{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if you don't have some library installed try using pip or pip3 to install it - you can do it from the notebook\n",
    "# example: !pip install tqdm\n",
    "# also on labs it's better to use python3 kernel - ipython3 notebook\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.sparse as sparse\n",
    "import time\n",
    "import random\n",
    "import evaluation_measures as ev\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# df = pd.DataFrame(np.loadtxt( './Datasets/ml-1m.dat',delimiter='::'))\n",
    "df=pd.read_csv('./Datasets/ml-100k/u.data',delimiter='\\t', header=None)\n",
    "df.columns=['user', 'item', 'rating', 'timestamp']\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train, test = train_test_split(df, test_size=0.2, random_state=30)\n",
    "\n",
    "train.to_csv('./Datasets/ml-100k/train.csv', sep='\\t', header=None, index=False)\n",
    "test.to_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interactions properties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How data looks like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>user</th>\n",
       "      <th>item</th>\n",
       "      <th>rating</th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>880606923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>886397596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user  item  rating  timestamp\n",
       "0   196   242       3  881250949\n",
       "1   186   302       3  891717742\n",
       "2    22   377       1  878887116\n",
       "3   244    51       2  880606923\n",
       "4   166   346       1  886397596"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 943 users, 1682 items and 100000 ratings.\n",
      "\n",
      "Average number of ratings per user is 106.04. \n",
      "\n",
      "Average number of ratings per item is 59.453.\n",
      "\n",
      "Data sparsity (% of missing entries) is 6.3047%.\n"
     ]
    }
   ],
   "source": [
    "users, items, ratings=len(set(df['user'])), len(set(df['item'])), len(df)\n",
    "\n",
    "print('We have {} users, {} items and {} ratings.\\n'.format(users, items, ratings))\n",
    "\n",
    "print('Average number of ratings per user is {}. \\n'.format(round(ratings/users,2)))\n",
    "print('Average number of ratings per item is {}.\\n'.format(round(ratings/items,4)))\n",
    "print('Data sparsity (% of missing entries) is {}%.'.format(round(100*ratings/(users*items),4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "items_per_user=df.groupby(['user']).count()['rating']\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.hist(items_per_user, bins=100)\n",
    "\n",
    "# Let's add median\n",
    "t=items_per_user.median()\n",
    "plt.axvline(t, color='k', linestyle='dashed', linewidth=1)\n",
    "plt.text(t*1.1, plt.ylim()[1]*0.9, 'Median: {:.0f}'.format(t))\n",
    "\n",
    "# Let's add also some percentiles\n",
    "t=items_per_user.quantile(0.25)\n",
    "plt.axvline(t, color='k', linestyle='dashed', linewidth=1)\n",
    "plt.text(t*1.1, plt.ylim()[1]*0.95, '25% quantile: {:.0f}'.format(t))\n",
    "\n",
    "t=items_per_user.quantile(0.75)\n",
    "plt.axvline(t, color='k', linestyle='dashed', linewidth=1)\n",
    "plt.text(t*1.05, plt.ylim()[1]*0.95, '75% quantile: {:.0f}'.format(t))\n",
    "\n",
    "plt.title('Number of ratings per user', fontsize=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ua5iSTvG/i8vdFsbMDlR0O5Ryik9ZrDibTkAzFd3HVIpmlK30t0H/DQ3k7svc/WeKrtGVosmCDtrQ/cW2l9SnQpuvFT3/U7N1TbdD2UrRPTLzqun4Kntsuft7kl6JX/YzszSugQaAtVCUAkjDXVpzW4lhiia5qaTpj6g9zKzcSM05G9OxQAaaWblZLM9RdG9USfq/4hXxfR+nxi//ycyyWJjeV/T8hxVGsS5sYbusaLpecRczK3e96yVl1jUpPm2y2lObN7l4gquH45ddJJ3UUlsz668WZt5dT3OKnidxKVGL9ww1s45acw/cFVrz89NkYtHz0WbWWvnUdHxVc2xNiP9tp2jSOQBIFUUpgODimTKbrr8zSd+rYrPiPySvKlXomNmlqjwylwVtJP0m/mN5LWY2RNIP45crFF1/29z/05qRrbvNrPl9F5vvcw8zu9rMdinXLkEPas2kP4dJ+nmp0Tcz+4mkIfHLuZLuCNO99dI0qmaSLm++0iKXKZphtpK/FT2vNLIX2i+Lnt9oZvs1b2Bm3SSNL7cTMzvQzC42s05l2uysNYWvq/x9T6v1r2a2zvXpZraNoi/Bmib3ua35iLe7P681X4gcLumOcqOHZtbGzI43s7MS6HeSmo6vncxs9wptb5L0Vvx8lJldUG6E3My2N7NzzCwPv18B5BATHQFIhbtPMbM/Kjp9s5pv9m9TdAuZDpJOlPQHM7tD0a0wdlc04lpQdP/BYZuk08l5QFER8xczGyfpz4pGLAYo+mO9qeC+0N3nNt/Y3aeb2XckjVN0i4bfmdkzkn6n6A/TfyjK05cVTS5TiDe9dpO9o7X797mZnaLoHqRbS/qBpK/Fn9fbik6J/WbcN8X9PS3BW9Ik6VeSzlA0cn2OmdUqGr1+R9GtNf5V0oGKRvI/ltS3zL7+oOi9biHpAjNrKsiabn/xvruncgqzuz9mZuMlDVd0Xfaf4td/VHRaaEFRHraTdK+in0Fp3VlUt1d0Sv7o+Jj8o6Q3FE1g1UHS/opy1iFuf4e7/30juz9D0URFN8f33Lxf0en+e0s6U2uu9f2b1nwZ1twZcfv9JX1L0gAzu0dSg6QPFB3HNYo+66MU/dzdupH9TtpjkobGz//PzG5WdH1302f0Z3efJ0nuvjzO1e8VfaY/kzTSzO5TdCwvi5d/SdE9Z/srup7/1DBvBcAXDUUpgDT9WGuu5yrL3ReZ2amKCoItFY3AHdas2RRFf1xmvSi9TtFkI2dJ+kmJ9S7pUne/saUduPutZvauosK0k0rno9hiScGKPnefYWZfVzQC1VnRyGCp0cH3Jf2ruz8Zqm/rI34f35N0o6Kzi47QutcdvirpGEn/VWFf75nZ1YqO+/Za93rq32sjrtdMQJ2ifp2o6PrK/9Das2N/rmgU/0OtKUqbz5bcdL1za5XOVbHfxDE31oeS/k3Rz/+A+NHcbEnfcPePSu3A3T8ys3+SVK+oKN0h7lu5/jWfMClttyn6nbK3oi9Hmh+P/6aike742D5Y0UjygYomHCt37+hPVfl+uACwQTh9F0Bq3P0prXt9V7n2Dym6/vR2SX9XNCPvIklPKPoGf6i7Z/XegGtx97MlHS1psqI/blfG//5G0mHuPqaKfUyWtKeiwmGSolNgP9aavDyr6N6wQyR1iSc4Ccbdn1V0f8/zFRVcixSNFC6O+3aRpO7uPi1kv9aXu9+sqOD/X0UjpP+Q9K6iUcDzJRXcfVaV+/qJpJMVHffvaM2s0qlz93+4+0mSTlDUv0WKCpG/Kzq1+jB3v0ZS8Wy57zfbx+8VjTb+UNEXEq8pGnX7PP73FUXF01fdfVhSP6/uPkNRYXW5pJcVFcvLJL2o6Iuf3u7+1wr7+Mjdhyn68uTaeNvFimZeXibpTUVnOZyv6Lit5jriYNx9maR+kq6QNF1RsV72fqDu/rqiAvYYRdeZviHpI0mfKRptnqnomtvhkjq7e9W/rwFgfVh0aRcAAEBl8Smex8cvd3L398u134T9aPoD5vfu3j+NPgAAksFIKQAAqEo82dHg+OXMtApSAMDmhaIUAADIzLqbWdcy62sUTSLUNl50S5COAQA2e0x0BAAAJOlQSbeb2VOKZgqerega5Z0UXav4TUWzREvSc4omBQIAYKNRlAIAgCZtJB0ZP1rypKQT3P2zID0CAGz2KEoBAIAUzQRdp+g+nD0V3a+0g6IZghdKel7S3fGszwAAJCYTs+/uvPPO3q1bt7S7scEWLVqkjh07bvYx84g8AQAAAOlrbGx8z91L/mGeiZHSbt26qaGhIe1uAAAAAAA2ATN7q6V1zL6bgDFjxnwhYuYReQIAAACyLROn7xYKBc/zSKmZKXQe04iZR+QJAAAASJ+ZNbp7odQ6RkoBAAAAAKmhKAUAAAAApIaiNAFpnHqc59OdQyJPAAAAQLZRlAIAAAAAUsNERwlgoqPsIk8AAABA+pjoCAAAAACQSRSlAAAAAIDUUJQmYPTo0V+ImHlEngAAAIBs45pSAAAAAMAmxTWlm1iXLl2+EDHziDwBAAAA2UZRmoAFCxZo7ty5+trXvqZ9991XvXr10nXXXbd6/ZgxY1RTU6Pa2lrV1tbqoYcekiQ988wz6t27twqFgt58801J0pIlS/SNb3xDn3/+ecWYm8oDDzygV155ZfXrSy65RI8++qgkqX///ht1788nn3xStbW16tWrl7761a9udF8raSlPr7/++urPo7a2Vtttt52uvfZaSZvm89qUNtXn9eGHH2rIkCE64IAD1KtXL91+++2r102YMEE9evRQjx49NGHChI17AwAAAPhCa5N2BzYXbdq00TXXXKM+ffpo6dKl6tu3r4466ijtu+++kqTvf//7+uEPf7jWNtdcc40eeughzZkzR7/+9a91zTXX6PLLL9dPfvITtWqV3vcFDzzwgAYPHry675deemki+12yZIm++93vaurUqdp999317rvvJrLfDbHPPvtoxowZkqTPPvtMNTU1Ou6441av5/OSbrrpJu27776aPHmyFi1apH322Uff/va3tWzZMo0dO1YNDQ0yM/Xt21dDhw7VjjvumEhcAAAAfLEwUpqAPn36qHPnzurTp48kadttt1XPnj01b968stttscUWWrFihVasWKEttthCs2fP1ty5c9W/f/8Wt5k6daq+/OUva+utt9Y555yjwYMHS4pG966++urV7fbbbz/NmTNHknTssceqb9++6tWrl+rr61e3ad++vS666CIdcMAB6tevnxYuXKg//vGPmjRpki644ALV1tZq9uzZGj58uO699951+vLwww/r0EMPVZ8+fXTSSSdp2bJlZd/vnXfeqeOPP1677767JGmXXXYp2z4JTZ9JOY899pi6d++uPfbYo2y7jfm8+vTpk7vPy8y0dOlSubuWLVumDh06qE2bNpo2bZqOOuoodejQQTvuuKOOOuooTZ06tey+AAAAgJZQlCagsbFxrddz5szRiy++qEMOOWT1shtvvFG9e/fWGWecoQ8++ECS9OMf/1innXaa/vM//1Nnn322LrroIl1++eUtxvnkk080YsQITZ48WcuXL9c777xTVf9uu+02NTY2qqGhQddff70WL14sSVq+fLn69eunmTNn6ogjjtC4ceP0la98RUOHDtXPf/5zzZgxQ927dy+5z/fee0+XX365Hn30UU2fPl2FQkG/+MUvJEWnj06aNGmdbd544w198MEH6t+/v/r27auJEydW1f+N0fyzKeXuu+/WySefvNaypD+vxsbG3H1eZ599tl599VV16dJF+++/v6677jq1atVK8+bN02677ba6XdeuXSt+AQMAAAC0hKI0AXV1daufL1u2TCeccIKuvfZabbfddpKk73znO5o9e7ZmzJihzp076wc/+IEkqba2Vs8995yeeOIJ/fWvf1Xnzp3l7vrWt76lU045RQsXLlwrzmuvvaY999xTPXr00MiRI3XKKadU1b/rr79+9eja3LlzV18P2bZt29Ujd3379l09UleN5557Tq+88ooOO+ww1dbWasKECXrrrbckRaePDh06dJ1tVq1apcbGRj344IOaNm2aLrvsMr3xxhtVx9wQxZ9NKStXrtSkSZN00kknrV62KT4vM8vd5zVt2jTV1tZq/vz5mjFjhs4++2x99NFHVccEAAAAqkFRmoBx48ZJkv7xj3/ohBNO0Le//W0df/zxq9d36tRJrVu3VqtWrTRixAi98MILa23v7rr88st18cUXa+zYsfrZz36mESNG6Prrr68Ys0mbNm3Wmmznk08+kRRNLPToo4/q2Wef1cyZM3XggQeuXrfFFlvIzCRJrVu31qpVq6p+z+6uo446SjNmzNCMGTP0yiuv6NZbby27TdeuXTVgwABts8022nnnnXXEEUdo5syZVcfcEM3z1Nzvfvc79enTR506dVq9bFN8Xs3l4fO6/fbbdfzxx8vMtNdee2nPPffUa6+9ppqaGs2dO3d1u7fffls1NTVV9wUAAAAoRlGaEHfXmWeeqZ49e+r8889fa13xDLD333+/9ttvv7XWT5w4UYMGDVKHDh20YsUKtWrVSq1atdKKFSvWavflL39Zc+bM0ezZsyVJd9111+p13bp10/Tp0yVJ06dP19/+9jdJ0QyqO+64o9q1a6fXXntNzz33XMX3su2222rp0qVl2/Tr10/PPPOMZs2aJSk6tbTSqOcxxxyjp59+WqtWrdKKFSv0/PPPq2fPnhX7synddddd65y6y+cV2X333fXYY49JkhYuXKjXX39dX/rSlzRgwAA9/PDD+uCDD/TBBx/o4Ycf1oABAyr2EwAAACiF2Xer1G3Ug2XXP/PMM/rv//5v7b///qqtrZUk/fSnP9WgQYP0ox/9SDNmzJCZqVu3brrllltWb7dixQqNHz9eDz/8sCTp/PPP16BBg9S2bVvdeeeda8XYaqutVF9fr6OPPlpSNFFQUzFywgknaOLEierVq5cOOeQQ7b333pKkgQMH6te//rV69uypffbZR/369av4XocNG7Z65K/UhDmS1LFjR40fP14nn3yyPv30U0nS5Zdfrr333luXXHKJCoXCOqeE9uzZUwMHDlTv3r3VqlUr/fu///s6BV9Iy5cv1yOPPLLW5yFpk3xe7dq10+GHH56rz+viiy/W8OHDtf/++8vdddVVV2nnnXdeve6ggw6SFF2T2qFDh4r9BAAAAEoxd0+7DyoUCr4x974MoVxRumrpYr1902kBeyPNnz9fb7zxhq6++mpNmTIlaOw8mT9/vrp06ZJ2NyRFp+byeQEAAOCLyMwa3b1Qah2n7yZg5cJZwWNWM6ssyBMAAACQdYyUVqncSOlbVw1W6DyaWfCYeUSeAAAAgPQxUgoAAAAAyCSKUgAAAABAaihKE9BhwNnBYzafMRalkScAAAAg2yhKE7Bt7cDgMevq6oLHzCPyBAAAAGQbRWkC3rpqcPCYZhY8Zh6RJwAAACDbKEoBAAAAAKmhKAUAAAAApIaiNAFbdz8oeMzBg8OfMpxH5AkAAADINorSBOxy4ujgMSdPnhw8Zh6RJwAAACDbKEoT8O69Y4PHHDJkSPCYeUSeAAAAgGyjKE3Ax7P/FDzmlClTgsfMI/IEAAAAZBtF6WbEzHTKKaesfr1q1Sp17Nhxva+r7N+/vxoaGiRJgwYN0pIlSxLt54wZM3TooYeqV69e6t27t37zm9+sXnf44YertrZWtbW16tKli4499thEYwMAAADIljZpdwDJ2WabbfTyyy/r448/1tZbb61HHnlENTU1G7XPhx56KKHerdGuXTtNnDhRPXr00Pz589W3b18NGDBAO+ywg/7whz+sbnfCCSfomGOOSTw+AAAAgOxgpDQBe1wY/hRRdy+5fNCgQXrwwQclSXfddZdOPvnk1euWL1+uM844QwcffLAOPPBA/fa3v5Ukffzxxxo2bJh69uyp4447Th9//PHqbbp166b33ntPknTssceqb9++6tWrl+rr61e3ad++vS666CIdcMAB6tevnxYuXFi273vvvbd69OghSerSpYt22WUXLVq0aK02H330kR5//PGNHiltKU8AAAAAsqHqotTMWpvZi2Y2JX69p5k9b2azzOw3ZtY2Xr5l/HpWvABhjiIAACAASURBVL7bpul6diydMTV4zOKisNiwYcN0991365NPPtFLL72kQw45ZPW6K664QkceeaReeOEFPfHEE7rgggu0fPly3XzzzWrXrp1effVVjR07Vo2NjSX3fdttt6mxsVENDQ26/vrrtXjxYklRsduvXz/NnDlTRxxxhMaNGydJmjRpki655JKy7+OFF17QypUr1b1797WWP/DAA/r617+u7bbbruqclNJSngAAAABkw/qMlJ4r6dWi11dJ+qW77yXpA0lnxsvPlPRBvPyXcbvN2vvTbgwec+TIkSWX9+7dW3PmzNFdd92lQYMGrbXu4Ycf1pVXXqna2lr1799fn3zyif7+97/rqaeeWn0tau/evdW7d++S+77++utXj4bOnTtXb775piSpbdu2q69b7du3r+bMmSNJGjp0qC699NIW38OCBQt06qmn6vbbb1erVmsfis1HeTdUS3kCAAAAkA1VXVNqZl0lHS3pCknnm5lJOlLSv8ZNJkgaI+lmScfEzyXpXkk3mpk551EGM3ToUP3whz/Uk08+uXo0U4pOZb3vvvu0zz77rPc+n3zyST366KN69tln1a5du9VFrSRtscUWig4JqXXr1lq1alXF/X300Uc6+uijdcUVV6hfv35rrXvvvff0wgsv6P7771/vfgIAAADIl2pHSq+V9CNJn8evd5K0xN2bqo+3JTXNqFMjaa4kxes/jNsjkDPOOEOjR4/W/vvvv9byAQMG6IYbblh9neWLL74oSTriiCN05513SpJefvllvfTSS+vs88MPP9SOO+6odu3a6bXXXtNzzz23wf1buXKljjvuOJ122mk68cQT11l/7733avDgwdpqq602OAYAAACAfKhYlJrZYEnvunvpCw03kJnVmVmDmTU0n+QmbzqecHHwmJMmTWpxXdeuXXXOOeess/ziiy/WP/7xD/Xu3Vu9evXSxRdH/f7Od76jZcuWqWfPnrrkkkvUt2/fdbYdOHCgVq1apZ49e2rUqFHrjG621MdS15Tec889euqppzR+/PjVt3+ZMWPG6vV33313IqfuNvUBAAAAQHZZpbNqzew/JZ0qaZWkrSRtJ+l+SQMk7eruq8zsUElj3H2AmU2Lnz9rZm0kvSOpY7nTdwuFgjfdFzOruo16sMV1q5Yu1ts3nRawN9L8+fPVpUuXoDHziDwBAAAA6TOzRncvlFpXcaTU3X/s7l3dvZukYZIed/dvS3pCUtO5l6dL+m38fFL8WvH6xzf360nn/er0yo0StrH3H/2iIE8AAABAtm3MfUovVDTp0SxF14zeGi+/VdJO8fLzJY3auC4CAAAAADZXVc2+28Tdn5T0ZPz8r5IOLtHmE0knJdA3AAAAAMBmbmNGShFrf8CA4DFHjBgRPGYekScAAAAg2yhKE7DTwO8Fj1lfXx88Zh6RJwAAACDbKEoTsGD8ucFjlrptC9ZFngAAAIBsoyhNwMqFs4PHnD59evCYeUSeAAAAgGyjKAUAAAAApIaiNAGt23cIHrNz587BY+YReQIAAACyjaI0AV3Pmhg85vz584PHzCPyBAAAAGQbRWkCljx9R/CYY8aMCR4zj8gTAAAAkG3m7mn3QYVCwRsaGtLuRlndRj3Y4rq3rhqs0Hk0s+Ax84g8AQAAAOkzs0Z3L5Rax0gpAAAAACA1FKUAAAAAgNRQlCZg19OvDR4z66c7ZwV5AgAAALKNohQAAAAAkBqK0gS8M+G84DELhZLXCKMZ8gQAAABkG0UpAAAAACA1FKUAAAAAgNRQlCZg+8NODh5z9OjRwWPmEXkCAAAAss3cPe0+qFAoeNZnSe026sGy6+dceXSgngAAAABAvphZo7uXnPCFkdIEvH3TacFjdunSJXjMPCJPAAAAQLZRlCbgs2XvB4+5YMGC4DHziDwBAAAA2UZRCgAAAABIDUVpAtp26h48Zp8+fYLHzCPyBAAAAGQbRWkCOg+/LnjMxsbG4DHziDwBAAAA2UZRmoDFU28IHrOuri54zDwiTwAAAEC2UZQmYNnMacFjjhs3LnjMPCJPAAAAQLZRlAIAAAAAUkNRCgAAAABIDUVpAmq+OyF4zHnz5gWPmUfkCQAAAMg2itIErFw4K3hMZpWtDnkCAAAAso2iNAGL7rsseMyhQ4cGj5lH5AkAAADINopSAAAAAEBqKEoBAAAAAKmhKE1AhwFnB495yy23BI+ZR+QJAAAAyDaK0gRsWzsweMy6urrgMfOIPAEAAADZRlGagLeuGhw8ppkFj5lH5AkAAADINopSAAAAAEBqKEoBAAAAAKmhKE3A1t0PCh5z8ODwpwznEXkCAAAAso2iNAG7nDg6eMzJkycHj5lH5AkAAADINorSBLx779jgMYcMGRI8Zh6RJwAAACDbKhalZraVmb1gZjPN7C9mNjZePt7M/mZmM+JHbbzczOx6M5tlZi+ZWZ9N/SbS9vHsPwWPOWXKlOAx84g8AQAAANnWpoo2n0o60t2XmdkWkp42s9/F6y5w93ubtf8XST3ixyGSbo7/BQAAAABgLRVHSj2yLH65RfzwMpscI2livN1zknYws84b31UAAAAAwOamqmtKzay1mc2Q9K6kR9z9+XjVFfEpur80sy3jZTWS5hZt/na8bLO1x4XhTxF1L/e9AJqQJwAAACDbqipK3f0zd6+V1FXSwWa2n6QfS/qypIMkdZB04foENrM6M2sws4ZFixatZ7ezZemMqcFj1tfXB4+ZR+QJAAAAyLb1mn3X3ZdIekLSQHdfEJ+i+6mk2yUdHDebJ2m3os26xsua76ve3QvuXujYseOG9T4j3p92Y/CYI0eODB4zj8gTAAAAkG3VzL7b0cx2iJ9vLekoSa81XSdqZibpWEkvx5tMknRaPAtvP0kfuvuCTdJ7AAAAAECuVTP7bmdJE8ystaIi9h53n2Jmj5tZR0kmaYak/4jbPyRpkKRZklZI+rfkuw0AAAAA2BxULErd/SVJB5ZYfmQL7V3SWRvftfzoeMLFwWNOmjQpeMw8Ik8AAABAtq3XNaUorW2nvYLH7Nu3b/CYeUSeAAAAgGyjKE3AvF+dHjxmTc1mfZedxJAnAAAAINsoSgEAAAAAqaEoBQAAAACkhqI0Ae0PGBA85ogRI4LHzCPyBAAAAGQbRWkCdhr4veAx6+vrg8fMI/IEAAAAZBtFaQIWjD83eExmla0OeQIAAACyjaI0ASsXzg4ec/r06cFj5hF5AgAAALKNohQAAAAAkBqK0gS0bt8heMzOnTsHj5lH5AkAAADINorSBHQ9a2LwmPPnzw8eM4/IEwAAAJBtFKUJWPL0HcFjjhkzJnjMPCJPAAAAQLaZu6fdBxUKBW9oaEi7G2V1G/Vgi+veumqwQufRzILHzCPyBAAAAKTPzBrdvVBqHSOlAAAAAIDUUJQCAAAAAFJDUZqAXU+/NnjMrJ/unBXkCQAAAMg2ilIAAAAAQGooShPwzoTzgscsFEpeI4xmyBMAAACQbRSlAAAAAIDUUJQCAAAAAFJDUZqA7Q87OXjM0aNHB4+ZR+QJAAAAyDZz97T7oEKh4FmfJbXbqAfLrp9z5dGBegIAAAAA+WJmje5ecsIXRkoT8PZNpwWP2aVLl+Ax84g8AQAAANlGUZqAz5a9HzzmggULgsfMI/IEAAAAZBtFKQAAAAAgNRSlCWjbqXvwmH369AkeM4/IEwAAAJBtFKUJ6Dz8uuAxGxsbg8fMI/IEAAAAZBtFaQIWT70heMy6urrgMfOIPAEAAADZRlGagGUzpwWPOW7cuOAx84g8AQAAANlGUQoAAAAASA1FKQAAAAAgNRSlCaj57oTgMefNmxc8Zh6RJwAAACDbKEoTsHLhrOAxmVW2OuQJAAAAyDaK0gQsuu+y4DGHDh0aPGYekScAAAAg2yhKAQAAAACpoSgFAAAAAKSGojQBHQacHTzmLbfcEjxmHpEnAAAAINsoShOwbe3A4DHr6uqCx8wj8gQAAABkG0VpAt66anDwmGYWPGYekScAAAAg2yhKAQAAAACpqViUmtlWZvaCmc00s7+Y2dh4+Z5m9ryZzTKz35hZ23j5lvHrWfH6bpv2LQAAAAAA8qqakdJPJR3p7gdIqpU00Mz6SbpK0i/dfS9JH0g6M25/pqQP4uW/jNtt1rbuflDwmIMHhz9lOI/IEwAAAJBtFYtSjyyLX24RP1zSkZLujZdPkHRs/PyY+LXi9V+3zfzCvl1OHB085uTJk4PHzCPyBAAAAGRbVdeUmllrM5sh6V1Jj0iaLWmJu6+Km7wtqSZ+XiNpriTF6z+UtFOSnc6ad+8dGzzmkCFDgsfMI/IEAAAAZFtVRam7f+butZK6SjpY0pc3NrCZ1ZlZg5k1LFq0aGN3l6qPZ/8peMwpU6YEj5lH5AkAAADItvWafdfdl0h6QtKhknYwszbxqq6S5sXP50naTZLi9dtLWlxiX/XuXnD3QseOHTew+wAAAACAPKtm9t2OZrZD/HxrSUdJelVRcXpi3Ox0Sb+Nn0+KXyte/7i7e5KdBgAAAABsHtpUbqLOkiaYWWtFRew97j7FzF6RdLeZXS7pRUm3xu1vlfTfZjZL0vuShm2CfmfKHheGP0WUOr865AkAAADItmpm333J3Q90997uvp+7Xxov/6u7H+zue7n7Se7+abz8k/j1XvH6v27qN5G2pTOmBo9ZX18fPGYekScAAAAg29brmlKU9v60G4PHHDlyZPCYeUSeAAAAgGyjKAUAAAAApIaiFAAAAACQGorSBHQ84eLgMSdNmhQ8Zh6RJwAAACDbKEoT0LbTXsFj9u3bN3jMPCJPAAAAQLZRlCZg3q9Or9woYTU1NcFj5hF5AgAAALKNohQAAAAAkBqKUgAAAABAaihKE9D+gAHBY44YMSJ4zDwiTwAAAEC2UZQmYKeB3wses76+PnjMPCJPAAAAQLZRlCZgwfhzg8dkVtnqkCcAAAAg2yhKE7By4ezgMadPnx48Zh6RJwAAACDbKEoBAAAAAKmhKE1A6/Ydgsfs3Llz8Jh5RJ4AAACAbKMoTUDXsyYGjzl//vzgMfOIPAEAAADZRlGagCVP3xE85pgxY4LHzCPyBAAAAGSbuXvafVChUPCGhoa0u1FWt1EPtrjurasGK3QezSx4zDwiTwAAAED6zKzR3Qul1jFSCgAAAABIDUUpAAAAACA1FKUJ2PX0a4PHzPrpzllBngAAAIBsoygFAAAAAKSGojQB70w4L3jMQqHkNcJohjwBAAAA2UZRCgAAAABIDUUpAAAAACA1FKUJ2P6wk4PHHD16dPCYeUSeAAAAgGwzd0+7DyoUCp71WVK7jXqw7Po5Vx4dqCcAAAAAkC9m1ujuJSd8YaQ0AW/fdFrwmF26dAkeM4/IEwAAAJBtFKUJ+GzZ+8FjLliwIHjMPCJPAAAAQLZRlAIAAAAAUkNRmoC2nboHj9mnT5/gMfOIPAEAAADZRlGagM7Drwses7GxMXjMPCJPAAAAQLZRlCZg8dQbgsesq6sLHjOPyBMAAACQbRSlCVg2c1rwmOPGjQseM4/IEwAAAJBtFKUAAAAAgNRQlAIAAAAAUkNRmoCa704IHnPevHnBY+YReQIAAACyjaI0ASsXzgoek1llq0OeAAAAgGyjKE3AovsuCx5z6NChwWPmEXkCAAAAso2iFAAAAACQGopSAAAAAEBqKhalZrabmT1hZq+Y2V/M7Nx4+Rgzm2dmM+LHoKJtfmxms8zsdTMbsCnfQBZ0GHB28Ji33HJL8Jh5RJ4AAACAbGtTRZtVkn7g7tPNbFtJjWb2SLzul+5+dXFjM9tX0jBJvSR1kfSome3t7p8l2fEs2bZ2YPCYdXV1wWPmEXkCAAAAsq3iSKm7L3D36fHzpZJelVRTZpNjJN3t7p+6+98kzZJ0cBKdzaq3rhocPKaZBY+ZR+QJAAAAyLb1uqbUzLpJOlDS8/Gis83sJTO7zcx2jJfVSJpbtNnbKl/EAgAAAAC+oKouSs2svaT7JJ3n7h9JullSd0m1khZIumZ9AptZnZk1mFnDokWL1mdTAAAAAMBmoqqi1My2UFSQ3uHu/ydJ7r7Q3T9z988ljdOaU3TnSdqtaPOu8bK1uHu9uxfcvdCxY8eNeQ+p27r7QcFjDh4c/pThPCJPAAAAQLZVM/uuSbpV0qvu/oui5Z2Lmh0n6eX4+SRJw8xsSzPbU1IPSS8k1+Xs2eXE0cFjTp48OXjMPCJPAAAAQLZVM1J6mKRTJR3Z7PYvPzOzP5vZS5K+Jun7kuTuf5F0j6RXJE2VdNbmPPOuJL1779jgMYcMGRI8Zh6RJwAAACDbzN3T7oMKhYI3NDSk3Y2yuo16sMV1b101WKHzaGbBY+YReQIAAADSZ2aN7l4otW69Zt8FAAAAACBJFKUAAAAAgNRQlCZgjwunBI/JKanVIU8AAABAtlGUJmDpjKnBY9bX1wePmUfkCQAAAMg2itIEvD/txuAxR44cGTxmHpEnAAAAINsoSgEAAAAAqaEoBQAAAACkhqI0AR1PuDh4zEmTJgWPmUfkCQAAAMg2itIEtO20V/CYffv2DR4zj8gTAAAAkG0UpQmY96vTg8esqakJHjOPyBMAAACQbRSlAAAAAIDUUJQCAAAAAFJDUZqA9gcMCB5zxIgRwWPmEXkCAAAAso2iNAE7Dfxe8Jj19fXBY+YReQIAAACyjaI0AQvGnxs8JrPKVoc8AQAAANlGUZqAlQtnB485ffr04DHziDwBAAAA2UZRCgAAAABIDUVpAlq37xA8ZufOnYPHzCPyBAAAAGQbRWkCup41MXjM+fPnB4+ZR+QJAAAAyDaK0gQsefqO4DHHjBkTPGYekScAAAAg28zd0+6DCoWCNzQ0pN2NsrqNerDFdW9dNVih82hmwWPmEXkCAAAA0mdmje5eKLWOkVIAAAAAQGooSgEAAAAAqaEoTcCup18bPGbWT3fOCvIEAAAAZBtFKQAAAAAgNRSlCXhnwnnBYxYKJa8RRjPkCQAAAMg2ilIAAAAAQGooSgEAAAAAqaEoTcD2h50cPObo0aODx8wj8gQAAABkm7l72n1QoVDwrM+S2m3Ug2XXz7ny6EA9AQAAAIB8MbNGdy854QsjpQl4+6bTgsfs0qVL8Jh5RJ4AAACAbKMoTcBny94PHnPBggXBY+YReQIAAACyjaIUAAAAAJAaitIEtO3UPXjMPn36BI+ZR+QJAAAAyDaK0gR0Hn5d8JiNjY3BY+YReQIAAACyjaI0AYun3hA8Zl1dXfCYeUSeAAAAgGyjKE3AspnTgsccN25c8Jh5RJ4AAACAbKMoBQAAAACkhqIUAAAAAJAaitIE1Hx3QvCY8+bNCx4zj8gTAAAAkG0Vi1Iz283MnjCzV8zsL2Z2bry8g5k9YmZvxv/uGC83M7vezGaZ2Utmttnfk2PlwlnBYzKrbHXIEwAAAJBt1YyUrpL0A3ffV1I/SWeZ2b6SRkl6zN17SHosfi1J/yKpR/yok3Rz4r3OmEX3XRY85tChQ4PHzCPyBAAAAGRbxaLU3Re4+/T4+VJJr0qqkXSMpKbzVidIOjZ+foykiR55TtIOZtY58Z4DAAAAAHJvva4pNbNukg6U9LykTu6+IF71jqRO8fMaSXOLNns7XgYAAAAAwFqqLkrNrL2k+ySd5+4fFa9zd5fk6xPYzOrMrMHMGhYtWrQ+m2ZOhwFnB495yy23BI+ZR+QJAAAAyLaqilIz20JRQXqHu/9fvHhh02m58b/vxsvnSdqtaPOu8bK1uHu9uxfcvdCxY8cN7X8mbFs7MHjMurq64DHziDwBAAAA2VbN7Lsm6VZJr7r7L4pWTZJ0evz8dEm/LVp+WjwLbz9JHxad5rtZeuuqwcFjRh8LKiFPAAAAQLa1qaLNYZJOlfRnM5sRL/uJpCsl3WNmZ0p6S9I343UPSRokaZakFZL+LdEeAwAAAAA2GxWLUnd/WlJLw01fL9HeJZ21kf0CAAAAAHwBrNfsuyht6+4HBY85eHD4U4bziDwBAAAA2UZRmoBdThwdPObkyZODx8wj8gQAAABkG0VpAt69d2zwmEOGDAkeM4/IEwAAAJBtFKUJ+Hj2n4LHnDJlSvCYeUSeAAAAgGyjKAUAAAAApIaiFAAAAACQGorSBOxxYfhTRKM776AS8gQAAABkW8X7lKKypTOmqtuo8m3mXHl0ojHr6+tVV1eX6D43R+QJAAAAyDZGShPw/rQbg8ccOXJk8Jh5RJ4AAACAbKMoBQAAAACkhqIUAAAAAJAaitIEdDzh4uAxJ02aFDxmHpEnAAAAINsoShPQttNewWP27ds3eMw8Ik8AAABAtlGUJmDer04PHrOmpiZ4zDwiTwAAAEC2UZQCAAAAAFJDUQoAAAAASA1FaQLaHzAgeMwRI0YEj5lH5AkAAADINorSBOw08HvBY9bX1wePmUfkCQAAAMg2itIELBh/bvCYzCpbHfIEAAAAZBtFaQJWLpwdPOb06dODx8wj8gQAAABkG0UpAAAAACA1FKUJaN2+Q/CYnTt3Dh4zj8gTAAAAkG0UpQnoetbE4DHnz58fPGYekScAAAAg2yhKE7Dk6TuCxxwzZkzwmHlEngAAAIBsoyhNwIfP3BU85tixY4PHzCPyBAAAAGQbRSkAAAAAIDUUpQAAAACA1FCUJmDX068NHrOhoSF4zDwiTwAAAEC2UZQCAAAAAFJDUZqAdyacFzxmoVAIHjOPyBMAAACQbRSlAAAAAIDUUJQCAAAAAFJDUZqA7Q87OXjM0aNHB4+ZR+QJAAAAyDZz97T7oEKh4FmfJbXbqAc3avs5Vx6dUE8AAAAAIF/MrNHdS074wkhpAt6+6bTgMbt06RI8Zh6RJwAAACDbKEoT8Nmy94PHXLBgQfCYeUSeAAAAgGyjKAUAAAAApIaiNAFtO3UPHrNPnz7BY+YReQIAAACyjaI0AZ2HXxc8ZmNjY/CYeUSeAAAAgGyjKE3A4qk3BI9ZV1cXPGYekScAAAAg2yoWpWZ2m5m9a2YvFy0bY2bzzGxG/BhUtO7HZjbLzF43swGbquNZsmzmtOAxx40bFzxmHpEnAAAAINuqGSkdL2lgieW/dPfa+PGQJJnZvpKGSeoVb/MrM2udVGcBAAAAAJuXikWpuz8lqdp7nhwj6W53/9Td/yZplqSDN6J/AAAAAIDN2MZcU3q2mb0Un967Y7ysRtLcojZvx8s2azXfnRA85rx584LHzCPyBAAAAGTbhhalN0vqLqlW0gJJ16zvDsyszswazKxh0aJFG9iNbFi5cFbwmMwqWx3yBAAAAGTbBhWl7r7Q3T9z988ljdOaU3TnSdqtqGnXeFmpfdS7e8HdCx07dtyQbmTGovsuCx5z6NChwWPmEXkCAAAAsm2DilIz61z08jhJTTPzTpI0zMy2NLM9JfWQ9MLGdREAAAAAsLlqU6mBmd0lqb+knc3sbUmjJfU3s1pJLmmOpJGS5O5/MbN7JL0iaZWks9z9s03TdQAAAABA3lUsSt395BKLby3T/gpJV2xMp/Kmw4Czg8e85ZZbgsfMI/IEAAAAZNvGzL6L2La1pW7jumnV1dUFj5lH5AkAAADINorSBLx11eDgMc0seMw8Ik8AAABAtlGUAgAAAABSQ1EKAAAAAEgNRWkCtu5+UPCYgweHP2U4j8gTAAAAkG0UpQnY5cTRwWNOnjw5eMw8Ik8AAABAtlGUJuDde8cGjzlkyJDgMfOIPAEAAADZRlGagI9n/yl4zClTpgSPmUfkCQAAAMg2ilIAAAAAQGooSgEAAAAAqaEoTcAeF4Y/RdTdg8fMI/IEAAAAZBtFaQKWzpgaPGZ9fX3wmHlEngAAAIBsoyhNwPvTbgwec+TIkcFj5hF5AgAAALKNohQAAAAAkBqKUgAAAABAaihKE9DxhIuDx5w0aVLwmHlEngAAAIBsoyhNQNtOewWP2bdv3+Ax84g8AQAAANlGUZqAeb86PXjMmpqa4DHziDwBAAAA2UZRCgAAAABIDUUpAAAAACA1FKUJaH/AgOAxR4wYETxmHpEnAAAAINsoShOw08DvBY9ZX18fPGYekScAAAAg2yhKE7Bg/LnBYzKrbHXIEwAAAJBtFKUJWLlwdvCY06dPDx4zj8gTAAAAkG0UpQAAAACA1FCUJqB1+w7BY3bu3Dl4zDwiTwAAAEC2UZQmoOtZE4PHnD9/fvCYeUSeAAAAgGyjKE3AkqfvCB5zzJgxwWPmEXkCAAAAso2iNAEfPnNX8Jhjx44NHjOPyBMAAACQbRSlAAAAAIDUtEm7A18U3UY9WHb9nCuPDtQTAAAAAMgORkoTsOvp1waP2dDQEDxmHpEnAAAAINsoSgEAAAAAqaEoTcA7E84LHrNQKASPmUfkCQAAAMg2ilIAAAAAQGooSgEAAAAAqaEoTcD2h50cPObo0aODx8wj8gQAAABkm7l72n1QoVDwrM+SWumWLhuLW8IAAAAA2FyZWaO7l5zwhZHSBLx902nBY3bp0iV4zDwiTwAAAEC2UZQm4LNl7wePuWDBguAx84g8AQAAANlWsSg1s9vM7F0ze7loWQcze8TM3oz/3TFebmZ2vZnNMrOXzKzPpuw8AAAAACDfqhkpHS9pYLNloyQ95u49JD0Wv5akf5HUI37USbo5mW5mW9tO3YPH7NOHer8a5AkAAADItopFqbs/Jan5+anHSJoQP58g6dii5RM98pykHcysc1KdzarOw68LHrOxsTF4zDwiTwAAAEC2beg1pZ3cvelivXckdYqf10iaW9Tu7XjZ/2/v/mP1LMs7gH+vFZlGiAysFVp+OCUaTQZiVYzEqKh0UsFEYjRudsZ5XASDyZbJlhhgxgT/mTp/heKvujjU4JwVDGjQZNNkSoswf0cgJVJLy/g1mEaCu/fHeYqH2p6ect73fd7nvJ9PcnLe536fnuvi7pXzcvW+n+dZ0e6+9sMTjzk3NzfxmENkngAAYLot+0ZHbf6ZgU01CwAADlhJREFUMof8XJmqmquqbVW17a677lpuGr168ObrJh7ziiuumHjMITJPAAAw3R5rU7p777bc7vuebnxnkuMXnLeuG/s9rbXNrbX1rbX1q1evfoxpAAAAMGSPtSndmmRT93pTkq8sGH9zdxfe05Pcv2CbLwAAADzKYQc7oaquTPLSJE+uqjuSXJzksiRfrKq3Jrk9yeu707+W5NVJbknyqyRvGUPOU2ftO7Yc/KQR27lzvwvQ7MM8AQDAdDtoU9pae+MB3jpzP+e2JOcvN6mheWj3LTnsyGMmGnP79u057rjjJhpziMwTAABMt2Xf6Ijkri+9d+IxzznnnInHHCLzBAAA001TCgAAQG80pQAAAPRGUzoCR591wcRjXn755ROPOUTmCQAAppumdASOPHXDxGPOzc1NPOYQmScAAJhumtIRuP39Gyces6omHnOIzBMAAEw3TSkAAAC90ZQCAADQG03pCDzh6c+feMyNGye/ZXiIzBMAAEw3TekIPOW8iyce86tf/erEYw6ReQIAgOl2WN8JrAR7rrp02Y3pSRdds+j7Oy47+1HHr3nNazRcS2CeAABgulkpHYFf33rDxGNeffXVE485ROYJAACmm6YUAACA3mhKAQAA6I2mdAROfPfkt4i21iYec4jMEwAATDdN6Qg8cNO1E4+5efPmicccIvMEAADTTVM6Avdc95GJx3z7298+8ZhDZJ4AAGC6aUoBAADojaYUAACA3mhKR2D1694z8Zhbt26deMwhMk8AADDdDus7gZXg8DXPGHuMky665lHHDz9wb975nd+N7bjs7LHnMETPe97z+k4BAABYhJXSEdj5sU0zEXOI1q5d23cKAADAIjSlAAAA9EZTCgAAQG80pSNwxClnzUTMIXrb297WdwoAAMAiNKUjcMyGd85EzCHavHlz3ykAAACL0JSOwK7PXDgTMYfI3XcBAGC6aUpH4KHdt85EzCG68cYb+04BAABYhKYUAACA3hzWdwIrwaojjl4RMU+66JpF399x2dkjjzluxx57bN8pAAAAi7BSOgLrzv/sTMQcol/+8pd9pwAAACxCUzoC9337czMRc4guueSSvlMAAAAWoSkdgfu/c+VMxByiSy+9tO8UAACARWhKAQAA6I2mFAAAgN5oSkfgqZs+OBMxh2jbtm19pwAAACzCI2FmxMEe9wIAANAHK6UjcOeWd81EzCFav3593ykAAACL0JQCAADQG9t3VwjbcwEAgCFaVlNaVTuSPJDkt0kebq2tr6qjk3whyUlJdiR5fWvt3uWlOd2e9OI3zkTMIbr44ov7TgEAAFjEKLbvvqy1dmprbe/Fexclub61dnKS67vjFe2oM940EzGH6JJLLuk7BQAAYBHjuKb03CRbutdbkrx2DDGmyh0fffNMxByi4447ru8UAACARSy3KW1Jvl5V26tqrhtb01rb1b2+M8maZcaYer998J6ZiDlEu3btOvhJAABAb5Z7o6MzWms7q+opSb5RVT9d+GZrrVVV298f7JrYuSQ54YQTlpkGAAAAQ7SsldLW2s7u+54kX07ygiS7q+rYJOm+7znAn93cWlvfWlu/evXq5aTRu8PXPH0mYg7Raaed1ncKAADAIh5zU1pVT6yqI/e+TvKqJD9MsjXJpu60TUm+stwkp92xf/GhmYg5RNu3b+87BQAAYBHLWSldk+TbVXVzku8luaa1dm2Sy5K8sqp+nuQV3fGKdve1H56JmEM0Nzd38JMAAIDePOamtLV2W2vtlO7rOa2193Xjd7fWzmytndxae0VrbcXfkefBm6+biZhDdMUVV/SdAgAAsIhxPBIGAAAAlmS5d99lhpx00TWLvr/jsrMnlAkAALBSWCkdgbXv2DITMYdo586dfacAAAAsQlM6Ag/tvmUmYg6Ru+8CAMB005SOwF1feu9MxByic845p+8UAACARbimlJFxzSkAAHCorJQCAADQGyulI3D0WRfMRMxxG8dK6+WXX/5Y0wEAACZAUzoCR566YSZiLtfBms5xmJubm3hMAABg6WzfHYHb379xJmIOUVX1nQIAALAITSkAAAC90ZQCAADQG03pCDzh6c+fiZhDtHGjbc4AADDN3OhoBJ5y3sUzEXPa7fdGSs/5q0fGPScVAACmj6Z0BPZcdenEm8Q+Yg7RrM3TOB6rAwAA42T77gj8+tYbZiLmEJknAACYblZKGYxJPOfUSiMAAEyWlVIAAAB6oykdgRPfffVMxBwi8wQAANNNUzoCD9x07UzEHCLzBAAA001TOgL3XPeRmYg5ROYJAACmm6YUAACA3rj7LoyQu/cCAMCh0ZSOwOrXvWcmYg7RwnmaxCNlAACAQ2P77ggcvuYZMxFziMwTAABMNyulI7DzY5sm/uiRPmIO0dDmyfbf5TOHAADDYqUUAACA3mhKAQAA6I2mdASOOOWsmYg5ROYJAACmm2tKR+CYDe+ciZhDNOp56vsOvn3HXwrXdAIAcCislI7Ars9cOBMxh8g8AQDAdLNSOgIP7b51JmIOkXk6NEtZibXSCQDAKGlKAVYYW6gBgCHRlI7AqiOOnomYQ2Se2JeGDQBgumhKR2Dd+Z+diZhDNG3z1PeNivqOPy05AAAwPdzoaATu+/bnZiLmEJknAACYblZKR+D+71yZo85404qPOUTmiSGyxRgAmCWaUuCQ2H7LUiy3Tmah8faPDwAwT1MKsMAkmm7NBkuhaQVgVmhKR+Cpmz44EzGHyDwxjZbb+FqtHj8NIQBMztia0qrakORDSVYl+URr7bJxxQJg6YawtXa5TaGmEgCGYyxNaVWtSvLRJK9MckeSG6pqa2vtx+OI17c7t7wrJ7776hUfc4jME4zeNKzUTkMOQ6dxB2BajGul9AVJbmmt3ZYkVfX5JOcmWZFNKQCzZRaa4mloWsedwzT8Nx7MEHJczNDzZ56/R8ZtXE3p2iS/WHB8R5IXjikWABwSTeX0//whWAlzMITt/MvVd0M1hBvorYRaXun6ruNxq9ba6H9o1XlJNrTW/rI7/vMkL2ytXbDgnLkkc93hM5P8bOSJjMaTk/x330kwGOqFpVIrHAr1wlKpFQ6FemGpRlErJ7bWVu/vjXGtlO5McvyC43Xd2CNaa5uTbB5T/JGpqm2ttfV958EwqBeWSq1wKNQLS6VWOBTqhaUad638wZh+7g1JTq6qp1XV4UnekGTrmGIBAAAwUGNZKW2tPVxVFyS5LvOPhPlUa+1H44gFAADAcI3tOaWtta8l+dq4fv4ETf0WY6aKemGp1AqHQr2wVGqFQ6FeWKqx1spYbnQEAAAASzGua0oBAADgoDSli6iqDVX1s6q6paou6jsf+ldVn6qqPVX1wwVjR1fVN6rq5933P+rGq6r+qauf/6qq0/rLnEmrquOr6ltV9eOq+lFVXdiNqxcepaoeX1Xfq6qbu1q5tBt/WlV9t6uJL3Q3DkxV/WF3fEv3/kl95s/kVdWqqvp+VV3dHasV9quqdlTVD6rqpqra1o35HOL3VNVRVXVVVf20qn5SVS+aZK1oSg+gqlYl+WiSP03y7CRvrKpn95sVU+AzSTbsM3ZRkutbaycnub47TuZr5+Tuay7JxyeUI9Ph4SR/3Vp7dpLTk5zf/Q5RL+zrN0le3lo7JcmpSTZU1elJ3p/kA621ZyS5N8lbu/PfmuTebvwD3XnMlguT/GTBsVphMS9rrZ264HEePofYnw8luba19qwkp2T+d8zEakVTemAvSHJLa+221tpDST6f5Nyec6JnrbV/T3LPPsPnJtnSvd6S5LULxj/b5v1nkqOq6tjJZErfWmu7Wms3dq8fyPwv97VRL+yj+zt/sDt8XPfVkrw8yVXd+L61sreGrkpyZlXVhNKlZ1W1LsnZST7RHVfUCofG5xCPUlVPSvKSJJ9MktbaQ621+zLBWtGUHtjaJL9YcHxHNwb7WtNa29W9vjPJmu61GiJJ0m2Ze26S70a9sB/ddsybkuxJ8o0ktya5r7X2cHfKwnp4pFa69+9PcsxkM6ZHH0zyt0n+rzs+JmqFA2tJvl5V26tqrhvzOcS+npbkriSf7i4N+ERVPTETrBVNKYxQm7+dtVta84iqOiLJl5K8q7X2PwvfUy/s1Vr7bWvt1CTrMr9T51k9p8QUqqqNSfa01rb3nQuDcUZr7bTMb7c8v6pesvBNn0N0DktyWpKPt9aem+R/87utuknGXyua0gPbmeT4BcfrujHY1+69Wxa673u6cTU046rqcZlvSD/XWvvXbli9cEDddqlvJXlR5rdD7X2e+MJ6eKRWuveflOTuCadKP16c5Jyq2pH5y4penvnrwNQK+9Va29l935Pky5n/Ry+fQ+zrjiR3tNa+2x1flfkmdWK1oik9sBuSnNzd0e7wJG9IsrXnnJhOW5Ns6l5vSvKVBeNv7u5QdnqS+xdsgWCF667b+mSSn7TW/nHBW+qFR6mq1VV1VPf6CUlemflrkL+V5LzutH1rZW8NnZfkm81Dx2dCa+3vWmvrWmsnZf7/S77ZWntT1Ar7UVVPrKoj975O8qokP4zPIfbRWrszyS+q6pnd0JlJfpwJ1kr53XRgVfXqzF+7sSrJp1pr7+s5JXpWVVcmeWmSJyfZneTiJP+W5ItJTkhye5LXt9bu6ZqSj2T+br2/SvKW1tq2PvJm8qrqjCT/keQH+d21X3+f+etK1QuPqKo/yfwNJFZl/h+Lv9ha+4eq+uPMr4YdneT7Sf6stfabqnp8kn/O/HXK9yR5Q2vttn6ypy9V9dIkf9Na26hW2J+uLr7cHR6W5F9aa++rqmPic4h9VNWpmb+B2uFJbkvylnSfSZlArWhKAQAA6I3tuwAAAPRGUwoAAEBvNKUAAAD0RlMKAABAbzSlAAAA9EZTCgAAQG80pQAAAPRGUwoAAEBv/h8FqDPMJfOR7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "items_per_user=df.groupby(['item']).count()['rating']\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.hist(items_per_user, bins=100)\n",
    "\n",
    "# Let's add median\n",
    "t=items_per_user.median()\n",
    "plt.axvline(t, color='k', linestyle='dashed', linewidth=1)\n",
    "plt.text(t*1.1, plt.ylim()[1]*0.9, 'Median: {:.0f}'.format(t))\n",
    "\n",
    "# Let's add also some percentiles\n",
    "t=items_per_user.quantile(0.25)\n",
    "plt.axvline(t, color='k', linestyle='dashed', linewidth=1)\n",
    "plt.text(t*1.1, plt.ylim()[1]*0.95, '25% quantile: {:.0f}'.format(t))\n",
    "\n",
    "t=items_per_user.quantile(0.75)\n",
    "plt.axvline(t, color='k', linestyle='dashed', linewidth=1)\n",
    "plt.text(t*1.05, plt.ylim()[1]*0.95, '75% quantile: {:.0f}'.format(t))\n",
    "\n",
    "plt.title('Number of ratings per item', fontsize=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rating\n",
       "1    0.06110\n",
       "2    0.11370\n",
       "3    0.27145\n",
       "4    0.34174\n",
       "5    0.21201\n",
       "Name: user, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['rating']).count()['user']/len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Item attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "genres = pd.read_csv('./Datasets/ml-100k/u.genre', sep='|', header=None,\n",
    "                     encoding='latin-1')\n",
    "genres=dict(zip(genres[1], genres[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'unknown',\n",
       " 1: 'Action',\n",
       " 2: 'Adventure',\n",
       " 3: 'Animation',\n",
       " 4: \"Children's\",\n",
       " 5: 'Comedy',\n",
       " 6: 'Crime',\n",
       " 7: 'Documentary',\n",
       " 8: 'Drama',\n",
       " 9: 'Fantasy',\n",
       " 10: 'Film-Noir',\n",
       " 11: 'Horror',\n",
       " 12: 'Musical',\n",
       " 13: 'Mystery',\n",
       " 14: 'Romance',\n",
       " 15: 'Sci-Fi',\n",
       " 16: 'Thriller',\n",
       " 17: 'War',\n",
       " 18: 'Western'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.read_csv('./Datasets/ml-100k/u.item', sep='|', encoding='latin-1', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</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": [
       "   0                  1            2   3   \\\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",
       "                                                  4   5   6   7   8   9   ...  \\\n",
       "0  http://us.imdb.com/M/title-exact?Toy%20Story%2...   0   0   0   1   1  ...   \n",
       "1  http://us.imdb.com/M/title-exact?GoldenEye%20(...   0   1   1   0   0  ...   \n",
       "2  http://us.imdb.com/M/title-exact?Four%20Rooms%...   0   0   0   0   0  ...   \n",
       "\n",
       "   14  15  16  17  18  19  20  21  22  23  \n",
       "0   0   0   0   0   0   0   0   0   0   0  \n",
       "1   0   0   0   0   0   0   0   1   0   0  \n",
       "2   0   0   0   0   0   0   0   1   0   0  \n",
       "\n",
       "[3 rows x 24 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(19):\n",
    "    movies[i+5]=movies[i+5].apply(lambda x: genres[i] if x==1 else '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['genre']=movies.iloc[:, 5:].apply(lambda x: ', '.join(x[x!='']), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies=movies[[0,1,'genre']]\n",
    "movies.columns=['id', 'title', 'genres']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>id</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</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>Animation, Children's, Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>Action, Adventure, Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "      <td>Action, Comedy, Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "      <td>Crime, Drama, Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id              title                         genres\n",
       "0   1   Toy Story (1995)  Animation, Children's, Comedy\n",
       "1   2   GoldenEye (1995)    Action, Adventure, Thriller\n",
       "2   3  Four Rooms (1995)                       Thriller\n",
       "3   4  Get Shorty (1995)          Action, Comedy, Drama\n",
       "4   5     Copycat (1995)         Crime, Drama, Thriller"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.to_csv('./Datasets/ml-100k/movies.csv', index=False)\n",
    "movies[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Toy example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "if not os.path.exists('./Datasets/toy-example/'):\n",
    "    os.mkdir('./Datasets/toy-example/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "toy_train=pd.DataFrame([[0,0,3,0], [0,10,4,0], [0,40,5,0], [0,70,4,0],\n",
    "              [10,10,1,0], [10,20,2,0], [10,30,3,0],\n",
    "              [20,30,5,0], [20,50,3,0], [20,60,4,0]])\n",
    "toy_test=pd.DataFrame([[0,60,3,0],\n",
    "              [10,40,5,0],\n",
    "              [20,0,5,0], [20,20,4,0], [20,70,2,0]])\n",
    "\n",
    "toy_train.to_csv('./Datasets/toy-example/train.csv', sep='\\t', header=None, index=False)\n",
    "toy_test.to_csv('./Datasets/toy-example/test.csv', sep='\\t', header=None, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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