cf_recommendation_clothing_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# All needed imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.mlab as mlab\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from surprise import *\n",
    "from surprise.accuracy import rmse, mae, fcp\n",
    "from UserDefinedAlgorithm import PredictMean\n",
    "from sklearn.grid_search import ParameterGrid\n",
    "import pickle\n",
    "from os import listdir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will start by parsing the json files into a pandas data frame and saving it in a csv file to avoid parsing it multiple times. Afterwards, we will clean the data by keeping the necessary features only. When we finish the data pre-processing, we will do some statistical analysis on the data that will help us understand the problem better. Finally we will save the resulting dataframe in a csv file to be loaded directly for usage in the recommendation algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### prepare data in pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviewerID</th>\n",
       "      <th>asin</th>\n",
       "      <th>reviewerName</th>\n",
       "      <th>helpful</th>\n",
       "      <th>unixReviewTime</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>overall</th>\n",
       "      <th>reviewTime</th>\n",
       "      <th>summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1KLRMWW2FWPL4</td>\n",
       "      <td>0000031887</td>\n",
       "      <td>Amazon Customer \"cameramom\"</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>1297468800</td>\n",
       "      <td>This is a great tutu and at a really great pri...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>02 12, 2011</td>\n",
       "      <td>Great tutu-  not cheaply made</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2G5TCU2WDFZ65</td>\n",
       "      <td>0000031887</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>1358553600</td>\n",
       "      <td>I bought this for my 4 yr old daughter for dan...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>01 19, 2013</td>\n",
       "      <td>Very Cute!!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1RLQXYNCMWRWN</td>\n",
       "      <td>0000031887</td>\n",
       "      <td>Carola</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>1357257600</td>\n",
       "      <td>What can I say... my daughters have it in oran...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>01 4, 2013</td>\n",
       "      <td>I have buy more than one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A8U3FAMSJVHS5</td>\n",
       "      <td>0000031887</td>\n",
       "      <td>Caromcg</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>1398556800</td>\n",
       "      <td>We bought several tutus at once, and they are ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>04 27, 2014</td>\n",
       "      <td>Adorable, Sturdy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A3GEOILWLK86XM</td>\n",
       "      <td>0000031887</td>\n",
       "      <td>CJ</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>1394841600</td>\n",
       "      <td>Thank you Halo Heaven great product for Little...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>03 15, 2014</td>\n",
       "      <td>Grammy's Angels Love it</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       reviewerID        asin                 reviewerName helpful  \\\n",
       "0  A1KLRMWW2FWPL4  0000031887  Amazon Customer \"cameramom\"  [0, 0]   \n",
       "1  A2G5TCU2WDFZ65  0000031887              Amazon Customer  [0, 0]   \n",
       "2  A1RLQXYNCMWRWN  0000031887                       Carola  [0, 0]   \n",
       "3   A8U3FAMSJVHS5  0000031887                      Caromcg  [0, 0]   \n",
       "4  A3GEOILWLK86XM  0000031887                           CJ  [0, 0]   \n",
       "\n",
       "   unixReviewTime                                         reviewText  overall  \\\n",
       "0      1297468800  This is a great tutu and at a really great pri...      5.0   \n",
       "1      1358553600  I bought this for my 4 yr old daughter for dan...      5.0   \n",
       "2      1357257600  What can I say... my daughters have it in oran...      5.0   \n",
       "3      1398556800  We bought several tutus at once, and they are ...      5.0   \n",
       "4      1394841600  Thank you Halo Heaven great product for Little...      5.0   \n",
       "\n",
       "    reviewTime                        summary  \n",
       "0  02 12, 2011  Great tutu-  not cheaply made  \n",
       "1  01 19, 2013                    Very Cute!!  \n",
       "2   01 4, 2013       I have buy more than one  \n",
       "3  04 27, 2014               Adorable, Sturdy  \n",
       "4  03 15, 2014        Grammy's Angels Love it  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read data from file \n",
    "def parse(path):\n",
    "\tg = open(path, 'rb')\n",
    "\tfor l in g:\n",
    "\t\tyield eval(l)\n",
    "        \n",
    "#parse data into a pandas dataframe\n",
    "def get_df(path):\n",
    "\ti = 0\n",
    "\tdf = {}\n",
    "\tfor d in parse(path):\n",
    "\t\tdf[i] = d\n",
    "\t\ti += 1\n",
    "\treturn pd.DataFrame.from_dict(df, orient='index')\n",
    "\n",
    "# if the data has been parsed:\n",
    "#   load it \n",
    "# else:\n",
    "#   parse it, save it and load it\n",
    "def possibly_get_df(path):\n",
    "    try:\n",
    "        df = pd.read_csv('use_item_ratings.csv')\n",
    "    except IOError:\n",
    "        df = get_df(path)\n",
    "        df.to_csv('use_item_ratings.csv', index=False)\n",
    "    return df\n",
    "\n",
    "df = possibly_get_df('Clothing_Shoes_and_Jewelry_5.json')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a data frame with users, items and ratings only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataframe after dropping unneccessary fields:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviewerID</th>\n",
       "      <th>asin</th>\n",
       "      <th>overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1KLRMWW2FWPL4</td>\n",
       "      <td>0000031887</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2G5TCU2WDFZ65</td>\n",
       "      <td>0000031887</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1RLQXYNCMWRWN</td>\n",
       "      <td>0000031887</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A8U3FAMSJVHS5</td>\n",
       "      <td>0000031887</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A3GEOILWLK86XM</td>\n",
       "      <td>0000031887</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       reviewerID        asin  overall\n",
       "0  A1KLRMWW2FWPL4  0000031887      5.0\n",
       "1  A2G5TCU2WDFZ65  0000031887      5.0\n",
       "2  A1RLQXYNCMWRWN  0000031887      5.0\n",
       "3   A8U3FAMSJVHS5  0000031887      5.0\n",
       "4  A3GEOILWLK86XM  0000031887      5.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_df = df[['reviewerID', 'asin','overall']]\n",
    "print \"Dataframe after dropping unneccessary fields:\"\n",
    "ratings_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Overall statistical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of user-items ratings is: 278677\n",
      "number of unique reviewers is: 39387\n",
      "number of unique items is: 23033\n",
      "average rating is 4.245 and standard deviation 1.104\n",
      "sparisity of the user-item matrix is 0.00030718\n"
     ]
    }
   ],
   "source": [
    "#number of rows in dataframe  which are the total number of ratings\n",
    "total_num_of_ratings = len(ratings_df)\n",
    "print \"total number of user-items ratings is: %d\" % total_num_of_ratings\n",
    "\n",
    "#number of unique reviewers\n",
    "num_of_users = len(ratings_df['reviewerID'].unique())\n",
    "print \"number of unique reviewers is: %d\" % num_of_users\n",
    "\n",
    "#number of unique items\n",
    "num_of_items = len(ratings_df['asin'].unique())\n",
    "print \"number of unique items is: %d\" % num_of_items\n",
    "\n",
    "#average rating and standar deviation\n",
    "ratings_mean = ratings_df['overall'].mean()\n",
    "ratings_std = ratings_df['overall'].std()\n",
    "print \"average rating is %.3f and standard deviation %.3f\" % (ratings_mean,ratings_std)\n",
    "\n",
    "# The sparsity of the user-item matrix\n",
    "sparsity = float(total_num_of_ratings) / (num_of_users * num_of_items)\n",
    "print \"sparisity of the user-item matrix is %.8f\" % sparsity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Users statitical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average number of items rated by a user is 7.075355\n",
      "The average number of items rated by a user is 3.585205\n",
      "The minimum number of items rated by a user is 5\n",
      "The maximum number of items rated by a user is 136\n"
     ]
    }
   ],
   "source": [
    "users_df = ratings_df.groupby('reviewerID').size()\n",
    "print \"The average number of items rated by a user is %f\" % np.mean(users_df)\n",
    "print \"The average number of items rated by a user is %f\" % np.std(users_df)\n",
    "print \"The minimum number of items rated by a user is %d\" % min(users_df)\n",
    "print \"The maximum number of items rated by a user is %d\" % max(users_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Items statitical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average number of users that rated an item is 12.099032\n",
      "The standard deviation of number of users that rated an item is 13.967952\n",
      "The minimum number of users that rated an item is 5\n",
      "The minimum number of users that rated an item is 441\n"
     ]
    }
   ],
   "source": [
    "items_df = ratings_df.groupby('asin').size()\n",
    "print \"The average number of users that rated an item is %f\" % np.mean(items_df)\n",
    "print \"The standard deviation of number of users that rated an item is %f\" % np.std(items_df)\n",
    "print \"The minimum number of users that rated an item is %d\" % min(items_df)\n",
    "print \"The minimum number of users that rated an item is %d\" % max(items_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Distribution of ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PxyOXy6U9e/bI5XJJks6cOaMFCxaotLRUDQ0NWr58uSorK8NCOgAAAGJTn4foxMRE1dTU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\no0TcJ1j+OooAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6af43ca290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IIUS50QBjDx7k0x49yJdpLsIBSAEoVLPzxx/5afx43khNVTuKEEKUOyPwyrZtvP/EE9jt\ndrXj3JIhQ4Ywa9YsVY49ffp0mjdvzoIFC1Q5fmUhewELVRzeupVZAwcy88wZ3NQOI4QQFShap+Pb\nIUN4ad48taOUWkZGBj///DO9e/dWPUfr1q2ZN28eHTp0uOLz48ePZ926dej1eqBwjrmLiwu7d++u\n6KgOT0YARYU7vX8/n44cyRQp/oQQVVCwzcZ9S5ey5O231Y5Sajt27OCbb75ROwbZ2dloNBpCQkKu\nepvnnnuOgwcPcvDgQSIjI6X4uwopAEWFOn/0KLPfe4nEpJPIMs9CiKqqWV4etWfO5MfFi1U5foMG\nDVi8eDHt2rVj/vz5AKxbt45u3bpx55130rFjR1asWAHAxo0bGTNmDJGRkTRt2pSYmBgGDBjARxd3\nOpk9ezYjR45kwYIFtGvXjlatWjF9+vSiY6WlpTFw4ECaNm1Kjx49+P3332nQoAFxcXElZjt16hQD\nBw6kZcuWtGnThilTpmCxWDh79ixdunQB4LHHHmPu3Lnl+S2q9KQAFBUm8fx5Pv/gTdq12cfKX8xM\nbGhim16ndiwhhFBFx9RU0idN4sD27aocf/Pmzaxbt45hw4YRExPD+PHjmTRpEgcOHGDatGm8/fbb\nnDhxgi5duvDcc8/RtGlTDh48SFBQ0BWPtX//fmw2G1u2bOGTTz5h6dKlHDp0CIAJEyZgs9nYtm0b\nM2fO5JNPPkGj0ZSYyWKxMHjwYJo1a8b27dtZuXIle/bsYdasWYSFhbFx40YURWHdunWMGDGixMfY\nuXMnPXr0oHnz5vTp04cjR46U3TetEpECUFSInIwMVnz0Lu6+v9Krcy4GAyz91syaB3UsdTGqHU8I\nIVTxVGwsPz/3HGlpaRV+7K5du+Lr6wtAUFAQO3fupE2bNgDcfffd+Pn5lbp40uv1DB8+HIPBQJs2\nbfD19eX06dMoisK2bdsYPHgwnp6ehIaG0rdv36s+zh9//EF+fj6jRo3CaDQSHBxMv379+PHHH4vd\n7mrtC8HBwYSFhTF//ny2bt3KXXfdxeDBg8nIyCjV11GVSAF4i6QT6vqsFgurZ87kTPIaxg4p/kfu\nw5kW0kfYeMvDhHQjCSGqohcPHWLWk09WeGdw7dq1i11etmwZDz30EM2aNaNJkyYkJydjKeW2nP9+\nLBcXF8xmM+np6Vit1mKfb9y48VUfJyYmhqCgIAwGQ9F1oaGhxMfHlyrHyJEjmTZtGtWrV8fd3Z1X\nX30Vo9HIJtmJ5QpSAN6gjIwMVq1aVXR54cKFjB49WpUcS5cu5eOPP2bo0KEl3iY5OZkhQ4bQoEGD\nUj+Jy5rdbmfj/PlsPbiUGWPiKWnU/4XhNtp9ZOZZbxOyD4gQoqpxAZ7ZtIl5r75aocfV6S5NwVm1\nahULFixgxowZHDhwgMjISAICAkr9WFptyeXEPyN1lxd0Vzv9C1z1tepa97lerlq1apGYmHhT96/M\npAC8Qc7SCXXixAl69+6Nr6/vTT9xysLvK5bz829fMPH507i5Xv12D7aHN9aYGRRgouRpwUIIUXmF\n2mzctmgRm1auVOX4hw4dokWLFrRs2RKNRkNSUlKZFE0+Pj7odDpiY2OLHetqQkJCiImJoaDgUpvg\n6dOni807vNZr2jvvvMPx48eLLlutVs6fP09wcPDNfgmVVpUvACtrJ1RKSgoff/yxqms2HfrrN9b+\nOI/720cSGnj924fUgsUbzYytb2KvNIcIIaqYjmlpnJwwgXOnT1f4sYOCgoiKiiIzM5PY2FimT59O\nYGAgCQkJAJhMJhITE8nIyLihM0parZYWLVqwePFisrOzOXPmzDUHUe699170ej1z5szBYrEQFRXF\n0qVL6dGjR9FtrrV8cUxMDFOnTiUhIYGcnBzef/99DAYDHTt2LHXmqqLKF4BQOTuh2rRpQ7Nmzcr2\nG3UDYqJP8MeW7/D2O0bPTuZS38/FBZZ/Z+aL9npWmqQ5RAhRtQw/fZrF/fuX+3Zx/37tefLJJwkJ\nCaFDhw6MGDGC/v37069fPxYtWsTy5cvp2LEjiqJw3333cfTo0eueWbr88zNmzCAzM5N27doxceLE\noteskk4bu7m5MW/ePHbv3s0999zD8OHD6d69O8OHD79q9svNmDGD0NBQevXqRbt27Th+/DhLlizB\nxcWlVN+XKkWp4urXr68sX7682HWZmZnFLrdr105ZvXq1oiiK8umnnypPPPFE0ef69++vfPjhh0Wf\nu+eee4rdt02bNsqaNWsUu92uNGrUSNm8eXPR577++mulQYMGSmxs7BW5fvnlF6Vly5aKxWIpum7Z\nsmXK/fffryiKosTExCj169dXoqKirvn17dq1S2nQoIFiNpuvebuylJGRoixd+IYyvH8dxXwQRfn7\n5j7ef9agzHA3KXZQFPmQD/mQjyrykQrKu/36Vdjf7IpgtVqL/r9jxw6lYcOGxa4TFU9GAKmcnVBq\nsVot/Pz9fPbt+Y1X+p3BeAuDeGNfttJ4uoXnvU2o08IihBAVrxrQafVqvr44vcjZTZw4kaFDh5KV\nlUVWVhaLFy+mbdu2Rdu1CXVIAUjV6ISqCIqisHnjIo4fP0KLuvupF3brSxo83Fnh5a/MDKxhIqkM\nMgohhDNolpeH5sMPVVskuiyNGzcOHx8fOnbsSKdOndDr9UybNk3tWFWelN//cnknFFAunVB16tQp\nOtbVXN4J9c+7pBvphFLD/t0/ce7sCbISf6bfkzll9ri3hcH8DWaG9DEx4ZyVpraKXStLCCHU0Ccu\njndeeIF6W7fi7u6udpyb5u3tzcyZM9WOIf5FRgD/pbJ0Qt3IbcpCfNxpDkf+zoE9P/Dmswll/vge\n7rBivZn/tTaw1mS4/h2EEKISGHngAP+9rAFCiLJS5QvAytoJ9cYbb9CkSZOiRaJbtGhB06ZNWbdu\n3fW/KTfIYsnn903LOXxoHyN6nrrmen+3QquFuQvNHH4CZrqZyucgQgjhQLyAe9atY8PSpWpHEZWM\nRqmoISIBUOyU7s6dOxkyZAgHDx502smwiqLw0w/zOXRwF97KKp59PKtCjrtqrZbtMwy8n2lGxgOF\nEJXdp/Xq0ef33wmoWVPtKKKSqPIjgBWpMnZCHfrrN2LOnSQuagPDelVM8QfQ+zE7Q5eaecbPRMVv\noS6EEBVrxIkTfDZoUIVN6xGVnxSAFaiydUIlJ8Xw175fOLBvM5OHl7zPb3lqVA/m/GhmeIiJvx2s\nIUYIIcqSAeizeTMr3n9f7SiikpBTwOKmWK0W1qz8gBPH/+aOgG/p8WCealnsdhg60ESfSDtdLFbV\ncgghRHlbGBpKx82bCb24moQQN0tGAMVN+ePXFWRlZZJ+YbOqxR8UNod8vtTMn49p+K80hwghKrFn\nzp3ji2efxW6X5bDErZECUNywY0d2EHP+GAf2bmbCYMfZmWTqVAuer1oZ52nCpnYYIYQoBzqg3x9/\n8OWMGWpHEU5OCkBxQ9LTktiz6wfi4y7QoclRfH3UTlTcgL52en9mZrCviYprSRFCiIpTp6AA5bPP\nOH3smNpRhBOTAlCUms1mY/NPi7DbICH6Z3p3ylU7UolaNoMP15sZEmgiSppDhBCV0IDoaFa88IJ0\nBYubJgWgKLU9O9aTm53B/r1bGPd0nNpxrqm6LyzfaOatxkZ+MzjvMjtCCFESLfDY1q1899lnakcR\nTkoKQFEqyUkxnDi2m5S0dBoGH6aWv9qJrk+vhy++NrOhi5bPXY1qxxFCiDLV2Gzm7KefkpUlE17E\njZMCUFyX3W7n983L0Wr1nDi8gWE9M9WOdEPefc9CwSgbkzxckL45IURlMvTIERa+8oraMYQTkgJQ\nXNe+XT+Sk53OoYN7GNHrPCVsXezwnh1so8ucfIb5mMhRO4wQQpQRTyBk3ToO792rdhThZJzwpVxU\npNSUeP4+8if5+Ta89LtpUs95F1hp1wreXmtmcC0T0WqHEUKIMtIjMZHvxo6VhhBxQ6QAFFelKAq/\nb16OTmcg8sBGxjydqnakW1bbH5ZuNDP+dhN/OvEezEII8Q8N0PvPP1n5ySdqRxFORApAcVWRB7aQ\nlZFMQkIibW4/hUsl2WTDaIQvV5tZeb+WZS7SHCKEcH71rVYS584lPT1d7SjCSUgBKEqUk5PJob+2\noDeYOHNiE091c8w1/27FzFkWUobaedvdhJw4EUI4uyHHj7PghRfUjiGchBSAokRbf10BaDh58hi9\n7j9PZV1PefTzBbT+0MwIbxP5aocRQohb4AbcvmED+7ZuVTuKcAJSAIornD55gIQLZwANGYnbua+l\nRe1I5apTBxj/jZmB/iYuqB1GCCFuQbeUFDZOnCgNIeK6pAAUxVitFvbsWI/B4MLBv3Yyoles2pEq\nRFgQLN5o5uXbTOzX6dSOI4QQN+2xXbtYv3ix2jGEg5MCUBSzZ8f3WK0WzGYrbvxFvbCq8y7S1RVW\nrDOzsJ2eb03SHCKEcE6NLBaOzZ2Lzea8y3aJ8icFoCiSk53BqRN70en0HNj3Ky8+lah2JFXMmWvm\nVH+F96U5RAjhpPrs2yfLwohrkgJQFPlz62p0OgPpGVnUCfgbXx+1E6nntbFW6r9t4QUvE5V7BqQQ\nojIKs9mI++ILzGaz2lGEg5ICUACQeOEcsdHH0Wg0HD34M8/1zlA7kuoe/T+FUcvNPFPdRIraYYQQ\n4gb1j4zky2nT1I4hHJQUgAJFUdj151oMBhOxsXG0bxKFUabAAdCgDny20czIMBOHnHETZCFElRUA\n5K5aRVZWltpRhAOSVzTB6VMHSE2OQ6PRcP70r/TpXPkWfb4VHu6w4gczn7Y0sN5oUDuOEEKU2tPH\nj7Nk4kS1YwgHJAVgFWez2Tiw52cMRhfOnDnDI+2iK+2iz7dCq4XPFps58LjCJ26VZE88IUSl5w0Y\n168nKSlJ7SjCwUgBWMUd+msLeTmF8/0SY7bzUBtpebiWN98oIGCClVe8TBSoHUYIIUphwNmzfDlu\nnNoxhIORArAKs1jyOXpoK3qDiXPnztGpdYyM/pVC3152nv7czDO+JqRVRgjh6FwA/59/5vyZM2pH\nEQ5ECsAqbPef67HZ7ABciN5O1/ayXEBpNbsDZv1gZliwieNaqZqFEI7tibg4Vk2apHYM4UCkAKyi\nMjNTiTp1AJ1OR0xMLPc3l7l/N8rXB77aaObdZkZ+keYQIYQD0wO+W7fKXEBRRArAKmrXtjXodIVF\ny09bNhBeO1/lRM5Jq4XPl5n57WEN81ylOUQI4bieiI7m6ylT1I4hHIQUgFVQWloicTEn0Wg0xMYn\nEnGfO+9vD6Pra75s3ad2Ouc0fboF48sFvO5hQnbfFEI4IjeATZvIyclRO4pwAFIAVkF7d3yP3lA4\nWrXvxF5adHGnw5O+PDIhlM+PR9BlnC/rtsivxo0aNMBGj3lmhlQzka12GCGEKEHfEyf4+r331I4h\nHIC8ylcxmRkpxMWcQKPRkJKWgXtwJlpd4eQ/vUFD2+4+9HgzlB/SIug8rjpL1utVTuxcWjeH99ab\nGVzbxBmZVCmEcDDVgfR16ygokIWsqjopAKuYPTvXF43+7Ty0i7u6ul5xG61WQ4suXvSaHMxefThd\nJtTgkxXS5FBa/n6w/Cczk+8w8rteCmghhGPpHhnJd599pnYMoTIpAKuQzMxUYs8fR6PRkJ9vQeOd\nisF49V8BjUZD4w6e9JoUTEztCLpM9GfKXBN2ewWGdlJ6PSxZZeb7zloWu8jGykIIxxFhtxO1bBmK\noqgdRahIo8hvQJWx/KuZxJw9RG3/QP7Ys4sGj8fjWe3GRvbOHcln3/oUbvfJ4L3R+cgA1/X99zMd\nCfP1TM42yzsuIYRD2OviQsby5TzYo4faUYRK5PWoisjNzSY5M5MCozeHz5wiMf/MDRd/AKF3uNDz\n9UB8OtXh0ek1efY/buTmlkPgSmTkszbum2nmWR8T8q0SQjiCFvn57Pvf/9SOIVQkI4BVxE9b1nAq\n6iharZbjx4/h3nAPYY2unP93o5Jjzfy5Kg0fSyafvpKNj1cZhK2kYi7AmL5GPkqwEKh2GCFElbfR\n25taP/9M01at1I4iVCAjgFWA2ZzPydOFxR9AdOKRMin+AKoHmnj0pZo0GxLOM/OC6DPJk7jEMnno\nSieoJnz6NXGGAAAgAElEQVSx0cKr9U3sknPnQgiVdc7IYNOHH6odQ6hECsAqYNf+31CUws6NlORU\n/CLKfhFQLz8D3Z73p+2ocEavCKb7696cPlfmh3F6Li6w/Dszyzro+MokzSFCCPVoAI+9e8nKylI7\nilCBFICVnN1u5/jJw+gvjjhF/r2LOx9yKbfjuXvp6TysBg+ODWPihlAefs2H/X+X2+Gc1qzZZuIH\nK0x3NyFzMIQQaukRFcWa2bPVjiFUIAVgJff3ib/IySvcl8JisaL1TEZvKP8fu4ubjgef9qPr+DA+\n3hlG13G+bNld7od1Ki+PttL8XTPPeZswqx1GCFEl+QNJGzeqHUOoQJpAKrnl384lIzMNgD17d3L7\no2fwqVHxizrbChT2/JBJ7IF0RnRJo/sDspjgP85Ew/h+JmYlmfFXO4wQN+GB8HDiSpjXepvFwvpz\nhXNBfnV35zNfX04bjdiAO/PzeSU5mTvM1377E6vX858aNdjh5oYOaJmXx6TERGpd3MnijMHAazVr\ncspopFl+Ph/Gx1PtX4uVflC9On+bTCyMjS2Tr7ey+cnLi9pbttC4eXO1o4gKJCOAlVhCYhyJSfFF\nl7Os0aoUfwA6vYa7H/Omx5sh/JRVh86vVWfRWmmEAAgPhs83mnmxrokDOp3acYS4KUPS0tgeFVXs\n48voaAD+cHPj+dq1uTs3l2/On2dFdDSudjtPBwURe42GqCytlgHBwQCsPH+ez2NiuKDXMzTwUh/9\n2/7+1Cwo4Nvz58nTavm0evVij3HcaOQrb2/eSkgoh6+6cngoM5PNn3yidgxRwaQArMR27f8No7Fw\n27fC5o88lRMVbjPX/CFPer0ZzF8uEXSZUIMPl0gzhJsbLFtr5rM2elabZNs94Xxc7Xb8bLZiH94X\nR+K+8/IiyGrlpZQUQq1W6lssvJWYSI5WyyYPj6s+5hIfH6waDR/Fx1PHaqWR2czH8fG8mJKCBSgA\ndru5MSA9nXCrlZ4ZGWx1cyv2GFMCAhiemkqQ7H17VVrAsGsX+fn5akcRFUgKwEoq35zHuejTaDQa\nAA4f20OT+8qv+eNGaTQaGrX3oNekYBIjIugyKYA3/ud428wNnhxIg+63EZd09VEKixXeXVSdDoPD\nadSrLl2fD2X15uILIm7a6U6nEWG07l+Hyf8r+UTvU+ODadbZjWN94UNpDhGVzL9fbAylmH30i4cH\nD2Vnc/lbxBCrlU4Xr0vV6bABNS8Wd4EFBSRcNqL4lbc3OVotg9PSbjl/Zffo8eOsXbBA7RiiAkkB\nWEnt2b+16P92ux2bMfma+/6qqW5zN3pNCIRWEXSbUpMXP3TFYlE7FXyzyYvdh924WENf1eT/BbB2\nixdTRyayYc45+nbJYNLsADZsLxzZyDdrGD+rJo8/lMHnb8WwcbsnG7cXH/VYscGbnDwtQ3ukMeF1\nK+FTrLzoZcJaXl+cEBXoiYwMYg0GvvTxwQ6YNRpm+fnhbbfT9SpLkBQAp0wmgi0WPvbz48GwMNpG\nRDCmZk1SL65pqlz80FwsJjWKUvTGKVmn4+Pq1ZmWkIBMrLi+YCBm7Vq1Y4gK5JgVgbgldrud46cO\nFS39curUaW5r4/jjSSG3u9Lz9dpU7xJBj3dqMXSGu2rbzCWm6nhvUQ36dk6/5u3ikvR8t8WLlwck\n06FFDsE1rTz9SDr/1y6LWcv9APjruAtmq4ZBj6VxRx0z996Vw9b97kWPkZyuY+ay6rz9fAL/TAHs\n+bCdEV+aecbPRGq5fZVClJ3DLi4MCwykfUQE94WH86a/P6kXf6Fb5+XxYXw8H1avTpPbbuPOunXZ\n5OHBopgYathsJT5ehk5HAfBFtWpYNBrmxMUxNSGBva6uDA4KAsDXZkMHJF/8W5eg1+N/cTTwPzVq\n8HBmJnfk5/NO9eo8EB5O19BQvvP0LPfvhbOqs38/p0+cUDuGqCBSAFZCp88eIysno+hy9IUjhN1h\nUjHRjakZbqL72FrUfSKcJ2bWpv9kD1KvXYeVuanz/Gl+ex6d2mZf83bbDhQWch3uKr64dvvmOZyN\nMxKToCc5XY+vlw3DxTNTtWtYSUi9dJpqxoIadG2XRbP6xeffNLwN/rfRzIhQE0e18lQVjsuvoIAc\nrZZ+6eksiolhTHIyv7u7MzAoCAuw29WV8QEB9E1PZ8X58yyOiaFxfj7P1a591SaQgotD78FWK68l\nJ9PAYqFjTg5TEhM5ZjKxyd0dA3BXXh7feXlhBdZ7eXFPbi7b3dzY4+rKy8nJfOXtzRYPD9aeO8d7\nFy4wsWZNzhhknm1JuqWm8sMHH6gdQ1QQeVWphCKP7sXFVLjVW36+GZNfZtFcQGfiV8vIoy/V5M5h\n4QxeEETviV7EXCj/427Y7sGOSDfeeu76XYNnYg0Y9Qr+vsVHMUJrFZ68jYo1oihwef2m1cA/05+2\n7ndjzxE3xjydXOLje3nAVz+a+aiFgQ1GedESjmlVdDRLY2K4LyeHuhYLj2Rl8c6FC5wyGvnR05N3\na9SgWX4+ryUn09hsplVeHh/Fx2PTaJjv61viY7pfHBls9K/GhJZ5hc1sx0yFb2rHJyWxycODprfd\nRrxez9C0NN7y9+eNpCQ8FIVdbm50yMnB026nkdlMXbOZnf9qFBGFDIB91y7sjjYZW5QLKQArGbM5\nn9j4s0WXIw/tp9lDzl04ePka6DbSn3ajw3h5ZQjdx/tw8mz5HCsjW8v0+f6MeTqZAL+ST01dLidP\ni7vrlX8sPdzsKApk52ipXq2AtExdUYNLQqoef98CzBYNU+f5M2lYInlmDS+8U4t7B4fT97VgDp28\nNGKr1cKCL8zs7Amz3ZxnJFdUbQ3MZhQgUa8nymjktn9N7DUAgVYr564yGuehKFS32Uj/19JI/zzb\nPC4+oW43m9kWFcW2qCh+PHeOb728qGOx8FB24eh9plaL+2UFjbvdToYst3RVbY4fZ+eWLWrHEBVA\nCsBK5q/DO4tdzi6IU23tv7Lm5qmn09DqdBwbyps/hdJtnA97j5TtMabP9yekloWn/i/j+jcupab1\n8tFqFTZs8yQ1Q8cf+9y5p1kuc772pU6whc5ts5k23x+dFrbMP0OXe7J44Z3a/LtJ8q3JVqq9bmWs\np4nrl6ZCVIzTBgOvBQQQ9a9CLtLFBQ0QbrVS22rllLH4ck9W4LzBQOA1lmfpkJPDVnd3Li8d97i6\nogHq/2sBaV+bjVNGI1/5+DAlMbHoei+7nZTLCr4UvR7vq8w7FNDKbGb30qVqxxAVoFIVgEOGDGHW\nrFmqHHv69Ok0b96cBSq30Z84fRSDvvAPcW5uHm7Vc65zD+djctXxwAA/uk0IY9aecP5vnC+bd17/\nftfzx343ftnpwbTnL536vd5KFZ7udnLyrnwaZeUUXuflYcfNRWHSsCTGfxpA+0ERNKufR90QM19t\n9GHKiMIXql2HXHm4QyY6HfR8IJMLKXqiYq8s3Pv1tvPkQjPP+JrIvIWvVYiyUquggD1ubrxcqxZ/\nurkRo9ezyd2dtwICuM1i4YHsbAakp/OnmxuzfX2JMhg4ZjQysWZNsnQ6emZcerPVJSyMxT4+RZef\nTU0lX6Ph5dq1OWMwsN3Njen+/tyZl0ebvCvXNZ3s78/zKSkEXFZUtsnN5VcPDw6bTKzz9CTGYKCN\nWt1lTkADcPAgsklY5efUWzFkZGTw888/07t3bwAWLlyoWo6lS5cyb948OnTocMXnFUVhzpw5rFmz\nhvT0dG677TbGjh1LixYtyjRHSloSKakJmEyF6/0dORpJk+6VY/SvJAajlvZ9qmEr8GHFhmq8vyad\nYQ+l0avjzc1f2bDNE7NFwyOjw4quU5TCj4eGh9OqUS6LphbfSioi0IKlQENCip4Av0svOmfjjGg0\nUDe4cOyixwOZPNIhk9w8LV4edvqND2LkEynUrF54n8xsXdGp5H/+zcjSQQkLwdzVGGZ+b2ZoHxMz\nYi3UlT/UQkVuisKX0dF84ufHhIAAUnQ6qtls3J+Tw0vJyeiAvheLvOU+Pnzm64sOaJifz/yYGJpf\nNsfvnMFA2mWjdaFWK0tiYni3Rg16hoZiVBQ6ZWXxelLSFTlWeXmRr9UyIL14x1jvjAxOmEwMCgrC\nw25n+oULhFllgaVraXH8OHu3b6dlu3ZqRxHlyKkLwB07dvDNN98UFYBqyc7ORqPREBISUuLnFy1a\nxOrVq5k/fz4hISHMmzeP559/nl9//RV3d/cS73Mz9h7YhsFw6TRLljUWL7/KWwD+Q6fX0PoRb1p2\n82LzZl8+ey2Nx1unM6znja38/3L/ZIZ0L75gbORJFybODmDB5BhCal35otG+eQ4a4Nfd7jx52Wnj\nX3Z6UD/UXFTgAeh1hSOCK3/2Js+sZeAjl16ovDxspKYXPh2TL/7r7Xn101R+1WDFRjND+psYcMTG\ng1bZ5UCop3ZBAe9eZ6u1vhkZRYXg1fx98uQV1zU0m/kiJua6GXpnZtI788pxcT0wOTGRyZedFhbX\n1jYvj08XLZICsJJzqFPADRo0YPHixbRr14758+cDsG7dOrp168add95Jx44dWbFiBQAbN25kzJgx\nREZG0rRpU2JiYhgwYAAfffQRALNnz2bkyJEsWLCAdu3a0apVK6ZPn150rLS0NAYOHEjTpk3p0aMH\nv//+Ow0aNCAuLq7EbKdOnWLgwIG0bNmSNm3aMGXKFCwWC2fPnqVLly4APPbYY8ydO/eK++r1el57\n7TXq1KmDwWBg8ODBZGRkcLKEP3Y3y263czbmFNqL7ab5eWZcfK69hEllo9VquPPiNnNHPCPoMsGf\nd78wlHp3EX9fG3VDLMU+ggKsKAqE1rYS6F9A5EkT//d8KPv/dim6z1P/l84ny/3YsseduCQ981dX\n4/e97rw84MrO3pR0HR9/6ce0UQnFFphu0ySXr37y5mycgfnfVqN2jQLqBF17lEKng8UrzGzqqmWB\nqzSHCCHKhhZQ5DRwpedwI4CbN29m3bp1+Pr6EhMTw/jx41mwYAFt2rRh586dDB48mLvuuosuXbpw\n6tQptm3bxldffVXiY+3fv5+mTZuyZcsW9u7dy6BBg3j00Udp3LgxEyZMwGazsW3bNlJTU3n55Zev\nulSKxWJh8ODB9OjRg/nz55OQkMCIESOYNWsWY8eOZePGjTz44IOsW7eOsLCwK+7/9NNPF7scHx+P\nRqPB37/kLcFuRtS54+TmZhUt/3L070M07upwP94KodFouOMeD+64x4NTB3zp+mYqd9XK4O3nzNzM\ncnqX/1rkm7WcjTOSm3/pgSYMTcLD3c5bc/1JzdQRHmhh5rh4Otx15TyjdxbV4LH7MmkYUXwC++uD\nk5jwaU16vhJCaC0rs14r+Y1ISf7zjoUFDXRM+K+JaVlmx3pXJ4RwSk2PHePg3r00a9lS7SiinDhc\nhdC1a1d8L64LFRQUxM6dO/G8uHL73XffjZ+fH0eOHKFevXrXfSy9Xs/w4cMBaNOmDb6+vpw+fZpG\njRqxbds2PvnkEzw9PfH09KRv375Mnjy5xMf5448/yM/PZ9SoURgMBoKDg+nXrx8LFixg7NixRbcr\nzbsli8XCpEmTeOyxx6hdu/Z1b19aR4/tLyr+ANLzoqkWYLzGPaqGune6UfdON2KOV+fht1Kp65nJ\nB6NzMZbyW9OqUR5/rzl51ctQuEzLS/1SeKlfynUf7/2XS17IMMDPxsIpsSV+rjSGPmNj+x02hrxo\nYnaambKbWCCEqIra5+Tw3wULpACsxByuAPx3UbRs2TK+/fZbkpKSsNvtWK1WLKXcKPbfj+Xi4oLZ\nbCY9PR2r1Vrs840bN77q48TExBAUFIThsmUOQkNDiY+PL1WOf+Tk5DBy5Ej0ej1Tpky5oftei91u\nJ/ZCdNFli9mC0ScLkMVO/xFU34Wg12qTcM6PHu+mEkAGs17JxaMSVUr3tIQ6a80MesLEB/FmSp6R\nKoQQ16cHbAcOoCiKU24kIK7P4c4W6S7rAFu1ahULFixgxowZHDhwgMjISAICAkr9WNqrnO/7Z6Tu\n8oLuWr/gVys4b+RJkZqaSv/+/fH29mbhwoW4uLiU+r7Xc/b8SfLNl043Hj12hDvulYVOSxIQaqL7\nmFrU7xfBk7MC6femB8mVaLPdmjXgy41mJjQ0sU0vvwNCiJt3x7FjHI2MVDuGKCcOVwBe7tChQ7Ro\n0YKWLVui0WhISkoisQw6uXx8fNDpdMTGXjrldujQoavePiQkhJiYGAouW1vq9OnTBF3ckByuX0CO\nGDGCxo0bM2vWLIylPf9YSkeO7cdkvFRQpmXHUCNImgKuxbemkUdfDOCu4eEMWxTE4xO9OH9jA7oO\ny2iEL781s+ZBHUtdZBqAEOLm3J+VxZaLDZmi8nHoAjAoKIioqCgyMzOJjY1l+vTpBAYGknBxuQGT\nyURiYiIZGRmlPi0MhSODLVq0YPHixWRnZ3PmzBm++eabq97+3nvvRa/XM2fOHCwWC1FRUSxdupQe\nPXoU3eZa8/8WLlyI0Whk6tSppc5YWna7nbgL54sKUEVRwCRLBJeWZzUDXZ/zp/3ocMZ8E8Kjr3vz\n92m1U5WND2daSB9uY4qHCenlE0LcKANgP3xY7RiinDhUAfjvUbQnn3ySkJAQOnTowIgRI+jfvz/9\n+vVj0aJFLF++nI4dO6IoCvfddx9Hjx697inZyz8/Y8YMMjMzadeuHRMnTmTEiBFAyaeN3dzcmDdv\nHrt37+aee+5h+PDhdO/evajBpKTsl1u9ejV//fUXTZo0oWnTpkX/lrRkzI2KiTtDbt6l3T5iY+IJ\nbChrwt0oN08dnYZUp9Or4by9OYxur1VjZyU48/HCCBvtPjDzrLeJK/dNEEKIa/M6dYrMEtZXFM5P\no1ThhX4KCgrQ6wv7YHbu3MmQIUM4ePBg0XXO4IdfVnIu+lRRAfr7tk3cOywVg9GhanunU2C1s3Nt\nBgmHMnileyqd7lE70a05Hw+v9jXxcaKZsus9F0JUdmeAw0uW8MiAAWpHEWWsylYJEydOZOjQoWRl\nZZGVlcXixYtp27atUxV/iqIQG3++2OijXZ8uxV8Z0Bu0tHu8Gj3eDGVVXB06v+bH1z85b1NFSC34\n4iczY+uZ2CvNIUKIUgoHon75Re0YohxU2Uph3Lhx+Pj40LFjRzp16oRer2fatGlqx7oh8RdiyMm5\nNDRvMVswecsm52VJq9PQqps3vd4M4Q9rBJ1er87cb5znTcLlXFxg+Vozi+/RsdIkzSFCiFI6cULt\nBKIcVOlTwM5u0+/rOHH6cNEI4JEjR/Bvc4Ba4WW3xIwoTlEUju3K4eimdO6tm87rz1huancRtb3/\nkQHrci3jc8zICl9CiGtZ6edH+0OHqFWrltpRRBlywpcu8Y8LCTHFTv8mpp6lZpgs/1KeNBoNt9/t\nQa9JQWQ1rEO3yQG8/qkLBU7Wd/PqK1YaT7fwvJeJ0vfPCyGqovtTUvh1+XK1Y4gyJgWgk8rNyyE5\nrfiaiBqXbFmxvQJFNHGl5/hAXNpH8Oi0mjz/nhv5+WqnKr1HOiu8/LWZgTVMJKkdRgjhsGoAybt2\nqR1DlDEpAJ3UsZORaC8r9nJz83DzdaLqoxIJrOdCj3G1qf1oBI9/UItBb3uQma12qtK5LQzmbzAz\nKtzEQZ38ORBCXMXx46Xa7144D/mL76TOx0RhMFyayH8mKoqI5tLdqSb/ECOPvVKL2weE0X92IE++\n6Uliitqprs/DHVZ8b+a/rQ2sNRmufwchRJVTPyqKE8eOqR1DlCEpAJ2QoigkJMUVuy4p/TwBoTL/\nzxFU8zfyyOgAWo0IZ8QXQfSc4M252OvfT01aLcxbaOZQH/jYTX6PhBDFtc/OZqvMA6xUpAB0Qimp\nieTkZhW/0pgj8/8cjIePnv8b4c99L4Xx6nchPPq6D0dOqZ3q2iZNsFL7DSsvepqwqh1GCOEw3IGc\nyEqwPZIoIgWgEzp64gAm46VRGluBDZ2LrP/nqFw9dDw0qDqdx4UxfUsY3cZV48+/1E51dU90tzN0\niZln/Eykqx1GCOEwNOfOqR1BlCEpAJ3QhcTYYnsWR0fHEtJExUCiVIwuWu7v58vDE8OYfziCLq/6\n8sNWtVOVrHEDmPOjmWEhJo7JyLIQAgiIjeXChQtqxxBlRApAJ6MoCimpxRftOB93ktCGsvizs9Ab\nNLTt6UOPN0NZm1CXLq/5sexHx2vg8fGCrzeYee8uIz8ZpTlEiKquRXIy+379Ve0YooxIAehkUtOS\nyMvLKXadTZuF3iA/Smej1Wlo2dWLnm+GsJMIOr9endlfO1ahpdXC50vNbH9Uw/9cpTlEiKosAoja\n6qCnLcQNk6rByZw4fQSjsfg+rhpjzlVuLZyBRqOhyX2ePP5mCGcDIugy0Z+p803Y7Wonu2Tq2xbc\nxhbwqqcJm9phhBCq0ABER6sdQ5QRKQCdTEJSHDqdvuhyZkYWXgFOtg+ZuKr6rdzpNTEIc5MIuk0O\n4NVPXB1mm7mBT9no85mZQdVMZF3/5kKIyuj8eVkQupLQKPKTdCoLv/wIi9VcdPno0b/xb7OPmmEy\nBxDgsxdyyEy58le6epCWZ95zu+L6jCQ781+8soNaAzTrZODBZwpPe57cU8Dvy83kZyvUv1vPQ0Ou\n/H4vn5xLnTv1tO5uvOJzNyv2VD67V6cS6pLJxy/l4uIAP+akVBjZx8S7cRYi5M+HEFXK6mrVaH3o\nEIGBgWpHEbdIf/2bCEeRm5tNVnYGJtOlKiApNYZGwTI3q4gGWj5soEW34nPpdLqrd7JqgMdecaHW\nbcUHxA2mwvtYLQob5+XT6lEjYY11rJqRR0ijAuq3vvT0+esXK5Y8aPlo2c7hC6xbuM1cUowfj3+Q\nhp89g09fycHLo0wPc0Nq+MKKjWYG9zMx+G8b91kdZIhSCFHuWqSlsffnnwkcNEjtKOIWySlgJ3L6\n3LErFntW9Llor1HcVEUGkwZ3b22xDxePq3+PFMDkfuV9jC6F94k/ZcNmgRZdDQSE64hopufswUtF\nT066nW0rzXQaZkKrLZ+fRY0gE4+9UpNGAyMY8N9A+r7hyYWk69+vvOj1sORrMz901vK5S9mNeAoh\nHFswcG7HDrVjiDIgBaATOR9zBqOx+GifxpCvUpqqIydNwdVLg05fWNx5VdeQlXrp1OeWJRYatDFQ\n+7byX8rFp4aBh0cFcPfIcJ7/Mpge4705o+Kc7Pfft2AdZWOShwkH6lkRQpQTDcD582rHEGVACkAn\nkpaRXOyyxWLF6G6+yq1FWSo28KqlcNgQOHOwgJhjNtr3rdhRMHdvPV2G1+CBV8J4fX0ID7/mQ+Tx\nCo1QZPgQG13mmBnmY0L2oxGi8tNER0sjSCUgcwCdhN1uJyMjtdgOIPGxF6hVX8VQDiohysa37+aR\ndM6ORgsRzXTc08eEm9fVT88e+9PKHyvMpCcouHrAHR0MtOhqQKfX4O6tIS9LQbEraLQaslMVPKpp\nKLAobF5k5oGBJgouzhOMP2XHq7qGB542UbNO+Y8Iurjr6PhMdaxmX95bnU7q55mM75NK+7vK/dDF\ntGsFEWvNDHrCxAcXzARX7OGFEBUoJC6O6OhoQkJC1I4iboGMADqJzKx0zJbip3tjE84QVE8aQC7n\n5qXBkg/NHjLw+ARX7n3SSNQBGyun5WEruPIdq1YL7t4aCixwXz8TvSe40KiDgT+/sbDp88LR1Zp1\ndWi0cHxnAbmZCmf+shHaWMeO1RZ8A7XUa61n82ILWq2GZz91o35rPWs/zq/Qd8gGk5YOT/ryyIRQ\nPj8eQZdxvqzbUrFP79r+sPQnM6/fbuJPvby3FKKyapCezrF9+9SOIW6R/JV2EueiT6HXFf9xWWxZ\nuLg53hZiauo/rfhSL9WDtLh5a1g1I5/jOwpo2L54l66nn5bn/ude7Dr/UB2WfIVd31lp28uOp5+W\nBwaa2DjXjN1upk5zHdWDtGxeZGbgu4XHiz5aQOdnXdBqNdzRwcCWLy2kxin4BVZsg47eoKFtdx/s\nj3rzw8++zBmXTr/26Tz9SMV06hqNsGy1mRdHGzmz1Ui/fEuFHFcIUXEigN927YIePdSOIm6BjAA6\niQuJMRgM/9oBxCDz/0rDP7SwSM5KK/2I3D/3yb54n0YdDIxe5M7Iee489oormxebadPTiKdf4VMo\nPweMF1fnMbpy8Tr15shotRpadPGi1+Rg9urD6Ty+BjOXV9w2c5/MspA8xM5UdxMyU0iIysUAWGNj\n1Y4hbpEUgE4iPSP1yiv1UgBeLiXWzo//zSc1rng/avzpws3LfGtd+et+am8BG+bmo9iVK+6j0YK3\n/6X7aHUaXNw1RG62YjVD8/+7VFC5uENuZuFj5KQrF69Tf3kejUZD4w6ePP5GMLGBhdvMTZlbMdvM\nvTiqgNYfmBnubUJ61YWoZBIT1U4gbpEUgE4iIyut2GWbzYbOJKfXLufppyHmbxvrZ+Vz7lABGUl2\nTu4pYNNCMzWCtdS5S0f8aRufj8kh9kRhUejhq+HYnwWsn5VPwhkb6Ql29v9k4cBGK43v11/ROJKT\nYWfrSgudhpmKrckY0kjPwc0FpMXb2b3OipefBr9Ax3p61WtZuM1cQYsIuk0J4JWPXMp9m7nO98H4\nVWYG+pu4UL6HEkJUpIQEtROIWyRbwTmB3LwcPlvyHibjZTuAJKaQbPqexve6X+OeVU9msp1tKy1E\nH7WRm6ng6qmhTnMd7fqYcPXUEH20sCGk1+suhDUpnFMZc8zGzjUWEs/aMecpeNfQ0KiDgZYPG9D8\na2HnH+fk4+ql4f4BxZtvslPtbJxnJu6kDZ8ALZ2GmagZ4djzM+OjzOz8NoUgQyYzX8rF7cqd8spM\nbi4MecLEq2cKaG6zld+BhBAVYlFgIH2OH8fdXV6DnJUUgE4g6uxx1m5chovJtei6o0ePEnDPfgJC\nHGBzWOHUkmPN/LkqDR9LJp++ko2PV/kcR1Fg1AgTD+xS6GWW0WshnNnvJhN+e/fSqFEjtaOIm+RY\n557uED4AACAASURBVKhEiaLjooqN/gEkp17Ar5YsASNuXfVAE4++VJNmQ8J5Zm4QfSZ5ElcO03s0\nGpgzz8zJ/grvSXOIEE4twmzm9N69ascQt0AKQCeQlZVx5R7A2nz0BvWbDETl4eVnoNsof9qOCueF\n5cF0f92b0+fK/jivj7VSb6qFF7xMyDigEM4pEIg9dEjtGOIWyDqATiA7N+vKK6UDWJQTdy89XZ6t\nQX6uLxNXpZN9NoOpz6TT/PayO0b3rgoN6psZ+IyJ2clm/MruoYUQFUAL2JOS1I4hboGMADqB7JwS\nCkCdFICifLm46XhwoB9dx4fx8c4wuo7zZcvusnv8BnVg3gYzz4WZOKSVP0VCOJ3k5OvfRjgs+avr\n4Gw2G7m52Vdcr9FbVUgjqiKDUcu9T/jy6KRQlp6OoMs4P777tWz+dHh5wFc/mJnV0sD3xopbqFoI\nUQZSUtROIG6BFIAOLis7A6uteLGXl5ePi2cFrOQrxGV0eg1tHvOhx5sh/JRVh86vVWfR2lufRaLV\nwvzFZvY/rvCJmzQ2CeE0sko4OyWchhSADi4hOQ6dpviPKT3t/9m77/Ao66yN49+pmfSekE4SSkJJ\npYUq0lFA7B0FAQv2tpZddV0Qu7LWta4Nwc7a1lXpEHovUhNIgBBIb9PfPygvSCYJkMwz5Xyua681\nM0+e3AGSOXN+rYqQdgoFEl5PrVaRMyyQy/6WwHpDCiMfieTFj/TNf2Iz/vZXC1GPmLk30AfnnFws\nhDgfupoaTCZZyuWupAB0cSUlRej1p3dFjh4tpbbaiLFeuoBCOSqVim4DArjsrwmUJKcw8vFo/vrm\n+R0zd83lNiZ8YOSmMB8qWy+qEKINRNbVcUTmAbot2Qjaxf3wyxz2Fe857bHVv68gu05FaX0t1RYT\n9XYz9TYTDZiox0SDyoghAgITbUSl6Inv6EtgqCz4Fm1v37Z6Vn9XTueQSp6bVo/+HBuDZRUw9Uof\n/lFsorNNfkUJ4YqW6HQErFxJVlaW0lHEOZCqwMXV1p+5AASTleu7556xN+AJVpuNo3V1HKqqpnBR\nFQXfl3PAVE+93UK93UyD/XihiBFNsJWAeIhMVROb6ktYOx1qWZEpzlFiui+J6b4c2hvO+JllxGir\nmHVP7VkfMxcWAnN+NjLxeh+u32xlqFkGhYVwNVFmMwV794IUgG5JCkAXV19fd8ZjOjsOiz8AjVpN\nVEAAUQEBZMTEOLzObrdT2dDAoepq9q+ponB+JXvqa6izmU8Wiie6ijaDmYBYCE2B2FQD0e0NaLVS\nKIrGtUv24ZIHYjh6MJyrXikj2FjFrHtrCAtp+T3UavjwMyOPPKpn988+TK2XrY+EcCVRwMrdu5WO\nIc6RFIAurr6hkQKwlYbEVCoVIb6+hPj6khYV1eS1dSYTJTU1FO+spmBNBftrqqi1mmiwm2nATL3d\nRD1GLDozvtEQlGQnJlVPbKoBg5/8M/NW4TF6xt7TjqqycCa+U46uooqX76wi/iwWMT0zw8T76Roe\nnuXDjBojmraLK4Q4C8FA5YEDSscQ50hemV2YxWLBaGpArzt9IpVWgbUffno9yWFhJIeF0Z8kh9eZ\nLBYO19Zy8GA1hTsq2VdVQY3FdKyryImC0UiDyog+3E5gop3oVB1xHQwEhZ3/SlLhmoLCdFx0RxR1\n1WHcO6cC84FKnr+1ko7tW/b5E2+wkt/FyqQ7fXit3EhAm6YVQrSECqCmkWlKwi1IAejCqqorOFJ8\nCN8Afwx+vvgYDKjVapf+S9NrtcQHBxMfHExP4h1eZzsxT7Gmhn1LKtn7QwUHjXXU2y3HisTj8xTr\nMaIJshEQZyM8VUNcBwPhMXqZp+im/AK1DL8lAmN9KH/7soKKHWU8PbGaHl2b/9w+uZA8z8jNV/nw\n3EETybJ+TQjlyV6AbsuVawmvZ6ptINQSCpV2jKU11FjLQaXCXu/++y6p1WoiAwKIDAigezvH44En\n5imW1NSwf30VBQsr2FVXTcPxQvFEV7EeIzaDBf8YG6EpKmJSDMS0N6DVS6Hoinx8NVx4QzhmUyiv\nflXO4fcqeOjKSob0afrzoiNg9n+N3HyND5O3WxlokcUhQihKCkC3JQWgC6sqLyc0LBzdn/bS+GNH\noUKJnO/UeYqdIyObvLbOZOJwTQ3Fu6opWHtsnmKd9dj8xBPzFBswYdKa8Iu2E5gEMSl64jrKPEWl\n6PRqBl4TjtUSxqf/Kef5L6uZPKKcy4Y5nueg1cLHXxh54H49e3/XM6HB/d8QCeG2pAB0W7IPoAtb\nvWgRW9etO2O4c/f//sc9ubkKpXJ/ZquVwzU1HKyuprC6isKqCqrMDce6iicLxePzFMPsBCTaiU7R\nEtfBl+AImafYlmw2Oxt+rWb30gou71PB5Mua7vC98S8Nh97R8mSNUXa1F0IBr+XkMG3NGqVjiHMg\nbQ8X1lBff0bxZ7fbUUvNfl50Gg1xwcHEBQfTo4nrbDYbZfX1HKquZt+yKgp+qmCHse7Yxtt/nqcY\naCMg3kZ4iobYFAMR8TJP8Vyo1SqyhweRNSyQLcvCGPmXCgZ3LufBCWYa++O8fYqV37tamfyAD/+s\nMHKW2w0KIc6Tymxu/iLhkqQAdGEm45n7nhlNJgLP9XgFcVbUajUR/v5E+PvTrZl5ilVGIyXV1RRt\nqGLv4kp211YfLxLNJ+cq1mPE6mMmIMZOcLKK2BQDsSkyT7ExKpWKrv0C6NovgF1rwxj9WBk5sZX8\n4w7jGYXghf2g4zdGbrpaz8slJuKUiSyEd5J5uG5LCkAXZmnknVV9QwNBUgC6FJVKRbDBQLDBQKdm\n5inWm82U1NRwcG81ezdUUFRdSa3NTL3NfGzT7RPzFDUmfKMgsL3t+H6KvvgFeOePa4ccPzrk+FG0\nPYKL/lZGh6AqXryn7rRj5hLawUc/m5h4tQ9377bSW16UhHAO+VlzW975iuImzA4KwHgpAN2Wr05H\n+9BQ2oeGkkeiw+vMViultbUcPFxN4a5j+ykWmhtOHudn5P+Hn31Cbfgn2o7vp+hLSKRn/vuITzMQ\n/2gsJYXhjJ9RRrSqklkP1BHgf+x5gwE++9bIndN82LtEzdVGWRwiRJuTAtBtySIQFzbvk0+oKi8/\n7bE9hYX0q6ykR7zjPfaE97DZbJTX1x/bT7G6koLKCo42nDpP8dgWOfV2I+pAKwFxEJaiIjbVl6gE\n956nWHbQxJLZ5QQ1VPLqfTVEhP3/cy/N0tHwkZpHao04PjRRCHG+3kpIYPLevWg0ckaPu5EOoAtr\nbAjYarWikx80cZxarSbc359wf3+6Rkc7vM5ut1NtNB47zm9zFXuXVbCnpvq0QrHh5DxFC37tILi9\nndgOPsS090VvcL1CMSxGz9j7oqkuD2Pye+Voyqp46a4qEmPgvrvM/NQVbnvMh1crjfgoHVYID+Vj\ntWI0GvHzkyVY7kYKQBdmbaS1brfZ0KikpyHOjkqlIshgIMhgoGNEBBc0cW3DiXmKhdUUbKpkf3Ul\ntTYT9TYz9ZxY/Ww8Nk8xEvyTrMSk+BDfwRe/IOf/SgkM1TH6jijqqsO5//NyjEWVPHtrJaOGQKcO\nRibc4MOsUiNNn3YthDgXPlYrDQ0NUgC6ISkAXVhjHUDsdtRSAIo2ZNDpSAoNJSk0lKYO5rAcn6d4\n6Eg1hXurKPjm2DzFBtuxrmI9x/dTxIQu1E5Ago2oE/spRmpbffjZL1DD8MkRGOvDePqLCirfrOSv\nN5Tz/s9Gbr7Ch0cKLWRZra36NYXwdj4WC8ZGdqwQrk8KQBdmMZtR/elF0mazoXHjeVvCc2g1GmKC\ngogJCiK7ic1X7Hb7sXmK1dXsX1lFwa8V7KqvOzY/8U/7Kar8rfjH2ghLUR+bp5ioR6M5u3/vPr5q\nBt8YhsUcwpvfhFDyUSX3PlLGmx/6MHK1mvFG2bdMiNZiMJulAHRTUgC6KJvNhsViOeMYOJvNJpNt\nhVtRqVSE+fkR5udHlybmKQLH5ilWV1O0tYqC/Er2VFdQZzVhUlmPndKCCSMmLDozvu0gqL2d2A4G\nYlPOnKeo1anpf2UotstC+PLHUPaFVDC3RxV71vtwnywOEaJVSAfQfUkB6KIsZjONLdC222xodDoF\nEgnR9gJ9fAj08aFDREST1xktFkpqajhQVE3B5jKKqqqotR7vJp4cfjbRoDZhiLQT1N5G97FhlBUH\nMdd0lB2brbxWb0F+koQ4Pz7SAXRbUgC6KJvNBg4KQJkDKLydj1ZLYkgIiSEh9ElIcHid1WY7Nk+x\nvJrC+ZUUVlYQGljPguhCppaU8n59nRNTC+F59GYzJpPsuemOZDKZq7LbaWyDRrvdLnMAhWghjVpN\nu8BAsmJjGZeWzi3Zufxh1tH5psc40L0XVUoHFMLNWTQadDIq5ZakA+iibDabw8elAyjE2Zu7bTsf\nV9sZ8Nf3CYyI4WBaDp9N6M+txgalownhthr0esJ9ZKdNdyStJBfl6HgWrVaLSbayEKLF6kwmrv99\nCfPTL2TU0x8RGBEDQEynDPJDm55rKIRomlGnw2AwKB1DnAPpALoou4M5gBq9HqOcvShEi3yx7Q8+\nqrYx4PH3ThZ+pyrpnMnRQ0WEK5BNCE/QoNPhIx1AtyQdQBdldzAHUKvV0iAFoBBNqjOZuOa3hfzS\nsd9pXb8/6/7AS/zb19fJ6YTwHEaNRjqAbko6gC7KUQdQq9NRX12tQCIh3MNX23fwfpWFgY9/QFBk\nbJPXhsUlsyYsCooLnZROCM/SoNVKB9BNSQHoohrbAxBAp9NRLx1AIc5QZzIxdekqAgZfzkVjbkLV\nwsVSZRl5FBcXNnGWiRDCkQaNRgpANyVDwC7K0SIQnU5HgywCEeI0X23fwVUb9tD90XfJGHtzi4s/\ngJz7nucD/4A2TCeE57JrtXI6lZuSDqCL0mg00MiLmF6rpVY6gEIA0GA2M3nJSvwvuIxRZ1n4nRAY\nHs268CiorWmDhEJ4ONkD0G1JAeiiNBpNoy9mMgQsxDHf/LGT96ss9H/kXwRFnd8Abl2fYezY9zad\nWimbEF5DK2WEu5IhYBel0WobLwC1WikAhVdrMJu5Yf5S/tuxP6P+/tF5F38AudOm81FAYCukE8LL\nSAfQbUnp7qI0Gg2NDWbpdDrqpAAUXuq7Hbt4p8LEgFbo+p3KEBDE6oh2UCMr7IU4G3bpALot6QC6\nKLVGA42c+atSqbDIUXDCyzSYzdw4fyk/pOQx+umPW7X4O8Fy4XjWyc+WEGfH31/pBOIcSQHootRq\ntcMzf63yIiW8yHc7dnH5up10feRfZF86+ZwWerREzi2P81lgUJvcWwiPFShTJ9yV9G5dmNpBa106\ngMIbnFjh69t/HKPbsPA7QW8wsCMyBntVZaPTL4QQjZAC0G1JB9CFaRoZAgawOTmHEM42b+cuLl+7\ng26PvE32ZVPavPg7wWfczSyVPc2EaDkpAN2WFIAuzNHmmtIBFJ7qxFy/75PzGP2PTwiKinfq18+4\n+k6+kGFgIVrEDlIAujEZAnZhWr0es9l8xuMyB1B4onk7d/KvMhP9//IWwdEJimTQarXsjk7AWlGO\n9AGFaFoFEBonhyi6K+kAujC9g/MVrTg+K1gId9NgNjNh/lK+b3+s66dU8XdC8LV384tsbSFEs0qA\nqNRUpWOIcyQFoAvT6fWNP+7rS7XR6OQ0QrS+73ft5vK1O0j/y1tOnevXlK4XXcd/gkKUjiGEyzus\n1xOVnKx0DHGOpAB0YY46gIGhoRyoqnJyGiFaj8liYcL8pXyX2NMlun6nUqvVFMQmYVI6iBAuriQo\niOjoaKVjiHMkBaALc9QBDA0Lo0AKQOGmfti1m0tXbyf9oTfJufxWl+j6/Vm7W/7Gt3LElRBNOuLn\nR3h4uNIxxDmSAtCF6Xx8Gp3rFxoSQkG1HFkl3IvJYuHmBUv5NrEno6d/SnBMotKRHOo0cBS/Bocp\nHUMIl2YNDEQr82XdlvzNuTB/f39sViuaP/2A6XU6aqxWhVK5ns+2b+f19esZnJDAP/r1O+259YcP\n8+bGjWw7ehSdWk2fmBjuzc0lwte3yXt+sm0b3+zaRUltLVF+flzVuTNXde588vnCqiqeWr6cvZWV\ndIuI4Ol+/Qj505D9a+vX80dZGf+88MLW+2bd1I87d/HG0Qb6P/imSxd+p9qf0IG6IyX4KR1ECBdl\nly1g3Jp0AF1YYEgIFoul0ecsDjaJ9iZVJhP3L1zIZ9u3Y2hkz8TCqirumj+f+IAAPhk1ipcvuICD\ntbXcNX8+Vpvj7bTfWL+etzZs4KpOnZhz8cXc2KULr65bx5c7dpy85oXVq4ny8+PDkSNpsFp5Z+PG\n0+6xq6KCr3fu5JFevVrvG3ZDJouFm+cv5Rs36Pr9WfKdM5irb3werhACVDL869akinBhAUFBWB0U\ngGYXnDflbP8tKKDBYuGTkSMJaGS+5EdbtxJqMPBY794kBgWRERnJE3l57K6o4Pf9+xu9Z4PFwuw/\n/mBMaipXdu5MXEAAl3TowBUdO/Le5s0AWGw21hw+zJWdO5MUFMTFKSksP3jwtPs8u2oVN3XtSmxA\nQOt/427ip917uHT1djo9+AY5V9zmknP9mpKYlceiEHmBE8KhyEilE4jzIAWgCwsICjpj+PcEk5u9\nmLaF/nFxvHbhhYQYDI0+v+LgQfrExKA+5c8qKSiI2IAAlh040Ojn7KmsxGS1khMVddrjg+LjKTca\n2VFeToXRiNVmI9rv2OBgjL8/pfX1J6/9eudOas1mrktLO99v0S2ZLBYmzl/KV3E5jJ7+KSGxSUpH\nOmcHU7tSoXQIIVyQEdDLJtBuTQpAF+bj64vOwUpEi1rd5DCmN4jx93fYVaq3WCitryeukQ5cfEAA\nhQ5WUWuPD61r/nTfUIMBu93O/urqkwtzTlyh4v835j5aX8+bGzfyWK9eDs9y9mT/3b2HS1dvo+OD\nb5B75e1u1/X7s7T7X+ATB28whPBme4DU3r2VjiHOg/e9QrkRlUqFwa/xKej+ISEclJXADtUeP0LP\nr5EC2l+no6aRI/YAEgIDUatUbDl69LTHd5SXA1BnNhNqMKBWqTja0ABAaX39yUUlL69dy/CkJNLC\nwnhl7VrGffcdV33/PT/u3dtq35srMlksTFywlC/ishk9/TO37vqdKjolnZWhEUrHEMLl7AwOpmNm\nptIxxHmQAtDFGfz9G308ol07tv+pSBHnz1erZUxKCl/v2sXi4mKsNhubjhzh423bUKlUaNVqtGo1\nWVFR/Lh3LxabjZ8LCugdE8OKgwdZf/gwt2dm8s3xz/901Cie7NuXp/PzHXYd3d1/d+9h/MqtdHzg\ndXKvvMPtu35/drhLLoeVDiGEi9kfEUF8fLzSMcR5kALQxfk62K4kOjKS7ce7UuJMAcc7f7WNdPpq\nzWYCHWyyDXBfbi4D4+J4aNEi+s+Zw9P5+dyRmYndbj+51cu9OTksLCpiwJw5lNTWckN6Os+tWsX9\nPXrgr9OxuqSEfrGxBOj1pIeFkRIczOqSkrb5ZhVisliYtGApc2OzuOiZ2YTEtlc6UpvIuu8lPvBr\n/I2YEN7KHhmJ2gunuXgS2QfQxTkaAtZqNNQ0skm0OMag1RLt50dRI8Pk+6qr6dmuXZOf+1TfvjzY\nsycmq5Uwg4EtR46gUqnoGBoKQKfQUH4cP57yhgZCDQbe2LCB5OBgBiccO9Ks2mQiOTj45D39dDqq\nTJ5zuNgve/by6qFq+j/wGiGxnn0WaEhMAhtCI6CuVukoQrgOWQHs9qR8d3EBQUFYHWz6bGpk7zvx\n//rFxbH84MHTFsv8UVbGodpaBjaxem1RURHrDx8mQKcj7PgCgJ8KCugWHn7GBtKhBgN7Kiv5Ztcu\nHurZ8+TjgXo9ZcfnCAKUNTQQ1ETX0V2Yj3f95rTLPN718+zi74SKnIEUKh1CCFfyp50ShPuRAtDF\nhUdHY3bQOTJ62Fyrs1VlMnG0vp4j9fXY7HZMVitH6+s5Wl+P0WrlhvR06sxm/rFiBfuqqthy5AhP\nr1hB94gIBp4yd+WK779n9vbtJz/+uaCAvyxZwvIDBzhYW8tn27fz3e7d3JWd3WiOmStXcku3bkSd\n0q3t2a4di4qK2FZWxs9793Kgpoaebn5o+q97C7hk5VZS7/8nPa6e5nFz/ZqSc8+zfOjvvXs6CnGq\nOsDv+GiHcF8yBOziQsPDGz0PGEAXEEBZXR1hDoaJPd3DixaxrrT05MelxcUsKi4G4K+9e3NRSgpv\nDBnCq2vXcv1PP+Gj1TIwLo67c3JOu8/+6mrKjcaTHz/euzcvrV3LU/n51JrNdAwJ4eULLiCjkSGP\n73bvxmi1cmWnTqc9Pi41ld0VFUz77Tf8dToeP74ZtTuyWK3cujgfe6+RXHSXdxV+JwSERrA2Ihpq\na5SOIoTidgOpffooHUOcJ5XdUXUhXILNZuPjV19tdEPogv37yTlyhH5JnrHlhnA9v+4t4OWDVfS7\ncwahcSlKx1HU0hfu47nPXiNd6SBCKOyrsDDyNm0iNjZW6SjiPMgQsItTq9X4OzhwOyY6ms2yEli0\nAYvVyi0LlvJZVDcuema21xd/ALm3/52PAt2ziytEazoQE0NMTIzSMcR5kiFgNxAQHEzDKUeNneCj\n11Ph4KxgIc7VbwWFvHSggn73/ZPQeCn8TjD4BbAxMgZ7dRXeNwguxCkSE71yKoinkQ6gG/APDHQ4\nD7Be9mESrcRitTJ54TI+jezKRc98LsVfY4ZfyWr5mRNezA6QmKh0DNEK5DeZG4iIjsbqoNNn1Gqx\nONgmRoiWmr+3gHH5m0m+dxY9r7lL3t07kHPTw8yWYWDhxQqA5P79lY4hWoEUgG4gOj4ei4Oza8Nj\nY9l+5IiTEwlPYbFambJwGR9HdmX0M7Ol69cMrV7P7qhYbM1fKoRHWh0RQe6QIUrHEK1ACkA3EBwa\nivb40WZ/lpiQwIpDh5ycSHiCBQWFjMvfTNI9r9Lz2rvlWKcW8r1sKgtlE3bhpQ7FxckCEA8hv/Hd\ngEarxS+g8U1ofX19KXXQHRSiMRarlSkLlvJRRDqjn5lNWEKq0pHcSsblU/k6MLj5C4XwRDL/z2PI\nKmA3ERAURNkpmx6fql66EaKFFhQU8nxxOX3veZWwxA5Kx3FLarWave0SsVSUyS9Q4VVsgEoKQI8h\nHUA3EdzEiSBmnQ6jbAcjmmCxWpm6cBn/Dktj9DOfS/F3nsImPMhPDqZlCOGpdgKdLrxQ6RiilUgB\n6Cbik5MxnXJc2ami4uPZVFLi5ETCXSwq3Me45ZtIvPsVel1/r8z1awVdRlzBj4EhSscQwqlWR0eT\nO2iQ0jFEK5FXAjfRLj7e4dYcCfHxrJACUPzJia7fB6GdGD1Tun6trTAumcbfkgnhmcri4ggPD1c6\nhmglUgC6CR+DweGRcHq9nnI50lmcYnHhPsYt30jC3S/T6/r7pOvXBuJue5Kv9HqlYwjhNHaZ/+dR\n5FXBjQSHhjp8rlatdjhHUHgPi9XKrQuX8V5oJ0bPnEN4YkelI3ms1D5DmR/s+GdSCE9SDfhnZCgd\nQ7QijykAJ02axKxZsxT52tOnTycnJ4d33323Tb9OSESEwyIvuF07dh092qZfX7i2Jce7fvF3v0xv\n6fo5RVFSZ2qUDiGEEywKCmLQ9dcrHUO0IpXdTdtGlZWV/PLLL1xxxRWK5+jduzdvv/02gxqZHGsy\nmXjuuef4+eefqa+vJzk5mbvuuouBAwee9dc6UFjIj59/jsHP74zn6uvrqczPZ3Jm5jl9H8J92Ww2\n7liygvqMQfSUws+pirevI+PmgUx2sEBLCE/xanY2d61ZI8dEehC3faVYvnw5X375pdIxqKmpQaVS\nkehgbsQLL7zA5s2b+frrr1m1ahUXX3wxd955J0fPoVsXERODxsGef76+vhyWDaG9ztL9RYxZuoHY\naS/Q+8YHpPhzsri0bJaGRCgdQ4i216mTFH8exmVeLdLS0vjwww/p378/77zzDgDz5s3joosuIjs7\nm6FDhzJ79mwAfv75Z+6//342btxIZmYmRUVF3HDDDbz00ksAvPbaa9x+++28++679O/fn169ejF9\n+vSTX6u8vJwJEyaQmZnJ+PHjWbhwIWlpaRw4cKDRbLt27WLChAn07NmTvLw8nnzySUwmEwUFBYwc\nORKAcePG8dZbb53xuXl5eUyfPp2oqCjUajWXX345RqORffv2nfWfkV6vJ7CJeYA1Go3MA/QSNpuN\n2xct553AZEY/O4eI9p2VjuS1Sjp1p0zpEEK0oUNAdL9+SscQrcylNrL/7bffmDdvHmFhYRQVFfHI\nI4/w7rvvkpeXR35+PhMnTiQ3N5eRI0eya9culixZwueff97ovdauXUtmZibz589n9erV3HzzzYwd\nO5bu3bvz6KOPYrVaWbJkCWVlZdx7770O39mYTCYmTpzI+PHjeeeddygpKeHWW29l1qxZPPDAA/z8\n888MGTKEefPm0b59+zM+f/DgwSf/u6amhrfffpvk5GS6du16Tn9GoRERHHRQPIbGxrKttJQuUVHn\ndG/hHpbuL2JGYSl9pr0ghZ8L6HLfi3y0aiH3NNQrHUWINvF7VBRDr7pK6RiilblMBxBg9OjRhIWF\nARAfH09+fj55eXkA9OnTh/DwcLZs2dKie2m1WqZOnYpOpyMvL4+wsDB2796N3W5nyZIlTJw4kcDA\nQJKSkrj66qsd3mfRokU0NDQwbdo09Ho9CQkJXHfddfz444+nXddc523SpEn06NGDxYsX88Ybb6A/\nx+0j4pKSMJtMjT6XkprKguLic7qvcH3Hun7L+FdAMqNnStfPVUQmdWRNWKTSMYRoM6UdOhAljQWP\n41IFYGxs7Gkff/rppwwbNoysrCwyMjI4cuQIJgfFT3P3MhgMGI1GKioqMJvNpz3fvXt3h/cpKioi\nPj4e3SnHPiUlJXHw4MEW5TjhvffeY82aNVx00UVcd911lDo417c5SR07YrPZGn3OR6+n1Go92iri\nNAAAIABJREFUp/sK15a/fz9jlq4n5o4X6DPhQZnr52KOdOvF2f1GEMI92AE6dVI6hmgDLvUqcuoC\nhy+++IJ3332XGTNmsG7dOjZu3Eh0dHSL7+XoBfJEp+7Ugq6pia2OCs5zmQzr7+/P5MmTCQoK4vvv\nvz/rzwfw9fcnMMTxEVRGg4G6FhbJwvXZbDamLVrOmwEpx7p+yWlKRxKNyL7/Bd7381c6hhCtbodK\nRefRo5WOIdqASxWAp9q0aRM9evSgZ8+eqFQqSktLOXz48HnfNyQkBI1GQ/EpQ6WbNm1yeH1iYiJF\nRUVYLJaTj+3evZv4+PiTHzdVDI4fP5758+ef9pharUarPffpl+FNtOJT0tL4raDgnO8tXMeJrl/0\nHc8f6/o5WAEulBcUGcvmMBkiE55ncWIiA6QA9EguWwDGx8ezZ88eqqqqKC4uZvr06cTFxVFy/Mxb\nHx8fDh8+TGVlZYuHheFY8dWjRw8+/PBDampq2Lt3b5PbyQwcOBCtVsvrr7+OyWRiz549fPzxx4wf\nP/7kNU3N/8vMzOTVV19l//79WCwW5syZQ1FREf37929x5j9rah5gdFQUmyorz/neQnnHun7LeMO/\nvXT93Eh1z8HsVjqEEK2srkMH/P2lu+2JXKYA/HMX7ZprriExMZFBgwZx6623cv3113PdddfxwQcf\n8NlnnzF06FDsdjsXXHABW7dubXZI9tTnZ8yYQVVVFf379+exxx7j1ltvBRofNvbz8+Ptt99m5cqV\n9OvXj6lTp3LJJZcwdepUh9lP9cgjj9C7d2+uuOIKevXqxRdffMHrr79OcnJyi/5cGtO+U6cmi85q\n2Q7Gba0oLmbM0vVE3f48eTc9LF0/N5Jz90w+DGj8vG4h3JEJ0HbrpnQM0Ubc9iSQ82WxWE4Ow+bn\n5zNp0iQ2bNhwXkOzzvTlu+/SUN/4thN/7NjBMLOZzJgYJ6cS58pms3H30hVUdu1P7xsekMLPTa2+\ntAtfFuxSOoYQreLnwECSli0jXYpAj+QyHUBneuyxx7jllluorq6murqaDz/8kL59+7pN8QfHTgVx\nVLunpqbym2wH4zZOdP0ib31Wun5uzjxoHBvltAThIbanpUnx58G8sgB86KGHCAkJYejQoQwfPhyt\nVss//vEPpWOdlY5du2JsaGj0Oa1GQ4W8CLk8m83GnYuW87ohgdEz5xCZ2kXpSOI85U59gk9kGFh4\nACugkrPlPZr7tLxaUXBwMK+88orSMc5LTGIiBl9fh8+rQ0I4XFNDVECAE1OJllpZXMxTew7R+7Zn\n6SKFn8fQGwzsiIzFXl2FvAUT7izfYCDvxhuVjiHakFd2AD2BWq0mol07h8+nd+vGN7tkLpKrsdls\n3LV4Oa/5JDB65lzp+nkg7cU3kK+WYXzh3lZ16kQPOf/Xo0kB6MYSUlMdbgfj5+tL8Sl7FwrlrSwu\nZuzSdYTf+gx5E/8ic/08VNb19zInUIaBhfuyA2RmyolDHk7+dt1Yhy5Nd4+04eEUyZ6AijvR9fun\nTxyjZs4lKlUmVXsyrVbL7uh4Gj+wUQjXt9LHh14TJigdQ7QxKQDdmI/BQGik40Po07t147s9e5yY\nSPzZmuIDjFmyjvCpM+g78VHp+nmJoKvu4Df5uxZuKj8tjT6DBysdQ7QxKQDdXHRcHDZb470Gg48P\nJQ6eE23LZrNx9+J8XvGJZfSzc4nq0F3pSMKJuo67mW+DHJ/ZLYSrsgFkZ8vwrxeQv2E3l5aVhdlo\ndPi8ITqaPWVlTkwk1hQfYOySdYRNnS5dPy+lVqspiEnCrHQQIc7ScoOBvJtvVjqGcAIpAN1caHg4\nQWFhDp9PS09n3t69TkzkvWw2G/cszudlXQwjZ86Rrp+Xi7rlUf7jRpvLCwGwKj2dngMGKB1DOIEU\ngB4gLinJ4TCwTqej1DtP+3OqdQcOMnbJOkKm/IN+tzyGRl74vV7nC8by3xDHb86EcDVGQJuX1+T5\n9sJzSAHoAbr16IGpiWHgkMRE1h886MRE3sNms3Hvknxe1EQxcuYcojtmKB1JuJD98ak0fmK3EK5n\nXkQEYx98UOkYwkmkAPQAQaGhhEZEOHy+U6dO/Lh/vxMTeYd1Bw4yZslagif9nX6T/ypdP3GGxDun\nM0evVzqGEC2yPzeXxPbtlY4hnEQKQA8Rn5yM1Wpt9Dm1Wk2NwUC9WaaktwabzcZ9S/J5URvNqJlz\nie6cpXQk4aLaZ/dncUi40jGEaNYetZrkyy5TOoZwIikAPUS3Hj2wNFHgpWVl8dUffzgxkWfacPAQ\nY5esJWjS3+l3y+PS9RPNKk5Oo0rpEEI044e0NC6SzZ+9ihSAHsI/MJDwqCiHz4eGhPBHvcxGOlcn\nun7PqSIYKV0/cRY63/8inxoMSscQwiELYOvTB71MV/AqUgB6kMTUVKxNnP/rFxvLH0eOODGRZ9hw\n8BBjF68hcNJT9J/yN+n6ibMS06Eb+aGO5+gKobQfQ0IYdf/9SscQTiYFoAfpkpvrcDsYgPQuXfhG\njoZrMZvNxv1LV/C8OpKRz35Bu87ZSkcSbupw5yxKlQ4hhAO7cnPp1MzZ8sLzSAHoQQy+vrRLSHD4\nvEajoVKnw9REl1Acs/FQCeOWrCVg4pOywlect4wHXuZDXz+lYwhxhiIgdswYpWMIBUgB6GG65ORg\namhw+HyHzEy+3bnTiYnci81m4/4l+TxLGCOemSNdP9EqQmOTWB8WqXQMIc7wXadOjJsyRekYQgFS\nAHqYxNRU/AIDHT4fFRHBhupqJyZyHxsPHZvrFzDxSfpPeQKNTqd0JOFByjL7IbtxCldiA0y9e+Pr\n66t0FKEAKQA9jEqlIjE1FZuDPQEBwlJSWFFU5MRUrs1ms/Hg0hU8SzgjZ86lXVqO0pGEB8q97zk+\n9PdXOoYQJ/0YGsoIWfzhtaQA9ECZffpgaWKeX8eOHfmpuNiJiVzX5pISxi5eg+9NT0jXT7SpgLAo\nNodHKx1DiJO25+XRJTNT6RhCIVIAeiD/wECi4uIcPq9SqVBFRLCvosKJqVyLzWbjwSX5zLCFMnLm\nXGLSpesn2l5d3ghkO3bhCpb5+ZF3111KxxAKkgLQQ6VlZGBsYjFIRnY2s3fscGIi13Fq12/A1Cel\n6yecJveOp/kowPEcXSGcZWmPHvQbMULpGEJBUgB6qJT0dHybmG+k1Wqp8vWl2mh0Yipl2Ww2Hl66\nghnWkGNdvy65SkcSXsYQEMS2iHbYlQ4ivNp2nY7ON92kdAyhMCkAPZRarSa5c2esTSwG6dajB59t\n2+bEVMrZWlLCuMVr8Lnxrwy49Snp+gnF2IddzlqVSukYwot9n5nJxXLur9eTAtCD5fTrh93uuNcQ\n4O9PgcWCtYnTQ9yd3W7nL8tW8A9LCCNmziWmaw+lIwkvlzvpMT4LDFI6hvBSB4B2l1+OWi0v/95O\n/gV4MB+Dgfj27ZssAjtmZ/PF9u1OTOU8W0tKGLtoNdobHmfAbdL1E65Bq9ezKzJWhoGFIr7o0oUr\n7rlH6RjCBUgB6OFyBw7EbDI5fD4yMpL1tbUe1wV8ZPlKnjYHMXzmHOK69lQ6jhCnMVwykcUajdIx\nhJepAnxGjcLHx0fpKMIFqOxNtYeER/jPp59SWVbm8PmSw4cJ2rmTK9PTnZiqbWw7fJiHthaQOekx\nKfyEy7LZbBwaGsOsinKlowgv8q+UFK5cs4aQkBClowgXIB1AL5DZuzemJlb7RkdFsaa6GpubdwEf\nWbaSv5sCGf7sXCn+hEtTq9XsiU7A8RItIVqXCagbNEiKP3GSFIBeICE1laBmfug7ZmfztZvuC7i9\ntJQxC1aiuf5RBt7+NFqdXulIQjQr9Pp7+VmrVTqG8BKz4+O58u9/VzqGcCFSAHoBlUpFWmZmk3MB\no6OjWVVZ6XZdwMeWr+KpBv9jXb9uvZSOI0SLpY+6hu+DpBsj2l4tUDFqFLHx8UpHES5ECkAvkZ6T\ng8HPr8lrUrOz+cZNuoDbS0sZs3AlXPsXBt7xD+n6CbejVqvZF5eM92zFLpTyUWoqNz77rNIxhIuR\nAtBLaDQaumRnYzabHV4T064dK92gC/h4/iqeaghg+My5xHfvrXQcIc5ZzJQn+Fa2JxJt6CigvvRS\nQkNDlY4iXIysAvYiVquVuW+/3eTpIIdKSgjcuZOru3RxYrKW2Vlayn1b9pJx08PEZ+QpHUeIVrF7\nRALvlJYoHUN4qFe6dmXKypX4NTMCJLyPdAC9iEajoUtODpYmuoDtoqNZV1uL0WJxYrLmPb58FX9t\n8GP4zDlS/AmPUpTYkVqlQwiPtF+lIuzaa6X4E42SAtDLdOvZE73B0OQ13fv25b1Nm5yUqGk7S0sZ\nu2AF9mseYtAd09HqZQNT4VlS7n6W2bIxr2gDs7OzuebBB5WOIVyUFIBeRqPRkJ6V1WQXMDgwkCKt\nlvK6OicmO9Nfl6/ir/V+DJs5V7p+wmMldOvJspBwpWMID7Ndp6PDlCnoZI6pcEAKQC+U0asXumY6\nDjl5eby9ebOTEp1u99GjjF24Eus1DzJomnT9hOc7mNoVORNEtKZve/bkksmTlY4hXJgUgF5Io9U2\n2wX00etpCAtjb7lzX5b+lr+Kx2r1DHtmDgkZfZ36tYVQStr9L/KxwVfpGMJDrPTzo8e996JWy0u8\ncEz+dXipzN698fFt+gUnu0cPPti+3Sl5dh89ytgFK7Bc9SCDpj0jXT/hVaKT01gdFqF0DOEB7MCv\neXkMvfxypaMIFycFoJfSaLVk9enT5BnBarWagORkVhYXt2mWJ1es5rEaPcNmziUhU7p+wjuVdunB\nIaVDCLf3TWQkY2fOVDqGcANSAHqxtKwsgpvZHDQtPZ0vCgvbZHPoE10/0xX3M+hO6foJ75Z1/0t8\nKNt1iPNQBRSOHUu3Hj2UjiLcgBSAXkylUtFr8OAmu4AqlYr03r3595Ytrfq1n8xfzaPVOoY+M4eE\nrH6tem8h3FFwdBwbwqKUjiHc2LtduzL5lVeUjiHchBSAXi4hJYWo2FiaOhAmMjKSHXY7R2rPf7va\nvce7fsYr7uOCu2ai82l6T0IhvElVziAKlA4h3NJGg4HUu+8mICBA6SjCTUgBKOg7dChmk6nJa3r2\n68fr57k59FMr1vCXKi1Dn5lDYnb/87qXEJ4o995n+cBfXsDF2bEB8/r3Z+wttygdRbgRKQAFYVFR\nJKamNjnPT6fT4ZeczJL9+8/6/ie6fg2X38sFdz8rXT8hHPALDmNrRLTSMYSbmRMby9WzZqFSqZSO\nItyIFIACgL7DhjW70CMtPZ3viouxWK0tvu9TK1ZL10+Is2AccDFb5IVctNBR4Ohll9EhPV3pKMLN\nqOxNTf4SXmXlwoVsXbMGbRNHB5WVl9Owbh1Ts7KavFdheTl3bfiDLtc/SGLOgNaOKoTHMtXXUTsy\nkWerq5SOItzA81lZTFu2DN9m9nUV4s+0SgcQriO3f3/2bt/e5HzAsNBQVuj1FFVWEh8c3Og1f1+5\nhs0RHRjyzFwZ7nUjO+Z/zbb/fkbVoX0YAkKIzehL7tV3YQgKA6Bk+1rWzJnF0T1bUGt1xGX0pdeN\nD+MXGtnkfTf95wN2/PYltUcP4hcWTZeR19Fl1PUnn688UMDiNx6long3kR0yGHTX8xgCQ067x+rP\nXuZowTZGPPqv1v/GXYze1491kTHYq6uQPqBoygp/fzIeekiKP3FOZAhYnKTRaOg7bBimhoYmr+uR\nl8drmzefsXK4sLyccQtWUDf+LgbfI3P93MnmH/7NsneeosPAcVzy7Ff0nfwExRuW8PvL9wFQeWAv\nvzwzlaDoBMbO/JJhD79BzZGD/PLMVGw2x1MC1sx+hXVz/0n6yOsY/+I8uo+dxKpPXmDbL5+fvCb/\ng+n4hUUzZvocLKYG1n3x+mn3KNu3gz9+nUvfW55om2/eBalHXstKOcZLNMEK/DZ4MCOuuUbpKMJN\nyW8YcZqElBRi27dvcj6gRqOhfU4Oc7ZtO/nY0yvW8GC5iiEzPicpd5AzoopWtPn7D+kwaBxdR99A\nYHQCcZn9yLrsVg5vX0vZvh1smvcehsBQ+k15iuCYJKI6ZTHgtumU799JQf4vjd7TYqxny0+f0PGC\n8XQZeS2BUfF0HnI56SOuZcPXbwFgs1o4tHUVXUZdR3BsezoOuoTiDUtOu8/y954m45LJBEbFtfmf\ng6vIvulBPg8MUjqGcGHvpaRw42uvKR1DuDEpAMUZBo4aBc1MDY2Li2Oj1cqa4mLGLcindvydDL7n\nOXRyoL1bGv/Cd/S+8eHTHvMLPbYa1dJQR/HG5cRm9kN1SlcqOLY9gZFxFK8/vWA7oaJoN1azkej0\n008lSOwxmIbKMsoKt9NQVY7NZsU/7NjXCoiMpa7s8Mlrt/86F3N9LV0vntAq36e70Gq17IqKo/XP\n3xGeYIPBQPh99xGflKR0FOHGpAAUZ/ALCKBbr17N7w3Yvz8Pbd3DkBlzSOpxgXPCiTbh4x+Eztf/\ntMf2rfkdrcGXkPgO1JUfJig64YzPC2yXSOWBvY3eU609NsVYrTl9qrEhOAw7dqoO7vv/Nxoq9Sn/\nf+yx+oojrJ0zi35TnkKt1pzHd+eeAq64jd813vd9i6YZge+HD+fS229XOopwc1IAikZl9elDoINF\nHidoNRpG985my8+fOSmVcJZ9axaw47evyLhkChZjPQA6w5nn1Op9AzDVVTd6j8DoRFRqDaW7Tt9A\nvKzgDwDMDbUYgkJRqTXUVx4BoK6sBN/QY8ehrfzoOVL6jiY8pQsrP36eL+4cwdf3j2XXonmt9n26\nsu6X3sK3QU3/DArv81Z6Ord98IHs+SfOmxSAolFqtZp+I0Y47ALabDYKDpXS4BuE/cAWKg4UODeg\naDN78//LglfuI3XAGDLGTTrn++gMfnQcdAl//DqX/WsWYLNZObxzA5v+8z4qVKg1WtRaHdFpOexe\nNA+bxcyepT8Ql5FH8cZlHNq+htyr7+aPX79g/5oFjHv2Swbe8QxL3v4blV7w702tVrO3XSIWpYMI\nl7E4KIguTzxBWFiY0lGEB5ACUDgUk5BASloaVsvpL0G1dXVsLiqB8CgSU1Po16cX6z5/qdmNpIXr\n2/rzpyyc9RCdh17JgNunA6D3O3Y0man+zLOgTXXV6P0dL1boPeFhEnMv4LcX7+aj63NY8ubj5F59\nD3bsJ7eX6XXDQxSu/p2PbuxBzZGDdLv4Zpa//w/63PQIOl9/Dm1ZSXz2QPR+gUSkdCU0PpWDW1a0\nwXfveiJueogfmtiXU3iPamDF2LEMu+oqpaMIDyH7AIom9Rs+nAP79mG1WLDZbOw/fJRKlZa4Tmlo\ntMfmJ6nVavK6d2bdV2+Se8UdCicW52r7/+aw8t/P0uO6++h28U0nH9f6+OIf3o7qkn1nfE7VwUJi\nuvdxeE+tjy8Dp82kz8THsZqN+AaHU7prEypUhCZ2AiC8fRpXvzmfhqoyDEFhrPn8VULiUknqNRQA\nY20VwXEpJ++pM/hjrKlspe/ataUPu5yfnrubcUdLlY4iFPZmVhZ3vPWW0jGEB5EOoGiSVqej/4gR\nlFdUsqmoBHvYsXODTxR/J0RERuJbWcTh3ZsVSirOx4HNK8j/YAa9bnzotOLvhITsgRStX3Lann9H\n926j5uhBEnMHO7zvvtXzKdm+Fr1fAL7B4QDsXvIfIjtmnLGBtCEojIqi3fzx2xfkTXr85OM+/kE0\nVB09+XF95VF8Arxnbty+uBSa3plTeLofw8MZ+Nxz+Pv7N3+xEC0kBaBoVkJKCqEd04jp0ImQsFCH\n1/XMzWLrN29hNZudmE60hvwPphPVOZvkvJHUVxw57X/mhjq6jZmIpaGOpW/9jcqDhZTu2sSSt/9K\nVMdMEnv8fwH49X1j2PLjxyc/3r3ke+a/fC/FG5ZSU3qALT98xI7fv6bHdfc3mmPZu38n69JbT24L\nAxDTrTf7Vs/nyJ4t7F7yPdWlxcR0c9x19DQJd/ydL/V6pWMIhRwGdl99NX2GDVM6ivAwchawaBGz\nxcL7s7/CbGl6SnplZQUr9xwl7+ZHnJRMnK+aIwf58s4RDp/Puvw2si67jSN7t7Lq4+c5snszGr0P\nibkX0PP6B0/rxn14TQbdx00i9+q7ATA31LHi3zMpWrsQU30tYUmdyb36bmK69jrj6+z4/Sv++HUu\nF0///LQVjjarhRX/nsmepT+i8/Un58ppdBg4rhX/BFzfrhGJvFt6SOkYwsnswPQ+fXho4UL08iZA\ntDIpAEWL7S8+yBff/xe9vulJ6Tt27KI6Kp3OF4x3UjIhPNvCqcOZu2oBgUoHEU71YWIifebNIy0z\nU+kowgPJELBosYS4GDK6dMJicXz2K0CnTh1o2L6U0j3bmrxOCNEyHe99jk/lbG2vsiwwkNDHHpPi\nT7QZKQDFWRnctzeBAf401zju27sn277+J8baxjcJFkK0XGxaFvmh4UrHEE5yCFhx1VWMmzJF6SjC\ng0kBKM6KRqNhzLDBWK1N7/mnUqkYOiCPZe88IfsDCtEKSjplcrT5y4SbswBvDRzInW++qXQU4eGk\nABRnLSoijB6ZXTGbml4QovfxoU+XVFbPfsVJyYTwXN3uf5F/+/oqHUO0sX+mpXHr7NlotbJNr2hb\nUgCKc9KvZw4R4SHNdvcioyKJ0dSxa8kPTkomhGcKT0hl3Z/2ThSe5buoKHq99BLtYmOVjiK8gBSA\n4pyoVCouHT0crUbT7LXpaZ2o2vg7R/ftdEIyITzX0YzeFCsdQrSJbTodZbfeSr9Ro5SOIryEFIDi\nnPn7+TJ2+BAszewNCDAgrxeb577S6HmyQoiWybrvRT7wk9MgPE0N8OXo0dz05JNKRxFeRApAcV7i\nY6Pp3ysXczOnf6hUKob068Wyd55sdgWxEKJxQRHt2BQe3fyFwm3YgZdzcrj3k09O2wBdiLYmBaA4\nbz0yu9GhfXss1qb3BzT4+tKzcyJr5r7mpGRCeJ7a3kOQyRSe492kJK784AMCAgKUjiK8jBSAolWM\nHjKA4MCAZrt70dHRRNrK2bP8FyclE8Kz9LjrGf4dIGeCeIJvIyPp8OKLdM7IUDqK8EJSAIpWodFo\nuOLikUDzQxhd09MoX/MT5Qf2tn0wITyMISCI7RHtlI4hztNif3/Mjz7K4MsuUzqK8FJSAIpWE+Dv\nx9jhgzGbW7AopG9v1n/6AuaGeickE8KzWAdfwgaZL+a2tmu1bJ46lSvuuUfpKMKLqewyI1+0shVr\nN7B01Xr0+qY3Mq2vq+N/KzYw6M7n0cimp0K0mKmhgeqRCTxfVal0FHGWSoD3rrqKR2bPlkUfQlHS\nARStrld2BqlJ8c1uD+Pr58egnC4sefMxOS5OiLOgNxjYGRmDvHt3L3XAa4MH8+DHH0vxJxQnBaBo\ndSqViouGXkBQQPOLQoKDg+mTnsSyd5+S7WGEOAv6sTexTC2/wt2FFZiRmclD332HTqdTOo4QMgQs\n2k5VdQ3/nvstKnXz73QPHTrE5pJ6+kx42AnJhHB/FouF0mFxvFpZrnQU0Qw7MCMlhZsXLCA2IUHp\nOEIA0gEUbSgoMICLhg7C0oJFIe3ataNTmJbVc/7phGRCuD+tVsue6Hia3n1TuIK3YmIY9/nnUvwJ\nlyIFoGhTKUkJ9MrJwNSCIjAxIYEEXR0b573vhGRCuL/ga+/if7KAyqXNDQkh/fXX6dazp9JRhDiN\nFICizeXlZtEpOalF28OkpiQTWn+Arb987oRkQri3LhffwLzAEKVjCAe+DQzE94UXuGD8eKWjCHEG\nKQBFmzu2KGQQiXExWCzND1ild+6E4fA2di7+3gnphHBfarWagtgkTEoHEWf40c8P64wZjJk0Seko\nQjRKCkDhFCqViktGDiE6MrxFRWBGt67Ydi1n76rfnZBOCPfV7pbH+U4rq0pdya8GAxVPPMFl06Yp\nHUUIh6QAFE5z4ri4sJBgrNbmi8Dc7Eyq1/1M8eYVTkgnhHvqNOgifg0JVTqGOG6hXk/hAw9w7UMP\nKR1FiCZJASicSqvVcPW40QT6+7do8+e+vXpwaOEcSnZtckI6IdzT/oQO1CkdQrBIr2fjbbcx6emn\nlY4iRLOkABROp9fruPbSizHofVq0+fPAvr3Z+8M7lO3f7YR0Qrif9tNmMFevVzqGV1uk17Nm4kTu\nfOUVpaMI0SJSAApFGHx8uPbSi9BoNM0WgSqViiED+7H1i5eoPLTPSQmFcB+J2X1ZFBKudAyvtVCv\nZ+WECdz75ptKRxGixaQAFIoJ8PfnmktGo0LVoiJw+AUD2Dr7eQ7v3uykhEK4j4MpXahUOoQXWqjX\ns/KGG3jgX/9SOooQZ0WOghOKKzlylLnzfmrR4eh2u53Fy1cSkXcJidkDnJBOCPdQsnsrqTf0YVpD\ng9JRvMZ/fXzYdN11PPDee0pHEeKsSQdQKC46IpxLRw3Daml+UYhKpWJg397UrfuZHQvnOSGdEO4h\nOrULq0IjlI7hNT7392fHlClS/Am3JQWgcAlxMdGMGXFBi/YIBOiRk4nPgfVs+s+HbRtMCDdyOC2H\nw0qH8AJvBAVRfc893DlrltJRhDhnMgQsXMofu/bw0/zFaFt4vumevXspqNPT89p7WjSELIQnKz9Q\nSMQVmTxYL5vCtAU7MD00lJRHH+XaBx5QOo4Q50U6gMKldO6QwoX981p0bjBASnIyaaFqlr7zZIv2\nFRTCk4XGJrEuLFLpGB7JAvwlIoLuM2dK8Sc8ghSAwuVkpHdi5OABLR4Ojo2NoVeHGBb+8yEsJmMb\npxPCtVXmDKBQ6RAeph64NyaGobNmMW7KFKXjCNEqZAhYuKyC/cXM++/vqNSqFg3v1tbU8Fv+Wvre\nOgNDQJATEgrhemrKj6Ab24knamuUjuIRyoGHExO5cdYs+o8bp3QcIVqNdACFy2qfEMdVl4xGrVK3\n6MQQ/4AARg7ozfI3Hqa69IATEgrhegJCI9gSHqV0DI+wH3ioQwemffyxFH/C40gBKFy+A9n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DuaiAl5SXMmkps7HfMNJ8e019eT7GnBkdwT8COKAqqKKzubGcXFTC8qkm1chJgEJAAKcRdTIhFO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\nAbfUTyXfyRlmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b32dc3510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# frequency of each rating in a histogram\n",
    "plt.figure(1)\n",
    "plt.hist(ratings_df['overall'],5, facecolor='g')\n",
    "plt.xlabel('Rating')\n",
    "plt.ylabel('Frequency')\n",
    "plt.axis([0, 5, 0, 180000])\n",
    "\n",
    "# percentage of each rating in a pie chart\n",
    "plt.figure(2)\n",
    "labels = 'rating of 1', 'rating of 2' ,'rating of 3', 'rating of 4','rating of 5'\n",
    "colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue','red']\n",
    "plt.pie(ratings_df.groupby('overall').size(),labels=labels, colors=colors,autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "plt.axis('equal')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dumping data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save data in CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ratings_df.to_csv('cleaned_user_item_ratings.csv', index=False,header=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# path to dataset file\n",
    "original_file_path = 'cleaned_user_item_ratings.csv'\n",
    "\n",
    "reader = Reader(line_format='user item rating', sep=',')\n",
    "original_data = Dataset.load_from_file(original_file_path, reader=reader)\n",
    "\n",
    "#Split data into 4 folds, 3 for training and 1 for testing.\n",
    "original_data.split(n_folds=4)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Method that will be used for all algorithm.\n",
    "# Input: Algorithm to use\n",
    "#       Dataset\n",
    "# Output: Error measurements\n",
    "def algorithm_to_results(data, algo):\n",
    "    algo = algo()\n",
    "    alogPerf = evaluate(algo, data,measures=['RMSE', 'FCP'], verbose=False)\n",
    "    return alogPerf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating RMSE, FCP of algorithm PredictMean.\n",
      "\n",
      "        Fold 1  Fold 2  Fold 3  Fold 4  Mean    \n",
      "FCP     0.0000  0.0000  0.0000  0.0000  0.0000  \n",
      "RMSE    1.1017  1.1002  1.1055  1.1075  1.1037  \n"
     ]
    }
   ],
   "source": [
    "print algorithm_to_results(original_data,PredictMean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SVD Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating RMSE, FCP of algorithm SVD.\n",
      "\n",
      "        Fold 1  Fold 2  Fold 3  Fold 4  Mean    \n",
      "FCP     0.5191  0.5192  0.5234  0.5216  0.5208  \n",
      "RMSE    1.0692  1.0665  1.0726  1.0745  1.0707  \n"
     ]
    }
   ],
   "source": [
    "print algorithm_to_results(original_data, SVD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Baseline Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating RMSE, FCP of algorithm BaselineOnly.\n",
      "\n",
      "Estimating biases using als...\n",
      "Estimating biases using als...\n",
      "Estimating biases using als...\n",
      "Estimating biases using als...\n",
      "        Fold 1  Fold 2  Fold 3  Fold 4  Mean    \n",
      "FCP     0.5272  0.5247  0.5303  0.5252  0.5268  \n",
      "RMSE    1.0502  1.0475  1.0539  1.0562  1.0520  \n"
     ]
    }
   ],
   "source": [
    "print algorithm_to_results(original_data, BaselineOnly)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Co-Clustering Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating RMSE, FCP of algorithm CoClustering.\n",
      "\n",
      "        Fold 1  Fold 2  Fold 3  Fold 4  Mean    \n",
      "FCP     0.5101  0.5102  0.5120  0.5158  0.5120  \n",
      "RMSE    1.1671  1.1676  1.1688  1.1824  1.1715  \n"
     ]
    }
   ],
   "source": [
    "print algorithm_to_results(original_data, CoClustering)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Optimizing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Functions to tune parameters\n",
    "We define a function where we give it a dictionary with different value for each parameter and it returns an accuracy measure for each combination. Insprised from GridSearchCV of sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# return the average accuracy measure on different test sets\n",
    "def get_folds_mean(algo, data, accuracy=['RMSE']):\n",
    "    measures_list = []\n",
    "    for trainset, testset in data.folds():\n",
    "        algo.train(trainset)\n",
    "        predictions = algo.test(testset)\n",
    "        accuracy_measure = []\n",
    "        if 'RMSE' in accuracy:\n",
    "            accuracy_measure.append(rmse(predictions, verbose=False))\n",
    "        if 'MAE' in accuracy:\n",
    "            accuracy_measure.append(mae(predictions, verbose=False))\n",
    "        if 'FCP' in accuracy:\n",
    "            accuracy_measure.append(fcp(predictions, verbose=False))\n",
    "        measures_list.append(accuracy_measure)\n",
    "    return np.mean(measures_list,axis=0)\n",
    "\n",
    "# return a list of tuples of each parameter combination and its correcsponding accuracy measure\n",
    "def compute_all_combinations(combinations_list,algo, data, accuracy=['RMSE']):\n",
    "    results = []\n",
    "    for combination in combinations_list:\n",
    "        algo_instance = algo(**combination)\n",
    "        mean_accuracy = get_folds_mean(algo_instance, data,accuracy)\n",
    "        combination['measure'] = mean_accuracy\n",
    "        results.append(combination)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optimize SVD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### with 20 iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "regularization is 0.05:\n",
      "done\n",
      "regularization is 0.1:\n",
      "done\n",
      "regularization is 0.2:\n",
      "done\n",
      "regularization is 0.4:\n",
      "done\n",
      "regularization is 0.6:\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "\"\"\" \n",
    "    Test the SVD algorithm on 5 different learning rates each time witha different reg_all factor. n_epochs = 20\n",
    "    NOTE: This block can take some time to finish\n",
    "\"\"\"\n",
    "print 'regularization is 0.05:'\n",
    "param_grid = {'n_epochs':[20], 'lr_all': [0.002, 0.005, 0.01, 0.015, 0.02], 'reg_all': [0.05]}\n",
    "combinations_list  = list(ParameterGrid(param_grid))\n",
    "vary_lr_rate_only_reg2 = compute_all_combinations(combinations_list, SVD, original_data, accuracy=['RMSE','FCP'])\n",
    "print 'done'\n",
    "\n",
    "print 'regularization is 0.1:'\n",
    "param_grid = {'n_epochs':[20], 'lr_all': [0.002, 0.005, 0.01, 0.015, 0.02], 'reg_all': [0.1]}\n",
    "combinations_list  = list(ParameterGrid(param_grid))\n",
    "vary_lr_rate_only_reg3 = compute_all_combinations(combinations_list, SVD, original_data, accuracy=['RMSE','FCP'])\n",
    "print 'done'\n",
    "\n",
    "print 'regularization is 0.2:'\n",
    "param_grid = {'n_epochs':[20], 'lr_all': [0.002, 0.005, 0.01, 0.015, 0.02], 'reg_all': [0.2]}\n",
    "combinations_list  = list(ParameterGrid(param_grid))\n",
    "vary_lr_rate_only_reg4 = compute_all_combinations(combinations_list, SVD, original_data, accuracy=['RMSE','FCP'])\n",
    "print 'done'\n",
    "\n",
    "print 'regularization is 0.4:'\n",
    "param_grid = {'n_epochs':[20], 'lr_all': [0.002, 0.005, 0.01, 0.015, 0.02], 'reg_all': [0.4]}\n",
    "combinations_list  = list(ParameterGrid(param_grid))\n",
    "vary_lr_rate_only_reg5 = compute_all_combinations(combinations_list, SVD, original_data, accuracy=['RMSE','FCP'])\n",
    "print 'done'\n",
    "\n",
    "print 'regularization is 0.6:'\n",
    "param_grid = {'n_epochs':[20], 'lr_all': [0.002, 0.005, 0.01, 0.015, 0.02], 'reg_all': [0.6]}\n",
    "combinations_list  = list(ParameterGrid(param_grid))\n",
    "vary_lr_rate_only_reg6 = compute_all_combinations(combinations_list, SVD, original_data, accuracy=['RMSE','FCP'])\n",
    "print 'done'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_learn_measure_rmse(iterable):\n",
    "    x_values = []\n",
    "    y_values = []\n",
    "    for item in iterable:\n",
    "        x_values.append(item['lr_all'])\n",
    "        y_values.append(item['measure'][0])\n",
    "    return [x_values, y_values]\n",
    "\n",
    "def get_learn_measure_fcp(iterable):\n",
    "    x_values = []\n",
    "    y_values = []\n",
    "    for item in iterable:\n",
    "        x_values.append(item['lr_all'])\n",
    "        y_values.append(item['measure'][1])\n",
    "    return [x_values, y_values]\n",
    "\n",
    "#RMSE\n",
    "reg2_rmse = get_learn_measure_rmse(vary_lr_rate_only_reg2)\n",
    "reg3_rmse = get_learn_measure_rmse(vary_lr_rate_only_reg3)\n",
    "reg4_rmse = get_learn_measure_rmse(vary_lr_rate_only_reg4)\n",
    "reg5_rmse = get_learn_measure_rmse(vary_lr_rate_only_reg5)\n",
    "reg6_rmse = get_learn_measure_rmse(vary_lr_rate_only_reg6)\n",
    "\n",
    "#FCP\n",
    "reg2_fcp = get_learn_measure_fcp(vary_lr_rate_only_reg2)\n",
    "reg3_fcp = get_learn_measure_fcp(vary_lr_rate_only_reg3)\n",
    "reg4_fcp = get_learn_measure_fcp(vary_lr_rate_only_reg4)\n",
    "reg5_fcp = get_learn_measure_fcp(vary_lr_rate_only_reg5)\n",
    "reg6_fcp = get_learn_measure_fcp(vary_lr_rate_only_reg6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KPeRWrq6uchBStWpVWrRowc6dO+X1a9eupVWrVvKZIT8/P+Li4uxS7fKS+/U6fvw4//vf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wcm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6af0ab3e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1= plt.subplots()\n",
    "ax1.plot(reg2_rmse[0], reg2_rmse[1], 'b-',reg3_rmse[0], reg3_rmse[1], 'y-',\\\n",
    "         reg4_rmse[0], reg4_rmse[1], 'r-',reg5_rmse[0], reg5_rmse[1], 'g-',reg6_rmse[0], reg6_rmse[1], 'o-')\n",
    "ax1.set_xlabel('Learning rate')\n",
    "ax1.set_ylabel('RMSE')\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(reg2_fcp[0], reg2_fcp[1], 'b-',reg3_fcp[0], reg3_fcp[1], 'y-',\\\n",
    "        reg4_fcp[0], reg4_fcp[1], 'r-',reg5_fcp[0], reg5_fcp[1], 'g-',reg6_fcp[0], reg6_fcp[1], 'o-')\n",
    "ax2.set_ylabel('FCP')\n",
    "labels = ['0.05','0.1','0.2','0.4','0.6']\n",
    "plt.legend(labels)\n",
    "plt.title('RMSE, FCP vs. learning rate and regularization factors - 20 iterations')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### With 40 Iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "regularization is 0.05:\n",
      "done\n",
      "regularization is 0.1:\n",
      "done\n",
      "regularization is 0.2:\n",
      "done\n",
      "regularization is 0.4:\n",
      "done\n",
      "regularization is 0.6:\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "\"\"\" \n",
    "    Test the SVD algorithm on 5 different learning rates each time witha different reg_all factor. n_epochs = 40\n",
    "    NOTE: This block can take some time to finish\n",
    "\"\"\"\n",
    "print 'regularization is 0.05:'\n",
    "param_grid = {'n_epochs':[40], 'lr_all': [0.002, 0.005, 0.01, 0.015, 0.02], 'reg_all': [0.05]}\n",
    "combinations_list  = list(ParameterGrid(param_grid))\n",
    "vary_lr_rate_only_reg2_40 = compute_all_combinations(combinations_list, SVD, original_data, accuracy=['RMSE','FCP'])\n",
    "print 'done'\n",
    "\n",
    "print 'regularization is 0.1:'\n",
    "param_grid = {'n_epochs':[40], 'lr_all': [0.002, 0.005, 0.01, 0.015, 0.02], 'reg_all': [0.1]}\n",
    "combinations_list  = list(ParameterGrid(param_grid))\n",
    "vary_lr_rate_only_reg3_40 = compute_all_combinations(combinations_list, SVD, original_data, accuracy=['RMSE','FCP'])\n",
    "print 'done'\n",
    "\n",
    "print 'regularization is 0.2:'\n",
    "param_grid = {'n_epochs':[40], 'lr_all': [0.002, 0.005, 0.01, 0.015, 0.02], 'reg_all': [0.2]}\n",
    "combinations_list  = list(ParameterGrid(param_grid))\n",
    "vary_lr_rate_only_reg4_40 = compute_all_combinations(combinations_list, SVD, original_data, accuracy=['RMSE','FCP'])\n",
    "print 'done'\n",
    "\n",
    "print 'regularization is 0.4:'\n",
    "param_grid = {'n_epochs':[40], 'lr_all': [0.002, 0.005, 0.01, 0.015, 0.02], 'reg_all': [0.4]}\n",
    "combinations_list  = list(ParameterGrid(param_grid))\n",
    "vary_lr_rate_only_reg5_40 = compute_all_combinations(combinations_list, SVD, original_data, accuracy=['RMSE','FCP'])\n",
    "print 'done'\n",
    "\n",
    "print 'regularization is 0.6:'\n",
    "param_grid = {'n_epochs':[40], 'lr_all': [0.002, 0.005, 0.01, 0.015, 0.02], 'reg_all': [0.6]}\n",
    "combinations_list  = list(ParameterGrid(param_grid))\n",
    "vary_lr_rate_only_reg6_40 = compute_all_combinations(combinations_list, SVD, original_data, accuracy=['RMSE','FCP'])\n",
    "print 'done'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#RMSE\n",
    "reg2_rmse_40 = get_learn_measure_rmse(vary_lr_rate_only_reg2_40)\n",
    "reg3_rmse_40 = get_learn_measure_rmse(vary_lr_rate_only_reg3_40)\n",
    "reg4_rmse_40 = get_learn_measure_rmse(vary_lr_rate_only_reg4_40)\n",
    "reg5_rmse_40 = get_learn_measure_rmse(vary_lr_rate_only_reg5_40)\n",
    "reg6_rmse_40 = get_learn_measure_rmse(vary_lr_rate_only_reg6_40)\n",
    "\n",
    "#FCP\n",
    "reg2_fcp_40 = get_learn_measure_fcp(vary_lr_rate_only_reg2_40)\n",
    "reg3_fcp_40 = get_learn_measure_fcp(vary_lr_rate_only_reg3_40)\n",
    "reg4_fcp_40 = get_learn_measure_fcp(vary_lr_rate_only_reg4_40)\n",
    "reg5_fcp_40 = get_learn_measure_fcp(vary_lr_rate_only_reg5_40)\n",
    "reg6_fcp_40 = get_learn_measure_fcp(vary_lr_rate_only_reg6_40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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St29f9Hr9Q7X9vdSvX9+S+mfOy+7Tpw+//vorBw8etOwTQO3atW3SBs3uTs/Yu3cvP/74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AeX6yMCAVfRdlmOIjr6HhyOwShKs2q0UCCoHYiV/dNQE96ec/MCPPzSZsw2N2q+hbfG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\nAa7mLVTadpKmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6aeda00390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1= plt.subplots()\n",
    "ax1.plot(reg2_rmse_40[0], reg2_rmse_40[1], 'b-', reg3_rmse_40[0], reg3_rmse_40[1], 'y-',\\\n",
    "         reg4_rmse_40[0], reg4_rmse_40[1], 'r-',reg5_rmse_40[0], reg5_rmse_40[1], 'g-',\\\n",
    "         reg6_rmse_40[0], reg6_rmse_40[1], 'o-')\n",
    "ax1.set_xlabel('Learning rate')\n",
    "ax1.set_ylabel('RMSE')\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(reg2_fcp_40[0], reg6_fcp_40[1], 'b-',reg3_fcp_40[0], reg3_fcp_40[1], 'y-',\\\n",
    "        reg2_fcp_40[0], reg4_fcp_40[1], 'r-',reg5_fcp_40[0], reg5_fcp_40[1], 'g-',\\\n",
    "        reg6_fcp_40[0], reg6_fcp_40[1], 'o-')\n",
    "ax2.set_ylabel('FCP')\n",
    "labels = ['0.05','0.1','0.2','0.4','0.6']\n",
    "plt.legend(labels)\n",
    "plt.title('RMSE, FCP vs. learning rate and regularization factors - 40 iterations')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### optimal SVD model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Orignal data:\n",
      "Evaluating RMSE, FCP of algorithm SVD.\n",
      "\n",
      "('The dump has been saved as file', u'./dumps/161227-16h08m38-SVD-fold1')\n",
      "('The dump has been saved as file', u'./dumps/161227-16h10m42-SVD-fold2')\n",
      "('The dump has been saved as file', u'./dumps/161227-16h12m16-SVD-fold3')\n",
      "('The dump has been saved as file', u'./dumps/161227-16h13m48-SVD-fold4')\n",
      "        Fold 1  Fold 2  Fold 3  Fold 4  Mean    \n",
      "FCP     0.5238  0.5250  0.5293  0.5234  0.5254  \n",
      "RMSE    1.0475  1.0446  1.0504  1.0532  1.0489  \n"
     ]
    }
   ],
   "source": [
    "# final model results with dump\n",
    "def svd_algorithm_final(data):\n",
    "    svdAlgo = SVD(n_epochs=40, lr_all=0.005, reg_all=0.6)\n",
    "    svdPerf = evaluate(svdAlgo, data, measures=['RMSE', 'FCP'], verbose=False, with_dump=True, dump_dir=\"./dumps/\")\n",
    "    return svdPerf\n",
    "print \"Orignal data:\"\n",
    "print svd_algorithm_final(original_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# The dump has been saved and we can now use it whenever we want.\n",
    "# Let's load it and see what we can do\n",
    "files = listdir(\"./dumps\")\n",
    "dump_obj = pickle.load(open('./dumps/' + files[0], 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# read results from file\n",
    "predictions = dump_obj['predictions']\n",
    "trainset = dump_obj['trainset']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial dataframe: \n",
      "          user_id     item_id  actual_rating  predicted_rating  \\\n",
      "0   AATP629AVSJJU  B009653L92            5.0          4.913908   \n",
      "1  A3FS5K0OLOA4VC  B00421FDA0            5.0          4.488684   \n",
      "2  A24KSM99CZS5OC  B004LP2RL0            5.0          3.958093   \n",
      "3  A3BZUQXA2QHDI2  B008FHJSK8            4.0          4.774726   \n",
      "4  A1VUQN40UCUU00  B008KK0ZJ8            4.0          4.553391   \n",
      "\n",
      "                      details  \n",
      "0  {u'was_impossible': False}  \n",
      "1  {u'was_impossible': False}  \n",
      "2  {u'was_impossible': False}  \n",
      "3  {u'was_impossible': False}  \n",
      "4  {u'was_impossible': False}  \n",
      "\n",
      "After dropping details column: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>actual_rating</th>\n",
       "      <th>predicted_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AATP629AVSJJU</td>\n",
       "      <td>B009653L92</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.913908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A3FS5K0OLOA4VC</td>\n",
       "      <td>B00421FDA0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.488684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A24KSM99CZS5OC</td>\n",
       "      <td>B004LP2RL0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.958093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3BZUQXA2QHDI2</td>\n",
       "      <td>B008FHJSK8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.774726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1VUQN40UCUU00</td>\n",
       "      <td>B008KK0ZJ8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.553391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id     item_id  actual_rating  predicted_rating\n",
       "0   AATP629AVSJJU  B009653L92            5.0          4.913908\n",
       "1  A3FS5K0OLOA4VC  B00421FDA0            5.0          4.488684\n",
       "2  A24KSM99CZS5OC  B004LP2RL0            5.0          3.958093\n",
       "3  A3BZUQXA2QHDI2  B008FHJSK8            4.0          4.774726\n",
       "4  A1VUQN40UCUU00  B008KK0ZJ8            4.0          4.553391"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print \"Initial dataframe: \"\n",
    "df = pd.DataFrame(predictions, columns=['user_id', 'item_id', 'actual_rating', 'predicted_rating', 'details']) \n",
    "print df.head()\n",
    "\n",
    "# We will not use the details field so we will drop it\n",
    "df.drop('details',axis=1,inplace=True)\n",
    "\n",
    "print \n",
    "print \"After dropping details column: \"\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>actual_rating</th>\n",
       "      <th>predicted_rating</th>\n",
       "      <th>user_num_of_ratings</th>\n",
       "      <th>item_num_of_ratings</th>\n",
       "      <th>mae</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AATP629AVSJJU</td>\n",
       "      <td>B009653L92</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.913908</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>0.086092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A3FS5K0OLOA4VC</td>\n",
       "      <td>B00421FDA0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.488684</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0.511316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A24KSM99CZS5OC</td>\n",
       "      <td>B004LP2RL0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.958093</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1.041907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3BZUQXA2QHDI2</td>\n",
       "      <td>B008FHJSK8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.774726</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>0.774726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1VUQN40UCUU00</td>\n",
       "      <td>B008KK0ZJ8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.553391</td>\n",
       "      <td>5</td>\n",
       "      <td>119</td>\n",
       "      <td>0.553391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id     item_id  actual_rating  predicted_rating  \\\n",
       "0   AATP629AVSJJU  B009653L92            5.0          4.913908   \n",
       "1  A3FS5K0OLOA4VC  B00421FDA0            5.0          4.488684   \n",
       "2  A24KSM99CZS5OC  B004LP2RL0            5.0          3.958093   \n",
       "3  A3BZUQXA2QHDI2  B008FHJSK8            4.0          4.774726   \n",
       "4  A1VUQN40UCUU00  B008KK0ZJ8            4.0          4.553391   \n",
       "\n",
       "   user_num_of_ratings  item_num_of_ratings       mae  \n",
       "0                    7                   17  0.086092  \n",
       "1                    3                   10  0.511316  \n",
       "2                    3                    8  1.041907  \n",
       "3                    9                    5  0.774726  \n",
       "4                    5                  119  0.553391  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_Iu(uid):\n",
    "    \"\"\"Return the number of items rated by given user\n",
    "    \n",
    "    Args:\n",
    "        uid: The raw id of the user.\n",
    "    Returns:\n",
    "        The number of items rated by the user.\n",
    "    \"\"\"\n",
    "    \n",
    "    try:\n",
    "        return len(trainset.ur[trainset.to_inner_uid(uid)])\n",
    "    except ValueError:  # user was not part of the trainset\n",
    "        return 0\n",
    "    \n",
    "def get_Ui(iid):\n",
    "    \"\"\"Return the number of users that have rated given item\n",
    "    \n",
    "    Args:\n",
    "        iid: The raw id of the item.\n",
    "    Returns:\n",
    "        The number of users that have rated the item.\n",
    "    \"\"\"\n",
    "    \n",
    "    try:\n",
    "        return len(trainset.ir[trainset.to_inner_iid(iid)])\n",
    "    except ValueError:  # item was not part of the trainset\n",
    "        return 0\n",
    "   \n",
    "df['user_num_of_ratings'] = df.user_id.apply(get_Iu)\n",
    "df['item_num_of_ratings'] = df.item_id.apply(get_Ui)\n",
    "df['mae'] = abs(df.predicted_rating - df.actual_rating)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "best_predictions = df.sort_values(by='mae')[:1]\n",
    "worst_predictions = df.sort_values(by='mae')[-1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Best predictions analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>actual_rating</th>\n",
       "      <th>predicted_rating</th>\n",
       "      <th>user_num_of_ratings</th>\n",
       "      <th>item_num_of_ratings</th>\n",
       "      <th>mae</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64648</th>\n",
       "      <td>A1UJOL9GT2GYCV</td>\n",
       "      <td>B004880XWK</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              user_id     item_id  actual_rating  predicted_rating  \\\n",
       "64648  A1UJOL9GT2GYCV  B004880XWK            5.0               5.0   \n",
       "\n",
       "       user_num_of_ratings  item_num_of_ratings  mae  \n",
       "64648                    4                    7  0.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0\n",
      "5.0\n"
     ]
    }
   ],
   "source": [
    "def get_user_average_rating(user_id):\n",
    "    tuples_list = trainset.ur[trainset.to_inner_uid(user_id)]\n",
    "    ratings_array = []\n",
    "    for tup in tuples_list:\n",
    "        ratings_array.append(tup[1])\n",
    "    return np.mean(ratings_array)\n",
    "\n",
    "def get_item_average_rating(item_id):\n",
    "    tuples_list = trainset.ir[trainset.to_inner_iid(item_id)]\n",
    "    ratings_array = []\n",
    "    for tup in tuples_list:\n",
    "        ratings_array.append(tup[1])\n",
    "    return np.mean(ratings_array)\n",
    "\n",
    "print get_user_average_rating('A1UJOL9GT2GYCV')\n",
    "print get_item_average_rating('B004880XWK')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### worst predictions analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>actual_rating</th>\n",
       "      <th>predicted_rating</th>\n",
       "      <th>user_num_of_ratings</th>\n",
       "      <th>item_num_of_ratings</th>\n",
       "      <th>mae</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>63002</th>\n",
       "      <td>A20301JOB907D3</td>\n",
       "      <td>B00B9DRN4S</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              user_id     item_id  actual_rating  predicted_rating  \\\n",
       "63002  A20301JOB907D3  B00B9DRN4S            1.0               5.0   \n",
       "\n",
       "       user_num_of_ratings  item_num_of_ratings  mae  \n",
       "63002                    3                    4  4.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's look at the prediction with the biggest error\n",
    "worst_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0\n",
      "4.75\n"
     ]
    }
   ],
   "source": [
    "print get_user_average_rating('A20301JOB907D3')\n",
    "print get_item_average_rating('B00B9DRN4S')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### error analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global mean of training set: \n",
      "4.24660778535\n",
      "\n",
      "statistics for rating 1\n",
      "actual_rating       1.000000\n",
      "predicted_rating    3.923809\n",
      "mae                 2.923809\n",
      "dtype: float64\n",
      "\n",
      "statistics for rating 2\n",
      "actual_rating       2.000000\n",
      "predicted_rating    4.023977\n",
      "mae                 2.023977\n",
      "dtype: float64\n",
      "\n",
      "statistics for rating 3\n",
      "actual_rating       3.000000\n",
      "predicted_rating    4.105728\n",
      "mae                 1.108803\n",
      "dtype: float64\n",
      "\n",
      "statistics for rating 4\n",
      "actual_rating       4.000000\n",
      "predicted_rating    4.189274\n",
      "mae                 0.352279\n",
      "dtype: float64\n",
      "\n",
      "statistics for rating 5\n",
      "actual_rating       5.000000\n",
      "predicted_rating    4.356946\n",
      "mae                 0.643054\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print \"Global mean of training set: \"\n",
    "print trainset.global_mean\n",
    "\n",
    "def statistic_per_rating(rating):\n",
    "    df_low_ratings = df[df['actual_rating'] == rating]\n",
    "    print \"\\nstatistics for rating %d\" % rating\n",
    "    return df_low_ratings[['actual_rating','predicted_rating','mae']].mean(axis=0)\n",
    "\n",
    "for rating in range(1,6):\n",
    "    print statistic_per_rating(rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KTkXyVZSZ+RvvfPcB5trBV+V8x3OsPNwy/oIfk+zff/ZjqXfs+JaxY5+mV68+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\nAAAAEIGSDAAAAESgJAMAAAAR/j9kK4zLMLkn8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6aeffd05d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df['predicted_rating']\n",
    "y = df['actual_rating']\n",
    "data = np.vstack([x, y]).T\n",
    "plt.hist(data,bins=5,alpha=0.6, label=['Predicted', 'Actual'])\n",
    "plt.axvline(x.mean(), color='b',linestyle='dashed', linewidth=1)\n",
    "plt.axvline(x.std(), color='b', linestyle='dashed', linewidth=1)\n",
    "\n",
    "plt.axvline(y.mean(), color='g', linestyle='dashed',linewidth=1)\n",
    "plt.axvline(y.std(), color='g', linestyle='dashed', linewidth=1)\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.title(\"Actual vs. Predicted ratings\")\n",
    "plt.xlabel(\"Rating\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}