code/day33 content based filtering for movie recommendation.ipynb

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    "# <img align=\"left\" src=\"./images/film_strip_vertical.png\"     style=\" width:40px;  \" > Practice lab: Deep Learning for Content-Based Filtering\n",
    "\n",
    "In this exercise, you will implement content-based filtering using a neural network to build a recommender system for movies. \n",
    "\n",
    "\n",
    "# Outline\n",
    "- [ 1 - Packages ](#1)\n",
    "- [ 2 - Movie ratings dataset ](#2)\n",
    "- [ 3 - Content-based filtering with a neural network](#3)\n",
    "  - [ 3.1 Training Data](#3.1)\n",
    "  - [ 3.2 Preparing the training data](#3.2)\n",
    "- [ 4 - Neural Network for content-based filtering](#4)\n",
    "  - [ Exercise 1](#ex01)\n",
    "- [ 5 - Predictions](#5)\n",
    "  - [ 5.1 - Predictions for a new user](#5.1)\n",
    "  - [ 5.2 - Predictions for an existing user.](#5.2)\n",
    "  - [ 5.3 - Finding Similar Items](#5.3)\n",
    "    - [ Exercise 2](#ex02)\n",
    "- [ 6 - Congratulations! ](#6)\n"
   ]
  },
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   "source": [
    "_**NOTE:** To prevent errors from the autograder, you are not allowed to edit or delete non-graded cells in this lab. Please also refrain from adding any new cells. \n",
    "**Once you have passed this assignment** and want to experiment with any of the non-graded code, you may follow the instructions at the bottom of this notebook._"
   ]
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   "source": [
    "<a name=\"1\"></a>\n",
    "## 1 - Packages <img align=\"left\" src=\"./images/movie_camera.png\"     style=\" width:40px;  \">\n",
    "We will use familiar packages, NumPy, TensorFlow and helpful routines from [scikit-learn](https://scikit-learn.org/stable/). We will also use [tabulate](https://pypi.org/project/tabulate/) to neatly print tables and [Pandas](https://pandas.pydata.org/) to organize tabular data."
   ]
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   "source": [
    "import numpy as np\n",
    "import numpy.ma as ma\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tabulate\n",
    "from recsysNN_utils import *\n",
    "pd.set_option(\"display.precision\", 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"2\"></a>\n",
    "## 2 - Movie ratings dataset <img align=\"left\" src=\"./images/film_rating.png\" style=\" width:40px;\" >\n",
    "The data set is derived from the [MovieLens ml-latest-small](https://grouplens.org/datasets/movielens/latest/) dataset. \n",
    "\n",
    "[F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. <https://doi.org/10.1145/2827872>]\n",
    "\n",
    "The original dataset has roughly 9000 movies rated by 600 users with ratings on a scale of 0.5 to 5 in 0.5 step increments. The dataset has been reduced in size to focus on movies from the years since 2000 and popular genres. The reduced dataset has $n_u = 397$ users, $n_m= 847$ movies and 25521 ratings. For each movie, the dataset provides a movie title, release date, and one or more genres. For example \"Toy Story 3\" was released in 2010 and has several genres: \"Adventure|Animation|Children|Comedy|Fantasy\". This dataset contains little information about users other than their ratings. This dataset is used to create training vectors for the neural networks described below. \n",
    "Let's learn a bit more about this data set. The table below shows the top 10 movies ranked by the number of ratings. These movies also happen to have high average ratings. How many of these movies have you watched? "
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie id</th>\n",
       "      <th>num ratings</th>\n",
       "      <th>ave rating</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4993</td>\n",
       "      <td>198</td>\n",
       "      <td>4.1</td>\n",
       "      <td>Lord of the Rings: The Fellowship of the Ring,...</td>\n",
       "      <td>Adventure|Fantasy</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5952</td>\n",
       "      <td>188</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Lord of the Rings: The Two Towers, The</td>\n",
       "      <td>Adventure|Fantasy</td>\n",
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       "      <th>2</th>\n",
       "      <td>7153</td>\n",
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       "      <td>Lord of the Rings: The Return of the King, The</td>\n",
       "      <td>Action|Adventure|Drama|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4306</td>\n",
       "      <td>170</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Shrek</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy|Ro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58559</td>\n",
       "      <td>149</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Dark Knight, The</td>\n",
       "      <td>Action|Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6539</td>\n",
       "      <td>149</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Pirates of the Caribbean: The Curse of the Bla...</td>\n",
       "      <td>Action|Adventure|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>79132</td>\n",
       "      <td>143</td>\n",
       "      <td>4.1</td>\n",
       "      <td>Inception</td>\n",
       "      <td>Action|Crime|Drama|Mystery|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6377</td>\n",
       "      <td>141</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Finding Nemo</td>\n",
       "      <td>Adventure|Animation|Children|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4886</td>\n",
       "      <td>132</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Monsters, Inc.</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7361</td>\n",
       "      <td>131</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Eternal Sunshine of the Spotless Mind</td>\n",
       "      <td>Drama|Romance|Sci-Fi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   movie id  num ratings  ave rating  \\\n",
       "0      4993          198         4.1   \n",
       "1      5952          188         4.0   \n",
       "2      7153          185         4.1   \n",
       "3      4306          170         3.9   \n",
       "4     58559          149         4.2   \n",
       "5      6539          149         3.8   \n",
       "6     79132          143         4.1   \n",
       "7      6377          141         4.0   \n",
       "8      4886          132         3.9   \n",
       "9      7361          131         4.2   \n",
       "\n",
       "                                               title  \\\n",
       "0  Lord of the Rings: The Fellowship of the Ring,...   \n",
       "1             Lord of the Rings: The Two Towers, The   \n",
       "2     Lord of the Rings: The Return of the King, The   \n",
       "3                                              Shrek   \n",
       "4                                   Dark Knight, The   \n",
       "5  Pirates of the Caribbean: The Curse of the Bla...   \n",
       "6                                          Inception   \n",
       "7                                       Finding Nemo   \n",
       "8                                     Monsters, Inc.   \n",
       "9              Eternal Sunshine of the Spotless Mind   \n",
       "\n",
       "                                              genres  \n",
       "0                                  Adventure|Fantasy  \n",
       "1                                  Adventure|Fantasy  \n",
       "2                     Action|Adventure|Drama|Fantasy  \n",
       "3  Adventure|Animation|Children|Comedy|Fantasy|Ro...  \n",
       "4                                 Action|Crime|Drama  \n",
       "5                    Action|Adventure|Comedy|Fantasy  \n",
       "6         Action|Crime|Drama|Mystery|Sci-Fi|Thriller  \n",
       "7                Adventure|Animation|Children|Comedy  \n",
       "8        Adventure|Animation|Children|Comedy|Fantasy  \n",
       "9                               Drama|Romance|Sci-Fi  "
      ]
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   "source": [
    "top10_df = pd.read_csv(\"./data/content_top10_df.csv\")\n",
    "bygenre_df = pd.read_csv(\"./data/content_bygenre_df.csv\")\n",
    "top10_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next table shows information sorted by genre. The number of ratings per genre vary substantially. Note that a movie may have multiple genre's so the sum of the ratings below is larger than the number of original ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre</th>\n",
       "      <th>num movies</th>\n",
       "      <th>ave rating/genre</th>\n",
       "      <th>ratings per genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action</td>\n",
       "      <td>321</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>234</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Animation</td>\n",
       "      <td>76</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Children</td>\n",
       "      <td>69</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>326</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Crime</td>\n",
       "      <td>139</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Documentary</td>\n",
       "      <td>13</td>\n",
       "      <td>3.8</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Drama</td>\n",
       "      <td>342</td>\n",
       "      <td>3.6</td>\n",
       "      <td>10201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>124</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Horror</td>\n",
       "      <td>56</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>68</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Romance</td>\n",
       "      <td>151</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>174</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>245</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7659</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          genre  num movies  ave rating/genre  ratings per genre\n",
       "0        Action         321               3.4              10377\n",
       "1     Adventure         234               3.4               8785\n",
       "2     Animation          76               3.6               2588\n",
       "3      Children          69               3.4               2472\n",
       "4        Comedy         326               3.4               8911\n",
       "5         Crime         139               3.5               4671\n",
       "6   Documentary          13               3.8                280\n",
       "7         Drama         342               3.6              10201\n",
       "8       Fantasy         124               3.4               4468\n",
       "9        Horror          56               3.2               1345\n",
       "10      Mystery          68               3.6               2497\n",
       "11      Romance         151               3.4               4468\n",
       "12       Sci-Fi         174               3.4               5894\n",
       "13     Thriller         245               3.4               7659"
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "bygenre_df"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"3\"></a>\n",
    "## 3 - Content-based filtering with a neural network\n",
    "\n",
    "In the collaborative filtering lab, you generated two vectors, a user vector and an item/movie vector whose dot product would predict a rating. The vectors were derived solely from the ratings.   \n",
    "\n",
    "Content-based filtering also generates a user and movie feature vector but recognizes there may be other information available about the user and/or movie that may improve the prediction. The additional information is provided to a neural network which then generates the user and movie vector as shown below.\n",
    "<figure>\n",
    "    <center> <img src=\"./images/RecSysNN.png\"   style=\"width:500px;height:280px;\" ></center>\n",
    "</figure>\n",
    "\n",
    "<a name=\"3.1\"></a>\n",
    "### 3.1 Training Data\n",
    "The movie content provided to the network is a combination of the original data and some 'engineered features'. Recall the feature engineering discussion and lab from Course 1, Week 2, lab 4. The original features are the year the movie was released and the movie's genre's presented as a one-hot vector. There are 14 genres. The engineered feature is an average rating derived from the user ratings. \n",
    "\n",
    "The user content is composed of engineered features. A per genre average rating is computed per user. Additionally, a user id, rating count and rating average are available but not included in the training or prediction content. They are carried with the data set because they are useful in interpreting data.\n",
    "\n",
    "The training set consists of all the ratings made by the users in the data set. Some ratings are repeated to boost the number of training examples of underrepresented genre's. The training set is split into two arrays with the same number of entries, a user array and a movie/item array.  \n",
    "\n",
    "Below, let's load and display some of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "M5gfMLYgxCD1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training vectors: 50884\n"
     ]
    }
   ],
   "source": [
    "# Load Data, set configuration variables\n",
    "item_train, user_train, y_train, item_features, user_features, item_vecs, movie_dict, user_to_genre = load_data()\n",
    "\n",
    "num_user_features = user_train.shape[1] - 3  # remove userid, rating count and ave rating during training\n",
    "num_item_features = item_train.shape[1] - 1  # remove movie id at train time\n",
    "uvs = 3  # user genre vector start\n",
    "ivs = 3  # item genre vector start\n",
    "u_s = 3  # start of columns to use in training, user\n",
    "i_s = 1  # start of columns to use in training, items\n",
    "print(f\"Number of training vectors: {len(item_train)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the first few entries in the user training array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: center;\"> [user id] </th><th style=\"text-align: center;\"> [rating count] </th><th style=\"text-align: center;\"> [rating ave] </th><th style=\"text-align: center;\"> Act ion </th><th style=\"text-align: center;\"> Adve nture </th><th style=\"text-align: center;\"> Anim ation </th><th style=\"text-align: center;\"> Chil dren </th><th style=\"text-align: center;\"> Com edy </th><th style=\"text-align: center;\"> Crime </th><th style=\"text-align: center;\"> Docum entary </th><th style=\"text-align: center;\"> Drama </th><th style=\"text-align: center;\"> Fan tasy </th><th style=\"text-align: center;\"> Hor ror </th><th style=\"text-align: center;\"> Mys tery </th><th style=\"text-align: center;\"> Rom ance </th><th style=\"text-align: center;\"> Sci -Fi </th><th style=\"text-align: center;\"> Thri ller </th></tr>\n",
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       "<tr><td style=\"text-align: center;\">     2     </td><td style=\"text-align: center;\">       22       </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">    4.2     </td><td style=\"text-align: center;\">    0.0     </td><td style=\"text-align: center;\">    0.0    </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">  4.1  </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">  4.0  </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.0   </td><td style=\"text-align: center;\">   4.0    </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.9   </td><td style=\"text-align: center;\">    3.9    </td></tr>\n",
       "<tr><td style=\"text-align: center;\">     2     </td><td style=\"text-align: center;\">       22       </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">    4.2     </td><td style=\"text-align: center;\">    0.0     </td><td style=\"text-align: center;\">    0.0    </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">  4.1  </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">  4.0  </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.0   </td><td style=\"text-align: center;\">   4.0    </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.9   </td><td style=\"text-align: center;\">    3.9    </td></tr>\n",
       "<tr><td style=\"text-align: center;\">     2     </td><td style=\"text-align: center;\">       22       </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">    4.2     </td><td style=\"text-align: center;\">    0.0     </td><td style=\"text-align: center;\">    0.0    </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">  4.1  </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">  4.0  </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.0   </td><td style=\"text-align: center;\">   4.0    </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.9   </td><td style=\"text-align: center;\">    3.9    </td></tr>\n",
       "<tr><td style=\"text-align: center;\">     2     </td><td style=\"text-align: center;\">       22       </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">    4.2     </td><td style=\"text-align: center;\">    0.0     </td><td style=\"text-align: center;\">    0.0    </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">  4.1  </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">  4.0  </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.0   </td><td style=\"text-align: center;\">   4.0    </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.9   </td><td style=\"text-align: center;\">    3.9    </td></tr>\n",
       "<tr><td style=\"text-align: center;\">     2     </td><td style=\"text-align: center;\">       22       </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">    4.2     </td><td style=\"text-align: center;\">    0.0     </td><td style=\"text-align: center;\">    0.0    </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">  4.1  </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">  4.0  </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.0   </td><td style=\"text-align: center;\">   4.0    </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.9   </td><td style=\"text-align: center;\">    3.9    </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "'<table>\\n<thead>\\n<tr><th style=\"text-align: center;\"> [user id] </th><th style=\"text-align: center;\"> [rating count] </th><th style=\"text-align: center;\"> [rating ave] </th><th style=\"text-align: center;\"> Act ion </th><th style=\"text-align: center;\"> Adve nture </th><th style=\"text-align: center;\"> Anim ation </th><th style=\"text-align: center;\"> Chil dren </th><th style=\"text-align: center;\"> Com edy </th><th style=\"text-align: center;\"> Crime </th><th style=\"text-align: center;\"> Docum entary </th><th style=\"text-align: center;\"> Drama </th><th style=\"text-align: center;\"> Fan tasy </th><th style=\"text-align: center;\"> Hor ror </th><th style=\"text-align: center;\"> Mys tery </th><th style=\"text-align: center;\"> Rom ance </th><th style=\"text-align: center;\"> Sci -Fi </th><th style=\"text-align: center;\"> Thri ller </th></tr>\\n</thead>\\n<tbody>\\n<tr><td style=\"text-align: center;\">     2     </td><td style=\"text-align: center;\">       22       </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">    4.2     </td><td style=\"text-align: center;\">    0.0     </td><td style=\"text-align: center;\">    0.0    </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">  4.1  </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">  4.0  </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.0   </td><td style=\"text-align: center;\">   4.0    </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.9   </td><td style=\"text-align: center;\">    3.9    </td></tr>\\n<tr><td style=\"text-align: center;\">     2     </td><td style=\"text-align: center;\">       22       </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">    4.2     </td><td style=\"text-align: center;\">    0.0     </td><td style=\"text-align: center;\">    0.0    </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">  4.1  </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">  4.0  </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.0   </td><td style=\"text-align: center;\">   4.0    </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.9   </td><td style=\"text-align: center;\">    3.9    </td></tr>\\n<tr><td style=\"text-align: center;\">     2     </td><td style=\"text-align: center;\">       22       </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">    4.2     </td><td style=\"text-align: center;\">    0.0     </td><td style=\"text-align: center;\">    0.0    </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">  4.1  </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">  4.0  </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.0   </td><td style=\"text-align: center;\">   4.0    </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.9   </td><td style=\"text-align: center;\">    3.9    </td></tr>\\n<tr><td style=\"text-align: center;\">     2     </td><td style=\"text-align: center;\">       22       </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">    4.2     </td><td style=\"text-align: center;\">    0.0     </td><td style=\"text-align: center;\">    0.0    </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">  4.1  </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">  4.0  </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.0   </td><td style=\"text-align: center;\">   4.0    </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.9   </td><td style=\"text-align: center;\">    3.9    </td></tr>\\n<tr><td style=\"text-align: center;\">     2     </td><td style=\"text-align: center;\">       22       </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">    4.2     </td><td style=\"text-align: center;\">    0.0     </td><td style=\"text-align: center;\">    0.0    </td><td style=\"text-align: center;\">   4.0   </td><td style=\"text-align: center;\">  4.1  </td><td style=\"text-align: center;\">     4.0      </td><td style=\"text-align: center;\">  4.0  </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.0   </td><td style=\"text-align: center;\">   4.0    </td><td style=\"text-align: center;\">   0.0    </td><td style=\"text-align: center;\">   3.9   </td><td style=\"text-align: center;\">    3.9    </td></tr>\\n</tbody>\\n</table>'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pprint_train(user_train, user_features, uvs,  u_s, maxcount=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the user and item/movie features are not used in training. In the table above, the features in brackets \"[]\" such as the \"user id\", \"rating count\" and \"rating ave\" are not included when the model is trained and used.\n",
    "Above you can see the per genre rating average for user 2. Zero entries are genre's which the user had not rated. The user vector is the same for all the movies rated by a user.  \n",
    "Let's look at the first few entries of the movie/item array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: center;\"> [movie id] </th><th style=\"text-align: center;\"> year </th><th style=\"text-align: center;\"> ave rating </th><th style=\"text-align: center;\"> Act ion </th><th style=\"text-align: center;\"> Adve nture </th><th style=\"text-align: center;\"> Anim ation </th><th style=\"text-align: center;\"> Chil dren </th><th style=\"text-align: center;\"> Com edy </th><th style=\"text-align: center;\"> Crime </th><th style=\"text-align: center;\"> Docum entary </th><th style=\"text-align: center;\"> Drama </th><th style=\"text-align: center;\"> Fan tasy </th><th style=\"text-align: center;\"> Hor ror </th><th style=\"text-align: center;\"> Mys tery </th><th style=\"text-align: center;\"> Rom ance </th><th style=\"text-align: center;\"> Sci -Fi </th><th style=\"text-align: center;\"> Thri ller </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: center;\">    6874    </td><td style=\"text-align: center;\"> 2003 </td><td style=\"text-align: center;\">    4.0     </td><td style=\"text-align: center;\">    1    </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">   1   </td><td style=\"text-align: center;\">      0       </td><td style=\"text-align: center;\">   0   </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">     1     </td></tr>\n",
       "<tr><td style=\"text-align: center;\">    8798    </td><td style=\"text-align: center;\"> 2004 </td><td style=\"text-align: center;\">    3.8     </td><td style=\"text-align: center;\">    1    </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">   1   </td><td style=\"text-align: center;\">      0       </td><td style=\"text-align: center;\">   1   </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">     1     </td></tr>\n",
       "<tr><td style=\"text-align: center;\">   46970    </td><td style=\"text-align: center;\"> 2006 </td><td style=\"text-align: center;\">    3.2     </td><td style=\"text-align: center;\">    1    </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0     </td><td style=\"text-align: center;\">    1    </td><td style=\"text-align: center;\">   0   </td><td style=\"text-align: center;\">      0       </td><td style=\"text-align: center;\">   0   </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">     0     </td></tr>\n",
       "<tr><td style=\"text-align: center;\">   48516    </td><td style=\"text-align: center;\"> 2006 </td><td style=\"text-align: center;\">    4.3     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">   1   </td><td style=\"text-align: center;\">      0       </td><td style=\"text-align: center;\">   1   </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">     1     </td></tr>\n",
       "<tr><td style=\"text-align: center;\">   58559    </td><td style=\"text-align: center;\"> 2008 </td><td style=\"text-align: center;\">    4.2     </td><td style=\"text-align: center;\">    1    </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">   1   </td><td style=\"text-align: center;\">      0       </td><td style=\"text-align: center;\">   1   </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">     0     </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "'<table>\\n<thead>\\n<tr><th style=\"text-align: center;\"> [movie id] </th><th style=\"text-align: center;\"> year </th><th style=\"text-align: center;\"> ave rating </th><th style=\"text-align: center;\"> Act ion </th><th style=\"text-align: center;\"> Adve nture </th><th style=\"text-align: center;\"> Anim ation </th><th style=\"text-align: center;\"> Chil dren </th><th style=\"text-align: center;\"> Com edy </th><th style=\"text-align: center;\"> Crime </th><th style=\"text-align: center;\"> Docum entary </th><th style=\"text-align: center;\"> Drama </th><th style=\"text-align: center;\"> Fan tasy </th><th style=\"text-align: center;\"> Hor ror </th><th style=\"text-align: center;\"> Mys tery </th><th style=\"text-align: center;\"> Rom ance </th><th style=\"text-align: center;\"> Sci -Fi </th><th style=\"text-align: center;\"> Thri ller </th></tr>\\n</thead>\\n<tbody>\\n<tr><td style=\"text-align: center;\">    6874    </td><td style=\"text-align: center;\"> 2003 </td><td style=\"text-align: center;\">    4.0     </td><td style=\"text-align: center;\">    1    </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">   1   </td><td style=\"text-align: center;\">      0       </td><td style=\"text-align: center;\">   0   </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">     1     </td></tr>\\n<tr><td style=\"text-align: center;\">    8798    </td><td style=\"text-align: center;\"> 2004 </td><td style=\"text-align: center;\">    3.8     </td><td style=\"text-align: center;\">    1    </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">   1   </td><td style=\"text-align: center;\">      0       </td><td style=\"text-align: center;\">   1   </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">     1     </td></tr>\\n<tr><td style=\"text-align: center;\">   46970    </td><td style=\"text-align: center;\"> 2006 </td><td style=\"text-align: center;\">    3.2     </td><td style=\"text-align: center;\">    1    </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0     </td><td style=\"text-align: center;\">    1    </td><td style=\"text-align: center;\">   0   </td><td style=\"text-align: center;\">      0       </td><td style=\"text-align: center;\">   0   </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">     0     </td></tr>\\n<tr><td style=\"text-align: center;\">   48516    </td><td style=\"text-align: center;\"> 2006 </td><td style=\"text-align: center;\">    4.3     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">   1   </td><td style=\"text-align: center;\">      0       </td><td style=\"text-align: center;\">   1   </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">     1     </td></tr>\\n<tr><td style=\"text-align: center;\">   58559    </td><td style=\"text-align: center;\"> 2008 </td><td style=\"text-align: center;\">    4.2     </td><td style=\"text-align: center;\">    1    </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0      </td><td style=\"text-align: center;\">     0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">   1   </td><td style=\"text-align: center;\">      0       </td><td style=\"text-align: center;\">   1   </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0     </td><td style=\"text-align: center;\">    0    </td><td style=\"text-align: center;\">     0     </td></tr>\\n</tbody>\\n</table>'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pprint_train(item_train, item_features, ivs, i_s, maxcount=5, user=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, the movie array contains the year the film was released, the average rating and an indicator for each potential genre. The indicator is one for each genre that applies to the movie. The movie id is not used in training but is useful when interpreting the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train[:5]: [4.  3.5 4.  4.  4.5]\n"
     ]
    }
   ],
   "source": [
    "print(f\"y_train[:5]: {y_train[:5]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target, y, is the movie rating given by the user. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, we can see that movie 6874 is an Action/Crime/Thriller movie released in 2003. User 2 rates action movies as 3.9 on average. MovieLens users gave the movie an average rating of 4. 'y' is 4 indicating user 2 rated movie 6874 as a 4 as well. A single training example consists of a row from both the user and item arrays and a rating from y_train."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"3.2\"></a>\n",
    "### 3.2 Preparing the training data\n",
    "Recall in Course 1, Week 2, you explored feature scaling as a means of improving convergence. We'll scale the input features using the [scikit learn StandardScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html). This was used in Course 1, Week 2, Lab 5.  Below, the inverse_transform is also shown to produce the original inputs. We'll scale the target ratings using a Min Max Scaler which scales the target to be between -1 and 1. [scikit learn MinMaxScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# scale training data\n",
    "item_train_unscaled = item_train\n",
    "user_train_unscaled = user_train\n",
    "y_train_unscaled    = y_train\n",
    "\n",
    "scalerItem = StandardScaler()\n",
    "scalerItem.fit(item_train)\n",
    "item_train = scalerItem.transform(item_train)\n",
    "\n",
    "scalerUser = StandardScaler()\n",
    "scalerUser.fit(user_train)\n",
    "user_train = scalerUser.transform(user_train)\n",
    "\n",
    "scalerTarget = MinMaxScaler((-1, 1))\n",
    "scalerTarget.fit(y_train.reshape(-1, 1))\n",
    "y_train = scalerTarget.transform(y_train.reshape(-1, 1))\n",
    "#ynorm_test = scalerTarget.transform(y_test.reshape(-1, 1))\n",
    "\n",
    "print(np.allclose(item_train_unscaled, scalerItem.inverse_transform(item_train)))\n",
    "print(np.allclose(user_train_unscaled, scalerUser.inverse_transform(user_train)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To allow us to evaluate the results, we will split the data into training and test sets as was discussed in Course 2, Week 3. Here we will use [sklean train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) to split and shuffle the data. Note that setting the initial random state to the same value ensures item, user, and y are shuffled identically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "movie/item training data shape: (40707, 17)\n",
      "movie/item test data shape: (10177, 17)\n"
     ]
    }
   ],
   "source": [
    "item_train, item_test = train_test_split(item_train, train_size=0.80, shuffle=True, random_state=1)\n",
    "user_train, user_test = train_test_split(user_train, train_size=0.80, shuffle=True, random_state=1)\n",
    "y_train, y_test       = train_test_split(y_train,    train_size=0.80, shuffle=True, random_state=1)\n",
    "print(f\"movie/item training data shape: {item_train.shape}\")\n",
    "print(f\"movie/item test data shape: {item_test.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scaled, shuffled data now has a mean of zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pprint_train(user_train, user_features, uvs, u_s, maxcount=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"4\"></a>\n",
    "## 4 - Neural Network for content-based filtering\n",
    "Now, let's construct a neural network as described in the figure above. It will have two networks that are combined by a dot product. You will construct the two networks. In this example, they will be identical. Note that these networks do not need to be the same. If the user content was substantially larger than the movie content, you might elect to increase the complexity of the user network relative to the movie network. In this case, the content is similar, so the networks are the same.\n",
    "\n",
    "<a name=\"ex01\"></a>\n",
    "### Exercise 1\n",
    "\n",
    "- Use a Keras sequential model\n",
    "    - The first layer is a dense layer with 256 units and a relu activation.\n",
    "    - The second layer is a dense layer with 128 units and a relu activation.\n",
    "    - The third layer is a dense layer with `num_outputs` units and a linear or no activation.   \n",
    "    \n",
    "The remainder of the network will be provided. The provided code does not use the Keras sequential model but instead uses the Keras [functional api](https://keras.io/guides/functional_api/). This format allows for more flexibility in how components are interconnected.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "deletable": false,
    "id": "CBjZ2HhRwpa0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 14)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_2 (InputLayer)            [(None, 16)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "sequential (Sequential)         (None, 32)           40864       input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "sequential_1 (Sequential)       (None, 32)           41376       input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_l2_normalize/Square [(None, 32)]         0           sequential[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_l2_normalize_1/Squa [(None, 32)]         0           sequential_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_l2_normalize/Sum (T [(None, 1)]          0           tf_op_layer_l2_normalize/Square[0\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_l2_normalize_1/Sum  [(None, 1)]          0           tf_op_layer_l2_normalize_1/Square\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_l2_normalize/Maximu [(None, 1)]          0           tf_op_layer_l2_normalize/Sum[0][0\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_l2_normalize_1/Maxi [(None, 1)]          0           tf_op_layer_l2_normalize_1/Sum[0]\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_l2_normalize/Rsqrt  [(None, 1)]          0           tf_op_layer_l2_normalize/Maximum[\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_l2_normalize_1/Rsqr [(None, 1)]          0           tf_op_layer_l2_normalize_1/Maximu\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_l2_normalize (Tenso [(None, 32)]         0           sequential[0][0]                 \n",
      "                                                                 tf_op_layer_l2_normalize/Rsqrt[0]\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_l2_normalize_1 (Ten [(None, 32)]         0           sequential_1[0][0]               \n",
      "                                                                 tf_op_layer_l2_normalize_1/Rsqrt[\n",
      "__________________________________________________________________________________________________\n",
      "dot (Dot)                       (None, 1)            0           tf_op_layer_l2_normalize[0][0]   \n",
      "                                                                 tf_op_layer_l2_normalize_1[0][0] \n",
      "==================================================================================================\n",
      "Total params: 82,240\n",
      "Trainable params: 82,240\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# GRADED_CELL\n",
    "# UNQ_C1\n",
    "\n",
    "num_outputs = 32\n",
    "tf.random.set_seed(1)\n",
    "user_NN = tf.keras.models.Sequential([\n",
    "    ### START CODE HERE ###     \n",
    "  tf.keras.layers.Dense(256,activation='relu'),\n",
    "  tf.keras.layers.Dense(128,activation='relu'),\n",
    "    tf.keras.layers.Dense(num_outputs),\n",
    "  \n",
    "  \n",
    "    ### END CODE HERE ###  \n",
    "])\n",
    "\n",
    "item_NN = tf.keras.models.Sequential([\n",
    "    ### START CODE HERE ###     \n",
    "  tf.keras.layers.Dense(256,activation='relu'),\n",
    "  tf.keras.layers.Dense(128,activation='relu'),\n",
    "    tf.keras.layers.Dense(num_outputs)\n",
    "  \n",
    "  \n",
    "  \n",
    "    ### END CODE HERE ###  \n",
    "])\n",
    "\n",
    "# create the user input and point to the base network\n",
    "input_user = tf.keras.layers.Input(shape=(num_user_features))\n",
    "vu = user_NN(input_user)\n",
    "vu = tf.linalg.l2_normalize(vu, axis=1)\n",
    "\n",
    "# create the item input and point to the base network\n",
    "input_item = tf.keras.layers.Input(shape=(num_item_features))\n",
    "vm = item_NN(input_item)\n",
    "vm = tf.linalg.l2_normalize(vm, axis=1)\n",
    "\n",
    "# compute the dot product of the two vectors vu and vm\n",
    "output = tf.keras.layers.Dot(axes=1)([vu, vm])\n",
    "\n",
    "# specify the inputs and output of the model\n",
    "model = tf.keras.Model([input_user, input_item], output)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mAll tests passed!\n",
      "\u001b[92mAll tests passed!\n"
     ]
    }
   ],
   "source": [
    "# Public tests\n",
    "from public_tests import *\n",
    "test_tower(user_NN)\n",
    "test_tower(item_NN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for hints</b></font></summary>\n",
    "    \n",
    "  You can create a dense layer with a relu activation as shown.\n",
    "    \n",
    "```python     \n",
    "user_NN = tf.keras.models.Sequential([\n",
    "    ### START CODE HERE ###     \n",
    "  tf.keras.layers.Dense(256, activation='relu'),\n",
    "\n",
    "    \n",
    "    ### END CODE HERE ###  \n",
    "])\n",
    "\n",
    "item_NN = tf.keras.models.Sequential([\n",
    "    ### START CODE HERE ###     \n",
    "  tf.keras.layers.Dense(256, activation='relu'),\n",
    "\n",
    "    \n",
    "    ### END CODE HERE ###  \n",
    "])\n",
    "```    \n",
    "<details>\n",
    "    <summary><font size=\"2\" color=\"darkblue\"><b> Click for solution</b></font></summary>\n",
    "    \n",
    "```python \n",
    "user_NN = tf.keras.models.Sequential([\n",
    "    ### START CODE HERE ###     \n",
    "  tf.keras.layers.Dense(256, activation='relu'),\n",
    "  tf.keras.layers.Dense(128, activation='relu'),\n",
    "  tf.keras.layers.Dense(num_outputs),\n",
    "    ### END CODE HERE ###  \n",
    "])\n",
    "\n",
    "item_NN = tf.keras.models.Sequential([\n",
    "    ### START CODE HERE ###     \n",
    "  tf.keras.layers.Dense(256, activation='relu'),\n",
    "  tf.keras.layers.Dense(128, activation='relu'),\n",
    "  tf.keras.layers.Dense(num_outputs),\n",
    "    ### END CODE HERE ###  \n",
    "])\n",
    "```\n",
    "</details>\n",
    "</details>\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use a mean squared error loss and an Adam optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "pGK5MEUowxN4"
   },
   "outputs": [],
   "source": [
    "tf.random.set_seed(1)\n",
    "cost_fn = tf.keras.losses.MeanSquaredError()\n",
    "opt = keras.optimizers.Adam(learning_rate=0.01)\n",
    "model.compile(optimizer=opt,\n",
    "              loss=cost_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "6zHf7eASw0tN"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 40707 samples\n",
      "Epoch 1/30\n",
      "40707/40707 [==============================] - 5s 131us/sample - loss: 0.1232\n",
      "Epoch 2/30\n",
      "40707/40707 [==============================] - 5s 120us/sample - loss: 0.1146\n",
      "Epoch 3/30\n",
      "40707/40707 [==============================] - 5s 121us/sample - loss: 0.1089\n",
      "Epoch 4/30\n",
      "40707/40707 [==============================] - 5s 120us/sample - loss: 0.1039\n",
      "Epoch 5/30\n",
      "40707/40707 [==============================] - 5s 121us/sample - loss: 0.1001\n",
      "Epoch 6/30\n",
      "40707/40707 [==============================] - 5s 120us/sample - loss: 0.0973\n",
      "Epoch 7/30\n",
      "40707/40707 [==============================] - 5s 122us/sample - loss: 0.0956\n",
      "Epoch 8/30\n",
      "40707/40707 [==============================] - 5s 119us/sample - loss: 0.0935\n",
      "Epoch 9/30\n",
      "40707/40707 [==============================] - 5s 120us/sample - loss: 0.0916\n",
      "Epoch 10/30\n",
      "40707/40707 [==============================] - 5s 121us/sample - loss: 0.0897\n",
      "Epoch 11/30\n",
      "40707/40707 [==============================] - 5s 119us/sample - loss: 0.0880\n",
      "Epoch 12/30\n",
      "40707/40707 [==============================] - 5s 120us/sample - loss: 0.0865\n",
      "Epoch 13/30\n",
      "40707/40707 [==============================] - 5s 120us/sample - loss: 0.0852\n",
      "Epoch 14/30\n",
      "40707/40707 [==============================] - 5s 121us/sample - loss: 0.0839\n",
      "Epoch 15/30\n",
      "40707/40707 [==============================] - 5s 120us/sample - loss: 0.0830\n",
      "Epoch 16/30\n",
      "40707/40707 [==============================] - 5s 121us/sample - loss: 0.0815\n",
      "Epoch 17/30\n",
      "40707/40707 [==============================] - 5s 122us/sample - loss: 0.0807\n",
      "Epoch 18/30\n",
      "40707/40707 [==============================] - 5s 122us/sample - loss: 0.0796\n",
      "Epoch 19/30\n",
      "40707/40707 [==============================] - 5s 120us/sample - loss: 0.0786\n",
      "Epoch 20/30\n",
      "40707/40707 [==============================] - 5s 121us/sample - loss: 0.0776\n",
      "Epoch 21/30\n",
      "40707/40707 [==============================] - 5s 120us/sample - loss: 0.0769\n",
      "Epoch 22/30\n",
      "40707/40707 [==============================] - 5s 120us/sample - loss: 0.0761\n",
      "Epoch 23/30\n",
      "40707/40707 [==============================] - 5s 121us/sample - loss: 0.0755\n",
      "Epoch 24/30\n",
      "40707/40707 [==============================] - 5s 122us/sample - loss: 0.0746\n",
      "Epoch 25/30\n",
      "40707/40707 [==============================] - 5s 121us/sample - loss: 0.0741\n",
      "Epoch 26/30\n",
      "40707/40707 [==============================] - 5s 122us/sample - loss: 0.0733\n",
      "Epoch 27/30\n",
      "40707/40707 [==============================] - 5s 119us/sample - loss: 0.0728\n",
      "Epoch 28/30\n",
      "40707/40707 [==============================] - 5s 120us/sample - loss: 0.0723\n",
      "Epoch 29/30\n",
      "40707/40707 [==============================] - 5s 122us/sample - loss: 0.0717\n",
      "Epoch 30/30\n",
      "40707/40707 [==============================] - 5s 120us/sample - loss: 0.0713\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f2a74f24710>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.random.set_seed(1)\n",
    "model.fit([user_train[:, u_s:], item_train[:, i_s:]], y_train, epochs=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the model to determine loss on the test data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10177/10177 [==============================] - 0s 36us/sample - loss: 0.0815\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.08146006993124337"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate([user_test[:, u_s:], item_test[:, i_s:]], y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is comparable to the training loss indicating the model has not substantially overfit the training data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xsre-gquwEls"
   },
   "source": [
    "<a name=\"5\"></a>\n",
    "## 5 - Predictions\n",
    "Below, you'll use your model to make predictions in a number of circumstances. \n",
    "<a name=\"5.1\"></a>\n",
    "### 5.1 - Predictions for a new user\n",
    "First, we'll create a new user and have the model suggest movies for that user. After you have tried this on the example user content, feel free to change the user content to match your own preferences and see what the model suggests. Note that ratings are between 0.5 and 5.0, inclusive, in half-step increments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "deletable": false,
    "id": "4_7nZyPiVJ4r"
   },
   "outputs": [],
   "source": [
    "new_user_id = 5000\n",
    "new_rating_ave = 0.0\n",
    "new_action = 0.0\n",
    "new_adventure = 5.0\n",
    "new_animation = 0.0\n",
    "new_childrens = 0.0\n",
    "new_comedy = 0.0\n",
    "new_crime = 0.0\n",
    "new_documentary = 0.0\n",
    "new_drama = 0.0\n",
    "new_fantasy = 5.0\n",
    "new_horror = 0.0\n",
    "new_mystery = 0.0\n",
    "new_romance = 0.0\n",
    "new_scifi = 0.0\n",
    "new_thriller = 0.0\n",
    "new_rating_count = 3\n",
    "\n",
    "user_vec = np.array([[new_user_id, new_rating_count, new_rating_ave,\n",
    "                      new_action, new_adventure, new_animation, new_childrens,\n",
    "                      new_comedy, new_crime, new_documentary,\n",
    "                      new_drama, new_fantasy, new_horror, new_mystery,\n",
    "                      new_romance, new_scifi, new_thriller]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new user enjoys movies from the adventure, fantasy genres. Let's find the top-rated movies for the new user.  \n",
    "Below, we'll use a set of movie/item vectors, `item_vecs` that have a vector for each movie in the training/test set. This is matched with the new user vector above and the scaled vectors are used to predict ratings for all the movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  y_p</th><th style=\"text-align: right;\">  movie id</th><th style=\"text-align: right;\">  rating ave</th><th>title                                              </th><th>genres                          </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">  4.5</td><td style=\"text-align: right;\">     98809</td><td style=\"text-align: right;\">         3.8</td><td>Hobbit: An Unexpected Journey, The (2012)          </td><td>Adventure|Fantasy               </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  4.4</td><td style=\"text-align: right;\">      8368</td><td style=\"text-align: right;\">         3.9</td><td>Harry Potter and the Prisoner of Azkaban (2004)    </td><td>Adventure|Fantasy               </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  4.4</td><td style=\"text-align: right;\">     54001</td><td style=\"text-align: right;\">         3.9</td><td>Harry Potter and the Order of the Phoenix (2007)   </td><td>Adventure|Drama|Fantasy         </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  4.3</td><td style=\"text-align: right;\">     40815</td><td style=\"text-align: right;\">         3.8</td><td>Harry Potter and the Goblet of Fire (2005)         </td><td>Adventure|Fantasy|Thriller      </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  4.3</td><td style=\"text-align: right;\">    106489</td><td style=\"text-align: right;\">         3.6</td><td>Hobbit: The Desolation of Smaug, The (2013)        </td><td>Adventure|Fantasy               </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  4.3</td><td style=\"text-align: right;\">     81834</td><td style=\"text-align: right;\">         4  </td><td>Harry Potter and the Deathly Hallows: Part 1 (2010)</td><td>Action|Adventure|Fantasy        </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  4.3</td><td style=\"text-align: right;\">     59387</td><td style=\"text-align: right;\">         4  </td><td>Fall, The (2006)                                   </td><td>Adventure|Drama|Fantasy         </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  4.3</td><td style=\"text-align: right;\">      5952</td><td style=\"text-align: right;\">         4  </td><td>Lord of the Rings: The Two Towers, The (2002)      </td><td>Adventure|Fantasy               </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  4.3</td><td style=\"text-align: right;\">      5816</td><td style=\"text-align: right;\">         3.6</td><td>Harry Potter and the Chamber of Secrets (2002)     </td><td>Adventure|Fantasy               </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  4.3</td><td style=\"text-align: right;\">     54259</td><td style=\"text-align: right;\">         3.6</td><td>Stardust (2007)                                    </td><td>Adventure|Comedy|Fantasy|Romance</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "'<table>\\n<thead>\\n<tr><th style=\"text-align: right;\">  y_p</th><th style=\"text-align: right;\">  movie id</th><th style=\"text-align: right;\">  rating ave</th><th>title                                              </th><th>genres                          </th></tr>\\n</thead>\\n<tbody>\\n<tr><td style=\"text-align: right;\">  4.5</td><td style=\"text-align: right;\">     98809</td><td style=\"text-align: right;\">         3.8</td><td>Hobbit: An Unexpected Journey, The (2012)          </td><td>Adventure|Fantasy               </td></tr>\\n<tr><td style=\"text-align: right;\">  4.4</td><td style=\"text-align: right;\">      8368</td><td style=\"text-align: right;\">         3.9</td><td>Harry Potter and the Prisoner of Azkaban (2004)    </td><td>Adventure|Fantasy               </td></tr>\\n<tr><td style=\"text-align: right;\">  4.4</td><td style=\"text-align: right;\">     54001</td><td style=\"text-align: right;\">         3.9</td><td>Harry Potter and the Order of the Phoenix (2007)   </td><td>Adventure|Drama|Fantasy         </td></tr>\\n<tr><td style=\"text-align: right;\">  4.3</td><td style=\"text-align: right;\">     40815</td><td style=\"text-align: right;\">         3.8</td><td>Harry Potter and the Goblet of Fire (2005)         </td><td>Adventure|Fantasy|Thriller      </td></tr>\\n<tr><td style=\"text-align: right;\">  4.3</td><td style=\"text-align: right;\">    106489</td><td style=\"text-align: right;\">         3.6</td><td>Hobbit: The Desolation of Smaug, The (2013)        </td><td>Adventure|Fantasy               </td></tr>\\n<tr><td style=\"text-align: right;\">  4.3</td><td style=\"text-align: right;\">     81834</td><td style=\"text-align: right;\">         4  </td><td>Harry Potter and the Deathly Hallows: Part 1 (2010)</td><td>Action|Adventure|Fantasy        </td></tr>\\n<tr><td style=\"text-align: right;\">  4.3</td><td style=\"text-align: right;\">     59387</td><td style=\"text-align: right;\">         4  </td><td>Fall, The (2006)                                   </td><td>Adventure|Drama|Fantasy         </td></tr>\\n<tr><td style=\"text-align: right;\">  4.3</td><td style=\"text-align: right;\">      5952</td><td style=\"text-align: right;\">         4  </td><td>Lord of the Rings: The Two Towers, The (2002)      </td><td>Adventure|Fantasy               </td></tr>\\n<tr><td style=\"text-align: right;\">  4.3</td><td style=\"text-align: right;\">      5816</td><td style=\"text-align: right;\">         3.6</td><td>Harry Potter and the Chamber of Secrets (2002)     </td><td>Adventure|Fantasy               </td></tr>\\n<tr><td style=\"text-align: right;\">  4.3</td><td style=\"text-align: right;\">     54259</td><td style=\"text-align: right;\">         3.6</td><td>Stardust (2007)                                    </td><td>Adventure|Comedy|Fantasy|Romance</td></tr>\\n</tbody>\\n</table>'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate and replicate the user vector to match the number movies in the data set.\n",
    "user_vecs = gen_user_vecs(user_vec,len(item_vecs))\n",
    "\n",
    "# scale our user and item vectors\n",
    "suser_vecs = scalerUser.transform(user_vecs)\n",
    "sitem_vecs = scalerItem.transform(item_vecs)\n",
    "\n",
    "# make a prediction\n",
    "y_p = model.predict([suser_vecs[:, u_s:], sitem_vecs[:, i_s:]])\n",
    "\n",
    "# unscale y prediction \n",
    "y_pu = scalerTarget.inverse_transform(y_p)\n",
    "\n",
    "# sort the results, highest prediction first\n",
    "sorted_index = np.argsort(-y_pu,axis=0).reshape(-1).tolist()  #negate to get largest rating first\n",
    "sorted_ypu   = y_pu[sorted_index]\n",
    "sorted_items = item_vecs[sorted_index]  #using unscaled vectors for display\n",
    "\n",
    "print_pred_movies(sorted_ypu, sorted_items, movie_dict, maxcount = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name=\"5.2\"></a>\n",
    "### 5.2 - Predictions for an existing user.\n",
    "Let's look at the predictions for \"user 2\", one of the users in the data set. We can compare the predicted ratings with the model's ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
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       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  y_p</th><th style=\"text-align: right;\">  y</th><th style=\"text-align: right;\">  user</th><th>user genre ave           </th><th style=\"text-align: right;\">  movie rating ave</th><th style=\"text-align: right;\">  movie id</th><th>title                                             </th><th>genres                                    </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">  4.5</td><td style=\"text-align: right;\">5.0</td><td style=\"text-align: right;\">     2</td><td>[4.0]                    </td><td style=\"text-align: right;\">               4.3</td><td style=\"text-align: right;\">     80906</td><td>Inside Job (2010)                                 </td><td>Documentary                               </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  4.2</td><td style=\"text-align: right;\">3.5</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.0]                </td><td style=\"text-align: right;\">               3.9</td><td style=\"text-align: right;\">     99114</td><td>Django Unchained (2012)                           </td><td>Action|Drama                              </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  4.1</td><td style=\"text-align: right;\">4.5</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.0]                </td><td style=\"text-align: right;\">               4.1</td><td style=\"text-align: right;\">     68157</td><td>Inglourious Basterds (2009)                       </td><td>Action|Drama                              </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  4.1</td><td style=\"text-align: right;\">3.5</td><td style=\"text-align: right;\">     2</td><td>[4.0,3.9,3.9]            </td><td style=\"text-align: right;\">               3.9</td><td style=\"text-align: right;\">    115713</td><td>Ex Machina (2015)                                 </td><td>Drama|Sci-Fi|Thriller                     </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  4.0</td><td style=\"text-align: right;\">4.0</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.1,4.0,4.0,3.9,3.9]</td><td style=\"text-align: right;\">               4.1</td><td style=\"text-align: right;\">     79132</td><td>Inception (2010)                                  </td><td>Action|Crime|Drama|Mystery|Sci-Fi|Thriller</td></tr>\n",
       "<tr><td style=\"text-align: right;\">  4.0</td><td style=\"text-align: right;\">4.0</td><td style=\"text-align: right;\">     2</td><td>[4.1,4.0,3.9]            </td><td style=\"text-align: right;\">               4.3</td><td style=\"text-align: right;\">     48516</td><td>Departed, The (2006)                              </td><td>Crime|Drama|Thriller                      </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  4.0</td><td style=\"text-align: right;\">4.5</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.1,4.0]            </td><td style=\"text-align: right;\">               4.2</td><td style=\"text-align: right;\">     58559</td><td>Dark Knight, The (2008)                           </td><td>Action|Crime|Drama                        </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  4.0</td><td style=\"text-align: right;\">4.0</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.1,3.9]            </td><td style=\"text-align: right;\">               4.0</td><td style=\"text-align: right;\">      6874</td><td>Kill Bill: Vol. 1 (2003)                          </td><td>Action|Crime|Thriller                     </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  4.0</td><td style=\"text-align: right;\">3.5</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.1,4.0,3.9]        </td><td style=\"text-align: right;\">               3.8</td><td style=\"text-align: right;\">      8798</td><td>Collateral (2004)                                 </td><td>Action|Crime|Drama|Thriller               </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  3.9</td><td style=\"text-align: right;\">5.0</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.1,4.0]            </td><td style=\"text-align: right;\">               3.9</td><td style=\"text-align: right;\">    106782</td><td>Wolf of Wall Street, The (2013)                   </td><td>Comedy|Crime|Drama                        </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  3.9</td><td style=\"text-align: right;\">3.5</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.2,4.1]            </td><td style=\"text-align: right;\">               4.0</td><td style=\"text-align: right;\">     91529</td><td>Dark Knight Rises, The (2012)                     </td><td>Action|Adventure|Crime                    </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  3.9</td><td style=\"text-align: right;\">4.0</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.0,3.9]            </td><td style=\"text-align: right;\">               4.0</td><td style=\"text-align: right;\">     74458</td><td>Shutter Island (2010)                             </td><td>Drama|Mystery|Thriller                    </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  3.9</td><td style=\"text-align: right;\">4.5</td><td style=\"text-align: right;\">     2</td><td>[4.1,4.0,3.9]            </td><td style=\"text-align: right;\">               4.0</td><td style=\"text-align: right;\">     80489</td><td>Town, The (2010)                                  </td><td>Crime|Drama|Thriller                      </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  3.8</td><td style=\"text-align: right;\">4.0</td><td style=\"text-align: right;\">     2</td><td>[4.0]                    </td><td style=\"text-align: right;\">               4.0</td><td style=\"text-align: right;\">    112552</td><td>Whiplash (2014)                                   </td><td>Drama                                     </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  3.8</td><td style=\"text-align: right;\">3.0</td><td style=\"text-align: right;\">     2</td><td>[3.9]                    </td><td style=\"text-align: right;\">               4.0</td><td style=\"text-align: right;\">    109487</td><td>Interstellar (2014)                               </td><td>Sci-Fi                                    </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  3.8</td><td style=\"text-align: right;\">5.0</td><td style=\"text-align: right;\">     2</td><td>[4.0]                    </td><td style=\"text-align: right;\">               3.7</td><td style=\"text-align: right;\">     89774</td><td>Warrior (2011)                                    </td><td>Drama                                     </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  3.7</td><td style=\"text-align: right;\">3.0</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.0,3.0]            </td><td style=\"text-align: right;\">               3.9</td><td style=\"text-align: right;\">     71535</td><td>Zombieland (2009)                                 </td><td>Action|Comedy|Horror                      </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  3.7</td><td style=\"text-align: right;\">5.0</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.2,3.9,3.9]        </td><td style=\"text-align: right;\">               3.8</td><td style=\"text-align: right;\">    122882</td><td>Mad Max: Fury Road (2015)                         </td><td>Action|Adventure|Sci-Fi|Thriller          </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  3.5</td><td style=\"text-align: right;\">5.0</td><td style=\"text-align: right;\">     2</td><td>[4.0]                    </td><td style=\"text-align: right;\">               3.6</td><td style=\"text-align: right;\">     60756</td><td>Step Brothers (2008)                              </td><td>Comedy                                    </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  3.5</td><td style=\"text-align: right;\">2.5</td><td style=\"text-align: right;\">     2</td><td>[4.0,3.9]                </td><td style=\"text-align: right;\">               3.5</td><td style=\"text-align: right;\">     91658</td><td>Girl with the Dragon Tattoo, The (2011)           </td><td>Drama|Thriller                            </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  3.1</td><td style=\"text-align: right;\">3.0</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.0]                </td><td style=\"text-align: right;\">               4.0</td><td style=\"text-align: right;\">     77455</td><td>Exit Through the Gift Shop (2010)                 </td><td>Comedy|Documentary                        </td></tr>\n",
       "<tr><td style=\"text-align: right;\">  3.1</td><td style=\"text-align: right;\">4.0</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.0]                </td><td style=\"text-align: right;\">               3.2</td><td style=\"text-align: right;\">     46970</td><td>Talladega Nights: The Ballad of Ricky Bobby (2006)</td><td>Action|Comedy                             </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "'<table>\\n<thead>\\n<tr><th style=\"text-align: right;\">  y_p</th><th style=\"text-align: right;\">  y</th><th style=\"text-align: right;\">  user</th><th>user genre ave           </th><th style=\"text-align: right;\">  movie rating ave</th><th style=\"text-align: right;\">  movie id</th><th>title                                             </th><th>genres                                    </th></tr>\\n</thead>\\n<tbody>\\n<tr><td style=\"text-align: right;\">  4.5</td><td style=\"text-align: right;\">5.0</td><td style=\"text-align: right;\">     2</td><td>[4.0]                    </td><td style=\"text-align: right;\">               4.3</td><td style=\"text-align: right;\">     80906</td><td>Inside Job (2010)                                 </td><td>Documentary                               </td></tr>\\n<tr><td style=\"text-align: right;\">  4.2</td><td style=\"text-align: right;\">3.5</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.0]                </td><td style=\"text-align: right;\">               3.9</td><td style=\"text-align: right;\">     99114</td><td>Django Unchained (2012)                           </td><td>Action|Drama                              </td></tr>\\n<tr><td style=\"text-align: right;\">  4.1</td><td style=\"text-align: right;\">4.5</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.0]                </td><td style=\"text-align: right;\">               4.1</td><td style=\"text-align: right;\">     68157</td><td>Inglourious Basterds (2009)                       </td><td>Action|Drama                              </td></tr>\\n<tr><td style=\"text-align: right;\">  4.1</td><td style=\"text-align: right;\">3.5</td><td style=\"text-align: right;\">     2</td><td>[4.0,3.9,3.9]            </td><td style=\"text-align: right;\">               3.9</td><td style=\"text-align: right;\">    115713</td><td>Ex Machina (2015)                                 </td><td>Drama|Sci-Fi|Thriller                     </td></tr>\\n<tr><td style=\"text-align: right;\">  4.0</td><td style=\"text-align: right;\">4.0</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.1,4.0,4.0,3.9,3.9]</td><td style=\"text-align: right;\">               4.1</td><td style=\"text-align: right;\">     79132</td><td>Inception (2010)                                  </td><td>Action|Crime|Drama|Mystery|Sci-Fi|Thriller</td></tr>\\n<tr><td style=\"text-align: right;\">  4.0</td><td style=\"text-align: right;\">4.0</td><td style=\"text-align: right;\">     2</td><td>[4.1,4.0,3.9]            </td><td style=\"text-align: right;\">               4.3</td><td style=\"text-align: right;\">     48516</td><td>Departed, The (2006)                              </td><td>Crime|Drama|Thriller                      </td></tr>\\n<tr><td style=\"text-align: right;\">  4.0</td><td style=\"text-align: right;\">4.5</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.1,4.0]            </td><td style=\"text-align: right;\">               4.2</td><td style=\"text-align: right;\">     58559</td><td>Dark Knight, The (2008)                           </td><td>Action|Crime|Drama                        </td></tr>\\n<tr><td style=\"text-align: right;\">  4.0</td><td style=\"text-align: right;\">4.0</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.1,3.9]            </td><td style=\"text-align: right;\">               4.0</td><td style=\"text-align: right;\">      6874</td><td>Kill Bill: Vol. 1 (2003)                          </td><td>Action|Crime|Thriller                     </td></tr>\\n<tr><td style=\"text-align: right;\">  4.0</td><td style=\"text-align: right;\">3.5</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.1,4.0,3.9]        </td><td style=\"text-align: right;\">               3.8</td><td style=\"text-align: right;\">      8798</td><td>Collateral (2004)                                 </td><td>Action|Crime|Drama|Thriller               </td></tr>\\n<tr><td style=\"text-align: right;\">  3.9</td><td style=\"text-align: right;\">5.0</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.1,4.0]            </td><td style=\"text-align: right;\">               3.9</td><td style=\"text-align: right;\">    106782</td><td>Wolf of Wall Street, The (2013)                   </td><td>Comedy|Crime|Drama                        </td></tr>\\n<tr><td style=\"text-align: right;\">  3.9</td><td style=\"text-align: right;\">3.5</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.2,4.1]            </td><td style=\"text-align: right;\">               4.0</td><td style=\"text-align: right;\">     91529</td><td>Dark Knight Rises, The (2012)                     </td><td>Action|Adventure|Crime                    </td></tr>\\n<tr><td style=\"text-align: right;\">  3.9</td><td style=\"text-align: right;\">4.0</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.0,3.9]            </td><td style=\"text-align: right;\">               4.0</td><td style=\"text-align: right;\">     74458</td><td>Shutter Island (2010)                             </td><td>Drama|Mystery|Thriller                    </td></tr>\\n<tr><td style=\"text-align: right;\">  3.9</td><td style=\"text-align: right;\">4.5</td><td style=\"text-align: right;\">     2</td><td>[4.1,4.0,3.9]            </td><td style=\"text-align: right;\">               4.0</td><td style=\"text-align: right;\">     80489</td><td>Town, The (2010)                                  </td><td>Crime|Drama|Thriller                      </td></tr>\\n<tr><td style=\"text-align: right;\">  3.8</td><td style=\"text-align: right;\">4.0</td><td style=\"text-align: right;\">     2</td><td>[4.0]                    </td><td style=\"text-align: right;\">               4.0</td><td style=\"text-align: right;\">    112552</td><td>Whiplash (2014)                                   </td><td>Drama                                     </td></tr>\\n<tr><td style=\"text-align: right;\">  3.8</td><td style=\"text-align: right;\">3.0</td><td style=\"text-align: right;\">     2</td><td>[3.9]                    </td><td style=\"text-align: right;\">               4.0</td><td style=\"text-align: right;\">    109487</td><td>Interstellar (2014)                               </td><td>Sci-Fi                                    </td></tr>\\n<tr><td style=\"text-align: right;\">  3.8</td><td style=\"text-align: right;\">5.0</td><td style=\"text-align: right;\">     2</td><td>[4.0]                    </td><td style=\"text-align: right;\">               3.7</td><td style=\"text-align: right;\">     89774</td><td>Warrior (2011)                                    </td><td>Drama                                     </td></tr>\\n<tr><td style=\"text-align: right;\">  3.7</td><td style=\"text-align: right;\">3.0</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.0,3.0]            </td><td style=\"text-align: right;\">               3.9</td><td style=\"text-align: right;\">     71535</td><td>Zombieland (2009)                                 </td><td>Action|Comedy|Horror                      </td></tr>\\n<tr><td style=\"text-align: right;\">  3.7</td><td style=\"text-align: right;\">5.0</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.2,3.9,3.9]        </td><td style=\"text-align: right;\">               3.8</td><td style=\"text-align: right;\">    122882</td><td>Mad Max: Fury Road (2015)                         </td><td>Action|Adventure|Sci-Fi|Thriller          </td></tr>\\n<tr><td style=\"text-align: right;\">  3.5</td><td style=\"text-align: right;\">5.0</td><td style=\"text-align: right;\">     2</td><td>[4.0]                    </td><td style=\"text-align: right;\">               3.6</td><td style=\"text-align: right;\">     60756</td><td>Step Brothers (2008)                              </td><td>Comedy                                    </td></tr>\\n<tr><td style=\"text-align: right;\">  3.5</td><td style=\"text-align: right;\">2.5</td><td style=\"text-align: right;\">     2</td><td>[4.0,3.9]                </td><td style=\"text-align: right;\">               3.5</td><td style=\"text-align: right;\">     91658</td><td>Girl with the Dragon Tattoo, The (2011)           </td><td>Drama|Thriller                            </td></tr>\\n<tr><td style=\"text-align: right;\">  3.1</td><td style=\"text-align: right;\">3.0</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.0]                </td><td style=\"text-align: right;\">               4.0</td><td style=\"text-align: right;\">     77455</td><td>Exit Through the Gift Shop (2010)                 </td><td>Comedy|Documentary                        </td></tr>\\n<tr><td style=\"text-align: right;\">  3.1</td><td style=\"text-align: right;\">4.0</td><td style=\"text-align: right;\">     2</td><td>[4.0,4.0]                </td><td style=\"text-align: right;\">               3.2</td><td style=\"text-align: right;\">     46970</td><td>Talladega Nights: The Ballad of Ricky Bobby (2006)</td><td>Action|Comedy                             </td></tr>\\n</tbody>\\n</table>'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uid = 2 \n",
    "# form a set of user vectors. This is the same vector, transformed and repeated.\n",
    "user_vecs, y_vecs = get_user_vecs(uid, user_train_unscaled, item_vecs, user_to_genre)\n",
    "\n",
    "# scale our user and item vectors\n",
    "suser_vecs = scalerUser.transform(user_vecs)\n",
    "sitem_vecs = scalerItem.transform(item_vecs)\n",
    "\n",
    "# make a prediction\n",
    "y_p = model.predict([suser_vecs[:, u_s:], sitem_vecs[:, i_s:]])\n",
    "\n",
    "# unscale y prediction \n",
    "y_pu = scalerTarget.inverse_transform(y_p)\n",
    "\n",
    "# sort the results, highest prediction first\n",
    "sorted_index = np.argsort(-y_pu,axis=0).reshape(-1).tolist()  #negate to get largest rating first\n",
    "sorted_ypu   = y_pu[sorted_index]\n",
    "sorted_items = item_vecs[sorted_index]  #using unscaled vectors for display\n",
    "sorted_user  = user_vecs[sorted_index]\n",
    "sorted_y     = y_vecs[sorted_index]\n",
    "\n",
    "#print sorted predictions for movies rated by the user\n",
    "print_existing_user(sorted_ypu, sorted_y.reshape(-1,1), sorted_user, sorted_items, ivs, uvs, movie_dict, maxcount = 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model prediction is generally within 1 of the actual rating though it is not a very accurate predictor of how a user rates specific movies. This is especially true if the user rating is significantly different than the user's genre average. You can vary the user id above to try different users. Not all user id's were used in the training set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"5.3\"></a>\n",
    "### 5.3 - Finding Similar Items\n",
    "The neural network above produces two feature vectors, a user feature vector $v_u$, and a movie feature vector, $v_m$. These are 32 entry vectors whose values are difficult to interpret. However, similar items will have similar vectors. This information can be used to make recommendations. For example, if a user has rated \"Toy Story 3\" highly, one could recommend similar movies by selecting movies with similar movie feature vectors.\n",
    "\n",
    "A similarity measure is the squared distance between the two vectors $ \\mathbf{v_m^{(k)}}$ and $\\mathbf{v_m^{(i)}}$ :\n",
    "$$\\left\\Vert \\mathbf{v_m^{(k)}} - \\mathbf{v_m^{(i)}}  \\right\\Vert^2 = \\sum_{l=1}^{n}(v_{m_l}^{(k)} - v_{m_l}^{(i)})^2\\tag{1}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"ex02\"></a>\n",
    "### Exercise 2\n",
    "\n",
    "Write a function to compute the square distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "# GRADED_FUNCTION: sq_dist\n",
    "# UNQ_C2\n",
    "def sq_dist(a,b):\n",
    "    \"\"\"\n",
    "    Returns the squared distance between two vectors\n",
    "    Args:\n",
    "      a (ndarray (n,)): vector with n features\n",
    "      b (ndarray (n,)): vector with n features\n",
    "    Returns:\n",
    "      d (float) : distance\n",
    "    \"\"\"\n",
    "    ### START CODE HERE ###     \n",
    "    d=np.sum(np.square(a-b))\n",
    "    ### END CODE HERE ###     \n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "squared distance between a1 and b1: 0.000\n",
      "squared distance between a2 and b2: 0.030\n",
      "squared distance between a3 and b3: 2.000\n"
     ]
    }
   ],
   "source": [
    "a1 = np.array([1.0, 2.0, 3.0]); b1 = np.array([1.0, 2.0, 3.0])\n",
    "a2 = np.array([1.1, 2.1, 3.1]); b2 = np.array([1.0, 2.0, 3.0])\n",
    "a3 = np.array([0, 1, 0]);       b3 = np.array([1, 0, 0])\n",
    "print(f\"squared distance between a1 and b1: {sq_dist(a1, b1):0.3f}\")\n",
    "print(f\"squared distance between a2 and b2: {sq_dist(a2, b2):0.3f}\")\n",
    "print(f\"squared distance between a3 and b3: {sq_dist(a3, b3):0.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "squared distance between a1 and b1: 0.000    \n",
    "squared distance between a2 and b2: 0.030   \n",
    "squared distance between a3 and b3: 2.000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mAll tests passed!\n"
     ]
    }
   ],
   "source": [
    "# Public tests\n",
    "test_sq_dist(sq_dist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for hints</b></font></summary>\n",
    "    \n",
    "  While a summation is often an indication a for loop should be used, here the subtraction can be element-wise in one statement. Further, you can utilized np.square to square, element-wise, the result of the subtraction. np.sum can be used to sum the squared elements.\n",
    "    \n",
    "</details>\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A matrix of distances between movies can be computed once when the model is trained and then reused for new recommendations without retraining. The first step, once a model is trained, is to obtain the movie feature vector, $v_m$, for each of the movies. To do this, we will use the trained `item_NN` and build a small model to allow us to run the movie vectors through it to generate $v_m$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            [(None, 16)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "sequential_1 (Sequential)       (None, 32)           41376       input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_l2_normalize_2/Squa [(None, 32)]         0           sequential_1[1][0]               \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_l2_normalize_2/Sum  [(None, 1)]          0           tf_op_layer_l2_normalize_2/Square\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_l2_normalize_2/Maxi [(None, 1)]          0           tf_op_layer_l2_normalize_2/Sum[0]\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_l2_normalize_2/Rsqr [(None, 1)]          0           tf_op_layer_l2_normalize_2/Maximu\n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_l2_normalize_2 (Ten [(None, 32)]         0           sequential_1[1][0]               \n",
      "                                                                 tf_op_layer_l2_normalize_2/Rsqrt[\n",
      "==================================================================================================\n",
      "Total params: 41,376\n",
      "Trainable params: 41,376\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "input_item_m = tf.keras.layers.Input(shape=(num_item_features))    # input layer\n",
    "vm_m = item_NN(input_item_m)                                       # use the trained item_NN\n",
    "vm_m = tf.linalg.l2_normalize(vm_m, axis=1)                        # incorporate normalization as was done in the original model\n",
    "model_m = tf.keras.Model(input_item_m, vm_m)                                \n",
    "model_m.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have a movie model, you can create a set of movie feature vectors by using the model to predict using a set of item/movie vectors as input. `item_vecs` is a set of all of the movie vectors. It must be scaled to use with the trained model. The result of the prediction is a 32 entry feature vector for each movie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size of all predicted movie feature vectors: (847, 32)\n"
     ]
    }
   ],
   "source": [
    "scaled_item_vecs = scalerItem.transform(item_vecs)\n",
    "vms = model_m.predict(scaled_item_vecs[:,i_s:])\n",
    "print(f\"size of all predicted movie feature vectors: {vms.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now compute a matrix of the squared distance between each movie feature vector and all other movie feature vectors:\n",
    "<figure>\n",
    "    <left> <img src=\"./images/distmatrix.PNG\"   style=\"width:400px;height:225px;\" ></center>\n",
    "</figure>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then find the closest movie by finding the minimum along each row. We will make use of [numpy masked arrays](https://numpy.org/doc/1.21/user/tutorial-ma.html) to avoid selecting the same movie. The masked values along the diagonal won't be included in the computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>movie1                                  </th><th>genres                                             </th><th>movie2                                             </th><th>genres                                             </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>Save the Last Dance (2001)              </td><td>Drama|Romance                                      </td><td>Mona Lisa Smile (2003)                             </td><td>Drama|Romance                                      </td></tr>\n",
       "<tr><td>Wedding Planner, The (2001)             </td><td>Comedy|Romance                                     </td><td>Mr. Deeds (2002)                                   </td><td>Comedy|Romance                                     </td></tr>\n",
       "<tr><td>Hannibal (2001)                         </td><td>Horror|Thriller                                    </td><td>Final Destination 2 (2003)                         </td><td>Horror|Thriller                                    </td></tr>\n",
       "<tr><td>Saving Silverman (Evil Woman) (2001)    </td><td>Comedy|Romance                                     </td><td>Down with Love (2003)                              </td><td>Comedy|Romance                                     </td></tr>\n",
       "<tr><td>Down to Earth (2001)                    </td><td>Comedy|Fantasy|Romance                             </td><td>Bewitched (2005)                                   </td><td>Comedy|Fantasy|Romance                             </td></tr>\n",
       "<tr><td>Mexican, The (2001)                     </td><td>Action|Comedy                                      </td><td>Rush Hour 2 (2001)                                 </td><td>Action|Comedy                                      </td></tr>\n",
       "<tr><td>15 Minutes (2001)                       </td><td>Thriller                                           </td><td>Panic Room (2002)                                  </td><td>Thriller                                           </td></tr>\n",
       "<tr><td>Enemy at the Gates (2001)               </td><td>Drama                                              </td><td>Kung Fu Hustle (Gong fu) (2004)                    </td><td>Drama                                              </td></tr>\n",
       "<tr><td>Heartbreakers (2001)                    </td><td>Comedy|Crime|Romance                               </td><td>Fun with Dick and Jane (2005)                      </td><td>Comedy|Crime|Romance                               </td></tr>\n",
       "<tr><td>Spy Kids (2001)                         </td><td>Action|Adventure|Children|Comedy                   </td><td>Tuxedo, The (2002)                                 </td><td>Action|Adventure|Children|Comedy                   </td></tr>\n",
       "<tr><td>Along Came a Spider (2001)              </td><td>Action|Crime|Mystery|Thriller                      </td><td>Insomnia (2002)                                    </td><td>Action|Crime|Mystery|Thriller                      </td></tr>\n",
       "<tr><td>Blow (2001)                             </td><td>Crime|Drama                                        </td><td>25th Hour (2002)                                   </td><td>Crime|Drama                                        </td></tr>\n",
       "<tr><td>Bridget Jones&#x27;s Diary (2001)            </td><td>Comedy|Drama|Romance                               </td><td>Punch-Drunk Love (2002)                            </td><td>Comedy|Drama|Romance                               </td></tr>\n",
       "<tr><td>Joe Dirt (2001)                         </td><td>Adventure|Comedy|Mystery|Romance                   </td><td>Polar Express, The (2004)                          </td><td>Adventure|Comedy|Mystery|Romance                   </td></tr>\n",
       "<tr><td>Crocodile Dundee in Los Angeles (2001)  </td><td>Comedy|Drama                                       </td><td>Bewitched (2005)                                   </td><td>Comedy|Drama                                       </td></tr>\n",
       "<tr><td>Mummy Returns, The (2001)               </td><td>Action|Adventure|Comedy|Thriller                   </td><td>Rundown, The (2003)                                </td><td>Action|Adventure|Comedy|Thriller                   </td></tr>\n",
       "<tr><td>Knight&#x27;s Tale, A (2001)                 </td><td>Action|Comedy|Romance                              </td><td>Legally Blonde (2001)                              </td><td>Action|Comedy|Romance                              </td></tr>\n",
       "<tr><td>Shrek (2001)                            </td><td>Adventure|Animation|Children|Comedy|Fantasy|Romance</td><td>Tangled (2010)                                     </td><td>Adventure|Animation|Children|Comedy|Fantasy|Romance</td></tr>\n",
       "<tr><td>Moulin Rouge (2001)                     </td><td>Drama|Romance                                      </td><td>Notebook, The (2004)                               </td><td>Drama|Romance                                      </td></tr>\n",
       "<tr><td>Pearl Harbor (2001)                     </td><td>Action|Drama|Romance                               </td><td>Bridget Jones: The Edge of Reason (2004)           </td><td>Action|Drama|Romance                               </td></tr>\n",
       "<tr><td>Animal, The (2001)                      </td><td>Comedy                                             </td><td>Dumb and Dumberer: When Harry Met Lloyd (2003)     </td><td>Comedy                                             </td></tr>\n",
       "<tr><td>Evolution (2001)                        </td><td>Comedy|Sci-Fi                                      </td><td>Behind Enemy Lines (2001)                          </td><td>Comedy|Sci-Fi                                      </td></tr>\n",
       "<tr><td>Swordfish (2001)                        </td><td>Action|Crime|Drama                                 </td><td>We Were Soldiers (2002)                            </td><td>Action|Crime|Drama                                 </td></tr>\n",
       "<tr><td>Atlantis: The Lost Empire (2001)        </td><td>Adventure|Animation|Children|Fantasy               </td><td>Cloudy with a Chance of Meatballs (2009)           </td><td>Adventure|Animation|Children|Fantasy               </td></tr>\n",
       "<tr><td>Lara Croft: Tomb Raider (2001)          </td><td>Action|Adventure                                   </td><td>National Treasure: Book of Secrets (2007)          </td><td>Action|Adventure                                   </td></tr>\n",
       "<tr><td>Dr. Dolittle 2 (2001)                   </td><td>Comedy                                             </td><td>Legally Blonde 2: Red, White &amp; Blonde (2003)       </td><td>Comedy                                             </td></tr>\n",
       "<tr><td>Fast and the Furious, The (2001)        </td><td>Action|Crime|Thriller                              </td><td>xXx (2002)                                         </td><td>Action|Crime|Thriller                              </td></tr>\n",
       "<tr><td>A.I. Artificial Intelligence (2001)     </td><td>Adventure|Drama|Sci-Fi                             </td><td>Bubba Ho-tep (2002)                                </td><td>Adventure|Drama|Sci-Fi                             </td></tr>\n",
       "<tr><td>Cats &amp; Dogs (2001)                      </td><td>Children|Comedy                                    </td><td>Robots (2005)                                      </td><td>Children|Comedy                                    </td></tr>\n",
       "<tr><td>Scary Movie 2 (2001)                    </td><td>Comedy                                             </td><td>Orange County (2002)                               </td><td>Comedy                                             </td></tr>\n",
       "<tr><td>Final Fantasy: The Spirits Within (2001)</td><td>Adventure|Animation|Fantasy|Sci-Fi                 </td><td>Madagascar: Escape 2 Africa (2008)                 </td><td>Adventure|Animation|Fantasy|Sci-Fi                 </td></tr>\n",
       "<tr><td>Legally Blonde (2001)                   </td><td>Comedy|Romance                                     </td><td>Serendipity (2001)                                 </td><td>Comedy|Romance                                     </td></tr>\n",
       "<tr><td>Score, The (2001)                       </td><td>Action|Drama                                       </td><td>Punisher, The (2004)                               </td><td>Action|Drama                                       </td></tr>\n",
       "<tr><td>Jurassic Park III (2001)                </td><td>Action|Adventure|Sci-Fi|Thriller                   </td><td>Men in Black II (a.k.a. MIIB) (a.k.a. MIB 2) (2002)</td><td>Action|Adventure|Sci-Fi|Thriller                   </td></tr>\n",
       "<tr><td>America&#x27;s Sweethearts (2001)            </td><td>Comedy|Romance                                     </td><td>Maid in Manhattan (2002)                           </td><td>Comedy|Romance                                     </td></tr>\n",
       "<tr><td>Ghost World (2001)                      </td><td>Comedy|Drama                                       </td><td>Station Agent, The (2003)                          </td><td>Comedy|Drama                                       </td></tr>\n",
       "<tr><td>Planet of the Apes (2001)               </td><td>Action|Adventure|Drama|Sci-Fi                      </td><td>Day After Tomorrow, The (2004)                     </td><td>Action|Adventure|Drama|Sci-Fi                      </td></tr>\n",
       "<tr><td>Princess Diaries, The (2001)            </td><td>Children|Comedy|Romance                            </td><td>Lake House, The (2006)                             </td><td>Children|Comedy|Romance                            </td></tr>\n",
       "<tr><td>Rush Hour 2 (2001)                      </td><td>Action|Comedy                                      </td><td>Mexican, The (2001)                                </td><td>Action|Comedy                                      </td></tr>\n",
       "<tr><td>American Pie 2 (2001)                   </td><td>Comedy                                             </td><td>Rat Race (2001)                                    </td><td>Comedy                                             </td></tr>\n",
       "<tr><td>Others, The (2001)                      </td><td>Drama|Horror|Mystery|Thriller                      </td><td>The Machinist (2004)                               </td><td>Drama|Horror|Mystery|Thriller                      </td></tr>\n",
       "<tr><td>Rat Race (2001)                         </td><td>Comedy                                             </td><td>American Pie 2 (2001)                              </td><td>Comedy                                             </td></tr>\n",
       "<tr><td>Jay and Silent Bob Strike Back (2001)   </td><td>Adventure|Comedy                                   </td><td>Mexican, The (2001)                                </td><td>Adventure|Comedy                                   </td></tr>\n",
       "<tr><td>Training Day (2001)                     </td><td>Crime|Drama|Thriller                               </td><td>Frailty (2001)                                     </td><td>Crime|Drama|Thriller                               </td></tr>\n",
       "<tr><td>Zoolander (2001)                        </td><td>Comedy                                             </td><td>Old School (2003)                                  </td><td>Comedy                                             </td></tr>\n",
       "<tr><td>Serendipity (2001)                      </td><td>Comedy|Romance                                     </td><td>Legally Blonde (2001)                              </td><td>Comedy|Romance                                     </td></tr>\n",
       "<tr><td>Mulholland Drive (2001)                 </td><td>Crime|Drama|Mystery|Thriller                       </td><td>Prisoners (2013)                                   </td><td>Crime|Drama|Mystery|Thriller                       </td></tr>\n",
       "<tr><td>From Hell (2001)                        </td><td>Crime|Horror|Mystery|Thriller                      </td><td>Identity (2003)                                    </td><td>Crime|Horror|Mystery|Thriller                      </td></tr>\n",
       "<tr><td>Waking Life (2001)                      </td><td>Animation|Drama|Fantasy                            </td><td>Warm Bodies (2013)                                 </td><td>Animation|Drama|Fantasy                            </td></tr>\n",
       "<tr><td>K-PAX (2001)                            </td><td>Drama|Fantasy|Mystery|Sci-Fi                       </td><td>Gosford Park (2001)                                </td><td>Drama|Fantasy|Mystery|Sci-Fi                       </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "'<table>\\n<thead>\\n<tr><th>movie1                                  </th><th>genres                                             </th><th>movie2                                             </th><th>genres                                             </th></tr>\\n</thead>\\n<tbody>\\n<tr><td>Save the Last Dance (2001)              </td><td>Drama|Romance                                      </td><td>Mona Lisa Smile (2003)                             </td><td>Drama|Romance                                      </td></tr>\\n<tr><td>Wedding Planner, The (2001)             </td><td>Comedy|Romance                                     </td><td>Mr. Deeds (2002)                                   </td><td>Comedy|Romance                                     </td></tr>\\n<tr><td>Hannibal (2001)                         </td><td>Horror|Thriller                                    </td><td>Final Destination 2 (2003)                         </td><td>Horror|Thriller                                    </td></tr>\\n<tr><td>Saving Silverman (Evil Woman) (2001)    </td><td>Comedy|Romance                                     </td><td>Down with Love (2003)                              </td><td>Comedy|Romance                                     </td></tr>\\n<tr><td>Down to Earth (2001)                    </td><td>Comedy|Fantasy|Romance                             </td><td>Bewitched (2005)                                   </td><td>Comedy|Fantasy|Romance                             </td></tr>\\n<tr><td>Mexican, The (2001)                     </td><td>Action|Comedy                                      </td><td>Rush Hour 2 (2001)                                 </td><td>Action|Comedy                                      </td></tr>\\n<tr><td>15 Minutes (2001)                       </td><td>Thriller                                           </td><td>Panic Room (2002)                                  </td><td>Thriller                                           </td></tr>\\n<tr><td>Enemy at the Gates (2001)               </td><td>Drama                                              </td><td>Kung Fu Hustle (Gong fu) (2004)                    </td><td>Drama                                              </td></tr>\\n<tr><td>Heartbreakers (2001)                    </td><td>Comedy|Crime|Romance                               </td><td>Fun with Dick and Jane (2005)                      </td><td>Comedy|Crime|Romance                               </td></tr>\\n<tr><td>Spy Kids (2001)                         </td><td>Action|Adventure|Children|Comedy                   </td><td>Tuxedo, The (2002)                                 </td><td>Action|Adventure|Children|Comedy                   </td></tr>\\n<tr><td>Along Came a Spider (2001)              </td><td>Action|Crime|Mystery|Thriller                      </td><td>Insomnia (2002)                                    </td><td>Action|Crime|Mystery|Thriller                      </td></tr>\\n<tr><td>Blow (2001)                             </td><td>Crime|Drama                                        </td><td>25th Hour (2002)                                   </td><td>Crime|Drama                                        </td></tr>\\n<tr><td>Bridget Jones&#x27;s Diary (2001)            </td><td>Comedy|Drama|Romance                               </td><td>Punch-Drunk Love (2002)                            </td><td>Comedy|Drama|Romance                               </td></tr>\\n<tr><td>Joe Dirt (2001)                         </td><td>Adventure|Comedy|Mystery|Romance                   </td><td>Polar Express, The (2004)                          </td><td>Adventure|Comedy|Mystery|Romance                   </td></tr>\\n<tr><td>Crocodile Dundee in Los Angeles (2001)  </td><td>Comedy|Drama                                       </td><td>Bewitched (2005)                                   </td><td>Comedy|Drama                                       </td></tr>\\n<tr><td>Mummy Returns, The (2001)               </td><td>Action|Adventure|Comedy|Thriller                   </td><td>Rundown, The (2003)                                </td><td>Action|Adventure|Comedy|Thriller                   </td></tr>\\n<tr><td>Knight&#x27;s Tale, A (2001)                 </td><td>Action|Comedy|Romance                              </td><td>Legally Blonde (2001)                              </td><td>Action|Comedy|Romance                              </td></tr>\\n<tr><td>Shrek (2001)                            </td><td>Adventure|Animation|Children|Comedy|Fantasy|Romance</td><td>Tangled (2010)                                     </td><td>Adventure|Animation|Children|Comedy|Fantasy|Romance</td></tr>\\n<tr><td>Moulin Rouge (2001)                     </td><td>Drama|Romance                                      </td><td>Notebook, The (2004)                               </td><td>Drama|Romance                                      </td></tr>\\n<tr><td>Pearl Harbor (2001)                     </td><td>Action|Drama|Romance                               </td><td>Bridget Jones: The Edge of Reason (2004)           </td><td>Action|Drama|Romance                               </td></tr>\\n<tr><td>Animal, The (2001)                      </td><td>Comedy                                             </td><td>Dumb and Dumberer: When Harry Met Lloyd (2003)     </td><td>Comedy                                             </td></tr>\\n<tr><td>Evolution (2001)                        </td><td>Comedy|Sci-Fi                                      </td><td>Behind Enemy Lines (2001)                          </td><td>Comedy|Sci-Fi                                      </td></tr>\\n<tr><td>Swordfish (2001)                        </td><td>Action|Crime|Drama                                 </td><td>We Were Soldiers (2002)                            </td><td>Action|Crime|Drama                                 </td></tr>\\n<tr><td>Atlantis: The Lost Empire (2001)        </td><td>Adventure|Animation|Children|Fantasy               </td><td>Cloudy with a Chance of Meatballs (2009)           </td><td>Adventure|Animation|Children|Fantasy               </td></tr>\\n<tr><td>Lara Croft: Tomb Raider (2001)          </td><td>Action|Adventure                                   </td><td>National Treasure: Book of Secrets (2007)          </td><td>Action|Adventure                                   </td></tr>\\n<tr><td>Dr. Dolittle 2 (2001)                   </td><td>Comedy                                             </td><td>Legally Blonde 2: Red, White &amp; Blonde (2003)       </td><td>Comedy                                             </td></tr>\\n<tr><td>Fast and the Furious, The (2001)        </td><td>Action|Crime|Thriller                              </td><td>xXx (2002)                                         </td><td>Action|Crime|Thriller                              </td></tr>\\n<tr><td>A.I. Artificial Intelligence (2001)     </td><td>Adventure|Drama|Sci-Fi                             </td><td>Bubba Ho-tep (2002)                                </td><td>Adventure|Drama|Sci-Fi                             </td></tr>\\n<tr><td>Cats &amp; Dogs (2001)                      </td><td>Children|Comedy                                    </td><td>Robots (2005)                                      </td><td>Children|Comedy                                    </td></tr>\\n<tr><td>Scary Movie 2 (2001)                    </td><td>Comedy                                             </td><td>Orange County (2002)                               </td><td>Comedy                                             </td></tr>\\n<tr><td>Final Fantasy: The Spirits Within (2001)</td><td>Adventure|Animation|Fantasy|Sci-Fi                 </td><td>Madagascar: Escape 2 Africa (2008)                 </td><td>Adventure|Animation|Fantasy|Sci-Fi                 </td></tr>\\n<tr><td>Legally Blonde (2001)                   </td><td>Comedy|Romance                                     </td><td>Serendipity (2001)                                 </td><td>Comedy|Romance                                     </td></tr>\\n<tr><td>Score, The (2001)                       </td><td>Action|Drama                                       </td><td>Punisher, The (2004)                               </td><td>Action|Drama                                       </td></tr>\\n<tr><td>Jurassic Park III (2001)                </td><td>Action|Adventure|Sci-Fi|Thriller                   </td><td>Men in Black II (a.k.a. MIIB) (a.k.a. MIB 2) (2002)</td><td>Action|Adventure|Sci-Fi|Thriller                   </td></tr>\\n<tr><td>America&#x27;s Sweethearts (2001)            </td><td>Comedy|Romance                                     </td><td>Maid in Manhattan (2002)                           </td><td>Comedy|Romance                                     </td></tr>\\n<tr><td>Ghost World (2001)                      </td><td>Comedy|Drama                                       </td><td>Station Agent, The (2003)                          </td><td>Comedy|Drama                                       </td></tr>\\n<tr><td>Planet of the Apes (2001)               </td><td>Action|Adventure|Drama|Sci-Fi                      </td><td>Day After Tomorrow, The (2004)                     </td><td>Action|Adventure|Drama|Sci-Fi                      </td></tr>\\n<tr><td>Princess Diaries, The (2001)            </td><td>Children|Comedy|Romance                            </td><td>Lake House, The (2006)                             </td><td>Children|Comedy|Romance                            </td></tr>\\n<tr><td>Rush Hour 2 (2001)                      </td><td>Action|Comedy                                      </td><td>Mexican, The (2001)                                </td><td>Action|Comedy                                      </td></tr>\\n<tr><td>American Pie 2 (2001)                   </td><td>Comedy                                             </td><td>Rat Race (2001)                                    </td><td>Comedy                                             </td></tr>\\n<tr><td>Others, The (2001)                      </td><td>Drama|Horror|Mystery|Thriller                      </td><td>The Machinist (2004)                               </td><td>Drama|Horror|Mystery|Thriller                      </td></tr>\\n<tr><td>Rat Race (2001)                         </td><td>Comedy                                             </td><td>American Pie 2 (2001)                              </td><td>Comedy                                             </td></tr>\\n<tr><td>Jay and Silent Bob Strike Back (2001)   </td><td>Adventure|Comedy                                   </td><td>Mexican, The (2001)                                </td><td>Adventure|Comedy                                   </td></tr>\\n<tr><td>Training Day (2001)                     </td><td>Crime|Drama|Thriller                               </td><td>Frailty (2001)                                     </td><td>Crime|Drama|Thriller                               </td></tr>\\n<tr><td>Zoolander (2001)                        </td><td>Comedy                                             </td><td>Old School (2003)                                  </td><td>Comedy                                             </td></tr>\\n<tr><td>Serendipity (2001)                      </td><td>Comedy|Romance                                     </td><td>Legally Blonde (2001)                              </td><td>Comedy|Romance                                     </td></tr>\\n<tr><td>Mulholland Drive (2001)                 </td><td>Crime|Drama|Mystery|Thriller                       </td><td>Prisoners (2013)                                   </td><td>Crime|Drama|Mystery|Thriller                       </td></tr>\\n<tr><td>From Hell (2001)                        </td><td>Crime|Horror|Mystery|Thriller                      </td><td>Identity (2003)                                    </td><td>Crime|Horror|Mystery|Thriller                      </td></tr>\\n<tr><td>Waking Life (2001)                      </td><td>Animation|Drama|Fantasy                            </td><td>Warm Bodies (2013)                                 </td><td>Animation|Drama|Fantasy                            </td></tr>\\n<tr><td>K-PAX (2001)                            </td><td>Drama|Fantasy|Mystery|Sci-Fi                       </td><td>Gosford Park (2001)                                </td><td>Drama|Fantasy|Mystery|Sci-Fi                       </td></tr>\\n</tbody>\\n</table>'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = 50  # number of movies to display\n",
    "dim = len(vms)\n",
    "dist = np.zeros((dim,dim))\n",
    "\n",
    "for i in range(dim):\n",
    "    for j in range(dim):\n",
    "        dist[i,j] = sq_dist(vms[i, :], vms[j, :])\n",
    "        \n",
    "m_dist = ma.masked_array(dist, mask=np.identity(dist.shape[0]))  # mask the diagonal\n",
    "\n",
    "disp = [[\"movie1\", \"genres\", \"movie2\", \"genres\"]]\n",
    "for i in range(count):\n",
    "    min_idx = np.argmin(m_dist[i])\n",
    "    movie1_id = int(item_vecs[i,0])\n",
    "    movie2_id = int(item_vecs[min_idx,0])\n",
    "    disp.append( [movie_dict[movie1_id]['title'], movie_dict[movie1_id]['genres'],\n",
    "                  movie_dict[movie2_id]['title'], movie_dict[movie1_id]['genres']]\n",
    "               )\n",
    "table = tabulate.tabulate(disp, tablefmt='html', headers=\"firstrow\")\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results show the model will generally suggest a movie with similar genre's."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"6\"></a>\n",
    "## 6 - Congratulations! <img align=\"left\" src=\"./images/film_award.png\" style=\" width:40px;\">\n",
    "You have completed a content-based recommender system.    \n",
    "\n",
    "This structure is the basis of many commercial recommender systems. The user content can be greatly expanded to incorporate more information about the user if it is available.  Items are not limited to movies. This can be used to recommend any item, books, cars or items that are similar to an item in your 'shopping cart'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"2\" color=\"darkgreen\"><b>Please click here if you want to experiment with any of the non-graded code.</b></font></summary>\n",
    "    <p><i><b>Important Note: Please only do this when you've already passed the assignment to avoid problems with the autograder.</b></i>\n",
    "    <ol>\n",
    "        <li> On the notebook’s menu, click “View” > “Cell Toolbar” > “Edit Metadata”</li>\n",
    "        <li> Hit the “Edit Metadata” button next to the code cell which you want to lock/unlock</li>\n",
    "        <li> Set the attribute value for “editable” to:\n",
    "            <ul>\n",
    "                <li> “true” if you want to unlock it </li>\n",
    "                <li> “false” if you want to lock it </li>\n",
    "            </ul>\n",
    "        </li>\n",
    "        <li> On the notebook’s menu, click “View” > “Cell Toolbar” > “None” </li>\n",
    "    </ol>\n",
    "    <p> Here's a short demo of how to do the steps above: \n",
    "        <br>\n",
    "        <img src=\"https://drive.google.com/uc?export=view&id=14Xy_Mb17CZVgzVAgq7NCjMVBvSae3xO1\" align=\"center\" alt=\"unlock_cells.gif\">\n",
    "</details>"
   ]
  }
 ],
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   "authorship_tag": "ABX9TyOFYdA6zQJ1FpgYwYmRIeXa",
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   "name": "Recsys_NN.ipynb",
   "private_outputs": true,
   "provenance": [
    {
     "file_id": "1RO0HLb7kRE0Tj_0D4E5I-vQz2QLu3CUm",
     "timestamp": 1655169179306
    }
   ]
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  "gpuClass": "standard",
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