final_report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IEOR 4571 - Personalization - Final Project\n",
    "\n",
    "#### Team members: \n",
    "Name, UNI/email, Github ID\n",
    "* Megala Kannan, msk2245@columbia.edu, thisismeg\n",
    "* Hojin Lee, hl3328@columbia.edu, hjlee9295\n",
    "* Jung Ah Shin, js5569@columbia.edu, juliajungahshin\n",
    "* Tiffany Zhu, tz2196@columbia.edu, tlzhu19\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TOC:\n",
    "* [1. Introduction](#1)\n",
    "* [2. Data Exploration](#2)\n",
    "* [3. Modeling](#3)\n",
    "    * [3.0 Sampline Data](#3-0)\n",
    "    * [3.1 Baseline Model: Learned Bias](#3-1)\n",
    "        * [3.1.1 Accuracy of Learned Bias Model](#3-1-1)\n",
    "        * [3.1.2 Coverage of Learned Bias Model](#3-1-2)\n",
    "    * [3.2 Baseline Model: Vanilla Matrix Factorization](#3-2)\n",
    "        * [3.2.1 Tuning Hyperparameters](#3-2-1)\n",
    "        * [3.2.2 Accuracy of Vanilla Matrix Factorization Model](#3-2-2)\n",
    "        * [3.2.3 Coverage of Vanilla Matrix Factorization Model](#3-2-3)\n",
    "    * [3.3 SVD and Nonnegative Matrix Factorization](#3-3)\n",
    "    * [3.4 Wide and Deep Model](#3-4)\n",
    "        * [3.4.1 Feature Selection](#3-4-1)\n",
    "        * [3.4.2 Feature Engineering](#3-4-2)\n",
    "        * [3.4.3 Modeling](#3-4-3)\n",
    "        * [3.4.4 Accuracy](#3-4-4)    \n",
    "        * [3.4.5 Coverage](#3-4-5)    \n",
    "* [4. Evaluation](#4)\n",
    "    * [4.1 Accuracy](#4-1)\n",
    "    * [4.2 Coverage](#4-2)\n",
    "    * [4.3 Hold out](#4-3)\n",
    "    * [4.4 Final Results Table](#4-4)\n",
    "    * [4.5 User Distribution](#4-5)\n",
    "* [5. Conclusion](#5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Introduction <a class=\"anchor\" id=\"1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yelp is a crowd-sourced review platform where users interact with businesses and other users through writing reviews and discovering new businesses. To make this platform more effective, we leverage various attributes of users, businesses, and reviews to provide better business recommendation for users. Our initial goal is to predict the last rating (final review) for each active user, where active users are those that gave 5 or more reviews. While aiming towards minimizing the difference between the actual rating and our prediction, we also want our recommendation model to provide diverse recommendations so that many users have an opportunity to explore new contents.\n",
    "\n",
    "In line with our objectives, we decided to explore several recommendation algorithms to gain a better understanding of the advantages and disadvantages each model provides.\n",
    "We chose Singular Value Decomposition (SVD), Non-negative Matrix Factorization (NMF), and Wide and Deep algorithms. We compare the aforementioned models with two baseline models, which are user and item bias baseline, and a vanilla Matrix Factorization model.\n",
    "\n",
    "To evaluate and compare the accuracy of our models, we used RMSE. These values would indicate how well our models perform compared to the baseline models. To evaluate the quality and diversity of our models, we used catalog coverage. This would indicate how diverse our business recommendations are. Detailed descriptions of each process is provided below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data Exploration <a class=\"anchor\" id=\"2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "from tqdm import tqdm\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "import itertools\n",
    "from collections import Counter\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we convert all of our json files to Pandas dataframes. We will use review.json, business.json, user.json, tip.json, and photo.json. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_json_to_df(path, file_name, column_names):\n",
    "    '''\n",
    "    converts the json file into a pandas dataframe\n",
    "    path: string of path\n",
    "    file_name: string of json file name\n",
    "    column_names: the names of the json file attributes (used as the dataframe column names)\n",
    "    returns: dataframe\n",
    "    '''\n",
    "    line_count = len(open(path + file_name).readlines())    \n",
    "    columns_dict = {name: [] for name in column_names}\n",
    "\n",
    "    with open(path + file_name) as f:\n",
    "        for line in tqdm(f, total=line_count):\n",
    "            blob = json.loads(line)\n",
    "            \n",
    "            for key in column_names:\n",
    "                columns_dict[key].append(blob[key])\n",
    "    \n",
    "    return pd.DataFrame(columns_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"/Users/tiffanyzhu/Downloads/yelp_dataset/\"\n",
    "file_name = \"review.json\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6685900/6685900 [01:20<00:00, 83031.22it/s]\n"
     ]
    }
   ],
   "source": [
    "# review.json\n",
    "ratings = convert_json_to_df(path, file_name, ['user_id', 'business_id', 'stars', 'date'])\n",
    "\n",
    "user_counts = ratings[\"user_id\"].value_counts()\n",
    "active_users = user_counts.loc[user_counts >= 5].index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "286130"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(active_users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>business_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>ujmEBvifdJM6h6RLv4wQIg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-07 04:34:36</td>\n",
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       "      <td>NZnhc2sEQy3RmzKTZnqtwQ</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2017-01-14 21:30:33</td>\n",
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       "      <td>dacAIZ6fTM6mqwW5uxkskg</td>\n",
       "      <td>ikCg8xy5JIg_NGPx-MSIDA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2018-01-09 20:56:38</td>\n",
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       "      <td>2018-01-30 23:07:38</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                  user_id             business_id  rating                 date\n",
       "0  hG7b0MtEbXx5QzbzE6C_VA  ujmEBvifdJM6h6RLv4wQIg     1.0  2013-05-07 04:34:36\n",
       "1  yXQM5uF2jS6es16SJzNHfg  NZnhc2sEQy3RmzKTZnqtwQ     5.0  2017-01-14 21:30:33\n",
       "2  n6-Gk65cPZL6Uz8qRm3NYw  WTqjgwHlXbSFevF32_DJVw     5.0  2016-11-09 20:09:03\n",
       "3  dacAIZ6fTM6mqwW5uxkskg  ikCg8xy5JIg_NGPx-MSIDA     5.0  2018-01-09 20:56:38\n",
       "4  ssoyf2_x0EQMed6fgHeMyQ  b1b1eb3uo-w561D0ZfCEiQ     1.0  2018-01-30 23:07:38"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.rename(columns={'stars': 'rating'}, inplace=True)\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for metadata info see https://www.yelp.com/dataset/documentation/main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 192609/192609 [00:03<00:00, 52207.81it/s]\n"
     ]
    }
   ],
   "source": [
    "# business.json\n",
    "# todo: how to add 'attributes'?\n",
    "'''\n",
    "\"attributes\": {\n",
    "        \"RestaurantsTakeOut\": true,\n",
    "        \"BusinessParking\": {\n",
    "            \"garage\": false,\n",
    "            \"street\": true,\n",
    "            \"validated\": false,\n",
    "            \"lot\": false,\n",
    "            \"valet\": false\n",
    "        },\n",
    "'''\n",
    "\n",
    "businesses = convert_json_to_df(path, 'business.json', ['business_id', 'city', 'state', 'stars', \n",
    "                                                        'review_count', 'is_open', 'attributes', \n",
    "                                                        'categories', 'hours', 'latitude', 'longitude', 'name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>business_id</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>is_open</th>\n",
       "      <th>attributes</th>\n",
       "      <th>categories</th>\n",
       "      <th>hours</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1SWheh84yJXfytovILXOAQ</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>AZ</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>{'GoodForKids': 'False'}</td>\n",
       "      <td>Golf, Active Life</td>\n",
       "      <td>None</td>\n",
       "      <td>33.522143</td>\n",
       "      <td>-112.018481</td>\n",
       "      <td>Arizona Biltmore Golf Club</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>QXAEGFB4oINsVuTFxEYKFQ</td>\n",
       "      <td>Mississauga</td>\n",
       "      <td>ON</td>\n",
       "      <td>2.5</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>{'RestaurantsReservations': 'True', 'GoodForMe...</td>\n",
       "      <td>Specialty Food, Restaurants, Dim Sum, Imported...</td>\n",
       "      <td>{'Monday': '9:0-0:0', 'Tuesday': '9:0-0:0', 'W...</td>\n",
       "      <td>43.605499</td>\n",
       "      <td>-79.652289</td>\n",
       "      <td>Emerald Chinese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gnKjwL_1w79qoiV3IC_xQQ</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>NC</td>\n",
       "      <td>4.0</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "      <td>{'GoodForKids': 'True', 'NoiseLevel': 'u'avera...</td>\n",
       "      <td>Sushi Bars, Restaurants, Japanese</td>\n",
       "      <td>{'Monday': '17:30-21:30', 'Wednesday': '17:30-...</td>\n",
       "      <td>35.092564</td>\n",
       "      <td>-80.859132</td>\n",
       "      <td>Musashi Japanese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>xvX2CttrVhyG2z1dFg_0xw</td>\n",
       "      <td>Goodyear</td>\n",
       "      <td>AZ</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>Insurance, Financial Services</td>\n",
       "      <td>{'Monday': '8:0-17:0', 'Tuesday': '8:0-17:0', ...</td>\n",
       "      <td>33.455613</td>\n",
       "      <td>-112.395596</td>\n",
       "      <td>Farmers Insurance - Paul Lorenz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HhyxOkGAM07SRYtlQ4wMFQ</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>NC</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>{'BusinessAcceptsBitcoin': 'False', 'ByAppoint...</td>\n",
       "      <td>Plumbing, Shopping, Local Services, Home Servi...</td>\n",
       "      <td>{'Monday': '7:0-23:0', 'Tuesday': '7:0-23:0', ...</td>\n",
       "      <td>35.190012</td>\n",
       "      <td>-80.887223</td>\n",
       "      <td>Queen City Plumbing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id         city state  stars  review_count  is_open  \\\n",
       "0  1SWheh84yJXfytovILXOAQ      Phoenix    AZ    3.0             5        0   \n",
       "1  QXAEGFB4oINsVuTFxEYKFQ  Mississauga    ON    2.5           128        1   \n",
       "2  gnKjwL_1w79qoiV3IC_xQQ    Charlotte    NC    4.0           170        1   \n",
       "3  xvX2CttrVhyG2z1dFg_0xw     Goodyear    AZ    5.0             3        1   \n",
       "4  HhyxOkGAM07SRYtlQ4wMFQ    Charlotte    NC    4.0             4        1   \n",
       "\n",
       "                                          attributes  \\\n",
       "0                           {'GoodForKids': 'False'}   \n",
       "1  {'RestaurantsReservations': 'True', 'GoodForMe...   \n",
       "2  {'GoodForKids': 'True', 'NoiseLevel': 'u'avera...   \n",
       "3                                               None   \n",
       "4  {'BusinessAcceptsBitcoin': 'False', 'ByAppoint...   \n",
       "\n",
       "                                          categories  \\\n",
       "0                                  Golf, Active Life   \n",
       "1  Specialty Food, Restaurants, Dim Sum, Imported...   \n",
       "2                  Sushi Bars, Restaurants, Japanese   \n",
       "3                      Insurance, Financial Services   \n",
       "4  Plumbing, Shopping, Local Services, Home Servi...   \n",
       "\n",
       "                                               hours   latitude   longitude  \\\n",
       "0                                               None  33.522143 -112.018481   \n",
       "1  {'Monday': '9:0-0:0', 'Tuesday': '9:0-0:0', 'W...  43.605499  -79.652289   \n",
       "2  {'Monday': '17:30-21:30', 'Wednesday': '17:30-...  35.092564  -80.859132   \n",
       "3  {'Monday': '8:0-17:0', 'Tuesday': '8:0-17:0', ...  33.455613 -112.395596   \n",
       "4  {'Monday': '7:0-23:0', 'Tuesday': '7:0-23:0', ...  35.190012  -80.887223   \n",
       "\n",
       "                              name  \n",
       "0       Arizona Biltmore Golf Club  \n",
       "1       Emerald Chinese Restaurant  \n",
       "2      Musashi Japanese Restaurant  \n",
       "3  Farmers Insurance - Paul Lorenz  \n",
       "4              Queen City Plumbing  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "businesses.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1637138/1637138 [00:42<00:00, 38463.04it/s]\n"
     ]
    }
   ],
   "source": [
    "# user.json\n",
    "users = convert_json_to_df(path, 'user.json', ['user_id', 'review_count', 'friends', 'useful', \n",
    "                                               'funny', 'cool', 'fans', 'elite', 'average_stars', \n",
    "                                               'compliment_hot', 'compliment_more', 'compliment_profile',\n",
    "                                               'compliment_cute', 'compliment_list', 'compliment_note',\n",
    "                                               'compliment_plain', 'compliment_cool', 'compliment_funny',\n",
    "                                               'compliment_writer', 'compliment_photos'\n",
    "                                              ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <td>84</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>2015,2016,2017</td>\n",
       "      <td>4.03</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4XChL029mKr5hydo79Ljxg</td>\n",
       "      <td>33</td>\n",
       "      <td>kEBTgDvFX754S68FllfCaA, aB2DynOxNOJK9st2ZeGTPg...</td>\n",
       "      <td>48</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td>3.63</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bc8C_eETBWL0olvFSJJd0w</td>\n",
       "      <td>16</td>\n",
       "      <td>4N-HU_T32hLENLntsNKNBg, pSY2vwWLgWfGVAAiKQzMng...</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>3.71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dD0gZpBctWGdWo9WlGuhlA</td>\n",
       "      <td>17</td>\n",
       "      <td>RZ6wS38wnlXyj-OOdTzBxA, l5jxZh1KsgI8rMunm-GN6A...</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td></td>\n",
       "      <td>4.85</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MM4RJAeH6yuaN8oZDSt0RA</td>\n",
       "      <td>361</td>\n",
       "      <td>mbwrZ-RS76V1HoJ0bF_Geg, g64lOV39xSLRZO0aQQ6DeQ...</td>\n",
       "      <td>1114</td>\n",
       "      <td>279</td>\n",
       "      <td>665</td>\n",
       "      <td>39</td>\n",
       "      <td>2015,2016,2017,2018</td>\n",
       "      <td>4.08</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>57</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  user_id  review_count  \\\n",
       "0  l6BmjZMeQD3rDxWUbiAiow            95   \n",
       "1  4XChL029mKr5hydo79Ljxg            33   \n",
       "2  bc8C_eETBWL0olvFSJJd0w            16   \n",
       "3  dD0gZpBctWGdWo9WlGuhlA            17   \n",
       "4  MM4RJAeH6yuaN8oZDSt0RA           361   \n",
       "\n",
       "                                             friends  useful  funny  cool  \\\n",
       "0  c78V-rj8NQcQjOI8KP3UEA, alRMgPcngYSCJ5naFRBz5g...      84     17    25   \n",
       "1  kEBTgDvFX754S68FllfCaA, aB2DynOxNOJK9st2ZeGTPg...      48     22    16   \n",
       "2  4N-HU_T32hLENLntsNKNBg, pSY2vwWLgWfGVAAiKQzMng...      28      8    10   \n",
       "3  RZ6wS38wnlXyj-OOdTzBxA, l5jxZh1KsgI8rMunm-GN6A...      30      4    14   \n",
       "4  mbwrZ-RS76V1HoJ0bF_Geg, g64lOV39xSLRZO0aQQ6DeQ...    1114    279   665   \n",
       "\n",
       "   fans                elite  average_stars  compliment_hot  compliment_more  \\\n",
       "0     5       2015,2016,2017           4.03               2                0   \n",
       "1     4                                3.63               1                0   \n",
       "2     0                                3.71               0                0   \n",
       "3     5                                4.85               1                0   \n",
       "4    39  2015,2016,2017,2018           4.08              28                1   \n",
       "\n",
       "   compliment_profile  compliment_cute  compliment_list  compliment_note  \\\n",
       "0                   0                0                0                1   \n",
       "1                   0                0                0                0   \n",
       "2                   0                0                0                1   \n",
       "3                   0                0                0                0   \n",
       "4                   0                0                1               16   \n",
       "\n",
       "   compliment_plain  compliment_cool  compliment_funny  compliment_writer  \\\n",
       "0                 1                1                 1                  2   \n",
       "1                 0                1                 1                  0   \n",
       "2                 0                0                 0                  0   \n",
       "3                 2                0                 0                  1   \n",
       "4                57               80                80                 25   \n",
       "\n",
       "   compliment_photos  \n",
       "0                  0  \n",
       "1                  0  \n",
       "2                  0  \n",
       "3                  0  \n",
       "4                  5  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1223094/1223094 [00:12<00:00, 99101.99it/s] \n"
     ]
    }
   ],
   "source": [
    "# tip.json\n",
    "tips =  convert_json_to_df(path, 'tip.json', ['text', 'date', 'compliment_count', 'business_id', 'user_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 200000/200000 [00:01<00:00, 149443.01it/s]\n"
     ]
    }
   ],
   "source": [
    "photos = convert_json_to_df(path, 'photo.json', ['photo_id', 'business_id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploratory analysis of our data shows some interesting patterns.\n",
    "The average ratings received by businesses imply that a large number of businesses received 3.5-5.0 stars from users, while only a small number of businesses received a low rating (rating <  3.0). Similarly, by looking at the distribution of ratings by users, the majority of the ratings are concentrated in the upper-half, indicating that most users gave 4.0 to 5.0 in their reviews. It shows that most ratings are positive, whereas only a small amount of ratings were negative (rating <  3.0).  We can hypothesize from this evaluation that since our models will be trained primarily by positive ratings,  it might perform better at predicting ratings for businesses they are likely to enjoy than those they are less likely to enjoy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart = sns.countplot(x='stars',data=businesses)\n",
    "var = chart.set(xlabel='Rating', title='Average Ratings of Businesses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart = sns.countplot(x='rating',data=ratings)\n",
    "var = chart.set(xlabel='Rating', title='Distribution of Ratings by Users')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, we also looked at which categories the businesses belong in. Our analysis indicate that the top-5 most common categories are restaurants, shopping, food, home services, and beauty & spas. \n",
    "Furthermore, the top-5 most common categories cover approximately 71% of the data, hence these categories are used as features for our Wide and Deep model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "catList = []\n",
    "businesses['categories'].fillna(value='',inplace=True)\n",
    "businesses['cat'] = businesses['categories'].apply(lambda x: x.split(','))\n",
    "catList.extend(businesses['cat'])\n",
    "merged = [x.strip() for x in list(itertools.chain(*catList))]\n",
    "\n",
    "mostCommon = Counter(merged).most_common(15)\n",
    "mostCommonKeys = [x for x in list(itertools.chain(*mostCommon)) if type(x) != int]\n",
    "mostCommonValues = [x for x in list(itertools.chain(*mostCommon)) if type(x) == int]\n",
    "\n",
    "df = pd.DataFrame({'y': mostCommonValues, 'categories': mostCommonKeys})\n",
    "ax = df.plot.barh(x='categories', y='y')\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "business covered percentage when k = 5 : 0.7069243908643936\n",
      "business covered percentage when k = 10 : 0.8770670114065282\n",
      "business covered percentage when k = 20 : 0.9306418703175864\n"
     ]
    }
   ],
   "source": [
    "topKBusiness = [5,10,20]\n",
    "\n",
    "for n in topKBusiness:\n",
    "    catList = []\n",
    "    businesses['categories'].fillna(value='',inplace=True)\n",
    "    businesses['cat'] = businesses['categories'].apply(lambda x: x.split(','))\n",
    "    catList.extend(businesses['cat'])\n",
    "    merged = [x.strip() for x in list(itertools.chain(*catList))]\n",
    "    mostCommon = Counter(merged).most_common(n)\n",
    "    mostCommonKeys = [x for x in list(itertools.chain(*mostCommon)) if type(x) != int]\n",
    "    mostCommonValues = [x for x in list(itertools.chain(*mostCommon)) if type(x) == int]\n",
    "\n",
    "    colName = 'Top' + str(n)\n",
    "    businesses[colName] = businesses['cat'].apply(lambda categories: 1 if bool(set([y.strip() for y in categories]).intersection(mostCommonKeys)) else 0)   \n",
    "    print('business covered percentage when k =', n, \":\", businesses[colName].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see which specific businesses might have received the highest number of ratings, Starbucks overall received the most number of ratings,  followed by McDonald’s and Hash House A Go Go. We could observe that franchise businesses tend to have many reviews due to having branches in various locations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = ratings.merge(businesses[['business_id', 'name']], on='business_id')\n",
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "chart = sns.countplot(x='name',data=df, order=pd.value_counts(df['name']).iloc[:10].index)\n",
    "chart.set(title='Distribution of Ratings by Business')\n",
    "var = chart.set_xticklabels(chart.get_xticklabels(), rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of ratings of active users indicate that while there are a small number of users who have reviewed around 4000 times (Maximum is 4129), the vast majority of the users have reviewed around 5 times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    286130.000000\n",
       "mean         15.860874\n",
       "std          32.607378\n",
       "min           5.000000\n",
       "25%           6.000000\n",
       "50%           8.000000\n",
       "75%          15.000000\n",
       "max        4129.000000\n",
       "Name: user_id, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_counts[user_counts.values >= 5].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart = sns.distplot(user_counts.values[user_counts.values > 15], norm_hist=True, hist=False, bins=5)\n",
    "chart.set(title='Distribution of Ratings of Active Users', xlabel='Ratings');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Modeling <a class=\"anchor\" id=\"3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.0 Sampling Data <a class=\"anchor\" id=\"3-0\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By identifying and filtering the users who gave 5 or more reviews, there are 286,130 active users and about 4.5 million ratings. Since we wanted to evaluate the performance on several models, we first sampled the active user data where there are 1000, 10,000, and 50,000 users (where 50,000 users account for approximately 17.5% of the entire data). For this given dataset, we wanted to sample by users to ensure that our sample dataset would contain an entire user-business interaction history of the sampled users. That way, it would align with our goal of predicting the last rating for each active user based on the entire user-business interaction history.\n",
    "\n",
    "Furthermore, since users’ preference towards restaurants could gradually change over time, we believe that it would be reasonable to split our train and test data so that the train data would contain the earlier reviews, and the test data would consist of the latter reviews given by the users. That way, while we are supposedly predicting “future” reviews,  it is also consistent with our objective of predicting the final star rating (by date) given by the user. \n",
    "We also analyzed the categories for each business. As stated earlier, we decided to use the top-5 categories to feed into our model. This is because they account for approximately 70% of the businesses in our data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4538272"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_user_only_ratings_df = ratings[ratings['user_id'].isin(active_users)]\n",
    "len(active_user_only_ratings_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "286130"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(active_user_only_ratings_df['user_id'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "\n",
    "def get_sample_size_n(active_user_only_ratings_df, n): \n",
    "    sample_size = [n]\n",
    "\n",
    "    for s in sample_size:\n",
    "        sampleUID = active_user_only_ratings_df['user_id'].unique()[np.random.randint(active_user_only_ratings_df['user_id'].unique().shape[0], size=s)]\n",
    "        active_user_only_ratings_df_sample = active_user_only_ratings_df[active_user_only_ratings_df['user_id'].isin(sampleUID)]\n",
    "    \n",
    "    active_user_only_ratings_df_sample['date'] = pd.to_datetime(active_user_only_ratings_df_sample['date'])\n",
    "\n",
    "    # take 80% of reviews for each user sampled\n",
    "    training_data = active_user_only_ratings_df_sample.sort_values(by=['user_id', 'date']).groupby('user_id').apply(lambda x: x[:round(len(x)*.8)]).reset_index(drop=True)\n",
    "\n",
    "    # take 20% of reviews for each user sampled\n",
    "    testing_data = active_user_only_ratings_df_sample.sort_values(by=['user_id', 'date']).groupby('user_id').apply(lambda x: x[round(len(x)*.8):]).reset_index(drop=True)\n",
    "    \n",
    "    return active_user_only_ratings_df_sample, training_data, testing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_df_info(n, total_df, training_df, testing_df):\n",
    "    print('Total Number of Ratings: ', len(total_df))\n",
    "    print('Total Number of Users: ', n)\n",
    "    print(\"Training Size: \", len(training_df))\n",
    "    print(\"Testing Size: \", len(testing_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Number of Ratings:  16404\n",
      "Total Number of Users:  1000\n",
      "Training Size:  13144\n",
      "Testing Size:  3260\n"
     ]
    }
   ],
   "source": [
    "sample_sizes = [1000, 10000, 50000]\n",
    "active_user_only_ratings_df_sample, training_data, testing_data = get_sample_size_n(active_user_only_ratings_df, sample_sizes[0])\n",
    "print_df_info(sample_sizes[0], active_user_only_ratings_df_sample, training_data, testing_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Number of Ratings:  150722\n",
      "Total Number of Users:  10000\n",
      "Training Size:  120959\n",
      "Testing Size:  29763\n"
     ]
    }
   ],
   "source": [
    "active_user_only_ratings_df_sample2, training_data2, testing_data2 = get_sample_size_n(active_user_only_ratings_df, sample_sizes[1])\n",
    "print_df_info(sample_sizes[1], active_user_only_ratings_df_sample2, training_data2, testing_data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Number of Ratings:  725909\n",
      "Total Number of Users:  50000\n",
      "Training Size:  582297\n",
      "Testing Size:  143612\n"
     ]
    }
   ],
   "source": [
    "active_user_only_ratings_df_sample3, training_data3, testing_data3 = get_sample_size_n(active_user_only_ratings_df, sample_sizes[2])\n",
    "print_df_info(sample_sizes[2], active_user_only_ratings_df_sample3, training_data3, testing_data3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_samples = [active_user_only_ratings_df_sample, \n",
    "                  active_user_only_ratings_df_sample2, \n",
    "                  active_user_only_ratings_df_sample3]\n",
    "\n",
    "all_training_samples = [training_data, training_data2, training_data3]\n",
    "\n",
    "all_testing_samples = [testing_data, testing_data2, testing_data3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Baseline Model: Learned Bias <a class=\"anchor\" id=\"3-1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first baseline model is learned bias, where for each user and business pair, we take the user's average rating and the business's average rating and take their mean to get a predicted rating. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_average_user_ratings(ratings_df):\n",
    "    ratings_series = ratings_df.groupby('user_id')['rating'].mean()\n",
    "    return pd.DataFrame({'user_id':ratings_series.index, 'avg_user_rating':ratings_series.values})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_average_business_ratings(ratings_df):    \n",
    "    ratings_series = ratings_df.groupby('business_id')['rating'].mean()\n",
    "    return pd.DataFrame({'business_id':ratings_series.index, 'avg_business_rating':ratings_series.values})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_learned_bias(df, average_user_ratings, average_business_ratings, missingVal=3.0):\n",
    "    \n",
    "    baseline_line_predictions = df.merge(average_user_ratings, how ='left', on='user_id')\n",
    "    baseline_line_predictions = baseline_line_predictions.merge(average_business_ratings, how ='left', on='business_id')\n",
    "    baseline_line_predictions['avg_business_rating'] = baseline_line_predictions['avg_business_rating'].fillna(value=missingVal)\n",
    "    baseline_line_predictions['avg_user_rating'] = baseline_line_predictions['avg_user_rating'].astype(float)\n",
    "    baseline_line_predictions['avg_business_rating'] = baseline_line_predictions['avg_business_rating'].astype(float)\n",
    "    baseline_line_predictions['predicted_rating'] = (baseline_line_predictions['avg_user_rating'] + baseline_line_predictions['avg_business_rating']) / 2\n",
    "    \n",
    "    return baseline_line_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_catalog_coverage(ratingsDict, N, k=3, pred_col_name='predicted_rating'):\n",
    "    '''\n",
    "    k: top k businesses\n",
    "    N: total number of businesses\n",
    "    '''\n",
    "    \n",
    "    recList = []\n",
    "    for keys in ratingsDict.keys():\n",
    "        \n",
    "        if k >= len(ratingsDict[keys]):\n",
    "            for row in ratingsDict[keys]:\n",
    "                recList.append(row['business_id'])\n",
    "        else:\n",
    "            for index, item in enumerate(sorted(ratingsDict[keys], key=lambda x: -x[pred_col_name])):\n",
    "                if index+1 <= k:\n",
    "                    recList.append(item['business_id'])\n",
    "                else:\n",
    "                    pass\n",
    "\n",
    "    recommended_set = set(recList)\n",
    "\n",
    "    coverage = (len(recommended_set) / N) * 100\n",
    "    print('Catalog Coverage: {}%'.format(coverage))\n",
    "    return coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_baseline_model(X_data, y_data):\n",
    "    average_user_ratings = find_average_user_ratings(X_data)\n",
    "    average_business_ratings = find_average_business_ratings(X_data)\n",
    "     \n",
    "    baseline_line_predictions_test = predict_learned_bias(y_data, average_user_ratings, average_business_ratings)\n",
    "    \n",
    "    N = len(baseline_line_predictions_test)\n",
    "    baseline_rmse_test = np.sum(np.sqrt(((baseline_line_predictions_test['predicted_rating'] - baseline_line_predictions_test['rating']) / N) ** 2))\n",
    "    \n",
    "    baseline_line_predictions_dict = dict(baseline_line_predictions_test.set_index('user_id').groupby(level = 0).apply(lambda x : x.to_dict(orient= 'records')))\n",
    "    N = len(baseline_line_predictions_test['business_id'].unique())\n",
    "    coverage = calculate_catalog_coverage(baseline_line_predictions_dict, N, k=5)\n",
    "\n",
    "    return baseline_rmse_test, coverage, baseline_line_predictions_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Catalog Coverage: 69.65493910690121%\n"
     ]
    }
   ],
   "source": [
    "rmse, coverage, baseline_predictions = run_baseline_model(training_data, testing_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.1790384113004118, 69.65493910690121)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse, coverage "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Catalog Coverage: 75.10193933366484%\n"
     ]
    }
   ],
   "source": [
    "rmse2, coverage2, baseline_predictions2 = run_baseline_model(training_data2, testing_data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.084912746514116, 75.10193933366484)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse2, coverage2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Catalog Coverage: 80.38108553810855%\n"
     ]
    }
   ],
   "source": [
    "rmse3, coverage3, baseline_predictions3 = run_baseline_model(training_data3, testing_data3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0360132065826084, 80.38108553810855)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse3, coverage3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.1 Accuracy of Learned Bias Model<a class=\"anchor\" id=\"3-1-1\"></a>\n",
    "\n",
    "We look at RMSE as a measure of accuracy. The first RMSE is for the sample with 1000 users, the second is for 10000 users and the third is when we sampled 50000 users. We can see that as the sample size increased, the RMSE improved (decreased)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.1790384113004118, 1.084912746514116, 1.0360132065826084]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baseline_rmses = [rmse, rmse2, rmse3]\n",
    "baseline_rmses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.2 Coverage of Learned Bias Model<a class=\"anchor\" id=\"3-1-2\"></a>\n",
    "\n",
    "We additionally looked at catalog coverage. The first catalog coverage is for the sample with 1000 users, the second is for 10000 users and the third is when we sampled 50000 users. We can see that as the sample size increased, the catalog coverage improved (increased)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[69.65493910690121, 75.10193933366484, 80.38108553810855]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baseline_coverages = [coverage, coverage2, coverage3]\n",
    "baseline_coverages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Baseline Model: Vanilla Matrix Factorization <a class=\"anchor\" id=\"3-2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a second baseline model, we used vanilla matrix factorization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.mllib.recommendation import ALS\n",
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql import SQLContext\n",
    "from pyspark.ml.tuning import (CrossValidator, ParamGridBuilder)\n",
    "\n",
    "import pyspark.sql.functions as sql_func\n",
    "from pyspark.sql.types import *\n",
    "from pyspark.ml.recommendation import ALS, ALSModel\n",
    "from pyspark.context import SparkContext\n",
    "from pyspark.sql.session import SparkSession\n",
    "from pyspark.mllib.evaluation import RegressionMetrics, RankingMetrics\n",
    "from pyspark.ml.evaluation import RegressionEvaluator, BinaryClassificationEvaluator, MulticlassClassificationEvaluator\n",
    "from pyspark.sql.functions import col\n",
    "\n",
    "# Load ratings data into PySpark dataframe\n",
    "data_schema = StructType([\n",
    "    StructField('numeric_user_id', IntegerType(), False),\n",
    "    StructField('numeric_business_id', IntegerType(), False),\n",
    "    StructField('rating',FloatType(), False)])\n",
    "\n",
    "#Spark Session\n",
    "spark =SparkSession.builder.appName('ALS').getOrCreate()\n",
    "sc = spark.sparkContext\n",
    "sqlContext = SQLContext(sc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_sample_dfs = [active_user_only_ratings_df_sample, active_user_only_ratings_df_sample2, active_user_only_ratings_df_sample3]\n",
    "all_training_dfs = [training_data, training_data2, training_data3]\n",
    "all_testing_dfs = [testing_data, testing_data2, testing_data3]\n",
    "\n",
    "\n",
    "for df in [all_training_dfs, all_testing_dfs]:\n",
    "    for train in df:\n",
    "        numeric_user_id_dict = {key: value + 1 for value, key in enumerate(train['user_id'].unique())}\n",
    "        numeric_business_id_dict = {key: value + 1 for value, key in enumerate(train['business_id'].unique())}\n",
    "\n",
    "        train['numeric_user_id'] = train['user_id'].apply(lambda u: numeric_user_id_dict[u])\n",
    "        train['numeric_business_id'] = train['business_id'].apply(lambda b: numeric_business_id_dict[b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Spark DF from Pandas DF\n",
    "all_sample_spark_dfs_train = []\n",
    "all_sample_spark_dfs_test = []\n",
    "\n",
    "for sample_df in all_training_dfs:\n",
    "    new_sample_df = sample_df[['numeric_user_id', 'numeric_business_id', 'rating']]\n",
    "    spark_sample_df = spark.createDataFrame(new_sample_df, schema=data_schema)\n",
    "    all_sample_spark_dfs_train.append(spark_sample_df)\n",
    "\n",
    "for sample_df in all_testing_dfs:\n",
    "    new_sample_df = sample_df[['numeric_user_id', 'numeric_business_id', 'rating']]\n",
    "    spark_sample_df = spark.createDataFrame(new_sample_df, schema=data_schema)\n",
    "    all_sample_spark_dfs_test.append(spark_sample_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_rmse_vs_param(cvModels, param_list, xlabel):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    for cvModel in cvModels: \n",
    "        plt.plot(param_list, cvModel.avgMetrics, marker='o')\n",
    "\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel('RMSE')\n",
    "    plt.title('RMSE vs {} by Number of Active Users'.format(xlabel))\n",
    "    plt.legend(['{} Active Users'.format(n) for n in sample_sizes])\n",
    "    plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.1 Tuning Hyperparameters  <a class=\"anchor\" id=\"3-2-1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Regularization Parameter**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSEs (tuning RegParam): [3.182061397411228, 2.0441366702852806, 1.987522157706846]\n",
      "RMSEs (tuning RegParam): [3.8560588923231442, 1.7424215836149237, 1.655989164770081]\n",
      "RMSEs (tuning RegParam): [3.612037802588028, 1.5457387527610602, 1.4780722464985914]\n"
     ]
    }
   ],
   "source": [
    "als = ALS(maxIter=5, userCol=\"numeric_user_id\", itemCol=\"numeric_business_id\", ratingCol=\"rating\",\n",
    "              coldStartStrategy=\"drop\", nonnegative= True)\n",
    "\n",
    "reg_list = [0.001, 0.1, 0.2]\n",
    "paramGrid = ParamGridBuilder().addGrid(als.regParam, reg_list).build()\n",
    "    \n",
    "crossval = CrossValidator(estimator=als,\n",
    "                              estimatorParamMaps=paramGrid,\n",
    "                              evaluator=RegressionEvaluator(metricName=\"rmse\",\n",
    "                                                            labelCol=\"rating\"),\n",
    "                              numFolds=3)\n",
    "\n",
    "cvs = []\n",
    "for i in range(3):\n",
    "    cv = crossval.fit(all_sample_spark_dfs_train[i])\n",
    "    cvs.append(cv)\n",
    "    print('RMSEs (tuning RegParam):', cv.avgMetrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BazDGfI51VX1zrPd8HdZn5BDFl0OUKpDAXoV12vr/sH6OMoGrjTFF7wRRmnuwEqnkEG1sxXqf/hUIPYL1eX4Aa/w+wz7TPA7r9PkvWOuOG5bS7mysWfIsrJ/Pwu2+iXUV+zVYn81VWJ/pHUd4LY9j3X3inkC/lwX6lxgYe7D+6IsFXsf6mf+Uw3evKPi5vBZrnfhUrBnWJYG+FJzlKMlBrP9DvsH6Y+R64IbA504px8gx/D+plFKlClxk9Scw1hgzo6L7U1FEpCtWktzQGJN6pP2VUup0pqfvlVLHTUSuxZq52YC1fnYc1inudyuyXxVFrNKpDbFOS8/RhFQppY5MT98rpU6EKljrAH/COn3vwrrP4u4K7VXFScI6Leyn+J0XlFJKhaCn75VSSimlVIXTmVKllFJKKVXhTqo1penp6Tqtq5RSSil1kouJiSlWsENnSpVSSimlVIXTpFQppZRSSlU4TUpD2LRpU0V3oVLT8SmZjk3pdHxKpmNTOh2f0un4lEzHpnSVaXw0KVVKKaWUUhVOk1KllFJKKVXhTqqr75VSSilVORljyMjIwO/3V3RXbDweD+np6RXdjUrLqfFxuVxER0cjUuwi+xJpUqqUUkqp45aRkUFkZCQREREV3RWbyMhIPB5PRXej0nJqfHJzc8nIyKBatWplfo6evldKKaXUcfP7/ZUuIVUVJyIi4qhnzTUpVUoppZRSFU6TUqWUUkqdEu6//37OPfdcLr/8clv8wIED9OjRg/j4eHr06EFaWhpgrYNNSkqidevWJCQksGbNmuBz5syZQ3x8PPHx8cyZM6fENvft20etWrWYNWvWEfv35Zdf8t133wUf/+c//2Hu3LlH+zKLue+++1i8eLEtduaZZx73ccubJqVKKaWUOiXccsstzJ8/v1h82rRpdOjQgZSUFDp06MC0adMAWLp0KVu3biUlJYXp06czfPhwwEpiJ06cyPLly1mxYgUTJ04MJrJFLVq0iDZt2oRst6jk5GRWrlwZfHzXXXdx8803H8tLLRc+n69c29OkVCmllFLlbt6WTJq/m0rsrB00fzeVeVsyj/uYbdu2JTY2tlj8o48+CiZ/N998M0uWLAnGb7rpJkSENm3akJ6eTmpqKsuXLycxMZHY2Fhq1KhBYmIiy5YtC9nme++9x/jx49m5cyc7d+4MxpctW0b79u1p27Yt1157Lb///juzZs1i5syZtGvXjq+//pqnn36aGTNm8Msvv9CxY8fgc3///XcSEhIAWLNmDV27dqVDhw707NmT1NTUoxqT1NRUunTpQrt27bj88sv5+uuvAVixYgWdO3emc+fO9O3bl4yMDACaN2/OxIkTufrqq1m0aBEvvfQSl156KQkJCdx1111H1fbR0qvvSxD29VIi5r+G7N+DOaMOub3uJj+hc0V3SymllDop1Ji1o8z7/pHpY8D/pTHg/0LPRgKk3Xnsp6P37NlDXFwcAHFxcezduxeAXbt22U5z169fn127dpUYL+rPP/9k9+7dXHTRRVx//fUsWLCAQYMGsW/fPoYMGcKSJUto1KgRBw4cIDY2ljvvvJPo6GgGDx4MwP/+9z8AzjvvPHJzc9m2bRuNGjVi4cKFXH/99eTl5ZGUlMScOXOoVasWCxYs4Mknn+TFF18s82ufN28enTp1YsSIEfh8PrKysti/fz+TJ09m0aJFuN1uXnrpJV588UUeeughwLpN1CeffAJA06ZNWbt2LZGRkSXOFp8ompSGEPvjt0R+9P+Q3BwAZP9uImdNAdDEVCmllDpFGGOKxUQkZDyUBQsWcP311wPQs2dPBg8ezKBBg1i1ahUJCQk0atQIIOTsbVHXX389CxcuZNiwYSxYsIBZs2axadMmNmzYQI8ePQDrDgd169YN2eeSYvHx8QwaNIi8vDyuueYaWrRowSeffMIvv/zCP//5T4wx5Ofn06ZNG1tfCjRr1owBAwZwzTXXcM0115RpXI6VJqUh1P98YTAhLSC5OUTMf1WTUqWUUuokU6dOHVJTU4mLiyM1NZXatWsD1gzojh2HZ3R37txJXFwc9evXJzk52RZv165dsePOnz+fvXv38u677wLWqfItW7ZgjDmqm8aDldT27duX7t27IyI0btyYn376iaZNm7J06dJSn1uzZk3bLOaBAweoWbMmYC1p+Oijj/jss8+49957GTx4cHBJwuuvv47X6y12n9KqVasGv3/33Xf56quv+Pjjj5k8eTLffvstYWHOpI+6pjSE8PS/QsZl/x7Izyvn3iillFLqeHTp0iV4lfvcuXPp2rVrMP72229jjGHVqlVUr16duLg4OnXqxIoVK0hLSyMtLY0VK1bQqVMn2zE3bdpEVlYWGzZsYP369axfv55hw4bx3nvvcckll/DVV1+xbds2wEoSAaKjozl06FDIPp5zzjm43W4mTZpEz549AWjSpAn79u0LXhyVl5fHhg0bij23Xbt2LFiwgNzcXADeeustrrjiCgC2b99O7dq16du3L7fddhtr166lTZs2fPfdd2zduhWArKwsNm/eXOy4fr+fP//8k/bt2/PEE0+Qnp4eXHvqBJ0pDSEvpiYRIRJTATzPjcY7+AmIjCr/jimllFInidLWgM7bkskDX6WT7Tt8mjzKLTzfNobejauW+Lwj6d+/P8nJyezfv58LLriAUaNGceONNzJs2DD69evHm2++SYMGDZg9ezYAV111FUuXLqV169ZUqVIluFYzNjaWkSNHkpiYCEBSUlKxU/Dz58+nW7dutti1115L//79SUpK4rnnnuP222/H7/dTu3ZtFi1aRJcuXbjjjjv46KOPmDRpUrH+9+zZk0ceeYS1a9cC1g3oZ8+ezUMPPcTBgwfx+Xzcd999nH/++bbnXX311axZs4Yrr7wSl8vFOeecE7zDQHJyMjNmzCAsLIzo6Gj+/e9/U6tWLV588UX69++P1+tFRBg7diznnnuu7bg+n4977rmHgwcPYoxh4MCB1KhR41jfniOSsq6bqAzS09PLpbP7Fr7J2YXWlBblO7cZ2Q8+A1XLXjrrVLJp0yaaNGlS0d2olHRsSqfjUzIdm9Lp+JSuMoxPeno6MTExZd5/3pZMnvjhEH9m+mhQ1c2jF1U7roS0JKFOT6vDnByf0j4TMTExxdY36ExpCAcuvIy4unHBq+9xuRD/4Xt1uTf/RNSEIXhHTsbUOKMCe6qUUkqdnHo3rupIEqpOXrqmtAT5CZ3JmvoOmbM/J2v6fHwN7VPa7j+3EvXUYGRv8VtEKKWUUkqpo6NJaRmY6rFkP/wcvr83t8Vde3YSNX4wrj9/q6CeKaWUUkqdGjQpLasq0WSPmEx+i0ttYVfaPqImDMG1pfjVcEoppZRSqmw0KT0akR68Q8aTd5n9thCSeZCoicNw//RDBXVMKaWUUurkpknp0QoLJ+eeMeR1vM4WlhwvnqmjcH//ZQV1TCmllFLq5KVJ6bFwuci5Yyi53W+zhSU/D88L4wj78uMK6phSSil1+rr//vs599xzufzyy23xAwcO0KNHD+Lj4+nRo0ew+pExhqSkJFq3bk1CQgJr1qwJPmfOnDnEx8cTHx/PnDlzgvE1a9aQkJBA69atSUpKKrUkadu2benfv/8R+/37778zb9684OPVq1eTlJRU5tddkrfeeouRI0faYtdccw2rV68+7mM7QZPSYyVCbq+7ybnpPnvY+PG8NpHwT+eV8ESllFJKOeGWW25h/vz5xeLTpk2jQ4cOpKSk0KFDh+CN5ZcuXcrWrVtJSUlh+vTpDB8+HLCS2IkTJ7J8+XJWrFjBxIkTg4nsgw8+yHPPPUdKSgpbt25l2bJlIfvyyy+/YIzh66+/JjMzs9R+b9++3dbv1q1bh7y5fmWRn5/vyHE1KT1OeV364O2fhBH7UEbOeZGIBf+Bk6g4gVJKKVVewr5eSpUH+1C1byJVHuxD2Nel13cvi7Zt2xarvATw0UcfcfPNNwNw8803s2TJkmD8pptuQkRo06YN6enppKamsnz5chITE4mNjQ3WiV+2bBmpqakcOnSISy65BBHhpptuCh6rqHnz5tGnTx8SExP5+OPDZ1C3bt3KddddR9u2bWnfvj2//fYbjz/+ON988w3t2rXjxRdf5Msvv6RPnz74/X6aN29uq2vfunVr9uzZw759+7j99ttJTEwkMTGRb7/99qjGqqA6VIcOHUhISAhWs/rtt9+44YYb6NChA126dOHXX38F4L777mP06NF069aNcePGkZycTLt27WjXrh1XXHFFieVTj4bePP8EyG/fFRNVFc9L45H8vGA8YvEbkHGQ3NseAJfm/0oppU4f0X2vLPO+sn83npefgpefKnGfjNlfHHNf9uzZQ1xcHABxcXHs3bsXgF27dnHmmYfLodavX59du3aVGq9fv36xeCgLFy5k4cKFbN68mVdeeYVevXoBMGDAAIYOHUr37t3xer34/X7GjRvHCy+8wDvvvAPAl19a16e4XC66du3Khx9+yG233cb3339Pw4YNqVOnDnfffTcDBw7k8ssv548//uCGG25g5cqVZR6T9evXs2vXLv73v//h8XiCie+QIUOYNm0ajRs35vvvv2f48OF88MEHAGzZsoXFixfjdrvp06cPU6ZM4bLLLiMjI+OEVIXSpPQE8bXpgLdKVTzTxyI53mA8YvkiJCuDnLtHQZgOt1JKKVVZhFoPKiIlrhMta2n2lJQUatWqRcOGDTnzzDMZNGgQaWlpuN1udu3aRffu3QHKlMj17NmTSZMmcdttt/Hee+/Rs2dPAL744gs2btwY3O/QoUMcOnSIatUOl0AXKVbJMxhv1KgR27ZtY/To0XTt2pWOHTuSkZHBypUr6du3b3Df3Nzc4PfXXXcdbrcbgMsuu4wxY8bQu3dvunfvTnR0dJnGpjQ6fXcC+ZpdTPZDUzFVq9ni4d8sw/P8WMjNqaCeKaWUUqevOnXqkJqaCkBqaiq1a9cGrJnOHTt2BPfbuXMncXFxIeP16tWjfv367Ny5s1i8qPnz5/Prr7/SvHlzWrVqxaFDh3j//ffLnNQWdskll7B161b27dvHkiVLggmt3+9n6dKlJCcnk5yczIYNG2wJKUDNmjVtp/7BWi9bs2ZNatSoQXJyMgkJCbz66qsMHjwYv99PTExM8JjJycm22deqVQ+XhR02bBjPP/88Xq+Xzp07B0/zHw9NSk8wf+MLyB49HX+NM2zxsLXfEjUlCbIyKqhnSiml1OmpS5cuzJ07F4C5c+fStWvXYPztt9/GGMOqVauoXr06cXFxdOrUiRUrVpCWlkZaWhorVqygU6dOxMXFER0dzapVqzDG8PbbbwePVcDv97N48WK++uor1q9fz/r163nrrbeYP38+1atXp379+nz44YcA5OTkkJWVRbVq1UpckykidOvWjdGjR3PeeedRs2ZNADp27Mirr74a3G/dunXFnhsfH8+3337L7t27Aeuq/tzcXBo0aMD+/fvx+/1069aNMWPGsHbtWqpXr87ZZ5/NokWLAGtmeP369SH79dtvv9GsWTOGDh1Kq1atTkhSqueTHeBv8Deyx8wgatIIXHsP/0Xl/mUtUc88SPaISVC9RgX2UCmllHJWaWtAw75eSuSsKUihM4gmIpKcO0eQn9D5mNvs378/ycnJ7N+/nwsuuIBRo0Zx4403MmzYMPr168ebb75JgwYNmD17NgBXXXUVS5cupXXr1lSpUiV4sU9sbCwjR44kMTERgKSkpOAFVFOnTmXgwIFkZ2fTuXNnOne29/err74KzqoWaNu2LQMGDCA1NZWXX36ZoUOHMmHCBMLDw5k9ezbNmjUjLCyMtm3bcsstt9CiRQvbMXv27EliYiIzZ84MxiZOnMiIESNISEjA5/ORkJAQvKtAgTp16vDMM8/Qu3dv/H4/0dHRvPbaa7hcLnbu3Mn999+Pz+dDRBg3bhwAr7zyCsOHD2fy5Mnk5+fTs2dPmje3l1kHmDlzJsnJybhcLpo2bVpsHI6FHMtUckVJT08vl85u2rSJJk2aHPdxJG0/nskjcP/5my3ur3cW2SOnYM6oe9xtVIQTNT6nIh2b0un4lEzHpnQ6PqWrDOOTnp5OTExMmfcP+3opEfNfQ/bvwZxRh9xed4GS/6gAACAASURBVB9XQloSr9d7Qi7COVU5OT6lfSZiYmKKLXjVmVIHmRpnkP3wdKKmjsK95edg3LXrD6LGDyY7aQqmXsMK7KFSSilVMfITOjuShKqTl64pdVp0dbIfepb8Zhfbwq6/9lDlqcG4th3/GgyllFJKqZOdJqXlITIK77AJ5LfpYAvLoXSinh6Ka+PaCuqYUkoppVTloElpeQmPwDvwUfLa26/SE28WUVNG4l7zTQV1TCmllFKq4mlSWp5cbnLuGklulz62sOTl4nl+LGHfhK6fq5RSSil1qtOktLyJkNvnXnJ6DbCHfT4iX36K8GULK6hjSimllFIVR5PSiiBCXvdb8fYdhilUAkyMIfLN6YQvfgNOolt1KaWUUpVB8+bNSUhIoF27dlx55ZXB+IEDB+jRowfx8fH06NEjWOXIGENSUhKtW7cmISGBNWvWBJ8zZ84c4uPjiY+PZ86cOcH4mjVrSEhIoHXr1iQlJZVapalt27b079//iP3+/fffmTdvXvDx6tWrSUpKOpqXHtJbb73FyJEjbbFrrrmG1atXH/exnaBJaQXK73gdOfc9ggnUkS0QueA/RMydCX5/BfVMKaWUOjl98MEHJCcn88UXXwRj06ZNo0OHDqSkpNChQ4fgTeaXLl3K1q1bSUlJYfr06QwfPhywktiJEyeyfPlyVqxYwcSJE4OJ7IMPPshzzz1HSkoKW7duZdmy0EvvfvnlF4wxfP3112RmZpba5+3btzN//vzg49atWzNp0qTjGQZH5efnO3JcTUorWP6lHfEOmYCJiLTFIz6dR+Trk8DnzBuvlFJKVaS81BVkfXUHmSu6kPXVHeSlrnCsrY8++oibb74ZgJtvvpklS5YE4zfddBMiQps2bUhPTyc1NZXly5eTmJhIbGwsNWrUIDExkWXLlpGamsqhQ4e45JJLEBFuuumm4LGKmjdvHn369CExMZGPP/44GN+6dSvXXXcdbdu2pX379vz22288/vjjfPPNN7Rr144XX3yRL7/8kj59+uD3+2nevLmtfn3r1q3Zs2cP+/bt4/bbbycxMZHExES+/fbboxoTn8/HfffdR4cOHUhISAhWs/rtt9+44YYb6NChA126dAmWD73vvvsYPXo03bp1Y9y4cSQnJ9OuXTvatWvHFVdcUWKZ1KOhN88vQV7qCvK2/BeTsxeJrE14436Ex3V0pC1fy0vJHjmZqGkPI1mH/5oKT/4Eyc7Ee+9YKJK0KqWUUpVZ5oqry7yvydlD7s+TyP255NnBqh0/OeJxRITrr78eEeHOO++kX79+AOzZs4e4uDgA4uLi2Lt3LwC7du3izDPPDD6/fv367Nq1q9R44fKhBfFQFi5cyMKFC9m8eTOvvPIKvXr1AmDAgAEMHTqU7t274/V68fv9jBs3jhdeeIF33nkHgC+//BIAl8tF165d+fDDD7ntttv4/vvvadiwIXXq1OHuu+9m4MCBXH755fzxxx/ccMMNrFy58ohjVGD9+vXs2rWL//3vf3g8nmDiO2TIEKZNm0bjxo35/vvvGT58OB988AEAW7ZsYfHixbjdbvr06cOUKVO47LLLyMjIOCFVoTQpDSEqcxW5O94Bv1WT1+TsIXfjdADHElP/31uQPeo5PFOScB08EIyH/fAlnmkP431gPERVcaRtpZRS6lTw6aefUq9ePfbu3UuPHj1o0qQJF110UYn7h1oPKiIlrhMta2n2lJQUatWqRcOGDTnzzDMZNGgQaWlpuN1udu3aRffu3QHKlMj17NmTSZMmcdttt/Hee+/Rs2dPAL744gs2btwY3O/QoUMcOnSIatWq2V5LKCJCo0aN2LZtG6NHj6Zr16507NiRjIwMVq5cSd++fYP75ubmBr+/7rrrcAeWHF522WWMGTOG3r170717d6Kjo8s0NqXR0/chVEv/IJiQBvlzyNvyX0fb9Z/dhOwxM/DXqmuLh/2cQtTEByEj3dH2lVJKqZNZvXr1AKhduzbdunUjJSUFgDp16pCamgpAamoqtWvXBqyZzh07dgSfv3PnTuLi4kLG69WrR/369dm5c2exeFHz58/n119/pXnz5rRq1YpDhw7x/vvvlzmpLeySSy5h69at7Nu3jyVLlgQTWr/fz9KlS0lOTiY5OZkNGzbYElKAmjVr2k79g7VetmbNmtSoUYPk5GQSEhJ49dVXGTx4MH6/n5iYmOAxk5OTbbOvVatWDX4/bNgwnn/+ebxeL507dw6e5j8empSG4PYdCBk3OXscb9vENbAS0/pn2/v020ainhqC/LXX8T4opZRSJ5vMzMzgusbMzEw+//xzzj//fAC6dOnC3LlzAZg7dy5du3YNxt9++22MMaxatYrq1asTFxdHp06dWLFiBWlpaaSlpbFixQo6depEXFwc0dHRrFq1CmMMb7/9dvBYBfx+P4sXL+arr75i/fr1rF+/nrfeeov58+dTvXp16tevz4cffghATk4OWVlZVKtWrcQ1mSJCt27dGD16NOeddx41a9YEoGPHjrz66qvB/datW1fsufHx8Xz77bfs3r0bsK7qz83NpUGDBuzfvx+/30+3bt0YM2YMa9eupXr16px99tksWrQIsGaG169fH7Jfv/32G82aNWPo0KG0atXqhCSlevo+BJ87lrASEtO8nZ8RXv8qR9s3NeuQNXo6Uc8+hPu3X4Jx985tRD01mOykKZi6DRztg1JKKXU8SlsDmpe6wloWV/ispCuSiKZDjnmZ3N69e7n11lsB6yKeXr168Y9//AOv18uwYcPo168fb775Jg0aNGD27NkAXHXVVSxdupTWrVtTpUqV4MU+sbGxjBw5ksTERACSkpKIjY0FYOrUqQwcOJDs7Gw6d+5M586dbf346quvgrOqBdq2bcuAAQNITU3l5ZdfZujQoUyYMIHw8HBmz55Ns2bNCAsLo23bttxyyy20aNHCdsyePXuSmJjIzJkzg7GJEycyYsQIEhIS8Pl8JCQkBO8qUKBOnTo888wz9O7dG7/fT3R0NK+99houl4udO3dy//334/P5EBHGjRsHwCuvvMLw4cOZPHky+fn59OzZk+bNmxcb75kzZ5KcnIzL5aJp06bFxuFYyLFMJVeU9PT0cunsn2vmEJv2TvFT+AHhf7uT8LNvLHGtxgmTnYnnuTGEbVxjC/tjYvGOmIK/YWNn2y/Bpk2baNKkSYW0Xdnp2JROx6dkOjal0/EpXWUYn/T0dGJiYsq8f3ldUOz1ek/IRTinKifHp7TPRExMTLEkSk/fh5BdtQ0RTYcgkXVCbs/bOovcza9gjMP3EY2qinf4RPJbt7WFXekHiHp6CK5NPzrbvlJKKeWQ8LiOVGn7BlU7fkyVtm84diGxOnloUlqCwz8snxDZ/BFwhdu25/+xkJyfJ2P8ec52JCIS7+DHyWv7T1tYsjKImjQc9/qy3/5BKaWUUqqy0qS0DMJqt8XT8ilw22/J5Nv9OTnrHsPkZzvbAXcYOXc/RG7nG2xhyc3BM200Yd997mz7SimllFIO06S0jNyxLfDET0EiYm1x318/4F0zCpPr8O2aXC5ybx1EzvV32sLiyyfy308Q9sWHzravlFJKKeUgTUqPgrva3/BcNBWJqm+L+w/+QnbKcPzZu53tgAh5PfqSc+tge9gYPLOmEL5krrPtK6WUUiVwuVy2G62r01tubi4u19GlmXpLqKPkiqpH1EVT8a4di//Q5mDcZP2JN2U4npbjcUU3crQPeVfdgKlajcjXnkH8hy+2inz3ZSTzILm9/wVO3xlAKaWUKiQ6OpqMjAyysx1e0naUDh48SPXq1Su6G5WWU+PjcrmOusqTJqXHQCJq4Gk9Ee/6J/EfOHy7JpOzj+yUEXhaPI67RjNH+5Df9ipMVFU8Mx9D8g5fbBWxZC6SmUFO36HgcjvaB6WUUqqAiBSrKFQZ7Nmzh7POOquiu1FpVabxKZfT9yLiEZGVIrJWRH4SkcdD7NNPRPaKyJrA193l0bdjJWFV8bR8Aned9vYN+Rl41zxM/r5vHe+DL74t3uGTMB77BVjhX3xA5MwnId/hOwMopZRSSp0g5bWmNAfoaIxpCbQCrhaRy0Ls944xplXg67Vy6tsxE1cEkc0eIuzM7vYN/lxy1j9B3s7PHO+D7/zWZI+aiom2T72Hr/oCz3OjIadynUZRSimllAqlXJJSY8kIPAwPfJ08paRKIeIm4u8DCT/ndvsG4yd341Ryf38Xp6tm+c9pStaYGfhr1rbFw9avImrSSMgMXU9XKaWUUqqyKLer70XELSJrgD3AUmPMdyF2u0FE1onIfBGpHAscykBEiDjnViLOe4CiQ5q35T/lUv3J1D+b7DEz8NdtYIu7N/9I1NNDkbT9jravlFJKKXU8xOlZvGINitQAFgKDjTE/FoqfAWQYY3JE5F7gRmOMreZYenp6sLObNm0qry4fFU/WGmL3z0bIt8WzqlxMWs1bQZy9tiws4yCN5z5Hld1/2OI5sbXZfMswcmNrl/BMpZRSSinnNGnSJPh9TExMsdsElXtSCiAi44BMY8yUEra7gb+MMTGF44WTUidt2rTJNnBHy3dgLd51j4MvyxZ317yIyOaPIG7P8XaxdJmHiHpuNO5f19vC/hq18I6cjL/BOcd1+OMdn1OZjk3pdHxKpmNTOh2f0un4lEzHpnQVNT6hktLyuvq+dmCGFBGJAv4BbCyyT71CD68FNpRH35zgjm2JJ35y6OpPqx9yvvpT1Wpkj5hMfotLbWFX2j6iJgzBteWkHVqllFJKnaLKa01pPeBzEVkHrMJaU/qhiDwhItcG9nkgcLuotcADQL9y6psj3NUaB6o/1bPFg9WfvHuc7UCkB++Q8eRdalsBgWQeJGrSg7h/TnG2faWUUkqpo1BeV9+vM8a0Nsa0MMZcaIx5IhB/1BjzfuD7h40xzYwxLY0xicaYjaUftfIrqP7kim5si5usP/H+8CD+jG3OdiAsnJx7x5CXeK0tLN5sPM8+hPuHL51tXymllFKqjMrt6vvTlUTE4omfhCu2lS1eUP3Jl/6zsx1wucnpO4zc7rfZ+5Wfh2fGOMKSP3G2faWUUkqpMtCktBwcrv50hX1Dfgbe1Q+Tvy/U3bFOZAeE3F53k9PnXnvY+PG8+gzhn85ztn2llFJKqSPQpLScWNWfRhF2Zjf7Bn8OOesfJ2+X89Wf8rrehPeukRixv+2Rc14kYsF/oALuxKCUUkopBZqUliur+tP9oas/bZhK7u/Oz1jmd7gG7/3jMGHhtnjE4jeI+H/Pg9/Zm/wrpZRSSoWiSWk5O1z9aTDFqz+9Ts6mVx2v/uRr0wHvsKcxkfb7pUYsW0jkKxMgP7+EZyqllFJKOUOT0goSfuY1RF44GsQ+Y5n/x3vk/DwF43c2MfRdeDHZSc9iqlaz9+ubZXhmPAK5OY62r5RSSilVmCalFSisTjs8rcaDu4ot7tu9gpz1j2N8Xkfb95/bjOyHp+OPqWnv15pviJqSBFkZjravlFJKKVVAk9IKVmL1p/2r8K4ehck76Gj7/rP+RvbYF/DXrm/v1y9riXrmQTiY5mj7SimllFKgSWml4K7WGE/8syGqP20k+wfnqz+ZOvXJHjsDX4Nz7P36/VeqTBiM7N/taPtKKaWUUpqUVhKuKvVLqP70h1X9KfN3R9s3Nc4g++Hp+BpfYO/Xrj+IGj8Y2bXd0faVUkopdXrTpLQSCVZ/qtHSFjc5+8j+Ybjz1Z+iq5OdNIX8Zhfbwq6/9hD11AO4tv3qbPtKKaWUOm1pUlrJWNWfnsRdu519Q3lVf/JUwTtsAvkXt7eFXYfSiHpmGK6Na51tXymllFKnJU1KKyFxRxB54cOlVH9a5mwHwiPwDnyUvPZd7f3KziRqykiqb1rnbPtKKaWUOu1oUlpJHa7+dJt9g/GTu2GK89Wf3GHk3DWS3C597P3Ky+Vv82YS9o3DibFSSimlTiualFZiVvWn2wLVn8S2rVyqP4mQ2+decnoNsIf9PiJffoqw5Yuca1sppZRSpxVNSk8CpVV/yt3wrLPVn0TI634r3r7DMHI4MRZj8LzxHOGL3wBjnGtfKaWUUqcFTUpPEmF1rghZ/Sk/dXm5VH/K73gdOfeOxbjdtnjkgv8Q8fa/NTFVSiml1HHRpPQkYlV/mlRh1Z/yL+uEd8hT+MPsM7YRn7xL5OuTwOfgjK1SSimlTmmalJ5k3NXOtao/eUJVfxqB37vX0fZ9LS9j8y3DMFWq2uLhX36M58XHITfH0faVUkopdWrSpPQk5KpSH89Fz4ao/rQd7w/DHK/+lNmwCdmjnsNf3T5jG/bDl3imPQzZWY62r5RSSqlTjyalJylXZM1A9acWtrhV/WmE49Wf/Gc3IXvM8/jPqGuLh/2cQtSk4ZCR7mj7SimllDq1aFJ6ErOqP40PUf3pUKD600pH2zdxZ5E9dgb+eg1tcffWDURNGIIc2Odo+0oppZQ6dWhSepI7XP3pGvsGfw456x9zvPqTqVmHrDHP42v0d1vcvWMbUeMHIbt3ONq+UkoppU4NmpSeAqzqT4MIb3SrfUOg+lPe9vnOdqBaDbJHTSO/aStb2LUvlainBuPavsXZ9pVSSil10tOk9BQhIkT87XYi/j6IotWfcje/Ru5mh6s/RVXFO3wi+a3b2sKu9L+IenoIrk0/Ote2UkoppU56mpSeYsIbdAtZ/Slv+3vkbpjqbPWniEi8gx8nL+EqW1iyMoiaNAL3emfXuCqllFLq5KVJ6SkorM4VeFo+GaL60zLnqz+5w8gZMIrczj1tYcn14pk2GvfKL5xrWymllFInLU1KT1Humq3wxE+E8Bq2+OHqT4eca9zlIvfWweT06GcLiy8fz8wnCPviQ+faVkoppdRJSZPSU5i7WhOiLppaQvWn4c5WfxIh7/p+5Nw6yB42fjyzphC+ZK5zbSullFLqpKNJ6SnucPWnv9nih6s/bXe0/byreuEd8DDGZf+oRb77MhHvvgzGONq+UkoppU4OmpSeBqzqT5NLqP40HF/6Bkfbz2/3T7yDn8SE2y++ilgyl8j/TgW/z9H2lVJKKVX5aVJ6mjhc/cl+yyar+tMo8vevcrR9X3xbvMMnYTxRtnj4Fx8Q+e/xkJ/naPtKKaWUqtw0KS3BvC2ZNH83ldhZO2j+birztmRWdJeOm1X9aTRh9bvaN/hzyFnnfPUn3/mtyR41DRNd3RYPX/k5nufGQI6DdwVQSimlVKWmSWkIH+9x88BX6fyR6cMAf2T6GPxV2qmRmIqbiPMGh6j+5AtUf3rP0fb95zQla8wM/LG1bPGw9SuJmjwCMh28K4BSSimlKi1NSkOY+Xs42T77BTheHwz7Op3UrJN//ePh6k/3U7z606vkbn4N4+AFSKb+2WSPfQF/3Qa2uHvTj0Q9MxRJ2+9Y20oppZSqnDQpDWF3joSMZ+QbWs1PZdR3aadEchreoDuRFz4covrTfHI3POto9SdTK47sMc/ja3iuLe7evoWoCQ8ge3c51rZSSimlKh9NSkOoG1nyLKHXBy/9nEmr+ak8fAokp2F12geqP9kvQLKqPz3haPUnE1OT7FHT8P29uS3u2r2DqPGDkR3bHGtbKaWUUpWLJqUhDDw7jyh36NnSAl4f/PsUSU6t6k+TQlR/Wol39cPOVn+qWo3sEZPJb3GpLexK20eVpx7AtXWjc20rpZRSqtLQpDSELnV8PN82hrOquhGgQVU3t54bRYOq7mL7Fk5OR69MY/dJmpwerv4UZ4v7D24gO8Xh6k+RHrxDxpN3aaItLJkHiZo4DPeG1c61rZRSSqlKQZPSEvRuXJX1N8Zx4M4z+fHGOF68oiY/3FCXqZfX4MwqoZPTmT9l0vIkTk5LrP6UuR3vDw86W/0pLJyce8eSl3itLSzebDzPJuH+4Uvn2lZKKaVUhdOk9ChEuoW7mlYlpVddnr085pRMTl2RZwSqP9nXeZqcvYHqTw6eTne5yek7jNzut9nCkpeHZ8Y4wpI/ca5tpZRSSlUoTUqPQaRb6N80ukzJaav5uxmzMv2kSk6t6k9PlVD96SEis39ysHEht9fd5PS51x42fjyvPkP4Z/Oda1sppZRSFUaT0uNQODmdclkM9asUH85sn+HFnzKCyeme7JMjOT1c/amLfYM/h5r7XiE/dbmj7ed1vQnvXSMxYh/TyLdeIGLBLHDwPqpKKaWUKn+alJ4AkW7h7vOjWd0r7ojJact5uxl7kiSnVvWnBwhvdIs9jp+cnyc7Xv0pv8M1eO9/FOMOs8UjFs8m4q0Z4Pc72r5SSimlyo8mpSdQQXKackMck0tJTl84iZJTq/rTHUT8fSChqz+97mj1J1+bK/E++AwmwmOLRyxdQOSrT0O+czf4V0oppVT50aTUAZ4wYcAplpyGN7iWyGYPg9hnLfO2zyN3w1SM37n++y68mOyHnsVUrWbv09dL8cx4FHJzHGtbKaWUUuVDk1IHFU1O6x0hOX1kVTp7K3FyGlbXqv7kl0hbPD91qePVn/znNiP74en4Y2ra+7Tma6KeTYLsTMfaVkoppZTzNCktBwXJ6eob4ph0acnJ6YwfM2g5v3Inp+6ardlf5wEIj7HFffu/w7tmtKPVn/xn/Y3ssS/gr13P3qeNa4l6ehgcTHOsbaWUUko5S5PScuQJE/51QenJaVb+4eT00UqanOZFNAxUf6pri/vTfyY7ZYSj1Z9Mnfpkj5mBr8E5trj791+pMmEwsn+PY20rpZRSyjmalFaAwsnpxEtjiIsKnZw+X4mTU1eVM/FcNBVXtD05NJm/B6o//eFY2ya2FtkPT8fX+Hx7n3b9QdT4QUiqc20rpZRSyhmalFYgT5hwzwXRrOlVtuR03Kp09nkrT3JqVX+aErr6U4rD1Z+iq5Od9Cz5zS6y9+mvPUSNH4xr26/Ota2UUkqpE06T0kqgIDld3SuOZ0pJTqf/mEGLeZUrOQ1Wf6qVYN+QdxDv6ofI3/+9c417quAd9jT5F7e3hV2H0oh6ZhiujWuda1sppZRSJ5QmpZVIVJhw70mYnFrVn8aErP6Us24c+akrnGs8PALvwEfJa9/V3qfsTKKmjMS95hvn2lZKKaXUCaNJaSVUNDmtW0py2nLebh77vuKTU3EVVH+62b7B+Mj5eRJ52xc417g7jJy7RpJ79Y32PuXl4nl+LGHfOFsSVSmllFLHr1ySUhHxiMhKEVkrIj+JyOMh9okUkXdEZLOIfCcijcqjb5VZQXK6plccT18SOjnNzDc8t/5wcrq/ApNTq/pTXyKa3Efx6k+vkLv5P85VfxIh96b7yOl1tz3s8xH58njCli92pl2llFJKnRDlNVOaA3Q0xrQEWgFXi8hlRfbpDxwwxpwLTAMmllPfKr2oMOG+ZmVLTlvM283jFZychp91HZHNRoWo/vQuuRunOVf9SYS87rfhvWMYRg4nxWIMnjemEf7+m+BgSVSllFJKHbtySUqNJSPwMDzwVTQ7uA6YHfh+PtBJRAQVVDg5nXBJDHVKSE6nVYLkNKxuBzwtnwB3lC2ev+szx6s/5Xe6jpx7xmLcbls88r3XiXj735qYKqWUUpVQua0pFRG3iKwB9gBLjTHfFdnlTOAPAGNMPpAOnFFe/TuZRIUJA5tFs6ZX3SMmpy3n7eaJHyomOXXXjMfTemKFVH/Kv7wT3iFPYcIjbPGIT94l8vVJ4Mt3rG2llFJKHT1xbI1fSQ2K1AAWAoONMT8Wiv8E/NMY82fg8RbgEmPM/oJ90tPTg53dtGlT+XW6kvP6YEFqGLP/DOevvNCTy1Xchhvr5XPrmXnUCC/f/rnz9nDG3hcJ8/1li+eF1WN/7YH4w2o41nbV7b/S+J0XcOdk2+JpTePZ1uNuTFg5D4ZSSil1mmrSpEnw+5iYmGIJS7knpQAiMg7INMZMKRT7FHjMGPONiIQBqUBtU6iDhZNSJ23atMk2cCeLrHw/s37JYvr6Q+zJ9ofcJzpM+NcFVRnULJqaHnfIfY7kWMbHn7Mf75oxmMxttrhE1sHT6ilcVc86pr6Uhev3TXgmj8R1KM0Wz78gHu+Q8eCpcsLaOlk/O+VFx6dkOjal0/EpnY5PyXRsSldR4xMqKS2vq+9rB2ZIEZEo4B9A0XI/7wN9A9/3AlaYisiYT2JVwlzcHzit/1QJp/Uz8g1T11lrTp/8IZ2/yum0vivyDKLip+CKudAWNzl7rOpPB39xrG3/2U3IHjsD/xl1bfGwn1OImjQcMg461rZSSimlyqa81pTWAz4XkXXAKqw1pR+KyBMicm1gn9eBM0RkM/AgMKqc+nbKKZycjm9Tndqe0Mnps+WcnEp4NJ5WJVR/SklytPqTiTuL7DEz8NdraIu7t2wgasIDyIF9jrWtlFJKqSMrr6vv1xljWhtjWhhjLjTGPBGIP2qMeT/wvdcY09sYc64x5hJjzNby6NuprEqYi0EXVmNt7yMnpy3n72b8Dwc5kBP6tP+JIu5Iq/pTvavtG4LVnz53rG1zRh2yxjyPr9HfbXH3jm1EjR+E7N7hWNtKKaWUKp1WdDoNFCSna3rV5ckSktNDeYYp6w7RYl6q48mpuNxENB1C+Nk32TcYHzk/TyTvj0WOtU21GmSPmoavaUtb2LUvlainBuPavsW5tpVSSilVIk1KTyNVw10MLpSc1jpScpriXHIqIkQ07he6+tOml8jd4mD1p6iqZA+fRH4r+zICV/pfRD09BNfmn5xpVymllFIl0qT0NFSQnK7tVZcnLy4lOV3rfHJqVX96qHj1p98drv4UEYl38BPkJXS2hSUrg6iJw3GvX+VMu0oppZQKSZPS01jVcBeDm1d8chpW98pA9SePLZ6/6zNyfnwS48s54W1aDYeRM+Bhcjv3tIUl14tn2sO4V33hTLtKKaWUKkaTUnVUyWnLeam89Hs4aSc4ObWqP00qXv1p37fOVn9yuci9dTC5PfrawuLLx/PiE4R98aEz7SqllFLKxVEaKAAAIABJREFURpNSFVQ4OX3i4uqcEVn843Ewz/D6H+G0mJfKUykHT2hy6q7+d6Iuehbx1LHF/ek/kZ0yEn+OQ7dtEiH3+jvJuXWQPWz8eGZNIfyjt51pVymllFJBmpSqYqqGu3iguXUrqdKS08mB0/oTVp+45NRVpQGei6YhVRvZ4iZzG94fHsSf+ccJaSeUvKt64R3wMMZlf72R77xExLuvgNZyUEoppRyjSakqUXSh5PTxUpLTSWtObHJaYvUn7x6yU0Y4Wv0pv90/8Q5+AhMebotHLJlD5Oyp4NSFV0oppdRpTpNSdUTR4S6GFEpOa4QVnzE80cnp4epPl9s35KXjXf0Q+ft/OK7jl8YX3w7v8EkYT5QtHv75B0S+NB7y8xxrWymllDpdaVKqyqwgOV3cJpvHLqpOzdJmTuen8vRxJqdW9aexhNX7p32Dz0vOukcdrf7kO7812Q9Nw0RXt8XDv/scz3NjIMfrWNtKKaXU6UiTUnXUqrhhaItqrOtdt+TkNNcw8QQkp1b1p6EVUv3J/7emZI2ZgT+2li0etn4lUZNHQKZDdwRQSimlTkOalKpjFh3uOqrk9JljTE4PV3+6t9g2q/rTLMeqP5n6Z5M99gX8dc+0xd2bfiTqmaFI+l+OtKuUUkqdbjQpVcetIDld27su40pJTp85zuQ0/KweRF4QqvrTO+RufM6x6k+mVhzZY2bga9jYFndv30LUU4ORvbscaVcppZQ6nWhSqk6YauEuhhVKTmMjpdg+Bclpy/mpTFxzkPTco0tOw+ISiWzxeIjqT586Wv3JxNQke9Rz+JrY7wjg2r3DSkx3bHOkXaWUUup0oUmpOuEKktN1veN4tITkND3X8PRq62r9o01Ow864CE/rieVf/alqNbJHTiG/+SW2sOvAPqpMeADX1o3OtKuUUkqdBjQpVY6pFu7iQYeSU3f184iKn1JK9af9J+Q1FBPpwTv0KfIuTbSFJeMgUROHEb1NE1OllFLqWGhSqhxXkJyu7RXHI/FHTk4nlTE5dVU9q5TqT8PwZ/15ol6CXVg4OfeOJS/xWltYvNk0njsdd0qyM+0qpZRSpzBNSlW5qR7hYnjLw8lpjYjQyemEo0hOrepPk3HFNLPFjXcP2T8Md676k8tNTt9h5Ha71R725eOZ8ShhyZ86065SSil1itKkVJW7guR0Xe84xh4hOW05L5XJaw5ysJTkVMKr4Wk1AXety+wbAtWffH+lnOiXEGhYyO09gJw+9ltVid+P59WnCf9svjPtKqWUUqcgTUpVhake4WLEEZLTtFzDU4GZ09KSU6v60yOE1bvKvsHnxbvW2epPeV1vwnvnCIzYf5wi33qBiIWzwKF7qCqllFKnkv/f3p2HuVXW/R9/f7Nnpu20dIXSUihlL7RsCsIjoKio/ERZRHEBARWRsrWFLoIgZd/agqKgIoqirKKCPDxuCIhKSwtlaQtlK3SBbkzb7Ll/fyQzk5PJzGQ6yWSWz+u6cjW5z3KfnGamn965z/kqlErNdTacXr+4sWQ4zVV/Op/gTl/0LnDpqld/Sh/xWeJnX0LW5/e0hx76BaG750N228utioiI9AcKpdJjNIXTxSeOYtbkgTS0EU6vWPhBm+E0V/3ptHaqP91ZtepPmYOOYMXJU3Ah7z1UQ48/QPj2qyCdrkq/IiIifYFCqfQ4DSEf0yYN4vkuhNOW6k/ekcvUm/dUtfpT4y57EbvoBlzdAO/xPP04kfmXQLI6N/cXERHp7ToMpWY2r+j16UWv76/0QYmAN5zO7CCc7nffam4oCqe56k+Xt1H96YqqVX/K7ro3sZnzyDZs52kPLHqa6A3TIbalKv2KiIj0ZuWMlJ5a9Pq6otdHV+ZQREprCPmY3kE43ZBw/KAgnDamcuG0pfrTIM/6mff/RXzRLFxqc1WOOTtmF2Kz5pMdvr2n3f/KYqJXnw8fbKxKvyIiIr1VOaG0OAG0TgQi3aApnC4+YRQzOgin+967mhufz4XTXPWnG7BwcfWnJcQWTq1a9Sc3cjSxWfPJjB7nafe/sYy6K6dg69ZWpV8REZHeqJxQWnxViO5vIzU1OOzjooJwOqiNcHr5gpZwuiU0msiBN7ZR/emCqlV/ckOGEZs5j8z4PT3tvlVvEb3iu9jqt6vSr4iISG9TTigNmNmRZnaUmR1V4rW/ox2IVENTOH2+jHC6371rmLs0TGbiNfga9vKs4+Jr8tWfllXnQAcMIjb9BtJ7H+Bp9q1fS/SKc/C9ubw6/YqIiPQi5YTStcDPgJ/mH+uKXus7SKmpwnB68aTS4XR9IstlCz5gv4e2cntgJm67D3lXqHb1p0gd8fOvIn3g/3iafY0biV51Hr6lz1enXxERkV6iw1DqnBvnnNu5vUd3HKhIRwaHfVw8ueNwesnCBJNfOpNXwkd6F2ZiuepPa/5enQMMhoh/5xJShx/jabbYFqLXTcW/+Jnq9CsiItILbNN9Ss1sdzP7vJntVOkDEumqwnB60aSBDAq2DqfvJYyPLTuFOzZ/2rvApUm8eA2pt39fnYPzB0icPp3kp07yNFsqSWTuLAL/+kt1+hUREenhyrlP6Q1m9pWC118DXgR+ArxiZse0ubFIDQ0O+5gxOXcrqdLh1Lh03fFcuv7konZHcvmPqlf9yYzkyWeROOEMb3MmQ/jHVxD4S5UCsYiISA9WzkjpccATBa+vBKY454YD3wYurcaBiVRKYTidXiKc3tF4NGe/dyYpV6r609zqVH8yI3XsV4h/7XyctRyPOUfkrpsIPvxLqFI5VBERkZ6onFA63Dn3FoCZ7QMMJXeBE8CvgN2qdGwiFTU47GNmG+H0oa0f5tS157A1G/Jsk171ZxJL5lSt+lP6Y58j8a3ZOL83EIfv/ymhe36kYCoiIv1GOaF0k5mNzD8/HHjWOdf0L3QQ3Uxfepm2wunf4xM5ac001me8desz7z/N1ueqV/0pfcjHiE+5Ahf0BuLQn39H+GfXQSZdlX5FRER6knJC6e+Ae8xsCnAx8OuCZR8CXqvGgYlUW1M4XXziKKbtN5CBQeO55C4ct/piVqa9dev5YAlvP30hW7a8X5VjyUw6hNi063HRek978IlHiPzwckglq9KviIhIT1FOKL0Y+Du5Gvc/AX5csGxSvk2k1xoS9jFr/9zI6bT9BrLWduC41TN4JbmDZ72hmTd55+nzuPO5ZWxJZSt+HNnd9yU242ayAwd72gPPPkHkphkQ31rxPkVERHqKcu5TmnLOXeacO9Y5N8c5ly1YNtc5p1AqfUJhOD1ln534+rqL+W98V886o/3vc+T7szj5oX8z/4XGiofT7E4TiM2eT3boSE974MUFRK+9EDZ/UNH+REREeopybgn1tY4e3XGgIt1lSNjH7P0H8c8TduWZkZfyt/h+nuXD/I3cMeRq/nfJM+x335qKh1M3agyxWfPJbj/W0+5/7WWiV07BNlRnCoGIiEgtlfP1/Z3AbOAM4MwSjzPa3FKkFxsS9nHxAcM59Mgf8GLwCM+yAb4Ed424mUN9z/C9Zz9g0n1rmL+kkViF7h7lho5g68x5ZMZ5b27hf+cNonPOwda8U5mOREREeohyQuk8oA5oBG4BPu6cO7zg8T/tby7Su20XDXHQYReRHn2Cpz1kGW4d9hNOHfgX3otn+d5/P+C4Z6PMX1KhkdNBg4ldfBOZPbwjtb73VhGdcw6+t1d0vQ8REZEeopw5pecBOwE/BL4AvGFmt5vZYdU+OJGewsxo2P0MQrt+09PuM8ec7X7NtIYHAcf6lPG9/7aMnG5NdzGcRuuJXXgt6UmHevvdtJ7oVefie/XFru1fRESkhyhnpBTnXMY59yfn3BeB3YENwN/N7MiqHp1IDxMc+wXCe00D897s/rzBf+Sa7e7CT+77+6aR0/3uXcMtXQ2noTDxcy4ndejRnmbb0kj0mgvxL3l22/ctIiLSQ5QVSgHMrMHMvgX8Gfg88ANgUbUOTKSnCoz6GOF9LwNf2NP+lYFP8OPhPyJMqrntvXiW2ZUIp4EAiTNnkDz6C55mS8aJ3Hgx/v/+fdv2KyIi0kOUc/X9Z83sXuBlYDIwzTk3IX+bqA1VP0KRHigw9EAik6+B4CBP+zF1z3HPqBsZZN57ijaF00n3reHWFzdvWzj1+Uiecg7J477uabZMmsitlxP4x586v08REZEeopyR0oeBfYC7gbXAJ83s8sJHVY9QpIfyN+xBdP8bsPBwT/vB4WX8Y9x1jAttarXN2liWWf/ZtO3h1Izk508jccp3vc0uS+Rn1xF85J5Ovw8REZGeoJxQehfwDDAMGFPisWPVjk6kh/PVjyFywI1YvfeeoiOyb/HPcddw2V6N1Aes1XZN4XTyfWv44YubiaVdp/pNfeIE4mdejPN5f4TDv72N0L23g+vc/kRERGqtnKvvT3XOnVbqAdwMDKz+YYr0XL7IcKL734CvYS9ve2INZyQv5YVjPuD8iQNKhtM1sSwz/7OJSfet7nQ4TR/2KeLnXI4LBj3toT/eTfgXN0K2QjdNFRER6QblzCmtM7MfmNkfzOxGMxtkZruY2YPAk+S+0hfp1yw4kMikK4lH9vYuSG0i/OIMZu/yGs+fOLLi4TSz/2HEL7wWF4l62oN/+wPh266AdKqNLUVERHqWcr6+vxU4FngJ+DhwP/AP4EVgZ+fc2dU7PJHew/wR1g87k8Coj3sXZGLEF19Cw6anuPTABhafOJLzyginPyoznGb2nEzsoptwA7wXXQX//Tcic2dDIt6l9yUiItIdygmlnwQ+4Zy7CPg08DHgy8652c45FeEWKWR+QnteSHDsid52lybx4lWkVj7MsIif75cRTmd0Ipxmd9mDrTPnkR0yzNMeeP7fRK+bClsau/zWREREqqmcUDrAObcWwDm3EtjsnPtndQ9LpPcyM0K7nk5o1zOLljiSy35IcsVdOOc84fTcfQZQ1044nXzfam57qf1w6kaPIzZrPtmRoz3t/uVLiF59HrZpfSXenoiISFWUE0oDZnakmR1lZkcBFL5uahMRr+DY40tWf0q98WuSS+fhXO5CpGERP5cd1MDz7YTT1bEsF/+743Dqhm9PbNZ8MmPHe9r9b71GdM452PurK/TuREREKqucULoW+Bnw0/xjXdHrOzragZmNMbO/mdnLZvaimZ1bYp0jzGyTmS3KPy7p1DsR6YFy1Z++36r6U/rdR0ksmYPLJJvbmsLp4hNGMqWMcPrjlzYTLxFOXcN2xC6+mcyEfTztvjXvEL3iu9g7b1TkvYmIiFRSObeEGuec27mdxy5l9JMGLnTO7Ql8GDjbzPYqsd4/nXOT8g/dlF/6hMDQg4hMvhoC3runZd57mvjiWbj0Fk/78Kify8sIpxf9OzfntGQ4rR9IbNr1pCce7Gn2bXifuiun4FvxSmXenIiISIWUM1LaZc65Vc65hfnnjeRKlo5ufyuRvsPfsCfRA1pXf8pufIH4wmlkE+tabdOZcDr5/hLhNBwhft4cUh860rONbf6A6DXn43/5ucq8ORERkQrollBayMzGAZOBf5dYfIiZLTazR81s7xLLRXotX/3YktWfsptXEF9wIdmt75bcrjCcnrPPAKL+1uF01daWcPqTwnAaCJL49mxSRx7rWd/iMSI3TMe/8MnKvDkREZEuMteN5QjNbAC5e5zOcc49ULRsEJB1zm02s08Dc51zEwrX2bRpU/PBLl++vDsOWaTiLLOFoe//mFDydU97xjeA9cO/Qyo0pt3t1yXhV+8EuXdVgES2dUAFGB7KcuqOaT43Kk3YBzjH9n97kFFPP+pZz5mPt479Ouv3PbRL70lERKQjEya0xLqGhoZW/4B1Wyg1syDwR+Ax59yNZaz/BnBg4b1QC0NpNS1fvtxz4sRL56dt5Z4bl4mTWHIlmXX/8S7w1xGZeAn+7SZ1uI+1sQzzXtjMT1/ZQixT+kdjhzof5+87kK9OqCcSMIKP3EP4t7e1Wi9xyndJfeKEDvvsKn122qZz0z6dn/bp/LRN56Z9tTo/pUJpt3x9b2ZG7kr9l9sKpGY2Kr8eZnZw/thaT7QT6QPMHyE88ZIS1Z+2El/8PdJrn+hwHyOifq44OHef0+/uXfpr/Xe3Zpn2zCb2v381t7+8mcZPfJH4aVNx5v3RD999C6EHfw7d+M2JiIhIoe6aU/oR4KvAUQW3fPq0mX3bzL6dX+cEYImZLQbmASe77pxbINLNzBfIV38qGqF0KRJLriK18g9l7acpnC46YSRnlxFObxt5BJvPugTnD3jWCT30C0J3z4dsdpvfk4iIyLbqrqvvn3TOmXNu34JbPj3inLvNOXdbfp1bnHN7O+f2c8592Dn3dHccm0gt5ao/ndFG9adbSa74JeX+32xknZ85ZYTTqc9sYu83dufBEy7BhSKe5aHHHyB8+9WQTm/rWxIREdkm3X71vYi0Fhx7PKE9p5ao/nQ3yaXzm6s/laM4nEb8rdd5d2uWk9aM5wsHzCQervcey9P/S+SWSyGZ2Kb3IiIisi0USkV6iOD2H2+j+tMjJJZc6an+VI6mcLr4hFF8Z+/6kuH0D+HxHDJxNmvDgz3tgeeeInLDRRDb0nojERGRKlAoFelB2q7+9BTxxbNbVX8qx8g6P1cePLjNcPrCgLEcNukSVkS8N/YPvLKI6NXnwwcbO92niIhIZymUivQwLdWfhnnasxufJ75wOtnE+m3ab1M4XXTCKM7ayxtOV0RH8tHJl7KkbkfvsbyxjLorp2Dr1m5TnyIiIuVSKBXpgXLVn27C6oqrP73WbvWncoyq83PVh1qH01XhIRw1eTb/GTjeeyyr3iIy5xxs9dvb3KeIiEhHFEpFeihfZDjRA67HN2gPT7uLryK24AIyja92af+F4fTbe9UT9sP64ECO3m8mfxnsrfLrX7cGu+wc0q+rkpqIiFSHQqlID2bBQUQmX41/6EHeBamNxBdOJ7N+UZf7GFXn5+qCcJoORzh232k8OOxAz3p1Wzfiu2IKf378PyTbqCAlIiKyrRRKRXq4XPWnS9up/vTPivSzfUE4/cY+g/n6xCn8fNRHPesMSsf49K9nc8GP/szPX9micCoiIhWjUCrSC+SqP11AcOzx3gUuRWLJlaRW/rFifTWF0wUnjebZ485j3thjPMvrskluf/Z6nnnoEfa/fw13LlU4FRGRrlMoFeklzHyEdj2T0K5nFC1xJJfd0qnqT+XYvs7PNYcM4ZiLL+Thg0/xLAu6DL98+Yd8ZtljnPf0RoVTERHpMoVSkV4mOPaEfPUn749v6o27SS67pVPVn8qxfX2Ao84+kzVfnEKWltKlPhy3Lv85F7/5e1ZuTnPe0xs54IE1/ELhVEREtoFCqUgvFNz+44Qnfr919ad3/kRiyVWdrv5UjvpPf4Hkt2fhfN6771/x+u+4ZsVvwDne3pzhXIVTERHZBgqlIr1UYNjBRCZfBYEBnvbMe09uc/WnjqQP+Tjxc6/ABUOe9gvf/hM/WXo7/mxulFbhVEREOkuhVKQX8zfsVZXqT+3JTDqE2LTrcdF6T/s3Vv+D37w0n1A21dzWFE4PfGANdy3bQjpb8cMREZE+QqFUpJfz1e+Ur/40xtNeiepPbcnuvi+xGTeTHTjY0/6F9//LH164nvp03NP+1uYMU57ayPELIty1bAuprEZORUTES6FUpA/IVX+6oWT1p/jCC7tc/amU7E4TiM2eT3a7EZ72j21YwqJl1zAys7nVNu8mfEx5aiMH3L9G4VRERDwUSkX6iLaqP7nkhlz1pw2LK96nGzWG2OxbyG7vHaXdee0yVrx2NReOSRAq8VumaeRU4VRERJoolIr0IU3Vn/wjj/IuyGwlvmh2xao/FXJDR7B15nwyO+3maQ+/+zpX/Xk2L3w0wxl71LcbTg+8fw2/VDgVEenXFEpF+hjzBQjvNZXAmDaqP73zp8p3OmgwsRk3kdljP0+z77132fmm87lxx/UsPH4kJ2yfKhlO39yc4RyFUxGRfk2hVKQPMvMRnnAmwfGnFy1xJJfOJ/n6rypa/QmAaD2xC68lPelQT7Nv4zqiV53L2NVLuWh8ioXHj+T0PeoJKpyKiEgBhVKRPiy004mE9rywdfWn139FctmtFa/+RChM/JzLSR16tKfZtjQSveZCBq54iR0HBLjhkMFlhdODHljDr5YrnIqI9AcKpSJ9XHD7owlPvLRE9ac/5qo/ZStc/SkQIHHmDJIf/7yn2ZJxdrlnHv7//gOAMWWE0zcaM3z3SYVTEZH+QKFUpB8IDPtQ29WfFn2v8tWffD6SX5lC8nNf9zZnM0RuvYzAE480txWG02/s3n44PfiBNdy9fAtphVMRkT5HoVSkn/A37EV0/+tLVH9aTHzhdFxyQ2U7NCP5hdNIfPlsb7PLEvnptQQfucfTPmZAgBsPbT+cvt6Y4ez8yKnCqYhI36JQKtKP+AaMI3LAjSWrP8UWXEA2tqrifaY+eSLxMy/G+by/bsK/vY3QvbdD0QVXTeF0wfEjOW33ug7D6a8VTkVE+gSFUpF+xhcZQXT/61tXf4qtIr7gAjKNr1W8z/RhnyL+3cvI+gOe9tAf7yb8i5sg2/qCq7EDAtx06JAOw+l3FE5FRPoEhVKRfshCDbnqT9sd6GnPVX+aVpXqT5kDDue1k6fgIlFPe/BvDxO+bQ6kUyW3Kwynp+5WR8Bar6NwKiLS+ymUivRT5o8Q3vf77VR/erLifW7eeU9i02/E1Q/ytAf//Vcic2dDIt7mtmMHBLj5I+WF04MfWMNvXt2qcCoi0osolIr0Yy3Vn77gXeBSJJbMqUr1p+z4Pdk6ax7ZId4LrgLP/5vo9dNgS2O72+80sONwuqIxw1n/3KBwKiLSiyiUivRzZj5Cu7ZX/enuild/cqPHEZs1n+zI0Z52/7IXiF59PrZpfYf7KAynX+8gnH7owTXco3AqItKjKZSKCGaWr/50QYnqT7+sSvUnN3x7YjPnkRkz3tPuf+tVonPOwd5fXdZ+dhoYYG4H4fS1DzJ8W+FURKRHUygVkWbB7T/RdvWnF6+uePUnN3gosRk3k9l1H0+7b807RK/4Lvbum2XvqymcPnv8SL6mcCoi0usolIqIR5vVn9b+k/jiSypf/al+ILHp15GeeLCn2bfhfermnIPv9Vc6tbtxAwPMKzOcfvjBtfz2NYVTEZGeQKFURFpps/rThkXEn7uo8tWfwlHi580hdfCRnmbb/AHRq8/H//Jznd5lYTj96oQ6/CXC6asfpPnWEy3hNKNwKiJSMwqlIlJSS/WnHT3t2cZXq1P9KRAkcdZsUkcc62m2eIzIDdPxL3xqm3Y7bmCA+Yfl5px2FE4/9OBafqdwKiJSEwqlItKmXPWnG/AN2t3TXrXqTz4/iVMvIPmZL3uaLZUiMv97BJ58bJt3XW44/eYTG/jwQwqnIiLdTaFURNqVq/50Df7tDvC0V636kxnJk75J4qRveZuzWSK3X0Xwf+/v0u4Lw+lX2ginyze1hNN7FU5FRLqFQqmIdKil+pN3zieZrcQXV6f6U+ozXyJ+2lSceVNj+O75BB+8E7p479RxAwPcUkY4PVPhVESkWyiUikhZzBckvNc0AmM+712QTZFYciWpdx6peJ/pIz5L/DuX4vwBT3v4oTsJ3X0LZLNd7qMpnD77hZGconAqIlIzCqUiUrZc9advEhz/jaIlWZJL51Wl+lPm4COIn38lLhTxtIcev5/wHddAJl2RfnYeFODWMsPpIQ+t5b4VCqciIpWkUCoinZKr/nQSoT3aqv70w4pXf8pMPJjYRTfg6rz3Tg0+9RiR+ZdCMlGxvprC6X+/MJIv71o6nC7blOaMfyiciohUkkKpiGyT4A6fIDzxEvCFPO3pd/5A4sVrKl79Kbvr3sRmzCXbMMTTHnjuKSI3XASxyt7Uf5dBAX54eHnh9NCH1nK/wqmISJcolIrINgsM+zCRSaWqPz2Rr/60taL9ZceOJzZrPtlho7zH8coiotdcAI0bK9oflBdOl25Kc7rCqYhIlyiUikiX+Afvnav+FBrqac9Vf5qOS1Y2KLqROxKbNZ/MDuO8x/H6UurmTMHWr61of00Kw+mXdq3D1044/cjv1/KAwqmISKcolIpIl3Vc/Wl1Rftz2w0nNmsumV329B7HqreIXnEOtnplRfsrtMugAD86fAj//Xzb4fSVjWm+oXAqItIpCqUiUhG+6Mg2qj+9S3zB+WQaV1S2wwENxKbfQHqv/b3HsW4N0Tnn4HtzeWX7KzK+oSWcnjw+WlY4zVb4zgQiIn2JQqmIVIyFGohMurqN6k9TCcUrHBSjdcQvuJr0AYd7mn0fbCB61Xn4lj1f2f5KGN8Q4Lb/2a6scHroQ2t58HWFUxGRUhRKRaSiLBBts/rT0Pd+SPq9pyrbYTBE/OxLSR32Ke9xxLYQvW4a/sXPVLa/NjSF0/98fgRfbCecnvb3DXxE4VREpBWFUhGpuLaqPxlpEi/MIfXOo5Xt0B8gcfp0kp880dtfMkFk7iwCz/ylsv21Y9eGID/uIJy+XBBOH3o9pnAqIoJCqYhUSXP1p11OK1qSJbl0LsnXf13Z6k8+H8kvfYfE8ad7jyOTIXzbFQT++vvK9VWGcsPpqX9fz4hfvMtBT0bZ67eruPOVzWxNZ3VxlIj0O4GOVxER2TZmRmjcF7HQYJKvzAVaatWnXr8Ll9xAaLezMKvQ/4/NSP2/r+LqBxL+5VwsH3rNOSK/uInEls2kPvtlsBIJsUqawunU/VJct7iR+1bEKM6baQdgvLs1y3n/2sR5/9oEgM8g5IOQ3wj5zPM86INwwfPmdfzk/8ytH/RZfr2W50Ffbp3m521sm9u3Efbntg35jbAPggXHY914LkWkb1MoFZGqC+7wSSw4iPiSKzGXam5Pv/MHXGoT4b2mYkWVoboi/bHjoG4A4duvwjItJU/D992ObfmA5Be/3a3BFGBCQ5Cf/M9jmQG4AAAgAElEQVR2TGsnnBbLOohnIJ5xQM8cOW0KuE2BtinEthlo88+LQ3UuIOeCbvPzgpC87j0/y0KxlsBcZjgPlBqiFpEeSaFURLpFYPghrBt+NsPW3wHpzc3tmbVPEE99QGTiJVigrmL9pQ/5OC5aT+SWS7FUS8nT0KO/xbY0kjjtQvD5K9ZfuZrC6dR9Uxz8YHVu9N+dUllIZR1b0lDd4ByGpes7vVXzaHOJ0eOmEBtuI9B6QnVRwC7eZ8hfOpCXCufFo98abRbJ6ZZQamZjgLuAUeS+v/uJc25u0ToGzAU+DWwFTnXOLeyO4xOR7pEMjye6/3XEF83GJdc1t+eqP11EZL8fYKHBFesvM+kQYlOvI3rzTCy2pbk9+MQj2NbNxL89G4KVG6HtjN0GBxlT7+ftLZmSy42eOjbau3hGm1M984wGm6dKdDxiXBiGC0eJt3wQZMTGTa1Dr7/0FI7OhHO/KThL9+iukdI0cKFzbqGZDQQWmNnjzrmXCtY5BpiQf3wI+FH+TxHpQ3wDdiZywI3EF8/EbX2nuT3buJzYgguJTJqDLzqqnT10TnaP/YhdfBOR66fja2wpeRp49gkiN80kPuVyiFRuhLYzLjlgIFOe2kQs0xKWon5j3kcaOHF8PemsI5l1JDPk/3SkspAofJ5xpLKOZNHzZKZl21TWkcjk2puep7K5fTY/b1q/xLa59VqeNx1PKtvOm5OyNY0252xrcA7Cu5s7Xm0bGJQ96tvWiHHTaHNhwA76IFwUsFsF8vbCecG0DZ9Cc5/QLaHUObcKWJV/3mhmLwOjgcJQ+jngLpe7HPcZMxtsZtvntxWRPqSp+lN88SVkG5c1t7vYO8QXnJ8LpgN2qVh/2XG7EZs1j+i1U/Gtb/nKPPDis0SvnUrsgqthwKCK9VeuE8fXA3D5gkZWbkmzY32ASw4Y2NweyM+JrOuhE62cy4fYrCOVcSTygTZVHGKLgm6yKFQ3Bdz2Avb6TY2E6ga0Harz+0wWPE9kNNpcCY6eP7c5YLS+cC//3KUiDFy6tryL/tqYktFqCkcZ4VyjzZ3X7b/qzGwcMBn4d9Gi0cDbBa9X5tsUSkX6IAsNJjL5GhJLfkBmfctMHZfcQGzhNCITv49/yMSK9ee2H0ts9nyi103Ft6rlV43/tZeIXnUu8WnX4wYPrVh/5TpxfD0njq9n+fLlTJgwodv77wqz3D/MYb9BsLp9LV++jgkTOv/3k8m6/Mhy61HidsNyh6PRjlSmk6PWBc+bttVoc2WkHaTTbY02+2BLqniTbmVAyE9+ikSJEeOOLvpr4y4ZHYXqUiG5KZA/+tZWbnh+M+9uibLjc6s9/yGu2Xmq6H0CO+rMbADwD2COc+6BomV/Aq5yzj2Zf/0XYLpzbkHTOps2bWo+2OXLq1vXWkS6iUszeP2vqNu6wNtMgA1DTyVet19FuwtsaWT8b26mbvVbnvbE4OG8esr5JIcMr2h/Iu1xLheoUg6S2fzzrJF0ua/10w6SWSPlIJ0l32759tx2xa/TzvJTNYq2b17HclMG8n0Uv27ePt9nBo3w9QcRn2PmrkmOGVF6nnslFP7Hu6GhodUHq9tCqZkFgT8Cjznnbiyx/MfA351zv8m/XgocUfj1fWEorabeOGLRnXR+2qZz0762zo9zWZLLf0J65UNFS3yE9jiH4A7HVPZAYluI3jQT/9LFnubs4KHEp11HdsfKTR0olz477dP5aV81z08m650WUTg3OZklP3WjrdHogjnITVM8mp/nRozbm4bRPKKcbyt8XjgCLZUxpt7PCydVbk5/e0qF0u66+t6AnwIvlwqkeQ8D3zWze8hd4LRJ80lF+gczH6EJ38JCg0mtuLNgSZbkK3NxyY0Edzq5cnOyovXEpl5L5NbvE1j0r+Zm38Z1RK88l9gFV5Pdde/K9CXSy/l9RtQH0R46YupcS6D1zFPOB9rX3niTUaPHlpx33FbALhmWSwTsVNP85fy2nqkgBc8zPXMqbisr27gbSHfprjmlHwG+CrxgZovybTOBsQDOuduAR8jdDupVcreEKq5NKCJ9WK7608n56k/z8FR/WvGLXPWnCd+uXPWnUJj4OT8gfMfVBP/1fy3HsaWR6LUXEj/3CjJ7H1iZvkSkaszy8yz9pUNz8H3HhBG1ufVbk8LR5lTRHOY25x2XCNglR4zLDNiFgXzllkzJ4h071nf/vZsLddfV909C+//Fyl91f3Z3HI+I9FzBHT6FBRtIvHgVZFtuep9e+TAu2VT9qUJX1QQCJL45E1c/kND/PdjcbIk4kRtnEP/2bDIHfbQyfYlIv9XTRpvvfW1LydvRXXLAwBoeFVRoyEFEpHICww8hMulKCHivBM2s/QfxxZfg0lsr15nPR/IrU0h+7uueZkuniNx6GYEnHqlcXyIiPcCJ4+uZ95EGxtT7MRxj6v3N90euJYVSEemR/IP3Ibr/9VhoO097dsNzxJ+7CJfc2MaW28CM5BdOI/Fl75c15rJEfnotwUd/W7m+RER6gBPH1/PCSaP4z2ExXjhpVM0DKSiUikgP1lT9yepGe9qbqj9lY6sr2l/qkycSP+MiXNG81fA9PyJ03x25+/eIiEhVKJSKSI/mi44iuv8N+AZ6b3eTq/50AdnNKyraX/rwY4ifcxku4J23GvrDrwjfdTNkdf8ZEZFqUCgVkR6vqfqTb8j+nnaXXE9s4TQyG5dUtL/MAYcTv+BqXCTqaQ/+9feEfzwH0umK9iciIgqlItJLWKCOyH6X4R9RdDV8egvxRTNIv/ev0htuo8zeBxCbfiOufpCnPfjMX4jMmw2JeEX7ExHp7xRKRaTXMF+Q8N4XEdjx/3kXZFMkXvgBqXcfrWh/2fF7snXWPLKDh3naA4ufIXr9NNjSWNH+RET6M4VSEelVctWfziK4y6lFS3LVn5Jv3EMlyye70eOIzZ5PdsQOnnb/sheIXn0+tml9xfoSEenPFEpFpNdpqv4U2uNcin+NpVbcSXL5bThXuQuS3PDtic2aT2bMeE+7/61Xic6Zgr1f2bsAiIj0RwqlItJrBXc4hvDE2VBU4Sm98vckXrwGl01VrC83eCixGTeT2XUfT7tvzUqic87B3n2zYn2JiPRHCqUi0qsFhh9KZL+2qj9dWtnqT/UDiU2/jvTEgzzNvvXvUXflFHyvv1K5vkRE+hmFUhHp9fxDJhLd/7oS1Z8WVr76UzhK/LwrSR18pKfZGjcRvfp8/C8/V7m+RET6EYVSEekTfAN2yVV/ipao/rSwwtWfAkESZ80mdcSxnmaLx4jcMB3/wqcq15eISD+hUCoifYYvOoroASWqP21tqv70egU785M49QKSn/mSp9lSKSLzv0fgqf+tXF8iIv2AQqmI9Ckt1Z8me9pz1Z+mVrb6kxnJk75F4qRvepuzWSI/uZLg4w9Uri8RkT5OoVRE+pxc9afL26j+NLPi1Z9Sn/ky8dOm4sw87eFfzSP40C+ggvdNFRHpqxRKRaRParv6UzJf/enPFe0vfcRniX/nUpw/4GkPP/hzQnffAtnK3TdVRKQvUigVkT6rpfrT14uWZEm+cnPFqz9lDj6C+PlX4kIRT3vo8fsJ33ENZNIV60tEpK9RKBWRPi1X/elLhHZvq/rTjyta/Skz8WBi06/H1Q3wtAefeozI/EshmahYXyIifYlCqYj0C8HRbVV/eojES9dWtPpTdsI+xGbMJdswxNMeeO4pIjdeDLEK3tBfRKSPUCgVkX6jzepPa/5O4vlLcelYxfrKjh1PbNZ8ssNGeY/h5eeIXnM+NFbwhv4iIn2AQqmI9Cst1Z+8o5iZ9ZWv/uRG7khs1nwyO4zzHsPrS6m78lxs/dqK9SUi0tsplIpIv5Or/nQTFt3B055tXJav/rSmYn257YYTmzWXzC57eo/h3TeJXnEOtnplxfoSEenNFEpFpF/KVX+6sXuqPw1oIDb9BtJ77e89hnVriM45B9+byyvXl4hIL6VQKiL9Vkv1p0medpdcR2zhtMpWf4rWET//KtL7H+Zp9n2wgejV51H/loKpiPRvgY5XERHpu5qqPyVeuoHM2n+0LEhvJr5oJuG9ZxAYfkhlOguFiX/3+4R/dj3BJ1tu3m9btzDhrmvh3ltJHXA42T32g1A4d7/TUBgXCuf+DEcgGMaFwxAMQzAEPo0tiEjfoFAqIv2e+UKE976IZKiB9MqHWxZkkySW/AC3+7kEd/hkZTrzB0icPh1XP5DQY/e2HANAbAuhJ/8MT5ZfbcqFwi1BNRTBhUL5P8MFwbZEWziMC5YIu/n1vYE4AgH9cyEi1aXfMiIitFR/suBgUq/f1bLAZUm+chMuuZHgTidhRfXtt4nPR/JL3yH4xCNYbEuXdmXJBCQTWNd20yHn8+WCbX6UtinYegNx2Duymw+2heHYE4jDrbchGNbor0g/pVAqIpJnZoR2/jIWGkJy6XygpdJTasXPcamNhHY9E7MKhCazXnUTfctmIb4Vi1f/mF0w5BmldaEwu2Ud4YbBEAwVBeJIri0cabUNReHYhfJBOBiCcAT8gdzfg4j0CAqlIiJFgqOPwUKDSLx4NRRUekq//SAuuZHwnhdgRZWhtoUbOgJb1/r2Uy4cIb3vh7FkPDcK2jQamkxAMt7yOpXs8jH0RJZKQiqJbWlsbqsHeKey/Tjz5UZt25riUGo+b2HYLZr2UHLqRFijvyLlUigVESkhMPwj2H5ziD//fci0jA5m1vyNROoDwvvMxgLRLvWRPOEMwj+/Phcy81woTOLUC0kfenTHO8hmIVUQWBPxXKBLFATXZBySyeaA29JWEHYTcUglsEQu9DavX9Bm2WzHx9PLmMtCPIbFK1fJqy0uGPRMe8hNX2hnioNn1DfkmTrRPO2hIBD7Y1sglYRAUKO/0msplIqItME/ZF8i+19PYvEsXHJDc3tm/QLiiy4msu/lWKhhm/ffFDxD992BrVuDGzqS5AlnlBdIITf6Fo5COIrLN7l2N+iCdNo7SpuMQyLhDa7NI7gJSBSHX284bhn1Teb+bNpXQUDvSyyVglQK27q5KvvfN/+nM+v0fN5S0x5avc5Pk/Bs5/NX5b1I/6VQKiLSDv/AXYgccCPxRbNwsXeb27MfLCW28EIi+83BFx25zftPH3o06UOPZvny5UyYMKHjDWolEIDAAFzdAKCK4TebzY34FYXdla+9ypiRw1uP+ibiua/6E8VhN1EUiEtMg8hkqvUuasacy42UJ+JV78sFgm2M7HY8n9c7Utz+1AmCIY3+9hMKpSIiHfBFtyd6wI3EF88m2/hqc7vbupL4gguITJqDb8C42h1gX+Lz5S5CCkdwufyLA7YkIVPp0J5O58OvN9iWnOJQFHZLTXtoHvUtNXWiD7J0CtIpbGt1b/3gzPLTFoqmPbQzn7ewbejGTQTee6OsqRP4FYtqSWdfRKQMTdWf4i/8gOyGRc3tuepPU4ns+338g/ep4RFKpwUCEAjgovVAFUd/ncuN/nY0n7eNaQ8lw27h1IlUgmxsK/50Csukq/Uuasacy492x9mW8dKxnVjX+QMl5/O2mvbQzq3QSk17KL47hEZ/S1MoFREpkwXq89Wfriez9omWBU3Vn/aZSWDYh2t3gNIz5ed5EgrjGARUPgA3T//IpNu4sC3ZHGZzc37zF8UVzOf1XiBXahpEwrtfV7UYXzOWSUMs3eX7B5ejzfm7ZczndaE2wm6JO0a0Vfgi8PTjhO67g0nbMp+9ShRKRUQ6obn6U7CB9Dt/aFmQTZJ44XLc7ucR3OETtTtA6d/8AYgGcNG65qaqRMem0d925vO2GW4Tce/FcB3cMcLSqY6Ppxdqfr9V7sf5/d4pDsEwloxj69Y0/8fC1q0h/PPrAWoaTBVKRUQ6ycxPaLfvYKHBpF7/ZcsClyX5yo241EaCY0+sTPUnkZ6ocPS3fiBQzYvfMp7R2s7M5yUZp/G9tQyKRopGipsCccLb5vrgrc8yGYht6XD015IJQvfdoVAqItLb5Ko/nZKv/nQLnupPr/0Ml9xQuepPIv2Zzw+ROlxk20Z/3yr3zhbOQTrV7nzekrcxSxRfFNfG1InC/aZ65uivrVtb0/4VSkVEuiA4+tNYcBCJl66pavUnEakys9zczWCoe0Z/U0nvHN8O5vM2T3so41ZonqkSnRj9dUNHVOsdl0WhVESkiwIjDsOCA4k/f1nVqj+JSB/i83dP4Qvn8he/eefz+p/9J6E//MozX9eFwiRPOKMaR1E2fa8kIlIB/iH7Edn/Oiw0xNPeVP3JJTfV6MhEpN8yy5WerRuAGzwUN3I02R13IXXc10mcPp3s0JE4IDt0JInTpurqexGRvsI/cHy++tNMXGxVc3ulqj+JiFRKT6wmp5FSEZEKaqr+5Bsw3tPutq4kvvBCspvfqM2BiYj0cAqlIiIVZqEhRPa/Ft+QSZ52l3if2MKpZDa+WKMjExHpuRRKRUSqoKn6k3/4Yd4F6c3EF80g/f6/a3NgIiI9lEKpiEiVmC9EeJ8ZBEZ/1rsgmyTxwmWkVv1vbQ5MRKQHUigVEamiXPWnswnu/FXvApcl+fKNJN+8F9cHa4iLiHSWrr4XEamylupPg/PVn1pCaOq1n+KSG8B9tHYHKCLSAyiUioh0k+Doz2DBBhIvXgOusPrTA2zPA2x9bzjB8acRHHVUDY9SRKQ2FEpFRLpRW9WfDHCJ90i+dC3JpT/CwoPAgpgvBL4g+EK5cqX5197nJdax/Dr+UNn7wQKYWe1Ojoj0awqlIiLdLFf96Vri/50ClKhLnWnEbW0Eqlh7uyQrCq7BltBrTeG1xLJWYbeddawwKJcO1phf4VikH1IoFRGpAf/AXSkZSGvKQTYJ2aQnDHf/ZVjWaiR3eNoR2zCgILgGMX+wOSyXDsSlnrcRmvNh2Tzh29/t71ykP1MoFRGpEQuPwCXW1voweiAH2QRkE82BOAhkN3f3ufJ1MCJcYrS31Uhw0zbtbd9WaG7aj8Kx9A/dEkrN7GfAZ4G1zrl9Siw/Avg98Hq+6QHn3OXdcWwiIrUSHH8qyVfm5gJYE1+I4C6nEhh6EC6byo9cpnDZZO7iqMLX2VTR82TRNqmC9oJ1XAoyKXDebXGZmp2LninbKhxDDUaOzdfBiHCQ7eJp4rGGtkOzBTF/4TSMwqDdXuguWMcUjqW6umuk9E7gFuCudtb5p3Pus+0sFxHpU5qusk+9difZxFp84REEx59as6vvncuUCLrthd58u2tnWWdCc1Podj1tWkONuSy49sNxBMjEq3wc5msJxFbuaG/7obc4LLcZiBWO+4VuCaXOuSfMbFx39CUi0psERx1FcNRRLF++nAkTJtT0WMz84PeDP0ItLzNy2Uw+oLYE2jdfX87YMTu0zHltDrTFo70pXCZZFJS3LTT3vDm/NeayueSbidd45NhfYsqE94K8wnA7eHOMRGZo2aG5+W4VJadhFIRmU/2hSutJc0oPMbPFwLvAVOfci7U+IBER6X65OZTecJwONuIfOL5bj6MlHBeG1fZGe4tDb8E6LgX5sNw6BLcfmhWOi7gMZGKQiZUVjuuA9NY2FnaFBdq/oM4zCtzRvOR25hU3rdNq6kUIfIFtDsep1X8l9dqdbJ9Yy9a1tf2Wpol1V3m7/EjpH9uYUzoIyDrnNpvZp4G5zrlWQwabNm1qPtjly5dX8WhFRER6CJfBXBpzKXBpjFT+dTr32qValpNuft5qHQr2UbBObp+Fr1u2NQpe1+A+DNIxRwBnuQcWwFmwua35tTWtEwQC+NIbCCdfxQr+w5O1IJuGfIlY/UFVO9bCb4MaGhpafSHTI0JpiXXfAA50zr1f2F4YSqupJ3yN1pPp/LRN56Z9Oj9t07lpn85P+6p9fpxzuRHKViO5+QvnPFMhSk2RaGsUucQ6JecoF06rUDiuFguPoO4j7V3+UzmlQmmP+PrezEYBa5xzzswOBnzAuhofloiIiECumIEFwJeLDbWac5wLx+nypj/kp0ysXr2SkcO3aycQtz1HuWU/Je5W0Qe5xHs17b+7bgn1G+AIYJiZrQQuJXfbOZxztwEnAGeZWRqIASe77hrCFRERkV4hF47zczCpKyscxxqXExxd2VHklnDceiS33btNNI0Ct3sxXonbuBWHZlc4clw5Fh5e0f11Vnddff+lDpbfQu6WUSIiIiI9mjcc13rkuK1R3rbvNpHZuITMmr/lgnUTX5jg+FNr9E5yesTX9yIiIiLSOblwnL8Sn/LDcXCHT5LabnKPuUdyE4VSERERkX6mJ90juYnu/CoiIiIiNadQKiIiIiI1p1AqIiIiIjWnUCoiIiIiNadQKiIiIiI1p1AqIiIiIjWnUCoiIiIiNadQKiIiIiI1p1AqIiIiIjVnzrlaH0PZNm3a1HsOVkRERERKamhoaFUVVSOlIiIiIlJzCqUiIiIiUnO96ut7EREREembNFIqIiIiIjXXL0KpmX3KzJaa2atmdnGJ5WEz+21++b/NbFzBshn59qVm9sly99lbbOu5MbOjzWyBmb2Q//Oogm3+nt/novxjRPe9o8rqwvkZZ2axgnNwW8E2B+TP26tmNs/MWk327g26cG5OKTgvi8wsa2aT8sv602fnf8xsoZmlzeyEomVfN7Pl+cfXC9r7y2en5Lkxs0lm9i8ze9HMnjezLxYsu9PMXi/47EzqrvdTaV387GQKzsHDBe07538Ol+d/LkPd8V4qrQufnSOLfu/Ezey4/LL+9Nm5wMxeyv/8/MXMdipYVvvfO865Pv0A/MBrwC5ACFgM7FW0zneA2/LPTwZ+m3++V379MLBzfj/+cvbZGx5dPDeTgR3yz/cB3inY5u/AgbV+fzU+P+OAJW3s9z/AIYABjwLH1Pq9due5KVpnIrCin352xgH7AncBJxS0bwesyP85JP98SD/77LR1bnYDJuSf7wCsAgbnX99ZuG5vfXTl/OSXbW5jv78DTs4/vw04q9bvtbvPTcE62wHrgbp++Nk5suB9n0XLv1k94vdOfxgpPRh41Tm3wjmXBO4BPle0zueAX+Sf3wd8LP8/gc8B9zjnEs6514FX8/srZ5+9wTafG+fcc865d/PtLwIRMwt3y1F3n658dkoys+2BQc65f7ncT/tdwHGVP/Sqq9S5+RLwm6oeaW10eH6cc284554HskXbfhJ43Dm33jm3AXgc+FR/+uy0dW6cc8ucc8vzz98F1gLDu+ewu01XPjsl5X/ujiL3cwi5n8t+9dkpcgLwqHNua/UOtSbKOT9/K3jfzwA75p/3iN87/SGUjgbeLni9Mt9Wch3nXBrYBAxtZ9ty9tkbdOXcFDoeeM45lyho+3n+a5Dv9davGOn6+dnZzJ4zs3+Y2eEF66/sYJ+9QaU+O1+kdSjtL5+dzm7bnz47HTKzg8mNBr1W0Dwn/7XkTb34P8ldPT8RM3vWzJ5p+nqa3M/dxvzP4bbss6eo1L+9J9P6905//OycTm7ks71tu/X3Tn8IpaX+USu+5UBb63S2vbfpyrnJLTTbG7gG+FbB8lOccxOBw/OPr3bxOGulK+dnFTDWOTcZuAD4tZkNKnOfvUElPjsfArY655YULO9Pn53ObtufPjvt7yA3evNL4DTnXNOI2AxgD+Agcl9BXtSVg6yhrp6fsc65A4EvAzeb2fgK7LOnqNRnZyLwWEFzv/vsmNlXgAOB6zrYtls/O/0hlK4ExhS83hF4t611zCwANJCbb9LWtuXsszfoyrnBzHYEHgS+5pxrHq1wzr2T/7MR+DW5rxR6o20+P/kpH+sAnHMLyI3m7JZff8eC7fvlZyev1WhFP/vsdHbb/vTZaVP+P3d/AmY7555panfOrXI5CeDn9M/PTtO0BpxzK8jN0Z4MvA8Mzv8cdnqfPUgl/u09CXjQOZdqauhvnx0z+zgwC/h/Bd9w9ojfO/0hlP4XmJC/8jBE7h/Ch4vWeRhoutLsBOCv+bkTDwMnW+4q4p2BCeQm/Jazz95gm8+NmQ0m9w/DDOfcU00rm1nAzIblnweBzwJL6J26cn6Gm5kfwMx2IffZWeGcWwU0mtmH819Nfw34fXe8mQrrys8VZuYDTiQ354l8W3/77LTlMeATZjbEzIYAnwAe62efnZLy6z8I3OWcu7do2fb5P43cnLd+99nJf2bC+efDgI8AL+V/7v5G7ucQcj+X/eqzU6DVPPb+9Nkxs8nAj8kF0rUFi3rG751qXUHVkx7Ap4Fl5EarZuXbLs//pQBEgHvJXcj0H2CXgm1n5bdbSsEVZ6X22Rsf23pugNnAFmBRwWMEUA8sAJ4ndwHUXMBf6/dZg/NzfP79LwYWAscW7PNAcr/0XgNuIV/Eorc9uvhzdQTwTNH++ttn5yByoxBbgHXAiwXbfiN/3l4l9xV1f/vslDw3wFeAVNHvnUn5ZX8FXsifn18BA2r9Pmtwfg7Nn4PF+T9PL9jnLvmfw1fzP5fhWr/P7jw3+WXjgHcAX9E++9Nn5/+ANQU/Pw8XbFvz3zuq6CQiIiIiNdcfvr4XERERkR5OoVREREREak6hVERERERqTqFURERERGpOoVREREREak6hVERERERqTqFURKQEM3vDzGJmttnMVpvZnWY2oAL7vdPMkvn9rjezx81sj0ocs4hIb6ZQKiLStmOdcwOASeTKNc6o0H6vze93R2AtcGdnd1BQMlJEpE9QKBUR6YBzbjW5MnyTAPKlh683s7fMbI2Z3WZm0ab1zWy6ma0ys3fN7Awzc2a2a4n9bgV+DeyT3+5gM/uXmW3Mb39Lvlxg036dmZ1tZsuB5fm2uWb2tpl9YGYLzOzwgvW/b2b3mtmvzKzRzF4ws93MbIaZrc1v94kqnTYRkU5RKBUR6YCZ7QgcQ678HsA1wG7kQuquwGjgkvy6nwIuAD6eX/bRdvY7ADgFeC7flAHOB4YBhwAfA75TtNlxwIeAvfKv/5s/ju3IBdx7zSxSsP6xwC+BIfl+Hr2yLQwAAAJESURBVCP3u380ufKDPy7rJIiIVJnKjIqIlGBmb5ALhw4YQK4+9vHAJmAzsK9z7rX8uocAv3bO7WxmPwPWOOdm5JftSm5Uc4Jz7lUzuxM4GYjnH/8Bzm/aV9ExnAd81Dn3+fxrB3zMOffXdo57A3CEc26xmX0f+Ihz7uj8smOB3wANzrmMmQ0EPgCGOOc2bvvZEhHpOs1JEhFp23HOuf8zs4+SG4UcBoSAOmCBmTWtZ4A//3wH4NmCfbxdYr/XO+dmFzea2W7AjcCB+T4CwIKi1d4u2uZC4Ix8vw4YlD/OJmsKnseA951zmYLXkAvdCqUiUlP6+l5EpAPOuX+QuxjpeuB9cmFub+fc4PyjIX/hEsAqchcwNRnTia5+BLxCblR1EDCTXOD1HE7Tk/z80YuAk8iNdg4mN5JbvI2ISI+nUCoiUp6bgaOBfYHbgZvMbASAmY02s0/m1/sdcJqZ7WlmdeTnmpap6ev0zfnbRJ1Vxvpp4D0gYGaXkBspFRHpdRRKRUTK4Jx7D7gL+B650clXgWfM7APg/4Dd8+s9CswD/pZf51/5XSTK6GYq8GWgkVzw/W0H6z8GPAosA94kN0e11HQBEZEeTxc6iYhUkZntCSwBws65dK2PR0Skp9JIqYhIhZnZ580sZGZDyN0+6g8KpCIi7VMoFRGpvG+Rm+f5Grl7j3Y0N1REpN/T1/ciIiIiUnMaKRURERGRmlMoFREREZGaUygVERERkZpTKBURERGRmlMoFREREZGaUygVERERkZr7/7A7soUPM2QwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_rmse_vs_param(cvs, reg_list, 'RegParam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_reg = 0.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Rank**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSEs (tuning Rank): [1.9795415279380575, 1.9875221577068463, 2.0196830374919377] Time:  54.69717979431152\n",
      "RMSEs (tuning Rank): [1.6467891256719405, 1.655989164770081, 1.654779850719891] Time:  134.6790840625763\n",
      "RMSEs (tuning Rank): [1.4729708803070627, 1.4780722464985914, 1.4775676566107976] Time:  229.45668387413025\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "als = ALS(regParam=best_reg, maxIter=5, userCol=\"numeric_user_id\", itemCol=\"numeric_business_id\", ratingCol=\"rating\",\n",
    "          coldStartStrategy=\"drop\", nonnegative= True)\n",
    "\n",
    "rank_list = [5, 10, 20]\n",
    "paramGrid = ParamGridBuilder().addGrid(als.rank, rank_list).build()\n",
    "crossval_rank = CrossValidator(estimator=als,\n",
    "                          estimatorParamMaps=paramGrid,\n",
    "                          evaluator=RegressionEvaluator(metricName=\"rmse\",\n",
    "                                                        labelCol=\"rating\"),\n",
    "                          numFolds=3)\n",
    "\n",
    "cv_ranks = []\n",
    "for i in range(3):\n",
    "    start = time.time()\n",
    "    cv_rank = crossval_rank.fit(all_sample_spark_dfs_train[i])\n",
    "    cv_ranks.append(cv_rank)\n",
    "    end = time.time()\n",
    "    print('RMSEs (tuning Rank):', cv_rank.avgMetrics, 'Time: ', end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_rmse_vs_param(cv_ranks, reg_list, 'Rank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_rank = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Final model\n",
    "als = ALS(regParam=best_reg, rank=best_rank, maxIter=5, userCol=\"numeric_user_id\", itemCol=\"numeric_business_id\", ratingCol=\"rating\",\n",
    "          coldStartStrategy=\"drop\", nonnegative= True)\n",
    "\n",
    "\n",
    "final_models = []\n",
    "for i in range(3):\n",
    "    final_model = als.fit(all_sample_spark_dfs_train[i])\n",
    "    final_models.append(final_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.2 Accuracy of Vanilla Matrix Factorization  <a class=\"anchor\" id=\"3-2-2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSEs (after tuning): 1.888621491292947\n",
      "RMSEs (after tuning): 1.8714080111560851\n",
      "RMSEs (after tuning): 1.7314241587469161\n"
     ]
    }
   ],
   "source": [
    "evaluator = RegressionEvaluator(metricName=\"rmse\", labelCol=\"rating\", predictionCol=\"prediction\")\n",
    "\n",
    "final_rmses = []\n",
    "final_predictions_tests = []\n",
    "for i in range(3):\n",
    "    final_predictions_test = final_model.transform(all_sample_spark_dfs_test[i])\n",
    "    final_predictions_tests.append(final_predictions_test)\n",
    "    \n",
    "    final_rmse = evaluator.evaluate(final_predictions_test)\n",
    "    final_rmses.append(final_rmse)\n",
    "    \n",
    "    print('RMSEs (after tuning):', final_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline_MF_rmses = final_rmses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.3 Coverage of Vanilla Matrix Factorization  <a class=\"anchor\" id=\"3-2-3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Catalog Coverage: 59.67523680649527%\n",
      "Catalog Coverage: 66.9617105917454%\n",
      "Catalog Coverage: 73.29174732917473%\n"
     ]
    }
   ],
   "source": [
    "als_catalog_coverages = []\n",
    "for spark_test_df in final_predictions_tests:\n",
    "    pandas_test_df = spark_test_df.toPandas()\n",
    "    pandas_test_df.rename(columns={\"numeric_user_id\": \"user_id\", 'numeric_business_id': 'business_id'}, inplace=True)\n",
    "    pandas_test_df.sort_values(by=['user_id', 'rating'])\n",
    "    ratings_dictionary = dict(pandas_test_df.set_index('user_id').groupby(level = 0).apply(lambda x : x.to_dict(orient= 'records')))\n",
    "    N = len(pandas_test_df['business_id'].unique())    \n",
    "    als_catalog_coverages.append(calculate_catalog_coverage(ratings_dictionary, N, pred_col_name='prediction'))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 SVD and Nonnegative Matrix Factorization <a class=\"anchor\" id=\"3-3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from surprise import SVD\n",
    "from surprise import SVDpp\n",
    "from surprise import NMF\n",
    "from surprise.model_selection.validation import cross_validate\n",
    "from surprise import Dataset\n",
    "from surprise import accuracy\n",
    "from surprise.model_selection import train_test_split as s_train_test_split\n",
    "from surprise.model_selection import KFold\n",
    "from surprise import Reader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_result_df(predictions):\n",
    "    predictions_list = []\n",
    "    for i in range(len(predictions)):\n",
    "        predictions_list.append(list(predictions[i][:-1]))\n",
    "        \n",
    "    return pd.DataFrame(predictions_list, columns = ['user_id', 'business_id', 'rating', 'predicted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.3279\n",
      "test_rmse     1.368833\n",
      "train_rmse    0.926641\n",
      "fit_time      0.192152\n",
      "test_time     0.007480\n",
      "dtype: float64\n",
      "Catalog Coverage: 70.09472259810555%\n",
      "RMSE: 1.4622\n",
      "test_rmse     1.441115\n",
      "train_rmse    0.234110\n",
      "fit_time      0.458858\n",
      "test_time     0.006535\n",
      "dtype: float64\n",
      "Catalog Coverage: 70.23004059539919%\n",
      "RMSE: 1.2966\n",
      "test_rmse     1.354950\n",
      "train_rmse    0.917092\n",
      "fit_time      1.697547\n",
      "test_time     0.115373\n",
      "dtype: float64\n",
      "Catalog Coverage: 74.97762307309796%\n",
      "RMSE: 1.4637\n",
      "test_rmse     1.489127\n",
      "train_rmse    0.217332\n",
      "fit_time      3.297451\n",
      "test_time     0.071762\n",
      "dtype: float64\n",
      "Catalog Coverage: 75.59920437593237%\n",
      "RMSE: 1.2693\n",
      "test_rmse     1.336636\n",
      "train_rmse    0.908585\n",
      "fit_time      8.938784\n",
      "test_time     0.710005\n",
      "dtype: float64\n",
      "Catalog Coverage: 79.4973829497383%\n",
      "RMSE: 1.4108\n",
      "test_rmse      1.515139\n",
      "train_rmse     0.196731\n",
      "fit_time      18.267997\n",
      "test_time      0.396762\n",
      "dtype: float64\n",
      "Catalog Coverage: 80.21976052197604%\n"
     ]
    }
   ],
   "source": [
    "all_mf_prediction_dfs = []\n",
    "all_benchmark_dfs = []\n",
    "\n",
    "mf_algo_dict = {'SVD': 0, 'NMF': 1}\n",
    "all_mf_coverages = {mf_algo_dict['SVD']: [], mf_algo_dict['NMF']: []}\n",
    "all_mf_rmses = {mf_algo_dict['SVD']: [], mf_algo_dict['NMF']: []}\n",
    "\n",
    "MF_algorithms = [SVD(), NMF()]\n",
    "# MF_algorithms = [SVD(), NMF(), SVDpp()]\n",
    "\n",
    "\n",
    "for i in range(len(all_training_samples)):\n",
    "    training_sample = all_training_samples[i]\n",
    "    testing_sample = all_testing_samples[i]\n",
    "    \n",
    "    reader = Reader(rating_scale=(1.0, 5.0))\n",
    "    \n",
    "    train_df = Dataset.load_from_df(training_sample[['user_id', 'business_id', 'rating']], reader)\n",
    "    test_df = Dataset.load_from_df(testing_sample[['user_id', 'business_id', 'rating']], reader)\n",
    "\n",
    "    benchmark = []\n",
    "    mf_prediction_dfs = []\n",
    "\n",
    "    for j in range(len(MF_algorithms)):\n",
    "        algorithm = MF_algorithms[j]\n",
    "        # cross validation\n",
    "        # measures available: RMSE, MAE\n",
    "        # cv: determines how the train & test is split: 3-fold \n",
    "        # If verbose = True: accuracy measures for each split are printed, as well as train and test times\n",
    "        results = cross_validate(algorithm, test_df, measures=['RMSE'], cv=3, return_train_measures=True,verbose=False)\n",
    "        algorithm.fit(train_df.build_full_trainset())\n",
    "        predictions_test = algorithm.test(test_df.build_full_trainset().build_testset())\n",
    "        rmse = accuracy.rmse(predictions_test)\n",
    "        \n",
    "        # save predictions \n",
    "        prediction_df = create_result_df(predictions_test)\n",
    "        mf_prediction_dfs.append(prediction_df)\n",
    "        all_mf_prediction_dfs.append(prediction_dfs)\n",
    "\n",
    "        # Get results & algorithm\n",
    "        tmp = pd.DataFrame.from_dict(results).mean(axis=0) #test_rmse, fit_time, test_time    \n",
    "        print(tmp)\n",
    "        tmp = tmp.append(pd.Series([str(algorithm).split(' ')[0].split('.')[-1]], index=['Algorithm']))\n",
    "        benchmark.append(tmp)\n",
    "\n",
    "        # save rmses\n",
    "        all_mf_rmses[j].append(rmse)\n",
    "        \n",
    "        # save coverages\n",
    "        predictions_dict = dict(prediction_df.set_index('user_id').groupby(level = 0).apply(lambda x : x.to_dict(orient= 'records')))\n",
    "        N = len(prediction_df['business_id'].unique())\n",
    "        coverage = calculate_catalog_coverage(predictions_dict, N, k=5, pred_col_name='predicted')\n",
    "        all_mf_coverages[j].append(coverage)\n",
    "\n",
    "    # save RMSEs\n",
    "    benchmark_df = pd.DataFrame(benchmark).set_index('Algorithm').sort_values('test_rmse')  \n",
    "    all_benchmark_dfs.append(benchmark_df)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: [1.3279055437029503, 1.2966316545824215, 1.2692921978532068],\n",
       " 1: [1.4622196408886925, 1.463684398293782, 1.4108392013036999]}"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_mf_rmses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: [70.09472259810555, 74.97762307309796, 79.4973829497383],\n",
       " 1: [70.23004059539919, 75.59920437593237, 80.21976052197604]}"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_mf_coverages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 Wide and Deep Model <a class=\"anchor\" id=\"3-4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this model a wide linear model and deep neural network are jointly trained to combine the benefits of memorization and generalization for recommender systems. Wide linear models can effectively memorize sparse feature interactions using cross-product feature transformations, while deep neural networks can generalize previously unseen feature interactions through low-dimensional embeddings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4.1 Feature Selection <a class=\"anchor\" id=\"3-4-1\"></a>\n",
    "\n",
    "A set of relevant features are selected from the original dataset to be used for training the model. We notice that the dataset is highly imbalanced hence we perform oversampling ( Up-sampling) to create a balanced dataset for the traning data only."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our Wide and Deep model, the following features were used to train the model:\n",
    "* Categories\n",
    "* Business location information (latitude, longitude)\n",
    "* Business review count\n",
    "* Average business rating\n",
    "* Average user rating \n",
    "* User review count\n",
    "* Business hours -> Number of days open per week\n",
    "* Photo (How many photos businesses received in their reviews)\n",
    "\n",
    "-> : Indicates how we altered our features for better representation.\n",
    "For business hours, they were converted to number of days so that all the businesses hours were put into the same format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_features(active_user_only_ratings_df_sample):\n",
    "    base_df = active_user_only_ratings_df_sample\n",
    "\n",
    "    #sparse alert - hopefully with bigger dataset, we will see some weird ratings..\n",
    "    real_average = base_df[['business_id','rating']].groupby('business_id').mean().reset_index()\n",
    "    real_average.rename(columns={\"rating\": \"average_business_rating\"}, inplace=True)\n",
    "\n",
    "    #Average ratings for business added\n",
    "    base_df = base_df.merge(real_average, how='left', on='business_id')\n",
    "\n",
    "    #one-hot encoding for top5 categories\n",
    "    catList = []\n",
    "    businesses['categories'].fillna(value='',inplace=True)\n",
    "    businesses['cat'] = businesses['categories'].apply(lambda x: x.split(','))\n",
    "    catList.extend(businesses['cat'])\n",
    "    merged = [x.strip() for x in list(itertools.chain(*catList))]\n",
    "\n",
    "    #Adding state, review_count, is_open\n",
    "    base_df = base_df.merge(businesses[['business_id','state','city', 'latitude', 'longitude','review_count','is_open', 'hours', 'cat']], on='business_id')\n",
    "    base_df.rename(columns={\"review_count\": \"business_review_count\"}, inplace=True)\n",
    "\n",
    "    #getting top 5 common categories items\n",
    "    top5List = [x for x in list(itertools.chain(*Counter(merged).most_common(5))) if type(x) != int]\n",
    "\n",
    "    #one-hot encoding if the business in top 5 common category\n",
    "    for item in top5List:\n",
    "        base_df[item] = base_df['cat'].apply(lambda categories: 'Y' if bool(set([y.strip() for y in categories]).intersection([item])) else 'N')\n",
    "        #base_df[item] = base_df['cat'].apply(lambda categories: 'Y' if bool(set([y.strip() for y in categories]).intersection(set(item))) else 'N')\n",
    "\n",
    "    # is_open (categorical) change from 1 and 0 to Y and N\n",
    "    base_df['is_open'] = base_df['is_open'].apply(lambda x: 'Y' if x else 'N')\n",
    "\n",
    "    # hours: how many days per week it's open\n",
    "    base_df['hours'] = base_df['hours'].apply(lambda x: len(x.keys()) if x else 0)\n",
    "    base_df.rename(columns={\"hours\": \"days_per_week_open\"}, inplace=True)\n",
    "\n",
    "    # user information\n",
    "    base_df = base_df.merge(users[['user_id', 'average_stars', 'review_count', 'friends']],  on='user_id')\n",
    "\n",
    "    # number_of_friends\n",
    "    base_df['friends'] = base_df['friends'].apply(lambda x: len(x.split(',')))\n",
    "    base_df.rename(columns={\"friends\": \"number_of_friends\", \"review_count\": \"user_review_count\", \"average_stars\": \"average_user_rating\"}, inplace=True)\n",
    "\n",
    "    #number of tips for popularity measure of business\n",
    "    business_numberOfTips = tips[['business_id','user_id']].groupby('business_id').count().reset_index()\n",
    "    business_numberOfTips.rename(columns={\"user_id\": \"business_numberOfTips\"}, inplace=True)\n",
    "    base_df = base_df.merge(business_numberOfTips, on='business_id')\n",
    "\n",
    "    #number of photo for popularity measure of business\n",
    "    business_numberOfPhotos = photos[['business_id','photo_id']].groupby('business_id').count().reset_index()\n",
    "    business_numberOfPhotos.rename(columns={\"photo_id\": \"business_numberOfPhotos\"}, inplace=True)\n",
    "    base_df = base_df.merge(business_numberOfPhotos, on='business_id')\n",
    "    \n",
    "    return base_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_features = create_features(training_data)\n",
    "testing_features = create_features(testing_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_features2 = create_features(training_data2)\n",
    "testing_features2 = create_features(testing_data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_features3 = create_features(training_data3)\n",
    "testing_features3 = create_features(testing_data3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['user_id', 'business_id', 'rating', 'date', 'numeric_user_id',\n",
       "       'numeric_business_id', 'average_business_rating', 'state', 'city',\n",
       "       'latitude', 'longitude', 'business_review_count', 'is_open',\n",
       "       'days_per_week_open', 'cat', 'Restaurants', 'Shopping', 'Food',\n",
       "       'Home Services', 'Beauty & Spas', 'average_user_rating',\n",
       "       'user_review_count', 'number_of_friends', 'business_numberOfTips',\n",
       "       'business_numberOfPhotos'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_features3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wd_feature_modeling(df, colNames, dfType):\n",
    "    \n",
    "    nn = df[colNames]\n",
    "    nn = nn.rename(columns= {'Home Services': 'Home_Services', 'Beauty & Spas': 'Beauty_and_Spas'})\n",
    "    \n",
    "    labels = nn.pop('rating')\n",
    "    \n",
    "    if dfType == 'train':\n",
    "        #sampling\n",
    "        class1 = np.where(labels==1)[0]\n",
    "        class2 = np.where(labels==2)[0]\n",
    "        class3 = np.where(labels==3)[0]\n",
    "        class4 = np.where(labels==4)[0]\n",
    "        class5 = np.where(labels==5)[0]\n",
    "\n",
    "        class1_upsampled = np.random.choice(class1, size = 100000, replace= True)\n",
    "        class2_upsampled = np.random.choice(class2, size = 100000, replace= True)\n",
    "        class3_upsampled = np.random.choice(class3, size = 100000, replace= True)\n",
    "        class4_upsampled = np.random.choice(class4, size = 100000, replace= True)\n",
    "\n",
    "        train_nn_1 = nn.iloc[class1_upsampled]\n",
    "        train_nn_2 = nn.iloc[class2_upsampled]\n",
    "        train_nn_3 = nn.iloc[class3_upsampled]\n",
    "        train_nn_4 = nn.iloc[class4_upsampled]\n",
    "        train_nn_5 = nn.iloc[class5]\n",
    "\n",
    "        labels_1 = labels.iloc[class1_upsampled]\n",
    "        labels_2 = labels.iloc[class2_upsampled]\n",
    "        labels_3 = labels.iloc[class3_upsampled]\n",
    "        labels_4 = labels.iloc[class4_upsampled]\n",
    "        labels_5 = labels.iloc[class5]\n",
    "\n",
    "        train_nn_balanced = train_nn_1\n",
    "        train_nn_balanced = train_nn_balanced.append(train_nn_2, ignore_index = True)\n",
    "        train_nn_balanced= train_nn_balanced.append(train_nn_3, ignore_index = True)\n",
    "        train_nn_balanced= train_nn_balanced.append(train_nn_4, ignore_index = True)\n",
    "        train_nn_balanced= train_nn_balanced.append(train_nn_5, ignore_index = True)\n",
    "\n",
    "        labels_balanced = labels_1\n",
    "        labels_balanced = labels_balanced.append(labels_2, ignore_index = True)\n",
    "        labels_balanced = labels_balanced.append(labels_3, ignore_index = True)\n",
    "        labels_balanced = labels_balanced.append(labels_4, ignore_index = True)\n",
    "        labels_balanced = labels_balanced.append(labels_5, ignore_index = True)\n",
    "\n",
    "        assert len(train_nn_balanced) == len(labels_balanced)\n",
    "        nn = train_nn_balanced\n",
    "        labels = labels_balanced\n",
    "    \n",
    "    labels = labels.apply(lambda x: int(x)-1)\n",
    "    \n",
    "    return nn, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "colNames = ['user_id','business_id','average_business_rating','state','city','latitude','longitude'\n",
    "            ,'business_review_count','days_per_week_open','Restaurants','Shopping','Food','Home Services'\n",
    "            ,'Beauty & Spas','average_user_rating','user_review_count','business_numberOfPhotos', 'rating']\n",
    "\n",
    "train_nn, train_labels = wd_feature_modeling(training_features, colNames, dfType='train')\n",
    "test_nn, test_labels = wd_feature_modeling(testing_features, colNames, dfType='test')\n",
    "\n",
    "train_nn2, train_labels2 = wd_feature_modeling(training_features2, colNames, dfType='train')\n",
    "test_nn2, test_labels2 = wd_feature_modeling(testing_features2, colNames, dfType='test')\n",
    "\n",
    "train_nn3, train_labels3 = wd_feature_modeling(training_features3, colNames, dfType='train')\n",
    "test_nn3, test_labels3 = wd_feature_modeling(testing_features3, colNames, dfType='test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4.2 Feature Engineering  <a class=\"anchor\" id=\"3-4-2\"></a>\n",
    "\n",
    "For the wide component the feature set includes raw input features and transformed features. The sparse features are one hot encoded and cross product transformation was implemented on certain features. This captures the interaction between binary features and adds non-linearity to the generalized linear model.\n",
    "\n",
    "For the deep component each of the sparse high dimensional categorical features are first converted into low dimensional and dense real valued vector with the help of embeddings.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "tf.get_logger().setLevel('ERROR')\n",
    "from tensorflow import feature_column\n",
    "from tensorflow.keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_engineering(df):\n",
    "    \n",
    "    numeric_features = ['average_business_rating','business_review_count','average_user_rating'\n",
    "                        ,'user_review_count', 'days_per_week_open','business_numberOfPhotos','latitude','longitude']\n",
    "    cat_features = ['user_id','business_id','state','city','Restaurants','Shopping','Food'\n",
    "                    ,'Home_Services', 'Beauty_and_Spas']\n",
    "\n",
    "    #continuous features\n",
    "    cont = {colname : feature_column.numeric_column(colname) for colname in numeric_features}\n",
    "\n",
    "    #categorical features\n",
    "    cat = {\n",
    "            'user_id' : feature_column.categorical_column_with_hash_bucket('user_id', hash_bucket_size=10000)\n",
    "            , 'business_id' : feature_column.categorical_column_with_hash_bucket('business_id', hash_bucket_size=10000)\n",
    "            , 'state' : feature_column.categorical_column_with_hash_bucket('state', hash_bucket_size=1000)\n",
    "            , 'city' : feature_column.categorical_column_with_hash_bucket('city', hash_bucket_size=1000)\n",
    "            , 'Restaurants' : feature_column.categorical_column_with_vocabulary_list('Restaurants', ['Y', 'N'], default_value = 0)\n",
    "            , 'Shopping' : feature_column.categorical_column_with_vocabulary_list('Shopping', ['Y', 'N'], default_value = 0)\n",
    "            , 'Food' : feature_column.categorical_column_with_vocabulary_list('Food', ['Y', 'N'], default_value = 0)\n",
    "            , 'Home_Services' : feature_column.categorical_column_with_vocabulary_list('Home_Services', ['Y', 'N'], default_value = 0)\n",
    "            , 'Beauty_and_Spas' : feature_column.categorical_column_with_vocabulary_list('Beauty_and_Spas', ['Y', 'N'], default_value = 0)\n",
    "        }\n",
    "\n",
    "    #embed sparse columns for dense layer\n",
    "    embed = {\n",
    "           'embed_{}'.format(colname) : tf.feature_column.embedding_column(col, 10)\n",
    "              for colname, col in cat.items()\n",
    "            }\n",
    "\n",
    "    #columns for dense network\n",
    "    dense = cont.copy()\n",
    "    dense.update(embed)\n",
    "\n",
    "    #one hot encode sparse columns for wide network\n",
    "    wide = {\n",
    "        'ohe_{}'.format(colname)  : tf.feature_column.indicator_column(col)\n",
    "              for colname, col in cat.items()\n",
    "        }\n",
    "\n",
    "    #crossed columns\n",
    "    lat_boundary = list(df.latitude.sort_values().unique())\n",
    "    long_boundary = list(df.longitude.sort_values().unique())\n",
    "\n",
    "    latitude_bucket_fc = feature_column.bucketized_column(cont.get('latitude'), lat_boundary)\n",
    "    longitude_bucket_fc = feature_column.bucketized_column(cont.get('longitude'), long_boundary)\n",
    "\n",
    "    #crossed_lat_lon = feature_column.crossed_column([latitude_bucket_fc, longitude_bucket_fc], 5000)\n",
    "    wide['crossed_lat_lon'] = feature_column.crossed_column([latitude_bucket_fc, longitude_bucket_fc], 5000)\n",
    "    #print(wide.keys())\n",
    "    \n",
    "    return wide, dense\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_to_dataset_train(dataframe, labels,  shuffle = True, batch_size = 32):\n",
    "    ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))\n",
    "    if shuffle:\n",
    "        ds = ds.shuffle(buffer_size=len(dataframe))\n",
    "    ds = ds.batch(batch_size)\n",
    "    return ds\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_to_dataset_eval(dataframe, labels, batch_size= 32):\n",
    "    if labels is None:\n",
    "        ds = tf.data.Dataset.from_tensor_slices(dict(dataframe))\n",
    "    else:\n",
    "        ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))\n",
    "    ds = ds.batch(batch_size)\n",
    "    return ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ohe_user_id',\n",
       " 'ohe_business_id',\n",
       " 'ohe_state',\n",
       " 'ohe_city',\n",
       " 'ohe_Restaurants',\n",
       " 'ohe_Shopping',\n",
       " 'ohe_Food',\n",
       " 'ohe_Home_Services',\n",
       " 'ohe_Beauty_and_Spas',\n",
       " 'crossed_lat_lon']"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(wide.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['average_business_rating',\n",
       " 'business_review_count',\n",
       " 'average_user_rating',\n",
       " 'user_review_count',\n",
       " 'days_per_week_open',\n",
       " 'business_numberOfPhotos',\n",
       " 'latitude',\n",
       " 'longitude',\n",
       " 'embed_user_id',\n",
       " 'embed_business_id',\n",
       " 'embed_state',\n",
       " 'embed_city',\n",
       " 'embed_Restaurants',\n",
       " 'embed_Shopping',\n",
       " 'embed_Food',\n",
       " 'embed_Home_Services',\n",
       " 'embed_Beauty_and_Spas']"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dense.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "wide_training, dense_training = feature_engineering(training_features)\n",
    "wide_training2, dense_training2 = feature_engineering(training_features2)\n",
    "wide_training3, dense_training3 = feature_engineering(training_features2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4.3 Modeling <a class=\"anchor\" id=\"3-4-3\"></a>\n",
    "\n",
    "The wide component consists of a generalized linear model and the deep component is a feed forward neural network. The wide and deep component are combined using a weighted sum of their output log odds at the prediction which is then fed into a common logistic loss function during training.\n",
    "\n",
    "Joint training is done by back-propagating the gradients from the output to both wide and deep part of the model using stochastic mini batch descent. The regularizers and optimizer parameters were set to the values used in the paper 'Wide and Deep Learning for Recommender Systems'. The number of hidden units were tuned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator = tf.estimator.DNNLinearCombinedClassifier(\n",
    "    # wide settings\n",
    "    linear_feature_columns=list(wide.values()),\n",
    "    linear_optimizer='Ftrl',\n",
    "    # deep settings\n",
    "    dnn_feature_columns=list(dense.values()),\n",
    "    dnn_hidden_units=[1000, 500, 100],\n",
    "#     dnn_optimizer=tf.train.ProximalAdagradOptimizer(learning_rate = 0.1),\n",
    "    n_classes = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To apply L1 and L2 regularization, you can set dnn_optimizer to:\n",
    "# tf.train.ProximalAdagradOptimizer(\n",
    "#     learning_rate=0.1,\n",
    "#     l1_regularization_strength=0.001,\n",
    "#     l2_regularization_strength=0.001)\n",
    "\n",
    "# To apply learning rate decay, you can set dnn_optimizer to a callable:\n",
    "# lambda: tf.AdamOptimizer(\n",
    "#     learning_rate=tf.exponential_decay(\n",
    "#         learning_rate=0.1,\n",
    "#         global_step=tf.get_global_step(),\n",
    "#         decay_steps=10000,\n",
    "#         decay_rate=0.96))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "def input_fn_train():\n",
    "    return df_to_dataset_train(train_nn, train_labels)\n",
    "\n",
    "def input_fn_train2():\n",
    "    return df_to_dataset_train(train_nn2, train_labels2)\n",
    "\n",
    "def input_fn_train3():\n",
    "    return df_to_dataset_train(train_nn3, train_labels3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "def input_fn_eval():\n",
    "    return df_to_dataset_train(test_nn, test_labels)\n",
    "\n",
    "def input_fn_eval2():\n",
    "    return df_to_dataset_train(test_nn2, test_labels2)\n",
    "\n",
    "def input_fn_eval3():\n",
    "    return df_to_dataset_train(test_nn3, test_labels3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "def input_fn_pred():\n",
    "    return df_to_dataset_eval(test_nn)\n",
    "\n",
    "def input_fn_pred():\n",
    "    return df_to_dataset_eval(test_nn2)\n",
    "\n",
    "def input_fn_pred():\n",
    "    return df_to_dataset_eval(test_nn3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wd_estimator(wide, dense, index):\n",
    "    \n",
    "    estimator = tf.estimator.DNNLinearCombinedClassifier(\n",
    "        # wide settings\n",
    "        linear_feature_columns=list(wide.values())\n",
    "        , linear_optimizer='Ftrl'\n",
    "        # deep settings\n",
    "        , dnn_feature_columns=list(dense.values())\n",
    "        , dnn_hidden_units=[1000, 500, 100]\n",
    "    #     dnn_optimizer=tf.train.ProximalAdagradOptimizer(learning_rate = 0.1),\n",
    "        , n_classes = 5)\n",
    "    \n",
    "    if index == 0:\n",
    "        estimator.train(input_fn=input_fn_train, steps=100)\n",
    "        results = estimator.evaluate(input_fn=input_fn_eval, steps = 100)\n",
    "    \n",
    "    elif index == 1:\n",
    "        estimator.train(input_fn=input_fn_train2, steps=100)\n",
    "        results = estimator.evaluate(input_fn=input_fn_eval2, steps = 100)\n",
    "    \n",
    "    else:\n",
    "        estimator.train(input_fn=input_fn_train3, steps=100)\n",
    "        results = estimator.evaluate(input_fn=input_fn_eval3, steps = 100)\n",
    "\n",
    "    return estimator, results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4.4 Accuracy <a class=\"anchor\" id=\"3-4-4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice that our model does not outperform the baseline model. This could be due to several reasons. The deep component of the model can be improved in several ways. We can include droput, change the optimizers to find the best parameters that fit our model. The highly imbalanced dataset also affects the accuracy of the test set. Other metrics can be used to better represent the results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_rmse_wd(pred, nn, labels):\n",
    "    \n",
    "    predictions = [val['class_ids'][0] + 1 for val in pred]\n",
    "    predictions_df = nn[['user_id', 'business_id']]\n",
    "    predictions_df['rating'] = labels + 1\n",
    "    predictions_df['predicted_ratings'] = predictions\n",
    "\n",
    "    predictions_size = len(predictions_df)\n",
    "    rmse_wd = (sum((predictions_df['rating'] - predictions_df['predicted_ratings']) **2) / predictions_size) ** 0.5\n",
    "    \n",
    "    return rmse_wd, predictions_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdList = [[wide_training, dense_training], [wide_training2, dense_training2], [wide_training3, dense_training3]]\n",
    "rmse_wd_list = []\n",
    "result_list = []\n",
    "predictions_dfs = []\n",
    "\n",
    "for index, item in enumerate(wdList):\n",
    "    estimator, result = wd_estimator(item[0],item[1],index)\n",
    "    \n",
    "    if index == 0:\n",
    "        predictor = estimator.predict(input_fn = input_fn_eval)\n",
    "        pred = list(predictor)\n",
    "        rmse, predictions_df = calculate_rmse_wd(pred, test_nn, test_labels)\n",
    "\n",
    "    elif index == 1:\n",
    "        predictor2 = estimator.predict(input_fn = input_fn_eval2)\n",
    "        pred2 = list(predictor2)\n",
    "        rmse, predictions_df = calculate_rmse_wd(pred2, test_nn2, test_labels2)\n",
    "    \n",
    "    else:\n",
    "        predictor3 = estimator.predict(input_fn = input_fn_eval3)  \n",
    "        pred3 = list(predictor3)\n",
    "        rmse, predictions_df = calculate_rmse_wd(pred3, test_nn3, test_labels3)\n",
    "    \n",
    "    rmse_wd_list.append(rmse)\n",
    "    result_list.append(result)\n",
    "    predictions_dfs.append(predictions_df)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.1828125\n",
      "average_loss: 5.174266\n",
      "global_step: 100\n",
      "loss: 5.174266\n",
      "accuracy: 0.1828125\n",
      "average_loss: 5.174266\n",
      "global_step: 100\n",
      "loss: 5.174266\n",
      "accuracy: 0.1828125\n",
      "average_loss: 5.174266\n",
      "global_step: 100\n",
      "loss: 5.174266\n"
     ]
    }
   ],
   "source": [
    "for result in result_list:\n",
    "    for key in sorted(result):\n",
    "        print( \"%s: %s\" % (key, results[key]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.211083193570267, 2.188702708144844, 1.4438217563594928]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_wd_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4.5 Coverage <a class=\"anchor\" id=\"3-4-5\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Catalog Coverage: 75.40453074433657%\n",
      "Catalog Coverage: 83.31609789727679%\n",
      "Catalog Coverage: 87.64760806283297%\n"
     ]
    }
   ],
   "source": [
    "coverage_wds = []\n",
    "for predictions_df in predictions_dfs:\n",
    "    predictions_wd_dict = dict(predictions_df.set_index('user_id').groupby(level = 0).apply(lambda x : x.to_dict(orient= 'records')))\n",
    "    N = len(predictions_df['business_id'].unique())\n",
    "    coverage_wd = calculate_catalog_coverage(predictions_wd_dict, N, k=5, pred_col_name='predicted_ratings')\n",
    "    coverage_wds.append(coverage_wd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Evaluation <a class=\"anchor\" id=\"4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Accuracy <a class=\"anchor\" id=\"4-1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "LEGEND = ['Baseline Model - Learned Bias', 'Baseline Model - MF', 'SVD', 'NMF', 'Wide and Deep']\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.plot(sample_sizes, baseline_rmses, marker='o')\n",
    "plt.plot(sample_sizes, baseline_MF_rmses, marker='o')\n",
    "plt.plot(sample_sizes, all_mf_rmses[0], marker='o')\n",
    "plt.plot(sample_sizes, all_mf_rmses[1], marker='o')\n",
    "plt.plot(sample_sizes, rmse_wd_list, marker='o')\n",
    "\n",
    "plt.xlabel('Number of Active Users Sampled')\n",
    "plt.ylabel('RMSE')\n",
    "plt.title('RMSE Test vs Sample Size')\n",
    "plt.legend(LEGEND, loc='upper center', bbox_to_anchor=(1.2, 0.8), ncol=1,fancybox=True, framealpha=0.1)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each model, as the sample size increased, the RMSE decreased. It would indicate that with a larger dataset, the performance of our models would improve. \n",
    "Interestingly, our baseline model - Learned Bias performed the best in terms of RMSE. This could  be attributed to the fact that since most users seem to rate positively (rating > 3.5) and average business ratings are also skewed to the left (based on our exploratory analysis), by predicting the values based on the learned bias of users and businesses, the gap between prediction versus the actual values could be very small. \n",
    "\n",
    "Moreover, although both SVD and NMF showed slightly higher RMSE than the baseline - Learned Bias model, SVD also had a fairly low RMSE among the models. \n",
    "It is also interesting to note that for SVD and NMF, despite the changes in the sample size, the RMSE values do not seem to decrease significantly. \n",
    "We can also observe that the RMSE for the Wide and Deep model is also decreasing gradually as data size becomes larger. This also indicates that by further expanding the data size, the Wide and Deep model would perform better. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Coverage <a class=\"anchor\" id=\"4-2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(sample_sizes, baseline_coverages, marker='o')\n",
    "plt.plot(sample_sizes, als_catalog_coverages, marker='o')\n",
    "plt.plot(sample_sizes, all_mf_coverages[0], marker='o')\n",
    "plt.plot(sample_sizes, all_mf_coverages[1], marker='o')\n",
    "plt.plot(sample_sizes, coverage_wds, marker='o')\n",
    "\n",
    "plt.xlabel('Number of Active Users')\n",
    "plt.ylabel('Catalog Coverage')\n",
    "plt.title('Catalog Coverage Test vs Sample Size')\n",
    "plt.legend(LEGEND)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Catalog coverage is a measure of how many unique businesses appear in the top-k businesses over the total number of businesses. By maximizing coverage, we can assume that the model provides a good variety of businesses to users. For our largest sample, the Wide and Deep model had the highest catalog coverage, having approximately 10% higher coverage than the second highest model. This is in line with our objective to provide diverse business recommendations to users.\n",
    "\n",
    "The vanilla MF baseline had the lowest catalog coverage. In addition, it can be noted that SVD and NMF performed almost equally in terms of catalog coverage. It could be due to the fact that these two models are very similar to one another. \n",
    "\n",
    "For a sparse sample data, despite the fact that we trained our model using active users only (users who have rated 5 or more times), this would not guarantee that the test data would contain 5 or more businesses for each user. In the test data set, it is likely that each user would have rated one to two items on average (since most active users rated about 5 items, and we split the train-test data based on time), so we would be recommending top one or two items out of one or two items they have rated. Although our initial objective was to recommend novel and diverse items to users, we would need to train the model using more dense dataset to have higher catalog coverage.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 Hold Out <a class=\"anchor\" id=\"4-3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_rmse(df, predicted_col='predicted_rating', rating_col='rating'):\n",
    "    return np.sqrt( sum( (df[predicted_col] - df[rating_col]) ** 2  / len(df) ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "bs_hold_outs = baseline_predictions3.sort_values(by=['user_id']).groupby('user_id').apply(lambda x: x[-1:]).reset_index(drop=True)\n",
    "rmse_bs_hold_out = calculate_rmse(bs_hold_outs)\n",
    "\n",
    "als_predicted = final_predictions_tests[-1].toPandas()\n",
    "als_hold_outs = als_predicted.sort_values(by=['numeric_user_id']).groupby('numeric_user_id').apply(lambda x: x[-1:]).reset_index(drop=True)\n",
    "rmse_als_hold_out = calculate_rmse(als_hold_outs, predicted_col='prediction', rating_col='rating')\n",
    "\n",
    "svd_hold_outs = mf_prediction_dfs[0].sort_values(by=['user_id']).groupby('user_id').apply(lambda x: x[-1:]).reset_index(drop=True)\n",
    "rmse_svd_hold_out = calculate_rmse(svd_hold_outs, predicted_col='predicted', rating_col='rating')\n",
    "\n",
    "nmf_hold_outs = mf_prediction_dfs[1].sort_values(by=['user_id']).groupby('user_id').apply(lambda x: x[-1:]).reset_index(drop=True)\n",
    "rmse_nmf_hold_out = calculate_rmse(nmf_hold_outs, predicted_col='predicted', rating_col='rating')\n",
    "\n",
    "wd_hold_outs = predictions_dfs[-1].sort_values(by=['user_id']).groupby('user_id').apply(lambda x: x[-1:]).reset_index(drop=True)\n",
    "rmse_wd_hold_out = calculate_rmse(wd_hold_outs, predicted_col='predicted_ratings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Baseline Model - Learned Bias</th>\n",
       "      <th>Baseline Model - MF</th>\n",
       "      <th>SVD</th>\n",
       "      <th>NMF</th>\n",
       "      <th>Wide and Deep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>1.368302</td>\n",
       "      <td>1.848548</td>\n",
       "      <td>1.365983</td>\n",
       "      <td>1.522066</td>\n",
       "      <td>1.529822</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Baseline Model - Learned Bias  Baseline Model - MF       SVD       NMF  \\\n",
       "RMSE                       1.368302             1.848548  1.365983  1.522066   \n",
       "\n",
       "      Wide and Deep  \n",
       "RMSE       1.529822  "
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holds_outs = pd.DataFrame({'Baseline Model - Learned Bias': rmse_bs_hold_out, \n",
    "                           'Baseline Model - MF': rmse_als_hold_out,\n",
    "                           'SVD': rmse_svd_hold_out, \n",
    "                           'NMF': rmse_nmf_hold_out, \n",
    "                           'Wide and Deep': rmse_wd_hold_out}, \n",
    "                          index=['RMSE'])\n",
    "\n",
    "holds_outs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since our initial objective was to predict the last rating for each active user, the table above indicates the RMSE of all of our models based on the star rating prediction of the final review (by date). Although the baseline model - learned bias and SVD have similar RMSE, our SVD model performed slightly better in predicting the last held out rating. Following these two models, both NMF and Wide and Deep models also showed similar RMSE for the held out star rating."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4 Final Results Table <a class=\"anchor\" id=\"4-4\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_rmses = [baseline_rmses, baseline_MF_rmses, all_mf_rmses[0], all_mf_rmses[1], rmse_wd_list]\n",
    "all_rmses = [rmses[-1] for rmses in all_rmses]\n",
    "\n",
    "all_coverages = [baseline_coverages, als_catalog_coverages, all_mf_coverages[0], all_mf_coverages[1], coverage_wds]\n",
    "all_coverages = [all_coverages[-1] for all_coverages in all_coverages]\n",
    "\n",
    "results = pd.DataFrame({'RMSE': all_rmses, 'Catalog Coverage (%)': all_coverages}, \n",
    "                       index=['Baseline Model - Learned Bias', 'Baseline Model - MF', 'SVD', 'NMF', \"Wide and Deep\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RMSE</th>\n",
       "      <th>Catalog Coverage (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baseline Model - Learned Bias</th>\n",
       "      <td>1.036013</td>\n",
       "      <td>80.381086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baseline Model - MF</th>\n",
       "      <td>1.731424</td>\n",
       "      <td>73.291747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVD</th>\n",
       "      <td>1.269292</td>\n",
       "      <td>79.497383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NMF</th>\n",
       "      <td>1.410839</td>\n",
       "      <td>80.219761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wide and Deep</th>\n",
       "      <td>1.443822</td>\n",
       "      <td>87.647608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   RMSE  Catalog Coverage (%)\n",
       "Baseline Model - Learned Bias  1.036013             80.381086\n",
       "Baseline Model - MF            1.731424             73.291747\n",
       "SVD                            1.269292             79.497383\n",
       "NMF                            1.410839             80.219761\n",
       "Wide and Deep                  1.443822             87.647608"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final results table show how our models and the baseline models perfored in the overall prediction of the test data. Since we have divided up the sample data into training and test data based on time, these results indicate the models' performance in terms of future star rating prediction.\n",
    "\n",
    "Similar to other aforementioned evaluations, the baseline model - learned bias had the lowest RMSE, followed by SVD having a test RMSE value of 1.269. The baseline - vanilla MF performed the worst. \n",
    "\n",
    "As stated previously, the Wide and Deep model showed the highest catalog coverage. This could be due to the fact that the model incorporates both linear model and a feed forward neural network model, and thus has capability to recommend businesses based on memorization and generalization. The recommendations based generalization would help recommend items that are potentially novel to the user."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.5 User Distribution <a class=\"anchor\" id=\"4-5\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.3070\n",
      "Catalog Coverage: 75.32796660703637%\n"
     ]
    }
   ],
   "source": [
    "inactive_users = user_counts.loc[user_counts < 5].index.tolist() #1351008\n",
    "inactive_user_only_ratings_df = ratings[ratings['user_id'].isin(inactive_users)]\n",
    "inactive_user_only_ratings_df_sample, inactive_training_data, inactive_testing_data = get_sample_size_n(active_user_only_ratings_df, 10000)\n",
    "\n",
    "\n",
    "training_sample = inactive_training_data\n",
    "testing_sample = inactive_testing_data\n",
    "reader = Reader(rating_scale=(1.0, 5.0))\n",
    "train_df = Dataset.load_from_df(training_sample[['user_id', 'business_id', 'rating']], reader)\n",
    "test_df = Dataset.load_from_df(testing_sample[['user_id', 'business_id', 'rating']], reader)\n",
    "\n",
    "algorithm = SVD()\n",
    "results = cross_validate(algorithm, test_df, measures=['RMSE'], cv=3, return_train_measures=True,verbose=False)\n",
    "algorithm.fit(train_df.build_full_trainset())\n",
    "predictions_test = algorithm.test(test_df.build_full_trainset().build_testset())\n",
    "# rmse\n",
    "inactive_rmse = accuracy.rmse(predictions_test)\n",
    "\n",
    "prediction_df = create_result_df(predictions_test)\n",
    "\n",
    "# coverages\n",
    "predictions_dict = dict(prediction_df.set_index('user_id').groupby(level = 0).apply(lambda x : x.to_dict(orient= 'records')))\n",
    "N = len(prediction_df['business_id'].unique())\n",
    "inactive_coverage = calculate_catalog_coverage(predictions_dict, N, k=5, pred_col_name='predicted')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3070190499114258"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inactive_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75.32796660703637"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inactive_coverage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We decided to see how well our model would do on inactive users. Therefore, we decided to look at users with less than 5 reviews. \n",
    "We chose to use our SVD model to run on this sample, since it had the highest RMSE among our models. \n",
    "The resulting RMSE and coverage are similar to when we ran our SVD model on active users. Having similar results between inactive and active users suggests that our model is robust, i.e. it produces similar results on different populations, despite having sparse dataset (i.e. users with only few ratings)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Conclusion <a class=\"anchor\" id=\"5\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, our models could perform better (especially the Wide and Deep model) with larger and denser datasets. One important future work could be to make our models more scalable to larger datasets.\n",
    "In line with larger dataset, the Wide and Deep model can be improved by additional features.\n",
    "For future work, by adding more features, it would provide more information to our Wide and Deep model. We expect the model to show better performance with more features and larger data. For additional features, we could incorporate the text reviews, and build a feature based on term frequencies (TF-IDF). \n",
    "\n",
    "In regards to improving SVD, one possibility may be to incorporate implicit rating data (SVD++). We can observe how the SVD++ model would perform in comparison to the simple SVD.\n",
    "\n",
    "Overall, SVD, NMF, and Wide and Deep models achieved our initial objective to a certain extent. We were able to recommend businesses to users with reasonable RMSE and coverage. The performance metrics (RMSE and coverage) were not as high as we expected (in comparison to the baseline - learned bias). As stated earlier, this can be due to the fact that since most users seem to rate positively (rating > 3.5) and average business ratings are also skewed to the left (based on our exploratory analysis), by predicting the values based on the learned bias of users and businesses, the gap between prediction versus the actual values could be very small. Hence, the low RMSEs achieved by the baseline model.\n",
    "\n",
    "Even though real-world datasets are extremely sparse and face cold start problems, by having a more scalable model with larger training and test sets would improve both of our models significantly."
   ]
  }
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