Predicting Customers Channel with SVM, RForests, XGBoost.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine learning algorithms on wholesale costumers data (ongoing)\n",
    "## Predicting sales channel with SVM, Random Forest and XGBoost algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem is the following: given the annual spending on 6 different type of products of 440 wholesale customers, is it possible to predict the customers channel? In another notebook on this repo, *Principal Component Analyis on wholesale customers data*, I segmented these same costumers into three different groups and I found some differences between customer segment and channel, which is a positive indication that there is a pattern to be predicted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset I used can be found [here](https://archive.ics.uci.edu/ml/datasets/Wholesale+customers).\n",
    "It is originated from a larger database referred on: \n",
    "\n",
    "*Abreu, N. (2011). Analise do perfil do cliente Recheio e desenvolvimento de um sistema promocional. Mestrado em Marketing, ISCTE-IUL, Lisbon*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset variable description:\n",
    "\n",
    "- FRESH: annual spending (m.u.) on fresh products (Continuous)\n",
    "- MILK: annual spending (m.u.) on milk products (Continuous)\n",
    "- GROCERY: annual spending (m.u.) on grocery products (Continuous)\n",
    "- FROZEN: annual spending (m.u.) on frozen products (Continuous) \n",
    "- DETERGENTS_PAPER: annual spending (m.u.) on detergents and paper products (Continuous) \n",
    "- DELICATESSEN: annual spending (m.u.) on and delicatessen products (Continuous) \n",
    "- **CHANNEL: customers Channel - Horeca (Hotel/Restaurant/Cafe) or Retail channel (Nominal)** \n",
    "- REGION: customers Region - Lisbon, Oporto or Other (Nominal) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "# Load the dataset\n",
    "dataset = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00292/Wholesale%20customers%20data.csv'\n",
    "df = pd.read_csv(dataset)\n",
    "\n",
    "# List only continous variables (products), I'll deal with nominal vars later\n",
    "products = ['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(440, 8)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0        2       3  12669  9656     7561     214              2674        1338\n",
       "1        2       3   7057  9810     9568    1762              3293        1776\n",
       "2        2       3   6353  8808     7684    2405              3516        7844\n",
       "3        1       3  13265  1196     4221    6404               507        1788\n",
       "4        2       3  22615  5410     7198    3915              1777        5185"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "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>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Fresh          Milk       Grocery        Frozen  \\\n",
       "count     440.000000    440.000000    440.000000    440.000000   \n",
       "mean    12000.297727   5796.265909   7951.277273   3071.931818   \n",
       "std     12647.328865   7380.377175   9503.162829   4854.673333   \n",
       "min         3.000000     55.000000      3.000000     25.000000   \n",
       "25%      3127.750000   1533.000000   2153.000000    742.250000   \n",
       "50%      8504.000000   3627.000000   4755.500000   1526.000000   \n",
       "75%     16933.750000   7190.250000  10655.750000   3554.250000   \n",
       "max    112151.000000  73498.000000  92780.000000  60869.000000   \n",
       "\n",
       "       Detergents_Paper    Delicassen  \n",
       "count        440.000000    440.000000  \n",
       "mean        2881.493182   1524.870455  \n",
       "std         4767.854448   2820.105937  \n",
       "min            3.000000      3.000000  \n",
       "25%          256.750000    408.250000  \n",
       "50%          816.500000    965.500000  \n",
       "75%         3922.000000   1820.250000  \n",
       "max        40827.000000  47943.000000  "
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[products].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## t-SNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "dtsne = TSNE(learning_rate=250).fit_transform(df[products])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f9543158c88>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xkH/nvdJCgutZMK5ZIt7bJxN884tf7clquhJJI3pJLN5nGMZjQD/wz0zTfLLZ\nE4eH4+j6+pduHlujSJ1GZTeOnH+FZy8cruRFuF3yyo7lV3MdiPb5z03baUZHEx2PedvAFmZyc3Xb\nR+PDbNs6uOz3YDs2aTtd9RmG+jS0yBBz+XnG+xNVGdF3vedDy77Wzx+b4rHnz7nn0FXS+TKPPX+O\nwYEYH9y3ur6BkZEE6XyZkF4vBul8mZHRPrQec2Sv1Xe6m2zEMW9EekUsTgL/ArgPuAb4n4ZhvNs0\nzYYFXObmco02ryljY/0dlxnolNoWo0E/xeMnfk7JKlVFLgncfAkLhXLZ8ifc4cgQMzPZjsd808gH\neGyhvqrtTSMfaPs4zd5Dv9Zf12s8ooTZkdjGgD5Q11Z1udf6kYNvUbbq228+cvAt9mzra/CK7jA2\n1s/sbJb+WKhpWYnZmfqyHuvJWnynu81GGvNGF7WeEAvTNM8D/73y8LRhGBeBHcDp9RvV2lJrT2/U\nYtR7vHf0PVzIXawSCg+BQFM1t1RIxVHdqopsM1t+oxavnWRWL/UempVXv+uaj/s5J6qirmgy6IWC\ncbKshGSz0BNiYRjGXwXeY5rmtw3D2AJsA86v87DWhGb29EYd8mCxSm2qsFCVbb2Y763QH+ojmXeL\nCG6LN7fNt2PLDyYMdmpbX+o9fG3iq/7fjYRouXb8oPD1QsE4WVZCslnoCbEAHga+YhjGL3BzP37T\nNM3SOo9p1WnWgMURjm+iEYKqMuPnsxd5+M0noJK05wmG96+mKNjCZktsFAW3DIV3B/+Jsdtanhsa\nN3/pdPKuTbwL4jnIVyJEtTQTvl64s5dlJSSbgZ4QC9M0M8Cvrfc41ppmkTLPvXoJZUeMS+lZLFv4\n1UzRy6QKC6iKgoNrYkIsCoWuaDgI0qUMiqLURUR94n2LYtGN5i9L9bsIVtStpTa6qRtC0Ur4euHO\nXgqFZCPTE2JxJbKUPf18MotS3oo16JqSLFswm8ughIuVpjqKW8684rPwDVCKilrZli3nfLEQonI3\n73hNjRqfWwjRli1/Kcd7kFZtX7tFK+GTd/YSycqRAd7rhGdPb0SxbBMt7CSx8D5UuzLhKzaqFXXL\nmBPsKOeuKhRFYTDS7+93q9LmSeZnuJSbJlVc4OWpVxueO1ewmJ7Lc3EmRypd5JU3L1eNx3YWHeme\n09pbMXhO60YlOSbG9nL3rjv90h7DkSHu3nWnLyyN2qx2SjtObA8pFBLJ8pEri3WkoT1dCMIh17QT\nye8gkt+JagOSAAAgAElEQVSBg01q288QQCIRYaHkNjPSFBVHOGiofsc7UPxudOnSYhRRWAvx58cf\n5ZNXfcyvI/WTX5whV7CYzyxOtpGQVjWmWvPNi/nmTutGq4tav4Tt2P7KZLYwx0h0Zb0resGJLVl/\n1iM7/0pDisU60sye7j32UNFQ7RhquEA8FAVlsdtcTI8SD8X9khzev27XOcvvOueZpLxJ3Tv3Dx83\nAdA0lf5YiFgla/qRQ2exHYHAQUFlNl3kLw6+Cddd9jOrg7TK6j52+QSHp45wPjvFQnFRxC4XZjmf\ndc1IyxWMXnBiS9aH549N8cjBt5hNFxnuj/AhGWm2akixWGea2dNrJ79YdjehwVMAVWUyPrPrTqA6\nBPWua27iibNP+3kWQYKT+sS1owz1RxjsC6HUTPJT5bcIb7vgls0uR7GTO7HntqIPKzBq1QnGUiU5\ngvkWC8U05UoXPk1RwXG3PXHm6WWLRS85sSVrx9GTSR57/hwLmRLpfJmpy1nMcyk+ftOC3ztE0j2k\nWPQIQaFoVkhQG7quqWO5NgTV69ddS3BSPz5zgtz2wxSE22gplt1NJL+DYvQ89J3E0VQcIbCVLGx9\nAxyBldxJKuI2JgoKxlJOay/fQgjhCwW4LVy9sVzKJ1cUQiud2FceByenyObLzKYL2I6ASrn7J4+8\nzZ4dA/JmoctIsehRGk9+Y0vmJQS3LRWJZDsOx2fctqbRhEUh4zbe8Zom5RNnCOkqQlDtIB59B+Xs\nrSQWohSUc8SjTsus7qXyLQC/01+3kEJxZeAFNsymi9iBBlIIKJcdHn7ujBSLLiPFosdpNPm1c/fd\nqFTHrtg+nvm5zY/TL5HbfphowjUnFcs2mXwZxxHMR07RH7eJhMKkMiWECPwQQ3n6YrrbXyO/g299\n8iZ0bemCjsF8C0VRCKl61erCe3fb4mOy+qqkbTRVZbg/wtuXGpSCUeDibF6uMruMFItNTDAS6ZVT\nMzz2/DnKVhGBQ0FkKGTcMN180UZTVTQV0EsUczEigzZDfWFmFgo4juv70J048agbmjvSH2kpFB7B\nVc5ApJ9UYR5bOGiKWnHAJ7jrmo+v1mWQbFJu37edyVOX67ZrihSI1UCKxRWAqqhViWsKKqodw9Hy\nZPJlP+RQINDsOLHsbjLhE2wdjgFRUhm38koiu9gLu5NIo+AqB6CvLwGK67MY6bA4oUTicbOxle2j\nCaZmsr49U1MUVFVhfCQmVxVdRorFFUDJKjObLlb1VYhld5MdeM1dNeglhFpCKA6eYSgy916GtqeA\nFAm9Hyu5k3J+65KRRkuV/2hUB0o2/pGslN+4dy+//+NXSedKOI5A01T6YiHuObBrvYe26ZBisYkJ\nluXIbVcJp69Bt9wVgdfXez5xHEer3JkBdmiBhZEXGC69m69PVHdya2YDbrf8B9B2TailhEci8fjg\nvnHmF/IcnJxiZqHA6EBUhk2vElIsNim1vSSiiRLzHCfuCF8oIvkdbBk7z2y5jKMV/OcKxSETP8Vk\n8njVpN9MKJr1rFiOaalWeLzmRxJJM2TY9NogbQCblNpeEvGozshAFHvQbTE60h/hsweuJt5fRg9b\nfhl0RQFdU0BxODT1YtPje3WdgjkUS52/HRrVnfrz4482rDslkdQihWJ1kSuLTUiz3IZELEQkZFeF\nvB55ZZCLyjQhTa3KedBUjVRxvs6vMJk8zqGpF0kV5xmKDHJ6/hyWsHyzkVdapFX5j0Ys1SxJOsAl\ntQQLXEpWHykWm5BWvSSCIa/7d9zKyfm3sB27KjkuEUrUlfD4i8lDPHvxGSxboOhlLmiXsLBQUKpK\ndyBgZ994R0LRTrMk6Qzf3LRbDDDY6Gp8S4JbjbEqH4UsKrg6SLHYpLTbS2JibC8fXtjPz88fCqwO\nEsT1aNVzj55M8sy553E0gVBLWEoBhA2KG3JrCwcHgRCCmcIcfaFEnc9jKTppliTZXLTT3jf43GDd\ntMup/JJVkqWju3tIsdikNMrgbuYs/ty1d7Nr4GrfvNQoounZyfM48TwAjlrCD5/ybVcCRwh/lWEL\nu2NH91o1S5L0Dp22923W6MqrktzucSSdI8ViE1Ob2zA21k8y2aA8QuW5e0ffg6IodXfxtuMwly6j\nRmI4Wq4qHwMCnfoQ6KqOrmp+tdtO/A2NBO5O43b2RK7r9K1LauhV00wn7X2XanQ1NZNjbChaV2X5\n2VcuSLHoElIsrgBamXBa5Ul4DYaKlUQ+RaiuYAgVFKeyknAFRAESobj/2k79DZ7A/XL6VV64+DI/\nfu1x+rX+rmd5e5Nnr06i3aITE89qsNT1bafLYTDCqWmjK7HYLdIjV7DIVMqWf/eBSWmS6gJSLK5w\n2s2TcBsMuT/S7MAJrNA8itCI6lFQHWyrSEjVGYj0+42WYHn+hsnkcZ44+zQAuq6tOHcjiDd5vpPM\nUirbREIaO8cSm3Iy6dTEU8tKhLQdkWo2+QshGB2INgyFbdjoSnHLe3jFZ4PdHzVNZXahIE1SXUCK\nxRVOu+Gqiz02Isxe2oE+conQ2AWEnmc4MsS2+Bivz52sO85y/A2rFULrTZ65gkUqXUBRFPJFtwLu\nZpxMOjHxBFnpaqQTkQpO/t5qwLYddE3l6Mlk3fNre71sGYpxq+Fu846TyZcBcNwa+0zN5NA0lUcO\nnd1Un+9aI8XiCqbTcNVGmbLB5+zuoOxHt8bUCQcnp8gVLC7P5yuOeYGmKKTzZWJRveUkupFo18RT\nu3roxmqkE5HyHj/y3BnmM0U0TWWoL4LtiKbnDX4Pt20bqPLDPfvKBaYuZ12XmmCxjI3t8M50hpfe\nmObmd29t+T4k9UixuIJZbrhq0DwQfM7e0fcu2ZxpNce0FG62ucKbFxZI50r4yeYCbCGgbINobCff\nqDS17+Nm77/y5uW61cOnxvpXvBqZWSiQTBWq+rl7NLu+N14/xsHJKezquImW5210nBuvH+O7D0zy\n+rkUiOqkPU1Tee7VixtCLAzD+EPgv5imeXCVz/Nt4JRpmn/c6rlSLK5wuhGu2shsMXHtCMCyigF2\nK4Q26LhXrBi58ABkqycKATgAijuJbgah8Gho3wd2bEk0XD3090XbdjgHVyTB1YjnZE5VfAZBwWh2\nff1VUINLvxwBP7BvO8dPz9Zt74uF/P4svfQ5G4ZxN/B/AEWgH3hifUfUmJZiYRjGp03TfHwtBiNZ\nexqFq3ZiPqo1W0xZp/nDU4+ivpNF0xS2xcf45NUf5QNb37+sMaXt9LJMWrWO+4vpWbQd0wgBdqpa\nMFTFdap20qNjI9C4l/t409XD0y+9veRqRFWVhjcGtcfri4WYzxRJ58tEoypKpQRds+vbahXUzsTu\nidfRk0meO3YRAMsRqAqEdI2QrpLJl8nky/zHH7/aMwENhmG8G/hnwKdM00wZhqECvw28C/iSYRh/\nDbgJ+CxgAT8A5ir7/xagAf8deBTYB7xgmua/MQzjVeCPge3Au0zT/JJhGBPAvwQuVU7/9U7GqtQW\ngGvwZh4F3g38CfDfTNM828kJVoNkMr30oNeApXIWepVWY16O+ei7D0z6P/Ji7ALpoV/iaAUUBTQV\nvyPeVf07uOuaj7c94Xs//tHRBDMz2Y7GBPD9yR/4piwh3Dh8y3ZwilFKp24k+LUfiIf46t3v6erk\n0Uvfj5JloasaqqpQsiz+nz8+2vB5IV3lrl95Fw8/V/8T/9ztuwDqVypCkCvaxGtMTgva2xT6TqNH\nS0SVPvbvuIVfndhf9Zxmq5Pa8y71ubx1KcMjB99iNl1EUyCTt4hFdfIFi1SmiBCCWDREoRLIMNQX\n8Vc7rY7dbcbG+utUzzCM3wJ00zT/fc32PwR+ZprmDwzD+P+A54FXgWtM03zSMIy/jysUDwCPm6b5\nHsMwEsBh0zTfbxjGGeA20zQvGobxIvBp4I+Af2Ca5muGYfxz4CXgRrplhjJN8zOGYQwDXwB+3zAM\nB/gD4H+Ypmm3er1k49CpUNQ6UfOJ0wjV7aonhHD9AbiCcSmXbCv8tfbO9Z479rBnW1+H46p2knuV\ndB2hIMJ5QpqCZ/PQNLXrQtEtVpoD8sAzb3Jwcop80ULXVPrjIaIRnVS6SDik1U3wW4Zi3PzuraiK\n0jAa6rsPTNafRFEolavFohi7gDVgEtUUxobiKIqDWXyByeQAE2N7l4y26iQK6+jJZKVVsOubmJrN\nY9vu354gpCuriUhIq/OjtPLDeIUKVzkPJ0xDAxwAb1b+zQExXDPVXzEM4x7gfcDPK/vfATBNM2sY\nhhe3bpmmebHyd7by+t3APzQMA2AYeKuTgbblszBNc84wjD+tDPa3gH8AfNswjN8wTfP5Tk4o2TwE\nzQcCB9vP7gZqsrwtx0awdPhro0ic+540ufuDV3c0mTdykvfFQqQyJVSRwNI0bNtB01Q+ftPOnhOK\nbiTSPfDMmzz54tuA27Qqb1nkixYDiTCRkOb7FIKT/J23Xg00jnpbKroqEtIQQvj+inziNOBe82BC\n9eGpI9iprUtGW3XSlyJo/hJC+ELhRbfFojqRsMql2Txbh2J1U3Izf8jRk0keOXSWqZkcAOMjMe45\nsGqrkEeA/2EYxh+bpjldMUP9ByDe4LnfAp42TfM+wzB+l85bTLwF/FPTNM8ahnE1MIs7l7dFy5MZ\nhvFhwzD+AHgN13b2G6ZpfhC4B/iPHQ5Wssnw7NAKKpodRxEqIOp+mLqqobAY/tqIZrb0ZyfPdzyu\nWmd4PKoz1BdmG+9m63CMvbtH+DuffS9f/Mi1HR97NfEE05uYvcn06MlkR8cJXkvbEX4U6XzWXfkN\n9UUolV3DwEh/hM/dvosP7qv2KTTKnm7EzrEEn79jNyP9EQQOarjAUF+4buUyV0w1/SyD423XRxEU\nL0VR0DR3OrNth1zeYnouz6XZPI4Qfj5NkEb+kKMnk9z/1Cnemc5g244bcpvMct9Tpzr+DNrBNM03\ngH+MKxhPAk8Db+CuJmp5EvhNwzD+LXAR+DXgqg5O90+Af28Yxg+Bfw9EOxlrOyuL3wX+M/B3TdP0\nPx3TNM8YhvFnnZxMsvkImg+K2d2IyAJCK/gmKABNUUmEEkDz8NdGd67F2AXm+05zSc3z+5OvsL8D\nJ3cjx/3du9zX91o0TJDlhq4GKVVWEeBe11oHXypTZKgvwlB/hH/y129G19u7QW0WXeWtfLxVwX85\n9nrD0OehyCAX0+WGx+406skTr3R+8XieYx1gPrv4XUpEQ4vRWRENb7nTyOF+cHKq6pgemXx51fJw\nTNN8CHhoif3fDjwMPu8/VP69M/Dc64L/Vv7+aOXPd4BfrTn8t2mTdnwWdyyx71+1eyLJ5mVxong/\nx2au44kzT3Mhe5GyY1VKgAwQ192bmGbhr7URMZnBV8n3vQmKg4LKhexUxyU/agspevSqUHRaK6kZ\nYV0nFtHJFy2cJqEg6XyZnWOJtoTC852041dQVaVp6PP+8Vt5pt+ueo+e+Wo5Yct3TIzz2PPn/Mfe\nSiZbKGNZrpmxL6YTj4YAmE0XUDMKsYje0LRnOw6zCwXfnFW1z3Z6Mux2LZF5FpKuoapK1QT96uXX\neH7qpbZDcr0712LsAvm+NxGKgwKomnCbKrG8kh8bpRdGN0JIPe6YGHd9FjVioXk+CNvh9n1Lhwo3\n85208issFY5tTyyWXPFKe2iayr7dI22/N48brx9jcCDmR0ON9Ef47P5rePjQWf/485kS89mSO15F\nYdtIDFVVOXZ6lj07BqoEQ1NVRgaiTKfqBUPT1Kb1qq4UpFhIVgVVUblhbB83jO1rOyTX++He9+Yh\nVygU9wfs/T6z5dym75q3lKmnEzxfzBMvnMO2XR+SpiqoFdv++EiMm4ylI42WckS3mjSbrepuvH6M\nty4s8PTL532h6I+FGk7e7fDBfePs2dZXJV6PPX/ON0cBbrSUADWkoAYimxqZle6YGOd8MksqU8QR\nwu2RURHcHVsSHY1tsyHFQrIqBMM+O5nYJ64d4S9TAjsfcst0KIprqsANiR0KD25aoYDmiXTLsZV/\n8SPXsmfHAA8+exqlMpF6Zp97Duxa8rXd8J1A48/+wuUsW4djVDV9X8axq84TFK+Av8wRwn9Ythxy\nBcs3VzUy7Xnn/9HTp5ieyy8uzBR44cT0sgRtsyDFQtJVVhr26YW95qwCqcI8tu0gRCVXQtXZv+PW\nVRx9b9BuCGk7eRjetX92coq5dJHRgWjLz6RbvpOWx24zlLXT49sCBhJhFnIlbCtghxOQSheAKPGo\n3tS0d+P1Yzzy3Bl0TVnskSFgPlPkkefOSLGQSFbKSiuWetw2fgsPvP44ohQBrQQ4CFuhcPEqfnIu\nh32gvnT1ZkTU3npXeMmc5rljF9vuWX2wIhTDfeEln+vZ6bvpO6llNY8NMPnmDKl0kXzRwnIcVNUN\nq/VMcYqikMmXiUf1pqY923GYms3Xdd0DN/HvSnVyS7GQdI1umS4mxvby8KEzaOpJsHNYhQjOzFUw\nv40pLb8pe08EabY6O3oyySPPneHt6Qy67mYkzwIPHjyN4whuNrbWHSco3nOZUsNr550vnS/THwtx\nx8R4le8kmHDXjfpZ3fLL1BJ8v+XKihRAVQWa5vprPD574JoN9f2591sP6g995/P1ySLLxDCMfcCD\nwL8zTfP32nmNFAtJV+im6cJ2HMqzWxliK8lUDidgSnDNUmJT9J5oZEZqtjp768ICL5yYZmahgHAE\nlmNRLFmoaQXbEXzvwePs2na2KtO4HfEOni+kq/75Pnf7LvbtHvHLhTQKN11uOZJu+mVq31euUCZf\ntNBUBavSOs9xoC8RYrg/AgJGBiJLlinXVJXx0TjvTGfq9o2Pxtd0VXHvtx78HG7BwHfd+60H3wb+\n4KHvfP4nKzlmpYbUd4GnOnldz4iFYRi/A3wcN6vw66ZpNm6XJulJumle0FSV4b4wc+kidk31MU1T\nURRlQ/eeaFUbqRYhBD97+TyW7SAqiROiEqTj2K7zXwjhZxoDTFw72pZ4NxOURw6dxXYEQ/0Rhvoi\noMCrb82wZ8eAP86VTPSdlvZoxUvmNK+fS1EoWr5TWlHc66SAn62O0t4K5p791/DDx00y+bI/xr5Y\niHv2X7PisbZLRSh+O7DpXcBv3/utB1mhYBSBzwD/qJMX9URYiWEYHwNuMU3zduCrwL9d5yFJlkGz\nH2En5oWjJ5N894FJLlzOMZ0qUJso0BdzE6w2au+Jpcp52I6b+OWRK7glK6YuZ8kVLbdNaLO3XNnu\nZRovVZ7Du3ZLrQanZnJ4FalzRXccF2dy/NeHT3DfU6dWXI7Eoxuf4dGTSR4+dNZddQa2+0FRilsf\na7gv3FGl2XBIIxxyy5t7f68xf6vD7W1hmqZlmma+09f1hFgAH8O1n2Ga5jFgh2EYjQppSXqYG68f\n43O37/InKa/mULs/zuBEGovq9MdCoCg4jkDTVAb7In7Y40btPbHUnfx/+vExkqkC03N55tJF5jPF\nquQw2xZVdncPQXWynZdp3Eq8mwpKZZZVFIVcwaoaR77kPs4Vqs3nzd7XWvDIobNMz+UpWfWZ1woQ\n0lT27h7hf//SDW1/Fw9OThGP6mwdjrF9NM7W4RjxSuvdteDebz2o464kGvGue7/14JorV6+YocaB\nVwKPk8A24PT6DEeyXDoxL9iOXdVJr/aHGIvqDPSFsW2Hob5IV+3b60GzO3mv94I1Gqc/5tYxKpQs\nNEVBVd3wTV1zxcIRrrPWs8eDW37dE5FgpnE7voGGzmZFYXwkhi3clUojvIgij/UyCx6avMA70xk/\nF6e2uok3ngPv3972MRsVKfRYq/f50Hc+b1V8FI0E4+2HvvP5NW8P0StiUap53Ohz9xkejqPray6s\ndYyN9a/3EDpmNcbsZeK2y5Hzr/Cz04eYyc0xGh/mY7v3c+P295POlwk1qFWkaSr/9GsH3L83kOmp\n0bUe35LgcmrRApDNl12ntRDMzBcYSIQZGYwyPZvDEYKIrjGQCANwOVXwncqaBpbluNVWA9dkqC/M\nPXfs8c/9qbF+PnVgD7YjGl67T431MzgQ46cvnuNyKs/4loRfqvxP//J1HEdUTZbhkAbCNesE8xC2\nDMXYtm2gC1etM77/8GuEdBXLFihOZf5cTLpGCEE4pDM8GO/ou1/7OXms8fv8A6p9FsHta06viMUU\nEAxPGGOx9V8dc3ONqveuLb3UCa1duj3m5STg1bY7vbRwmfteeYj5+Tx9sRBzNXfeId0tBzE7Ux+Z\n0ss0u9a3GmP+nby3onAqE3nZcpiZLzDUFyES1rFth9HBqD8hD/W7ZcWH+iKMDETYsSXBibNzdX0X\n9mzr6+hz3rOtj6999n2MjPZVXefP3OY6efNFyy/LAW7VWk1TK6sb4b+vRudcaQOnpY5hOw6XU3ni\nUbfarKoofjl2WFxxxSI6f/TYCeYX8m2vSIOfU+325f6GOr1Re+g7n//Jvd96ECrRUEC3oqFuBr4D\n7ALKhmF8CfiCaZr1jcsD9IpYPIbbG/Z7hmHcBLy1HAeMZO1oNwGv9od+eKo+yC2bL/MnLz0NyVvI\nVGL9gx3N7pgY78qk0wsETUOvz+Ubrsi8fId0vlx1Vx+P6vzVT1zHDdduqTKDOJUIqZWaRmpXHt5Y\nf3LwTJ1jvS+mYwua3iR0o4FTq2NoqsroYNTvlJfJl6FsYzmuSSqka/TFQr65rJNw69UK7+2UijD8\n5N5vPah1y/RkmuZLwEc7fV1PiIVpmi8ZhvGKYRgv4zYl/9vrPSbJ0rSK4W/0Q5+4dqSqz0HOyrNQ\nyFKyLRRNpX/4En1s8/sJ7BxLcP3VwxycnOInvzizof0VQW68foyJa0f5nR8cqXIie9i2QzSicet7\nt3LhcrblZLWa9vOGk2YlaKGZ7f7F1y/x6OHF0uHLyeRvdTPifb8uzuZIZUr0xUJsHY7hOA6XZvMM\nJsLEKyuh4DE68Td0O7x3JayHj6KWnhALANM0/xEdxv1K1odWCXgvvTHNw8+drdrm/dC9dqc5K89C\nMU3ZWYxgyQ2cIIHC1ugOvzRFsMfycsuH9CqjA1Fm00X/zjeTL1O23DkhX7S5cDnLHRPjdSuJtabZ\npFk7Jq/n90KuhKq4eQlDgWirTu7sl7oZAfzvQSIWwraFf4Nx1ViCkK651WKpzj4f7gtXtYltd6W6\n3kLRK/SMWEg2Dq0S8J579WKDV7k/9I982G2Mky3n3FafXkkGx3Xi5hOnieR3MJcp8ewrF5oeZ6OK\nRXDFpSmu3yIW1YlHdbftbNpmqN8tdNdr4rjUpOn1/BaVbEFHCBa8Fq4VwWj3zr7VzcjBySn86pLg\n99se6Y/wzS9OcPRkkvueOuX3y/DCZfIFi3/xwyMgxJImNEljNr4RWLIuNIvhP7Bve/UPPVAuejZd\nZN/o+7jrmo8D7m9YRUWzoygVsXC0PALBcH+EuUxtkNzicTw7/UaiNiHPFtU5EsXyolAEWc8chnbx\nxqgoSpV/Ixh6224iZTD/Q4jqz1lF8Pq5FFMzOabn8mQDxw9+L7yzOEJQthwcISiWbd6ZzvBOMkuu\nYDGbLvLgwdOr0lt7MyLFQrIsmiXg3WxsZaQ/Qt7LPq78qPMFy58sPrD1/bx76Fq2JbYyEhnxhQJA\ntWMoKHxoYrxlBvJGo9GkH4/qDCbC/F9//SaG+iN1QgG9L47Bnt8AmlLjfK9M+J0kUu7YkvCzxqfn\n8uQKFrmCRbawaLq3bYfZhSL5SoKg9704ODlFrJJQF9I1QpqKqihVvbXns0X/+D983JSC0QbSDCVZ\nNs1s2Tu2JDh+ejEKz7YdUpkit753MTra69MctNdbtmDMuZ67A1nfwR7LHhsxe7uVaWUp095wf6in\nxTHY8xsWzVW2cDPOR2p6aLTyFxw9meTY6Vk/Isy2HTL5MgPxENGIjoCqgIB0vkysUnK86joLUZUB\nLxyBUNzVnG0JhOb6M/JFy434ojfMfb2KFAvJiqmdyC5czjLYF6nqsdwXC3HhctZ/TrBPM6TYObiF\nD26/hRu2LvbXvvH6Mfr7ojz23OkNn73dTqHF2mzqYuwC+cRpnEGb70++0rKH+Xri9/yuoKoKKgp3\n3nIVX/rodYArAs++coG5TKnuswwKiLcC83wRbpKd4NJsju0RveoGw1tx3bt/8QbDv86VFrKeYCiV\nbHir4sdQAlnvKL3tC/vy/d/Qf/SV73WzRPnv4pZZCgH/yjTNP2v1GikWkq7i3dnFK07bYDRKrYOz\nWZ9mqO+xcO/+XUv2jN4ItOrjEAxTnbJOUxw+wUAlT2CumPKTGXtRMH71Q7sB6kqae73AH3jmzaq+\n27mC5ZdeD4YH1/m8gLlM0ReGt6cz9MXccuPxqI6uKQzEw1XfjeB19sqneH/nixaI6pwSL9lwNu2W\nWYmGe2da/PL93/BLlH/5/m+8DfzBj77yvZUm5X0Y+IBpmvsNwxgBJgEpFpK1pfYOOphU1szX0Ego\nanssPHToDIq6sc0E7SR6eaa93588SqoYqzvG4akjPSUWtfk0f/PTBu/fPUrwIz16MukLBbhmyflM\nkVLZ5umXz7v9uHEn64cPnUVTFT/0dS5dJF2JqlIU15TkPR7uj6AoSp1ZcuLaUaBynYG+eMiPgBod\njHJxJodVES0vAXRuoUC2UObv/4eDRMMaH7phhy9060VFKOpKlH/5/m+wQsF4Dvhy5e95IGwYhmqa\nZn0lxgBSLCRd58C+7Tz03Jm6tpTt+hq61XFvvWlkm28n0ct2bFKB5MUgc8VUw5XYetAoce6+p07x\nI+UUCznXmTw+EnN9BHb9PJTOl9FVpWr1CVRF0AWjqfzKuo4gky9z7Y4B7rljD3u29fnjqRXiYI6K\nd82Pnkz6Wem5gsU70xlfnGzHoWQ5PHr4LJdmc/zmr72/G5dquSxVonzZYmGapgV4dV3+NvBoK6EA\nKRaSLuK1/ZyazWM7DrqqkoiFuGos0bavoZsd99aLoyeTvPjwa0xdzjb1syz1HjRV85MXaxmODPWE\nUAMRdO4AACAASURBVEC9qOcKFnPpArYjCFXKmLw9nUFQeb81AV3CEai6WiUUQghsFO7dv4tnJ89z\nznF7eGjqYmVdVXP//cbn9zE+PkgymW6r/Ix3zb3Hjzx3xh9vLULA0ZOXOXpyffq9f/n+byxZovzL\n939D+9FXvreirG7DMD4P/K/AJ9t5fm986yQbHi8R6p1k1r2LFLiOROjIKd1O055expu0vIqly20O\ndNv4LR1tX2saiXomX3Yn3sDcq1T6kTRCURX6427YtNfo6eJMjlS6iKLC3/srH2AgEfZDX2Ex7yIW\n0dEDFYpbZXzXcuP1Ywz2RRpWOfZwHMGzr6xPjkvFmf12k91vd0Eo7sI1cX3aNM3Gy9gapFhIusLB\nyamGvQ+8zm2d0I2Oe+tFp5NWMybG9nL3rjsZjgwB7ori7l13rpm/omQtHXhTK+rCC1MV+BlxjiMo\n2w6OgLLlEIvofuFETVO56fotxKN6XYOlSEjzBdb7zL1jWbb77/VXDfrnbmc1WovtOMwuFLDt5vkr\niqowl1nXHJdmpchXVKLcMIxB3G6knzFNc6bd10kzlGTFeO1AG9mlvcSpTgu4AX40VNCUU9swqZfo\ntgltqWix1cKr79QooqmWYNSRUglTLdsOmuquJjzzjqK43epKlkN/LMTOgFny6MkkP3zcBKhyOIP7\n+X/zixNcms1x9ORlEPi9sJPzBY6eTPKpsf5l9X/XVJWRgSjTqQJaYKxB+mOhdV3N/ugr3/vJl+//\nBtSUKF9pNBTwFWAYuN8wDG/b3zRNsz6pKYAUC8mK0VS3O1syVS8YmqYyMrD0D24pR7DXY2EyeZzv\nTz7KXDHFcGSoJ3MOljNpNSN4TdZKKP7o0deqciXyRct/3EgwaqO7xkdizCwUyRetqgKRmqow1Bch\nFtUZ7gvzzS9O+Psmrh1lqD/CUF+krgy6J7C2I7hqa19VPSjvvJ86sAdoHZYcxLu2d0yM89aFhars\nc494RGeocpOynlSE4Sfd8FF4mKb5feD7nb5OioWkK9wxMc47yWxVZi24d2fNfnDt9DzQVKWuYVKr\nnIP17H3RyaTViG70gVguT77Q+Mby4ORU09VFbXTX0ZNJHn7uDGcupkFxQ5+HEhF/tTCXKVWtsBoJ\nrBcdNdIfQSAW99VE182mi/6KoJ2w5Npru2NLwq9oXEt/PNRR//jVpltCsRKkWEi6QjDCZGrWde6O\nj8a5Z/81DX9wL5nTPHyocRnz2uc3apjkbQ+KxXpOtB7e+V40k0tGQzWi3YZSq0HJcmsvNSJftLAs\np8qhXEsw0ujG68f47gOTzC4U61YLjVZYnsDmClZV1v++3SMtV2vB5LqlwpIbXdvXz6Uolhq/50LJ\n7hmh6BWkWEi6RvDHCo3DQ70J/fVzbgBGbVe82lwK27EbhpBCdc7Bciba1VqB3Hj9GJ86sIdLlxY6\nMj2tZ35JWHcz7rMNghRqI4/aoZMV1o3Xj/HWhYWqDO/+WIhjp2fZs2Og49Vao2tee209h3wz33Um\nV2opkFcaUiwkXafZBOlN6CJQ4M0rxeAJRp0jWCht5Rx0MtEudwXSqbh06qNY7/yST/7K1fzFM2/W\nbV+O3b7TtqQXLmfdTO5ANJX3es/HsdxVY6Nr6znkaWKGUlRFCkUNUiwkXWWpCTXY8yBY4M2rGgqL\nZopgbSgGtlAcvlxXvtvLOWg40VacobUTbbMViOMIbja20oi1MG910zm+XP7GZ95HNldqOxqqFe22\nJa36/Jo4uVfa4rTRtU1E9YbObXdfaEMkgK4lUiwkXaHVhFo7offFQr4zPBiff8fEeF1tqPLsVsp5\nC+XqSwg9XxcNFZxo8wXLL2utaSrjo3FE4Ha1UdZxJl/mvz36Os8du9jQKbpWfoSVOse7wRc/ci1f\n/Mi1XTXBtJpwOxHKTibvqq6EqtvrPHjDkYiF0DSFmfkCViXfQlWgPx5mz44BKRQ1SLGQrJh2JtTa\nCSFYZhpgZGBRYL77wGTdOSL5HcQu7Oa3vrCvYSjpHRPj3P/UKd+sBVAu27yTzPBPvv88O8cSdRVN\nc/ky89nFbnyzC/XjXks/Qqemm9VkrU0w3RbK2u+k7QgUQFOoaqkK+KZRBfyIq/UOme1FpFhcoXTT\nudvuhFo7IXhlzD974BpufvdWf1xL2e4RSp2pwiOTL1OyHJRKLSGv93I6X16saKpAJu+uPgqVSBhN\nVQjpmn9cb9xesmFtQURvLKthplipuWWj0m2hbPSdjEV1Bvsi/Navvb/u2vaCQPc6UiyuMLptf+/E\nMdvOhLAc2/3Rk0n+9KdvkC9Z7nwvwLIFuqbUlaHIFixSmaJbY6gSCWPbgkR0UThn00VeNpP84tgU\nyVQBcM1mQRPGavsRVFVZ13yR9aBbQtnyhmOVzrvZkWJxBfH8samm5qKJa0eXNTF1Orl7P8yyZbt3\n8w3oxCRx9GSS//rwCfJFy69f553Rsl3TgqJCrmgRi2gs5MoMJMJkCxaWs7iyKAWiYjQFHjrknt9r\nnuP5VzzBWE0zRa2gH9i3vanzfTOy0gl7ucECUiiWRorFFcRPX6zP0M0VLH74uMlQf2RZIYle2YRO\nJvdmKwvveEvVhqo91k8OniZfk1jVoOA0l+fzbs0i4a4Stg7HyObLLFR8FlVF8BTF76ngRWml82Uy\n+XJH5daXQ9DW7lViPX56lseeP9c0wVFSTy8EC2w2pFhcIdiO45fN9vCqfQIM9UWYWSi0tdJoNOF/\n7vZdLc1bzRzhb11Y4HwyU9ebOVgbqhEHJ6dwAo1yKi6KahQQjncNBCFdZb5ihkrEQiiKUuVkP7Bv\ne1VmOVT3gm5k7+4mnq09+NkATM3k1iybezPQS8ECmwUpFlcImqqyZSjG1OWsvy1YUnw6lXfvrhX4\nrw+fYGw41rS+TqMJ/3O37+KbX5xoave1Haeh0zGVLvLY8+fQVaWqNzO4P/hgOYegDf8lc5rXz6X8\nXA1/URD4W9PclYQQiw10omGNdK7MzEKBbMEiEdXZOhyrcrI/d+xiYxNGi4KIKyVoa68t9+6tfOoz\n3B1A9Gwl3vVE+iK6ixSLK4g7b72aP3rsBBAod1DpRIbt4AiBbQnKOH7Zh05DSRvV5Dk4OcXMQoFk\nqlBV3iNXsFjIldzZXVX83szB44ErDN4E7hWAO3Z61j+HpihYonpNoWlupdOFbMkNnq9QKNp+j+dC\nyaJQstC0xS5s0MSEIcSyTBidOKk9W3ujcu+apoICMwsFHEfwypuXeezECyTVk6jhAtv6R/j0dbf3\nXCXeXkAKRXfQvv3tb6/3GDomlyt9e73HkEhEyOVKrZ/YQ7x79yi6Ikim8hRKNrYtsIVAqbiELa9Q\nTsVkn4iFAEim8nzwfdsoWRZPHnmn4bHzJZsPT+yoCjP1ViH5ko2iKGQLFvmiha6qWJbDzELeb7es\nKPivtWz3TnAgFuZ7P57k6ZfeYTZdRFUULEdw7PQsqqIQCWkUKsf2YuRDmsqWwSjRsE4iFiJftP3u\nap7nW+C+v5CmoqkKZUvwTjLLYCLM+GiC8dEEg4kwyVSe2YUi2XwZR7ilScIhlfHRRMtrfez0LH/8\nxOs88eLbvPrmTNuvC4dU3nh7vnrcQDTsOudzBYtfnrrMC+dfJd3/GkK1cASkC/9/e2ceHMd53umn\nu2cwGFzEQYAEddGipE8HTUUSdcuRJTuSHStKOUylUpVKNptVkt1KaVMVO9narUpls3ZqazfZ2mzs\nHC57cyiX5Vhx7JiWbMmUvKYkUxJJkxIttShKFCUSJAYXAQxmBn18+0dPN3oGM5gZXIPjff6QgJ7B\n8AMw+H79vcfvzfHO5Dv0tW1hW/vikuHr8T29ntbc3p76/WavYSmIWCyS9fQmDWlvT9HVmuD267fx\n43t2MNCT5og9N+4zNAC0rGBeckcxpj86WeCNd8d5+pX3mZjKBxttWdNWb2eKO27YXnLtq8+dIjdb\ndFYu2k4XZj3yjkeu4BIbeRCNKjCMoNx1S1sS+/2LXBidwfc1Wmvysx6WYQTzEjxNd2eKhGniFLtv\nkwmLX3noWn7pwWvZuqU1EkXX03S1JSkU1+L6Gss0InHSWtORTjIykef267cBMNjXTkvS5NS5i6RT\nCZIJk9ysh/3eRCQqnu+XnEhCjp7M8C//722yRRfX8q9biFCohkayTM44WJZJa4sVVHtpzZb2FkYn\nCzj9JzASbok4u55Pnilu2fZjC/4b1Viv7+n1sub1LhYShtqkmKbBLWqAJw+dYWh0Bs/zMU0DwwCz\n6N1kGEZkh/F+Jst0zsFxvKD81GmhOzZWszxEE8bfy+030qkEUzmHhGlgmMUcQ1GkPF9jWsV/2zTR\nxTGacabz7pyvlC5NPvd2pbhVBZt9PF597NRIMPa16HTra12yyYffa3lfyMHjQxUb8va/cDpKnPZ0\ntPChG3eU5BGW2vUdrj3s9XjjzMTcFLmUxcR0DjOZw/cNTCsuFpqx/MSqTtZbLjZbT8l6RMRik/OJ\nO68I4vM6mFsQ2mV0FENQ0zmHlmIFERTjv35QSppKWiUjMuN/8JYZhHji9hue5zPjeiQsg+29beQK\nHhenC/hGIBToYOO+76YdnDg9jmEYJKwgTBR/jS3tKabzTmknt1G5LNI0jZLN919fPM3weK4kJxB+\nr/Ea/FnXrZjkDquUpmZcpvMOQyNZ7Pcucv/Nk+y7d1ckkuUnL4CxYr6h3hj6zaqfG6/u47N/c3ju\ne9Uay0qgnTQ6WVrdlrAMelu715VQrIUZJEJ9iFhsckpLDKGjLQla42no6WiJTgZxTNPABHZsbePR\nfXs4ejLDn3z12LzSV8qSzhCEmRJmcCcf94cyPZ90yuKXPnYtN13dz9ATxxmbKtDV3sLoxXz09ZZl\n0pZOcNv1A5wbyTa0ydys+jFM2P/iu7w/PI1lmSWd2aGJYbh5TUwVaEla0eO5vBslmEcmg74N0wgS\n8weOnOXKHV1BuW9nquRnFv8Z/unXXm1oQwxmRad4PzPNdM6NKtb0yCUkdpTaiXekk5ET73qgmcOe\nhMYRsRAqlhiGH3/mr18iczE3V49a3P9N0+DcyAxPPHeKA0fnhtaEpa+u6+Np2NKRKpl+1t6aCEI7\nxdcL/aG01vz0PR+INomwIqk9ncT1dPQa5dP3Gi2LDL/Xw28O88Kr50vEBijZvFJJKzoZGcDoZC7K\ns+jIUoRIMMIw0z17BnnyUNAAmcvPnda2dKQa3hCPnsxwdiTLyEQ+Kv81MdAXt5Fua8HrPrNuq6Ga\nOexJaBwRi01ItfhwuOmGjx89mSGbD5LCOvpPgGEE1UFPH34v5rHkMz6V5+K0wV8/9QYtCYuWpBV1\nS2fzLpPZWdKpBLddt/DJID6e1HF9Lu1v564Pbo96IcrX3Ci3XDPALdcMlIhNudttWOJbCBPyFaaq\nBZYiwWuczWSj2QtbutLsP/g2b4zn5p1goL4N8YnvneLAkbPkZwMrE0MHPlZm0qCno5VL01fxGz/5\nSTD0ugo9wdoY9iQ0hojFJuLQa0PsP/h23QPtL2ZnSbcm6HJaSqy8wyQ4gOP6JK1go/K1LiakNYYb\n5CGyeYeCkyQfGzKTSlq89s4YD9+9kxt3ba26KSx2PGkt4mIZF8hKm1e6NUE6ZVFwXJKYse9xDk3w\nM5nKORw7NcJNV/dz++5Bruhv4zN/80ppkrzKUKZyjp7MRGNG0XMpC9MIQlNtrYlovetNKGBtDHsS\nGkPEYpNw9GSGJw+dwSka5pWHQ8rjx6OTec6PztDdkaK7o4VszsGLObV2tiWZminrMo7desdPItmc\ng1Xs0I435dUbbliujWOhZOpCm1dPVyvDE3kgKJU1TV1S9gvB996WMEu+J8s06etqrTqUaaHv6/vH\nh+aS8LHwn09wgtNa09fVuq43VfFvWl+sv1sSYVEsFB+u9Hg4+nQq5wTNbkmLpGWSsAxaWxJ0tLVg\nWWZJ1U88n50olnSahoGvNdt72xjoSUdCAXPhhtUgFMNQDEKxPHpyrs+k2ib1oT2DDPa1AcHpqTxv\nH87PyBdc3i+GouKvGeYtws3f83ymZpySfzuO5/uMTxWCrm0osTxBUyxxNpZlU/XKVW8Vuenqfh6+\neye9xRLs3s4UD9+9U/IVaxQ5WWwCFiznnCrguF7FO+po9Kmes+o2DCMqNU0mTLzZwCYkvuebJiV9\nDIZReWDRaoYb6kmm1jKfe+wpe86eJEZYFaW1ZtbxSr6nm67uZ/8Lp0tOFWH+otrJKjzlhCJjGgZY\ncye3HX1tfOKupW2qlU5ZD/R3Lvr1Fov4N60fRCw2AeHmU14CC8GGnUxYFUMwba0JOtOJYhVPaVmt\nZUBr0iKVtKJKJUdrDAMSZcnz/u7Wis1tqxVuaHRAU3zzCu+8w01/1vVxXQ/XD04YBsUktxGE6HKz\nLkfsDA8WN17PD6rCBnrSaN/HiP1sFspbxEM0UzkHPJ9ki8X9N13Cvg/vWtLPo1rJ6pauNFdu61jS\nay8WEYq1T9PFQin1y8BngLBo/Gnbtv+geSvamMTLOcuvh/+vFD8O72DLy2r/9GuvEuZ5w9LXsMku\n7LC2LIOOdAs/d99VQPPsoheTTA27vuMDiKJNX2syE3kc1ytJdluWgWWa/OuLp+nuDjbesDkx7JKP\n520WOlmtpMV2tVPWMy+f4dceun7Jry9sTJouFkUet237081exEYmXs5ZafOptTnFNzWNnrfxxpvs\nOtIJhkZnAIPOdCJ6/WaGGxod0FR+5/3NF9+N3GrDUNzFaT84UUBUEdZZDNGFG+/RkxmmZpySfEXY\nd3HP3TsXXPNK/MwWOmWNTOQkHCRUZa2IhbAK3L57kCu3dVTdEPbs6qtrc1roTr2zLYnnawZ60hiG\ngadLbc6btRE1cqde7c47ntkOT1Ojk/mKlV7hxnvw+FD03GzejeUtEnWfEpbzZ7bQ725rd1qEQqjK\nWhGLe5VSTwMW8Gnbto80e0EbmWozJxoJd1S7Uw831PIcxVroyq3nTn2hO29Pw0/duZPnXwt+VpcN\ndNDV3oLn6XkJ/K3daTQ6MmAMRaKrvYX2dBJPN959Xg3P9xoaflTtd/fRWy9f8lqEjcuqioVS6hHg\nkbLL/wh81rbtryulbgMeA3Yv9Do9PW0kEs2fDNbfhOqRpVK+5kOvDUW5jGQiKJV98tAZtnSluX13\n9QT0A/2dbOlK88zLZxiZyLG1O839t1zGV777ZsV67KmcQ29fR2kZ6CLXvNIMbm2fN4IWAgF48J4r\nefCeK/GKNueHXhviy0/bJc/zfc1Hb72cdzMzzOQd/GLoyvc1UzMOyYTJFYNdbNvWtaR1vnL2GM++\n8yKjM+P0tfVw3wfuZO8lN9b8ukq/u4/eevmCv++1zHr8O1yPGLqC2VszUUoNAZfatu1Ve04mM9X0\nRff3d5LJTDV7GQ1Rac2fKxr2RQMlivR2pnh03566XrfcMqNaIrne16u15pWmPGcRUq0HIDyZvX1u\nkmw+qDjrbGuhJWmifUqcdyEwQ3zkoeuWdNI6njnBk6efmXf94zs/2pA/VPx3t1He02uV/v7OdR3j\na3pTnlLqU0qpXy1+fC2QWUgohMXh+f68UZ2e73M2k2V4PMfQ6AzD4zlyxYE9jTTMxUMp1cph12JX\nrudXfps12ix209X97NjaTq7gBh3ehsFM3mF4LEfB8ejuSM012BVzGzfu2rqktf9g6JWGrldDchRC\nvayFnMU/AH+nlPpFgshveZhKWAJHT2bY/8JphsZymEZQ+vlQsRz2+KlRpnIOrhuMJo1X6lzS376o\njWQlSz6Xi+OZE/xg6BXGCxP0pLq5Y3DvvLvxRiuR5ifFg0bEqZxDd2cqGtCEsfRmRM/3GC9MlFwL\nD4bjhfU5/EhY+zRdLGzbHgI+0ux1bESOnszw5e++FQwY0hrf15wemuIL3zjBT+y9jNdPj+H5xWl0\nhsYyDEzTYCrncNfu7fNer95pZs0uk12I8vDNeGEi+rxS+Kae9c+6wWzxcizTwPM02tcY5lwX+1JP\nWZZp0ZPqZrwwEU0yDOzSDbZ19opQCCtC08VCWDkOHh9iOufga11SseO4Pk+//F40XtSygv6ByAjQ\n8PnmC+/ywmvno41tMSeFtSYUEIRpytIz0fXFzoJoSSRIpxLzBMM0DFpSFn1bWquOYF0sdwzu5Yk3\nnmJies4N2PU0E2e2cXRrZk2d5ISNgdyCbFA832d0Mo/n+SVusABocDw/ykmYhkHSCjqNPa2D04MR\n5C2+/N23ePy7by1owLde+OHwq7w5cYoLM8NkcqPMuHMT+MLwzWKpdlq47+ZLuGv3dno7U4xPz3Lw\n+NCy/Oz29N9AavxaTC8NgOmlaZ+8nlRuR/U+EUFYAnKy2KCE9tiZiRxOeYSkeFddfufv+hqDoAM7\nZLroJxV3i4W10TfRCMczJ/j2uweizz3fY7IwCUBbopWe1NJmV++7dxdaa55/9TzZvINB0NX97JGz\nzDoeyaRFZzrJGCzL6FDP93HGBuhmAI3GiDV6yPAgYSWQk8UG5p49g3SkW+Y1jFlmcJJIJiy6O4KK\nn3DORfDc4Au01sHJpDiAJ85q2osvB2GVUHuyreR61skCLGp2dWgyePRkhs89cZwTp8fp6wrmf2zd\n0orr+eRmXTxf4zgeE9OFqNpsqXf/YSc2EPxuYiXwMjxIWAnkZLGBCe9cv/LsWwyP5zAA0wpKO31f\ng+EznXNwYiW1pmEEtuRAOmVFJZ/lgrOeNqR49VBbIgjbZJ2ZqHT2Yzs/0lC+It7xbpkGUzNO5Is1\nNJbD83wMs3icC0fOao1JUDyQLk65W+rd/46t7Zw4PYbj+mgNLQmT7o5UTc8pQVgMIhYbnLAy6fCb\nw3zn5fd4d2gKyzLp7kiSdzwmi+NSDQOsoohoAs+jRLGCJ90y/22yFvsmoLL1Rbx6CALBaEsE7rE9\nrd3c2L+gYUAJ5Q17oZssBOIafuw4HgnLnJtyp+dOamjo7Vqa2B49meHl14dxPT86VMy6fkUbekFY\nDkQsNgm3XDPA4TdHmJ31o1PC5MxsFHUKXVM1wchQA42VNOlMtwDB/ApPsyb7JmB+78Rt22/mxwY+\nGD1+x+DekpLZsOR0cryHz508Xtf35Pl+afgo3PwJcjttrYnInh2CqXrMTaJFQyQgSxXbg8eHmJgu\noP3SQ19+1mX/C6fX3O9HWP+IWGwSPN8P/I6M4A4XKIaiKMlHhAN9WlMWAz1z8f0tHSl+45MfXJOh\np3jvxIybJ5M7xetjb/L0u8/x4M772dN/QxRm+sHQK5y9OMLkRYt09ipSuR2MUVgw6RyGnUYn82Qm\n8nPussXRs2FeR2sdTRe0LBPXLeuY9zR9Xckljw71fJ+xyTyO55enkkDD0NjMMpoU1tdbI2x8RCw2\nCZZpYpoGw+O5yAGVYujJK245OjZf2nF9hsdz0cZYzYl1LRAmr2fcfFThBHAhl4lE5Ia+6yLR+JMn\nfog5NT9cU6nCKx52Cp10wy73dGsiGjdrWWbJTI9cwUUXx81aQDJp0d6aYMfW9iXf9VumSU9nijPD\n0xUfn1cqvQgW40QsbGxELDYJR09mmMrOlgzh8X1d7KswgiR3cY8JZ0ovh/3HShNPXoeVTfHHsjmH\nvz98gLbzM9HEu/EKQgGVS07Lq5bCk0OYqE7HBj6FYbqH7ryCb7/8HrNOYKOitY6EZrnKWj904w6O\nnRqt+FjCMpecD6k0dhWWVu4rrG9ELDYJB48PBXMUPM1UzsFxPLziScLXcycM05jf3TyVc9ZsQjtM\nXo/lJ/B8L7RfCv6vTS5mHcAhXZzu980X343yL+WUV3hVmm0Rnhymc06UqL7n7vmjZw+fHGFoJBAv\no8zNdzlEd8+uPlpb5neNQyAWSxGkamW96623RlheJBi5CYhveunWBB3pZLSRGEDCNDBMA9OAhGkW\nLUDmXFI7lsEldSW5Y3AvOS+Pqz0c32HWc3Fcj9m8FViaeOmSprV5alikXBBLehlitLUmuPbybn73\n3+zl0X17Ko6erTZIaLlE1zJNWhJm8Lsr2k4ZRvF3yeKtVhYa/rTeemuE5UXEYhNQvumFXdnhbbjn\naxzXx/OD8kvX9xnoTrO9r42BnjSXrtEQVCkaU5vFUFqYgwnCbeZ46cbt+ZqH7rqiLgvyhSzXF/qZ\n3L57sKLN+Z5dfYv43ubj+T6ppIVZbLBMWMH/TdMglbQWvalXE0hYX701wvIjYahNwj17Bnny0Jm5\nWn8IXbTnJUT94sCe7uKmsVZDUCE/GHqFtkSaaRc8nQfTQRsehuXhnb2O/PRWunrmnt/bmeKWawa4\n5ZqBmuGapViux913j50a4eDxIb7x/OllSRhbpskl/e1AECYMixY608kl55eqjV1d6+8DYWURsdgk\n3HR1P1u60uw/+DaZicBAL51KcDE7O++5RnEOw5U7utZ8FUyY4NY6cF01TKN4rjDA9AEdlbWGuYP4\nplfPprpUy/Vjp0ZWJGEcburxWRnh9aWwHmaSCKuPiMUm4vbdg1y5rYMjdoZ/ffE0Wut5YpEo2oEA\n/Ief3k0isbYjlfHubCPh4BtzTrIGkLzkbcxkAoMrlrzprbWE8Upu6mt5JonQHEQsNiE3q34MM9hk\nzmay+Hpu8FFIOpVY80IREnZnGwkHYpNSTb8FwzC4RI3x6dv3NWXTq5Qw1r6PYZrLUka70pu6CIUQ\nsj52A2HZuenqfh7dt4cHb7ssSozGWU/x6T39N/DgFfdjWQamCRhB+Elbs6TbNTqRA6M5VTzxhPHE\nVIH3hqc5M5zlveFp8rMupmlE7rVLQTZ1YaWRk8Um52c/fBWGYXDw+BC5gks6leCePYPsu3dXs5fW\nEB/cej3b2vo5O32eBGYUwi/4WUyjs6mjRu/ZM8hjT9klIT/f12TGc/znL7xIayoheQFhzSNiIbDv\n3l3su3cXruuvm9BTSNxAcCQ3htY+hmHONeZRMuqhKezZ1Vexec7XMDyR4/JtnVHS2/c1t6iB7IGA\nHgAAEuFJREFUJqxSEBZGxEKIWI9CEXo/aQ2uH2zIoY1JaIp4dnqILxz7a+7cceui52wvhtBfaeRi\njtmiqWAYLIqcaDX4vk9+Npgt8pffeiOafS6nDGEtIWIhrFtCA0EozuMwLXxP43kGht+CtgpowHMN\nzk2O8uTsM/jaL7Eur4dKMzJqcei1oXkGhFBS4RqRK8zNFQEYm6xdWitusMJqI2IhrEviBoIh7cl2\nRp0JtKHR5tzma/gtXMxnyWqXv3vjn3jp/BHuGNxb85RRPiOjnq8JeeblM8Dc3IxqJCyDbH4uRBW6\nAUN1F1zpfxCagYiFsC4pn34HkLZaMd1WfMNDmw6GNjH8YHiTY+TAD3IZY4WJKHxVbfOPh7gAxuv4\nmpBwdshM3o1G1CZMA7fYKa8Bs2jm1N2RKjlVdKaT0cflpbXiBis0EznHCuuWOwb3lnxuGJA0UnSN\n30IyP4DldmD6LWhzNjLbs0wrCgPFw1jlVHtsoa8JsUyTrd3pkhOFaRokzKCXJZ1KcONVW/n47Zdz\n2UAHlmUWR92mIstzmO/FtFBznyCsNHKyENYt8el3YahIbb+NY+eCt3W260doNNrwg3niBKEqrQNh\nGS9M4Gt/XlltpRBXSLWvKef+Wy7jyBvDJddM06C3I0VryiqZOhh21JcT73Wpxw1Wei2ElUTEQljX\nhNPv4hv4FekMB4+nGJoEb8sZZq0clmWQNJNknSyThUks02JbW3/FTb9SiCukJ9VdV8/GnXt2cOnT\nNkOjMyUmf+nWoKdCx1Ld8Y76armIsLmvkmCIG6ywGohYCBuC+AY+Z4ER3L0fy7zGP7+1v2Tkqud7\nTDtZjmdOVMxBhBYila7XyyfuvCLIKcRKoHJ5l4sGfPaxwyWisJBtR1j5JG6wQjMRsRA2LOGmu7vv\nOr5z+lmyTjYqg21PttGWSPODoVcqikWlEFcj1VAw3+jPKjrihlP6Ri/m5iWo40Jx9GSG7x8fYjx2\n2nj47p1SDSU0BRELYcMSlr6O5ccZyY/RnmwnbaVK+h4WykFUCnE1SvzE8KdfexXP10xMBTO8tR9Y\nqn/l2bfmbfhPfO8UB46cjUJYubzLN54/zcN37+TRfXskRyGsOlINJWxIwtLX8cJEJA6ThUlyXmnM\nv54cxHL4SuniDPCJqQIXs7P4fpC18H3NhbEcX33urei5R09mIqGAYNrfxHSBXN6NKp9EKITVRsRC\n2JCUl7i2J9oAyDrZkuuN5CCWQpignpyZP2wK4Lkfnos+/v6xc3PTDGNM5RyZgy00DRELYcMRL32d\ncfNkcqNMzk5Fj2mCE8XHd350Vb2i7rh+W1VTw1zBxXV9PN9nfHo26OQuw/N8qXwSmobkLIQNR1j6\nejZ7vqQCKnzsY1fcX7c/VL0eTPU872bVj2HMd8ENmrmDhj3TNOjtTJV0f0drt0ypfBKahoiFsCG5\nY3Avj73++Lzr7ck2Xjp/hA9uvX5Bc8B6PZga8WqyTJOB7jTD47l5j/VvaY1ODPES2emcEyW577/5\nEql8EpqGiIWwISi/s7+h71rak21knZmSclmANydO8YeHP09vsRz2hr5rS4SjXg+mhZ73QH9nxXX+\n3P1X8TdP2UzHqqE60kl+7v6roueUl9z2dLTwoRt3iFAITUXEQljXVLuzt0yLS9oHGS9MoLXGMAxm\n3ByThanIH+ps9jyPvf447ck2LmkfjPooFvJgim/YJc8LPUSK1x+468qKrxEXgpGLObZuSVc8jdSa\nrS0W5cJqI2IhrFtqnQDCLuywdDbrzACBP9SMm4/yGVlnJnKV9bVmdCqHUaH2I+7BFHo15fIuU7FQ\nUWc6yRjgLVCxVEsI4pQ/LhblQrMQsRDWLbVOAPEu7LH8OABdqS7aEq1kcqPR8z3fQ2tNzsvzZfuf\ncXa04M+2ks5+gFRuR/S8eCWSZZpYpsFELAkd9kN0tCWx6qhYarSqqVwcRyfzYlEurBoiFsK6pF4X\n1ngX9pde/dtiWCoQiBDLtJiYnWR6NujBSCQdPF0g2/UjgEgw5lUiVauDXaGh36E4hgOVwtPM/hdO\ni1gIK86qBz2VUvcqpYaVUg/Frl2jlPqeUuplpdSfKaWkkFxYkLDJrRKVehFMw4wa8MIRrCEJMxEJ\nRfC4j9lSwEg45NrfobczxcN372TPrt7oOZ7v42nY0pGKeiIsy2RLRwpPLxyGqoTnz2/CK398bKoQ\nldTGu7vfG57miJ1p6N8ThEZZ1ZOFUmoX8FvA82UPfRH4Hdu2DymlvgrcBxxYzbUJ649GXVjjYakZ\nN0/WyQYVU7Mz0XOsorWHaRhYKZetWww+tNPgpfPf4juHSw0FQ7Fqa01ESXQIxKqeMBTUn4MIxTFe\nduv5PqEm/e13bAxTwlHCyrHaYagh4GeA/xteUEq1ALts2z5UvPR14EFELIQalJeY1pPwjYelXht5\nnRfPvcwbhZNBUxxG5AOlCUJVpmHw7Xfn3orx8apxsYqbE9bbONfomNS7dm/nxDtj+L6ORrRCkPvI\nFVy+cbD61wrCUllVsbBtewZAKRW/3A+Mxz4fBrYv9Do9PW0kEtUbqlaL/iq19GuZjbbmB/o7eeCu\nK/F8XffdfMi2gTv4yPV38Iff/wvOTJ5jPHcR39dBSEiDYZjMupp06/z32pGxH/Lb9/x7tnSleebl\nM4xM5Njaneajt17O7bsHa64b4OVv/ohkYn4k+GU7E3xPxZxEyMf6O/mX77/D0GgW8AmjyL6vcbWm\n4Hq89EamatluPWy094ewfKyYWCilHgEeKbv8e7Ztf7vsWrmzmkFwY1eV8fGZhR5eFfr7O8lkppq9\njIaQNVfm5r4f48LUCCmjjWk3izbA0CapqSvJzA7T3dFCW2vpn8qFyREuDF/kym0d/NpD15eUwWYy\nUzXX7fk+QyPZio+9/f5FfvfPDzI+PTvvtKS7zpPseQtacuhCK+7IpfgTA/gazo/OcGFshv/4Rwd4\n6K6dDZ8w5P2xsqx3UVsxsbBt+0vAl+p46ijQHft8O3CuynMFYdkJcxl/f/gAljYwvXRUNjuRfJ7p\nXH6eWJRbmzdaBlttTGrYtzE+HdxDxUNTxpYLTHX+iITWuB4YqTzJS97CAfyJASAoxHpveJrHvxtY\nnktISlgumt4Catu2D/xQKXVn8dIngf1NXJKwCbmh7zrazt9Bz4WP0D1yd1Qum85+ANfT86phl8Pa\nvFJuYyrn0JFOzrt+8PgQL50/TMIyMCwHIzWD0TqNkZohseNUyXM9TzORLVTtQxGExbCqYqGU+oRS\n6jngY8B/V0p9p/jQfwL+WCl1GHjLtu3yailBWFHCO32D0hNCKreDbc4eeluDw+9yWpvfdHU/D9+9\nM6qq6ulM0ZFOzjvFAIxO5RjLT5Bs9fGsPIZRVC/Dx0xPYXZfKHm+4/qMTcrsC2H5WO0E934qnBps\n2/4RcPtqrkUQyqlWivvx624L7DmWMF61GuXWH5974njFZsO+zjSp1m5G8qMkLAMvftrRJomtZ5md\n2FbyNb1dMvtCWD6aHoYShLVC+Z1+2IwXxv2XWyjixO3JK3HPnkFu235zVM6bTJgkEiYGBtpNYqTy\ngI7ORcmEzL4Qlhex+xCEGI2Y/MHyu78u3DvSz9PvPseFXAbP92ixEiTMFFnHwHNSUa9H0jL5ib2X\nSXJbWFZELAShArWEYiXdXxcSrAd33s+Tp58p6RhPmS4p91omulqZdVxaW5KczUxz9GRGBENYNkQs\nBKFBFuq83rOrLzppLPXUUUmw4pYl44WJYsJ9Lyd/1MqBU2dxHI/pvMfYVB77vYvcf/Mk++7dteg1\nCEKIiIUgNEilktRc3uWxp2y6i75QiYRJYdZbkZkTN/RdF1mWmIbJ0ZMZDhx5HcfxIgNDr/jfA0fO\ncuWOLjlhCEtGxEIQGqCSNXou70ZzLVoSJhezsxiGQVd7C8CyzZyoFvr6/rFzeJ6PVyyPCoukXE/j\n+S77X3hXxEJYMlINJQgNUMkafSrnBI9ZJtN5N7o+XbwO1Qc11UsY+gqFKgx9HbaHGZ+eDUJWurJP\nzvuZaQ6/Obykf18QRCwEoUFKSlK1jmZLdLQmoo8hmDWhi3f74UCmxVJNbF547Ty9nSk621qgQk5e\na5h1fb7w9RM88b1T858gCHUiYiEIDVLSj2EYpFMJtnSkaEsnS1xiLcssmXGx2Aa5WlMB7949SFtr\noqJNSPQanuY7L50RwRAWjeQsBGERxMtbj50aifISnelklL+Ib95LaZCrZjoIgQjdrPoxzOD0ceKd\nMVzfp3zwngZcX/PskbNSHSUsChELQVgCpmmUNtIBHW3JZa+GqjUVMBSvI3aGb7zwDmcuTM9/EQ25\ngssrbwyz99qBJa1H2HyIWAjCMlDeSNff38mFC5PL5s1U71TA8JTx5//yGq43lyMJV2GYBi+eOC9i\nITSMiIUgLCNxcVhuE796rUhuurqfB269jG/94EwkEppAMDrTySjZLiaDQiNIglsQ1hn1bPI/++Gr\n2NabBmOunNayDFqS1pKS7cLmRcRCEDYoe9UAlmlgGMGpwvM141N5dmxtb/bShHWIiIUgbFBef3e8\noeuCsBCSsxCEDYjn+wyNzmAaBqZVGnIaGp2RnIXQMHKyEARBEGoiYiEIGxDLNBnsTeNrjeP5OK6P\n4/n4WjPYm5ZThdAwIhaCsEG5bmfvfGdBXbwuCA0iOQtB2KCcG8nS29XKVM7BdT0SCYvOdJJzI9lm\nL01Yh4hYCMIGJDQfTLcmSLcm5rryQJryhEUhYShB2IDMm7sR0wVpyhMWg4iFIGxQqjndLsUBV9i8\nSBhKEDYo9ZoPCkI9iFgIwgamXvNBQaiFhKEEYRMgQiEsFRELQRAEoSYiFoIgCEJNRCwEQRCEmohY\nCIIgCDURsRAEQRBqImIhCIIg1ETEQhAEQaiJiIUgCIJQE0PrcsN7QRAEQShFThaCIAhCTUQsBEEQ\nhJqIWAiCIAg1EbEQBEEQaiJiIQiCINRExEIQBEGoiYiFIAiCUBOZlNcASql7gX8CfsW27W8Wrz0H\ntAPZ4tM+Zdv24eassDJV1n0N8EWgDXgZ+A3bttdc041S6peBzwCnipeetm37D5q3ooVRSn0GuB9o\nBX7dtu1XmrykmiilPkzw/jhRvPSqbduPNm9FC6OU2g18Hfjftm1/Xik1ADwGdAPvA79g23ahmWvc\niIhY1IlSahfwW8DzFR7+t7Ztv7bKS6qLBdb9ReB3bNs+pJT6KnAfcGC111cnj9u2/elmL6IWSqn7\ngL22bd9d3ND+DPjxJi+rXr5n2/bPNnsRtVBKtQOfA74bu/yHwF/Ztv24UuqPgF8A/rIZ69vISBiq\nfoaAnwEuNnshDTJv3UqpFmCXbduHipe+DjzYhLVtNO4j+FlSvHnYoZRqa+6SNhwF4CeBc7FrHwa+\nUfxY3ssrhJws6sS27RkApVSlhz+rlOoDXgd+07bt3GqubSGqrLsfGI99PgxsX8VlNcq9SqmnAQv4\ntG3bR5q9oCoMAsdin2eAbcA7zVlOQ1yvlHoS6AR+37btp5u9oErYtu0Cbtn7uTP2N7fW38vrFhGL\nCiilHgEeKbv8e7Ztf7vC0/8PcMK27TeVUp8HHgX+50qvsRINrHu27HMDaHq+osr6/xH4rG3bX1dK\n3UYQm9696ourjzX5c62Dk8BngS8DVwDPKaWuWUdx//jPfb38zNcdIhYVsG37S8CX6nzu12Kf7gd+\nfkUWVd9a6l33KEEyMGQ7pcf6plBr/bZtv6SU6lNKWbZte6u4tHoZAgZin/cDF5q0lrqxbfss8A/F\nT99RSp0HdrA+TkQAU0qptuIpek28lzcikrNYAkopQyn1rFIqPPbeA6zJRHcc27Z94IdKqTuLlz5J\nIHRrDqXUp5RSv1r8+Fogs0aFAuBJ4KcBlFI3A2+vpZBkNZRSP6+U+q/Fj7cShM7ONnVRjfEUxZ87\nQX5uTb6X1ztiUV4nSqlPAL8NXEsQix6ybfsBpdQ+4L8A0wR/YP9uLW0QC6z7euCvCE6Xz9m2/akm\nLrMqSqlB4O+AJEGI4VO2bb/U3FVVRyn1P4CfAFyC98KrTV5STZRSHcDfEpyKTOAztm1/q7mrqoxS\n6hbgfwE7AYfgb+4XgL8nKGG3gV8u5jaEZUTEQhAEQaiJhKEEQRCEmohYCIIgCDURsRAEQRBqImIh\nCIIg1ETEQhAEQaiJiIUgCIJQExELQRAEoSYiFsKmRyn1W0qpLxY/VkqpN5RSnc1elyCsJUQsBAH+\nmEAn7iaYQfHrtm1PNXlNgrCmELEQNj1Fr6xfAb5CMCXue01ekiCsOUQsBCGgl8Df6/JmL0QQ1iIi\nFsKmRynVCvwF8FPArFLqF5u8JEFYc4hYCAL8N+Brtm2/Cfwm8PtKqUubvCZBWFOI66wgCIJQEzlZ\nCIIgCDURsRAEQRBqImIhCIIg1ETEQhAEQaiJiIUgCIJQExELQRAEoSYiFoIgCEJN/j+mMW2guBp+\nDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9542e73be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "tsnedf = pd.DataFrame({'x':dtsne[:,0],\n",
    "                       'y':dtsne[:,1], \n",
    "                       'Channel':df['Channel'],\n",
    "                       'Region':df['Region']})\n",
    "sns.lmplot('x', 'y', \n",
    "           data=tsnedf, \n",
    "           fit_reg=False,\n",
    "           hue='Channel',  \n",
    "           scatter_kws={\"marker\": \"D\", \n",
    "                        \"s\": 50})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f9542e50198>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ntel84ABDB7+Hk05jWwVsRSWnNfGzzh7/mPFckZ9tuI1P/OsvgGOj6rqcyJYY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\n478BjncCwzBuBzpN0/x1wzB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dGnLemKUQ6C7EM97jnCEYPfzKkjAUi83STaVQ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\nmOd6cAsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9542e46cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('x', 'y', \n",
    "           data=tsnedf, \n",
    "           fit_reg=False,\n",
    "           hue='Region',  \n",
    "           scatter_kws={\"marker\": \"D\", \n",
    "                        \"s\": 50})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Judging by the tSNE plot, an unsupervised clustering method, the channel has two obvious clusters. The Region, on the other side, is scattered around the plot without any obvious clustering. The fact that the Channel is clustered is a good indication for the application of machine learning algorithms. I'll start with a simple algorithm, a Linear SVM."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params={}, iid=True, n_jobs=4,\n",
       "       param_grid=[{'kernel': ['linear'], 'C': [75, 100, 125]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# do a 66-33 train/test data split. \n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df[products], \n",
    "                                                    df['Channel'], \n",
    "                                                    test_size=0.33, \n",
    "                                                    random_state=42)\n",
    "\n",
    "# try different params for GridSearch.\n",
    "\n",
    "tuned_parameters = [{'kernel': ['linear'], 'C': [75, 100, 125]}]\n",
    "\n",
    "# build a classifier with gridsearch\n",
    "clf = GridSearchCV(SVC(C=1), \n",
    "                   tuned_parameters, \n",
    "                   cv=5,\n",
    "                   scoring=\"accuracy\",\n",
    "                   n_jobs=8)\n",
    "\n",
    "# Fit the classifier\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=75, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 90.41%\n",
      "Error: 9.59%\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, clf.predict(X_test))\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))\n",
    "print(\"Error: %.2f%%\" % (100-accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The linear SVM got around 90% of accuracy in a cross-validated gridsearch, with c=75 being the best predictor. 90% for the first attempt is good, but I'll try another methods to raise it a little bit (it still mislabels 1 customers channel out of 10)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# build the classifier\n",
    "clf = RandomForestClassifier(criterion='entropy')\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 90.41%\n",
      "Error: 9.59%\n",
      "5-CV Accuracy: 90.49%\n"
     ]
    }
   ],
   "source": [
    "# print accuracy and error\n",
    "accuracy = accuracy_score(y_test, clf.predict(X_test))\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))\n",
    "print(\"Error: %.2f%%\" % (100-accuracy * 100.0))\n",
    "accv = cross_val_score(cv=5, estimator=clf, X=X_train, y=y_train)\n",
    "print(\"5-CV Accuracy: %.2f%%\" % (np.mean(accv)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "90.4% accuracy without any parameter tuning at all is good. It performs even better with 5-CV subsetting. Lets check if there are any near-optimal parameters we can search with Bayesian Optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier as RFC\n",
    "from bayes_opt import BayesianOptimization\n",
    "\n",
    "\n",
    "def rfccv(n_estimators, min_samples_split, max_features):\n",
    "    \n",
    "    val = cross_val_score(\n",
    "        RFC(n_estimators=int(n_estimators),\n",
    "            min_samples_split=int(min_samples_split),\n",
    "            max_features=min(max_features, 0.999),\n",
    "            random_state=42\n",
    "        ),\n",
    "        X_train, y_train, 'f1', cv=5\n",
    "    ).mean()\n",
    "    return val\n",
    "\n",
    "\n",
    "rfcBO = BayesianOptimization(\n",
    "    rfccv,\n",
    "    {'n_estimators': (10, 250),\n",
    "    'min_samples_split': (2, 25),\n",
    "    'max_features': (0.1, 0.999)}\n",
    ")\n",
    "\n",
    "rfcBO.maximize(n_iter=10)\n",
    "print('-' * 53)\n",
    "print('Final Results')\n",
    "print('RFC: %f' % rfcBO.res['max']['max_val'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optimized parameters:\n",
    "\n",
    "- Max features:0.9881\n",
    "- Min samples split: 2\n",
    "- Number of estimators = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 92.47%\n",
      "Error: 7.53%\n",
      "5-CV Accuracy: 92.54%\n"
     ]
    }
   ],
   "source": [
    "clfBO = RandomForestClassifier( max_features=0.9881, \n",
    "                               min_samples_split= 2, \n",
    "                               n_estimators= 30, random_state = 42)\n",
    "clfBO.fit(X_train, y_train)\n",
    "\n",
    "accuracy = accuracy_score(y_test, clfBO.predict(X_test))\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))\n",
    "print(\"Error: %.2f%%\" % (100-accuracy * 100.0))\n",
    "accvbo = cross_val_score(cv=5, estimator=clfBO, X=X_train, y=y_train)\n",
    "print(\"5-CV Accuracy: %.2f%%\" % (np.mean(accvbo)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Bayesian parameter optimization resulted in ~92.5% 5-cross-validation accuracy - a 2% increase over the baseline model. Lets check the feature importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f86d05c0cf8>"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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85OfH7BMAALj8eQxUcXFxys3N1ebNmyVJixcvVnx8vEJCQtxjQkJC1L59ey1atEiSlJmZ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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bc4482e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scikitplot import plotters as skplt\n",
    "\n",
    "# Plot feature importance\n",
    "skplt.plot_feature_importances(clfBO, feature_names=products, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the plot above, the most important features in this dataset to predict the customers channel are  Detergents/Paper and Grocery."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost\n",
    "\n",
    "Next I'll try to predict the Channel using XGBoost, a gradient boosting trees model which is very popular amongst Kaggle competitors, who boast its high speed and good accuracies without overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 93.84%\n"
     ]
    }
   ],
   "source": [
    "import xgboost\n",
    "\n",
    "#build a classifier\n",
    "xgb = xgboost.XGBClassifier(learning_rate =0.2, gamma=0,\n",
    "                            objective='binary:logistic', seed=42)\n",
    "\n",
    "#fit the classifier\n",
    "xgb.fit(X_train, y_train)\n",
    "\n",
    "accuracy = accuracy_score(y_test, xgb.predict(X_test))\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "~94% accuracy just out-of-the-box is way too impressive. It was also a very quick fit, opposed to the linear SVM which took around 15 minutes. I'll do a 5-fold cross validation to check how well the model performs in practice. This approach will create 5 different subsets of the dataset and average the scores to get a more robust estimate of model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 5-fold CV: 89.79% (±3.65%)\n",
      "Max accuracy 5-fold CV: 96.43%\n",
      "Min accuracy 5-fold CV : 86.21%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "# Try a 5-fold CV\n",
    "accuracy = cross_val_score(xgb, X_test, y_test, cv=5)\n",
    "\n",
    "print(\"Accuracy 5-fold CV: %.2f%% (±%.2f%%)\" % (accuracy.mean()*100, accuracy.std()*100))\n",
    "print(\"Max accuracy 5-fold CV: %.2f%%\" % (accuracy.max()*100))\n",
    "print(\"Min accuracy 5-fold CV : %.2f%%\" % (accuracy.min()*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model performs well enough with 5-fold cross validation sets, but its far from the 94% against the test dataset. I'll try a Bayesian parameter optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Load Bayesian Optimization library\n",
    "from bayes_opt import BayesianOptimization\n",
    "import xgboost as xgb\n",
    "\n",
    "\n",
    "## Define xgb classification model with 5-fold cross-validation\n",
    "def xgb_BayesOpt(\n",
    "                n_estimators,\n",
    "                eta,\n",
    "                max_depth, \n",
    "                gamma, \n",
    "                min_child_weight, \n",
    "                max_delta_step, \n",
    "                subsample,\n",
    "                colsample_bytree\n",
    "                ):\n",
    "    params = {\n",
    "                'gamma': gamma,\n",
    "                'n_estimators': n_estimators,\n",
    "                'booster': 'gbtree',\n",
    "                'max_depth': max_depth.astype(int),\n",
    "                'eta': eta,\n",
    "                'objective': 'binary:logistic',\n",
    "                'nthread' : 6,\n",
    "                'silent': True,\n",
    "                'subsample': subsample,\n",
    "                'colsample_bytree': colsample_bytree,\n",
    "                'min_child_weight': min_child_weight,\n",
    "                'max_delta_step': max_delta_step.astype(int),\n",
    "                'seed': 42\n",
    "             }\n",
    "    \n",
    "    cv_r=xgb.cv(\n",
    "                params,\n",
    "                dtrain,\n",
    "                num_boost_round=10000,\n",
    "                stratified=True,\n",
    "                nfold=folds,\n",
    "                verbose_eval=False,\n",
    "                metrics=[\"error\"],\n",
    "                callbacks=[xgb.callback.early_stop(1000)]\n",
    "                )\n",
    "\n",
    "    return(-cv_r['test-error-mean'].values[-1])\n",
    "\n",
    "## Place BayesOpt object with dict of arrays w/ possible values for each parameter.\n",
    "xgb_BO = BayesianOptimization(xgb_BayesOpt, {'max_depth': (2,4),\n",
    "                                             'n_estimators': (25,200),\n",
    "                                             'eta': (0.075, 0.4),\n",
    "                                             'gamma': (0, 0.1),\n",
    "                                             'min_child_weight': (1, 2),\n",
    "                                             'max_delta_step': (0, 1),\n",
    "                                             'subsample': (0.5, 1),\n",
    "                                             'colsample_bytree': (0.5, 1)})\n",
    "\n",
    "## Define BayesOpt parameters and dataset.\n",
    "dtrain = xgb.DMatrix(X_train, y_train-1)\n",
    "random_state = 42\n",
    "num_iter = 15\n",
    "init_points = 5\n",
    "folds = 5\n",
    "\n",
    "## Start parameter tuning -> attempt to search parameters that minimize error mean.\n",
    "xgb_BO.maximize(init_points=init_points, \n",
    "                n_iter=num_iter,\n",
    "                acq='ucb', kappa=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best iteration:\n",
    " \n",
    "train-error:0.0476164+0.00561967\t\n",
    "test-error:0.0747516+0.0448799\n",
    "\n",
    "-0.07475 |             1.0000 |    0.4000 |    0.1000 |           1.0000 |      4.0000 |             2.0000 |        44.1783 |      0.5000 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,\n",
       "       gamma=0.1, learning_rate=0.4, max_delta_step=1, max_depth=4,\n",
       "       min_child_weight=2, missing=None, n_estimators=44, nthread=-1,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=42, silent=True, subsample=0.5)"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Optimized parameters\n",
    "xgb2 = xgboost.XGBClassifier(colsample_bytree=  1,\n",
    "                             learning_rate = 0.4 ,\n",
    "                             gamma = 0.1,\n",
    "                             max_delta_step = 1,\n",
    "                             max_depth = 4,\n",
    "                             min_child_weight = 2,\n",
    "                             n_estimators = 44,\n",
    "                             subsample = 0.5 ,\n",
    "                             objective='binary:logistic', \n",
    "                             seed=42)\n",
    "xgb2.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 5-fold CV: 91.17% (±4.05%)\n",
      "Max accuracy 5-fold CV: 94.92%\n",
      "Min accuracy 5-fold CV : 84.75%\n"
     ]
    }
   ],
   "source": [
    "# Try a 10-fold CV eval\n",
    "accuracy = cross_val_score(xgb2, X_train, y_train, cv=5)\n",
    "\n",
    "print(\"Accuracy 5-fold CV: %.2f%% (±%.2f%%)\" % (accuracy.mean()*100, accuracy.std()*100))\n",
    "print(\"Max accuracy 5-fold CV: %.2f%%\" % (accuracy.max()*100))\n",
    "print(\"Min accuracy 5-fold CV : %.2f%%\" % (accuracy.min()*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This optimized model performs slightly better (+1.4%) than the baseline.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f86bc368860>"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lAhUAAIAlAhUAAIAlf28XgEtXZKN63i5BkV46bu7Bo146MgCgOnCFCgAAwBKBCgAAwBKB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86b5a533c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skplt.plot_feature_importances(xgb2, feature_names=products, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The more a feature is used to make key decisions within decision trees, the higher its relative importance is. In this case, the Detergents/Paper and Gorcery features had the highest importance in predicting the channel while Delicatessen and Fresh had the lowest. his result seems concordant with the previous importance plot obtained with the RandomForest classifier. \n",
    "\n",
    "I'll plot the first decision tree for examples sake (as an ensemble method, this model has various predictor decision trees)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f86b4c57d68>"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0adPCjiLpIMgomYcNG0bPnj1DTqPcNmrUKAC6d+9eoErmgrEKSZIk7dPQoUOp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\nRjvXZLbMX8r5rPWkn4f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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86b4c21b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(25,15))\n",
    "xgboost.plot_tree(xgb2, rankdir='LR', num_trees= 0, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "    div.cell{\n",
       "        width:800px;\n",
       "        margin-left:16% !important;\n",
       "        margin-right:auto;\n",
       "    }\n",
       "    h1 {\n",
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       "    .CodeMirror{\n",
       "            font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
       "    }\n",
       "    .prompt{\n",
       "        display: None;\n",
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       "    .text_cell_render h5 {\n",
       "        font-weight: 300;\n",
       "        font-size: 18pt;\n",
       "        color: #4057A1;\n",
       "        font-style: italic;\n",
       "        margin-bottom: .5em;\n",
       "        margin-top: 0.5em;\n",
       "        display: block;\n",
       "    }\n",
       "    \n",
       "    .warning{\n",
       "        color: rgb( 240, 20, 20 )\n",
       "        }\n",
       "          \n",
       "    .output_png {\n",
       "        display: table-cell;\n",
       "        text-align: center;\n",
       "        vertical-align: middle;\n",
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       "</style>\n",
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       "                },\n",
       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
       "                \"HTML-CSS\": {\n",
       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
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       "        });\n",
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      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## This chunk is only relevant for Jupyter Notebook aesthetics.\n",
    "from pylab import rcParams\n",
    "import json\n",
    "s = json.load( open(\"/home/hypathia/bmh_matplotlibrc.json\") ) \n",
    "rcParams.update(s)\n",
    "\n",
    "from IPython.core.display import HTML\n",
    "def css_styling():\n",
    "    styles = open(\"/home/hypathia/custom.css\", \"r\").read() \n",
    "    return HTML(styles)\n",
    "css_styling()"
   ]
  }
 ],
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   "file_extension": ".py",
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