notebook_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building a Recommendation Engine from scratch using the MovieLens dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "plt.style.use('ggplot')\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading file and passing columns names\n",
    "\n",
    "col_users = ['user_id','age','sex','occupation','zip_code']\n",
    "users = pd.read_csv('/Users/gonzaloferreiro/Documents/GitHub/Recommendations_system_with_MovieLens_dataset/ml-100k/u.user',\n",
    "                   sep='|', names=col_users)\n",
    "\n",
    "col_rat = ['user_id','movie_id','rating','unix_timestamp']\n",
    "ratings = pd.read_csv('/Users/gonzaloferreiro/Documents/GitHub/Recommendations_system_with_MovieLens_dataset/ml-100k/u.data',\n",
    "                       sep='\\t', names=col_rat)\n",
    "\n",
    "col_items = ['movie_id', 'movie_title' ,'release_date','video_release_date', 'IMDb_URL', 'unknown', 'Action', 'Adventure',\n",
    "'Animation', 'Childrens', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy',\n",
    "'Film-Noir', 'Horror', 'Musical', 'Mystery', 'Romance', 'Sci_Fi', 'Thriller', 'War', 'Western']\n",
    "items = pd.read_csv('/Users/gonzaloferreiro/Documents/GitHub/Recommendations_system_with_MovieLens_dataset/ml-100k/u.item',\n",
    "                   sep='|', names=col_items, encoding='latin-1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some basic EDA, starting with shape and duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Users(943, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>94043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>32067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  age sex  occupation zip_code\n",
       "0        1   24   M  technician    85711\n",
       "1        2   53   F       other    94043\n",
       "2        3   23   M      writer    32067"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Users'+ str(users.shape))\n",
    "users.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "# Checking duplicates\n",
    "print((users.shape[0] - len([x for x in (users.user_id.value_counts() > 1).values if x == False]))/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item(1682, 24)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>video_release_date</th>\n",
       "      <th>IMDb_URL</th>\n",
       "      <th>unknown</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Childrens</th>\n",
       "      <th>...</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci_Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?GoldenEye%20(...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Four%20Rooms%...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id        movie_title release_date  video_release_date  \\\n",
       "0         1   Toy Story (1995)  01-Jan-1995                 NaN   \n",
       "1         2   GoldenEye (1995)  01-Jan-1995                 NaN   \n",
       "2         3  Four Rooms (1995)  01-Jan-1995                 NaN   \n",
       "\n",
       "                                            IMDb_URL  unknown  Action  \\\n",
       "0  http://us.imdb.com/M/title-exact?Toy%20Story%2...        0       0   \n",
       "1  http://us.imdb.com/M/title-exact?GoldenEye%20(...        0       1   \n",
       "2  http://us.imdb.com/M/title-exact?Four%20Rooms%...        0       0   \n",
       "\n",
       "   Adventure  Animation  Childrens   ...     Fantasy  Film-Noir  Horror  \\\n",
       "0          0          1          1   ...           0          0       0   \n",
       "1          1          0          0   ...           0          0       0   \n",
       "2          0          0          0   ...           0          0       0   \n",
       "\n",
       "   Musical  Mystery  Romance  Sci_Fi  Thriller  War  Western  \n",
       "0        0        0        0       0         0    0        0  \n",
       "1        0        0        0       0         1    0        0  \n",
       "2        0        0        0       0         1    0        0  \n",
       "\n",
       "[3 rows x 24 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Item'+ str(items.shape))\n",
    "items.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "# Checking non repeated-movies\n",
    "print((items.shape[0] - len([x for x in (items.movie_id.value_counts() > 1).values if x == False]))/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ratings(100000, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>unix_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>891717742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>377</td>\n",
       "      <td>1</td>\n",
       "      <td>878887116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  rating  unix_timestamp\n",
       "0      196       242       3       881250949\n",
       "1      186       302       3       891717742\n",
       "2       22       377       1       878887116"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Ratings' + str(ratings.shape))\n",
    "ratings.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking non repeated-movies\n",
    "#ratings['user-movie'] = ratings.user_id.map(str) + ratings.movie_id.map(str)\n",
    "#ratings.drop_duplicates(subset='user-movie',keep='last',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ratings.drop('user-movie',axis=1,inplace=True)\n",
    "#ratings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing our datasets as networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A bipartite graph consists of two groups of nodes. There are links between nodes of differing groups, but no links among nodes from the same group. Common examples are customers and purchased products or meetups and people attending. Which of the groups a node belongs to can be indicated by the keyword \"bipartite\" and the corresponding group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gonzaloferreiro/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "network = ratings[['user_id','movie_id']]\n",
    "network['user_id'] = network.user_id + network.movie_id.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from networkx.algorithms import bipartite\n",
    "\n",
    "B = nx.Graph()\n",
    "\n",
    "B.add_nodes_from(network.movie_id.unique(), bipartite=0)\n",
    "B.add_nodes_from(network.user_id.unique(), bipartite=1)\n",
    "B.add_edges_from(network.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 2625\n",
      "Number of edges: 100000\n",
      "Average degree:  76.1905\n"
     ]
    }
   ],
   "source": [
    "print(nx.info(B))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = bipartite.gnmk_random_graph(5,6,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeView((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EdgeView([(0, 8), (0, 10), (1, 8), (1, 6), (2, 10), (3, 7), (3, 6), (4, 6), (4, 8), (4, 9)])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeDataView({0: {'bipartite': 0}, 1: {'bipartite': 0}, 2: {'bipartite': 0}, 3: {'bipartite': 0}, 4: {'bipartite': 0}, 5: {'bipartite': 1}, 6: {'bipartite': 1}, 7: {'bipartite': 1}, 8: {'bipartite': 1}, 9: {'bipartite': 1}, 10: {'bipartite': 1}})"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.nodes(data=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movies with rating: 1682\n",
      "Users that rated movies: 943\n",
      "Total nodes 2625\n"
     ]
    }
   ],
   "source": [
    "bottom_nodes, top_nodes = bipartite.sets(B)\n",
    "print('Movies with rating:', len(bottom_nodes))\n",
    "print('Users that rated movies:', len(top_nodes))\n",
    "print('Total nodes', len(bottom_nodes) + len(top_nodes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To represent the bipartite graph, we fix the positions of the \n",
    "# nodes according to which group they belong to.\n",
    "\n",
    "# Just creating a list with the connections from:\n",
    "# Bottom nodes, being represented as 0, with as many posisitions as elements\n",
    "# Top nodes, being represented as 1, with as many posisitions as elements\n",
    "\n",
    "position = list(zip([0]*len(bottom_nodes), list(range(len(bottom_nodes))))) + \\\n",
    "    list(zip([1]*len(top_nodes), list(range(len(top_nodes)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Going over the lists of nodes and \n",
    "# adding the position of each on the bipartite graph\n",
    "\n",
    "positions = {}\n",
    "for node in bottom_nodes:\n",
    "    positions[node] = np.array(position[node-1])\n",
    "for node in top_nodes:\n",
    "    positions[node] = np.array(position[node-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are not going to visualize the network, since it's huge and it would be hard to see anything, but we can project on the bottom and top nodes to find relationship between movies and users.\n",
    "\n",
    "For the convenience of directly showing the relation structure among a particular set of nodes, bipartite networks are usually compressed by one-mode projection. This means that the ensuing network contains nodes of only either of the two sets, and two X (or, alternatively, Y) nodes are connected only if when they have at least one common neighboring Y (or, alternatively, X) node.\n",
    "\n",
    "Take the following example:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/f/f9/Bipartite_network_projection.png\" style=\"float: left; margin: 20px; height: 250px\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_top = bipartite.projected_graph(B, top_nodes)\n",
    "G_bottom = bipartite.projected_graph(B, bottom_nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 943\n",
      "Number of edges: 429110\n",
      "Average degree: 910.0954\n",
      "Average clustering coefficient: 0.9716768012393279\n"
     ]
    }
   ],
   "source": [
    "print(nx.info(G_top))\n",
    "print('Average clustering coefficient:',nx.average_clustering(G_top))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 393
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(list(dict(G_top.degree()).values()))\n",
    "plt.xlabel('Degree')\n",
    "plt.ylabel('Counts')\n",
    "plt.title('Degree distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 1682\n",
      "Number of edges: 983206\n",
      "Average degree: 1169.0916\n",
      "Average clustering coefficient: 0.8666145955851908\n"
     ]
    }
   ],
   "source": [
    "print(nx.info(G_bottom))\n",
    "print('Average clustering coefficient:',nx.average_clustering(G_bottom))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 393
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(list(dict(G_bottom.degree()).values()))\n",
    "plt.xlabel('Degree')\n",
    "plt.ylabel('Counts')\n",
    "plt.title('Degree distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The clustering coefficient represents the fraction of possible interconnections between the neighbours of each node. Since it's a faction, we can expect a number between 0 and 1. In fact we can get zero if we have a network in which each node has one or several edges going out, but none of those edges connect with each other. And it can be all the way to one if we have a network where each node is connected with each other.\n",
    "\n",
    "What we can se here is that in both, our projected network of movies and users, have an average clustering coefficient close to one. That means that our networks are very tightly connected.\n",
    "\n",
    "We can see that also by checking the average degree of each one. Where, for example, for our users projected network, we have almost as many degrees as nodes we have in our network. \n",
    "\n",
    "Let's check the number of components for each network, to see if there's some kind of clustering in between elements. We can extract the various connected components from a graph. Here we are interested in the largest of these connected components. We will call it the giant component (we will not define that more precisely here)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [],
   "source": [
    "def giant_component_size(G):\n",
    "    components = [(len(G), list(G)) for G in nx.connected_components(G)]\n",
    "    num_components = len(components)\n",
    "    return num_componentsa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_top = giant_component_size(G_top)\n",
    "n_bottom = giant_component_size(G_bottom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of components users:  1\n",
      "Number of components movies:  1\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of components users: \", n_top)\n",
    "print(\"Number of components movies: \", n_bottom)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our networks are so strongly connected, that we can identify only one component in each network of users and movies. Let's check only for the movies the betweenness centrality, to find which movies have more importance for the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_centrality = nx.betweenness_centrality(G_bottom)\n",
    "edge_centrality = nx.edge_betweenness_centrality(G_bottom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node: 288\n",
      "Max node centrality: 0.0011161495115620378\n",
      "Movie Scream (1996)\n",
      "Number of ratings: 478\n",
      "Average rating: 3.4414225941422596\n"
     ]
    }
   ],
   "source": [
    "# Checking out the node with the biggest betwenness centrality\n",
    "\n",
    "max_key = max(node_centrality, key=lambda k: node_centrality[k])\n",
    "print('Node:',max_key)\n",
    "print('Max node centrality:', node_centrality[max_key])\n",
    "print('Movie',items.movie_title[items.movie_id == max_key].item())\n",
    "print('Number of ratings:', len(ratings[ratings.movie_id == max_key]))\n",
    "print('Average rating:', np.mean(ratings.rating[ratings.movie_id == max_key]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 318,
       "width": 484
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing that node just for fun :)\n",
    "\n",
    "sub_net = [x for x in list(G_bottom.edges()) if x[0] == max_key]\n",
    "sub_G = nx.Graph()\n",
    "sub_G.add_edges_from(sub_net)\n",
    "nx.draw_spectral(sub_G, with_labels=False, node_size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8/xDr3ArAX1X/KkIIITA2+8U36yCpFdg2A/7m/83efchfzFq99x8e4JhOHvCYep2n43md9jvfW2V1KqfVVusADw9wbK1fg393XFVVPUIe+gl5XP3NIYTPhDwL1zN7VWwyeceh9Rq6aYBAfDVT+BqcTJtNxkCfySGEzUKemenn5I6Pm8nD5X7S+Gt1CnadXWm61L8cLqkf7t7+a0jIs2y9qn74hW5ffEtgIF+2TRr/3901VWf/QB7XuQp4EvkinzOB60KeouysEMKuvQqYoI4/QU7Cr/tsv73x/3iHH41Cs463d001pvWTb69jW2OYS5tmkD+RwHqidX4CYz9rTrVBn4P2438qOSiH8R0brP68NP8fzznbbmMmfiO/DSaYr6VnvesvOa0eufbzcaK/+k22zq0AfEPyRZqQv/TOJ4+L/VZbuuYQnFYgf1v9a0vTZH7F7HpMnYakNbaN53U66PneKQYYVlv9FfmiVcjnw0H145tDnjLxtBBCt57byeQdROuzc/kE8k7Va3Ci71GT1fczOeSpMa8lX5D+HPrHjpN93Q7LP5Nf5wF4Z9u2Zi99scNqwDu7lm7Hxv/9vs2vpjWmLeS5Yt9Evjp+Z/K3/U3JY67fFkL4MnmKw6GMkx/gJ+hxF9lne8e5oQvR79gmmnYqjbceM6XegxjWsU3mmJvv2efT5R4SXfT7AtHPIPXu9npr1ftK8swzg5rsF/9vNv7fnXwhditA/2k9Jh9yIP83wMJ6HunHyDNztLa1a7bDh8gzhQzqpnGkHYXWsd3G2PChQaw2XKwePvbnIYTjyLMT7UqePeeJ5KktDwUODSGcRJ4VphpG3nGaSL5RvgaHrt9ncghhY/Kwk3nkawI+TR6qeS35Or1HWs9/COGXwJbMkM/dqqpuCCF8gzzD0AEhhA9XVbWq/qW1dV3Z/3S7DqQUBvKFqn8mag2DuZcJDlmpquom8sV+Hw8hPIHcW7UX+Y1yU/LFIDeQp9+biTYlz8jSTbN3qf3CqGYPV78ehqkYp9lJs46DDCvYrF6O9xeZYZponR8nn7sz2d2M9eiM59haeVuaz9Gm9NZre2vWn0CeseOnA9RpWHrWu75QdOP6Yftr7U5gC/LsH9NW56qqbg0h/IL8Xrk78BFWHx/f8l3ytIzrk2ejuZ+xY+l0weWdjf/Xn+Z2mGp3kntdNwFumOyQg/q5+Sn87qLvncgXH76bHBy+B/gZufd0aHn7uJMccPa8qV4Xo3wNjsJ+jA2VWVRfkN/Nxj22jcrp5EB+AXlK2PPJF+O2fpX5zIjqNTQOrSnXexn71vtvfcZAD6Sqqserqvrfqqo+Qu6db/3Ut/9ky55CLx/H9h+3bWuOxe36BhTy3RSna8xfs447d00FhBAWkHunIM/tPSrjqfN65PHFkHtEh/0LzVDVv0RdWz/cMfS/A+7CVlZWf16a/4/nnG2vz2ONsl7edkHtVOtX7x0Ym22k/bXWGoP+jBDCCydRh4n0oLYC9p1DCPPI85FDo7e+Dlb/p374anqPj4d8PUDrPbd9hp7StdpqLjDQ3YoHVVXVY1VVXV5V1fvayu77GTOZvB1cWS+3Ge+4+xG/Bkeh+Xr9UrdEIYTnkYfpTqWJvP7PZ2wI1bvqZWtYzX3ke4kUzUC+QCHfCvnQ+uEq8k0+hqqeeaQVwGzSIUlrKqpR3874wG4bQghPJv8sC3k6tvbg4obG/y/tsY+/GKAew3o+fsjYtKL714FHN4c2/v/6JPc7YVVV3cHYF4nXhBC26pH8LYxdpD2yOo9Tq57tU+ytJoSwHWMB4JVVVTV75Jvt+qYQQq8xpO/qsQ3GpjKcSx6zOl3+OISwRY/tzXr/V9u2f2v8f8wk6tCcAm/Q11orEF+XPE3qPPL75rfb0rUC+90Za8dl9a+Wq6nbtpX/90MIsymYb7bV+6fqgvSqqq5gbOhUp8+YKclba87idVTXVN2N6jXYy1R9JjdHbvT6ZfqwHtuGZdzHWE8L25rc409DCK9k7FqYL/a6LqUUBvIFCSGsG0I4gvwm1Gq793Wa17pPOU8NIbyhHkrTLc1WjM0pfmOHJK3pK6d6lpt+XhtCOKB9ZT0zxamM9aR/usM8z99jbJaNw0MIT+xQzosZmwe4l6E8H3Vvz6n1w/nA6Z3aKYTwKuDo+uHd9J62bjqcWC/XAc4KIazfniCE8FzgH+uHj7L69Jkz2anksaEAn2if/QB+Nx3h5xm7EO2Tze11u7Z+kt6MfAvxNYQQ3kX/ccknMTZ0ZXEIoeuX2brMp4YQFvUpcxDrAp/r9KtECOE1jF1MdgP5Lr9N/8pYx0AMIfxtrx2FEDYIIRzR4dy/tfH/oK+11o2UIP+SCfmLVvtMX62A/6WM9dp36o1vObZR7udDCDv1SEsIYbsQwpsGqvEIVVV1KWPH/Qrg06HHPThCCOuEEN4R8jS0rXXbhhDap/xsz/cyxn4JvbGxfsJ5x+FfG/kODyF0vQ9HCGFuCKF9WN2oXoO9TNVn8nWN/9svGAUghLAfq3csTZXWMT49hLDROPKdwdgc919mbDRD8cNqwDHyM82GIc/H2rIOeVaPLck/a7+R1ccXf6yqqk9MYD/zyHczXB5COI/8k/KN5IuV5pOnHzuMPF4UVp86reU75Plsd6w/lC+gMQXmeL9cTMIPyMHFLuS7MK6o67WIsYvVfs5YAPk7VVXdFUL4Ivnues8Hvh1C+CdyILIx+XbVf0l+83iE3j0/w3w+Pk4eB/oi4M3AtvVFXdeSe7P3rI9vPXIgcVBVVb/tUtZ0OYt80fQe5AvTfhhC+AT52o31yT2cRzE2ddrf9JlvfsaoquoXIYQPk3/52gy4MoRwPLlH9lHykJKjyVO5Avx7VVWdfoJuXbj3bOBdIYTfI3+ZuZF8br2ZfAHWDxibArBTfX5Tf3B+nfwe8S8hhL8g31nxGnKv1cbkc3p38tCPu8i3Ip+MH5DHmv6gbtufkc/H15PfL+aQ35cObh8yVVXVyhDCPuQvzxsBx4YQ9iRPD/d/5OnsNiKPzd6FfJ3Ok+s6P94o55YQwjLyHbHfGUL4MXkoSGve8YfqaVmb+14RQvgpeYhA68O/U4D+ffJ74IaMfTZ2vSFRVVWXhhA+BHyM/L55WQjhS+TX/rI62dPJ1x39GXnY1b9Sxk/5byE/H1uShyHsGkI4g3z34nvJQyieRT6mvcnH/2zyL5+Q7wT6zRDCDeSOpx+Qp8p9mPyc7MrqgV/z3JxM3oFUVfVY/aXqO+T3p7Pqx/8K/IIc9G1Jnvf9TcAnGOusGOVrsJep+kw+B/h78oXGH6k7Ms4lz661Bfn5eTP5eduYif1CMqjvkL9MPAE4I4TwKcbunAtwa6drOqqq+lUI4ULyZ2frS9kVVVUNezrs0ahmwO1l1+Y/8gdSNc6/7wCvHKDsjrdsHsc+VwHHdSn7+eQPvY5529Iu6bS+R717pmf120tvRe4x6HYM1wLP7LGvp5IDiW75ryd/QC2rH18yhOfj2Ma2rbuUtwk56OnVPg+SZxTqdmw969xIt1ujzAMmcS5vSJ69o1edHwP+eqrrMujzPJ7nCvgwuWe+1/Elet/KfGvyB17P87XxeEmPsl7RqHe/v6sn+Byu1h6MTefW6e8R8l19e5X3XHq/3pp/9wFzOpRxQI883V6fJ7ale3WXdBe1pVswwHN0MPmLyCDHdGqH/Eta2wfYV9fzor2t+pTTd5/kiwO/NeBxPQxs2aUuvf4eAQ7rcRzjyjuB8/tl5C8J/fZ1xFS8BtvO5d361PWSOt2yLtun8jP5zeROi27H9gvynZhbz0W312G/7f2O8YnkDoRu9ej6HJK/TDfTHjSZc2cm/dkjP7M9SP4wu4t8s4WrgP+o1pzTeLxuJvcS7U5+03wWeUaKjclvBDcBlwKfraqqfVw5AFVV/az+KXkx+WfoLcgvsmlVVdXNdT2OIN9tbxvyt/VfkHsSPlX1mHWhqqq76zFzR5Cvzn8WuQfwJvJPcCdWVXV/HqnTsx5DfT6qqrqzHj7T6u3Yidzr9VBdt6+Tby1+a/dSpleVp4x7Qz1e+O3kD7lNycH7r8hjkE+pqura7qXMXFVVfTSE8BXyrzS7k9t4LrkX8jLgzKqq/rtPGcvq4VpHkHvnW+fbjeQ7EQ50vtVlXR7yDdz+nNyDvSP5C+A65F7TG8k9qF8nB6iTVlXVu0MIXycHry8hv2fcQW7bf6qqquc0uFVV/Tzk+b/fQH69vpx8jqxPvvh8Gfl6gv8GLqg6XAxdVdWSEMLt5F7Z1jGv256uzTfJc5NDDgC/1yNd666011ZV1Xee8aqqPlOfF61hUc8ndxA8Tn7vvo58flxQVVW/m2rNGPWx/2EI4Y/I70ELycH9huQvLr8kX3f0DeC8qqp+08j+HXLb7k7uQd+a3M5PrvP+gvyryGeqqmofGjOZvOM9xh/U17a8g/zL0ovJ89bfT75A8sfk3uf2oWKt/NP+GuxxLFP2mVxV1ZdCvhHUYnLMsAk5NrmBfL3AqVVV/XaQ961J1uOh+vP6aPKvHNuSz4tBdnwh+b1qU3L7dr1wtzSh/qYiSZIkzTr1hfo3kzv6PlNV1btHXKWh8WJXSZIkzWYHMRbzjve+AzOaPfKSJEmaleoZbq4nD0+9vKqqhX2yFMUx8pIkSZo1QgjPIF8jsCXwt4xNRf13I6vUFLFHXpIkSbNGCOES8sXSTf9aVdUgN3gsij3ykiRJmo0eJs8etIQuN+IrnT3ykiRJUoGctUaSJEkqkIG8JEmSVCADeUmSJKlABvKSJElSgZy1ZoxX/UqSJGk6hclktkdekiRJKpA98m2WL18+rfubPz/fbGzFihXTul9NnG1WHtusTLZbeWyz8thmo7FgwYKhlGOPvCRJklQgA3lJkiSpQAbykiRJUoEM5CVJkqQCGchLkiRJBTKQlyRJkgpkIC9JkiQVaNLzyMcYDwDO7JPs8ZTSnLZ8C4FjgJ2B9YHrgc8BJ6eUVnXZ157AYmAHYA5wNXBaSumsyRyDJEmSVJph3BDqR8BHumz7A2B34MLmyhjj64GvAA8D5wB3A68DPgm8EtivvaAY4yLgZOAu4GzgUWBfYEmM8YUppcVDOBZJkiSpCKGqqikrPMZ4ObnH/fUppfPrdfPIve8bAa9MKV1Zr18fuBh4BfDmlNLSRjlbAz8HHgB2TCktq9dvDFwB/B6wMKV0+SSqW4F3dlV/tll5bLMy2W7lsc3KY5uNRuPOrmEy5UzZGPkY4/bkIP5W4D8bm/YFNgGWtoJ4gJTSw+ShNgCHthV3ILAecEoriK/z3AMcVz88ZJj1lyRJkmayqbzY9d318l/axrzvXi8v6pDnUuBBYGGMcb0B81zYlkaSJEma9YYxRn4NMcYnAm8BHgc+27b5OfXyuvZ8KaWVMcabgBcA2wLXDJDnthjjA8AWMcYNUkoP9qnbVZ3Wp5SAsZ+YpsvcuXNHsl9NnG1WHtusTLZbeWyz8thmZZuqHvkIPAW4MKX0q7ZtG9XLe7vkba1/ygTybNRluyRJkjSrTEmPPHBwvfznCeRtDfofz1W4A+dJKe3YZVMF03+xhxeZlMc2K49tVibbrTy2WXlss9FoXOw6KUPvkY8xPh9YCNwCfK1Dkn695/Pa0o0nz30DVlOSJEkq2lQMrel2kWvLtfVyu/YNMca5wDbASuDGAfNsDmwI3NJvfLwkSZI0Www1kK/ngn8r+SLXf+mS7OJ6uUeHbbsAGwCXpZQeGTDPa9vSSJIkSbPesMfI7wdsDPxHh4tcW84F/hHYP8Z4ctsNoT5Wp/l0W54zgfcBi2KMZ7bdEOoDdZrTh3YU0+yOvReOugrTbs4Z54+6CpIkSUUbdiDfusj1M90SpJTuizEeRA7oL4kxLgXuBvYiTzN5LnBOW56bYoxHAycBV8YYzwEeJd9cagvghEne1VWSJEkqytCG1sQYnwe8iu4Xuf5OSuk8YFfyDaD2AQ4HHgPeC+yfUlpj9pmU0snkYP9q4G3kLw23AweklBYP6zgkSZKkEgytRz6ldA1j00AOkv57wJ+Ocx8XABeMs2qSJEnSrDNVN4SSJEmSNIUM5CVJkqQCGchLkiRJBTKQlyRJkgpkIC9JkiQVyEBekiRJKpCBvCRKGhkfAAAgAElEQVRJklQgA3lJkiSpQAbykiRJUoEM5CVJkqQCGchLkiRJBTKQlyRJkgpkIC9JkiQVyEBekiRJKpCBvCRJklQgA3lJkiSpQAbykiRJUoEM5CVJkqQCGchLkiRJBTKQlyRJkgpkIC9JkiQVyEBekiRJKpCBvCRJklQgA3lJkiSpQAbykiRJUoEM5CVJkqQCGchLkiRJBTKQlyRJkgpkIC9JkiQVyEBekiRJKpCBvCRJklQgA3lJkiSpQAbykiRJUoEM5CVJkqQCGchLkiRJBTKQlyRJkgpkIC9JkiQVyEBekiRJKpCBvCRJklQgA3lJkiSpQAbykiRJUoEM5CVJkqQCGchLkiRJBTKQlyRJkgpkIC9JkiQVaO4wC4sx/gFwBLAQeCpwN/AT4MSU0tfa0i4EjgF2BtYHrgc+B5ycUlrVpfw9gcXADsAc4GrgtJTSWcM8DkmSJGmmG1qPfIzxGOBSYBfgIuAE4AJgY2C3trSvb6T9KnAqsC7wSWBpl/IX1eVtD5wNnAEsAJbEGI8f1nFIkiRJJRhKj3yMcT/g74BvAG9MKd3ftn2dxv/zyEH4KmC3lNKV9foPARcD+8YY908pLW3k2Ro4ntzDv1NKaVm9/qPAFcBRMcavpJQuH8bxSJIkSTPdpHvkY4xPAP4ReBD48/YgHiCl9Fjj4b7AJsDSVhBfp3mYPNQG4NC2Ig4E1gNOaQXxdZ57gOPqh4dM7kgkSZKkcgyjR34hsA1wLnBPjPHPyMNfHgZ+0KGXfPd6eVGHsi4lfyFYGGNcL6X0yAB5LmxLI0mSJM16wwjkX1ov7wB+CLywuTHGeCmwb0rpznrVc+rlde0FpZRWxhhvAl4AbAtcM0Ce22KMDwBbxBg3SCk92KuyMcarOq1PKQEwf/78XtmHbu7coV5vXIzpfp6HqdVmJR/D2sY2K5PtVh7brDy2WdmGcbHr0+vlIcATgT8Cnkzulf86+YLWLzfSb1Qv7+1SXmv9UyaQZ6Mu2yVJkqRZZRjdwXPqZSD3vP9f/fjqGOPe5F70XWOMrxjwYtRQL6tx1GHgPCmlHbtsqgBWrFgxjt1O3tr6DXi6n+dharVZycewtrHNymS7lcc2K49tNhoLFiwYSjnD6JG/p17e2AjiAUgpPUTulQd4Wb3s13s+ry3dePLc17e2kiRJ0iwwjED+2nr5my7bW4H+E9vSb9eeMMY4l3zh7Ergxg776JRnc2BD4JZ+4+MlSZKk2WIYgfyl5MD72THGdTts375eLquXF9fLPTqk3QXYALisMWNNvzyvbUsjSZIkzXqTDuRTSiuAc8jDXj7c3BZj/GPgNeShMa2pI88FVgD7xxh3aqRdH/hY/fDTbbs5E3gEWFTfHKqVZ2PgA/XD0yd7LJIkSVIphjX34XuBlwMfjDHuAvwA2ArYm3wH14NSSr8BSCndF2M8iBzQXxJjXEq+Y+te5GkmzyV/MfidlNJNMcajgZOAK2OM5wCPkm8utQVwgnd1lSRJ0tpkGENrSCn9mhzIfxLYEngP+QZN/wn8QUrpy23pzwN2JQ/L2Qc4HHiM/IVg/5TSGrPPpJROJgf7VwNvAw4GbgcOSCktHsZxSJIkSaUIVTWeWR5ntQpg+fLl07rT+fPnc8feC6d1nzPBnDPOH3UVJsypuspjm5XJdiuPbVYe22w0GtNPhl7p+hlKj7wkSZKk6WUgL0mSJBXIQF6SJEkqkIG8JEmSVCADeUmSJKlABvKSJElSgQzkJUmSpAIZyEuSJEkFMpCXJEmSCmQgL0mSJBXIQF6SJEkqkIG8JEmSVCADeUmSJKlABvKSJElSgQzkJUmSpAIZyEuSJEkFMpCXJEmSCmQgL0mSJBXIQF6SJEkqkIG8JEmSVCADeUmSJKlABvKSJElSgQzkJUmSpAIZyEuSJEkFMpCXJEmSCmQgL0mSJBXIQF6SJEkqkIG8JEmSVCADeUmSJKlABvKSJElSgQzkJUmSpAIZyEuSJEkFMpCXJEmSCmQgL0mSJBXIQF6SJEkqkIG8JEmSVCADeUmSJKlABvKSJElSgQzkJUmSpAIZyEuSJEkFMpCXJEmSCmQgL0mSJBXIQF6SJEkqkIG8JEmSVCADeUmSJKlAc4dRSIxxGbBVl813pJQ265BnIXAMsDOwPnA98Dng5JTSqi772RNYDOwAzAGuBk5LKZ012WOQJEmSSjKUQL52L3Bih/W/bV8RY3w98BXgYeAc4G7gdcAngVcC+3XIswg4GbgLOBt4FNgXWBJjfGFKafFwDkOSJEma+YYZyP8mpXRsv0QxxnnAGcAqYLeU0pX1+g8BFwP7xhj3TyktbeTZGjieHPDvlFJaVq//KHAFcFSM8SsppcuHeDySJEnSjDWKMfL7ApsAS1tBPEBK6WHyUBuAQ9vyHAisB5zSCuLrPPcAx9UPD5mqCkuSJEkzzTB75NeLMb4FeCbwAPBj4NIO4913r5cXdSjjUuBBYGGMcb2U0iMD5LmwLY0kSZI06w0zkN8M+ELbuptijO9IKX27se459fK69gJSSitjjDcBLwC2Ba4ZIM9tMcYHgC1ijBuklB7sVckY41Wd1qeUAJg/f36v7EM3d+4wm6Ac0/08D1OrzUo+hrWNbVYm2608tll5bLOyDWtozZnAq8nB/IbAC4F/BrYGLowxvriRdqN6eW+XslrrnzKBPBt12S5JkiTNKkPpDk4pfaRt1U+BQ2KMvwWOAo4F9h6wuFAvq3FUYeA8KaUdu2yqAFasWDGO3U7e2voNeLqf52FqtVnJx7C2sc3KZLuVxzYrj202GgsWLBhKOVN9sevp9XKXxrp+vefz2tKNJ89946qdJEmSVKipDuR/XS83bKy7tl5u1544xjgX2AZYCdw4YJ7N6/Jv6Tc+XpIkSZotpjqQf0W9bAblF9fLPTqk3wXYALisMWNNvzyvbUsjSZIkzXqTDuRjjC+IMT61w/qtgFPqh2c3Np0LrAD2jzHu1Ei/PvCx+uGn24o7E3gEWFTfHKqVZ2PgA/XD05EkSZLWEsO42HU/4G9ijN8CbgLuB34P+DNgfeBr5LuyApBSui/GeBA5oL8kxriUfMfWvcjTTJ4LnNPcQUrpphjj0cBJwJUxxnOAR8k3l9oCOMG7ukqSJGltMoyhNd8Cvkoe2/7nwHuBXYHvAm8H9kwpPdrMkFI6r05zKbAPcDjwWJ13/5TSGrPPpJROJgf7VwNvAw4GbgcOSCktHsJxSJIkScUIVTWeWR5ntQpg+fLl07rT+fPnc8feC6d1nzPBnDPOH3UVJsypuspjm5XJdiuPbVYe22w0GtNPhl7p+pnqi10lSZIkTQEDeUmSJKlABvKSJElSgQzkJUmSpAIZyEuSJEkFMpCXJEmSCmQgL0mSJBXIQF6SJEkqkIG8JEmSVCADeUmSJKlABvKSJElSgQzkJUmSpAIZyEuSJEkFMpCXJEmSCmQgL0mSJBXIQF6SJEkqkIG8JEmSVCADeUmSJKlABvKSJElSgQzkJUmSpAIZyEuSJEkFMpCXJEmSCmQgL0mSJBXIQF6SJEkqkIG8JEmSVCADeUmSJKlABvKSJElSgQzkJUmSpAIZyEuSJEkFMpCXJEmSCmQgL0mSJBXIQF6SJEkqkIG8JEmSVCADeUmSJKlABvKSJElSgQzkJUmSpAIZyEuSJEkFMpCXJEmSCjR31BXQ2mnVQXuNugoTdscE88054/yh1kOSJK3d7JGXJEmSCmQgL0mSJBXIQF6SJEkqkIG8JEmSVCADeUmSJKlABvKSJElSgaZk+skY41uBz9cPD0opfbZDmj2BxcAOwBzgauC0lNJZPcp9O3AY8HxgFfC/wPEppf8Y7hFIkiRJM9vQe+RjjFsCJwO/7ZFmEXABsD1wNnAGsABYEmM8vkue44ElwOZ1+rOBFwIX1OVJkiRJa42hBvIxxgCcCdwFnN4lzdbA8cDdwE4ppcNSSkcCLwJuAI6KMb6iLc9C4Kh6+4tSSkemlA4DdqzLOb4uV5IkSVorDLtH/j3A7sA7gAe6pDkQWA84JaW0rLUypXQPcFz98JC2PK3Hf1+na+VZBpxal/eOSdZdkiRJKsbQAvkY4/OAjwOfSild2iPp7vXyog7bLmxLM5k8kiRJ0qw1lItdY4xzgS8AvwQ+0Cf5c+rlde0bUkq3xRgfALaIMW6QUnowxrgh8Azgtyml2zqU94t6ud2Adb2q0/qUEgDz588fpJihmTt3Sq431gw03eeWxrReZ7ZBWWy38thm5bHNyjasKPLD5NlnXpVSeqhP2o3q5b1dtt8LbFine3DA9ABPGayqkiRJUvkmHcjHGF9G7oU/IaV0+eSrRKiX1TjzDZQ+pbRjr/wrVqwY524nx2/Aa4/pPrc0pvU6sw3KYruVxzYrj202GgsWLBhKOZMaI98YUnMd8KEBs7V60Dfqsn1evbxvwPT9euwlSZKkWWeyF7s+iTw2/XnAwzHGqvUH/G2d5ox63Yn142vr5Rpj2mOMm5OH1dySUnoQIKX0AHAr8KR6e7tn18s1xtxLkiRJs9Vkh9Y8AvxLl22/Tx43/11y8N4adnMx8Epgj8a6ltc20jRdDLy1znPmgHkkSZKkWWtSgXx9Yeu7Om2LMR5LDuTPSil9trHpTOB9wKIY45mtueRjjBszNuNN+82kTicH8h+MMZ7Xmku+vgnUYeQvFO0BviRJkjRrTfvchymlm2KMRwMnAVfGGM8BHgX2Bbagw0WzKaXLYoyfAN4L/DjGeC6wLvAm4KnA4c2bS0mSJEmz3bDv7DqQlNLJwF7A1cDbgIOB24EDUkqLu+Q5CjigTndwne9q4HUppVOmodqSJEnSjBGqaryzPM5aFcDy5cundafz58/njr0XTus+NRpzzjh/1FVYazm9Wplst/LYZuWxzUajMf1k6JWun5H0yEuSJEmaHAN5SZIkqUAG8pIkSVKBDOQlSZKkAhnIS5IkSQUykJckSZIKZCAvSZIkFchAXpIkSSqQgbwkSZJUIAN5SZIkqUAG8pIkSVKBDOQlSZKkAhnIS5IkSQUykJckSZIKZCAvSZIkFchAXpIkSSqQgbwkSZJUIAN5SZIkqUAG8pIkSVKBDOQlSZKkAhnIS5IkSQUykJckSZIKZCAvSZIkFchAXpIkSSqQgbwkSZJUIAN5SZIkqUAG8pIkSVKBDOQlSZKkAhnIS5IkSQUykJckSZIKZCAvSZIkFchAXpIkSSqQgbwkSZJUIAN5SZIkqUAG8pIkSVKBDOQlSZKkAhnIS5IkSQUykJckSZIKZCAvSZIkFchAXpIkSSqQgbwkSZJUIAN5SZIkqUAG8pIkSVKBDOQlSZKkAhnIS5IkSQWaO4xCYoz/COwEbAfMBx4CbgbOA05JKd3VIc9C4BhgZ2B94Hrgc8DJKaVVXfazJ7AY2AGYA1wNnJZSOmsYxyFJkiSVYlg98kcCGwL/DXwK+CKwEjgW+HGMcctm4hjj64FLgV2ArwKnAusCnwSWdtpBjHERcAGwPXA2cAawAFgSYzx+SMchSZIkFWEoPfLAvJTSw+0rY4x/D3wAeD/wl/W6eeQgfBWwW0rpynr9h4CLgX1jjPunlJY2ytkaOB64G9gppbSsXv9R4ArgqBjjV1JKlw/peCRJkqQZbSg98p2C+Namevnsxrp9gU2Apa0gvlHGMfXDQ9vKORBYjzxMZ1kjzz3AcfXDQyZUeUmSJKlAU32x6+vq5Y8b63avlxd1SH8p8CCwMMa43oB5LmxLI0mSJM16wxpaA0CMcTHwJGAj8sWvryIH8R9vJHtOvbyuPX9KaWWM8SbgBcC2wDUD5LktxvgAsEWMcYOU0oN96nhVp/Up5R8P5s+f3yv70M2dO9Qm0Aw23eeWxrReZ7ZBWWy38thm5bHNyjbsKHIxsGnj8UXAASmlOxvrNqqX93Ypo7X+KePMs2GdrmcgL0mSJM0GQw3kU0qbAcQYNwUWknvi/zfGuGdK6YcDFhPqZTWOXQ+cJ6W0Y5dNFcCKFSvGsdvJ8xvw2mO6zy2Nab3ObIOy2G7lsc3KY5uNxoIFC4ZSzpSMkU8p3ZFS+irwJ8DTgM83Nrd61TdaI2M2ry3dePLcN86qSpIkSUWa0otdU0o3Az8DXhBjbHU9X1svt2tPH2OcC2xDnoP+xsamXnk2Jw+ruaXf+HhJkiRptpjqWWsg37QJ8rzxkOeKB9ijQ9pdgA2Ay1JKjzTW98rz2rY0kiRJ0qw36UA+xvjcGONmHdY/ob4h1NPJgfk99aZzgRXA/jHGnRrp1wc+Vj/8dFtxZwKPAIvqm0O18mxMvuEUwOmTPRZJkiSpFMO42HUP4P/FGC8FbgDuIs9csyt5CsnbgYNaiVNK98UYDyIH9JfEGJeS79i6F3mayXOBc5o7SCndFGM8GjgJuDLGeA7wKPnmUlsAJ3hXV0mSJK1NhjG05hvAZ8gXtb4ROBrYhxycfwR4QUrpZ80MKaXzyIH+pXXaw4HHgPcC+6eU1ph9JqV0MjnYvxp4G3Aw+UvCASmlxUM4DkmSJKkYoarGM8vjrFYBLF++fFp3On/+fO7Ye+G07lOjMeeM80ddhbWW06uVyXYrj21WHttsNBrTT4Ze6fqZjotdJUmSJA2ZgbwkSZJUIAN5SZIkqUAG8pIkSVKBDOQlSZKkAg1jHnlJA1h10F6jrsK0c6YeSZKmjj3ykiRJUoEM5CVJkqQCGchLkiRJBTKQlyRJkgpkIC9JkiQVyEBekiRJKpCBvCRJklQgA3lJkiSpQAbykiRJUoEM5CVJkqQCGchLkiRJBTKQlyRJkgpkIC9JkiQVyEBekiRJKpCBvCRJklQgA3lJkiSpQAbykiRJUoEM5CVJkqQCGchLkiRJBTKQlyRJkgpkIC9JkiQVyEBekiRJKpCBvCRJklQgA3lJkiSpQAbykiRJUoEM5CVJkqQCGchLkiRJBTKQlyRJkgpkIC9JkiQVyEBekiRJKpCBvCRJklQgA3lJkiSpQAbykiRJUoEM5CVJkqQCGchLkiRJBTKQlyRJkgpkIC9JkiQVyEBekiRJKpCBvCRJklSguZMtIMb4NGBv4M+AFwLPAB4FfgKcCZyZUnq8Q76FwDHAzsD6wPXA54CTU0qruuxrT2AxsAMwB7gaOC2ldNZkj0OSJEkqyTB65PcDzgBeDnwfOBH4CrA98FkgxRhDM0OM8fXApcAuwFeBU4F1gU8CSzvtJMa4CLigLvfsep8LgCUxxuOHcBySJElSMYYRyF8H7AVskVL6i5TS+1NKBwLPBX4F7AO8sZU4xjiPHISvAnZLKb0zpXQ08BLgcmDfGOP+zR3EGLcGjgfuBnZKKR2WUjoSeBFwA3BUjPEVQzgWSZIkqQiTDuRTShenlC5oHz6TUrodOL1+uFtj077AJsDSlNKVjfQPk4faABzatpsDgfWAU1JKyxp57gGOqx8eMrkjkSRJksox1Re7PlYvVzbW7V4vL+qQ/lLgQWBhjHG9AfNc2JZGkiRJmvUmfbFrNzHGucDb6ofNAPw59fK69jwppZUxxpuAFwDbAtcMkOe2GOMDwBYxxg1SSg/2qddVndanlACYP39+r+xDN3fulDWBNHLT/XrqpvU6myn10WBst/LYZuWxzco2lT3yHydfmPq1lNLXG+s3qpf3dsnXWv+UCeTZqMt2SZIkaVaZku7gGON7gKOAnwNvHWf21gw31VTkSSnt2GVTBbBixYpx7Hby/Aas2Wy6X0/dtF5nM6U+GoztVh7brDy22WgsWLBgKOUMvUc+xngY8CngZ8AfppTubkvSr/d8Xlu68eS5bxxVlSRJkoo11EA+xngEcArwU3IQf3uHZNfWy+065J8LbEO+OPbGAfNsDmwI3NJvfLwkSZI0WwwtkI8x/jX5hk4/Igfxv+6S9OJ6uUeHbbsAGwCXpZQeGTDPa9vSSJIkSbPeUAL5GOOHyBe3XgW8OqXUa6DVucAKYP8Y406NMtYHPlY//HRbnjOBR4BF9c2hWnk2Bj5QPzwdSZIkaS0Rqmo815SuKcb4dmAJ+U6tJ9N5ZpllKaUljTxvIAf0DwNLyXds3Ys8zeS5QEwprVaxGOPhwEnAXcA5wKPkm0ttAZyQUlo8qQOpL3Zdvnz5JIsZn/nz53PH3gundZ/SdJlzxvmjrgLgxVylst3KY5uVxzYbjcbFrqFXun6GMWvNNvVyDnBElzTfJgf7AKSUzosx7gp8ENgHWB+4HngvcFJ7EF/nOTnGuAxYTJ6f/gnkC2qPSSmdNYTjkCRJkoox6R75WcQeeWnI7JHXZNhu5bHNymObjcaweuSn8oZQkiRJkqaIgbwkSZJUIAN5SZIkqUAG8pIkSVKBDOQlSZKkAg1j+klJ6mjVQXuNugoA3DGN+5opM/VIkmY/e+QlSZKkAhnIS5IkSQUykJckSZIKZCAvSZIkFchAXpIkSSqQgbwkSZJUIAN5SZIkqUAG8pIkSVKBDOQlSZKkAhnIS5IkSQUykJckSZIKZCAvSZIkFchAXpIkSSqQgbwkSZJUIAN5SZIkqUAG8pIkSVKBDOQlSZKkAhnIS5IkSQUykJckSZIKZCAvSZIkFchAXpIkSSqQgbwkSZJUIAN5SZIkqUAG8pIkSVKBDOQlSZKkAhnIS5IkSQUykJckSZIKZCAvSZIkFchAXpIkSSqQgbwkSZJUIAN5SZIkqUAG8pIkSVKBDOQlSZKkAhnIS5IkSQUykJckSZIKZCAvSZIkFchAXpIkSSqQgbwkSZJUIAN5SZIkqUBzh1FIjHFfYFfgJcCLgScDX0wpvaVHnoXAMcDOwPrA9cDngJNTSqu65NkTWAzsAMwBrgZOSymdNYzjkCRJkkoxrB75Y4BF5ED+1n6JY4yvBy4FdgG+CpwKrAt8EljaJc8i4AJge+Bs4AxgAbAkxnj85A9BkiRJKsewAvkjge2AecChvRLGGOeRg/BVwG4ppXemlI4mfwm4HNg3xrh/W56tgeOBu4GdUkqHpZSOBF4E3AAcFWN8xZCORZIkSZrxhhLIp5S+lVL6RUqpGiD5vsAmwNKU0pWNMh4m9+zDml8GDgTWA05JKS1r5LkHOK5+eMgEqy9JkiQVZxQXu+5eLy/qsO1S4EFgYYxxvQHzXNiWRpIkSZr1hnKx6zg9p15e174hpbQyxngT8AJgW+CaAfLcFmN8ANgixrhBSunBXjuPMV7VaX1KCYD58+cPcgxDM3fuKJpA0lSZ7veQ2az1/uhzWg7brDy2WdlG0SO/Ub28t8v21vqnTCDPRl22S5IkSbPKTOwODvVykPH2486TUtqxy6YKYMWKFePY7eT5DViaXab7PWQ2a70/+pyWwzYrj202GgsWLBhKOaPoke/Xez6vLd148tw3iXpJkiRJxRhFIH9tvdyufUOMcS6wDbASuHHAPJsDGwK39BsfL0mSJM0WowjkL66Xe3TYtguwAXBZSumRAfO8ti2NJEmSNOuNIpA/F1gB7B9j3Km1Msa4PvCx+uGn2/KcCTwCLKpvDtXKszHwgfrh6VNVYUmSJGmmCVU1nmtKO4sxvgF4Q/1wM+A15KEx36nXrUgpLW5Lfy7wMLCUfMfWvcjTTJ4LxPabS8UYDwdOAu4CzgEeJd9cagvghGb5E1QBLF++fJLFjM/8+fO5Y++F07pPSVNnzhnnj7oKs4YX4ZXHNiuPbTYajYtdQ690/QyrR/4lwNvrv9fU67ZtrNu3mTildB6wK/kGUPsAhwOPAe8F9u90h9iU0snkYP9q4G3AwcDtwAFDCOIlSZKkogylR36WsEde0qTZIz889hSWxzYrj202GjOtR16SJEnSNDKQlyRJkgpkIC9JkiQVyEBekiRJKtDcUVdAkmaTVQftNeoqTDsv8JWk0bBHXpIkSSqQgbwkSZJUIAN5SZIkqUAG8pIkSVKBDOQlSZKkAhnIS5IkSQUykJckSZIKZCAvSZIkFchAXpIkSSqQgbwkSZJUIAN5SZIkqUAG8pIkSVKBDOQlSZKkAhnIS5IkSQUykJckSZIKZCAvSZIkFchAXpIkSSqQgbwkSZJUIAN5SZIkqUAG8pIkSVKBDOQlSZKkAhnIS5IkSQUykJckSZIKZCAvSZIkFchAXpIkSSqQgbwkSZJUIAN5SZIkqUAG8pIkSVKBDOQlSZKkAhnIS5IkSQUykJckSZIKNHfUFZAklW3VQXtNSbl3TEmpwzHnjPNHXQVJskdekiRJKpGBvCRJklQgA3lJkiSpQAbykiRJUoEM5CVJkqQCGchLkiRJBTKQlyRJkgpkIC9JkiQVqKgbQsUYtwA+CuwBPA24DTgP+EhK6Z5R1k2SJEmaTsUE8jHG3wMuA54O/Dvwc+BlwF8Be8QYX5lSumuEVZQkrSWm6m62M5V3spVmpmICeeA0chD/npTSya2VMcZPAEcCfw8cMqK6SZIkSdOqiEA+xrgt8CfAMuDUts1/CxwMvDXGeFRK6YFprp4kSbPaoL9A3DHF9ZhO/gqhEhQRyAO718v/Sik93tyQUro/xvg9cqC/M/DN6a6cJEmaXdaW4VPNL1//v717D7aqLOM4/lXzBgmWWsrgeAW1i2OKklCCMeJlxEvikzXeMHVIDUelpvAGTpaOkQlqamNeM3jEvEwGmSmSmqVNOqUoimLpyFRe8K6p9Mfzbthu9j57n3ME1rvW7zNzZp299nrXu97znLPPs971rnfp5CU/uSTy26XlghbvP0kk8oNpk8ib2V+brXd3AAYMGNCzI+yFzW9/aJXXKSIiIiJ5y2X6yf5puaTF+7X1G66CYxERERERWe1y6ZFvZ420XNpuQ3ffZSUfS7fUrhAU7bikNcUsP4pZnhS3/Chm+VHM8pZLj3ytx71/i/f7NWwnIiIiIlJquSTyT6Tl4BbvD0rLVmPoRURERERKJZdE/u60HG1mHzpmM9sAGA68BTywqg9MRERERGR1yCKRd/eFwB3AlsCJDW9PAfoC12oOeRERERGpimWVFMIAAAp8SURBVJxudj0BuB+YZmajgPnAUGBPYkjN6avx2EREREREVqk1li5tO9FLYZjZ5sA5wD7ARsALwC3AFHd/aXUem4iIiIjIqpRVIi8iIiIiIiGLMfIiIiIiIvJhSuRFRERERDKkRF5EREREJENK5EVEREREMqREXkREREQkQ0rkRUREREQylNMDoVYaMxtI6/npX+7Gfj4JnAUcBGwGvAjMAc5y9+c+qrrN7DPAZGAk0A94FpgBnOfub7UoMww4A/gisB7wFPALYLq7v99pG4ukzHEzs0HAV4G9gUHAp4GXgQeAn7r73Z22r0jKHLMW5a8EjkkvB7n7U521sDiqEDMzWwM4EhgH7AisDywGHgTOcPcFnbazCMoeMzNbFzgWOArYmvif9i/g98BUd3+20zYWRU4xM7NvArsBOwGfJ/5eznX3M9oc2/7AROALwFrAo8Cl7n5Np+2TFVV+Hnkz24Z4YuyngFuBx4lf0D2BJ4Dh7v5iB/vZKO1nMHAX8Q9ge+BA4N/A7u7+dG/rNrOhaf9rA7OID6+vAEOA+4BR7v5OQ5kDgZuAt4GZwEvAGGA7YJa7H9qufUVT9riZ2Qzga8BjwL1EzLYDDiA+AE9292ntf1LFUfaYNTnOMcBtwOvAx8kwka9CzMxsPeBGYP+03zuB14ABwJeBCe7+m3ZtLIqyx8zMPgbMBYan/d8JvAPsCuwBLAGGuftj7dpYFBnG7BWgP9G59BKwDW0SeTM7CZhOnFjMBN4FxgIDiZOvie3aJ82pRx4uJX6BJ7j79NpKM/sJcApwLjC+g/38kPjjudDdT63bzwTgolTPPr2p28zWAq4C+gAHuvttaf2agAOHpHLn1ZXpB/wceB8Y6e4PpfVnEn/oY83sMHef0UEbi6TUcSN6UM5397/VV2xmI4hepwvM7EZ3f6GDNhZF2WNGXflNiL+7mcCmwIgO2lVEVYjZVCKJ/xHR+/5B/ZtmtnYH7SuSssfsYCKJ/wMwuj5eZjaF6I2eyPIrYTnIJmbJYcB8d3/WzI4mYtiSmW0J/JhI+oe4+6K0/hziZOM0M7vJ3f/UQRulQaXHyJvZ1sBoYBFwScPbZwNvAEeYWd82++kLHJG2P7vh7YvT/vdO9fWm7hHADsC82gceQPog+256OT5dJq4ZC2wCzKgl8anM28RQG4BvddW+oqlC3Nz96sYkPq2/h+iNWgcY1lX7iqQKMWtwRVqe2FV7iqwKMUu9keOJZOL0xiQ+lf9fV+0rkirEjBhKA3B7k3jdmpabdNW+IskwZrj7nG4OXzoGWBe4uJbEp/28TJx8QGcnKtJEpRN54vIdwB2NHwju/hpxWa8PMa68K7sTY8TuS+Xq9/MBcEd6uWcv666VmdN4AOly2QJgC5Z/0HVZBpgHvAkMS2MOc1GFuHWllli81+H2RVCZmKUeqoOA8Z1cDi+wKsTs68T/wWuAfmZ2uJl938yON7Nt27SriKoQs0fTct/Uc19v/7S8s3mzCim3mPVEV3nI7IZtpJuqnshvl5atbmR6Mi0Hr4T9rPYy7v4e8AwxxKrTJLIIqhC3psxsC2AUcQI2r932BVKJmKX4XARc7+63tCibiyrEbNe07A8sBK4jeggvBxaY2SVp+EcuqhCz24FfA3sBfzezi8zsAjO7i7jKPJ3ogc5FbjHria7ykBeInv+BZtanl/VUUtUT+f5puaTF+7X1G66E/RS5TNFVIW4rSFdNfklcopzs3ZjJoABKH7PUO3gNcXPrhBblclL6mBFjgyFm7HiImIFjA+JkeSFwAnBmi/0VUelj5u5LiSGjk4kEcQIxJn5PonPjBs9rJrbcYtYTndbTv8X70oWqJ/Lt1Mbl9XZqn57sp8hliq50cUu9gtcRN3nNJG4cKpMyxOwUYszvcZmdZPVUGWJW621/ATjY3f/h7q+7+11EsvgBcKqZrdONeoos+5ilWYZmEsn7icQUi/2B/YhhOPPSTG1lkVvMeqKMecgqU/VZa9qdBfZr2O6j3E+RyxRdFeK2TErirwcOJWZyODz1SuWk1DGzmPf/XOAqd/9tizK5KXXMktoJ1xxvmK/c3R8xs2eIqfV2AB5psd8iqULMvkd8Fp7s7pfXrZ9tZmOBh4nhbbeSh9xi1hNLgI1TPc3uG6rV82ov66mkqvfIP5GWrcZ/DUrLdg8D6cl+VnuZNB/vVsRNk083vl9gVYgbsCxGvyKm+7oB+Ea6tyE3ZY/ZZ4khT+PMbGn9F8unnnwyrTuoxT6Lpuwxqy/zSosytUR//RbvF00VYla7oXWFh+K5+yPEFIdbpDnVc5BbzHqiqzxkM6Av8Jy7v9nLeiqp6ol87YNgdOPd72a2ATGM4S3iaZpdeSBtNzyVq9/PmsT0TvX19bTuu9KycR7Y2jRSg4kn4j3dSRni4Rl9gPsbH5JScFWIG+ly/iyi9+la4IjMxn7WK3vMFgFXtvhanLa5Mb1e1KaNRVH2mEHMRQ7wuSZl1mV5IrOoSbuKqAoxq82wtsIUkylmtd7dd5s1rIByi1lPdJWH7NuwjXRTpRN5d19ITMm0JSvO9zyFOEu81t3fqK00s+3NbPuG/bxOjF/uS9yAU++ktP/f1T9RrSd1A/cA84E9zOyAumNaEzg/vbysYdjFLOC/wGFmNqSuzHrAD9LLn5GRKsQt/UO6mXgi35XAuGZzXOei7DFz94fd/dhmXyzvjZqU1j3c5EdUOGWPWTKbSBL3NrO9Guo5kxgKcI+7LyYDFYnZH9NyUpNpkycTQ4YfbJyCsagyjFlPXEU8ffcki4dD1drxCWBSenlZL+uorKqPkYeYleB+YJqZjSI+VIYSd8AvAE5v2H5+WjY+CGYSMJK4MWon4C/EuMrao5GbPRimW3W7+/tmNo44c51lZrOAfxIzLNQeZ31hQ5lXzew4IqGfa2YziEuPBxB3/M8ibhzKTanjRnyo7UechD0PnGVmjccx193nNjm+oip7zMqo1DFz93fN7CgimZltZjcTPcC7Elcs/wMc3/rHU0iljhlxL8qYtM3jZjaH1BMN7Ja+P7n5j6awsokZgJkdC3wpvaw9b2GMmQ1M3z/u7suexuvuz5jZd4BpwENmNpO4YjIWGAhMdT3Vtccq3SMPy85IhwBXE7+8pxE3N00Ddu/0gS5pu91TuW3TfoYSZ6K7pHp6Xbe7/5n4J3MrcansFKLX6Bxgr2ZDZNJ81iOIqbkOAb5NPFToVOCwDG+crELctkrLjYlHjp/d5GtkJ20sigrErHSqEDN3vzfVcxPxOTmBeK7GFcDO7t7b8cGrVNlj5u7PAzsDU4G3gXFEj/Omqd6dc0sKc4sZkcQflb6Gp3U71q1bYQiNu08nOhAfBY4kTpAXA0e7+8RO2ifNrbF0aXY5nIiIiIhI5VW+R15EREREJEdK5EVEREREMqREXkREREQkQ0rkRUREREQypEReRERERCRDSuRFRERERDKkRF5EREREJENK5EVEREREMqREXkREREQkQ0rkRUREREQypEReRERERCRDSuRFRERERDKkRF5EREREJENK5EVEREREMqREXkREREQkQ0rkRUREREQypEReRERERCRD/we+OxJWFa0TUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 265,
       "width": 377
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(list(node_centrality.values()))\n",
    "plt.title('Distribution of node betwenness centrality')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.cluster import hierarchy\n",
    "from sklearn.metrics import pairwise_distances\n",
    "from scipy.spatial.distance import pdist, cosine, jaccard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 245,
       "width": 378
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "A = nx.adjacency_matrix(G_bottom).toarray()\n",
    "dist_out = pdist(A, metric='cosine')\n",
    "Z = hierarchy.linkage(dist_out, method='average')\n",
    "hierarchy.dendrogram(Z, leaf_rotation=0, labels=list(G_bottom.nodes()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters = hierarchy.fcluster(Z, 100, criterion='maxclust')\n",
    "dict_cluster = dict(list(zip(G_bottom.nodes(), clusters)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29 : 14\n",
      "37 : 50\n",
      "54 : 66\n",
      "55 : 20\n",
      "56 : 82\n",
      "57 : 23\n",
      "59 : 12\n",
      "60 : 1236\n",
      "61 : 19\n",
      "68 : 22\n"
     ]
    }
   ],
   "source": [
    "for each in np.unique(list(dict_cluster.values())):\n",
    "    if list(dict_cluster.values()).count(each) > 10:\n",
    "        print(each, ':', list(dict_cluster.values()).count(each))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our network is so strongly connected, that we have just ten clusters with more than 10 elements, and one cluster (number 60) with 1236 elements within.\n",
    "\n",
    "Let's visualize one cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [],
   "source": [
    "filter_list = [y for x,y in zip(dict_cluster.values(), dict_cluster.keys()) if x == 68]\n",
    "cluster_net = [x for x in list(G_bottom.edges()) if x[0] in filter_list]\n",
    "cluster_G = nx.Graph()\n",
    "cluster_G.add_edges_from(cluster_net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 318,
       "width": 483
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = dict(cluster_G.degree)\n",
    "nx.draw_spring(cluster_G, with_labels=False, \n",
    "               node_size=[v*2 for v in d.values()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's see which are those movies that have larger degrees in our network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [],
   "source": [
    "well_connected = [y for x,y in zip(d.values(),d.keys()) if x>10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "metadata": {},
   "outputs": [],
   "source": [
    "items['year'] = [x[0] for x in [x[-1].split(')') for x in [x for x in items.movie_title.str.split('(').values]]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {},
   "outputs": [],
   "source": [
    "well_connected_df = items[items.movie_id.isin(well_connected)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg = np.mean([int(x) for x in items[items.year.str.isnumeric()].year.values])\n",
    "items.year.replace('unknown',avg,inplace=True)\n",
    "items.year.replace('V',avg,inplace=True)\n",
    "items.year = items.year.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 265,
       "width": 387
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(items.year)\n",
    "plt.title('Release year distribution | COMPLETE DATASET')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 265,
       "width": 374
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(well_connected_df.year)\n",
    "plt.title('Release year distribution | CLUSTER MOVIES')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At first sight, it seems that one of the criteria used by our hierarchical clustering to find this specific cluster, is the release data, since the year distribution it's completely different to the distribution we had in the original dataset. However, we still have a lot of movies around the 90's, so there are probably more underlying reasons around our cluster simmilarity that we're just not seeing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Our datasets are strongly connected. Therefore the similarity analysis and the technique used for predicting in our system are gonna be very important factors to make good recommendations. At first sight our users dataset seems to be more tightly connected than our movies dataset. That's a good insight to maybe start thinking about working based on item-item similarity.\n",
    "\n",
    "\n",
    "* Also, the hierarchical decomposition of the movies network allowed us to find some clusters, so probably working with SVM it's gonna be a good idea to find underlying latent features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating our recommendation system"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Collaborative filtering model from scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First we need to crate a user-item matrix to be used \n",
    "# to calculate the similarity between users and items.\n",
    "\n",
    "n_users = ratings.user_id.unique().shape[0]\n",
    "n_items = ratings.movie_id.unique().shape[0]\n",
    "\n",
    "# We'll create a matrix full of zeros\n",
    "# And then we'll go by everyline in ratings, filling the user row\n",
    "# and the movie column, with the rating.\n",
    "\n",
    "data_matrix = np.zeros((n_users, n_items))\n",
    "for line in ratings.itertuples():\n",
    "    data_matrix[line[1]-1, line[2]-1] = line[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 701,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, we will calculate the similarity. \n",
    "# Regular Cosine Similarity by the definition reflects differences \n",
    "# in direction, but not the location. Therefore it does not consider \n",
    "# the difference in ratings of users. \n",
    "# Adjusted cosine similarity offsets this drawback by subtracting \n",
    "# the average rating.\n",
    "# So we'll create a function to find the Adjusted Cosine Similarity:\n",
    "\n",
    "from scipy import spatial\n",
    "\n",
    "def adjusted_cos_distance_matrix(size, matrix, row_column):\n",
    "    distances = np.zeros((size,size))\n",
    "    if row_column == 0:\n",
    "        M_u = matrix.mean(axis=1)\n",
    "        m_sub = matrix - M_u[:,None]\n",
    "    if row_column == 1:\n",
    "        M_u = matrix.T.mean(axis=1)\n",
    "        m_sub = matrix.T - M_u[:,None]\n",
    "    for first in range(0,size):\n",
    "        for sec in range(0,size):\n",
    "            distance = spatial.distance.cosine(m_sub[first],m_sub[sec])\n",
    "            distances[first,sec] = distance\n",
    "    return distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 702,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_similarity = adjusted_cos_distance_matrix(n_users,data_matrix,0)\n",
    "item_similarity = adjusted_cos_distance_matrix(n_items,data_matrix,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 704,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(ratings, similarity, type='user'):\n",
    "    if type == 'user':\n",
    "        mean_user_rating = ratings.mean(axis=1)\n",
    "        #We use np.newaxis so that mean_user_rating \n",
    "        # has same format as ratings. Find more about np.newaxis here:\n",
    "        # https://stackoverflow.com/questions/29241056/how-does-numpy-newaxis-work-and-when-to-use-it\n",
    "        ratings_diff = (ratings - mean_user_rating[:, np.newaxis])\n",
    "        pred = mean_user_rating[:, np.newaxis] + similarity.dot(ratings_diff) / np.array([np.abs(similarity).sum(axis=1)]).T\n",
    "    elif type == 'item':\n",
    "        pred = ratings.dot(similarity) / np.array([np.abs(similarity).sum(axis=1)])\n",
    "    return pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 709,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_prediction = predict(data_matrix, user_similarity, type='user')\n",
    "item_prediction = predict(data_matrix, item_similarity, type='item')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(943, 1682)"
      ]
     },
     "execution_count": 761,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_prediction.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 757,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(943, 1682)"
      ]
     },
     "execution_count": 757,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_prediction.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 767,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The following function uses the predictions previously created,\n",
    "# finds the movies not rated by the user (and therefore not seen),\n",
    "# a passes the top 5 ranked movies according to our predictions.\n",
    "\n",
    "def finding_movies_for_user(user, ratings, preds):\n",
    "    recos = {'movie_id': [],'rating': []}\n",
    "    user_array = preds[user]\n",
    "    orig_rat = ratings[user]\n",
    "    for each in range(0,len(orig_rat)):\n",
    "        if orig_rat[each] == 0:\n",
    "            recos['movie_id'].append(each+1)\n",
    "            recos['rating'].append(user_array[each])\n",
    "    recos_df = pd.DataFrame(recos)\n",
    "    final_recos_df = pd.merge(recos_df, items[['movie_id','movie_title']],\n",
    "                              how='left', on='movie_id')\n",
    "    return final_recos_df.sort_values(by='rating',ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 768,
   "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>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>movie_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>50</td>\n",
       "      <td>2.766617</td>\n",
       "      <td>Star Wars (1977)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>181</td>\n",
       "      <td>2.234739</td>\n",
       "      <td>Return of the Jedi (1983)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>127</td>\n",
       "      <td>1.982965</td>\n",
       "      <td>Godfather, The (1972)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.950807</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>288</td>\n",
       "      <td>1.928047</td>\n",
       "      <td>Scream (1996)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     movie_id    rating                movie_title\n",
       "34         50  2.766617           Star Wars (1977)\n",
       "134       181  2.234739  Return of the Jedi (1983)\n",
       "86        127  1.982965      Godfather, The (1972)\n",
       "0           1  1.950807           Toy Story (1995)\n",
       "213       288  1.928047              Scream (1996)"
      ]
     },
     "execution_count": 768,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finding_movies_for_user(10, data_matrix, user_prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building a popularity and collaborative filtering model using Turicreate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As almost always, we also have a library which generates all these recommendations automatically. Use 'pip install turicreate' to follow the next steps to create a recommendation engine using turicreate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import turicreate as tc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The dataset has already divided into train and test our data. Let's load that:\n",
    "\n",
    "r_cols = ['user_id', 'movie_id', 'rating', 'unix_timestamp']\n",
    "\n",
    "ratings_train = pd.read_csv('/Users/gonzaloferreiro/Documents/GitHub/Recommendations_system_with_MovieLens_dataset/ml-100k/ua.base', \n",
    "                            sep='\\t', names=r_cols, encoding='latin-1')\n",
    "ratings_test = pd.read_csv('/Users/gonzaloferreiro/Documents/GitHub/Recommendations_system_with_MovieLens_dataset/ml-100k/ua.test', \n",
    "                           sep='\\t', names=r_cols, encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = tc.SFrame(ratings_train)\n",
    "test_data = tc.SFrame(ratings_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Warning: Ignoring columns unix_timestamp;</pre>"
      ],
      "text/plain": [
       "Warning: Ignoring columns unix_timestamp;"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    To use these columns in scoring predictions, use a model that allows the use of additional features.</pre>"
      ],
      "text/plain": [
       "    To use these columns in scoring predictions, use a model that allows the use of additional features."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 90570 observations with 943 users and 1680 items.</pre>"
      ],
      "text/plain": [
       "    Data has 90570 observations with 943 users and 1680 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.147107s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.147107s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>90570 observations to process; with 1680 unique items.</pre>"
      ],
      "text/plain": [
       "90570 observations to process; with 1680 unique items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "popularity_model = tc.popularity_recommender.create(train_data, user_id='user_id',\n",
    "                                                    item_id='movie_id', target='rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+----------+-------+------+\n",
      "| user_id | movie_id | score | rank |\n",
      "+---------+----------+-------+------+\n",
      "|    1    |   1201   |  5.0  |  1   |\n",
      "|    1    |   1189   |  5.0  |  2   |\n",
      "|    1    |   1122   |  5.0  |  3   |\n",
      "|    1    |   814    |  5.0  |  4   |\n",
      "|    10   |   1201   |  5.0  |  1   |\n",
      "|    10   |   1189   |  5.0  |  2   |\n",
      "|    10   |   1122   |  5.0  |  3   |\n",
      "|    10   |   814    |  5.0  |  4   |\n",
      "|   100   |   1201   |  5.0  |  1   |\n",
      "|   100   |   1189   |  5.0  |  2   |\n",
      "|   100   |   1122   |  5.0  |  3   |\n",
      "|   100   |   814    |  5.0  |  4   |\n",
      "|   500   |   1201   |  5.0  |  1   |\n",
      "|   500   |   1189   |  5.0  |  2   |\n",
      "|   500   |   1122   |  5.0  |  3   |\n",
      "|   500   |   814    |  5.0  |  4   |\n",
      "+---------+----------+-------+------+\n",
      "[16 rows x 4 columns]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "popularity_recomm = popularity_model.recommend(users=[1,10,100,500],k=4)\n",
    "popularity_recomm.print_rows(num_rows=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are recommending the same for every user, since we're just doing it based on popularity, hence we're recommending the most popular items rated with 5. We'll now build a colaborative filtering model to correct that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Warning: Ignoring columns unix_timestamp;</pre>"
      ],
      "text/plain": [
       "Warning: Ignoring columns unix_timestamp;"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    To use these columns in scoring predictions, use a model that allows the use of additional features.</pre>"
      ],
      "text/plain": [
       "    To use these columns in scoring predictions, use a model that allows the use of additional features."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 90570 observations with 943 users and 1680 items.</pre>"
      ],
      "text/plain": [
       "    Data has 90570 observations with 943 users and 1680 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.167833s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.167833s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training model from provided data.</pre>"
      ],
      "text/plain": [
       "Training model from provided data."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Gathering per-item and per-user statistics.</pre>"
      ],
      "text/plain": [
       "Gathering per-item and per-user statistics."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+------------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Elapsed Time (Item Statistics) | % Complete |</pre>"
      ],
      "text/plain": [
       "| Elapsed Time (Item Statistics) | % Complete |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+------------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6.182ms                        | 100        |</pre>"
      ],
      "text/plain": [
       "| 6.182ms                        | 100        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+------------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Setting up lookup tables.</pre>"
      ],
      "text/plain": [
       "Setting up lookup tables."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Processing data in one pass using dense lookup tables.</pre>"
      ],
      "text/plain": [
       "Processing data in one pass using dense lookup tables."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-------------------------------------+------------------+-----------------+</pre>"
      ],
      "text/plain": [
       "+-------------------------------------+------------------+-----------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Elapsed Time (Constructing Lookups) | Total % Complete | Items Processed |</pre>"
      ],
      "text/plain": [
       "| Elapsed Time (Constructing Lookups) | Total % Complete | Items Processed |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-------------------------------------+------------------+-----------------+</pre>"
      ],
      "text/plain": [
       "+-------------------------------------+------------------+-----------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 33.696ms                            | 0.25             | 6               |</pre>"
      ],
      "text/plain": [
       "| 33.696ms                            | 0.25             | 6               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 259.975ms                           | 100              | 1680            |</pre>"
      ],
      "text/plain": [
       "| 259.975ms                           | 100              | 1680            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-------------------------------------+------------------+-----------------+</pre>"
      ],
      "text/plain": [
       "+-------------------------------------+------------------+-----------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Finalizing lookup tables.</pre>"
      ],
      "text/plain": [
       "Finalizing lookup tables."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Generating candidate set for working with new users.</pre>"
      ],
      "text/plain": [
       "Generating candidate set for working with new users."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished training in 1.32187s</pre>"
      ],
      "text/plain": [
       "Finished training in 1.32187s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "items_sim_model = tc.item_similarity_recommender.create(train_data, user_id='user_id',\n",
    "                                                        item_id='movie_id', target='rating',\n",
    "                                                        similarity_type='cosine')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+----------+--------------------+------+\n",
      "| user_id | movie_id |       score        | rank |\n",
      "+---------+----------+--------------------+------+\n",
      "|    1    |   423    | 0.9874823143464009 |  1   |\n",
      "|    1    |   202    | 0.9495907992352056 |  2   |\n",
      "|    1    |   655    | 0.7977067143407487 |  3   |\n",
      "|    1    |   403    | 0.765623665037956  |  4   |\n",
      "|    10   |   204    | 1.3055568549824856 |  1   |\n",
      "|    10   |   423    | 1.2247030108139432 |  2   |\n",
      "|    10   |   172    | 1.2214704402561845 |  3   |\n",
      "|    10   |    89    | 1.0962715104393574 |  4   |\n",
      "|   100   |   302    | 0.7020295469128356 |  1   |\n",
      "|   100   |   307    | 0.6980765315951133 |  2   |\n",
      "|   100   |   288    | 0.631126876996488  |  3   |\n",
      "|   100   |   750    | 0.5718726126515136 |  4   |\n",
      "|   500   |   195    | 0.6959708718366401 |  1   |\n",
      "|   500   |    79    | 0.6801476062730302 |  2   |\n",
      "|   500   |   173    | 0.6755023279855418 |  3   |\n",
      "|   500   |    22    | 0.6326416647711466 |  4   |\n",
      "+---------+----------+--------------------+------+\n",
      "[16 rows x 4 columns]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "item_sim_recomm = items_sim_model.recommend(users=[1,10,100,500],k=4)\n",
    "item_sim_recomm.print_rows(num_rows=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now let's use surprise's SVM model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part of the project we will make use of the surprise package, a package dedicated to recommendation systems. You can install with: conda install -c conda-forge scikit-surprise\n",
    "\n",
    "After importing the package the next thing will be to instantiate the reader function, which serves to specify which columns are referring to user, items and ratings as well as the rating scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import surprise as sur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = sur.Reader(rating_scale=(1,5))\n",
    "\n",
    "data_1 = sur.Dataset.load_from_df(ratings[['user_id','movie_id','rating']], reader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way to handle the scalability and sparsity issue created by CF is to leverage a latent factor model to capture the similarity between users and items. In short, we are talking about applying PCA on our matrix of users and ratings. Just as in any other case, PCA can reduce our original dataset into a much smaller set of features which describes the variance in our data. And often, the dimensions itself represent features that humans have learned to associate with whatever we’re trying to recommend. For example, in movies, how romantic it is, how funny it is, etc. PCA will find latent features in the data, that differentiate items between them. PCA won’t know what all these features represent, but it will find them anyway.\n",
    "\n",
    "When using SVM or Singular Value Decomposition, what we’re doing is applying PCA on both the users and the items, to find less sparse matrices to predict ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.9168\n",
      "0.9167802882204997\n"
     ]
    }
   ],
   "source": [
    "# Instantiate the algorithm\n",
    "\n",
    "algo = sur.SVD(random_state = 1,\n",
    "              reg_all=0.2, # Using some regularization (the same for all)\n",
    "              n_epochs=20, # The number of epochs for stochastic gradient descendent search\n",
    "              n_factors=100) # Number of latent factors to retain\n",
    "\n",
    "# We have to buld a training set from the data\n",
    "trainset_full = data_1.build_full_trainset()\n",
    "\n",
    "# Now let's fit the model\n",
    "algo.fit(trainset_full)\n",
    "\n",
    "# We prepare a test set from the training set\n",
    "trainsetfull_build = trainset_full.build_testset()\n",
    "\n",
    "# Obtain the predictions\n",
    "predictions_full = algo.test(trainsetfull_build)\n",
    "\n",
    "# And finally, we shall evaluate the predictions\n",
    "print(sur.accuracy.rmse(predictions_full))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.9168\n",
      "MAE:  0.7305\n"
     ]
    }
   ],
   "source": [
    "# Let's evaluate the model\n",
    "\n",
    "rmse = sur.accuracy.rmse(predictions_full)\n",
    "mae = sur.accuracy.mae(predictions_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# And finally let's put the predictions in a dataframe\n",
    "\n",
    "df_pred = pd.DataFrame([(x.r_ui, x.est) for x in predictions_full], \n",
    "                       columns=['Rating','Predicted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE:  0.9167802882205027 \n",
      "MAE:  0.7305196298517427\n"
     ]
    }
   ],
   "source": [
    "# Let's reconstruct in a very simple way the RMSE and MAE\n",
    "\n",
    "rmse_check = np.sqrt(df_pred.apply(lambda x: (x[0] - x[1])**2, axis=1).mean())\n",
    "mae_check = df_pred.apply(lambda x: abs(x[0]-x[1]), axis=1).mean()\n",
    "print('RMSE: ',rmse_check,'\\nMAE: ',mae_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now let's do some cross validation, train-test split and grid search\n",
    "\n",
    "import random\n",
    "raw_ratings = data_1.raw_ratings\n",
    "np.random.seed(1)\n",
    "\n",
    "# Shuffle ratings just in case\n",
    "random.shuffle(raw_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's use 90% of our data as training and 10% as our test group\n",
    "\n",
    "threshold = int(0.9 * len(raw_ratings))\n",
    "train_raw_ratings = raw_ratings[:threshold]\n",
    "test_raw_ratings = raw_ratings[threshold:]\n",
    "\n",
    "# And let's reassign our raw_data as our training data\n",
    "data_1.raw_ratings = train_raw_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90000"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check the length of our new data_1. It should be 9.000\n",
    "\n",
    "len(data_1.raw_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>test_rmse</th>\n",
       "      <th>test_mae</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>test_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.945228</td>\n",
       "      <td>0.743629</td>\n",
       "      <td>5.269720</td>\n",
       "      <td>0.156090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.943112</td>\n",
       "      <td>0.745776</td>\n",
       "      <td>5.039385</td>\n",
       "      <td>0.150592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.939213</td>\n",
       "      <td>0.740355</td>\n",
       "      <td>5.090267</td>\n",
       "      <td>0.561844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.932606</td>\n",
       "      <td>0.735689</td>\n",
       "      <td>5.065041</td>\n",
       "      <td>0.150829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.940561</td>\n",
       "      <td>0.739157</td>\n",
       "      <td>5.050787</td>\n",
       "      <td>0.149576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   test_rmse  test_mae  fit_time  test_time\n",
       "0   0.945228  0.743629  5.269720   0.156090\n",
       "1   0.943112  0.745776  5.039385   0.150592\n",
       "2   0.939213  0.740355  5.090267   0.561844\n",
       "3   0.932606  0.735689  5.065041   0.150829\n",
       "4   0.940561  0.739157  5.050787   0.149576"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate the algorithm again and run the cv\n",
    "\n",
    "algo = sur.SVD(random_state = 1)\n",
    "cv_results = sur.model_selection.cross_validate(algo, data_1, measures=['RMSE','MAE'], cv=5)\n",
    "pd.DataFrame(cv_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we are gonna run some Grid Search to find a better model\n",
    "\n",
    "param_grid = {'n_epochs': [15,20,25],\n",
    "            'lr_all': [0.002, 0.02, 0.2, 2]}\n",
    "\n",
    "grid_search = sur.model_selection.GridSearchCV(sur.SVD,\n",
    "                                              param_grid,\n",
    "                                              measures=['rmse'],\n",
    "                                              cv = 3,\n",
    "                                              refit = True)\n",
    "\n",
    "grid_search.fit(data_1)\n",
    "\n",
    "# Taking the best estimator to work with\n",
    "\n",
    "algo = grid_search.best_estimator['rmse']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score (rated items):\n",
      "RMSE: 0.8379\n",
      "Test score (rated items)\n",
      "RMSE: 0.9398\n",
      "Test score (unrated items):\n",
      "RMSE: 0.5195\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5195418202147132"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retraining on the whole training set\n",
    "# We build and fit the full trainset out of our suprise training dataset\n",
    "# We need a surprise object to fit our model\n",
    "\n",
    "trainset = data_1.build_full_trainset()\n",
    "algo.fit(trainset)\n",
    "\n",
    "# Computing score on training\n",
    "# For that we'll build a trainset with the proper structure out of our surprise dataset\n",
    "# We need a list to make predictions\n",
    "\n",
    "trainset_build = trainset.build_testset()\n",
    "predictions_train = algo.test(trainset_build)\n",
    "print('Training score (rated items):')\n",
    "sur.accuracy.rmse(predictions_train)\n",
    "\n",
    "# Compute score on rated test set\n",
    "# For that we have to construct a test list out of our suprise object\n",
    "\n",
    "testset = data_1.construct_testset(test_raw_ratings)\n",
    "predictions_test = algo.test(testset)\n",
    "print('Test score (rated items)')\n",
    "sur.accuracy.rmse(predictions_test)\n",
    "\n",
    "# Compute score on unrated data\n",
    "# The anti-test set is the parte where we did not have any ratings\n",
    "# For it the algorithm takes the baseline for each user for each movie\n",
    "# We'll be building a list with our anti testset out of our surprise object\n",
    "\n",
    "no_ratings = trainset.build_anti_testset()\n",
    "predictions_no_ratings = algo.test(no_ratings)\n",
    "print('Test score (unrated items):')\n",
    "sur.accuracy.rmse(predictions_no_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user: 804        item: 216        r_ui = 4.00   est = 4.38   {'was_impossible': False}\n",
      "user: 804        item: 396        r_ui = 3.00   est = 3.14   {'was_impossible': False}\n",
      "\n",
      "user: 62         item: 155        r_ui = 1.00   est = 2.72   {'was_impossible': False}\n",
      "user: 393        item: 419        r_ui = 4.00   est = 3.81   {'was_impossible': False}\n",
      "\n",
      "user: 804        item: 804        r_ui = 3.53   est = 3.56   {'was_impossible': False}\n",
      "user: 804        item: 273        r_ui = 3.53   est = 3.59   {'was_impossible': False}\n"
     ]
    }
   ],
   "source": [
    "print(predictions_train[0])\n",
    "print(predictions_train[1])\n",
    "print(\"\")\n",
    "print(predictions_test[0])\n",
    "print(predictions_test[1])\n",
    "print(\"\")\n",
    "print(predictions_no_ratings[0])\n",
    "print(predictions_no_ratings[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's predict now using Slope One"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is just another way of predicting for which we will obtain a number out of the combination of both the information from other users who rated the same item and from the other items rated by the same user to predict a rating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "algo = sur.SlopeOne()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Biased accuracy on train group:\n",
      "RMSE: 0.8469\n",
      "Ubiased accuracy on test group:\n",
      "RMSE: 0.9380\n",
      "Biased accuracy on no rated group:\n",
      "RMSE: 0.9466\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9466122157960265"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo.fit(trainset)\n",
    "\n",
    "# Compute biased accuracy on trainset\n",
    "predictions_train = algo.test(trainset_build)\n",
    "print('Biased accuracy on train group:')\n",
    "sur.accuracy.rmse(predictions_train)\n",
    "\n",
    "# Compute biased accuracy on testset\n",
    "predictions_test = algo.test(testset)\n",
    "print('Ubiased accuracy on test group:')\n",
    "sur.accuracy.rmse(predictions_test)\n",
    "\n",
    "# Compute biased accuracy on trainset\n",
    "# It's biased because it's using the baseline prediction\n",
    "predictions_no_ratings = algo.test(no_ratings)\n",
    "print('Biased accuracy on no rated group:')\n",
    "sur.accuracy.rmse(predictions_no_ratings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's repeat the same steps with the KNN with means model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This algorithm works similar to user-user collaborative filtering with just a little change – instead of taking the weighted sum of ratings of “user-neighbors”, we take the weighted sum of ratings of “item-neighbours”.\n",
    "\n",
    "This algorithm can work with users and items similarity by specifying the similarity measure configuration. Let's try with both."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_options = {'name': 'cosine',\n",
    "               'user_based': False} # compute similarities between items\n",
    "\n",
    "# We could also specify the number of neighbours using the 'k' parameter\n",
    "# Refer to the documentation for more information about it:\n",
    "# https://surprise.readthedocs.io/en/stable/knn_inspired.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "algo = sur.KNNWithMeans(sim_options = sim_options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Biased accuracy on train group:\n",
      "RMSE: 0.7844\n",
      "Unbiased accuracy on test group:\n",
      "RMSE: 0.9334\n",
      "Biased accuracy on no rated group:\n",
      "RMSE: 0.9746\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9745597126096517"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo.fit(trainset)\n",
    "\n",
    "# Compute biased accuracy on train group\n",
    "\n",
    "predictions_train = algo.test(trainset_build)\n",
    "print('Biased accuracy on train group:')\n",
    "sur.accuracy.rmse(predictions_train)\n",
    "\n",
    "# Compute unbiased accuracy on test group\n",
    "\n",
    "predictions_test = algo.test(testset)\n",
    "print('Unbiased accuracy on test group:')\n",
    "sur.accuracy.rmse(predictions_test)\n",
    "\n",
    "# Compute biased accuracy on no rated group\n",
    "\n",
    "predictions_no_ratings = algo.test(no_ratings)\n",
    "print('Biased accuracy on no rated group:')\n",
    "sur.accuracy.rmse(predictions_no_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's try now with user similarity\n",
    "\n",
    "sim_options = {'name': 'cosine',\n",
    "               'user_based': True} # compute similarities between users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "algo = sur.KNNWithMeans(sim_options = sim_options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Biased accuracy on train group:\n",
      "RMSE: 0.8249\n",
      "Unbiased accuracy on test group:\n",
      "RMSE: 0.9495\n",
      "Biased accuracy on no rated group:\n",
      "RMSE: 0.7773\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7773121267720133"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo.fit(trainset)\n",
    "\n",
    "# Compute biased accuracy on train group\n",
    "\n",
    "predictions_train = algo.test(trainset_build)\n",
    "print('Biased accuracy on train group:')\n",
    "sur.accuracy.rmse(predictions_train)\n",
    "\n",
    "# Compute unbiased accuracy on test group\n",
    "\n",
    "predictions_test = algo.test(testset)\n",
    "print('Unbiased accuracy on test group:')\n",
    "sur.accuracy.rmse(predictions_test)\n",
    "\n",
    "# Compute biased accuracy on no rated group\n",
    "\n",
    "predictions_no_ratings = algo.test(no_ratings)\n",
    "print('Biased accuracy on no rated group:')\n",
    "sur.accuracy.rmse(predictions_no_ratings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finally we need to evaluate our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Precision@k and Recall@k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First a little reminder about these metrics:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://kflu.github.io/2016/08/26/2016-08-26-visualizing-precision-recall/2016-08-26-visualizing-precision-recall-1.png\" style=\"float: left; margin: 20px; height: 250px\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Recommendations Systems, often users will not really care about all the rating predictions we are making, but instead they will have a major interest only in a few top-ranked items, let's say the 𝑘 top-ranked items. So we could take only these 𝑘 ratings into account. We then ask how many of these 𝑘 items are relevant to the user. The relevance is in general difficult to measure, but we can for example ask how many out of the 𝑘 top-ranked items have a score beyond a certain threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's write a function to find our precision at recall at k\n",
    "\n",
    "from collections import defaultdict\n",
    "\n",
    "\n",
    "def precision_recall_at_k(predictions, k=10, threshold=3.5):\n",
    "    '''Return precision and recall at k metrics for each user.'''\n",
    "\n",
    "    # First map the predictions to each user.\n",
    "    user_est_true = defaultdict(list)\n",
    "    for uid, _, true_r, est, _ in predictions:\n",
    "        user_est_true[uid].append((est, true_r))\n",
    "\n",
    "    precisions = dict()\n",
    "    recalls = dict()\n",
    "    f1s = dict()\n",
    "    for uid, user_ratings in user_est_true.items():\n",
    "\n",
    "        # Sort user ratings by estimated value\n",
    "        user_ratings.sort(key=lambda x: x[0], reverse=True)\n",
    "\n",
    "        # Number of relevant items\n",
    "        n_rel = sum((true_r >= threshold) for (_, true_r) in user_ratings)\n",
    "\n",
    "        # Number of recommended items in top k\n",
    "        n_rec_k = sum((est >= threshold) for (est, _) in user_ratings[:k])\n",
    "\n",
    "        # Number of relevant and recommended items in top k\n",
    "        n_rel_and_rec_k = sum(((true_r >= threshold) and (est >= threshold))\n",
    "                              for (est, true_r) in user_ratings[:k])\n",
    "\n",
    "        # Precision@K: Proportion of recommended items that are relevant\n",
    "        precisions[uid] = n_rel_and_rec_k / n_rec_k if n_rec_k != 0 else 1\n",
    "\n",
    "        # Recall@K: Proportion of relevant items that are recommended\n",
    "        recalls[uid] = n_rel_and_rec_k / n_rel if n_rel != 0 else 1\n",
    "\n",
    "        # F1@K:\n",
    "        f1s[uid] = 2*precisions[uid]*recalls[uid] / \\\n",
    "            (precisions[uid]+recalls[uid]\n",
    "             ) if (precisions[uid]+recalls[uid]) != 0 else 1\n",
    "\n",
    "    return precisions, recalls, f1s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "precisions, recalls, f1s = precision_recall_at_k(predictions_train, k=5, threshold=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9911099328384588\n",
      "0.11336309853357228\n",
      "0.18813408954915717\n"
     ]
    }
   ],
   "source": [
    "# Precision and recall can be then averaged over all users\n",
    "\n",
    "print(np.mean(list(precisions.values())))\n",
    "print(np.mean(list(recalls.values())))\n",
    "print(np.mean(list(f1s.values())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "precisions, recalls, f1s = precision_recall_at_k(predictions_test, k=5, threshold=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9027597402597403\n",
      "0.32821882569427013\n",
      "0.3891054069682789\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(list(precisions.values())))\n",
    "print(np.mean(list(recalls.values())))\n",
    "print(np.mean(list(f1s.values())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's get the top X predictions for each user\n",
    "\n",
    "def get_top_n(predictions, n=10):\n",
    "\n",
    "    # First map the predictions to each user.\n",
    "    \n",
    "    top_n = {}\n",
    "    for uid, iid, true_r, est, _ in predictions:\n",
    "        if uid in top_n.keys():\n",
    "            top_n[uid].append((iid, est))\n",
    "        else:\n",
    "            top_n[uid] = [(iid,est)]\n",
    "\n",
    "    # Then sort the predictions for each user and retrieve the k highest ones.\n",
    "    \n",
    "    for uid, user_ratings in top_n.items():\n",
    "        user_ratings.sort(key=lambda x: x[1], reverse=True)\n",
    "        top_n[uid] = user_ratings[:n]\n",
    "\n",
    "    return top_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_n = get_top_n(predictions_train, n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we're gonna create a function to search for the recommended movies for an user\n",
    "\n",
    "def get_ratings(user, top_n, ratings):\n",
    "\n",
    "    filtered = ratings[user][top_n:]\n",
    "    filtered.sort(key=lambda tup: tup[1], reverse=True)\n",
    "\n",
    "    top_df = pd.DataFrame(filtered, columns=['movie_id','prediction'])\n",
    "    top_df = pd.merge(left=top_df, right=items[['movie_id','movie_title']], on='movie_id')\n",
    "    \n",
    "    return top_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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>movie_id</th>\n",
       "      <th>prediction</th>\n",
       "      <th>movie_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>4.917951</td>\n",
       "      <td>Fargo (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>4.913669</td>\n",
       "      <td>Braveheart (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>4.912094</td>\n",
       "      <td>Usual Suspects, The (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>4.910871</td>\n",
       "      <td>Pulp Fiction (1994)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>134</td>\n",
       "      <td>4.906965</td>\n",
       "      <td>Citizen Kane (1941)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id  prediction                 movie_title\n",
       "0       100    4.917951                Fargo (1996)\n",
       "1        22    4.913669           Braveheart (1995)\n",
       "2        12    4.912094  Usual Suspects, The (1995)\n",
       "3        56    4.910871         Pulp Fiction (1994)\n",
       "4       134    4.906965         Citizen Kane (1941)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_ratings(10, 5, top_n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Coverage of the top-ranked recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "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>uid</th>\n",
       "      <th>iid</th>\n",
       "      <th>est</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>804</td>\n",
       "      <td>318</td>\n",
       "      <td>4.538451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>804</td>\n",
       "      <td>483</td>\n",
       "      <td>4.454358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>804</td>\n",
       "      <td>64</td>\n",
       "      <td>4.387604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>804</td>\n",
       "      <td>496</td>\n",
       "      <td>4.380314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>804</td>\n",
       "      <td>515</td>\n",
       "      <td>4.358676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   uid  iid       est\n",
       "0  804  318  4.538451\n",
       "1  804  483  4.454358\n",
       "2  804   64  4.387604\n",
       "3  804  496  4.380314\n",
       "4  804  515  4.358676"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_n_list = []\n",
    "for key in top_n.keys():\n",
    "    for val in top_n[key]:\n",
    "        top_n_list.append([key, val[0], val[1]])\n",
    "        \n",
    "df_top = pd.DataFrame(top_n_list, columns=['uid', 'iid', 'est'])\n",
    "df_top.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.29369797859690844"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_top.iid.unique())/1682"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Novelty of the top-ranked recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the novelty is kind of tricky. We have to mix our networks knowledge, with the predictions of our recommendations system. First, let's remember how bipartite networks work:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://imgur.com/LZlqFBp.png\" style=\"float: left; height: 450px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Knowing this we could take the degree of each item in the bipartite user-item network and average this degree over the recommendation list for each user. Sounds easy, but the implementation it's kind of tricky."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We need to offset our items id so they don't match our users id\n",
    "\n",
    "df_top['iid_offset'] = df_top.iid+1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 1437\n",
      "Number of edges: 9430\n",
      "Average degree:  13.1246\n"
     ]
    }
   ],
   "source": [
    "# Nos let's create our bipartite network using our recommendations dataframe\n",
    "\n",
    "import networkx as nx\n",
    "from networkx.algorithms import bipartite\n",
    "\n",
    "G = nx.Graph()\n",
    "\n",
    "G.add_nodes_from(df_top['uid'], bipartite=0)\n",
    "G.add_nodes_from(df_top['iid_offset'], bipartite=1)\n",
    "G.add_edges_from(df_top[['uid','iid_offset']].values)\n",
    "\n",
    "print(nx.info(G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we need to obtain the average degree for each user-movie pair\n",
    "\n",
    "grouped = df_top.groupby('uid')['iid_offset'].apply(lambda x: G.degree(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "uid\n",
       "1    ((1119, 4), (1251, 41), (1169, 96), (1114, 58)...\n",
       "2    ((1313, 244), (1100, 267), (1285, 112), (1251,...\n",
       "3    ((1346, 28), (1318, 242), (1320, 16), (1347, 1...\n",
       "4    ((1258, 165), (1011, 20), (1050, 363), (1357, ...\n",
       "5    ((1169, 96), (1408, 99), (1100, 267), (1174, 2...\n",
       "Name: iid_offset, dtype: object"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouped it's gonna be just a container with all the users\n",
    "# and each movie-degree tuple for our recommendations\n",
    "\n",
    "grouped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Novelty: 107.42396606574762\n"
     ]
    }
   ],
   "source": [
    "# So now we can find our novelty degree by just doing\n",
    "\n",
    "average_degree_per_user = grouped.apply(lambda x: np.mean([xx[1] for xx in x]))\n",
    "\n",
    "print('Novelty:', np.mean([x for x in average_degree_per_user]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does this number tell us? Well, we want this number to be as much laerger as we can versus our average degree for the bipartite network. Because more degrees of distance mean we are recommending items far away from each user links."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Almost the end, we only have left to evaluate the similarity of the top-k predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To achieve this we need to form a user-item matriz with ones indicating the top movies recommended to each user."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import sparse\n",
    "from scipy.spatial.distance import pdist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's do this for our user-user similarity\n",
    "\n",
    "A = sparse.csc_matrix(\n",
    "    (np.ones(len(df_top)), (df_top.uid.values-1, df_top.iid.values-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [1., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check our sparse matrix to confirm everything is ok\n",
    "\n",
    "A.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8870233905883781"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distances = pdist(A.toarray(), metric='cosine')\n",
    "distances.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JklSVIUOSJElSVYYMSZIkSVUZMiRJkiRVZciQJEmSVJUhQ5IkSVJVhgxJkiRJVRkyJEmSJFVlyJAkSZJUlSFDkiRJUlWGDEmSJElVjdU4SETsB+wGbAO8EtgA+GJmHtRn362AtwCvA7YCngP8HLgB+OvMvLZPzaHA+RM04d2ZeU6fuvWAE4ADgRcCjwDXAadk5m0D3ssmwMnAvsBmwMPAVcDJmXn/gJrnAx8B9gI2BR4ALgNOzcyfT9BuSZIkaeTUGsk4CTiKJmT8eCX7fhT43zTh4ivAp4BvAW8AromIYyaovRw4tc/XTb07RsQ6wNdoAsMjwKeBrwNvBm6KiO371GwKfBt4L3A3cAbwHeAwYElEbNGnZktgSdnnO6XmnnKMb5djSpIkSWuMKiMZwLHA/cBdNCMavzUa0eUq4BOZeXP3yojYjSYU/FVE/FNmPtCn9rLMvGCSbToO2Bm4BDggM58s57mYZpThvIjYurO++DiwADgjM4/ratsxNCHlszSjFd0+CzwbOCYzP9NVczpNv3wMOGKSbZYkSZJmvSojGZl5bWbemZnjk9j3gt6AUdZ/g+ZSprWBnabSnoiYw9O/2H+gO0hk5uXAN4GX0gSiTs36wMHAo8ApPYc8C7gPeF33aEb5fs+y7eyemlPKsQ4ux5YkSZLWCDNt4vd/leXyAdu3iYj3RcQJEXFwmQvRz5bAC4A7MvPePtuvLMvdu9btCKwHfCszf9m9cwkpXy0vX9O1qVP/1Z4REcoxvgU8E9hhQDslSZKkkVPrcqkpi4gXAnsAjwHXD9jtvT2vV0TE54D3Zeavu9a/pCzvGHCcO8tywTTU7Flqrh6wjyRJkjRSZkTIKJO0vwisQ3N5U+8dme4FjqYZTbgfmAv8EfCXwLuADYE/7dp/bln+YsApO+s3GkLNQBGxpN/6zARg3rx5kzlMNWNjY0M572xlf7Vjf7Vjf7Vjf7Vjf7U3zD57cNrPqGEYGxub1f8mhx4yImIt4As0k7QvBhb17lPma3yja9VjwD9FxA3A94C3RcQnMvN7kzztnLJc6RySIdRIkiRJs9pQQ0YJGBcC+wMJHDSZyeMdmfmfEfEV4M+AXWkCBzw9gjC3b2Ez8tG933TWDJSZ2w7YNA6wbNmyyRymmk56nu7zzlb2Vzv2Vzv2Vzv2Vzv2V3v2mVa35cuXT/vna/78+dWONbSJ3xExBvwjzYPy/g/wp5k5aML3RH5alt13cLq9LBfQ31Zl2T2XYrpqJEmSpJE2lJAREWvTPL9if+DzwMGZuWIVD9d5qN49XevuBn4ELIiIF/WpeX1ZXtO17gbgcWDniNigp73PoJnADf/9GSCd7/cs+3TXbEBzCdjj5diSJEnSGmHaQ0aZ5H0psA/w98Bhvbd/7VOzS591cyLif9HcenYZzUP+ACiXXJ1TXn6yOwBExD7ALsAP6JrnkZm/opkbsj7w4Z7THQVsDvxrZt7TVXM3zWT0zYEje2pOLcf6fGY+OtH7kyRJkkZJlTkZEbEvsG95+dyy3DEiLijfL8vM95fvzwH+hCYY/Bg4OSJ6D3ldZl7X9fr6iLgD+G6pmUszSvBymkngf5aZj/Qc43TgjcB+wI0RcTXNszP2LzWH9wk3HwReDRwXEdsA3wF+nyYQPcRvBwmA9wCLgTMjYg/gNprRldfQXCZ1Yp8aSZIkaWTVmvi9DXBIz7otyhfAfwCdkNG5fGkecPIEx7yu6/tFwB/SPPxuE+BJmsuhzgZO7x5d6MjMJyLitcAJNLe3PRZ4BLgMOCUzf9Cn5uGI2JHmad370ox4PAycD5ycmff3qbk7Il4FfATYiyZAPQCcCZyamT+b4D1KkiRJI2fO+Lh3V50FxgGWLl06rSf1zhnt2F/t2F/t2F/t2F/t2F/tDbPPVizce9rPqen3nEsXD/PuUnMm2m8yhnZ3KUmSJEmjyZAhSZIkqSpDhiRJkqSqDBmSJEmSqjJkSJIkSarKkCFJkiSpKkOGJEmSpKoMGZIkSZKqMmRIkiRJqsqQIUmSJKkqQ4YkSZKkqgwZkiRJkqoyZEiSJEmqypAhSZIkqSpDhiRJkqSqDBmSJEmSqjJkSJIkSarKkCFJkiSpKkOGJEmSpKoMGZIkSZKqMmRIkiRJqsqQIUmSJKkqQ4YkSZKkqgwZkiRJkqoyZEiSJEmqypAhSZIkqSpDhiRJkqSqDBmSJEmSqjJkSJIkSarKkCFJkiSpKkOGJEmSpKoMGZIkSZKqMmRIkiRJqsqQIUmSJKkqQ4YkSZKkqgwZkiRJkqoyZEiSJEmqypAhSZIkqSpDhiRJkqSqDBmSJEmSqjJkSJIkSarKkCFJkiSpKkOGJEmSpKoMGZIkSZKqMmRIkiRJqmqsxkEiYj9gN2Ab4JXABsAXM/OgCWp2Ak4CdgDWBe4CzgM+k5krBtS8EXg/8AfAWsCtwGcz8x8mOM8hwJHAS4EVwM3Aosz88oD91wKOBg4HtgIeB24ATsvMxQNq1gNOAA4EXgg8AlwHnJKZtw1qmyRJkjSKao1knAQcRRMyfryynSNiH+B6YFfgUuBsYG3gDOCiATVHAVcALwcuBM4F5gMXRMSiATWLgAuAzcr+FwJbA1eU4/XuP6ec/4zSnrNK+3YFri/t7q1ZB/gacDJNuPg08HXgzcBNEbH9yvpDkiRJGiW1QsaxwAJgQ+DdE+0YERvS/MK/Anh1Zv7PzPxzmoDybWC/iDiwp2ZzYBHwM+BVmXlkZh4LvAK4Gzg+InbsqdkJOL5sf0VmHpuZRwLbluMsKsftdiCwH7AY2CYz/zwz/yfwmtLecyNig56a44CdgUuA7TPzLzLzT8txngmcFxFeliZJkqQ1RpVffjPz2sy8MzPHJ7H7fsDvABdl5k1dx/g1zYgI/HZQORxYBzgrM+/rqvk58PHy8oiems7rj5X9OjX30YycrAMc1lPTOe9JpT2dmu8CF5d279dZX0Y+Ouf5QGY+2VVzOfBNmsu0dkOSJElaQwzjL+y7l+VVfbZdDzwG7FQuQ5pMzZU9+6xSTTnfTuX835zkebYEXgDckZn3tmibJEmSNLKGETJeUpZ39G7IzOXAvTQT0reYZM0DwKPA8yPimQARsT7wPOBXZXuvO8tyQde6F9NMJr+ntGMyNQPbNUGNJEmSNNKq3F2qpbll+YsB2zvrN2pZs37Z77HVeI4aNQNFxJJ+6zMTgHnz5k3mMNWMjY0N5byzlf3Vjv3Vjv3Vjv3Vjv3V3jD77MFpP6OGYWxsbFb/m5yJE5LnlOVk5ndMpWY6zrGq7ZIkSZJmrWGMZHT+uj93wPYNe/brfD+v1Dw8Qc0jkzxHvxGIVW1X25qBMnPbAZvGAZYtWzaZw1TTSc/Tfd7Zyv5qx/5qx/5qx/5qx/5qzz7T6rZ8+fJp/3zNnz+/2rGGMZJxe1n+1jyFiBgDXgQsB+6ZZM1mNJdK3Z+ZjwFk5qM0z+t4Vtnea6uy7J5LcRfNbWq3KO2YTM3Adk1QI0mSJI20YYSMa8pyrz7bdqV5tsTizHxikjWv79lnlWrK+RaX8+8yyfPcDfwIWBARL2rRNkmSJGlkDSNkXAIsAw6MiFd1VkbEusBp5eXf9NScDzwBHNX9AL2I2Bj4YHl5Tk9N5/WJZb9OzebAkeV45/fUdM57WmlPp2Y74ADgp8CXOuvLc0E65/lk90P3ytPBdwF+AHwDSZIkaQ0xZ3x86nOSI2JfYN/y8rnA62gud+o8b2JZZr6/Z/9LgF8DF9E8gXtvmlvCXgJE74P9IuJo4EyaORkXA7+heTDe84FPdR+/q+ZTNE/kvr8cd22asLApcHRmntWz/xwgy3F/CFxR9j0AWBd4a3nIXnfNOjQjFTsBNwFX0zw7Y//Sxt0z88aJ+m8SxgGWLl06xcO04/Wm7dhf7dhf7dhf7dhf7dhf7Q2zz1Ys3Hvaz6np95xLFw9zTsacifabjFojGdsAh5Sv15V1W3St269758y8jOYp2NcDbwWOBv6LJhAc2O/J4Zn5GZogcivwduCdwE+AQ/sFjFJzPHBo2e+dpe5W4E29AaPsPw68rbRjeWnXW0o7d+0NGKXmCeC1wEdoblV7LPDHwGXAdhUChiRJkjSrVBnJ0GrnSMYsYH+1Y3+1Y3+1Y3+1Y3+150iGVjdHMiRJkiSpiyFDkiRJUlWGDEmSJElVGTIkSZIkVWXIkCRJklSVIUOSJElSVYYMSZIkSVUZMiRJkiRVZciQJEmSVJUhQ5IkSVJVhgxJkiRJVRkyJEmSJFVlyJAkSZJUlSFDkiRJUlWGDEmSJElVGTIkSZIkVWXIkCRJklSVIUOSJElSVYYMSZIkSVUZMiRJkiRVZciQJEmSVJUhQ5IkSVJVhgxJkiRJVRkyJEmSJFVlyJAkSZJUlSFDkiRJUlWGDEmSJElVGTIkSZIkVWXIkCRJklSVIUOSJElSVYYMSZIkSVUZMiRJkiRVZciQJEmSVJUhQ5IkSVJVhgxJkiRJVRkyJEmSJFVlyJAkSZJUlSFDkiRJUlWGDEmSJElVGTIkSZIkVWXIkCRJklSVIUOSJElSVYYMSZIkSVWNDbsBkiRJq2rFwr2Hct4Hh3JWafZwJEOSJElSVYYMSZIkSVUN5XKpiDgUOH8luz2ZmWuV/TcH7p1g34sz88AB5zoEOBJ4KbACuBlYlJlfHrD/WsDRwOHAVsDjwA3AaZm5eEDNesAJwIHAC4FHgOuAUzLztgnfpSRJkjRihjUn4xbg1AHbdgF2B67ss+17wGV91v97vwNFxCLgeOB+4FxgbZogcEVEHJ2ZZ/XsPwe4CNgPuB04C9gEOAC4PiLempmX99SsA3wN2Bm4Cfg08LvA/sAbImL3zLxxwHuVJEmSRs5QQkZm3kITNH5LRHy7fPt3fTbfkpkfnsw5ImInmoBxN7BdZv68rP8rYAmwKCK+nJn3dZUdSBMwFgN7ZOavS805wL8B50bENZn5y66a42gCxiXAAZn5ZKm5mCYQnRcRW3fWS5IkSaNuRs3JiIiXAzsAPwb+ZYqHO6IsP9YJGAAlVJwNrAMc1lPz7rI8qRMwSs13gYuB36EJIZ32zuk6zwe6g0QZ8fgmzWVau03xvUiSJEmzxky7he27yvLvM3NFn+3zI+JdwKbAw8C3M/P7A461e1le1WfblcCHyj6nwFOXPe0EPEYTDvrVHFxqOvNJtgReANyRmf3mjFzJ05d/XTugnZIkSdJImTEho0yePgh4EvjcgN3+uHx1110HHJKZP+patz7wPOBXmflAn+PcWZYLuta9GFgLuCczl0+y5iVleceA9varGSgilvRbn5kAzJs3bzKHqWZsbGwo552t7K927K927K927K92ZnN/+bwKjaqxsbFZ+W+yYyZdLhXARsCVmfmfPdseAz4KbAtsXL52oxkdeDVwdQkWHXPL8hcDztVZv9EQaiRJkqSRNmNGMoB3luXf9m7IzIeAk3tWXx8Re9JMyN4eeAfNnZ3aGG+x75zVXZOZ2w7YNA6wbNmyFqeeuk56nu7zzlb2Vzv2Vzv2Vzv2Vzv2lzTzLF++fNr/Tc6fP7/asWbESEZEvJRmPsT9wFcmW1cua+pcWrVr16bOCMJc+us3ArGymg0r1UiSJEkjbUaEDFY+4XsiPy3Lpy6XysxHae5Q9ayI2KxPzVZl2T2X4i6ah/VtERH9Rnj61dxeloPmXPSrkSRJkkba0ENGRKxLc9emJ4G/X4VD7FCW9/Ssv6Ys9+pT8/qefcjMJ2iej/FMmjtCrbSG5hkcPwIWRMSLJlkjSZIkjbShhwyaJ2NvDHylz4RvACJi+4hYu8/63YFjy8sLezafU5YnRsTGXTWbA0cCT/D0rWg7/qYsTyvhp1OzHc1Tv38KfKmzPjPHu87zyYh4RlfNPjRh5QfAN/q9L0mSJGkUzYSJ350J3/2e8N3xCeBl5Xa195d1r+DpZ2F8KDMXdxdk5uKIOJ3midzfj4hLgLVpwsImwNE9T/sGuAh4C80D926OiCtonslxAM3tbRdm5iM9NacDbyw1N0bE1TTPztif5q5Yh/u0b0mSJK1JhjqSERG/D/wRK5/w/QXgRmA7YCHwHpr5Dgnsmpmn9SvKzOOBQ4Gf0ISZtwO3Am/KzLP67D8OvI0mmCwHjqYJHddupKPeAAAcB0lEQVSX81zep+YJ4LXAR2huVXsszbM8LgO2y8wbJ+oDSZIkadTMGR9vc0dWDck4wNKlS6f1pN7SsB37qx37qx37qx37q53Z3F8rFu497CZIq8VzLl08zFvYzplov8mYCXMyJEmSJI0QQ4YkSZKkqgwZkiRJkqoyZEiSJEmqypAhSZIkqSpDhiRJkqSqDBmSJEmSqjJkSJIkSarKkCFJkiSpKkOGJEmSpKoMGZIkSZKqMmRIkiRJqsqQIUmSJKkqQ4YkSZKkqgwZkiRJkqoyZEiSJEmqypAhSZIkqSpDhiRJkqSqDBmSJEmSqjJkSJIkSarKkCFJkiSpKkOGJEmSpKoMGZIkSZKqMmRIkiRJqsqQIUmSJKkqQ4YkSZKkqgwZkiRJkqoyZEiSJEmqypAhSZIkqSpDhiRJkqSqDBmSJEmSqjJkSJIkSarKkCFJkiSpKkOGJEmSpKoMGZIkSZKqMmRIkiRJqsqQIUmSJKkqQ4YkSZKkqgwZkiRJkqoyZEiSJEmqypAhSZIkqSpDhiRJkqSqDBmSJEmSqjJkSJIkSarKkCFJkiSpqrFhnTgi7gNeOGDzg5n53D41OwEnATsA6wJ3AecBn8nMFQPO80bg/cAfAGsBtwKfzcx/mKBthwBHAi8FVgA3A4sy88sD9l8LOBo4HNgKeBy4ATgtMxcPOo8kSZI0ioY9kvEL4NQ+X4t6d4yIfYDrgV2BS4GzgbWBM4CL+h08Io4CrgBeDlwInAvMBy6IiN86R6lZBFwAbFb2vxDYGriiHK93/znl/GeU9pxV2rcrcH1ptyRJkrTGmDM+Pj6UE5eRDDJz80nsuyHNqMVcYOfMvKmsXxe4BtgReFtmXtRVsznwQ+BRYNvMvK+s3xj4LrAlsFNmfrurZifgW8DdwHaZ+fOuYy0B1gd+r3Ossu1twP8BFgN7ZOavy/rtgH+jCVJbZuYvJ987v2UcYOnSpVM4RHvz5s0DYNmyZdN63tnK/mrH/mrH/mrH/mpnNvfXioV7D7sJ0mrxnEsXT/u/yfnz53e+nTPVYw17JGOy9gN+B7ioEzAAyi/0J5WX7+6pORxYBzirOxSU4PDx8vKInprO6491AkapuY9m5GQd4LCems55T+oEjFLzXeDi0u79VvoOJUmSpBExtDkZxToRcRDwApoRh+8D1/eZX7F7WV7V5xjXA48BO0XEOpn5xCRqruzZZzLnuRL4UNnnFICIWAfYqZz/mwNqDi415/fZLkmSJI2cYYeM5wJf6Fl3b0Qclpnf6Fr3krK8o/cAmbk8Iu4FXgZsAdw2iZoHIuJR4PkR8czMfCwi1geeB/wqMx/o09Y7y3JB17oX00wmvyczl0+yZqCIWNJvfWYCTw9nT5exsbGhnHe2sr/asb/asb/asb/amc399eCwGyCtJmNjY7Py32THMC+XOh/YgyZorE8zufpvgc2BKyPilV37zi3LXww4Vmf9RqtQM7dnuTrOsdGA7ZIkSdLIGdpIRmae2rPq34EjIuJXwPHAh4E3T/JwnckpbWaxr0rNaj1HZm470Tmne/LPbJ4IOAz2Vzv2Vzv2Vzv2Vzv2lzTzLF++fJgTv6dsJk78Pqcsd+1a1zvq0GvDnv3a1Dwyyf37jVqsSrskSZKkkTYTQ8ZDZbl+17rby/K35jZExBjwImA5cM8kazYrx78/Mx8DyMxHgR8Dzyrbe21Vlt1zPO6ieVjfFqUdk6mRJEmSRtpMDBk7lmV3YLimLPfqs/+uwDOBxV13llpZzet79lmlmnK+xeX8u7Q4jyRJkjSyhhIyIuJlEbFJn/UvpHliNjRP2u64BFgGHBgRr+raf13gtPLyb3oOdz7wBHBUeZhep2Zj4IPl5Tk9NZ3XJ5b9OjWbA0eW4/XeirZz3tNKezo12wEHAD8FvtT7XiVJkqRRNayJ3/sDJ0TEtcC9wC9pnsD9BmBd4CvAos7OmflIRCykCRvXRcRFwM+AvWluVXsJzYPv6Kq5NyL+HDgTuCkiLgZ+Q/NgvOcDn+p+2nepWRwRpwPHAd+PiEuAtWnCwibA0d0P9isuAt5SjntzRFwBbFpq1gIWZuYjSJIkSWuIYV0udS1wKc1cij+l+aV+N+DfgEOAN2bmb7oLMvOyss/1wFuBo4H/KrUHZuZv3cEpMz9DE0RuBd4OvBP4CXBoZr6/X8My83jg0LLfO0vdrcCbMvOsPvuPA28r7Vhe2vWW0s5dM/PySfaJJEmSNBLmjI+3vYOrhmAcYOnSpdN6Um9p2I791Y791Y791Y791c5s7q8VC/cedhOk1eI5ly4e5i1s50y032TMxInfkiRJkmYxQ4YkSZKkqgwZkiRJkqoyZEiSJEmqypAhSZIkqSpDhiRJkqSqDBmSJEmSqjJkSJIkSarKkCFJkiSpKkOGJEmSpKoMGZIkSZKqMmRIkiRJqsqQIUmSJKkqQ4YkSZKkqgwZkiRJkqoyZEiSJEmqypAhSZIkqSpDhiRJkqSqDBmSJEmSqhobdgMkSVIdKxbuvUp1D1ZuhyQ5kiFJkiSpKkOGJEmSpKoMGZIkSZKqMmRIkiRJqsqQIUmSJKkqQ4YkSZKkqgwZkiRJkqoyZEiSJEmqypAhSZIkqSpDhiRJkqSqDBmSJEmSqjJkSJIkSarKkCFJkiSpKkOGJEmSpKoMGZIkSZKqMmRIkiRJqsqQIUmSJKkqQ4YkSZKkqgwZkiRJkqoyZEiSJEmqypAhSZIkqSpDhiRJkqSqDBmSJEmSqjJkSJIkSarKkCFJkiSpqrFhnDQiNgXeDLwB2Bp4HvAb4P8DzgfOz8wnu/bfHLh3gkNenJkHDjjXIcCRwEuBFcDNwKLM/PKA/dcCjgYOB7YCHgduAE7LzMUDatYDTgAOBF4IPAJcB5ySmbdN0G5JkiRp5AxrJGN/4Fxge+BG4K+BLwEvBz4HZETM6VP3PeDUPl+X9DtJRCwCLgA2K+e7kCbUXBERR/XZfw5wEXAGsDZwFnApsCtwfUTs06dmHeBrwMk04eLTwNdpQtRNEbH9yjpDkiRJGiVDGckA7gD2Bv6lZ8Tig8B3gLcCb6EJHt1uycwPT+YEEbETcDxwN7BdZv68rP8rYAmwKCK+nJn3dZUdCOwHLAb2yMxfl5pzgH8Dzo2IazLzl101xwE70wSdAzrvJyIuBi4DzouIrbvfpyRJkjTKhjKSkZnXZOYVvb94Z+ZPgHPKy1dP8TRHlOXHOgGjnOM+4GxgHeCwnpp3l+VJnYBRar4LXAz8Dk0IAZ4a+eic5wPd7yczLwe+SXOZ1m5TfC+SJEnSrDGskYyJ/FdZLu+zbX5EvAvYFHgY+HZmfn/AcXYvy6v6bLsS+FDZ5xR46rKnnYDHaMJBv5qDS835Zd2WwAuAOzKz35yRK4FdSs21A9opSZIkjZQZFTIiYgx4e3nZLxz8cfnqrrkOOCQzf9S1bn2ayeS/yswH+hznzrJc0LXuxcBawD2Z2S/g9Kt5SVne0Wf/QTUDRcSSfuszE4B58+ZN5jDVjI2NDeW8s5X91Y791Y791c6a2l8PDrsBkqoZGxub1T/DZtotbP83zeTvr2Tmv3atfwz4KLAtsHH52o1mdODVwNUlWHTMLctfDDhPZ/1GQ6iRJEmSRtqMGcmIiGNoJmr/kOaypKdk5kM0d2/qdn1E7EkzIXt74B00d3ZqY7zFvp27Xa22mszcdsCmcYBly5a1OPXUddLzdJ93trK/2rG/2rG/2rG/JM12y5cvn/afYfPnz692rBkxkhERR9IEhB8Ar8nMn02mrlzW9LnycteuTZ0RhLn0128EYmU1G1aqkSRJkkba0ENGRLyP5nkU/04TMH7S8hA/LcunLpfKzEeBHwPPiojN+tRsVZbdcynuonlY3xZlbshkam4vy0FzLvrVSJIkSSNtqCEjIv6C5sF3t9AEjIdW4TA7lOU9PeuvKcu9+tS8vmcfMvMJmudjPJPmjlArraF5BsePgAUR8aJJ1kiSJEkjbWghIyI+RDPRewnNg+8GXnQWEdtHxNp91u8OHFteXtizufO8jRMjYuOums2BI4EnePpWtB1/U5anRcS6XTXbAQfQjJo89YDAzBzvOs8nI+IZXTX70ISVHwDfGPTeJEmSpFEzlInfEXEI8BGay5O+CRwTEb273ZeZF5TvPwG8rNyu9v6y7hU8/SyMD2Xm4u7izFwcEafTPJH7+xFxCbA2TVjYBDi652nfABfRPGl8P+DmiLiC5pkcB9Dc3nZhZj7SU3M68MZSc2NEXE3z7Iz9ae6KdbhP+5YkSdKaZFgjGZ1Li9YC3kfzQLzer0O79v8CcCOwHbAQeA/NfIcEds3M0/qdJDOPL8f5CfBOmmdw3Aq8KTPP6rP/OPA2mmCyHDiaJnRcX85zeZ+aJ4DX0oSmjWhGVv4YuAzYLjNvXHl3SJIkSaNjzvh4mzuyakjGAZYuXTqtJ/UWkO3YX+3YX+3YX+2sqf21YuHew26CpEqec+niYd7Cds5E+03G0O8uJUmSJGm0GDIkSZIkVWXIkCRJklSVIUOSJElSVYYMSZIkSVUZMiRJkiRVZciQJEmSVJUhQ5IkSVJVhgxJkiRJVRkyJEmSJFVlyJAkSZJUlSFDkiRJUlWGDEmSJElVGTIkSZIkVTU27AZIkrQ6PPjmnYbdBElaYzmSIUmSJKkqQ4YkSZKkqgwZkiRJkqoyZEiSJEmqypAhSZIkqSpDhiRJkqSqDBmSJEmSqjJkSJIkSarKkCFJkiSpKkOGJEmSpKoMGZIkSZKqMmRIkiRJqsqQIUmSJKkqQ4YkSZKkqgwZkiRJkqoyZEiSJEmqypAhSZIkqSpDhiRJkqSqDBmSJEmSqjJkSJIkSarKkCFJkiSpKkOGJEmSpKoMGZIkSZKqMmRIkiRJqsqQIUmSJKmqsWE3QJKGYcXCvYfdhFYenGL9Wuf+v1XaIUnSZDiSIUmSJKkqQ4YkSZKkqgwZkiRJkqoyZEiSJEmqyonflUTE84GPAHsBmwIPAJcBp2bmz4fZNkmSJGk6OZJRQURsCSwBDgO+A5wB3AO8F/h2RGw6xOZJkiRJ08qRjDo+CzwbOCYzP9NZGRGnA8cCHwOOGFLbJEmSpGnlSMYURcQWwJ7AfcDZPZtPAR4FDo6I9ae5aZIkSdJQOJIxdbuX5Vcz88nuDZn5y4j4Fk0I2QG4erobJ03GMB5MN9WHy0mSpJnLkYype0lZ3jFg+51luWAa2iJJkiQNnSMZUze3LH8xYHtn/UYrO1BELOm3PjMBmD9/ftu2VTGs885Ws7K//uWmYbdAqu53/VxLmuVm5e8UhSMZq9+cshwfaiskSZKkaeJIxtR1RirmDti+Yc9+A2XmtlVaVElnZGWmtWumsr/asb/asb/asb/asb/as8/asb/aGYX+ciRj6m4vy0FzLrYqy0FzNiRJkqSRYsiYumvLcs+I+G/9GREbADsDjwM3THfDJEmSpGEwZExRZt4NfBXYHDiyZ/OpwPrA5zPz0WlumiRJkjQUzsmo4z3AYuDMiNgDuA3YHngNzWVSJw6xbZIkSdK0ciSjgjKa8SrgAppwcTywJXAmsGNmPjy81kmSJEnTa874uHdWlSRJklSPIxmSJEmSqjJkSJIkSarKkCFJkiSpKkOGJEmSpKoMGZIkSZKqMmRIkiRJqsqQIUmSJKkqn/i9BomI5wMfAfYCNgUeAC4DTs3Mn0+ifn1gX+ANwP8Afhd4Ergd+EfgM5n5m9XT+uk31f4qx/hzmie/vxSYR9Nf/wF8DTg9M+9fDU0fmhp91ueYuwLX0vxR5GOZeVKl5g5dpc/YdcBuE+yyXmb+eopNnRFqfr4iYmug8+/z2cAvgNuAv8/Mz9ds97BU+Jn/app/eyvzgsz8zyk0dUao9fmKiD+i+Wy9Engu8BDw78CZmXlV7XYPS8X+ejNwNM3vFesC99L8TvHJEfrZtR/Nz+ltaD4XGwBfzMyDVuFY1f+fXV0cyVhDRMSWwBLgMOA7wBnAPcB7gW9HxKaTOMwuwIXA62h+YH6G5gfB84BFwLURsW791k+/Sv0F8C5gPvAN4LPA3wMPA8cCt0bEH1Ru+tBU7LPuY24A/APwWMWmzgirob9OHfC1vFabh6lmf0XEocDNNH80+SbwKeASYA7wJ1UbPiSV+us+Bn+u/rnsc+uIBIwqn6+IeDfNZ2qPsjyD5uf/bsCVEXFi/dZPv4r99VGaz9J2NL8onw08QvMZ+3pErFe/9UNxEnAUTcj48aoeZHX8P7s6OZKx5vgszV/rjsnMz3RWRsTpNL/wfgw4YiXH+AlwEPBP3SMW5RfB64CdgCNp/sOe7Wr0F8DL+/0lJiIWAn9XjjMSv9RQr8+6fRqYC/xlqR8lVfsrMz9cu4EzTJX+iogdgM/R/KFkr8z8Sc/2/6dmo4doyv2VmfcBH+63LSL+sXz7dxXaOhNMub/KZ+cvgV8D22bm7V3bPk4TbE+MiEWZ+UT9tzCtavTXHwAnAv+Xpr/uKevnAGfS/FL+Fwz4DM4yxwL3A3fRBM7JjBD2szr+n11tHMlYA0TEFsCeNH+VOrtn8ynAo8DB5XKogTLzlsz8Yu8lUZn5S54OFq+u0eZhqtVfABMM9WZZbrWKzZxRavZZ1zH3oflrzTHA0jotnRlWR3+Nssr99UlgLeCg3oABkJn/NbXWDt/q/nyVv5a+GXgc+MKqt3RmqNhfm9D8UeSO7oABkJm3AXcA6wHPqtDsoanYX2+mGT38XCdgAGTmOPBBYBx4d0SsVanpQ5OZ12bmneW9rZLZ+P+GIWPNsHtZfjUzn+zeUALCt4BnAjtM4Ryd/5hH4dKM6eivN5Xl96dwjJmkap9FxLOBc4HLMvPCmg2dIap/xiLigIg4ISKOi4jXR8Q69Zo7dFX6q1zLvAtwE83liq+JiPdHxPERsUdEjMr/iav7Z9ihwDo0o9oz6hrwVVSrvx4CfgosiIj/9gekiFhA80elWzLz4SqtHp5a/fXcsrynd0M5zjKav9pvPaXWjo7p+N2kqlH5gaqJvaQs7xiw/c6yXDCFcxxelqMwqa16f0XEOyLiwxGxKCL+lWaewX8AJ6x6M2eU2n32dzQ/n2bMsG9lq+Pf5EU0l2p8CvgK8KMy2XAU1Oqv7br2v6Z8/RXNnLKvA7dExIun0M6ZYnX/zH9HWf7tKtbPNFX6q/yV+kian11LIuIfIuIvI+LzNNfR3wrsX6G9w1br87WsLF/Uu6Fchj2vvPy9Vq0bXdPxu1xVhow1w9yy/MWA7Z31G63KwSPiKJq7HNwCnLcqx5hhVkd/vYNmOPN4muHOJcBrM/POCatmj2p9FhGHA/sA78nMByu0bSaq+Rm7nGZk7Pk0l2L8Hk3Y2Ai4OCJeP4V2zhS1+uvZZRnA7wNvKcd+Mc1lP1sD/xIRa696U2eE1fYzPyJ2o/mM3ZqZi1ehbTNRtf7KzH+i+Yvz/wXeTvOHpINpLmU5nz5/tZ+FavXXl8vyHRGxec+202gupQLYuFXrRtdq/V1udXDit+Dpf8itrxWMiLcAf00zKfyto3A98yS07q/M3AGeupb5f9BMzloSEQeM0i0NJzCpPiv/0fw1zWUYOdG+I27Sn7HMPKNn1e3AByNiKc0d4D4OXFm3eTPOZPtrra7lOzKz80vOIxFxCE3weBXwVpo7542qVf6ZD7yzLEdlFGMyJt1fEXEQzaWe/wx8lGbE+oXAh4CzaCb9xupp5owxqf7KzMUR8bc0d2H8fkR8CfgZsDPNqOOtwMuAFauxraNkKv+uVwtHMtYMnXQ7d8D2DXv2m5SI2JfmEo2HgFd3T9ya5VZLfwFk5sOZ+TWa0YzHgc+PyC36avXZeTT98p4ajZrBVttnrMvnaOZIbVMuPZjNavVXZ/7AEzSXlD2lXOpyeXn5h20bOMOsrp/5m9AEsJGY8N2lSn+VeRfn0fxyfHBm/jAzH8/MH9KMZiwB9i/PH5nNqn2+MvOI/7+9+wuRqooDOP4tMIKgiIwehBR9KCrD/kCYZJgkURSB9gN7kdIoCqRQevHJlx6CSny2MouIH1QE9VIQroVGUkT/fEmIgqA/kGBFiGUP5153GXdjHc/M7Nz9fmC4s/fODmd+nJ27v3vP+R1gM/AtJfl6DDhBKZX/VfOyX/puabcM47xRlXcy5oe2ysVM4/TaCWozjfM7Q0Q8ALxOuYNxR4eG/cAA4tUrM49FxCFKnf5rKRNRx1mtmN1I+QL9NWLai307mjrz72Tm/WfdyrljGH3s74g4ThlqcBFwvN/3mgNqxat9n+O9EycbbRIy7on/oPrXJsqE71cy81g/DZujasVrHbAAmJhmYu6/EXEAuKl57O+vqXNC1f6VmS8xzVDriNjTPD18Vq3rroGfN2rzTsb80NZjXtdbPaW5wrmKcmXqk9m8WUQ8SBlK8BNwe8cSDKgcr/+xqNl2oSJXrZjtoyxY2Ps40Bz/ovn5gzrNHpmB97GIuIqSYLRVWsZZrXh9SYnFwoi4Yprj1zXb7/tv6pwwqP71SLPtytoYrVrxaiu6XT7D8Xb/iRmOj4thfH+towwzm8jMvhev65hh/W9SjUnGPJCZR4H3gSWUyhdT7aRc5dyXmX+2OyPi6og4o6JDM275VeAHYHWHhkidViteEbG4qWt9hoh4lDLm9EcmbwmPrVoxy8ytmbml90GZMAnwXrOvt0b4WKnYx5ZGxKKe3yciFjIZszcyc6wT2Yr96ySTcwmenXqijojllNKsJymrf4+tmt/5U47fRpmz8nWHJnwDVeP1UbPdEBHXTz0QESuADZTx8h/Wa/3wVf6f4uJp9i2jJLL/0J0KjLMWEQuaeC2bur+fuI+aw6Xmj8eBg8DuiFgLHAFuAdZQbq3t6Hn9kWbbTiQiItZQbmmeT8moH5pmSMuxzNxVvfXDd87xAm4A3oqIg83v/AxcRqlhvRz4gzJutyuT2mrEbD6pEa/VwJ6ImACOUiZNXklZRf4SyjC8pwf1AYasVv96BlhLqfyzPCL2U64wrwcuBLZl5neD+ABDVvvvsZ3w3bW7GK1zjldmfhoRL1MWET0cEW9TJn4voQyNvQDYlZnfDPBzDEut/vViRCymzFf5nVLp7V7KsLMtmTlnrsqfi2YOazvEt10fZGVE7G2e/5aZ25vniyjxavvOVGcb95HyTsY80WTANwN7KR1yG7AM2A2snOXiQIuZ7DMPU0qy9j6erNrwEakUr8+BFygnlnuA7cBGypWs54BrMnOieuNHpFLM5o1K8foMeI1SmnV98x53Ue6ObQVWdWXsfK3+lZl/UZKMnZSFq54A7qOcuO/OzOerN34Eav49RsSllKvwXZvwfVrFeG2mJBmHKJOXtwF3Ah8DGzPzqbotH42K8XqXsphvUM6RtwJvAjdm5t66rR6pFZQ5TZso/QJg6ZR9s1rTaNzOs+edOjVnKl1JkiRJ6gDvZEiSJEmqyiRDkiRJUlUmGZIkSZKqMsmQJEmSVJVJhiRJkqSqTDIkSZIkVWWSIUmSJKkqkwxJkiRJVZlkSJIkSarKJEOSJElSVSYZkiRJkqoyyZAkSZJUlUmGJEmSpKpMMiRJkiRVZZIhSZIkqSqTDEmSJElVmWRIkiRJquo/InSOxwAqxGAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 396
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(distances);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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