sentence_quality_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentence Quality Classification\n",
    "\n",
    "Classifiy sentences into high / low quality. This is used to improve data quality for texts from different sources (scanned PDFs, crawled HTML, parsed Wikipedia, ..). It's mainly used to filter out artifacts from faulty parsing, HTML fragments, navigation elements, non-sentences (references, titles, ..)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import load_config\n",
    "import json\n",
    "import nltk\n",
    "\n",
    "### define a new configuration\n",
    "config_dict = {\n",
    "    # training data\n",
    "    \"train_file\": \"data/sentence-quality-8k.csv\",\n",
    "     \n",
    "    # model\n",
    "   \n",
    "    # chose tokenizer. possible values: \"nst\" and \"sgt\"\n",
    "    ## NonStemmingTokenizer: 'nst'\n",
    "    # - no stemming, only remove punctuation marks\n",
    "    # - lowercase letters\n",
    "\n",
    "    ## SimpleGermanTokenizer: 'sgt'\n",
    "    # - remove punctuation marks\n",
    "    # - stemming\n",
    "    # - lowercase letters\n",
    "    \"tokenizer\": \"nst\",\n",
    "    \n",
    "    # where to store the configuration file\n",
    "    \"config_path\": \"data/configuration-sentence-quality-classifier.json\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### save config file (don't change)\n",
    "config_src = config_dict[\"config_path\"]\n",
    "\n",
    "with open(config_src, 'w+') as f:\n",
    "    json.dump(config_dict, f, indent=4)\n",
    "    \n",
    "# load config object based on config file (don't change)\n",
    "config = load_config.Configuration(config_src, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tokenizer import get_tokenizer\n",
    "tokenizer = get_tokenizer(config_dict['tokenizer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load training data\n",
    "import pandas as pd \n",
    "\n",
    "data = pd.read_csv(config['train_file']) \n",
    "data = data.sample(frac=1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8007\n",
      "8005\n",
      "['SENTENCE', 'LOW_QUALITY', 'TITLE', 'REFERENCE', 'FOREIGN']\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAFHCAYAAACoKpuzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3X2YHXV99/H3x/BkBUzQldIkCmos\nQlsCXYEKd0vhbghoDSpYqGiq1GhvaLW3VcHb+gCmxcu2WFrhMhYErEqpqEQaiylgrSKQABEISLPy\nUJIrkMUgglQq8Ln/mN+yh5N9OPuQM7s7n9d1nWtnvr+ZOd8zyZ7vzsxv5ifbRERE8zyn7gQiIqIe\nKQAREQ2VAhAR0VApABERDZUCEBHRUCkAEREN1XEBkDRL0i2SrizzF0m6R9K68lpY4pJ0rqQ+SbdK\nOqhlG0slbSivpZP/cSIiolM7jGHZdwN3Aru3xN5n+8ttyx0DLCivQ4DzgUMk7QF8BOgFDNwkaaXt\nh8ebfEREjF9HRwCS5gGvAf6hg8WXAJe4cj0wW9JewNHAattby5f+amDxOPOOiIgJ6vQU0KeA9wNP\nt8WXl9M850jaucTmAve3LLOxxIaLR0REDUY9BSTptcAW2zdJOqKl6QzgAWAnYAXwAeDMiSYkaRmw\nDOB5z3ver++7774T3WRERKPcdNNND9nuGW25Tq4BHAa8TtKxwC7A7pL+0fbJpf0JSZ8D/qzMbwLm\nt6w/r8Q2AUe0xb/V/ma2V1AVFHp7e7127doOUoyIiAGS7utkuVFPAdk+w/Y823sDJwLX2D65nNdH\nkoDjgNvLKiuBt5beQIcCj9jeDFwFLJI0R9IcYFGJRUREDcbSC6jdFyT1AALWAe8q8VXAsUAf8Djw\nNgDbWyWdBawpy51pe+sE3j8iIiZAU/lx0DkFFBExdpJust072nK5EzgioqFSACIiGioFICKioVIA\nIiIaKgUgIqKhJtINNCJmgL1P/5e6UwDg3rNfU3cKjZMjgIiIhkoBiIhoqBSAiIiGSgGIiGioFICI\niIZKAYiIaKgUgIiIhkoBiIhoqBSAiIiG6rgASJol6RZJV5b5fSTdIKlP0j9J2qnEdy7zfaV975Zt\nnFHid0k6erI/TEREdG4sRwDvBu5smf8EcI7tlwMPA6eU+CnAwyV+TlkOSftRDSm5P7AYOE/SrIml\nHxER49VRAZA0D3gN8A9lXsCRwJfLIhdTjQsMsKTMU9qPKssvAS61/YTte6iGjDx4Mj5ERESMXadH\nAJ8C3g88XeZfAPzY9pNlfiMwt0zPBe4HKO2PlOWfiQ+xTkREdNmoBUDSa4Ettm/qQj5IWiZpraS1\n/f393XjLiIhG6uQI4DDgdZLuBS6lOvXzt8BsSQOPk54HbCrTm4D5AKX9+cCPWuNDrPMM2yts99ru\n7enpGfMHioiIzoxaAGyfYXue7b2pLuJeY/vNwLXA8WWxpcAVZXplmae0X2PbJX5i6SW0D7AAuHHS\nPklERIzJRAaE+QBwqaSPA7cAF5T4BcDnJfUBW6mKBrbXS7oMuAN4EjjV9lMTeP+IiJiAMRUA298C\nvlWm72aIXjy2fwacMMz6y4HlY00yIiImX+4EjohoqBSAiIiGSgGIiGioFICIiIZKAYiIaKgUgIiI\nhkoBiIhoqBSAiIiGSgGIiGioFICIiIZKAYiIaKgUgIiIhkoBiIhoqBSAiIiGSgGIiGioFICIiIbq\nZFD4XSTdKOn7ktZL+liJXyTpHknrymthiUvSuZL6JN0q6aCWbS2VtKG8lg73nhERsf11MiLYE8CR\nth+TtCPwHUnfKG3vs/3ltuWPoRrvdwFwCHA+cIikPYCPAL2AgZskrbT98GR8kIiIGJtOBoW37cfK\n7I7l5RFWWQJcUta7HpgtaS/gaGC17a3lS381sHhi6UdExHh1dA1A0ixJ64AtVF/iN5Sm5eU0zzmS\ndi6xucD9LatvLLHh4u3vtUzSWklr+/v7x/hxIiKiUx0VANtP2V4IzAMOlvQrwBnAvsCrgD2AD0xG\nQrZX2O613dvT0zMZm4yIiCGMqReQ7R8D1wKLbW8up3meAD4HHFwW2wTMb1ltXokNF4+IiBp00guo\nR9LsMv1c4HeAH5Tz+kgScBxwe1llJfDW0hvoUOAR25uBq4BFkuZImgMsKrGIiKhBJ72A9gIuljSL\nqmBcZvtKSddI6gEErAPeVZZfBRwL9AGPA28DsL1V0lnAmrLcmba3Tt5HiYiIsRi1ANi+FThwiPiR\nwyxv4NRh2i4ELhxjjhERsR3kTuCIiIZKAYiIaKgUgIiIhkoBiIhoqBSAiIiGSgGIiGioFICIiIZK\nAYiIaKgUgIiIhkoBiIhoqBSAiIiGSgGIiGioFICIiIZKAYiIaKgUgIiIhupkRLBdJN0o6fuS1kv6\nWInvI+kGSX2S/knSTiW+c5nvK+17t2zrjBK/S9LR2+tDRUTE6Do5AngCONL2AcBCYHEZ6vETwDm2\nXw48DJxSlj8FeLjEzynLIWk/4ERgf2AxcF4ZZSwiImowagEoA78/VmZ3LC8DRwJfLvGLqcYFBlhS\n5intR5Vxg5cAl9p+wvY9VENGDgwkHxERXdbRNQBJsyStA7YAq4EfAj+2/WRZZCMwt0zPBe4HKO2P\nAC9ojQ+xTut7LZO0VtLa/v7+sX+iiIjoSEcFwPZTthcC86j+at93eyVke4XtXtu9PT092+ttIiIa\nb0y9gGz/GLgW+A1gtqSBQeXnAZvK9CZgPkBpfz7wo9b4EOtERESXddILqEfS7DL9XOB3gDupCsHx\nZbGlwBVlemWZp7RfY9slfmLpJbQPsAC4cbI+SEREjM0Ooy/CXsDFpcfOc4DLbF8p6Q7gUkkfB24B\nLijLXwB8XlIfsJWq5w+210u6DLgDeBI41fZTk/txIiKiU6MWANu3AgcOEb+bIXrx2P4ZcMIw21oO\nLB97mhERMdlyJ3BEREOlAERENFQKQEREQ6UAREQ0VApARERDpQBERDRUCkBEREOlAERENFQKQERE\nQ6UAREQ0VApARERDpQBERDRUCkBEREOlAERENFQKQEREQ3UyIth8SddKukPSeknvLvGPStokaV15\nHduyzhmS+iTdJenolvjiEuuTdPr2+UgREdGJTkYEexJ4r+2bJe0G3CRpdWk7x/ZftS4saT+qUcD2\nB34J+DdJryjNn6YaUnIjsEbSStt3TMYHiYiIselkRLDNwOYy/aikO4G5I6yyBLjU9hPAPWVoyIGR\nw/rKSGJIurQsmwIQEVGDMV0DkLQ31fCQN5TQaZJulXShpDklNhe4v2W1jSU2XLz9PZZJWitpbX9/\n/1jSi4iIMejkFBAAknYFLgfeY/snks4HzgJcfv418PaJJmR7BbACoLe31xPdXgza+/R/qTsFAO49\n+zV1pxARdFgAJO1I9eX/BdtfAbD9YEv7Z4Ery+wmYH7L6vNKjBHiERHRZZ30AhJwAXCn7b9pie/V\nstjrgdvL9ErgREk7S9oHWADcCKwBFkjaR9JOVBeKV07Ox4iIiLHq5AjgMOAtwG2S1pXYB4GTJC2k\nOgV0L/BOANvrJV1GdXH3SeBU208BSDoNuAqYBVxoe/0kfpaIiBiDTnoBfQfQEE2rRlhnObB8iPiq\nkdaLiIjuyZ3AERENlQIQEdFQKQAREQ2VAhAR0VApABERDZUCEBHRUCkAERENlQIQEdFQKQAREQ2V\nAhAR0VApABERDZUCEBHRUCkAERENlQIQEdFQKQAREQ3VyYhg8yVdK+kOSeslvbvE95C0WtKG8nNO\niUvSuZL6yoDxB7Vsa2lZfoOkpdvvY0VExGg6OQJ4Eniv7f2AQ4FTJe0HnA5cbXsBcHWZBziGahjI\nBcAy4HyoCgbwEeAQ4GDgIwNFIyIium/UAmB7s+2by/SjwJ3AXGAJcHFZ7GLguDK9BLjEleuB2WX8\n4KOB1ba32n4YWA0sntRPExERHRvTNQBJewMHAjcAe9reXJoeAPYs03OB+1tW21hiw8UjIqIGHRcA\nSbsClwPvsf2T1jbbphocfsIkLZO0VtLa/v7+ydhkREQMoaMCIGlHqi//L9j+Sgk/WE7tUH5uKfFN\nwPyW1eeV2HDxZ7G9wnav7d6enp6xfJaIiBiDTnoBCbgAuNP237Q0rQQGevIsBa5oib+19AY6FHik\nnCq6ClgkaU65+LuoxCIiogY7dLDMYcBbgNskrSuxDwJnA5dJOgW4D3hTaVsFHAv0AY8DbwOwvVXS\nWcCastyZtrdOyqeIiIgxG7UA2P4OoGGajxpieQOnDrOtC4ELx5JgRERsH7kTOCKioVIAIiIaKgUg\nIqKhUgAiIhoqBSAioqFSACIiGioFICKioVIAIiIaKgUgIqKhUgAiIhoqBSAioqFSACIiGioFICKi\noVIAIiIaKgUgIqKhOhkR7EJJWyTd3hL7qKRNktaV17EtbWdI6pN0l6SjW+KLS6xP0umT/1EiImIs\nOjkCuAhYPET8HNsLy2sVgKT9gBOB/cs650maJWkW8GngGGA/4KSybERE1KSTEcG+LWnvDre3BLjU\n9hPAPZL6gINLW5/tuwEkXVqWvWPMGUdExKSYyDWA0yTdWk4RzSmxucD9LctsLLHh4hERUZPxFoDz\ngZcBC4HNwF9PVkKSlklaK2ltf3//ZG02IiLajKsA2H7Q9lO2nwY+y+Bpnk3A/JZF55XYcPGhtr3C\ndq/t3p6envGkFxERHRhXAZC0V8vs64GBHkIrgRMl7SxpH2ABcCOwBlggaR9JO1FdKF45/rQjImKi\nRr0ILOlLwBHACyVtBD4CHCFpIWDgXuCdALbXS7qM6uLuk8Cptp8q2zkNuAqYBVxoe/2kf5qIiOhY\nJ72AThoifMEIyy8Hlg8RXwWsGlN2ERGx3eRO4IiIhkoBiIhoqBSAiIiGSgGIiGioFICIiIZKAYiI\naKgUgIiIhkoBiIhoqBSAiIiGSgGIiGioFICIiIZKAYiIaKgUgIiIhkoBiIhoqBSAiIiGSgGIiGio\nUQuApAslbZF0e0tsD0mrJW0oP+eUuCSdK6lP0q2SDmpZZ2lZfoOkpdvn40RERKc6OQK4CFjcFjsd\nuNr2AuDqMg9wDNU4wAuAZcD5UBUMqqEkD6EaQP4jA0UjIiLqMWoBsP1tYGtbeAlwcZm+GDiuJX6J\nK9cDs8sA8kcDq21vtf0wsJpti0pERHTReK8B7Gl7c5l+ANizTM8F7m9ZbmOJDRffhqRlktZKWtvf\n3z/O9CIiYjQTvghs24AnIZeB7a2w3Wu7t6enZ7I2GxERbcZbAB4sp3YoP7eU+CZgfsty80psuHhE\nRNRkvAVgJTDQk2cpcEVL/K2lN9ChwCPlVNFVwCJJc8rF30UlFhERNdlhtAUkfQk4AnihpI1UvXnO\nBi6TdApwH/Cmsvgq4FigD3gceBuA7a2SzgLWlOXOtN1+YTkiIrpo1AJg+6Rhmo4aYlkDpw6znQuB\nC8eUXUREbDe5EzgioqFSACIiGioFICKioUa9BhAxE+19+r/UnQIA9579mrpTiAbLEUBEREOlAERE\nNFQKQEREQ6UAREQ0VApARERDpQBERDRUCkBEREOlAERENFQKQEREQ6UAREQ0VApARERDTagASLpX\n0m2S1klaW2J7SFotaUP5OafEJelcSX2SbpV00GR8gIiIGJ/JOAL4bdsLbfeW+dOBq20vAK4u8wDH\nAAvKaxlw/iS8d0REjNP2OAW0BLi4TF8MHNcSv8SV64HZAwPLR0RE9020ABj4pqSbJC0rsT3LQPAA\nDwB7lum5wP0t624ssWeRtEzSWklr+/v7J5heREQMZ6LjARxue5OkFwGrJf2gtdG2JXksG7S9AlgB\n0NvbO6Z1IyKicxM6ArC9qfzcAnwVOBh4cODUTvm5pSy+CZjfsvq8EouIiBqMuwBIep6k3QamgUXA\n7cBKYGlZbClwRZleCby19AY6FHik5VRRRER02UROAe0JfFXSwHa+aPtfJa0BLpN0CnAf8Kay/Crg\nWKAPeBx42wTeOyIiJmjcBcD23cABQ8R/BBw1RNzAqeN9v4iI7a1pY0XnTuCIiIZKAYiIaKgUgIiI\nhkoBiIhoqBSAiIiGSgGIiGioFICIiIZKAYiIaKgUgIiIhkoBiIhoqBSAiIiGSgGIiGioFICIiIZK\nAYiIaKgUgIiIhup6AZC0WNJdkvoknd7t94+IiEpXC4CkWcCngWOA/YCTJO3XzRwiIqIykSEhx+Ng\noK+MJoakS4ElwB3b6w2bNsJPRESnVI3U2KU3k44HFtv+wzL/FuAQ26e1LLMMWFZmfxm4q2sJDu+F\nwEN1JzFFZF8Myr4YlH0xaCrsi5fY7hltoW4fAYzK9gpgRd15tJK01nZv3XlMBdkXg7IvBmVfDJpO\n+6LbF4E3AfNb5ueVWEREdFm3C8AaYIGkfSTtBJwIrOxyDhERQZdPAdl+UtJpwFXALOBC2+u7mcM4\nTalTUjXLvhiUfTEo+2LQtNkXXb0IHBERU0fuBI6IaKgUgIiIhkoBiIhoqBSAGJakQ+vOIWI6kjTl\n7rEaSgpAjOQ8SZ+RNLvuROomad+W6Z3b2hpTKLMfBkn6Tsv059uab+xyOuOSAtBG0rWSrhnmdXXd\n+XVZL3AncGN5bEeTfbFl+nttbed1M5GaZT8Mel7L9P5tbepmIuM1LQ5TuuzPhogdCrwf2NLlXGpl\n+2ngU5K+CXxP0nmAqf5z2/butSbYXRpmeqj5mSz7YdBIfeinRf/6FIA2tm8amJb0W8CfA7sA77L9\njdoSq4mkU4DTgf8HfNrNvXHEw0wPNT+TZT8Mmi3p9VRnUmZLekOJC3h+fWl1LgVgCJKOBj4EPAEs\nt31tzSnVQtJ1wL3A/7L9QM3p1G2epHOpfrkHpinzc+tLq+uyHwb9O/C6lunfbWn7dvfTGbvcCdxG\n0hqgB/gk257jxPbNXU+qJpL+xPa5oy8580laOlK77Yu7lUudsh86I+mNti+vO4/RpAC0kfQtBg9l\nB853D7DtI7ueVE0k3Wz7oLrzmAok7QLsZru/Ld4DPGr7Z/Vk1l3ZD52R9F+2X1x3HqPJKaA2to+o\nO4eYks4F/hX4Slv8cGAR8Eddz6ge2Q+dmRYXxHME0EbS7sCetjeU+ROA55bmq2w/WFtyXSbpSeDx\noZpoWC8gSTfZ/vVh2tbbbu8GOCNlP3QmRwDT118B1wEbyvxfAt+gKgKvBt5VU151uM32gXUnMUX8\nwghtTbqfJvuhkHQbQ/d8ErBnl9MZlxSAbb0KeGfL/KO2/xiefedfNM4WSQfbftYdnpJeBfQPs85M\nlP0w6LV1JzBRKQDb2qGtr3vrHbBNeyTCP9edwBTyPuAySRcBA/eK9AJvpRrZrimyHwZ91vaiupOY\niBSAbT0t6RcH+r3bvh1A0lzg6Voz6769Wvp5b8P2n3QzmTrZvlHSIcD/Af6ghNcDh9huzB3i2Q/P\n0lN3AhOVi8BtJJ0MvBt4L3BLCR9EdW3gXNvtD32asUbp823bl3QtmYgpRtLdDP3oGABst/eUmnJy\nBNDG9j9Kegj4OIMPeLod+HDTHgUx0k09kv6qm7nUTdK1DP+oA9s+qpv51CX74VmeT3UdYKgun2bb\nrrJTTo4AYlymSze3ySJpqK6Pzzwk0ParupxSLbIfBs2EGyVzBNBG0t8xwkOtmnTeexTT4kaXyZKH\nBFayH55l2v8OpABsa23dCUwVkvYYrokZ8J9/rPKQwEr2wzNOrjuBicopoDGQtIPtJ+vOo1sk3cO2\nz0N6hu19uptRffKQwEr2wyBJPwWeGqqJaXKnfApAG0nfsX14mf687be0tE37c34xPm0PCWzXmIcE\nZj8MknTLdL9TPqeAtjXth3nbniS9DPh94MQmPfclDwmsZD88y7T/67lRz+7o0LQf5m2ySfolSX9a\nDv/XU/2/adRdn5Le3zJ9QlvbX3Q/o3pkPzzLiyT93+FedSfXiRSAbc2W9HpJbyzTbyivNzJNhnmb\nLJKWlX7f3wJeAJwCbLb9Mdu31Zpc97UWvDPa2hZ3M5GaZT8MmgXsCuw2zGvKyymgbU37Yd4m0d9T\nXej7fdtrASQ18iiIDIY+IPth0GbbZ9adxESkALSx/ba6c5hC9gJOAP5a0i8ClwE71ptSbTIYeiX7\nYdC0L3jpBTQESbOAObYfKvM7UT346k9tv7LO3OoiaR7we8BJVBfKv2r7g/Vm1T2SngJ+SvVL/1wG\nB8oRsIvtRhTG7IdBkvawvbXuPCYiBaCNpBOBz1D9J98ALAcuBNYAZzWpn/NwJL2CqhfQtD78jbGT\ntKPtn9edR0yOFIA2km4HjrPdJ+kgqnPgx9v+es2pdZ2kN7SFDDwErLP9aA0pTTmSZgOn2l5edy7d\nkHthZpZcA9jW/9jug+quRkkbmvjlX/zuELE9gF+TdIrta7qdUF0kzad67s0vAV8DvgScSTUQyhdr\nTK3bpv157xiUArCtF7X14Z3dOm/7b2rIqRbDXRCX9BKqC8KHdDejWl1C1SvscqrujmuBdcCvDgwe\n1BA9I/Vxb9Lvx0yQArCtz/LsPrzt841n+z5JjbnYV+xh+6Nl+qpyE9SbbTdtlLiBvu85EpgBUgDa\n2P5Y3TlMdZJ+mepJkI0iaQ6DX3w/Ap4vSQDTvTfIGEz7vu8xKAVgCJKOobrLcb8SWg98wvaq+rLq\nPklfZ9u+3XtQ3R8w7R+FO0bPpxoEvfUv34EeYQZe2vWM6pG//GeQ9AJqI+kdwDupRjgaGBugFzgb\n+AfbK+rKrdvKgB+tTPWX7wbb/1NDSlEzSXNsP1x3HjE5UgDaSLoDOLz9kF7SC4DvNPFGMEkLgIHP\nfbPtjXXmUwdJJ9v+xzJ9mO3vtrSdZvvv68uueyQ9yuBR4cDRgKnOJuxkO2cVppE8DG5bGup8ru0f\n1ZFMnSTNlvQ14CqqO6H/APh3SZ9RpUkP/2rt+fJ3bW1v72YidbK9m+3dy2s3qtOBy4EHgL+tN7sY\nq1Trbf1E0gG2v98alHQA0LSbn/6OqqvjGwZ6u5SLnh8Cvg68oryaIA9Ba1FugHsPg/dBvKqJfyRN\ndykA23ovsFLS56gu+kF1DWApzbvweWjriGhQDfkEnCVpC3BYPWnVIg9BAyS9kOp35PeoHpFyoO1H\n6s0qxivXAIYgaU/gVAZHBLsD+HTDbvih3AW9YJi2Ptsv73ZOdZH0ONBH9df+y8o0Zf6ltp833Loz\nSRkHtx/4HEMcEedGsOklRwBtJL3Y9n8BH647lyngOkkfpnoI3jN/KUj6EHBdfWnVonEX/4fxSQaP\neHKD5DSXI4A2rQ+7knS57TfWnVNdJO0OXAAcRHUtAGAhcAvwdts/qSu3bpN0qO3r684jYjKlF9C2\nWi/oNeXmniHZ/ontE4BFwEXltcj28a1f/pKaMDj8eQMTkr5XZyJ1knRZy/Qn2tq+2f2MYiJSALY1\n0sW+RrL9Q9tfL68fDrHI57ueVPe1/mGwS21Z1K/1mtDvtLX1dDORmLhcA9jWAZJ+QhnxqExT5m17\n9/pSm7Ka0A3yOeVZQM9pmX7mczfoWUAj/VGUP5immRSANrZn1Z3DNNSEX/z2ZwG1jgzXpGcB/YKk\nA6kK4XPLoEkwOERkTCO5CBwTllGimkPStVQFr/UxEM802/7t7mcV45VrADEZZvyD4SSd3DJ9WFvb\nad3PqDYfoBoH4bfLl/3FwGPA7UBje8xNVzkCiGFJugL4bnmtafITQNu6Bz/riKdJR0CSbgb+t+2t\nkn4TuBT4Y6ruwa+0fXytCcaY5BpAjOSzwKupHvZ1gKQ7qW4A+y5wne0H60yuy/IsoMqslgvevwes\nsH05cLmkdSOsF1NQCkAMy/aVwJUAkmYBBwJHUN0Nug/V8IBNkWcBVWZJ2sH2k8BRwLKWtnyfTDP5\nB4sRlYd/vbq8DqXqA/9vQNNuhtpX0q2UZwGVacp8U3oAAXyJ6pHgDwH/DfwHgKSXA3ko3DSTawAx\nLEkbqH6pLweup7oO8Fi9WdVD0ktGard9X7dyqZukQ6nGAfim7Z+W2CuAXW3fPOLKMaWkAMSwJJ1B\n9Vf/XOA/qf7q/x5wi+2n6sxtqpD0HOAk21+oO5eIsUoBiI6Uv/BeDfwGcDjwkO32MYNnrPJgvFOp\niuFKYDVwGtWz8b9ve0mN6UWMS64BxKgkvRQ4GDiE6ojgRcA9tSbVfZ8HHqY6AvpD4INU5/+Ps53e\nLzEt5QgghiXpq1Rf+o9Sun4C37V9Z62J1UDSbbZ/tUzPAjYDL7b9s3ozixi/HAHESD4HvMP2Q3Un\nMgX8fGDC9lOSNubLP6a7HAHEiCS9iGcPj7keOK9hN4Eh6SngpwOzVA8+e5w8JTamsTwLKIZVnnmz\npsxeUl4AN7Q/D2emsz3L9u7ltZvtHVqm8+Uf01KOAGJYkq4H/sj2LW3xhcBnbB9ST2YRMRlyBBAj\n2b39yx+g9HrJgOAR01wKQIxEZeSr9uAe5P9OxLSXX+IYyTnANyX9lqTdyusI4BvAp+pNLSImKtcA\nYkSSXgu8n6oXkIE7gE/a/nqtiUXEhKUAxLhIeo/tHAVETGMpADEukv7L9ovrziMixi/XAGK8mjQK\nVsSMlAIQ45VDx4hpLs8CimFJepShv+gHHoUQEdNYrgFERDRUTgFFRDRUCkBEREOlAMSMIemxlulj\nJf2npJdI+qikP5vE97lusrYVUacUgJhxJB0FnAscY/u+yd6+7VePIReVgeMjppz8x4wZRdJvAp8F\nXmv7h0O0v0PSGknfl3S5pF8o8RMk3V7i3y6x/SXdKGmdpFslLSjx1iON95Xt3SrpYyW2t6S7JF0C\n3A7Mb1vneEkXlemLJJ0r6TpJd0s6vsSfI+k8ST+QtFrSqoG2iMmSAhAzyc7A16gGav/BMMt8xfar\nbB8A3AmcUuIfBo4u8deV2LuAv7W9EOgFNrZuSNIiYAFwMLAQ+PVSgCjx82zv38FRyF7A4cBrgbNL\n7A3A3sB+wFuA3xhlGxFjlgIQM8nPqQauP2WEZX5F0n9Iug14M4NDXX4XuEjSO4BZJfY94IOSPgC8\nxPZ/t21rUXndAtwM7Ev1xQ9wn+3rO8z7a7aftn0HsGeJHQ78c4k/AFzb4bYiOpYCEDPJ08CbgIMl\nfXCYZS4CTrP9q8DHgF0AbL9NCQt4AAABPklEQVQL+BAwH7hJ0gtsf5HqaOC/gVWSjmzbloC/tL2w\nvF5u+4LS9tO2ZVtvuNmlre2Jtm1GdEUKQMwoth8HXgO8WdJQRwK7AZsl7Uh1BACApJfZvsH2h4F+\nqvP2LwXutn0ucAXwa23bugp4u6RdyzbmSnrRMKk9KOmV5YLw6zv4KN8F3liuBewJHNHBOhFjkkdB\nxIxje6ukxcC3JfW3Nf85cAPVl/wNDA5t+clykVfA1cD3gQ8Ab5H0c+AB4C/a3uebkl4JfE8SwGPA\nycBTQ6R1OnBled+1wK6jfIzLgaOoxl+4n+oU0yOjrBMxJnkURMQUJWlX249JegFwI3BYuR4QMSly\nBBAxdV0paTawE3BWvvxjsuUIICKioXIROCKioVIAIiIaKgUgIqKhUgAiIhoqBSAioqFSACIiGur/\nAx+tswcZciSzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4b1e6c080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set targets\n",
    "print(len(data.index))\n",
    "data = data.dropna(subset= ['Klassierung'])\n",
    "print(len(data.index))\n",
    "data.groupby('Klassierung').Klassierung.count().plot.bar()\n",
    "\n",
    "#data['label'] = pd.Series(['high' if a else 'low' for a in data['Klassierung'].isin(['SENTENCE'])], index=data.index)\n",
    "data['label'] = pd.Series(data['Klassierung'], index=data.index)\n",
    "\n",
    "# set label / classes\n",
    "classes = list(data['label'].unique())\n",
    "data.head()\n",
    "print(classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZsAAAEJCAYAAABCNoqwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSMIp2YiISMIp2YiISML9/3S4pBodlVXEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4b2c786d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZsAAAEJCAYAAABCNoqwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4b21333c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEnCAYAAAC+F6RaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH1G2DiAtWByRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4a43e7f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAE2CAYAAAB2nXVkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFDhERCQv/x9xXC+uvOHd0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe476a3be80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# character frequency analysis\n",
    "\n",
    "high_quality = data.loc[data['Klassierung'].isin(['SENTENCE', 'REFERENCE'])]\n",
    "low_quality = data.loc[[not a for a in data['Klassierung'].isin(['SENTENCE', 'REFERENCE'])]]\n",
    "\n",
    "fdc = nltk.FreqDist()\n",
    "tokens_h = []\n",
    "for s in high_quality['Satz']:\n",
    "    tokens_h.extend(tokenizer.tokenize(s))\n",
    "    fdc.update(s)\n",
    "    \n",
    "# character frequencies\n",
    "fdc.plot(40,cumulative=False)\n",
    "\n",
    "tokens_l = []\n",
    "fdc2 = nltk.FreqDist()\n",
    "for s in low_quality['Satz']:\n",
    "    tokens_l.extend(tokenizer.tokenize(s))\n",
    "    fdc2.update(s)\n",
    "    fdc.update(s)\n",
    "    \n",
    "# character frequencies\n",
    "fdc2.plot(40,cumulative=False)\n",
    "\n",
    "# word frequencies\n",
    "fd = nltk.FreqDist(tokens_h)\n",
    "fd.plot(30,cumulative=False) \n",
    "fd = nltk.FreqDist(tokens_l)\n",
    "fd.plot(30,cumulative=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keras LSTM input data\n",
    "\n",
    "# one hot encoded characters\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "tokenizer_char = Tokenizer(char_level=True)\n",
    "tokenizer_char.fit_on_texts(data['Satz'])\n",
    "char_int_encoded = tokenizer_char.texts_to_sequences(data['Satz'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Satz</th>\n",
       "      <th>Tokens</th>\n",
       "      <th>Klassierung</th>\n",
       "      <th>Bemerkungen</th>\n",
       "      <th>label</th>\n",
       "      <th>input</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Durch die Ausnutzung der  Schwerkraft  kann di...</td>\n",
       "      <td>['durch', 'die', 'ausnutzung', 'der', 'schwerk...</td>\n",
       "      <td>SENTENCE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SENTENCE</td>\n",
       "      <td>[[0.0384615, 0.0769231, 0.0384615, 0.0384615, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Er ist vor allem mit seinen Arbeiten zum Thema...</td>\n",
       "      <td>['er', 'ist', 'vor', 'allem', 'mit', 'seinen',...</td>\n",
       "      <td>SENTENCE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SENTENCE</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0588235...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>An anderen [[Monoamin]]transportern wirken sie...</td>\n",
       "      <td>['an', 'anderen', 'monoamin', 'transportern', ...</td>\n",
       "      <td>SENTENCE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SENTENCE</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Etwa die Hälfte der an einem Schlaganfall-Erkr...</td>\n",
       "      <td>['etwa', 'die', 'hälfte', 'der', 'an', 'einem'...</td>\n",
       "      <td>LOW_QUALITY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LOW_QUALITY</td>\n",
       "      <td>[[0.0, 0.0294118, 0.0, 0.0294118, 0.0, 0.0, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Schwerpunktthema: Was ist gesichert in der The...</td>\n",
       "      <td>['schwerpunktthema', 'was', 'ist', 'gesichert'...</td>\n",
       "      <td>TITLE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TITLE</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.142857, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Satz  \\\n",
       "0  Durch die Ausnutzung der  Schwerkraft  kann di...   \n",
       "1  Er ist vor allem mit seinen Arbeiten zum Thema...   \n",
       "2  An anderen [[Monoamin]]transportern wirken sie...   \n",
       "3  Etwa die Hälfte der an einem Schlaganfall-Erkr...   \n",
       "4  Schwerpunktthema: Was ist gesichert in der The...   \n",
       "\n",
       "                                              Tokens  Klassierung Bemerkungen  \\\n",
       "0  ['durch', 'die', 'ausnutzung', 'der', 'schwerk...     SENTENCE         NaN   \n",
       "1  ['er', 'ist', 'vor', 'allem', 'mit', 'seinen',...     SENTENCE         NaN   \n",
       "2  ['an', 'anderen', 'monoamin', 'transportern', ...     SENTENCE         NaN   \n",
       "3  ['etwa', 'die', 'hälfte', 'der', 'an', 'einem'...  LOW_QUALITY         NaN   \n",
       "4  ['schwerpunktthema', 'was', 'ist', 'gesichert'...        TITLE         NaN   \n",
       "\n",
       "         label                                              input  \n",
       "0     SENTENCE  [[0.0384615, 0.0769231, 0.0384615, 0.0384615, ...  \n",
       "1     SENTENCE  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0588235...  \n",
       "2     SENTENCE  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
       "3  LOW_QUALITY  [[0.0, 0.0294118, 0.0, 0.0294118, 0.0, 0.0, 0....  \n",
       "4        TITLE  [[0.0, 0.0, 0.0, 0.142857, 0.0, 0.0, 0.0, 0.0,...  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set input data\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tfidf = TfidfVectorizer(sublinear_tf=True, min_df=8, norm='l2', encoding='utf8', ngram_range=(2, 2), stop_words='english')\n",
    "tfidf = tfidf.fit(data.Satz)\n",
    "\n",
    "fd = nltk.FreqDist(tokens_h + tokens_l)\n",
    "\n",
    "voc_chars = list(fdc.keys())[:200]\n",
    "voc_words = list(fd.keys())[:500]\n",
    "\n",
    "def convert_sentence_to_input(s):\n",
    "    tokens = tokenizer.tokenize(s)\n",
    "    fd_words = nltk.FreqDist(tokens)\n",
    "    fd_chars = nltk.FreqDist(s)\n",
    "    \n",
    "    words = [fd_words.freq(w) for w in voc_words]\n",
    "    chars = [fd_chars.freq(c) for c in voc_chars]\n",
    "    \n",
    "    words_in_vocab = sum(1 for i in words if i > 0.0)\n",
    "    long_tail = float(words_in_vocab) /  len(tokens)\n",
    "    \n",
    "    tfidf_features = list(tfidf.transform([s]).toarray()[0])\n",
    "    \n",
    "    return np.array(words + chars + [long_tail/10.0], dtype=np.float32)\n",
    "\n",
    "data['input'] = pd.Series([[convert_sentence_to_input(s)] for s in data['Satz']], index=data.index)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6003, 701)\n"
     ]
    }
   ],
   "source": [
    "# fit scikit learn models\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import LinearSVC, SVC\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "models = [\n",
    "    RandomForestClassifier(n_estimators=200, max_depth=4, random_state=0),\n",
    "    RandomForestClassifier(n_estimators=200, max_depth=8, random_state=0),\n",
    "    RandomForestClassifier(n_estimators=200, max_depth=16, random_state=0),\n",
    "    RandomForestClassifier(n_estimators=200, max_depth=32, random_state=0),\n",
    "    RandomForestClassifier(n_estimators=200, max_depth=64, random_state=0),\n",
    "    RandomForestClassifier(n_estimators=200, max_depth=128, random_state=0),\n",
    "    LinearSVC(),\n",
    "    # MultinomialNB(),\n",
    "    LogisticRegression(random_state=0),\n",
    "    SVC(kernel='linear', gamma=2),\n",
    "    # SVC(kernel='poly', gamma=2),\n",
    "    SVC(kernel='rbf', gamma=2),\n",
    "    MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(8), random_state=1),\n",
    "    MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(16), random_state=1),\n",
    "    MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(32), random_state=1),\n",
    "    MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(32, 16), random_state=1),\n",
    "    MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(32, 16, 8), random_state=1)\n",
    "]\n",
    "\n",
    "X_train, X_test, char_int_encoded_train, char_int_encoded_test, y_train, y_test = train_test_split(np.array([e[0] for e in data['input']]), char_int_encoded, data['label'], random_state = 2)\n",
    "\n",
    "print(X_train.shape)\n",
    "\n",
    "# count_vect = CountVectorizer()\n",
    "# X_train_counts = count_vect.fit_transform(X_train)\n",
    "# tfidf_transformer = TfidfTransformer()\n",
    "# X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)\n",
    "\n",
    "models = [model.fit(X_train, y_train) for model in models]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier: 0.665334665335\n",
      "RandomForestClassifier: 0.743256743257\n",
      "RandomForestClassifier: 0.775724275724\n",
      "RandomForestClassifier: 0.792707292707\n",
      "RandomForestClassifier: 0.789210789211\n",
      "RandomForestClassifier: 0.79020979021\n",
      "LinearSVC: 0.733266733267\n",
      "LogisticRegression: 0.656343656344\n",
      "SVC: 0.682317682318\n",
      "SVC: 0.732267732268\n",
      "MLPClassifier: 0.762737262737\n",
      "MLPClassifier: 0.746753246753\n",
      "MLPClassifier: 0.754245754246\n",
      "MLPClassifier: 0.742257742258\n",
      "MLPClassifier: 0.729270729271\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkMAAAJRCAYAAABGJ7MLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3Xe8HVW58PHfk4QSQgkdkqCAYOMi\nxQABRKo0aVcwgIKoaF6vKEWBa0FBsQMiXAsGUQJKiYo06R0klAARCKAh1DQ6BKQlOc/7x56EnXBO\nsk84s/fOmd+Xz3yYWTN71jqTyT5PnllrTWQmkiRJVdWn1Q2QJElqJYMhSZJUaQZDkiSp0gyGJElS\npRkMSZKkSjMYkiRJlWYwJEmSKs1gSJIkVZrBkCRJqrR+rW7A/PRbfLDTY0taoGh1AyrCL+TmmPnm\n5Kbd0jOefaSpf6yLrbR2W/51NTMkSZIqzWBIkiRVWls/JpMkSSXqmNXqFrQFM0OSJKnSzAxJklRV\n2dHqFrQFM0OSJKnSzAxJklRVHWaGwMyQJEmqODNDkiRVVNpnCDAzJEmSKs7MkCRJVWWfIcDMkCRJ\nqjiDIUmSVGk+JpMkqarsQA2YGZIkSRVnMCRJUlV1zGrusgAR8fuIeDoi7q8rWyEiro6ICcX/ly/K\nIyJOjYiHI+LeiNi47jMHFcdPiIiDFlSvwZAkSWoXZwI7z1P2DeDazFwXuLbYBtgFWLdYRgC/gVrw\nBBwLbAZsChw7O4DqisGQJElVlR3NXRbUnMybgOfnKd4TGFWsjwL2qis/K2tuAwZGxOrATsDVmfl8\nZr4AXM3bA6y5GAxJkqR2tmpmTi3WpwGrFuuDgSfrjptUlHVV3iVHk0mSVFVNnnQxIkZQe6Q128jM\nHNno5zMzIyJ7ul0GQ5IkqSmKwKfh4KfwVESsnplTi8dgTxflk4E16o4bUpRNBraZp/yG+VXgYzJJ\nkioqs6Opy0K6GJg9Iuwg4KK68s8Uo8qGAS8Vj9OuBHaMiOWLjtM7FmVdMjMkSZLaQkScSy2rs1JE\nTKI2KuwnwOiIOBh4HBheHH4ZsCvwMPAq8DmAzHw+Io4H7iyO+35mztspe+56M3v80VuP6bf44PZt\nnKS2Ea1uQEX4hdwcM9+c3LRb+o0Jtzb1j3WJdbdoy7+uPiaTJEmV5mMySZKqyneTAWaGJElSxZkZ\nkiSpqhp4X1gVmBmSJEmVZjAkSZIqzcdkkiRVlR2oATNDkiSp4swMSZJUVU1+UWu7MjMkSZIqzcyQ\nJElVZZ8hwMyQJEmqODNDkiRVlX2GADNDkiSp4swMSZJUUZm+jgPMDEmSpIozMyRJUlU5mgwwMyRJ\nkirOzJAkSVXlaDLAzJAkSao4M0OSJFWVfYYAM0OSJKniDIYkSVKl+ZhMkqSq6nDSRTAzJEmSKs5g\nqIfstOM2jL//Jh564BaOPuqQVjen1/I6l89rXL7DDv0i48Zdxz33XMvZZ/+KJZZYotVN6nW8jxuU\nHc1d2pTBUA/o06cPp57yQ3bb/QDW32Bb9t13Lz7wgXVb3axex+tcPq9x+QYNWo1DDvk8w4btykYb\nbU/fvn3Zd/ierW5Wr+J9rO4yGOoBm26yERMnPsajjz7BjBkzGD36IvbYfadWN6vX8TqXz2vcHP36\n9aN//yXp27cvS/Xvz5Sp01rdpF7F+7gbOjqau7Qpg6EeMGjwajw5acqc7UmTpzJo0GotbFHv5HUu\nn9e4fFOmTOPkk0/jkYl38OQT9zB9+nSuueamVjerV/E+VneVNposIq4HsovdmZnbl1W3JLWrgQOX\nY/fdd2Ld9w7jxRenc955v+VTn/oE55xzQaubpipq4348zVTm0PojOykbBhwNPN3VhyJiBDACIPou\nR58+A8ppXQ+aMnkaawwZNGd7yODVmTLFtHdP8zqXz2tcvu2334rHHnuCZ599HoALL7yczYcNNRjq\nQd7H6q7SHpNl5l2zF2Bp4KfA/sCXMnOT+XxuZGYOzcyhi0IgBHDn2HGss85arLnmGiy22GIMH74n\nl1x6Vaub1et4ncvnNS7fk09MZtPNNqZ//yUB2G7bj/DQQxNa3Krexfu4G+wzBJQ86WJE7AQcA7wB\n/DAzry+zvlaZNWsWhx1+DJf9/Rz69unDmaPO54EH/t3qZvU6XufyeY3Ld8ed93DBBX/njjuuZObM\nmfxz3HhO/92fWt2sXsX7WN0VmV1163mHJ464E1gZOAEYM+/+zLx7Qefot/jgchonqVeJVjegIvxC\nbo6Zb05u2i39+s1nN/WPdcmtDmzLv65lZob+A7wC7APszdzfVwlsV2LdkiRJDSktGMrMbco6tyRJ\neucyfTcZlDu0fllg1cycUGx/Euhf7L4yM58qq25JkqRGlfmY7ETgVmD2MIkfA5dTC4i2AL5UYt2S\nJGlB2niEVzOVGQxtAvy/uu2XM/OrABFxS4n1SpIkNazMYKhfzj1U7cC69YEl1itJkhrhDNRAue8m\n64iIOS+Dycz7ASJiMODVlyRJbaHMYOgE4JKI+GhELFMsWwMXFvskSZJarsyh9X+MiGeBHwDrFcX3\nA9/NzMvLqleSJDXIDtRAya/jyMwrgCvKrEOSJOmdKHOeof9jPrO3Z+ahZdUtSZIaYAdqoNzM0NgS\nzy1JktQjyuwzNKqrfRFR6uM5SZLUAPsMASWOJqufWDEizp5n9x1l1StJktQdZWZoBtStrzfPvkCS\nJLWWfYaAcucZ6rLz9AL2SZIkNU2ZmaGBEfHf1AKugRHxiaI8gOVKrFeSJDXCPkNAucHQjcAedeu7\n1+27qcR6JUmSGlbmaLLPlXVuSZLUA8wMAeX2GSIi+kbESnXbi0fEiIh4sMx6JUmSGlXm0Pr9gOeB\neyPixojYEXgE2AX4dFn1SpKkBmVHc5c2VWafoWOAD2fmwxGxMTAG2CczLymxTkmSpG4pMxh6MzMf\nBsjMuyNigoGQJEltxD5DQLnB0CoR8bW67YH125n58xLrliRJakiZwdDpwDLz2ZYkSWq5MofWf6+s\nc0uSpB7Qxp2am6nsofW7RMRNEfFssdwYEbuWWackSVJ3lJYZiogvAv8POBoYWxQPBX4SEUMyc2RZ\ndUuSpAbYgRoot8/QEcBHMvP5urLrImIX4BbAYEiSJLVcmcFQzBMIAZCZz0VEidVKkqSG2GcIKLfP\n0PSI2GDewqLs5RLrlSRJaliZmaGvAxdHxB+Au4qyocBBwAEl1itJkhphnyGgxMxQZt4CbFrU8dli\n6QMMK/ZJkiS1XJmjyd6VmU8A3y2rDkmS9A6YGQLK7TN04eyViPhrifVIkiQttFJHk9Wtr11iPZIk\naWFktroFbaHMzFB2sS5JktQ2yswMbRAR06lliPoX6xTbmZnLlli3JElaEPsMAeW+qLVvWeeWJEnq\nKWVmhiRJUjszMwSU/NZ6SZKkdmcwJEmSKs3HZJIkVZUvagXMDEmSpIozMyRJUlXZgRowMyRJkirO\nzJAkSVXl6zgAM0OSJKnizAxJklRV9hkCzAxJkqSKMzNUcdutun6rm9DrXffUfa1uQq93/gpbt7oJ\nlTD8+Rtb3QT1NDNDgJkhSZJUcWaGJEmqKmegBswMSZKkijMzJElSRWWH8wyBmSFJklRxZoYkSaoq\nR5MBZoYkSVLFGQxJkqRK8zGZJElV5dB6wMyQJEmqODNDkiRVlUPrATNDkiSpTUTEERExPiLuj4hz\nI2LJiFgrIm6PiIcj4vyIWLw4doli++Fi/5oLW6/BkCRJVdXR0dxlPiJiMHAoMDQz/wvoC+wH/BQ4\nOTPXAV4ADi4+cjDwQlF+cnHcQjEYkiRJ7aIf0D8i+gFLAVOB7YC/FPtHAXsV63sW2xT7t4+IWNhK\nJUlSFbXRpIuZOTkiTgSeAF4DrgLuAl7MzJnFYZOAwcX6YODJ4rMzI+IlYEXg2e7WbWZIkiQ1RUSM\niIixdcuIun3LU8v2rAUMAgYAOzejXWaGJEmqqmzuaLLMHAmM7GL3DsCjmfkMQERcAGwJDIyIfkV2\naAgwuTh+MrAGMKl4rLYc8NzCtMvMkCRJagdPAMMiYqmi78/2wAPA9cA+xTEHARcV6xcX2xT7r8tc\nuOjOzJAkSVXVXn2Gbo+IvwB3AzOBe6hlkf4OnBcRPyjKzig+cgZwdkQ8DDxPbeTZQjEYkiRJbSEz\njwWOnaf4EWDTTo59HfhkT9RrMCRJUlU5AzVgnyFJklRxZoYkSaoq31oPmBmSJEkVZzAkSZIqzcdk\nkiRVlR2oATNDkiSp4swMSZJUUdlGky62kpkhSZJUaWaGJEmqKvsMAWaGJElSxZkZkiSpqpx0ETAz\nJEmSKs7MkCRJVWWfIcDMkCRJqjgzQ5IkVZXzDAFmhiRJUsWZGZIkqarsMwSYGZIkSRVnZkiSpKpy\nniHAzJAkSao4gyFJklRpPiaTJKmq7EANmBmSJEkVZ2ZIkqSKSiddBMwMSZKkijMzJElSVdlnCDAz\nJEmSKs7MkCRJVWVmCDAz1GN22nEbxt9/Ew89cAtHH3VIq5uzSPvaiUcw+p7zGHnNaXPK1v7g2pxy\n0cn85opf8cu/n8r7NnwvAEsvtzTHnv4dTrvqN5x6ySms+b53t6rZvcLpI09iyqR/Mu6ea1vdlN6j\nT7Dd1T9i87OPnKv4Qz/4DHtM/P2c7bU+sz3bX/8TtrvmR3z0omNZ5r2Dm93SXsXvZHWHwVAP6NOn\nD6ee8kN22/0A1t9gW/bddy8+8IF1W92sRdbVf76abx14zFxlX/z2wfzx5D/xPzsfwqgTz+YL3/oC\nAPt/ZT8mjn+EL+34P5xw+An8z3FfakWTe42zzhrNx3f7dKub0aus88VdeHnC5LnKBm6wFosvN2Cu\nsicvuJVrt/0G1+3wLSb86hI+dNwBzWxmr+J3cjdkR3OXNlVaMBQRw8o6d7vZdJONmDjxMR599Alm\nzJjB6NEXscfuO7W6WYus+26/n5dffHmuskxYapmlABiw7ACee+o5AN617rsYd+s4AJ6cOIlV11iV\ngSsNbG6De5Gbb7md5194sdXN6DX6r74Cq+2wIY/96fq3CvsE63/3U9x//LlzHTvzldfmrPddagkS\nH18sLL+T1V1l9hn6dUTcCfxvZvbqb9dBg1fjyUlT5mxPmjyVTTfZqIUt6n1+c9xp/PiPP2TEMV8k\n+gSH7/U1AB558BE+ssuW3H/HeN634XtZdfCqrLz6Srz4bK++5bSI+NDxB3L/8efSb+kl55S95/M7\nMfXKu3n96bffo2t/7mOs8/92pc9i/bh5nx82s6m9it/J3WCfIaDcx2RDgQeBOyLiwEY/FBEjImJs\nRIzt6PhPea3TImX3A3fjtO/9lk9vdiCnfe+3fO2EIwA4/1ejGbDs0vzmil+x52f35OHxE5k1q31T\nsaqO1T62EW88O50X7310TtmSqw5k8O6bMfGMKzv9zCN/uJqrhh3B/T84l/cfsVezmipVXmmZoczs\nAH4REVcBYyLi10ACUdudy3bxuZHASIB+iw9eJELWKZOnscaQQXO2hwxenSlTprWwRb3Px/bZgV8f\n+xsAbrr0Zo742eEAvPrKq5z09Z/POe6sW0cx7QmvvVpvxU3ey+o7bsyq229I3yUWo9/S/dnhxhPo\neHMGO952MgB9+y/OjmN+zlWbf22uz066cAwb/fTz3NWKhvcCfic3Ls0MASUPrY+Ig4FvAN8GfpWZ\nvfKq3zl2HOussxZrrrkGkydPY/jwPTnwM45e6EnPPfUcHxr2Ie697V423HJDpjxaS4EPWHYAb7z2\nBjNnzGSX/Xfmvtvv49VXXm1xayUY/6PzGf+j8wFYaYsPsO7/fJwxB5441zF7TPz9nEBowFqr8Z9H\na7+wV9thI1551F/eC8vvZHVXacFQRNwKPAZslZm9+m/1rFmzOOzwY7js7+fQt08fzhx1Pg888O9W\nN2uR9c1ffoMPDfsQy62wLH+642zOPumPnPy/p/Dl475En359mfHGm/ziG6cA8K513sVRJ3+dTHj8\n34/z86NObnHrF21/PPtXbP3RzVlppRV47JGxfO/7J/KHM89rdbMq4T2f35FVPvpfdMyYyYyX/sPY\nQ3/T6iYtsvxO7gYzQwBEWcmaiDg0M099J+dYVB6TLcq2W3X9Vjeh17vuqfta3YReb/QKW7e6CZUw\n/PkbW92ESpj55uRoVl0vH7pbU3/PLnPqpU372bqjzA7Uny3x3JIkST3C13FIklRVHY6+hXKDoQ9F\nxPROyuc7mkySJKmZygyG7stMZ7mSJKld2YEa8N1kkiSp4srMDP25xHNLkqR3yswQUG4wtHpEdDm0\nPjMPLbFuSZKkhpQZDI2dzz5DUUmSWqyXvhii28p8N9morvZFxIld7ZMkSWqmVnWgHt6ieiVJ0mwd\n2dylTbUqGGrL6bglSVL1lPmi1hW62oXBkCRJrdfG2ZpmKrMD9V3UOkp3FvjMKLFeSZKkhpXZgXqt\nss4tSZLeuTQzBDS5z1BEvCcivhMR45tZryRJUldKD4YiYlBEHBERdwLjizr3K7teSZK0AI4mA0oM\nhiJiRERcD9wArAgcDEzNzO9l5n1l1StJktQdZXag/iUwBvhUZo4FiIj2DQslSVIllfpuMuCTwEkR\nsRowGlisxPokSVJ3dLS6Ae2htMdkmflcZp6WmVsD2wMvAk9FxIMR8aOy6pUkSeqOMjNDc2TmJOAk\nalmi92IHakmSWs6h9TVlzkD9iXmKEngWGJeZ3y+rXkmSpO4oMzO0eydlKwAfioiDM/O6EuuWJEkL\nYmYIKHcG6s91Vh4R76bWmXqzsuqWJElqVFP6DNXLzMcjwlFlkiS1mqPJgCa/jgMgIt4HvNHseiVJ\nkjpTZgfqS6h1mq63ArX5hw4oq15JktQYR5PVlPmY7MR5thN4DpiQmW+WWK8kSVLDyuxAfSNARKwL\nfKAofsRASJKkNmGfIaDcF7UOjIgLgSuBzxbLjRHx26jZuay6JUmSGlXmY7L/A8YBn8jMDoCICOAY\n4BLgvcUiSZJawD5DNWUGQ8My88D6gsxM4PiIeBrYssS6JUmSGtL0eYYK0zNzQovqliRJYJ+hQpnz\nDN0aEd8tHo3NERHHALeWWK8kSVLDyswMfRU4A3g4IsYVZRsC9wCfL7FeSZKkhpU5tH468MmIeA/w\nwaL4gcycWH9cRKyXmePLaockSepc+pgMaEKfoSL4mTifQ84GNi67HZIkSZ1pVQfqerHgQyRJUo8z\nMwS04EWtnXCSA0mS1DLtkBmSJEktYJ+hmnbIDPmuMkmS1DKlZYYi4iLgH8VyZ1cvaM3MYWW1QZIk\nzYeZIaDczNDpwEDgh8BTEXFrRJwYEf8dEauWWK8kSVLDypxn6FLgUoCI6AtsBGwDnACsBfQtq25J\nkrRg9hmqKbUDdUSsBGxRLMOAJYFrgDFl1itJktSoMvsMTQBeAv4KXAn8IDNfKas+SZLUPWaGasrM\nDP2eWjZob2B94L8iYgxwT2bOKrFeSZKkhpXZZ+jHs9cj4r3UHpV9EfhIRDybmVuXVbckSVowM0M1\npc8zFBFrA5sCm1HLFK0CvFx2vZIkSY0os8/Q36gFQC9Tm2voVuDUzHywrDrVfTc/6x9H2dYZOKjV\nTej1Dn/zvlY3QVo0pa8HhXL7DP0B+GJmPltiHZIkSe9ImX2GLo6IVSLie8B6RfF44NeZ+VRZ9UqS\nJHVHaX2GImJL4M5i86xiAbi92CdJklooO5q7tKsyH5OdBOyVmffUlV1c9CX6LbX+RJIkSS1VZjC0\n7DyBEACZOS4ilimxXkmS1IDssAM1lDu0PiJi+U4KVyi5XkmSpIaVGZScDFwVEVtHxDLFsg1wOfCL\nEuuVJEkNsM9QTZmjyUZGxBTgeGqjyRJ4gNo7yi4pq15JkqTuKPVxVWZempkfzcwVM3OlYv2SiDi8\nzHolSdKCZUZTlwWJiIER8ZeIeCgiHoyIzSNihYi4OiImFP9fvjg2IuLUiHg4Iu6NiI0X9jq0qu/O\n11pUryRJal+nAFdk5vuBDYAHgW8A12bmusC1xTbALsC6xTIC+M3CVtqqYMju65IktVg79RmKiOWA\njwJnAGTmm5n5IrAnMKo4bBSwV7G+J3BW1twGDIyI1RfmOrQqGMoW1StJklokIkZExNi6ZUTd7rWA\nZ4A/RMQ9EfG7iBgArJqZU4tjpgGrFuuDgSfrPj+pKOu2Ml/U+jKdBz0B9C+rXkmS1JhmzzOUmSOB\nkV3s7gdsDHw1M2+PiFN465HY7M9nRPR4QqXM0WROrChJkho1CZiUmbcX23+hFgw9FRGrZ+bU4jHY\n08X+ycAadZ8fUpR1m5MfSpJUUZnNXebflpwGPBkR7yuKtqc2Jc/FwEFF2UHARcX6xcBnilFlw4CX\n6h6ndUuZr+OQJEnqjq8Cf4qIxYFHgM9RS9yMjoiDgceB4cWxlwG7Ag8DrxbHLhSDIUmSKqrd3k2W\nmeOAoZ3s2r6TYxM4pCfq9TGZJEmqNDNDkiRVVLtlhlrFzJAkSao0gyFJklRpPiaTJKmiFjTcvSrM\nDEmSpEozMyRJUkXZgbrGzJAkSao0M0OSJFVUppkhMDMkSZIqzsyQJEkVlR2tbkF7MDMkSZIqzcyQ\nJEkV1WGfIcDMkCRJqjgzQ5IkVZSjyWrMDEmSpEozMyRJUkU5A3WNmSFJklRpZoYkSaoo31pfY2ZI\nkiRVmsGQJEmqNB+TSZJUUXagrjEzJEmSKs3MkCRJFeXrOGq6DIYi4hKgy37mmblHKS2SJElqovll\nhk5sWiskSVLT+TqOmi6Docy8sZkNkSRJaoUF9hmKiHWBHwMfBJacXZ6Za5fYLkmSVDInXaxpZDTZ\nH4DfADOBbYGzgD+W2ShJkqRmaWQ0Wf/MvDYiIjMfB46LiLuA75bcNkmSVCJHk9U0Egy9ERF9gAkR\n8RVgMrB0uc2SJElqjkaCocOApYBDgeOB7YCDymyUJEkqn6PJahbYZygz78zMVzJzUmZ+LjM/kZm3\nNaNxi5KddtyG8fffxEMP3MLRRx3S6ub0CkOGrM4VV5zH3Xdfw113Xc0hh3xurv2HHfZFXnvtcVZc\ncfkWtXDR9cNffId/jL+Si288b07ZcgOX5Yw//5IrbvsrZ/z5lyy73DJz9m26xcb87bo/cclN53P2\nhb9tRZMXaWuvsyaX3/jnOcv4x8dw8JcO4FdnnDCn7B/jruDyG//c6qb2Gn4nqzsiF9CVPCKup5PJ\nFzNzu7IaNVu/xQcvEv3c+/Tpw4Pjb2bnXfdn0qSp3DbmMg448Ms8+OCEVjdtgRbr276TkK+22iqs\nttoqjBt3P0svPYBbb72U4cNH8NBDExgyZHV+/euf8r73vYctttiN5557odXN7dK7l1ml1U14m6HD\nNuLV/7zKT375PfbYej8AjvzuV3nphemc/n+j+OJXD2LZgctw0vG/ZJlll+bcv5/BF/c7lKmTn2KF\nlZbn+Wfb63q/OvP1VjehYX369OGO8dey58c+xeRJU+eUH3P8kbw8/RVOOeG0FrZu/qa88nyrm9CQ\nRfk7GWDmm5Oblq65e409m/p7duMnL2rLVFQjo8mOBI4qlu8A44CxZTZqUbPpJhsxceJjPProE8yY\nMYPRoy9ij913anWzFnnTpj3NuHH3A/DKK//hoYceZtCgVQH42c++y7e//WMWFMyrc2Nvu4eXXpw+\nV9n2O2/NhedfCsCF51/KDrtsA8Bue+/M1X+/nqmTnwJou0BoUbPl1pvxxGNPzhUIAey2105c9NfL\nWtSq3sXvZHXXAtMCmXnXPEX/iIg7FvS5iHh/Zj5UrC+RmW/U7RvWmx61DRq8Gk9OmjJne9LkqWy6\nyUYtbFHv8653DWHDDdfjzjvHsdtuH2PKlGncd9+DrW5Wr7LiyivwzNPPAfDM08+x4sorALDm2u+i\n32L9OOtvpzFg6aU46/TzuGi0v7QX1h6f2IWL/nr5XGWbbv5hnn36OR575IkWtap38Tu5cY4mq2lk\n0sUV6jb7AB8Glmvg3OcAGxfrY+rWAX49z7bUpQEDluLcc0/jqKO+z8yZMzn66EPYbbcDW92sXm92\n1q1fv76s96H387l9vswSSy7BeZf9nn+Ovd9f3AthscX68bGdt+Gn3z9lrvI9996Fiy4wwJRapZEO\nI3dR6zMU1CZefBQ4uIHPRRfrnW2/tSNiBDACIPouR58+AxqoqrWmTJ7GGkMGzdkeMnh1pkyZ1sIW\n9R79+vXj3HNP4/zzL+Sii65gvfXex7vfvQZ33FH7l/XgwaszZszf2WqrPXnqqWda3NpF23PPPM/K\nq6zIM08/x8qrrDjncdi0KU/z4gsv8dqrr/Paq68zdsw9vG+9dQ2GFsI2O2zF/fc+yLPPPDenrG/f\nvuy82w58fLt9W9iy3sXvZHVXI32GPpCZa2fmWpm5bmbuCNzZwOeyi/XOtt/akTkyM4dm5tBFIRAC\nuHPsONZZZy3WXHMNFltsMYYP35NLLr2q1c3qFU477Wf8618Pc+qpvwNg/Ph/8e53f5j3v/8jvP/9\nH2Hy5KlsvvnHDYR6wHVX3sRe++4GwF777sa1V9ReT3jtFTey8aYb0rdvX5bsvwQf2vi/eGTCYy1s\n6aJrz73f/ojsI9sMY+KER5k25akWtar38Tu5cZnR1KVdNZIZupW3P9Ka97FXZ4ZExKnUskCz1ym2\nB3erlW1u1qxZHHb4MVz293Po26cPZ446nwce+Herm7XI22KLoXz603tz330PcttttUcIxx57Alde\neX2LW7boO+m0H7DJlh9m+RUGcsO4S/m/n43k9FNHcfLpP2bvT+/BlEnTOOIL3wTgkQmPcfP1t3LR\nDefQ0ZH85U8XMeGhiS3+CRY9/Zfqz1bbbM43j/j+XOV7/PcuXGzH6R7ld7K6q8uh9RGxGrWg5Y/A\np3jr0daywGmZ+f75njhivhMzZuaoBTVuURlavyhr56H1vUU7Dq3vbRalofWLskVlaP2irplD628f\n9Imm/p7dbMoFbZkemt9vwp2AzwJDgJN4KxiaDnyrgXOfDyyTmXM9v4iIlYGXu91SSZKkEnQZDBWZ\nm1ERsXdm/nUhzn0qcAVwwTzlHwF2BP5nIc4pSZJ6iI9fahrpQP3hiBg4eyMilo+IHzTyucycNxAi\nM/8GfLQbbZQkSSpNI8HQLpnuY7mLAAAgAElEQVT54uyNzHwB2LWBzy31DuuVJEkl6sho6tKuGglK\n+kbEErM3IqI/sMR8jp/t6YjYdN7CiNgEcBy0JElqC40MJfoTcG1E/IFaJ+rPAgscCUbtXWajI+JM\nahM3AgwFPgPs1+2WSpKkHtXOc/80UyPvJvtpRPwT2IFaX6srgXc38Lk7ImIz4MvUAiiA8cBmmfn0\nQrdYkiSpBzU6ycxT1AKhT1J7HUdDo8sy8yng2IVrmiRJKlNHqxvQJroMhiLivcD+xfIstXmDIjO3\nbeTEEXE9XY/ay8zcvpttlSRJ6nHzyww9BNwM7JaZDwNExBHdOPeRnZQNA44GfEwmSVKLZdfvTa+U\n+QVDn6DW0fn6iLgCOI/5vG1+Xpk5u9M0EbE18B1gSeBLmXl5lx+UJElqovnNQH0hcGFEDAD2BA4H\nVomI3wB/y8wFvgI4InYCjgHeAH6Ymb5hU5KkNtHhFNRAY6PJ/gOcA5wTEctT60T9v8B8g6GIuBNY\nGTiB2lvuiYg5b7rPzLsXvtmSJEk9o1uvLC9mnx5ZLAvyH+AVYJ9imetUwHbdqVuSJKkM3QqGuiMz\ntynr3JIk6Z3rsAM1UOI7wiLi6Lr1T86z70dl1StJktQdZb4wtf6VG9+cZ9/OJdYrSZIakERTl3ZV\nZjAUXax3ti1JktQSpfUZYu7Zp+cdvOdgPkmSWszXcdSUGQxtEBHTqWWB+hfrFNtLllivJElSw8oc\nTda3rHNLkqR3rp378TRTmX2GOhURAyPi282uV5IkqTNlDq1fIyJGRsSlEfGFiBgQEScBE4BVyqpX\nkiQ1pqPJS7sqs8/QWcCNwF+pDaUfC4wD1s/MaSXWK0mS1LAyg6EVMvO4Yv3KYuLFT2dmOweHkiRV\nhr+Qa8oMhihe7Dq7d9ZzwHIREQCZ+XyZdUuSJDWizGBoOeAu5p5gcfab6hNYu8S6JUnSAjiarKbM\nofVrlnVuSZKknlLmaLID6ta3nGffV8qqV5IkNaYjmru0qzLnGfpa3fr/zbPv8yXWK0mS1DBf1CpJ\nkirNF7VKklRRHeYmgHKDofdHxL3UskDvKdYpth1JJkmS2kKZwdAHSjy3JEl6h3xMU1NmMLR6Zt5W\n4vklSZLesTI7UP969kpEjCmxHkmStBB8UWtNs0aTLVliPZIkSQutzMdkfYp3k/WpW58TIPluMkmS\nWqsjHE0GzX032d11+3w3mSRJagu+m0ySpIpyNFmN7yaTJEmV5rvJJEmqKEeT1fhuMkmSVGm+m0yS\npIrqMDUB+G4ySZJUcb6bTJKkivKt9TVlDq1/vLPyiOgD7A90ul+SJKmZyhxav2xEfDMifhkRO0bN\nV4FHgOFl1StJktQdZT4mOxt4ARgDfAH4FrX+Qntl5rgS65UkSQ1wNFNNmcHQ2pm5PkBE/A6YCrwr\nM18vsU5JkqRuKTMYmjF7JTNnRcQkA6H2M2PWzFY3odd7+MUprW5Cr/falJtb3YRK6D9oq1Y3QT3M\nofU1ZQZDG0TE9GI9gP7FdgCZmcuWWLckSVJDyhxN1resc0uSpHeunV+R0Uxlvo5DkiSp7ZX5mEyS\nJLUxR5PVmBmSJEltIyL6RsQ9EXFpsb1WRNweEQ9HxPkRsXhRvkSx/XCxf82FrdNgSJKkiuqI5i4N\nOgx4sG77p8DJmbkOtfkLDy7KDwZeKMpPLo5bKAZDkiSpLUTEEODjwO+K7QC2A/5SHDIK2KtY37PY\npti/fXF8txkMSZJUUR1NXiJiRESMrVtGzNOkXwBH89ZAtxWBFzNz9qR4k4DBxfpg4EmAYv9LxfHd\nZgdqSZLUFJk5EhjZ2b6I2A14OjPviohtmtkugyFJkiqqzeYZ2hLYIyJ2BZYElgVOAQZGRL8i+zME\nmFwcPxlYA5gUEf2A5YDnFqZiH5NJkqSWy8xvZuaQzFwT2A+4LjM/DVwP7FMcdhBwUbF+cbFNsf+6\nzFyo2QLMDEmSVFG5aLyb7H+B8yLiB8A9wBlF+RnA2RHxMPA8tQBqoRgMSZKktpKZNwA3FOuPAJt2\ncszrwCd7oj4fk0mSpEozMyRJUkW1WQfqljEzJEmSKs3MkCRJFWVmqMbMkCRJqjQzQ5IkVdRCTcrT\nC5kZkiRJlWZmSJKkiupYNCZdLJ2ZIUmSVGlmhiRJqihHk9WYGZIkSZVmZkiSpIoyM1RjZkiSJFWa\nmSFJkirKeYZqzAxJkqRKMzMkSVJFOc9QjZkhSZJUaQZDkiSp0nxMJklSRTm0vsbMkCRJqjQzQ5Ik\nVZRD62vMDEmSpEozMyRJUkV1mBsCzAxJkqSKMzMkSVJFOZqsxsyQJEmqNDNDkiRVlD2GaswMSZKk\nSjMzJElSRdlnqMbMkCRJqjQzQ5IkVVRHtLoF7cHMkCRJqjQzQ5IkVZQzUNeYGZIkSZVmMCRJkirN\nx2SSJFWUD8lqzAz1kJ123Ibx99/EQw/cwtFHHdLq5vRKQ4YM4pqr/sy9/7yef467jq9+5eBWN6lX\n8l5eeMf86Od89OP7sdcBX5pT9tL0l/nCYd9i130P5guHfYuXpr8MwHU3j+G/P/M/7H3QIQz//KHc\n/c/7Abjjrn+y90GHzFk23nYPrr3p1pb8PIsy72N1R2S2b1zYb/HB7du4On369OHB8Tez8677M2nS\nVG4bcxkHHPhlHnxwQqub1qusttoqrL7aKtwz7n6WXnoAd9x+BXvv83mvcw9aVO/l16bc3OomADB2\n3H0s1b8/3zr+RC7842kAnPSrM1hu2WX4woHD+d3Zo5n+8st87csH8+qrr9G//5JEBP96+FGO/M6P\nuOTc0+c630vTX2aX4Z/n2gvPpv+SS7biR5pL/0FbtboJDVlU7+PZZr45uWkD3r+55qea+nv2x4+d\n05aD+c0M9YBNN9mIiRMf49FHn2DGjBmMHn0Re+y+U6ub1etMm/Y094yr/ev5lVf+w0MPTWDwoNVa\n3KrexXv5nRm64fost+wyc5Vdf/MY9txlBwD23GUHrrtpDABLLdWfiNrvhddefx3i7b8jrrr+ZrYa\nNrQtAqFFifexuqu0YCgi3l+3vsQ8+4aVVW8rDBq8Gk9OmjJne9LkqQzyl3Sp3v3uIWy4wX9x+x33\ntLopvYr3cs977oUXWXmlFQBYacXlee6FF+fsu+bGf7D7/l/ky0d+l+O/dcTbPnv5NTexy8e2aVZT\new3v48Z1kE1d2lWZmaFz6tbHzLPv1yXWq15uwIClGH3+6XztyGN5+eVXWt0cqWERMScbBLDD1lty\nybmnc+pPvssvTz9rrmOfefZ5JjzyKFtu9uFmN1OqnDKDoehivbPtt3ZEjIiIsRExtqPjP+W0rIdN\nmTyNNYYMmrM9ZPDqTJkyrYUt6r369evHn88/nXPP/RsXXnh5q5vT63gv97wVlx/IM88+D9QCnBUG\nLve2Y4ZuuD6TpkzjhRdfmlN2xXU3sf1Ht2Cxfg767S7v48Zlk5d2VWYwlF2sd7b91o7MkZk5NDOH\n9ukzoJyW9bA7x45jnXXWYs0112CxxRZj+PA9ueTSq1rdrF7p9JEn8eBDD/OLU0a2uim9kvdyz9vm\nI8O46PJrALjo8mvYdqvNAXhi0hRmD2B54F8P8+abMxi43LJzPnf51Tew6w7bNL29vYH3sbqrzH9y\nDImIU6llgWavU2wPLrHepps1axaHHX4Ml/39HPr26cOZo87ngQf+3epm9TpbbrEJBx6wD/fe9wBj\n76x9sX3nOz/h8iuua3HLeg/v5XfmqGN/wp333MuLL05n+70O4MsHH8gXDhzO17/zIy649EoGrbYK\nJx3/LQCuvuEWLr78Wvr168eSSyzOid//xpxHaJOnPsW0p59l6Ebrt/LHWWR5Hzeuo9UNaBOlDa2P\niIPmtz8zRy3oHIvK0HpJrdUuQ+t7u0VlaP2irplD649cc/+m/p498bFz23JofZmZofOBZTLzmfrC\niFgZeLnEeiVJUgPaeYRXM5XZZ+hUoLN/RnwEOLnEeiVJkhpWZjD04cy8YN7CzPwb8NES65UkSQ1w\nNFlNmcHQUi2qV5IkqWFlBiVPR8Sm8xZGxCbAM50cL0mSmqijyUu7KrMD9VHA6Ig4E7irKBsKfAbY\nr8R6JUmSGlZaZigz7wA2ozav0GeLJYDNMvP2suqVJEnqjlLnec/Mp4Bjy6xDkiQtnGzrbs3NU1ow\nFBHX03Xn8czM7cuqW5IkqVFlZoaO7KRsGHA08HSJ9UqSpAa0c6fmZiotGMrM2Z2miYitge8ASwJf\nykxfNy5JktpCqX2GImIn4BjgDeCHmXl9mfVJkqTG+TqOmjL7DN0JrAycAIwpyjaevT8z7y6rbkmS\npEaVmRn6D/AKsE+x1EtguxLrliRJC2BeqKbMPkPblHVuSZKknlLapIsRcXTd+ifn2fejsuqVJEmN\n6SCburSrMt9NVv/KjW/Os2/nEuuVJElqWJl9hqKL9c62JUlSkznPUE2ZmaHsYr2zbUmSpJYoMzO0\nQURMp5YF6l+sU2wvWWK9kiSpAb6brKbMYGjJzJxR4vklSZLesTKDoduBjRd4lCRJagn7DNWU2WfI\nTtKSJKntlZkZWjkivtbVzsz8eYl1S5KkBbDPUE2ZwVBfYGnMEEmSpDZWZjA0NTO/X+L5JUmS3rFm\nTbooSZLajB2oa8rsQO1b6SVJUtsrMzP0RETM7pk1O0uURZ2LZ2aZdUuSpAXoSDtQQ4nBUGYuU78d\nEUsDhwD/D/hbWfVKkiR1R+nZmYgYCBwOfAY4B9gkM58ru15JkjR/5oVqSguGImIl4OvAvsDvgY0y\n86Wy6pMkSVoYZWaGHgeeAf4AvAocHPHWADMnXZQkqbU6zA0B5QZDJ/BWBm6Z+R0oSZLUKmV2oD6u\nrHNLkqR3ztdx1JQ2z1BEjK5b/+k8+64qq15JkqTuKHPSxXXr1j82z76VS6xXkiQ1oKPJS7sqMxia\nX+7NvJwkSWoLZXagXioiNqIWcPWPiI2L8gD6l1ivJElqgKPJakp9az1wErXgZxpwYt2+aSXWK0mS\n1LAyg6H/BZ7MzKkAEXEQsDfwGHBcifVKkqQGOJqspsw+Q6cBbwBExEeBHwOjgJeAkSXWK0mS1LAy\nM0N9M/P5Yn1fYGRm/hX4a0SMK7FeSZKkhpUaDEVEv8ycCWwPjGhSvZIkqQHtPNy9mcoMSs4FboyI\nZ4HXgJsBImIdao/KJEmSWq7M13H8MCKuBVYHrsrM2b20+gBfLateSZLUmLd+NVdbqY+rMvO2Tsr+\nXWadkiRJ3WHfHUmSKspJF2vKHFovSZLU9swMSZJUUY4mqzEYkrTI+9AH92t1EyS9QxGxBnAWsCq1\nF7qPzMxTImIF4HxgTWpvsRiemS9ERACnALsCrwKfzcy7F6ZuH5NJklRR2eT/FmAm8PXM/CAwDDgk\nIj4IfAO4NjPXBa4ttgF2AdYtlhHAbxb2OhgMSZKklsvMqbMzO5n5MvAgMBjYk9rrvCj+v1exvidw\nVtbcBgyMiNUXpm4fk0mSVFHtOposItYENgJuB1ad/dJ3YBq1x2hQC5SerPvYpKJsKt1kZkiSJDVF\nRIyIiLF1y4hOjlka+CtweGZOr99XTODc4xGcmSFJkiqq2TNQZ+ZIYGRX+yNiMWqB0J8y84Ki+KmI\nWD0zpxaPwZ4uyicDa9R9fEhR1m1mhiRJUssVo8POAB7MzJ/X7boYOKhYPwi4qK78M1EzDHip7nFa\nt5gZkiSpotpsnqEtgQOB+yJiXFH2LeAnwOiIOBh4HBhe7LuM2rD6h6kNrf/cwlZsMCRJklouM28B\noovd23dyfAKH9ETdPiaTJEmVZmZIkqSKamAixEowMyRJkirNzJAkSRXVrpMuNpuZIUmSVGlmhiRJ\nqqhmT7rYrswMSZKkSjMzJElSRdlnqMbMkCRJqjQzQ5IkVZTzDNWYGZIkSZVmZkiSpIrqcDQZYGZI\nkiRVnJkhSZIqyrxQjZkhSZJUaWaGJEmqKOcZqjEzJEmSKs1gSJIkVZqPySRJqigfk9WYGZIkSZVm\nZkiSpIpKJ10EzAxJkqSKMzMkSVJF2WeoxsyQJEmqNDNDkiRVVJoZAswMSZKkijMzJElSRTmarMbM\nkCRJqjQzQ5IkVZSjyWrMDEmSpEozMyRJUkXZZ6jGzJAkSao0M0OSJFWUfYZqzAxJkqRKMxiSJEmV\n5mMySZIqytdx1JgZkiRJlWZmSJKkiupwaD1gZkiSJFWcmSFJkirKPkM1ZoYkSVKlGQz1kJ123Ibx\n99/EQw/cwtFHHdLq5vRaffr04c47ruSiv41qdVN6Le/lnvGDXxzDLeOv4OIbz51TttPu23PJTecx\nftptrLfBB+aUD1x+Oc684NeMffQGjvnxka1obq/jfdyYjsymLu3KYKgH9OnTh1NP+SG77X4A62+w\nLfvuuxcf+MC6rW5Wr3ToV7/AQw9NaHUzei3v5Z5z4Xl/Z8R+h81VNuGhiXz1c0czdsw9c5W/8cYb\nnPrT33LCcac2s4m9lvexuqvpwVBE9Lp+SptushETJz7Go48+wYwZMxg9+iL22H2nVjer1xk8eHV2\n3WV7fv/7cxd8sBaK93LPGXvbPbz44vS5yh6Z8BiPTXzibce+9urr3H37P3nj9Tea1bxezfu4cdnk\n/9pVKcFQRNxSt372PLvvKKPOVho0eDWenDRlzvakyVMZNGi1Fraod/r5Sd/jG9/8AR0dHa1uSq/l\nvazewPtY3VVWZmhA3fp68+yLkupUL/bxXXfg6aef5e577mt1UySp17DPUE1Zj6zm9xPP92pExAhg\nBED0XY4+fQbM7/C2MGXyNNYYMmjO9pDBqzNlyrQWtqj32WKLoey+247ssvN2LLnkEiy77DKMOvNU\nDvrsoa1uWq/ivazewPtY3VVWZmhgRPx3ROxdrH+iWPYGlpvfBzNzZGYOzcyhi0IgBHDn2HGss85a\nrLnmGiy22GIMH74nl1x6Vaub1at8+5ifsObaQ1nnvcP49AFf5vrr/2EgVALvZfUG3seNs89QTVmZ\noRuBPerWd6/bd1NJdbbMrFmzOOzwY7js7+fQt08fzhx1Pg888O9WN0vqNu/lnnPiacez6ZYfZuAK\nA7l+3CX88men89KL0/n2j77OCisuz2nn/JyH7p/AF/etBfXXjL2QAcsMYLHFF2P7XbbmC8MPZeK/\nH23xT7Fo8j5Wd0U2+RleROydmX9t5Nh+iw9u3zBSUttYd+DgVjehEia8OLnVTaiEmW9Oblrf2ves\ntHFTf89OfPbutuw33Ip5hk5uQZ2SJEmdakUw1JZRoSRJqqZWTIDooy9JktpAO3dqbqZSgqGIuI/O\ng54AVi2jTkmSpIVRVmZot5LOK0mSekimM/pDecHQ6Zm5Y0nnliRJ6jFlBUMrl3ReSZLUQzrsMwSU\nFwwtFxGf6GpnZl5QUr2SJEndUlowRK3fUGfD6BMwGJIkqcWaPfFyuyorGHo8Mz9f0rklSZJ6TFnB\nkBMrSpLU5uwzVFPWDNQHlHReSZKkHlVWZuiOiJjVSXkAmZnLllSvJElqkH2GasoKhv6dmRuVdG5J\nkqQeU1YwZKgpSVKb6zAzBJQXDK0SEV/ramdm/rykeiVJkrqlrGCoL7A0jiqTJKlt+db6mrKCoamZ\n+f2Szi1JktRjyhpab0ZIkiQtEsrKDG1f0nklSVIPcWh9TSmZocx8vozzSpIk9bSyMkOSJKnN+TqO\nmrL6DEmSJC0SzAxJklRR9hmqMTMkSZIqzcyQJEkV5es4aswMSZKkSjMzJElSRdlnqMbMkCRJqjQz\nQ5IkVZTzDNWYGZIkSZVmZkiSpIqyz1CNmSFJklRpZoYkSaoo5xmqMTMkSZIqzWBIkiRVmo/JJEmq\nqHRoPWBmSJIkVZyZIUmSKsoO1DVmhiRJUqWZGZIkqaKcdLHGzJAkSao0M0OSJFWUo8lqzAxJkqRK\nMzMkSVJF2WeoxsyQJEmqNIMhSZIqKjObuixIROwcEf+KiIcj4htNuASAwZAkSWoDEdEX+BWwC/BB\nYP+I+GAz6jYYkiSporLJywJsCjycmY9k5pvAecCePfFzLojBkCRJageDgSfrticVZaVr69FkM9+c\nHK1uQ3dFxIjMHNnqdvRmXuPyeY2bw+tcPq/x/DX792xEjABG1BWNbIc/HzNDPW/Egg/RO+Q1Lp/X\nuDm8zuXzGreRzByZmUPrlvpAaDKwRt32kKKsdAZDkiSpHdwJrBsRa0XE4sB+wMXNqLitH5NJkqRq\nyMyZEfEV4EqgL/D7zBzfjLoNhnpey599VoDXuHxe4+bwOpfPa7wIyczLgMuaXW84FbckSaoy+wxJ\nkqRKq2wwFBGzImJc3bJmUf6RiLgjIh4qlhF1nzkuIiYXxz8QEfvX7TszIh6tO9+tRflnI+KXdccd\nEBH3RsT4iPhnRPwuIgYW+26IiLF1xw6NiBtKvxhqe3X36/0RcUndPbNmRLw2z738mWLfYxFxX135\nFg0ef29E3BgR7+6k/tnLN4ry+d6zEbFpRNxUTK9/T3G/L1X8vXhmnnM2ZabZRkXEt4u/p/cW7dus\n+Hn/VdfmvxTHHhcRr0bEKnWffyUiVqw7dlrd98e4iFi8itd1YS3gWr4aEevXbT9f9318TXHf39/J\nOTv93lYFNfu9JO2yAK90UrYa8ASwcbG9EnAX8PFi+zjgyGJ9XWA6sFixfSawTyfn/Czwy2J95+J8\ng4vtvsDngfcV2zcU9e9SbA8Fbmj1tZrfNSvKRwAPFcsdwEeK8j2BC+uO+ya12UVnb+8OXDyf+pYD\nzgIeBiYCfwKWL/ZtA1w6z/Fz/RkUf34zgC/Nc9xjwEqd/TkB3wbGFcusuvXDgDG89Wi5L3APsEWz\nrz0wCvh2sb4mcH8Xn+ns52zoeOB7wOkN/Nl3ec8CqwKPA5vXHb9PUT7n70U7LsDmxZ/3EnX30qDi\n5x3ayfHHFdfhp11dM+q+P6p6XXvwz2eua9nJtZ73u6DT+37e41yqu1Q2M9SFQ4AzM/NugMx8Fjga\neNvL4jJzAvAqsHw3zv9tan+BJxfnmJWZv8/Mf9Udc0Jx3CIhInYD/h+1AOj9wJeAcyJiNeBWYFjd\n4ZsD0+v+9bxFcUxXzgAeycx1MvM91IKiM7vRvE8CtwH7L+jA2TLzh5m5YWZuCLw2ez0zT6H2C+jg\n4tCvAmMzsxX/khxD+bOydqeOru7ZQ4BRmTlmdkFm/iUzn+qB9pVtdeDZzHwDat8FmTllAZ/5PbBv\nRKzQQ23ojddVaktVDob616VG/1aUrUctc1NvbFE+l4jYGJiQmU/XFZ9Qd84/dVLnesDdC2jXGODN\niNi2sR+j5f4XOKoIHCkCyVHAIZn5DLXgZ53i2MHAX6kFQRT//0dnJy0+82Hg+Lri7wMbRMT7Gmzb\n/sDXgcERMaTxH6lLRwDfjIj1gK9Q+9mbKmovMtyeuefeeM88j0W2qtt3fVF2e4PHz7YzcGHddv95\nPrNv3b6u7tn/4u1/n+rtO885+8/n2Ga7ClgjIv4dEb+OiK3r9v2prs0n1JW/Qi0gOqwb9VTturaj\nBX1vqwKqPLT+teJf/911RER8Dngvtcc89Y7KzL80cpKIWB84G1gG+FZmnl+3+wfAMbTgl+1C6CqA\nPKhY/wewRfFLfAK1TM1OEXEpsAG1SbY680FgXGbOml2QmbMi4h7gA8CL82tURKwBrJ6Zd0TEaGBf\n4KRu/WTzyMypEfELar+kDs3M59/J+bqpf0SMoxZQPghcXbdv4nzu5W1nB6oNHn99kdl4BfhOXfmC\n/r4szD17fmZ+pRvHN01mvhIRHwa2ArYFzp/dnwf4dGaO7eKjpwLjIuLEBquq1HVtUw1/b6v3qnJm\nqDMPUMtG1PswUD/p08mZuR6wN3BGRCzZjfOPBzYGyMz7ii/By4G5/uWWmdcVZcPedoZFz63UMkBb\nUAsi7gA2AzYCHsrM1xfyvF3NCTG7fF9gdLF+Ht14VLYAvwL6ZuaZPXS+Rs3+pfluIKg9KinDtkUd\n46j1G2pIF/fseN7+92mRUTzGviEzj6WWCdy7gc+8CJxDD/359MbrKrUjg6G5/Qr4bERsCLXRC8BP\ngZ/Ne2BmXszcGZBG/Pj/t3dvIVZVcRzHvz+E0ppJiUjQhwbykiE2D1GhEiVh9WCUECSUDj1E9mCS\nRVJCFkGm4UNJEFJRRNIFBibUzHxI8wLSpOOQRkIXgiDxIcyMKP89rHV0ezzHmXHOcS7794EDZ/bZ\ne+29z/U/a/3X/gOvVQ3Z1OvCfpmUrzTc9RVA7qYQDEXECWAsKQH6Qvk23wHtks68R/P9m0hDjcc5\nP1/raqDSC7KI9Fr+RBpSmiVp6kBOrJaIOE39QKzpIuIvYBmwQlJTenYj4l9gObB4gPkv1e/ZDcAS\nSbdWFkhaKGliY460eSRNr3q/tJNyxvpjPSmPrlGvz6h5Xs2GKwdDBRHxG/AwsFHSEdKP9TsR8Vmd\nTV4Cnir8YBfHng8o1VYptr+F1I2+VWlq/h7SjKVtNY5lC3CsMWfWVGuBV3PgSA4kO4A38+OHSbNw\n5pJmX0HqdXicOvlCABFxNK+/qrB4FbAjIn4hDblNkjQj7/c6UqB0QNI0oCUiJkdEW0S0kQLRRvUO\nDamI+Bbo4ez5VOcALeujiT7Xz5+FTZzt4ajObVlTY5tz3rM5ofch0j8A30s6DNwNnMirVOe2zK5u\ncwi1AO/lz2kPadh2dX6smDP0ZfWGeViyE7i8H/sp2/M6lKZL+rVwezAvv+D3tpWDr0Bt/SbpNFCc\nUbM+ItZLWkrqSQjSF/KKiNhZ2G4zMD4i5ua/O4B3gUn5R7fe/iYAb5B6la4i5RctqOQRSZpDygMa\nS5pC/1xEbJf0AjAuIlYW2ppFyqWYkXuLLgNO54c/JgUXNxdzLST9GREtNY6r5nIzMxuZHAzZiKA0\ng2wzKXH5ktetMTOz0cvBkJmZmZVamafW2zChdA2c6vyKRyLi0FAcj5mZlYt7hszMzKzUPJvMzMzM\nSs3BkJmZmZWagyGzEdNASpEAAAJXSURBVEzSf/naKL2SPpF0xSDauiOXSUHSfYXyE7XWnSDpiYvY\nx2pJT1/sMZqZNYODIbOR7VREtEfETOAf0sUsz1Ay4M95RHRFxHkXACyYAAw4GDIzG44cDJmNHruA\nKZLa8pWJ3wd6SdXX50vaK6k79yC1AEi6R9IRSd3AwkpDkjokbcj3J0rqlHQw32YDazh7Jet1eb1n\nJO2X1CPpxUJbzytVf/8amH7Jng0zs37y1HqzUSDXKbsX+DwvmgosiYh9kq4hlTK5KyJOSnqWVEZm\nLbARmAccBT6q0/zrwFcR8YCkMaRSFSuBmZWK65Lm533eQiok2yXpduAkqXREO+n7phv4prFnb2Y2\nOA6GzEa2cZIO5Pu7gLdJteB+joh9efltpNpauyVBKkWyF7gB+DEifgCQ9AHwWI19zAMWQ6rkDvwh\nqbpI7vx8q9SfayEFR61AZy4wi6SuQZ2tmVkTOBgyG9lOVXpnKnLAc7K4CNgeEYuq1jtnu0ES8EpE\nvFW1j+UN3IeZWVM4Z8hs9NsHzJE0BUDSlZKmAUeANknX5/UW1dl+B7A0bztG0nhSQd7WwjrbgEcL\nuUiTJV0L7ATulzROUiuwoMHnZmY2aA6GzEa5iDgGdACbJPWQh8gi4m/SsNjmnED9e50mngTulHSI\nlO9zY0QcJw279UpaFxFfAB8Ce/N6nwKtEdFNykU6CGwF9jftRM3MLpLLcZiZmVmpuWfIzMzMSs3B\nkJmZmZWagyEzMzMrNQdDZmZmVmoOhszMzKzUHAyZmZlZqTkYMjMzs1JzMGRmZmal9j9VPrq3o5ix\nIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe476eb5630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkMAAAJRCAYAAABGJ7MLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe476fa67f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkMAAAJRCAYAAABGJ7MLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4770ab0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj0AAAJRCAYAAACupwYgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4b1ddfb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj0AAAJRCAYAAACupwYgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMzOzSvg/WsHmy3A8wjwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4b1e6a3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj0AAAJRCAYAAACupwYgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4b1f2cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj0AAAJRCAYAAACupwYgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVkjQExEREa3wf9McjlPbF2I+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4b1f964e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj0AAAJRCAYAAACupwYgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe476f71e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkMAAAJRCAYAAABGJ7MLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwZCZmZmV2v8BdO2Xuw5td90AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4768aac88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj0AAAJRCAYAAACupwYgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4a41f8cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj0AAAJRCAYAAACupwYgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAeih2WJInDOKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4a4364f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj0AAAJRCAYAAACupwYgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngx4zMzMrhX8AHPYPviBvbLgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4a43aac18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj0AAAJRCAYAAACupwYgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4a420a390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj0AAAJRCAYAAACupwYgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4b1f6b898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj0AAAJRCAYAAACupwYgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe476baab38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# model evaluation\n",
    "\n",
    "for model in models:\n",
    "    y_pred = model.predict(X_test)\n",
    "    from sklearn.metrics import confusion_matrix\n",
    "    from sklearn.metrics import accuracy_score\n",
    "\n",
    "    print(model.__class__.__name__ + \": \" + str(accuracy_score(y_test, y_pred)))\n",
    "\n",
    "    conf_mat = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "    import matplotlib.pyplot as plt\n",
    "    import seaborn as sns\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(10,10))\n",
    "    sns.heatmap(conf_mat, annot=True, fmt='d',\n",
    "                xticklabels=model.classes_, yticklabels=model.classes_)\n",
    "    plt.ylabel('Actual')\n",
    "    plt.xlabel('Predicted')\n",
    "    # plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_4 (Dense)              (None, 128)               89856     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 5)                 645       \n",
      "=================================================================\n",
      "Total params: 90,501\n",
      "Trainable params: 90,501\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 6003 samples, validate on 2002 samples\n",
      "Epoch 1/1000\n",
      "6003/6003 [==============================] - 0s - loss: 1.2974 - acc: 0.5332 - val_loss: 1.1266 - val_acc: 0.5195\n",
      "Epoch 2/1000\n",
      "6003/6003 [==============================] - 0s - loss: 1.0379 - acc: 0.5579 - val_loss: 1.0213 - val_acc: 0.5509\n",
      "Epoch 3/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.9490 - acc: 0.5994 - val_loss: 0.9472 - val_acc: 0.6049\n",
      "Epoch 4/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.8797 - acc: 0.6483 - val_loss: 0.8846 - val_acc: 0.6508\n",
      "Epoch 5/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.8245 - acc: 0.6835 - val_loss: 0.8370 - val_acc: 0.6933\n",
      "Epoch 6/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.7818 - acc: 0.7091 - val_loss: 0.7996 - val_acc: 0.7133\n",
      "Epoch 7/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.7511 - acc: 0.7223 - val_loss: 0.7723 - val_acc: 0.7133\n",
      "Epoch 8/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.7244 - acc: 0.7333 - val_loss: 0.7525 - val_acc: 0.7263\n",
      "Epoch 9/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.7051 - acc: 0.7393 - val_loss: 0.7328 - val_acc: 0.7243\n",
      "Epoch 10/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.6902 - acc: 0.7448 - val_loss: 0.7207 - val_acc: 0.7288\n",
      "Epoch 11/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.6747 - acc: 0.7466 - val_loss: 0.7088 - val_acc: 0.7328\n",
      "Epoch 12/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.6635 - acc: 0.7518 - val_loss: 0.6981 - val_acc: 0.7368\n",
      "Epoch 13/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.6510 - acc: 0.7588 - val_loss: 0.6909 - val_acc: 0.7368\n",
      "Epoch 14/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.6407 - acc: 0.7643 - val_loss: 0.6829 - val_acc: 0.7398\n",
      "Epoch 15/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.6324 - acc: 0.7611 - val_loss: 0.6753 - val_acc: 0.7478\n",
      "Epoch 16/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.6227 - acc: 0.7643 - val_loss: 0.6702 - val_acc: 0.7478\n",
      "Epoch 17/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.6157 - acc: 0.7674 - val_loss: 0.6659 - val_acc: 0.7448\n",
      "Epoch 18/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.6065 - acc: 0.7724 - val_loss: 0.6585 - val_acc: 0.7502\n",
      "Epoch 19/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5985 - acc: 0.7753 - val_loss: 0.6555 - val_acc: 0.7483\n",
      "Epoch 20/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5931 - acc: 0.7764 - val_loss: 0.6487 - val_acc: 0.7493\n",
      "Epoch 21/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5881 - acc: 0.7766 - val_loss: 0.6466 - val_acc: 0.7493\n",
      "Epoch 22/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5810 - acc: 0.7776 - val_loss: 0.6403 - val_acc: 0.7532\n",
      "Epoch 23/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5739 - acc: 0.7806 - val_loss: 0.6368 - val_acc: 0.7542\n",
      "Epoch 24/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5670 - acc: 0.7851 - val_loss: 0.6348 - val_acc: 0.7537\n",
      "Epoch 25/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5629 - acc: 0.7858 - val_loss: 0.6332 - val_acc: 0.7562\n",
      "Epoch 26/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5586 - acc: 0.7879 - val_loss: 0.6284 - val_acc: 0.7562\n",
      "Epoch 27/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5533 - acc: 0.7896 - val_loss: 0.6299 - val_acc: 0.7587\n",
      "Epoch 28/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5471 - acc: 0.7941 - val_loss: 0.6229 - val_acc: 0.7572\n",
      "Epoch 29/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5442 - acc: 0.7894 - val_loss: 0.6212 - val_acc: 0.7552\n",
      "Epoch 30/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5380 - acc: 0.7946 - val_loss: 0.6218 - val_acc: 0.7547\n",
      "Epoch 31/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5353 - acc: 0.7939 - val_loss: 0.6185 - val_acc: 0.7592\n",
      "Epoch 32/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5339 - acc: 0.7958 - val_loss: 0.6162 - val_acc: 0.7587\n",
      "Epoch 33/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5297 - acc: 0.7953 - val_loss: 0.6157 - val_acc: 0.7592\n",
      "Epoch 34/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5244 - acc: 0.7966 - val_loss: 0.6140 - val_acc: 0.7627\n",
      "Epoch 35/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5213 - acc: 0.8011 - val_loss: 0.6125 - val_acc: 0.7562\n",
      "Epoch 36/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5183 - acc: 0.8016 - val_loss: 0.6127 - val_acc: 0.7592\n",
      "Epoch 37/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5150 - acc: 0.8011 - val_loss: 0.6121 - val_acc: 0.7622\n",
      "Epoch 38/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5104 - acc: 0.8014 - val_loss: 0.6098 - val_acc: 0.7567\n",
      "Epoch 39/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5110 - acc: 0.8026 - val_loss: 0.6095 - val_acc: 0.7597\n",
      "Epoch 40/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5079 - acc: 0.8064 - val_loss: 0.6075 - val_acc: 0.7637\n",
      "Epoch 41/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5037 - acc: 0.8058 - val_loss: 0.6072 - val_acc: 0.7612\n",
      "Epoch 42/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.5017 - acc: 0.8074 - val_loss: 0.6077 - val_acc: 0.7647\n",
      "Epoch 43/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4997 - acc: 0.8083 - val_loss: 0.6076 - val_acc: 0.7627\n",
      "Epoch 44/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4958 - acc: 0.8069 - val_loss: 0.6086 - val_acc: 0.7647\n",
      "Epoch 45/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4953 - acc: 0.8103 - val_loss: 0.6043 - val_acc: 0.7627\n",
      "Epoch 46/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4917 - acc: 0.8134 - val_loss: 0.6062 - val_acc: 0.7652\n",
      "Epoch 47/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4900 - acc: 0.8096 - val_loss: 0.6039 - val_acc: 0.7687\n",
      "Epoch 48/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4898 - acc: 0.8088 - val_loss: 0.6054 - val_acc: 0.7662\n",
      "Epoch 49/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4855 - acc: 0.8131 - val_loss: 0.6031 - val_acc: 0.7667\n",
      "Epoch 50/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4858 - acc: 0.8161 - val_loss: 0.6044 - val_acc: 0.7662\n",
      "Epoch 51/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4822 - acc: 0.8116 - val_loss: 0.6034 - val_acc: 0.7687\n",
      "Epoch 52/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4811 - acc: 0.8134 - val_loss: 0.6018 - val_acc: 0.7672\n",
      "Epoch 53/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4805 - acc: 0.8133 - val_loss: 0.6053 - val_acc: 0.7697\n",
      "Epoch 54/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4757 - acc: 0.8183 - val_loss: 0.6075 - val_acc: 0.7632\n",
      "Epoch 55/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4767 - acc: 0.8181 - val_loss: 0.6063 - val_acc: 0.7682\n",
      "Epoch 56/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4754 - acc: 0.8171 - val_loss: 0.6015 - val_acc: 0.7692\n",
      "Epoch 57/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4706 - acc: 0.8198 - val_loss: 0.6040 - val_acc: 0.7682\n",
      "Epoch 58/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4697 - acc: 0.8181 - val_loss: 0.6096 - val_acc: 0.7612\n",
      "Epoch 59/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6003/6003 [==============================] - 0s - loss: 0.4666 - acc: 0.8213 - val_loss: 0.6041 - val_acc: 0.7702\n",
      "Epoch 60/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4670 - acc: 0.8204 - val_loss: 0.6093 - val_acc: 0.7617\n",
      "Epoch 61/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4643 - acc: 0.8214 - val_loss: 0.6045 - val_acc: 0.7732\n",
      "Epoch 62/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4623 - acc: 0.8204 - val_loss: 0.6027 - val_acc: 0.7702\n",
      "Epoch 63/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4600 - acc: 0.8263 - val_loss: 0.6030 - val_acc: 0.7717\n",
      "Epoch 64/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4590 - acc: 0.8254 - val_loss: 0.6050 - val_acc: 0.7692\n",
      "Epoch 65/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4576 - acc: 0.8241 - val_loss: 0.6046 - val_acc: 0.7682\n",
      "Epoch 66/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4582 - acc: 0.8219 - val_loss: 0.6054 - val_acc: 0.7722\n",
      "Epoch 67/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4555 - acc: 0.8263 - val_loss: 0.6049 - val_acc: 0.7687\n",
      "Epoch 68/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4517 - acc: 0.8248 - val_loss: 0.6076 - val_acc: 0.7647\n",
      "Epoch 69/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4489 - acc: 0.8249 - val_loss: 0.6053 - val_acc: 0.7712\n",
      "Epoch 70/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4497 - acc: 0.8256 - val_loss: 0.6050 - val_acc: 0.7727\n",
      "Epoch 71/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4466 - acc: 0.8298 - val_loss: 0.6062 - val_acc: 0.7702\n",
      "Epoch 72/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4458 - acc: 0.8281 - val_loss: 0.6045 - val_acc: 0.7722\n",
      "Epoch 73/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4434 - acc: 0.8271 - val_loss: 0.6074 - val_acc: 0.7672\n",
      "Epoch 74/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4448 - acc: 0.8304 - val_loss: 0.6122 - val_acc: 0.7632\n",
      "Epoch 75/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4419 - acc: 0.8298 - val_loss: 0.6059 - val_acc: 0.7742\n",
      "Epoch 76/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4378 - acc: 0.8354 - val_loss: 0.6052 - val_acc: 0.7732\n",
      "Epoch 77/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4373 - acc: 0.8318 - val_loss: 0.6049 - val_acc: 0.7712\n",
      "Epoch 78/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4388 - acc: 0.8314 - val_loss: 0.6101 - val_acc: 0.7682\n",
      "Epoch 79/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4356 - acc: 0.8309 - val_loss: 0.6053 - val_acc: 0.7757\n",
      "Epoch 80/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4369 - acc: 0.8339 - val_loss: 0.6061 - val_acc: 0.7737\n",
      "Epoch 81/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4323 - acc: 0.8311 - val_loss: 0.6084 - val_acc: 0.7732\n",
      "Epoch 82/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4308 - acc: 0.8359 - val_loss: 0.6093 - val_acc: 0.7687\n",
      "Epoch 83/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4332 - acc: 0.8326 - val_loss: 0.6062 - val_acc: 0.7757\n",
      "Epoch 84/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4337 - acc: 0.8329 - val_loss: 0.6072 - val_acc: 0.7747\n",
      "Epoch 85/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4302 - acc: 0.8356 - val_loss: 0.6104 - val_acc: 0.7702\n",
      "Epoch 86/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4294 - acc: 0.8354 - val_loss: 0.6098 - val_acc: 0.7742\n",
      "Epoch 87/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4265 - acc: 0.8357 - val_loss: 0.6123 - val_acc: 0.7732\n",
      "Epoch 88/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4239 - acc: 0.8374 - val_loss: 0.6077 - val_acc: 0.7772\n",
      "Epoch 89/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4224 - acc: 0.8396 - val_loss: 0.6082 - val_acc: 0.7747\n",
      "Epoch 90/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4235 - acc: 0.8351 - val_loss: 0.6120 - val_acc: 0.7737\n",
      "Epoch 91/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4225 - acc: 0.8367 - val_loss: 0.6105 - val_acc: 0.7742\n",
      "Epoch 92/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4214 - acc: 0.8394 - val_loss: 0.6106 - val_acc: 0.7767\n",
      "Epoch 93/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4185 - acc: 0.8411 - val_loss: 0.6114 - val_acc: 0.7762\n",
      "Epoch 94/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4184 - acc: 0.8391 - val_loss: 0.6149 - val_acc: 0.7697\n",
      "Epoch 95/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4188 - acc: 0.8379 - val_loss: 0.6117 - val_acc: 0.7757\n",
      "Epoch 96/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4181 - acc: 0.8431 - val_loss: 0.6139 - val_acc: 0.7757\n",
      "Epoch 97/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4118 - acc: 0.8437 - val_loss: 0.6127 - val_acc: 0.7717\n",
      "Epoch 98/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4105 - acc: 0.8429 - val_loss: 0.6162 - val_acc: 0.7772\n",
      "Epoch 99/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4127 - acc: 0.8429 - val_loss: 0.6156 - val_acc: 0.7757\n",
      "Epoch 100/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4102 - acc: 0.8414 - val_loss: 0.6146 - val_acc: 0.7772\n",
      "Epoch 101/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4085 - acc: 0.8487 - val_loss: 0.6177 - val_acc: 0.7752\n",
      "Epoch 102/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4109 - acc: 0.8426 - val_loss: 0.6151 - val_acc: 0.7767\n",
      "Epoch 103/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4076 - acc: 0.8444 - val_loss: 0.6152 - val_acc: 0.7762\n",
      "Epoch 104/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4072 - acc: 0.8446 - val_loss: 0.6216 - val_acc: 0.7712\n",
      "Epoch 105/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4031 - acc: 0.8447 - val_loss: 0.6165 - val_acc: 0.7782\n",
      "Epoch 106/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4041 - acc: 0.8499 - val_loss: 0.6212 - val_acc: 0.7742\n",
      "Epoch 107/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4050 - acc: 0.8469 - val_loss: 0.6173 - val_acc: 0.7777\n",
      "Epoch 108/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4007 - acc: 0.8481 - val_loss: 0.6173 - val_acc: 0.7742\n",
      "Epoch 109/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4008 - acc: 0.8507 - val_loss: 0.6185 - val_acc: 0.7762\n",
      "Epoch 110/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.4014 - acc: 0.8487 - val_loss: 0.6240 - val_acc: 0.7697\n",
      "Epoch 111/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.3957 - acc: 0.8479 - val_loss: 0.6229 - val_acc: 0.7692\n",
      "Epoch 112/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.3943 - acc: 0.8484 - val_loss: 0.6208 - val_acc: 0.7777\n",
      "Epoch 113/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.3971 - acc: 0.8509 - val_loss: 0.6214 - val_acc: 0.7737\n",
      "Epoch 114/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.3959 - acc: 0.8511 - val_loss: 0.6219 - val_acc: 0.7742\n",
      "Epoch 115/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.3919 - acc: 0.8507 - val_loss: 0.6232 - val_acc: 0.7752\n",
      "Epoch 116/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.3946 - acc: 0.8511 - val_loss: 0.6290 - val_acc: 0.7707\n",
      "Epoch 117/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.3911 - acc: 0.8534 - val_loss: 0.6246 - val_acc: 0.7747\n",
      "Epoch 118/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.3936 - acc: 0.8502 - val_loss: 0.6231 - val_acc: 0.7747\n",
      "Epoch 119/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.3910 - acc: 0.8512 - val_loss: 0.6228 - val_acc: 0.7757\n",
      "Epoch 120/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.3889 - acc: 0.8517 - val_loss: 0.6230 - val_acc: 0.7747\n",
      "Epoch 121/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.3852 - acc: 0.8569 - val_loss: 0.6299 - val_acc: 0.7727\n",
      "Epoch 122/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6003/6003 [==============================] - 0s - loss: 0.3862 - acc: 0.8577 - val_loss: 0.6289 - val_acc: 0.7752\n",
      "Epoch 123/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.3880 - acc: 0.8506 - val_loss: 0.6272 - val_acc: 0.7742\n",
      "Epoch 124/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.3839 - acc: 0.8546 - val_loss: 0.6328 - val_acc: 0.7672\n",
      "Epoch 125/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.3866 - acc: 0.8549 - val_loss: 0.6363 - val_acc: 0.7682\n",
      "Epoch 126/1000\n",
      "6003/6003 [==============================] - 0s - loss: 0.3853 - acc: 0.8556 - val_loss: 0.6301 - val_acc: 0.7777\n",
      "Test loss: 0.630098594831\n",
      "Test accuracy: 0.777722277544\n"
     ]
    }
   ],
   "source": [
    "# Keras MLP\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.callbacks import EarlyStopping\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from keras.utils import np_utils\n",
    "\n",
    "batch_size = 128\n",
    "num_classes = len(classes)\n",
    "epochs = 1000\n",
    "\n",
    "\n",
    "# encode class values as integers\n",
    "encoder = LabelEncoder()\n",
    "encoder.fit(y_train)\n",
    "encoded_y_train = encoder.transform(y_train)\n",
    "encoded_y_test = encoder.transform(y_test)\n",
    "# convert integers to dummy variables (i.e. one hot encoded)\n",
    "dummy_y_train = np_utils.to_categorical(encoded_y_train)\n",
    "dummy_y_test = np_utils.to_categorical(encoded_y_test)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(128, activation='relu', input_shape=(X_train.shape[1],)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.summary()\n",
    "\n",
    "# early stopping criterion\n",
    "early_stopping = EarlyStopping(monitor='val_acc', patience=20)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=RMSprop(),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(X_train, dummy_y_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=epochs,\n",
    "                    verbose=1,\n",
    "                    validation_data=(X_test, dummy_y_test),\n",
    "                    callbacks=[early_stopping])\n",
    "score = model.evaluate(X_test, dummy_y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 64, 294)           86436     \n",
      "_________________________________________________________________\n",
      "gru_1 (GRU)                  (None, 32)                31392     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 16)                528       \n",
      "=================================================================\n",
      "Total params: 118,356\n",
      "Trainable params: 31,920\n",
      "Non-trainable params: 86,436\n",
      "_________________________________________________________________\n",
      "Train on 3003 samples, validate on 1001 samples\n",
      "Epoch 1/1000\n",
      "3003/3003 [==============================] - 3s - loss: 1.8205 - acc: 0.4276 - val_loss: 1.1439 - val_acc: 0.5075\n",
      "Epoch 2/1000\n",
      "3003/3003 [==============================] - 3s - loss: 1.1391 - acc: 0.5135 - val_loss: 1.1232 - val_acc: 0.5075\n",
      "Epoch 3/1000\n",
      "3003/3003 [==============================] - 3s - loss: 1.1234 - acc: 0.5198 - val_loss: 1.1061 - val_acc: 0.5155\n",
      "Epoch 4/1000\n",
      "3003/3003 [==============================] - 5s - loss: 1.1079 - acc: 0.5405 - val_loss: 1.0871 - val_acc: 0.5524\n",
      "Epoch 5/1000\n",
      "3003/3003 [==============================] - 4s - loss: 1.0878 - acc: 0.5541 - val_loss: 1.0643 - val_acc: 0.5684\n",
      "Epoch 6/1000\n",
      "3003/3003 [==============================] - 4s - loss: 1.0627 - acc: 0.5778 - val_loss: 1.0436 - val_acc: 0.5744\n",
      "Epoch 7/1000\n",
      "3003/3003 [==============================] - 4s - loss: 1.0364 - acc: 0.5818 - val_loss: 1.0249 - val_acc: 0.5914\n",
      "Epoch 8/1000\n",
      "3003/3003 [==============================] - 4s - loss: 1.0035 - acc: 0.6021 - val_loss: 1.0029 - val_acc: 0.5904\n",
      "Epoch 9/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.9791 - acc: 0.6277 - val_loss: 0.9904 - val_acc: 0.6004\n",
      "Epoch 10/1000\n",
      "3003/3003 [==============================] - 5s - loss: 0.9563 - acc: 0.6347 - val_loss: 0.9787 - val_acc: 0.6044\n",
      "Epoch 11/1000\n",
      "3003/3003 [==============================] - 5s - loss: 0.9397 - acc: 0.6424 - val_loss: 0.9834 - val_acc: 0.6094\n",
      "Epoch 12/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.9240 - acc: 0.6507 - val_loss: 0.9586 - val_acc: 0.6204\n",
      "Epoch 13/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.9127 - acc: 0.6573 - val_loss: 0.9566 - val_acc: 0.6264\n",
      "Epoch 14/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.8880 - acc: 0.6673 - val_loss: 0.9487 - val_acc: 0.6294\n",
      "Epoch 15/1000\n",
      "3003/3003 [==============================] - 5s - loss: 0.8759 - acc: 0.6687 - val_loss: 0.9512 - val_acc: 0.6314\n",
      "Epoch 16/1000\n",
      "3003/3003 [==============================] - 5s - loss: 0.8572 - acc: 0.6773 - val_loss: 0.9450 - val_acc: 0.6274\n",
      "Epoch 17/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.8500 - acc: 0.6820 - val_loss: 0.9372 - val_acc: 0.6354\n",
      "Epoch 18/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.8342 - acc: 0.6923 - val_loss: 0.9333 - val_acc: 0.6454\n",
      "Epoch 19/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.8184 - acc: 0.6936 - val_loss: 0.9434 - val_acc: 0.6384\n",
      "Epoch 20/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.8134 - acc: 0.7003 - val_loss: 0.9419 - val_acc: 0.6214\n",
      "Epoch 21/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.7927 - acc: 0.7070 - val_loss: 0.9415 - val_acc: 0.6464\n",
      "Epoch 22/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.7790 - acc: 0.7106 - val_loss: 0.9557 - val_acc: 0.6494\n",
      "Epoch 23/1000\n",
      "3003/3003 [==============================] - 3s - loss: 0.7683 - acc: 0.7169 - val_loss: 0.9377 - val_acc: 0.6314\n",
      "Epoch 24/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.7520 - acc: 0.7189 - val_loss: 0.9358 - val_acc: 0.6344\n",
      "Epoch 25/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.7413 - acc: 0.7219 - val_loss: 0.9399 - val_acc: 0.6414\n",
      "Epoch 26/1000\n",
      "3003/3003 [==============================] - 3s - loss: 0.7305 - acc: 0.7253 - val_loss: 0.9465 - val_acc: 0.6334\n",
      "Epoch 27/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.7255 - acc: 0.7346 - val_loss: 0.9390 - val_acc: 0.6444\n",
      "Epoch 28/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.7040 - acc: 0.7393 - val_loss: 0.9566 - val_acc: 0.6344\n",
      "Epoch 29/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.6957 - acc: 0.7436 - val_loss: 0.9665 - val_acc: 0.6264\n",
      "Epoch 30/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.6865 - acc: 0.7486 - val_loss: 0.9560 - val_acc: 0.6354\n",
      "Epoch 31/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.6702 - acc: 0.7586 - val_loss: 0.9688 - val_acc: 0.6364\n",
      "Epoch 32/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.6605 - acc: 0.7572 - val_loss: 0.9812 - val_acc: 0.6354\n",
      "Epoch 33/1000\n",
      "3003/3003 [==============================] - 3s - loss: 0.6568 - acc: 0.7579 - val_loss: 0.9795 - val_acc: 0.6364\n",
      "Epoch 34/1000\n",
      "3003/3003 [==============================] - 3s - loss: 0.6361 - acc: 0.7716 - val_loss: 1.0014 - val_acc: 0.6354\n",
      "Epoch 35/1000\n",
      "3003/3003 [==============================] - 3s - loss: 0.6268 - acc: 0.7756 - val_loss: 1.0121 - val_acc: 0.6334\n",
      "Epoch 36/1000\n",
      "3003/3003 [==============================] - 3s - loss: 0.6162 - acc: 0.7819 - val_loss: 1.0134 - val_acc: 0.6254\n",
      "Epoch 37/1000\n",
      "3003/3003 [==============================] - 3s - loss: 0.6025 - acc: 0.7822 - val_loss: 1.0243 - val_acc: 0.6324\n",
      "Epoch 38/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.5877 - acc: 0.7922 - val_loss: 1.0566 - val_acc: 0.6124\n",
      "Epoch 39/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.5802 - acc: 0.7939 - val_loss: 1.0556 - val_acc: 0.6224\n",
      "Epoch 40/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.5736 - acc: 0.7982 - val_loss: 1.0675 - val_acc: 0.6144\n",
      "Epoch 41/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.5552 - acc: 0.8049 - val_loss: 1.0784 - val_acc: 0.6284\n",
      "Epoch 42/1000\n",
      "3003/3003 [==============================] - 5s - loss: 0.5436 - acc: 0.8125 - val_loss: 1.1219 - val_acc: 0.6114\n",
      "Epoch 43/1000\n",
      "3003/3003 [==============================] - 4s - loss: 0.5324 - acc: 0.8182 - val_loss: 1.1466 - val_acc: 0.5894\n",
      "Test loss: 1.146633152\n",
      "Test accuracy: 0.58941058944\n"
     ]
    }
   ],
   "source": [
    "# Keras GRU\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import *\n",
    "n_symbols = len(tokenizer_char.word_index) + 1\n",
    "seq_length = 64\n",
    "embedding_size = 20\n",
    "batch_size = 32\n",
    "\n",
    "char_int_encoded_train = keras.preprocessing.sequence.pad_sequences(\n",
    "            char_int_encoded_train, maxlen=seq_length, dtype='int', truncating='pre')\n",
    "char_int_encoded_test = keras.preprocessing.sequence.pad_sequences(\n",
    "            char_int_encoded_test, maxlen=seq_length, dtype='int', truncating='pre')\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(input_dim=n_symbols,\n",
    "                          output_dim=n_symbols,\n",
    "                          input_length=seq_length,\n",
    "                          mask_zero=False, embeddings_initializer='identity', trainable=False))\n",
    "model.add(GRU(32, dropout=0.0, recurrent_dropout=0.0))\n",
    "model.add(Dense(16, activation='softmax'))\n",
    "model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "model.summary()\n",
    "\n",
    "history = model.fit(char_int_encoded_train, encoded_y_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=epochs,\n",
    "                    verbose=1,\n",
    "                    validation_data=(char_int_encoded_test, encoded_y_test),\n",
    "                    callbacks=[early_stopping])\n",
    "score = model.evaluate(char_int_encoded_test, encoded_y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}