",
       "      <th>3944</th>\n",
       "      <th>3945</th>\n",
       "      <th>3946</th>\n",
       "      <th>3947</th>\n",
       "      <th>3948</th>\n",
       "      <th>3949</th>\n",
       "      <th>3950</th>\n",
       "      <th>3951</th>\n",
       "      <th>3952</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</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>5.0</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>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>3</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>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",
       "    <tr>\n",
       "      <th>5</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>2.0</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>5 rows × 3706 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "movie_id  1     2     3     4     5     6     7     8     9     10    ...   \\\n",
       "user_id                                                               ...    \n",
       "1          5.0   0.0   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   0.0   0.0  ...    \n",
       "3          0.0   0.0   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   0.0   0.0  ...    \n",
       "5          0.0   0.0   0.0   0.0   0.0   2.0   0.0   0.0   0.0   0.0  ...    \n",
       "\n",
       "movie_id  3943  3944  3945  3946  3947  3948  3949  3950  3951  3952  \n",
       "user_id                                                               \n",
       "1          0.0   0.0   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   0.0   0.0  \n",
       "3          0.0   0.0   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   0.0   0.0  \n",
       "5          0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "\n",
       "[5 rows x 3706 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rp = rp.fillna(0); # Replace NaN\n",
    "rp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting values from rp dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = rp.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[5., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [3., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R = np.asmatrix(r)\n",
    "R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6040, 3706)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialization of parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "lambda_p = 0.1\n",
    "lambda_q = 0.1\n",
    "f = 100\n",
    "U, I = R.shape\n",
    "max_training_round = 20\n",
    "\n",
    "t = 1            #Training Epoch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialization of Factor matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = 5 * np.random.rand(U, f) \n",
    "q = 5 * np.random.rand(f, I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "P = np.asmatrix(p)\n",
    "Q = np.asmatrix(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[1.32669816, 2.15733512, 4.22489242, ..., 2.86199696, 3.9496379 ,\n",
       "         0.76311139],\n",
       "        [3.78808824, 2.8674695 , 2.67242092, ..., 4.58771151, 2.66560289,\n",
       "         2.51683236],\n",
       "        [2.30920035, 2.31980716, 3.82209771, ..., 3.87119193, 0.1203963 ,\n",
       "         2.24059126],\n",
       "        ...,\n",
       "        [0.50679877, 3.49281426, 4.40260623, ..., 3.72922823, 4.50933687,\n",
       "         1.32512072],\n",
       "        [2.26319962, 4.7966294 , 4.60343997, ..., 1.18271106, 1.96100533,\n",
       "         4.56273527],\n",
       "        [0.09626228, 2.48794385, 1.43650511, ..., 4.71794274, 0.62865997,\n",
       "         3.96563537]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[2.88972554, 3.99031   , 2.00686291, ..., 0.86256032, 0.52806533,\n",
       "         4.61415958],\n",
       "        [1.21099218, 3.89952411, 0.75376672, ..., 3.780225  , 3.4106275 ,\n",
       "         3.43483437],\n",
       "        [0.27109345, 4.69098372, 1.21964291, ..., 3.36529976, 0.08325299,\n",
       "         2.34128491],\n",
       "        ...,\n",
       "        [1.31240838, 4.24174721, 4.80133005, ..., 3.8368064 , 4.52157916,\n",
       "         3.66066186],\n",
       "        [1.521531  , 2.17473108, 2.57697319, ..., 2.70402347, 2.50691272,\n",
       "         4.14286617],\n",
       "        [2.85309122, 1.11094758, 2.20695549, ..., 0.74121035, 3.75801508,\n",
       "         0.64674265]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple error measure (Sum of elementwise squared error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_error(R, P, Q):\n",
    "    return np.sum(np.square(R - np.dot(P,Q)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Auxiliary matrix to determine Ui and Iu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "W = R>0.01\n",
    "W[W == True] = 1\n",
    "W[W == False] = 0\n",
    "# To be consistent with our Q matrix\n",
    "W = W.astype(np.int64, copy=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Counting no of users who rated item i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ui = []\n",
    "for iterator in range(I):\n",
    "    Ui.append(W[:,iterator].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Counting no of items which was being rated by user u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Iu = []\n",
    "for iterator in range(U):\n",
    "    Iu.append(W[iterator].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3706"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Ui)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6040"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Iu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RSNMF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1th epoch is completed\n",
      "2th epoch is completed\n",
      "3th epoch is completed\n",
      "4th epoch is completed\n",
      "5th epoch is completed\n",
      "6th epoch is completed\n",
      "7th epoch is completed\n",
      "8th epoch is completed\n",
      "9th epoch is completed\n",
      "10th epoch is completed\n",
      "11th epoch is completed\n",
      "12th epoch is completed\n",
      "13th epoch is completed\n",
      "14th epoch is completed\n",
      "15th epoch is completed\n",
      "16th epoch is completed\n",
      "17th epoch is completed\n",
      "18th epoch is completed\n",
      "19th epoch is completed\n",
      "20th epoch is completed\n"
     ]
    }
   ],
   "source": [
    "error = []\n",
    "while t <= max_training_round:\n",
    "    UserUp = np.zeros((U,f), dtype=float)\n",
    "    UserDown = np.zeros((U,f), dtype=float)\n",
    "    ItemUp = np.zeros((f,I), dtype=float)\n",
    "    ItemDown = np.zeros((f,I), dtype=float)\n",
    "    for u in range(U):\n",
    "        for i in range(I):\n",
    "            if(R[u,i] != 0):\n",
    "                rui_cap = P[u,:]*Q[:,i]\n",
    "                for k in range(f):\n",
    "                    UserUp[u,k] = UserUp[u,k] + Q[k,i] * R[u,i]\n",
    "                    UserDown[u,k] = UserDown[u,k] + Q[k,i] * rui_cap\n",
    "                    ItemUp[k,i] = ItemUp[k,i] + P[u,k] * R[u,i]\n",
    "                    ItemDown[k,i] = ItemDown[k,i] + P[u,k]*rui_cap\n",
    "    for u in range(U):\n",
    "        for k in range(f):\n",
    "            UserDown[u,k] = UserDown[u,k] + Iu[u] * lambda_p * P[u,k]\n",
    "            P[u,k] = P[u,k] * (UserUp[u,k]/UserDown[u,k])\n",
    "    for i in range(I):\n",
    "        for k in range(f):\n",
    "            ItemDown[k,i] = ItemDown[k,i] + Ui[i] * lambda_q * Q[k,i]\n",
    "            Q[k,i] = Q[k,i] * (ItemUp[k,i]/ItemDown[k,i])\n",
    "    error.append(get_error(R, P, Q))\n",
    "    print('{}th epoch is completed'.format(t))\n",
    "    t = t+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total error of rated movies: 569551283.3613634\n"
     ]
    }
   ],
   "source": [
    "print('Total error of rated movies: {}'.format(get_error(R,P,Q)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28756772208>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(error);\n",
    "plt.xlabel('Iteration Number');\n",
    "plt.ylabel('Sum of Squared Error');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'movie_titles' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-29-068ae87d6212>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0mprint_recommendations\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mQ_hat\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mW\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mW\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mQ\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mQ\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmovie_titles\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmovie_titles\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m     \u001b[1;31m#Q_hat -= np.min(Q_hat)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[1;31m#Q_hat[Q_hat < 1] *= 5\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mQ_hat\u001b[0m \u001b[1;33m-=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mQ_hat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mQ_hat\u001b[0m \u001b[1;33m*=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mQ_hat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'movie_titles' is not defined"
     ]
    }
   ],
   "source": [
    "def print_recommendations(Q_hat,W=W, Q=Q, movie_titles=movie_titles):\n",
    "    #Q_hat -= np.min(Q_hat)\n",
    "    #Q_hat[Q_hat < 1] *= 5\n",
    "    Q_hat -= np.min(Q_hat)\n",
    "    Q_hat *= float(5) / np.max(Q_hat)\n",
    "    movie_ids = np.argmax(Q_hat - 5 * W, axis=1)\n",
    "    for jj, movie_id in zip(range(m), movie_ids):\n",
    "        #if Q_hat[jj, movie_id] < 0.1: continue\n",
    "        print('User {} liked {}\\n'.format(jj + 1, ', '.join([movie_titles[ii] for ii, qq in enumerate(Q[jj]) if qq > 3])))\n",
    "        print('User {} did not like {}\\n'.format(jj + 1, ', '.join([movie_titles[ii] for ii, qq in enumerate(Q[jj]) if qq < 3 and qq != 0])))\n",
    "        print('\\n User {} recommended movie is {} - with predicted rating: {}'.format(\n",
    "                    jj + 1, movie_titles[movie_id], Q_hat[jj, movie_id]))\n",
    "        print('\\n' + 100 *  '-' + '\\n')\n",
    "#print_recommendations()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "R_estimate = np.dot(P,Q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print_recommendations(R_estimate)"
   ]
  }
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