Experiment_Framework.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiments for Stance Classification\n",
    "Author: Xiaochi (George) Li: [Github](https://xc-li.github.io/)  \n",
    "Capstone Project for George Washington University Data Science Program Spring 2019  \n",
    "Completed at [FiscalNote](https://www.fiscalnote.com/) as Data Scientist Intern\n",
    "\n",
    "**NOTE:** The data is provided by FiscalNote and not public available. This notebook is just for demonstration purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\tqdm\\autonotebook\\__init__.py:14: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  \" (e.g. in jupyter console)\", TqdmExperimentalWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from corpus_loader import corpus_loader, untagged_corpus_loader\n",
    "import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "tqdm = tqdm.tqdm_notebook\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 1\n",
    "%aimport plot_function\n",
    "summary_bar = plot_function.summary_bar\n",
    "import warnings \n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 200 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "try:\n",
    "    df = pd.read_pickle('./tagged_df.pickle',compression='zip')\n",
    "except:\n",
    "    df = corpus_loader(parser='bs')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2077,)\n"
     ]
    }
   ],
   "source": [
    "X = df['text']\n",
    "y = df['support']\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess\n",
    "Three methods:\n",
    "\n",
    "1. Do nothing\n",
    "2. Remove stop words (numbers and punctuation)\n",
    "3. Remove stop words and lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use spcay to lemmatization\n",
    "import spacy\n",
    "spacy = spacy.load('en_core_web_sm', disable=['parser', 'ner'])\n",
    "\n",
    "def spacy_lemma(speech):\n",
    "    doc = spacy(speech)\n",
    "    return \" \".join([token.lemma_ for token in doc])\n",
    "\n",
    "# remove numbers and punctuation\n",
    "import re\n",
    "import string\n",
    "re_number_punctuation = re.compile('['+string.punctuation+']|\\d')\n",
    "\n",
    "def remove_number_punctuation(text):\n",
    "    return re.sub(re_number_punctuation, '', text.lower())\n",
    "\n",
    "# remove stopwords\n",
    "from nltk.corpus import stopwords\n",
    "from nltk import word_tokenize\n",
    "nltk_stop_words = set(stopwords.words('english'))\n",
    "\n",
    "def remove_stopwords(text, cutoff=10000):\n",
    "    tokens = word_tokenize(remove_number_punctuation(text))\n",
    "#     tokens = [token.strip() for token in tokens]  # no need for this line\n",
    "    filtered_tokens = [token for token in tokens if token not in nltk_stop_words]\n",
    "    filtered_text = ' '.join(filtered_tokens[:cutoff])\n",
    "    return filtered_text\n",
    "\n",
    "def word_count_helper(text):\n",
    "    tokens = word_tokenize(remove_number_punctuation(text))\n",
    "    filtered_tokens = [token for token in tokens if token not in nltk_stop_words]\n",
    "    return len(filtered_tokens)\n",
    "\n",
    "def word_count(text_array):\n",
    "    count = text_array.apply(word_count_helper)\n",
    "    _ = plt.hist(count, bins=100)\n",
    "    return count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 9.43 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "X_remove_stopword = X.apply(remove_stopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 55.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "X_lemmatized = X_remove_stopword.apply(spacy_lemma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 10.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "X_500_remove_stopword = X.apply(remove_stopwords, cutoff=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 36.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "X_500_lemmatized = X_500_remove_stopword.apply(spacy_lemma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2077.000000\n",
       "mean      260.343284\n",
       "std       164.572041\n",
       "min         9.000000\n",
       "25%       108.000000\n",
       "50%       207.000000\n",
       "75%       439.000000\n",
       "max       500.000000\n",
       "Name: text, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/l+T3F7aBPweeYb2f55N+o2LINzOuY/DHTJ4H/mbS/Rnx2L4CvAz8D4Oz+T4Ga5GHgKPt/rzWNgyuQHoe+DEwM+n+r2Hcf8LgJenTwFPtdl3vYwf+CHiyjfsZ4G9b/XuA7wNzwL8AZ7f6c1p5ru1/z6THMIJjcBXw0FYYdxvfj9rt2YX8Wu/nuZ+MlaTObYalG0nSGhj0ktQ5g16SOmfQS1LnDHpJ6pxBL0mdM+glqXMGvSR17n8BgnyC/nAm9WgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "word_count(X_500_remove_stopword)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1634, 443]\n"
     ]
    },
    {
     "data": {
      "image/png": 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9KlvIhZR8ZxGRNVzb/9a9+rzTJXDl8hdgmnWIVqp0IdfTuI986kIncWQN/YzpmtO/h24SKZfTqTUqvSVXpQsZoJ7Gi6ng5TEyehf1nrCNdQbZYN+j1rjGOkSrVb6QAepp/DXgR9Y5JCz3+J13XeHHPmidQ17RQuDT1iHaoSMKuXA6cKd1CAnLr/sOrOTZ+gp5Cji67DuBbKiOKeR6Gq8EjgX0F1Be9vXe43bxnn7rHLJWK4BjqDXq1kHapWMKGV7eXeRY8v/QIvyJSds+y6ba3ilMp1Jr3G4dop06qpAB6mk8l3z6QgSAK3oP11174TmfWuNn1iHazXnvrTOYiJLsC8C/WucQe5vw0gv39/ztWOfQzSJhuJJa40PWISx03Ah5QD2NP4v24hNgGRM2e8Rvq7VPwnAb8FHrEFY6tpALnwJ+YB1C7H2r9/1jrTMItwLvqcp2TCPRsVMWA6IkGwNcAZxonUXsdNHX+2DPyc+PcX4r6ywd6r+B91JrvGQdxFKnj5Cpp3E/cDLwY+ssYqePru67/K6LrHN0qBuAuNPLGFTIwMulfAq6m6+jXdw7dZJ1hg50LfmNH9rlBxXyy4pS/ghwsXEUMXJH/x67r/Ldj1jn6CC/Bo6l1tDiXwUV8iD1NO6vp/HZwFmgu7c60XX9+z9mnaFDXAUcT62hTSQGUSGvRT2NvwkcA7xonUXa6+Le43f0ns4+0916FwIfoNZYbR0kNB1/lcX6REk2GbgG0DKNHeTentPvn+hefLN1jgpaDZxBraFLTddBI+T1qKfxfOBtwGLrLNI+P+077DnrDBX0NPBulfH6aYS8AaIkm0h+Wdx7rbNI601k2fMLev5uvHP0WGepiLvJT95pfv4VaIS8Aepp3ADeB3yG/McuqbAGm2z+uJ+0wDpHRfwIOGgkZeyc8865rw769T8452rNDFe87zlDfj2n2cfYUCrkDVRPY19P44uAg4A/WOeR1pred7R+dBydFcCZ1Bonj+JW6JXAcc65Vt89uUYhe+8PbPHx1kmFPEz1NL4L2Bf4hXUWaZ1f9L1j337P89Y5SmoesC+1xrdG+T69wAzWsn2Tc26Sc+6Xzrm7isdBg56/wTl3t3PuUufcowOF7pz7lXNuvnNukXPu74rnUmC8c26Bc+4nxXPLiq8znXNHDTrm5c65qc65Lufcl4vj3uecO2OUn/NlKuQRqKdxo57GHyDfkrxjF0KpstV0j7vX76SF64enFzgfOIBaY0mT3vNbwInOuYlDnv86cLH3fn9gKnBZ8fzngZu89/sCVwOvG/SaU733k4H9gLOcc1t67xNgufd+b+/90PVsrgQ+COCcGwe8k/zOwtOARnHs/YHTnXM7NOPDqpBHoZ7G04G3AFq6sYK+1jt1C+sMJfIA+Vzx56k1epv1pt77F8gX/zpryG+9C7jEObeA/I6/zZxzmwJvJy9SvPfXA4OvmDnLOXcvcAfwWmDnVzj8dcBhzrke4EjgFu/9cuBw4OTi2HcCW27Ae20QFfIo1dP4fvJ/Jf8R6PjFUapkdv9eb1rtux63zhE4Tz6K3YdaY26LjvE18lHpxoOeGwMcUIxs9/beb++9Xwq4tb2Bc+4d5CV+gPd+L/JB1EbrO6j3fgVwM3AE+Uj5yoG3Az4x6Ng7eO9njfjTDflQMkr1NO6rp/FXgDcBTfkPIyFw7rf9+z5snSJgjwNHUGuc2cqV2rz3zwI/Jy/lAbOAMwd+4Zzbu/j2d8AHiucOBwZ+ypkIPOe9f8k5txv5/QUDVjvn1rUe9pXkC+YfDPymeO43wLSB1zjndnHObbyO1w+LCrmJ6mn8SD2NjwA+DPyvdR4ZvYt6j3+9dYYArQC+COxGrXFDm475VWDw1RZnAfsVJ9UWAx8rnj8PONw5dzf5NMOfgaXA9UC3c+4+4Avk0xYDZgD3DZzUG2IWMAW40Xs/sO7GZeQ3i93tnFsIXAp0N+Ez6saQVomSbEvyP0SnWGeR0bm/57RFm7rle1jnCMSvgLOpNYJcFa+Y7+3z3vc65w4AvuO93/uVXhcKFXKLRUk2BbgImGydRUbmc91X3HJq9/VTrHMYWwx8klrjRusg6+Oc25l8emMMsAr4uPf+LttUG06F3AZRkjnyLaK+xJqX4UgJbEnj6Xk90zZ3rjk/lpZMA6gBlzTz6glZOxVyG0VJthH53FfCX082SAnc3nPmXdu6Z/e3ztFGq4HvA5+l1tD5kDZRIRuIkmxz8svkPsmal/JIoE7tum7O58b+yOyW2jZaRb4T+wXUGo9ah+k0KmRDUZK9mny0fDoq5qD1sGrF73s+sso5NrPO0iIrya8eSKk1/mgdplOpkAMQJdmrgI+TT2doo81AZeP+5Xd7jHn07dY5mmw58F3gQmqNJ6zDdDoVckCiJBtPvtHqZ4CdbNPIUIePueueGeMu3sc6R5O8BEwHvkyt8RfrMJJTIQcoSrIu8gVT/gldLhcQ7x/qOenP3a5/O+sko/AweRF/n1rjWeswsiYVcuCiJDuUfFW59wPjjON0vO+P/febD+ta8A7rHMPUR7435LeBG6g19Jc+UCrkkoiSbCvgZPL7+Xc3jtOx9nCPPJT1nPsG6xwb6C/kJ+pmUGtokaQSUCGXUJRkBwJ/S76Iiq7OaLPFPR/9nwlu5a7WOdbBA7OB7wBXU2toy7ESUSGXWJRkmwIfAk4F3mocp2N8qfuy2Sd233SIdY4h5gEzgZ9rM9HyUiFXRJRkryM/ETgVOJB1rAsro7cNzz55e8+ZWzlHl3GU+8hLeCa1hpYJrQAVcgVFSbYtcCxwPPnSgdbFUTlze6bN39o1LK6AWcJfS/j3BseXFlIhV1yUZJPIr9CYChwK9NgmqoZpXf952z+PnXlQGw61CriNfF3ejFrj/jYcU4yokDtIcePJIeRb0hyOrtYYsQmseHFRz6k415KTqkvIC3gWMJta48UWHEMCpELuYFGSbQ8cRj5yPhSITAOVzKxx/zhnlzF/asaCQ88Av2WghHWJWsdSIcvLoiTbgXzO+S3FY090M8o6HT1mzrxvjrtkv2G+rA9YBMwl37F4LrCQWqO/2fmkfFTIsk5Rko0D9iLfVXt/8pLeDe3FCMAY+vse7DnpmS7nt17P/+xR1izf+a3cEFTKTYUswxIl2Sbk62vsA+xKXtC7Atta5rLyk7Ffmn1Q16JDyBfr+T35VkdLyC9Jm0ut8ZRlPikXFbI0RZRkm5EX8+CS3o181brxhtGaqReoAw8CDwEPThlz75Irxl34EPCo1oiQ0VIhS8tFSbYFsN2gx/ZDfr0dsA0w1ihiH/Ak8AT5tvGDvw7+/ql6GmuuV1pGhSzBiJJsY2DikMcmwATyNTsGHmvbbHRtdyb2AksHPZat4/sX62msvwhiToUsIhIInS0XEQmECllEJBAqZBGRQKiQRUQCoUIWEQmECllEJBAqZBGRQKiQRUQCoUIWEQmECllEJBAqZBGRQKiQRUQCoUIWEQmECllEJBAqZBGRQKiQRUQCoUIWEQmECllEJBAqZBGRQKiQRUQCoUIWEQmECllEJBAqZBGRQKiQRUQCoUIWEQmECllEJBAqZBGRQKiQRUQCoUIWEQmECllEJBAqZBGRQPwf3XPr2CRrpdkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie([y[y==1].count(), y[y==-1].count()], labels=['Positive', 'Negative'])\n",
    "print([y[y==1].count(), y[y==-1].count()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment with Pipeline\n",
    "\n",
    "Preprocessing\n",
    "1. Do nothing --> None\n",
    "2. Remove stop words (numbers and punctuation) -->RS\n",
    "3. Remove stop words and lemmatization -->Lema\n",
    "\n",
    "Vectorization:\n",
    "\n",
    "1. Count Vectorization -->C\n",
    "2. Tf-idf Vectorization -->T\n",
    "\n",
    "Balancing the data:\n",
    "\n",
    "1. Do nothing -->N\n",
    "2. Smote -->S\n",
    "\n",
    "\\begin{equation*}\n",
    "N_{experiments} = N_{Preprocessing} \\times N_{Vectorization} \\times N_{Balancing} \\times N_{Models}\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the experiment pipeline to test all combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "%aimport Pipeline_V1\n",
    "Pipeline = Pipeline_V1.Pipeline\n",
    "count = CountVectorizer()\n",
    "tfidf = TfidfVectorizer()\n",
    "smote = SMOTE(random_state=42)\n",
    "\n",
    "original_logistic_regression = LogisticRegression()\n",
    "%aimport plot_function\n",
    "summary_bar = plot_function.summary_bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def experiment_pipeline(model,X,y,X_remove_stopword,X_lemmatized):\n",
    "    experiment_in = {\n",
    "        # Do nothing\n",
    "        'None_Count_None': Pipeline(X, y, vectorizer=count, model=model, sampler=None),\n",
    "        'None_Count_Smote': Pipeline(X, y, vectorizer=count, model=model, sampler=smote),\n",
    "        'None_Tfidf_None': Pipeline(X, y, vectorizer=tfidf, model=model, sampler=None),\n",
    "        'None_Tfidf_Smote': Pipeline(X, y, vectorizer=tfidf, model=model, sampler=smote),\n",
    "        # remove stop word\n",
    "        'RS_Count_None': Pipeline(X_remove_stopword, y, vectorizer=count, model=model, sampler=None),\n",
    "        'RS_Count_Smote': Pipeline(X_remove_stopword, y, vectorizer=count, model=model, sampler=smote),\n",
    "        'RS_Tfidf_None': Pipeline(X_remove_stopword, y, vectorizer=tfidf, model=model, sampler=None),\n",
    "        'RS_Tfidf_Smote': Pipeline(X_remove_stopword, y, vectorizer=tfidf, model=model, sampler=smote),\n",
    "        # Remove stop words and Lemmatization\n",
    "        'Lema_Count_None': Pipeline(X_lemmatized, y, vectorizer=count, model=model, sampler=None),\n",
    "        'Lema_Count_Smote': Pipeline(X_lemmatized, y, vectorizer=count, model=model, sampler=smote),\n",
    "        'Lema_Tfidf_None': Pipeline(X_lemmatized, y, vectorizer=tfidf, model=model, sampler=None),\n",
    "        'Lema_Tfidf_Smote': Pipeline(X_lemmatized, y, vectorizer=tfidf, model=model, sampler=smote)\n",
    "    }\n",
    "    \n",
    "    experiment_out = []\n",
    "    for key, pipeline in tqdm(experiment_in.items()):\n",
    "        report = pipeline.exec(silent=True)\n",
    "        experiment_out.append([key, report['test_f1_neg'], report['test_f1_pos'], report['train_f1_neg'], report['train_f1_pos']])\n",
    "    \n",
    "    experiment_table = pd.DataFrame(experiment_out)\n",
    "    experiment_table.columns = ['Combination', 'test_neg', 'test_pos', 'train_neg', 'train_pos']\n",
    "    return experiment_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb  # version 0.80\n",
    "xgb_original = xgb.XGBClassifier()\n",
    "xgb_after_grid_search = xgb.XGBClassifier(n_estimators=550,\n",
    "                                          max_depth=4,\n",
    "                                          gamma=0.1,\n",
    "                                          learning_rate=0.1,\n",
    "                                          subsample=0.7,\n",
    "                                          colsample_bytree=0.7,\n",
    "                                          scale_pos_weight=0.25,\n",
    "                                          #max_delta_step=4,\n",
    "                                          scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1650837bf6534b4194e064b6a1b6a8a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=12), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb_500_cutoff = experiment_pipeline(xgb_after_grid_search, X, y, X_500_remove_stopword, X_500_lemmatized)\n",
    "summary_bar(xgb_500_cutoff, 'XGBoost Classifier with 500 word cutoff')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Combination</th>\n",
       "      <th>test_neg</th>\n",
       "      <th>test_pos</th>\n",
       "      <th>train_neg</th>\n",
       "      <th>train_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None_Count_None</td>\n",
       "      <td>0.721311</td>\n",
       "      <td>0.932271</td>\n",
       "      <td>0.971787</td>\n",
       "      <td>0.992063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None_Count_Smote</td>\n",
       "      <td>0.752941</td>\n",
       "      <td>0.936556</td>\n",
       "      <td>0.992188</td>\n",
       "      <td>0.992063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None_Tfidf_None</td>\n",
       "      <td>0.750988</td>\n",
       "      <td>0.936683</td>\n",
       "      <td>0.971787</td>\n",
       "      <td>0.992063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None_Tfidf_Smote</td>\n",
       "      <td>0.743295</td>\n",
       "      <td>0.932118</td>\n",
       "      <td>0.992188</td>\n",
       "      <td>0.992063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RS_Count_None</td>\n",
       "      <td>0.748092</td>\n",
       "      <td>0.933063</td>\n",
       "      <td>0.971787</td>\n",
       "      <td>0.992063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RS_Count_Smote</td>\n",
       "      <td>0.766284</td>\n",
       "      <td>0.938197</td>\n",
       "      <td>0.992188</td>\n",
       "      <td>0.992063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RS_Tfidf_None</td>\n",
       "      <td>0.758621</td>\n",
       "      <td>0.936170</td>\n",
       "      <td>0.971787</td>\n",
       "      <td>0.992063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>RS_Tfidf_Smote</td>\n",
       "      <td>0.771536</td>\n",
       "      <td>0.937819</td>\n",
       "      <td>0.992188</td>\n",
       "      <td>0.992063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lema_Count_None</td>\n",
       "      <td>0.758621</td>\n",
       "      <td>0.936170</td>\n",
       "      <td>0.971787</td>\n",
       "      <td>0.992063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lema_Count_Smote</td>\n",
       "      <td>0.787879</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.992188</td>\n",
       "      <td>0.992063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Lema_Tfidf_None</td>\n",
       "      <td>0.778626</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.971787</td>\n",
       "      <td>0.992063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lema_Tfidf_Smote</td>\n",
       "      <td>0.765152</td>\n",
       "      <td>0.936992</td>\n",
       "      <td>0.992188</td>\n",
       "      <td>0.992063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Combination  test_neg  test_pos  train_neg  train_pos\n",
       "0    None_Count_None  0.721311  0.932271   0.971787   0.992063\n",
       "1   None_Count_Smote  0.752941  0.936556   0.992188   0.992063\n",
       "2    None_Tfidf_None  0.750988  0.936683   0.971787   0.992063\n",
       "3   None_Tfidf_Smote  0.743295  0.932118   0.992188   0.992063\n",
       "4      RS_Count_None  0.748092  0.933063   0.971787   0.992063\n",
       "5     RS_Count_Smote  0.766284  0.938197   0.992188   0.992063\n",
       "6      RS_Tfidf_None  0.758621  0.936170   0.971787   0.992063\n",
       "7     RS_Tfidf_Smote  0.771536  0.937819   0.992188   0.992063\n",
       "8    Lema_Count_None  0.758621  0.936170   0.971787   0.992063\n",
       "9   Lema_Count_Smote  0.787879  0.943089   0.992188   0.992063\n",
       "10   Lema_Tfidf_None  0.778626  0.941176   0.971787   0.992063\n",
       "11  Lema_Tfidf_Smote  0.765152  0.936992   0.992188   0.992063"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_500_cutoff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "33e3b61b7c414ae7ba99aa0b33883f70",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=12), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "experiment_table = experiment_pipeline(original_logistic_regression,X, y, X_remove_stopword, X_lemmatized)\n",
    "summary_bar(experiment_table, 'Logistic Regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Combination</th>\n",
       "      <th>test_neg</th>\n",
       "      <th>test_pos</th>\n",
       "      <th>train_neg</th>\n",
       "      <th>train_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None_Count_None</td>\n",
       "      <td>0.732283</td>\n",
       "      <td>0.931590</td>\n",
       "      <td>0.976000</td>\n",
       "      <td>0.993424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None_Count_Smote</td>\n",
       "      <td>0.724528</td>\n",
       "      <td>0.925738</td>\n",
       "      <td>0.993453</td>\n",
       "      <td>0.993424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None_Tfidf_None</td>\n",
       "      <td>0.270968</td>\n",
       "      <td>0.896615</td>\n",
       "      <td>0.513064</td>\n",
       "      <td>0.917505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None_Tfidf_Smote</td>\n",
       "      <td>0.695312</td>\n",
       "      <td>0.921371</td>\n",
       "      <td>0.962768</td>\n",
       "      <td>0.962866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RS_Count_None</td>\n",
       "      <td>0.731518</td>\n",
       "      <td>0.930373</td>\n",
       "      <td>0.976077</td>\n",
       "      <td>0.993418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RS_Count_Smote</td>\n",
       "      <td>0.689922</td>\n",
       "      <td>0.919192</td>\n",
       "      <td>0.993441</td>\n",
       "      <td>0.993435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RS_Tfidf_None</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>0.900735</td>\n",
       "      <td>0.603104</td>\n",
       "      <td>0.927088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>RS_Tfidf_Smote</td>\n",
       "      <td>0.752000</td>\n",
       "      <td>0.937876</td>\n",
       "      <td>0.973444</td>\n",
       "      <td>0.973187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lema_Count_None</td>\n",
       "      <td>0.714859</td>\n",
       "      <td>0.928929</td>\n",
       "      <td>0.976000</td>\n",
       "      <td>0.993424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lema_Count_Smote</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.922607</td>\n",
       "      <td>0.993447</td>\n",
       "      <td>0.993430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Lema_Tfidf_None</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>0.905556</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.931260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lema_Tfidf_Smote</td>\n",
       "      <td>0.756757</td>\n",
       "      <td>0.936299</td>\n",
       "      <td>0.969197</td>\n",
       "      <td>0.968681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Combination  test_neg  test_pos  train_neg  train_pos\n",
       "0    None_Count_None  0.732283  0.931590   0.976000   0.993424\n",
       "1   None_Count_Smote  0.724528  0.925738   0.993453   0.993424\n",
       "2    None_Tfidf_None  0.270968  0.896615   0.513064   0.917505\n",
       "3   None_Tfidf_Smote  0.695312  0.921371   0.962768   0.962866\n",
       "4      RS_Count_None  0.731518  0.930373   0.976077   0.993418\n",
       "5     RS_Count_Smote  0.689922  0.919192   0.993441   0.993435\n",
       "6      RS_Tfidf_None  0.325000  0.900735   0.603104   0.927088\n",
       "7     RS_Tfidf_Smote  0.752000  0.937876   0.973444   0.973187\n",
       "8    Lema_Count_None  0.714859  0.928929   0.976000   0.993424\n",
       "9   Lema_Count_Smote  0.714286  0.922607   0.993447   0.993430\n",
       "10   Lema_Tfidf_None  0.392857  0.905556   0.636364   0.931260\n",
       "11  Lema_Tfidf_Smote  0.756757  0.936299   0.969197   0.968681"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "609d40e48a1b47989d992feef5d02e20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=12), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table_500_cutoff = experiment_pipeline(original_logistic_regression, X, y, X_500_remove_stopword, X_500_lemmatized)\n",
    "summary_bar(table_500_cutoff, 'Logistic Regression with 500 word cutoff')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression with L1,L2 Penalty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d5e0e714c757425b93785a4cb9ef474f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=12), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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CJiIiIhIxdVlGSV05IiIiglrIRERERCKnhExEREQkYuqyFBER+aU0BEV+IbWQiYiIiERMCZmIiIhIxJSQiYiIiERMCZmIiIhIxJSQiYiIiERMCZmIiIhIxJSQiYiIiERMCZmIiIhIxJSQiYiIiEQsqQmZmXUzs6VmtszMBpdS5jwzW2xmH5nZ88mMR0RERCQVJe3WSWaWATwGdAFygdlm9pq7L44pcxhwC3Cyu39vZvsmKx4RERGRVJXMFrITgGXuvtzdtwE5QM8iZS4HHnP37wHcfVUS4xERERFJSebuydmxWS+gm7tfFi5fCJzo7tfGlHkV+AQ4GcgAhrr7v0vY1wBgAECjRo1a5+TkFC6wcmX8gGrWjFtkbTVna52tccvVolbcMvWpHz+mROKGcou93OIG1Tmqc1CdA6pzVOeA6hzVOZQcd4cOHea6e1a858btsjSzWsCNwMHufnnYzXiEu78e76klrCua/VUFDgOygQOBaWaW6e4/FHqS+0hgJEBWVpZnZ2cX3svQofHeBjRvHrfICw13sDR7adxyLWkZt0w22fFjSiRuKLfYyy1uUJ2jOgfVOaA6R3UOqM5RncMu1HkJEumyfArYCrQJl3OBexJ4Xi5wUMzygcDXJZQZ5+7b3f1zYClBgiYiIiJSaSSSkB3q7vcB2wHcfQslt34VNRs4zMyamFl1oA/wWpEyrwIdAMxsb+BwYHmCsYuIiIhUCIkkZNvMbA/C7kYzO5SgxaxM7p4HXAtMApYAL7n7R2Z2l5n1CItNAtaY2WLgP8BN7r7mZ7wPERERkbSVyLQXdwD/Bg4yszEEA/AvTmTn7j4RmFhk3ZCYxw7cEP6JiIiIVEplJmRmZsDHwG+Akwi6Kn/v7qt3Q2wiIiIilUKZCZm7u5m96u6tgQm7KSYRERGRSiWRMWTvm9nxSY9EREREpJJKZAxZB+AKM/sS2ETQbenufmxSIxMRERGpJBJJyE5PehQiIiIilVjcLkt3/xJoAHQP/xqE60RERESkHMRNyMzs98AYYN/w7zkzuy7ZgYmIiIhUFol0WfYnuCn4JgAz+wvwHvC3ZAYmIiIiUlkkcpWlATtilneQ2K2TRERERCQBibSQPQXMNLOx4fJZwKjkhSQiIiJSucRNyNz9r2b2DnAKQcvYJe4+P9mBiYiIiFQWcRMyMzsJ+Mjd54XLdc3sRHefmfToRERERCqBRMaQPQFsjFneFK4TERERkXKQ0KB+d/f8BXffSWJjz0REREQkAYkkZMvN7HdmVi38+z2wPNmBiYiIiFQWiSRkVwJtgf+FfycCA5IZlIiIiEhlkshVlquAPrshFhEREZFKqdQWMjO73MwOCx+bmY02s3Vm9oGZtdp9IYqIiIhUbGV1Wf4e+CJ83Bc4DmgK3AA8nNywRERERCqPshKyPHffHj4+E3jG3de4+xSgdvJDExEREakcykrIdprZfmZWE+gETInZtkdywxIRERGpPMoa1D8EmANkAK+5+0cAZtYeTXshIiIiUm5KTcjc/XUzOwSo6+7fx2yaA/ROemQiIiIilUSZ0164ex7wfZF1m5IakYiIiEglk8jEsCIiIiKSRErIRERERCL2sxIyMzuyvAMRERERqax+bgvZm+UahYiIiEglVuqgfjN7pLRNQIPkhCMiIiJS+ZR1leUlwI3A1hK29U1OOCIiIiKVT1kJ2WxgkbvPKLrBzIYmLSIRERGRSqashKwX8GNJG9y9SXLCEREREal8yhrUX8fdN++2SEREREQqqbISslfzH5jZv3ZDLCIiIiKVUlkJmcU8bprsQEREREQqq7ISMi/lsYiIiIiUo7IG9R9nZusJWsr2CB8TLru710t6dCIiIiKVQKkJmbtn7M5ARERERCor3VxcREREJGJKyEREREQipoRMREREJGJKyEREREQipoRMREREJGJJTcjMrJuZLTWzZWY2uIxyvczMzSwrmfGIiIiIpKKkJWRmlgE8BpwONAf6mlnzEsrVBX4HzExWLCIiIiKpLJktZCcAy9x9ubtvA3KAniWUuxu4D/gxibGIiIiIpCxzT85dkcysF9DN3S8Lly8ETnT3a2PKtARuc/dzzOwdYJC7zylhXwOAAQCNGjVqnZOTU7jAypXxA6pZM26RtdWcrXW2xi1Xi1pxy9SnfvyYEokbyi32cosbVOeozkF1DqjOUZ0DqnNU51By3B06dJjr7nGHZJV166RfykpYV5D9mVkVYDhwcbwduftIYCRAVlaWZ2dnFy4wdGj8aJoX6y0t5oWGO1iavTRuuZa0jFsmm+z4MSUSN5Rb7OUWN6jOUZ2D6hxQnaM6B1TnqM5hF+q8BMnssswFDopZPhD4Oma5LpAJvGNmXwAnAa9pYL+IiIhUNslMyGYDh5lZEzOrDvQBXsvf6O7r3H1vd2/s7o2B94EeJXVZioiIiFRkSUvI3D0PuBaYBCwBXnL3j8zsLjPrkazXFREREUk3yRxDhrtPBCYWWTeklLLZyYxFREREJFVppn4RERGRiCkhExEREYmYEjIRERGRiCkhExEREYmYEjIRERGRiCkhExEREYmYEjIRERGRiCkhExEREYmYEjIRERGRiCkhExEREYmYEjIRERGRiCkhExEREYmYEjIRERGRiCkhExEREYmYEjIRERGRiCkhExEREYmYEjIRERGRiCkhExEREYmYEjIRERGRiCkhExEREYmYEjIRERGRiCkhExEREYmYEjIRERGRiCkhExEREYmYEjIRERGRiCkhExEREYmYEjIRERGRiCkhExEREYmYEjIRERGRiCkhExEREYlY1agDKA/bq1cnt1Urfqxfv/RC1arF3c9hGdB4SeO45apTPW6ZJSyJW4auXeOXgXKLvdzihsRiLyPumlu3cuA33wA7Ens9ERGRCqxCJGS5rVpRt2lTGteujZmVXKhmzbj7WVPN2Vp3a9xytagVt0wDGsQtw9dfxy8D5RZ7ucUNicVeStzuzpoNG8gF2L4isdcTERGpwCpEl+WP9euzV1nJmKQUM2OvunX5sUaNqEMRERFJCRUiIQOUjKUZ/b9ERER+UmESMhEREZF0VSHGkBXz4IPF11WN/1b3qOJUr1F8kPmGoTeW+by1a9bSs1NPAFZ9s4qMjAz23WdfAGbNmkX16vEH0wOMzsnhjI4d+dW++xbb1u+KK5g6YwafzZ1L9erV+ebbbznl179m2Zw5Ce07Ua+88grNmzfnyCOPBODWW2+lc+fOdOjQoVxfR0RERH5SMROy3WzPvfZk2oJpAAwbOozadWpz+6Dbd3k/o3NyaJWZWWJCBkE339M5OVx+0UW/KN6yvPLKK1SpUqUgIfvTn/6UtNcSERGRgLosk+zpp5/mhBNOoEWLFlx99dXs3LmTvLw8LrzwQo7p1InMjh15ZNQoXhw3jgUffUTvq66iRZcubNu2rdi+Bl51FQ88+ig7dhRvxXv4rw/z6xN+TedjOzP8ruEF6x+44wHaHdmOPl36cGnvS3nioScAGD1iNB2P78gpx53CxedezJYtW5gxbQYTJ05k4MCBtGjRgi+++IJ+/frx6quvMn78eM4///yC/U55913O7t8fgDfefps23bvTqmtXel95JZs2by7vahQREanQlJAl0aJFixg7diwzZsxgwYIF5OXlkZOTw9y5c1m9ejUfvvUWi95+m4t69aJ3z560OPpoXnziCRZMnlxiN2eTgw/mxNatef7llwutnzh5Mrlf5fL6zNd5c8GbzJkxh9kzZjP3/blMeX0KkxdO5v/+9X/Mmz2v4DlnnXsWb89+m+kLp9P40MY8/4/naXtqW8444wyGDx/OggULaNy4cUH5rl27Mm3aNLZs2QLAi+PH07tHD1atXs2wxx7jrZdeYt6kSRx71FE8/OSTyalQERGRCkpdlkk0ZcoUZs+eTVZWFgBbtmzhoIMOomvXrixdupTfDxnCGR07clr79gnv848DB9Lrkkvo1K5dwbo3//Mf3pryFjNbzgRg88bNLP9kOd+v/p6uZ3WlRo0a1KhRg65n/jSZ60cffMS9Q+5l3Q/r2LhhY6FtJalevTpdunRhwoQJ9OzZk0nvvMNDd97J5HffZfEnn9C2Rw8Atm3fziknnJDw+xERERElZEnl7lx66aXcfffdxbZ98MEHvDFmDI+MGsW/Jk5k5H33JbTPIw8/nOZHHMErr79e6HVuuOkGzr3m3EJln7j/iVL3c+VFV/LPN/5J88zmPPPkM8x5P/7FAb1792bUqFHUqlWLNq1bU7tWLdydbtnZPPu3vyUUv4iIiBSnLssk6ty5My+99BKrV68GYM2aNaxYsYLvvvsOd+fc7t25c9Ag5n34IQB169Rhw6ZNcfd76w03cP+jjxYsd+3YkTHPjGHzpmDs1te5X7N29VqOP+V4Jr82ma1bt7Jxw0YmT5xc8JzNmzbT6FeN2L59Oy8//1MXaN26ddmwYUOJr9upUydmzpzJqFGj6B22iLXNymLq+++z/MsvAdi0eTOfLl++K9UkIiJS6VXMFrIbS5imIoHbD21J8NZJiTrmmGO444476Ny5Mzt37qRatWqMGDGCjIwM+vfvj2/bhpnxl1tvBeCS887jskGD2KNmTWZNmFDqdBnHZWZyXGYmi5cuBeCMLl2Yu/wTup/UHYDadWvz2POPkdUmi+xu2XQ+tjMHNT6Ilse3pF79egDcctctdDqhEwcefCBHZR7F1h+D9923b1+uuOIKHnzwQV599dVCr1u1alVOP/10nn/+ecbcfz8AjfbZh1EPPkjvq64quBDh3sGDOaxp03KrRxERkYouqQmZmXUDHgYygCfdfViR7TcAlwF5wHfApe7+ZTJjSrbBQwcXWj7//PMLXZ2Yb/78+cXuB3lejx6cF7Y8FfXc3/9eaPm1MWMKLV997dX0v6V/sedd84druPnum9m8aTO/OeU3DLxlIAADrh3AgGsHFCvfrl07liz56Qbjzz33XKHtI0aMYMSIEYVi79KuHV1ixrSJiIjIrklaQmZmGcBjQBcgF5htZq+5++KYYvOBLHffbGZXAfcBvZMVU2V0Y/8b+WzpZ2z9cSsXXnohmcdmRh2SiIiIFJHMFrITgGXuvhzAzHKAnkBBQubu/4kp/z7QL4nxVEojXhxR8LgWtSKMREREREpj7p6cHZv1Arq5+2Xh8oXAie5+bSnlHwW+cfd7Stg2ABgA0KhRo9Y5OTmFttevU4dmTZqUHVCV+Ncv5BnszNgZt1wGGeVShhImfy1ROcVebnFDYrHHiXvZ8uV8vvJLttaJP24vkWSyPvXjx7RyZfwykNCYw7XVPG7s5RY3JBZ7OcUNqnNAdY7qHFTngOqcnx93hw4d5rp7VrznJrOFzEpYV2L2Z2b9gCygxAm53H0kMBIgKyvLs7OzC21f8t571N0a5yBI4B+ypmpig/oT+afUpW7cMkXHkJWqnGIvt7ghsdjjxF3Tgw/A0uylcXfVkpZxy2STHT+moUPjlwFo3jxukRca7ogbe7nFDYnFXk5xg+ocUJ2jOgfVOaA6p5zrvATJTMhygYNilg8Ein2Lm1ln4FagvbuX3yWOIiIiImkimfOQzQYOM7MmZlYd6AO8FlvAzFoCfwd6uPuqJMYiIiIikrKS1kLm7nlmdi0wiWDai9Hu/pGZ3QXMcffXgPuBOsA/zQxghbuXPO/DrnjjjeLrqsZ/q3tUcarXKH7j7g3nnV7m89auWUvPTj0BWPXNKjIyMth3n30BmDVrVqnziRU1OieHMzp25Ff77ltsW78rruC/s2ZRv149MjIyePy++zgxK26XdCGvj32d5cuW87ubfsf4V8ZzRPMjOPzIwwG4+9a7ye6cTfcO3XdpnyIiIvLLJXUeMnefCEwssm5IzOPOyXz93WXPvfZk2oJpAAwbOozadWpz+6Dbd3k/o3NyaJWZWWJCBjD8nns469e/ZuLkyVx1003M+89/SixXmjPPPrPg8fhXxlOlSpWChOz2P+16vCIiIlI+dOukJHv66ac54YQTaNGiBVdffTU7d+4kLy+PCy+8kGM6dSKzY0ceGTWKF8eNY8FHH9H7qqto0aVLwaz3JWnXpg3LwtsTzVu4kBO7dKFdm3Zcfs7lrF+3HoCRw0eS3Tybzsd15rofFxxYAAAgAElEQVR+1wHwzJPPcMv1tzBj2gwmT5zMHwf+kVNbnMqKL1YwoN8AJrw6gfHjxxeayHbKlCmcffbZALzxxhu0adOGVq1a0fvKK9m0eXOyqk1ERKRSUUKWRIsWLWLs2LHMmDGDBQsWkJeXR05ODnPnzmX16tV8+NZbLHr7bS7q1YvePXvS4uijefGJJ1gweXKZ3ZzjJ03imPCqkX5XXsmDd9/Nu++9S9MjmvLQ3Q8B8MR9T/DmgjeZsnAK9zxaeCaRtqe2pcsZXbh3+L1MWzCNgxsfXLCta9euTJs2jS1btgDw4osv0rt3b1atWsWwYcN46623mDdvHscedRQPP/lkeVeZiIhIpaSELImmTJnC7NmzycrKokWLFkydOpXPPvuMZs2asXTpUn4/ZAiT3nmH+vXqJbS/gbfdRov27Xnq+ef5v+HDWbN2LT9u3copJ50EwLm/PZeZ784E4PCjD+e6ftfxyphXqFatWsIxV69enS5dujBhwgS2b9/OpEmT6N69OzNmzGDx4sW0bduWFi1aMGbsWL7Izd31ShEREZFiKubNxVOEu3PppZdy9913F9v2wQcf8MaYMTwyahT/mjiRkffdF3d/+WPI8q1es6bUss9Pep73pr7Hm+Pe5OF7HubtRW8nHHfv3r0ZNWoUtWrVok2bNtSuXRt3p1u3bjz77LNBoUTnUBMREZG41EKWRJ07d+all15i9erVAKxZs4YVK1bw3Xff4e6c2707dw4axLwPPwSgbp06bNi0KeH9773XXuxRsyYzZs0C4F/P/ouT2p/Ejh07WJm7klM6nsJt99/Gmu/WsGXzlkLPrVO3Dhs2bChxv506dWLmzJmMGjWK3r2DW4u2bduWqVOnsjwcu7Zp82Y+DR+LiIjIL1MxW8hOL2GaigRmu99SLbGZ+hN1zDHHcMcdd9C5c2d27txJtWrVGDFiBBkZGfTv3x/ftg0z4y+33grAJeedx2WDBrFHzZrMmjAhoekynn3iCa4aNIiNW3/kkMMP4a9P/ZW8vDyuOf8aNm3YxM6dO7nmD9dQp26dQs87p+853HDFDTz24GOMeXVMoW1Vq1bl9NNP5/nnn2fMmGBbo0aNChK0bdu2wfbt3Dt4MIc1bVpOtSUiIlJ5VcyELEKDhw4utHz++ecXumox3/z584t1+53Xowfn9Sh5Grbn/v73Ete3Ou44Zk6ezJoiyeS4/44rVvaiyy4qeHxyu5OZuWRmwfLI50YWKjtixAhGjBhRaF2XLl3o0qVLsKAuSxERkXKjLksRERGRiCkhExEREYmYEjIRERGRiCkhExEREYmYEjIRERGRiCkhExEREYlYhZz2YmjdB4uvrBr/rW6u4uxgR7H1N3Jjmc9bu2YtPTv1BGDVN6vIyMhg3332BWDWrFkJzSd2ycCBDL7mGo5o1ixuWYAnn32WAQMH8uH06Rx95JEAtDuyHTlTctj/wP0T2kci5s2bx6pVq+jWrRsAY8eOZdmyZdx0wQXl9hoiIiKVXYVMyHa3Pffak2kLpgEwbOgwatepze2Dbi9Uxt1xd6pUKblR8qnhw3f5dQ/cf3/uHT6cMaXMUVYe5s2bx6JFiwoSsrPPPjvYoHnIREREyo26LJNo2bJlZGZmcuWVV9KqVStWrlzJgAEDyMrK4uijj+aumCTslLPOYsGiReTl5dHgqKMYfO+9HNe5M226d2fVd9+VuP+zzjiDeQsXsqyEWxi9/cbbdG/Tna6tunJl7yvZvGkzABNfm8jxRxzP6aeezs3X3cwFZwUtXbPfn81pbU6jZcuWnHzyyXz66ads2bKFu+66izFjxtCiRQtefvllnnzySa6//nrWfv89TU46CXcHYOOmTRx8/PHk5eXx6fLldD3/fFp37Ei7M8/kk2XLyrtqRUREKhQlZEm2ePFi+vfvz/z58znggAMYNmwYc+bMYeHChUx+910Wf/JJseesW7+e9iedxMIpU2jTujWjx4wpYc9QpUoVbrruOv780EOF1q9etZrHhj3GS2+9xKR5kzjq2KN48uEn2bx5MzddfRP/evNfTHx3Iqu+WVXwnMOPOpw3pr/B/Pnzuf3227ntttvYY489GDJkCBdccAELFiygV69eBeX3bNiQ5ocdxvTwPprjJk3ijI4dqVq1KgNuvpnH772XuW+/zZ9vv51r//CH8qhKERGRCktdlkl26KGHcvzxxxcsv/DCC4waNYq8vDy+zs1l8Sef0Pzwwws9Z4+aNTm9Y0cAWh97LNPmzi11/xeedx5/fughcr/KLVg3Z8YcPln8CT3aBrdh2r5tOyeccgJLFy+l2RHNOPiQg4HgfpY5z+QAsO6HdVx10VWs+GxFwu+td48evPjaa5x64onkjBvHDQMG8MO6dbw/fz7nXH45hN2zeXl5Ce9TRESkMlJClmS1a9cuePzpp5/y8MMPM2vWLBo0aEC/c87hx63Fb2YeexFARkZGmQlNtWrVGHjllfztob8VrHN3srtl87dn/1ao7MezPy51P/fceg8du3Zk0NWDWLZsWcGYsbKc1a0bQx54gDtvvJEPP/6Y9m3asG79evZu2JAFkycndEN3ERERUZflbrV+/Xrq1q1LvXr1WLlyJZPeeadc9tu/Xz/emvwWP6z9AYCstlm8P/V9vlz+JQCbN21m+afLOfLoI1m2dBm5X+Xi7ox9cexPsa1bz/4HBFdn/uMf/yhYX7duXTZs2FDi69arW5eWmZlcf8cd9DjtNKpUqULDBg3Yr1Ejxr7xBgA7d+5k4aJF5fI+RUREKqoK2UI2dEMJ01Qk0FqzppqztUbxFqvy0qpVK5o3b05mZiZNmzbl5JiuzF+iRo0aXDrgUob8cQgA+zTahwdHPchVva9i27ZtAAy+dzCZh2Vy36P3cXbns9lrn71odXwrvl/7PQC//8Pvue7S63jsvsfo0KFDwb47duzI/fffT8uWLbn11luLvXbvHj3oe/XVTH/11YJ1OY8/zlW33MLQ4cPZtm0b/c49l+MyM8vlvYqIiFREFTIhi9LgoYMLHjdr1owFCxYULJsZzz777E+FY6aOiE1ofliypOBxn5496dO7d7HXuezCCwstX33t1fS/pX/Bcrsu7WjXpV2x57Xv3J7ZS2fj7gy8YiAts1oC0OaUNsz5ZA4NaADAPffcA8A+++zDnDlzir/RMPY+PXvSp2fPQpuaHnIIk55/Xl2WIiIiCVKXZSUz+onRnNriVE5qfhJbtmzhossvijokERGRSk8tZJXM7276Hb+76XdRhyEiIiIx1EImIiIiEjElZCIiIiIRU0ImIiIiEjElZCIiIiIRq5CD+sfVfbv4yqrx3+qWKpBH8VnxO9KxzOetXbOWnp2CqR9WfbOKjIwM9t1nXwBmzZpVaOb90lwycCCDr7mGI5o1i1sW4Mlnn+WWu+/mgP32Y+vWrVx1/bX0urpX/CfGyP0qlyGDhjD6xdEsnLeQ71Z9R69uwT7Gjh3LsmXLuOmmm3ZpnyIiIrLrKmRCtrvtudeeTFswDYBhQ4dRu05tbh90e6Ey7o67U6VKyY2STw0fvsuve0GvXjx077188+23HH3KKXQ8ryN77r1nws8/8KADGf3iaAAWzlvIkkVLChKys88+e5fjERERkZ9HXZZJtGzZMjIzM7nyyitp1aoVK1euZMCAAWRlZXH00UdzV0wSdspZZ7Fg0SLy8vJocNRRDL73Xo7r3Jk23buz6rvvynydXzVqxEGHHMT/VvyPtavXcnGPi+l8bGd6tO3Bx4uC+1dOf3s6pxx3Cqe2OJX2rdqzadMmli9bzqktTmXLli3cd9d9/HPMP2nRogUvv/wyTz75JNdffz1r166lSZMmuDsAGzdu5OCDDyYvL49Ply+n6/nn07pbN9r95jd88tlnyatMERGRCkwJWZItXryY/v37M3/+fA444ACGDRvGnDlzWLhwIZPffZfFn3xS7Dnr1q+n/UknsXDKFNq0bs3oMWPKfI1ly5eTuyKXg5sezH2330fLE1sy5YMp3Dj0RgZePBCAEfeP4KGRDzFtwTQmvDuBmjGz6O+xxx7cPORmzr3gXBYsWECvXj91fe655540b96c6dOnAzBu3DjOOOMMqlatyoCbb+bxe+9l7r//zZ9vuYVrb7utPKpMRESk0lGXZZIdeuihHB9zz8oXXniBUaNGkZeXx9e5uSz+5BOaH354oefsUbMmp3cMxq21PvZYps2dW+K+x7z8MlNnzKB6tWoMf3Q49RvUZ/b02Twz4RkA2p/WnoEXD2Tzps1knZzFH6//I73O70X3c7pTp06dhN9D7969efHFFzn11FPJycnhhhtu4Id163h//nzOufzygnJ5O3YkvE8RERH5iRKyJKtdu3bB408//ZSHH36YWbNm0aBBA/qdcw4/bi1+M/PYiwAyMjLIyyt+oQH8NIYMwhujs7WgazFf/vL1t11Pjx49eHPCm3Q6vhPj3xmPmSX0Hs466yyGDBnCnXfeyYcffkj79u1Z9/HH7N2wIQsmT05oHyIiIlI6dVnuRuvXr6du3brUq1ePlStXMumdd8r9NU5sdyKvjHkFgHenvMt+B+5Hrdq1+OKzL8g8NpMbbrmBY1oew6dLPy30vDp167Bxw8YS91mvXj1atmzJ9dcHSV2VKlVo2KAB+zVqxNg33gBg586dLPzoo3J/PyIiIpVBhWwh67mhhGkqYsZMlWZNNWdrjeItVuWlVatWNG/enMzMTJo2bcrJMV2Z5eWmu25i4CUD6XxsZ2rVqcVfn/orACMeGMHsabOpUqUKRx97NB1P60juityC57Xr2I6/3f83WrZsya233lpsv71796Zv374FY8kAch5/nKtuuYWhDz7Itu3b6feb33Dc0UeX+3sSERGp6CpkQhalwUMHFzxu1qwZCxYsKFg2M5599tmfCn/9dcHD6a++WvD4hyVLCh736dmTPr17F3udyy68sMTX33PvPXl6/NPF1g97Yhi1qFVoXdNmTQum69h7n735z5z/0IAGJe63T58+9OnTp/DzDzmESc8/X2J5ERERSZy6LEVEREQipoRMREREJGIVJiErenWhpDb9v0RERH5SIRKymuvWsWbTJn3Jpwl3Z82GDdQsYcoPERGRyqhCDOo/cN48coHv6tcvvVC1anH3sykDttfcHrdcdeLfLLzoAPoS/fBD/DJQbrGXW9yQWOxlxF1z61YO/OYb2CuxlxMREanIKkRCVm3bNpq8/37ZhZo3j7ufF/bdwdKWS+OWa0nLuGV60jNuGYYOjV8Gyi32cosbEos9gbhFREQkyV2WZtbNzJaa2TIzG1zC9hpm9mK4faaZNU5mPCIiIiKpKGkJmZllAI8BpwPNgb5mVrTJpD/wvbs3A4YDf0lWPCIiIiKpKpktZCcAy9x9ubtvA3KgWH9YTyB/FtOXgU6W6A0WRURERCqIZI4hOwD4KmY5FzixtDLunmdm6wiGea+OLWRmA4AB4eJGM4s/0Ovn2bvoa6eRdI09XeOG9I09XeOG9I09XeOG9I09XeOG9I09XeOG5MZ+SCKFkpmQldTSVXReikTK4O4jgZHlEVRZzGyOu2cl+3WSIV1jT9e4IX1jT9e4IX1jT9e4IX1jT9e4IX1jT9e4ITViT2aXZS5wUMzygcDXpZUxs6pAfWBtEmMSERERSTnJTMhmA4eZWRMzqw70AV4rUuY14Lfh417A267ZXUVERKSSSVqXZTgm7FpgEpABjHb3j8zsLmCOu78GjAKeNbNlBC1jfZIVT4KS3i2aROkae7rGDekbe7rGDekbe7rGDekbe7rGDekbe7rGDSkQu6lBSkRERCRaFeJeliIiIiLpTAmZiIiISMSUkImIiIhETAmZiIiIREJ35/mJErJKSh+C3S+d69zMdK4QSQPpdp7RVFc/0Um2HJjZQfFLpRZ3dzM70sxuMbMrw4l5U1J+MmBme5hZlpndZGb7Rh3XrgrrvFHUcewKM6sG4O47Y9al7Ak/HY+V/Po0s5pm9mszO6Kk7enAzPaIOoafI+Z/UMvMjgjPjb8quj0dpFOCEx7vw8zsd2ZWN+p4doWZ1Taz5mbW0MwyYtb/7GNFCdnPEPPh3dPMhgCjzWyqmVWP/cekopgvrI7AA0AN4OJw3rhmZpbQPbci8jDQCbga6A9gZoemcp3nx2Zm3c3sZmCcmV0duy3VxMTcHrjDzHLN7Jz87Wlywk+bYyVM1KsDzwPnAIvNbL/wfFI1TeobM+sLPGJm08xs/6jj2UX5X6J3ArcD7wHHAphZ9VT+H8R8Xk80swfMbLyZpezti2K+g04n+GxWBwa4+wYz29/MGkcYXplivvv3AYYDzwLTgOr5237JsaKE7OfJr7fLgDrA34H17r4N6GBmvSKLLHH9gUeB74H/hutOAC6MLKJSuPvO8AurFcGH4DNgfLj5JqBFVLHF4+47woc3AguAH4BV4bo+ZnZoJIGVISbmB4EXgS+BagBmdqGZHRxVbPGk27ES0xV8HsGt5f4IvOvuKwlifTiVW2divqCOBa4lOBfuB3xrZk3N7Kz8VtZUFh43vwK6uns/gtv6LQx7Dh41swOjjbB0MZ/XUcC/CM7jNQDM7IwUbLXMT1jOIvjh9C7wZrjuN8A9UQSVoPzPa3+C8+LlwAp33wL0MLO7y2PnsgtiPgBdCA6ozsC4cF0HIBNSs5k7puvpe2Ah0A34W7iuF+H9RlNwzNBxwHTgEGCnuy8KT/QdgA8jjSwOMzsS2OHubwKNgAnhpt8T3MUi5ZjZUcBq4GOCk/vYcNPV4XIqyz9WDibFj5WYz+PxBHV8HfDvcF1LoE7YgpZqn8eiLgT+DzgAeC88Rx4MXOTu2yONLHEnAm+EvQdfuvu3BPdgPsHdc6MNrWQxCfGpwEfu/h7wubv/18xqA8MIGg1SRkwL0kqC8/mtwD/CdacS3N0nJXsQYr77M4G3gCuBl8N1WUBN+Pnfn6n+IU91TxMkMce7+5Phuo789OWVcglZjGcIWsaygDpmdgrQDBgDhccMpQJ3nw1sJ2geXhKuHgr81923peKHFwpOmN8AM8zsIWCmu28xs3bAj+7+SbQRlmoTMBm4GXjL3beaWRsAd/800sjKYGYWHivbCH55Lw43DSW1j5VHCFoH+hG0ijUC+hJ0iaSsmC/X9wiOmQH81MLRG5gJqfnlWoLpBC3YDxAMLagBXAFMhdR8DzH1vw2YHZ5jJofr2gIr3f27FE3oRxL8SMoA9jazPxEk9C9CoeQnFT0K/JogiR8Ttq6eRvj9+XOl7EDuNPEeQbdIczN7FtgAfOXuCyH1khozy3D3HWZ2DfA4wRdAX4LkbClwS/jFWyXVYg/dA6wALjezjwhaJf8abkvJMR7hCfMHM1sEDAHWm9kjwL7AS/DT/yXCMItx9xVmth34A/BxOO7t18BTkHoxxxyzNcPE8T5gHXBhWPdjSe1j5QuCL/4TCVrx5hC0NE2B1DuXxApbH6cRfBl1BhaZ2TcEXa5Dw2IpG38+d19jZm8BrQnGH3YHvuWnBDMVj5v8RPFDgsaAHsAoM7uKoLHg7/nFIgqvROEPp6/N7I8Ere59CboAbwp/NKXqdxDh+LyNBHVag6BluAYw1d0XwM//vOpelr+AmdUBthI0a59J0L3zX3ffnGoHVNh8fSiwB5ADHBqOm6jq7nnRRleymASyGXAQUI8gcVwG1HP3tZEGWAYzqxEmt92A3u5+iZntR9A11RYYDXyWYkmN5f/ijmkNqE3Q6rEP8AKwOBwrmVLCMT7VgGuAvdz9kphtTdz988iCi8OCq7SPAla5+4Lwvfzo7qvD7QX/l1RiZvXcfb2ZPQCMcvclZnYy8FuCRGa0u3+eqvHDT4m8mTUkGHv1jbsvNLPDgB+B3BSOvba7bzKzDkBjd3/KzM4mGI+4DHgNmJsq30P5x4EFV1P2BboC84Cn3P3raKMrW9jTkQl8R9CClx2+lwMIzulL3H1pftmfe8yohWwXxHx4TwJuA9YSdEd9DEwkaB7eDCn5i7YhQXfI+cAaoLWZfePuX5nZ4cAf3L1/pBEWEZOsPEXQ+vg5wS/XdcDnZjbJ3f8XVXxxtA0HOp8JvB2uW+Pur5nZZIIri1LtGKkC7DCzQUATgmRsOcGJ/WN3/zFFuz4AjiC4QvE84HEzywQ2hYnYiWZ2aH5rUyqI+XI6juAH0lSgathNNheYH65LyatawzjbWXA15W8IfmDg7v8F/mtmB7v7inBdysWfL+Y8/RpBEtMi/PL9JzAuPD+mVGtwjNZm1pYguXkCwN3HAmMtmDrl6xT7HjKCVsaBwMkE3X5nAv8xs43A0+7+SITxlaUhwdjUS4D9gWwzWxJ+//zPzAaY2XJ33/5LjnclZLsmv6IvJ8jspwFNgcMIBiP+F3iy5KdGKxyUenN44Ncl6EqoYmZvExxoWyB1uqJivrCaEPxqPdfMmgLNCca6ZREkwalqCcFFH60BD0/yU81sJkGC9qi7/6LxBuUtbI2sTtDKdAvBMXFEuPwrM7vF3RdFGWNp3P0tM9tM0JK3F0H8X4TdlQ8T/BpPxdambIIhA/cRTLPQhKCl5jTChCxFGbADuIig1b1HmFzmXzTxtJmdmKqt71DoHNMG2JbfqhqO77wQmGBmrdx9TaSBliD8YfQNwSDy5gTJ2V0Eifw4gqk7hhMk9ykhJjlsANzh7u8D482sJsGPqepQaPhBKlkHvAOcS3Dl9ukEV8kvJbhQK9PdR/7S84u6LHeRBZdBPwEMdPeN4brGBAnCInf/ONVO+kW6ohoCGe6+2syOIRh4+xUw1t1XpcqHIaY1chBwCtDH3X8Mt2UATdx9WaRBJsDMOgH/IzjBn0JwOX1D4KxU6vqL6R5uDXR297+E6/ch6JJvAryaCsdGUUWO7wPdPTfs9utJ8Gt2mbs/FWmQpTCz0cC/3T1/PGEVgvre7u4rU+1cUpSZtQL2JIi5E5BH0HW8zd0vTZUfeCWJOcf0JfhR+idgS34SaWbV3H17Kv8PwuP8VILzyskEQzv2Bw5396OjjK0k4Xfl4wQX2zxA0GuQLlfhFgh/fBwDHE5wpehL7j7hlx7vSsh2UdhdORX4gCDLT+VWGqDQl+1vgZMIxpLtTfDL/G8EUwOk5IFgZpcTzM20E3gdeM6Dq+hSsbUDKPTL+wCgPVCf4MqnzwkGOq/0YEBrysVvZm8QDA6+0d0fjVmfcrEWZWY3ELQunUAwr9GfCeYISsm4wy/T6QTJzFjgobDLLy1YMJj/VwSJ2Cx3Xxx+UVUDPvFgfFlK/MArTZgAv04wRccEgnm8PiOYVzLlE4Xwf9CR4GInCHpxqgEbPRi/l1IJcTg85vfAkQRDZ6YSfAY+y2/gSDVWeCzzOQSt2i8B072crzhXQvYzhGODfk1wyW4Dgvm87vUUHjgMYGazgHsJrg5tBAwCJrp7Tip/4YYnnWMJWjy6EXSXtM8fr5dKYpKxfYFXgfcJunZOJbgK5w+RBhiHmdUnaM27giBRmACMcPfpqXiMxLRydCaYZf0yYDPBSf8o4Hx3/z7KGEtSpFXvAIILJ/oRJDiD3P2JKOMrixW+WrsTsJ7gS+oH4Dp3nxZlfImKTRYtmHfvAoJWpjXAZHf/e1nPj0rMMX8UwTxjexNMCbQC+Ke7jy9zBxELe5nyz4l9gHbADR7M05hyYup7CsEcgRsJJp7uSPC9P7rcXivFzq8pLRwHtCfBmIkGBBNnNiYYUzbC3Wen4pcWgJm1BB5z97Yx69oSJGhnptKvk5gPQCbBOKwfCeY4mkQwrunYMEFIuV/fMbEPBg5w9+vC9YcQjNt72d0nlLWP3S0miaxCMLC/nruvDROFwUAHd8+MNsqSxcT+CMFVivfEbHsA+C6/+zWVhOeStgTjT6sQzM6/zMy6EHSbTU+11o2izGwaMDi/Vc/MLiIYMznYg5nLU1bMcdOEYKqRfQl+fHxBkCTs5e6PpOL5POYc8wDwvbv/KRyH1Y/gCtd+7v5ltFH+pEgC2Zuge7g+wTQpowm6WVen4g/sfGa2FzDJ3bNi1rUmmMj5d+6+vjxeR4P6ExBzYjwLuJQgKfsvwaXdj3nM1Ymp9uGNsQj41Mz+DNzj7psITkIb3X1jKp14wg9vDYKxBksJLjWGcF4jd5+eXy6iEEsVE9OPBGPH8td/aWZrCH5ZTUix+s6P4zbC8ShmtoNgXMR1+eVSMQGOif1l4FIzO8jdvwrXHUwwyDllYo85l5xD0Cq2imAg/B/MbLy7v5ZfNsWTsb0IfiQVtD66+zNht/H+BN1+KalIK/ZzBJPX7iBIEAq1YqfKZzRWzHFcH/gmfD8/Ak9aMMF3R8L5AlNE/hxoNxEcL38iuBjhKqCuuz9kqXv1dr6awHIzu4ygFXIdQfLetrySMVBClpCYE+PdBAnZ1wRXnw0EDjGzm8MPRMryYHDqAwQfig/M7P/bO/O4T8eyjX8PjLEvxTDIEjKWMMmWIoo3QiEalCVZe19kiSRRtGixZC3bIGuUQmVfypp9ZGdkp2RnwpzvH8d1P8/9/Ob5PTNqZu7reuY+Ph+fmee+fzif+3fd13le53kc5/kilnmfnD4yHd6UGkUtUNkUZzx2TNeXxs//y8CxDZo4qbgQj2H5FB4LUqkus5oVWnNOK2EJ+pdxv7clgX0kvRkR16fPNR7QDICbcTn7Tkn3YY7n3KTmuxnZXtmxBym7JHf5Xh/4P0mPRURW4536Q7iJ6nk4CDgaK4c3wtyrbIOxhGqv+wpwe0TsBb1ZbEkb5V72SzgW82s3lfQwDoSXT9ey4X2m8vYQnIlcJdw7TfigfaykKyJT9XaFiHhK0i/xqKQFbD4r44B+snUnaAOyiaDjNPUMcGdYHfckcKXc2XkuLEHOCjWux7yYMzYvnhv2MlaI/DUixkFWp/Fqs3wFGJoCsfvDTSd/j5WK2Ww23RDudP8RHFhuixsIHxoRd6f7Wdhes+OjODvwQDZhcpgAACAASURBVPr51rTmNwOuz8XeOmrv5hyYL3Y0zvJ9Gqv9bonMVHLJ3hnwoPmFcSPpZ4FTJX0Z0yGyX98AEXGKpOcxF/VbuA3NYZBP+5z+ULNrHP1nsVfE7Riy/Q6SbXdKOg53up8O75mnRYaCofQeXgTsBXwvvQePY/7bAwP/282gtr8Iczuvwge9TfGh9WjglvTxyXLgawOyAdCxqF/DpYWLJB2FU/LDgGFpQ80Rle2npr8/jkc8PIf7qM2ON6VsUNssr8SE4a/hUuvcOBg7PN2vmgxmB0n74nLCeJy5+XzuGVTcY2dPSa9intvfsKS7GjaflYOtbZbDsUruVmA4fk8vwcFYVqX4ChHxjqTfAT+SVYlPY0L8sIi4JX0mK5thArXZWsCyWLSyIZ4s0NNzLKe1UkfiWv07ZUzPwk1J18FZ7PvJMIvdiVTe20TS+sAfMOdt+shQFZoOpU9GxPP4eR+RsnkP4nX/lxSsZbW/JFTJga9hKseaWBX68+jlTQom3/vakvoHQHp5162nryXtgYn8K+FO/SdFxMWZLigkzYVHPWyMyZNLYke7IvCjiLi/QfP6QB6MuzBwWETclq5tiXtgDcfKp98N8J9oDLUAYV3cSuRwHPAuDsyGs6uHRF69x5YAXo2I59LPKwOb4Oe9Epaj75vKU1kFNrXn/RXc5mJXTCP4CFb8zRoRWzdo4gRIpb1LcMbxDbmb+ro4OzkO7yW3ZryXVOTs3+LA92pgJN4PX8PZmT82aOJEkbKQs2Bl/B3p8laYEP8acEZE/Loh8wZE7fnvghWhZ+Ps+1L4Xf1FTvtjKgEfigPdhzDv+hnsh9bHTWvvjrznJ5MyeRvjfp274We/FLBRTGaBVhuQDQBJKwLXYfLqZcARKU38fnpTlC/l5Kgq1BzWQrjuPTpSz5RU4lkoZUGygdyjZh/MRfk3PrUeFRnPrOyEpFHADBFxZsrqLYQzCdNHxK9yCmxSuWM93CPtIrypvw/3MRqKT7aTjbA6JSDpk7in0Ukp81QpoWdKvI8sNnpJs2DntCquTFyLMwaPR4ZtObohZWd+gnswvirpfbgx5to423FzTmu8jrQ2vo4PpcKCiruAu8IK1yzt7kTKwN8bqQempHkwN3hoROyZ0ZofiidkLIunq7yNubT34b1mXI4HD+jjP2fEXRTOrvshScviHoevTs510wZkk4D08HfCtePX8UiKrDrbd4OkH+IAZzY8YeC8iHi0WasmDplkvgfwBVy7/31EnJjzppk2/FH4Bd63yvKlezPmlB2rIGkEzhB8Cjupc4GrImNieS1TsBgWeqyPFbl/wo4q6wAnlXG+gHsZ3o0zNedHrzo0O9Se+QdwCWcccHSksUKSZoiMxyTVIQ+3XgpnUhfG7YvG4/LrlTnuL7UAYSh+V7+EW0bcGWkIfednm7CzG9LeuBWwOuaNLY73yGuatKsbauX5nfC7+m98gHoAc5pfnxK+vw3IJgJJs6aHPyf+Ug7ABFaADXJM0dde3mVwZmxlSZ/Fg1FXxI0P/yciXmrU0BokzYpf0kVx0PsxnOreCG8+f4t8e2FVzmp7vOm8iefLjcXtGM7LMctXL42lzMfncPZjPszxWCm3jR0m6MP0JvB7/NyXw4HCORFxRpM2dkLSbDgI+CcuB7+KhTXfxxnJfSPipzk6U+izp5yN+ZEL4VY6NwE3APdEEgjlClnNOh4LJ1bEHKZd8V7zIRyMrduchd1RW/N74f3waZxpehWX0m6PiLuatLGOVK5cHz/v4fgZ34wPIUNxlnjT8HSHXNf8jPjAdCSejzt3uvUWcOKUOEC1pP4uSNyU1YHF5aHFL+AuzqcDJ+IX4YrmLBwQ02OV2eokFUiqdV8iaWbgs5kFY7Ph5/sYXpOX4ZPICjirdwppNEim/JpqM1kF+HZE3JT4h6OA3XFwtntTxvUHSV/Ha3tevKFvgMvzV+Hywm+TA87ueddOpQtgvuG9wC1pbY/CGY+ceo/tAnwXc5TG4kzeSunP7fBecnlD5k0UtWBsHmDOiNggOavPYae7A274mQ0ftRMpQKgU8afhoGAezIN7GPOZ7k+fzXnNL4Z5Y4/jMvFSWOz0Ii6/5oKfY7HHP3Dz1EtxBv6nwDxRa42SWzBWy/augHmRJ6TrIzBHdXFq6tzJ+v/O7FlkA0nn4AZ752EF1DnAvZgL9E4um/1AkHQA3vBvwJvRX6pyZU72p83yW7j53u04wLkCD4XO+tRdIZVBLsCb+xFRm3FWlStzeeaSlsTDfR/CAcL5uFQ5R0Q8Kw9vr/oH5Xp6XRi/m8NxufJ3EXFfs1b1D1mYsg3wKObRXItL8KWU+GZIe97mwDeBXSKpQdP9YWEVXbZIfOCjcUD2Cm4wneuBug9qAfEIHEweEhF/qN1fDngiIl7O5X2Vh4h/Kv2zDD5k/yYibmzQrPcEea7v2nis03G161MsYG8Dsi6QFWgjcVlhCJbAPozTxA9FUqblhmT3a8mxLokd1kq4xDAHPrF8Jzc+U8oorYCzTAthztsLWJVzTfT2x8oSaQPaBpdCXkj/3IFt/2dzlk2IRDBfCZPh34dPfM8Bf8Wlj5y5TMMj4pn0OyyKT6wjceZAWGn2hwH+E1Mdct+xBfH6XgyXx4biYHgMcHEJwZmsIP5f3JPpfpxR/UO471V2WaX+kN7TDXB/vffjMtqvI+LyXIKZbkiB15n4vf0zcHxEXNCsVQNDbgi7HOZfrwPMiLPw38zhcNqJZG+kA8gcmDu+I25xdSkekXj9lDpctwHZRJAyH8tjR7swbmUwNiIOH/BfbAiSLsRd4s8N93eZAds8HJ9UZgorALPdfFIZbSR+kZcFjomIO3LJMNUh6UDgVxHxWOLBzQashp3uSHwqPL9JG7shBcGL4YBsBA4aFsCKxexKaJI+CJwBbBsRD9eufwg/90+Sv9JvDhxIfgg3sx0G7JH4QdnZLGkVzCm8BB9Kh2CV4ki8zpcBvhj59mLsFykLvAIuu44Cto6IvzZr1YRI7+h6uGnzy+nanJhHth0+WG0YSXGZMxKlYC1gqYg4KvFWc5qigaTv4urBb6PWO1Lud/gN3AZrinGZ24CsC/rbHFOgsDru3XR1M5Z1h6w6uzAiRqafl8Gct9dwyfKHwPhcT7JdnvmiuDVAdgtV0ib4BPX5cC+dWfH4nqdx5mMJ4JGIeCUnZ9vlOVeBwoeBP+aW1QOQdAjuL7ZP4jB9HnPzbsTCiVsbNXAAdHnm8+FZfg93+dcah6QLcBbpp6mEPQIf8MDcw+ERcUfX/0ABkDRLZDrYWtKuwPoRsXH6eV58cFI6pC6Dy5WTtf3CtApJdwNbRcSYFEDug8vcD0bEibXPTZHkQEvq74L6wq4WekS8APyudn3BiJgi5L7/EFvgjuVV24g9Sc4Kk4ofjIhzmzNvYHR55mPTzx8A5svsFLsFJn2OS897V8w5GI8VW7tUH8xpo+zynF/BiqK70/XsspGYj1INO98e9zi6Fu9jh0r6SmbvYw/6+/4T7aFqyvsJPF0gG85kCtIXj4jN0s+fBo7BBPLXgW9FjUtWMN4CkHs2Phd5dbzfHLd2QR4jdxjOWD4h6dLwQPeK85nNHgMTb78h6VDgoFz2GUkb4cP/GLm/3u5YvX0MsJ6kpyOiGqk1RWzOfcJ6FuhwYNOlP2fFc/Nywh3Aa/IMwh/hmZVHRcT1uIz5SejpCdM4JA1L5OwJUD3zarMB9saE6Cwg9wMajtPb4Jf3bWDViFgSmF/uN5U1OoOz2vP+gqT1GjJrAiTO2O3AMvJEgb2ASyLiWxGxH25lMF+TNnYiOfiB7tffw++S0fpOWAdzOJH0YdylfDRWm18E7JA4N4MFx+Mu8lkgZcOG4kM1uOXS3zGP71RgS0lL5FTxkLRBta77C8Zq/nMVYEQuwVjCnMAbcuP3nXFJe7uIOBqLEjaHKRv4tgFZF6SSSL+30p9bY9VlTvgLlnL/AXeh/l6tHLIx7okFvb9D0/gOsJSkBVMGbALUNps1cRkwC6RMxqnAdyR9H4/A+XH0Nmlckikkjf5v0c2Jpo2m2iC/ilsBZIFUUjofZ8b2xeOHToYeDtksEXF7gyb2h19JmknS8onL2QkBSNoYuCEz5wQmXz8kaTccrLyMM8KBBQkLRxre3qCNA6Kyrdv+ot7+XrMB74tMmmYnu1/GavNjJI3GAcJJEXFHRPwJly4fb9DMPpC0Fu5jKEk7dvtY+vNL9M4lzgVn4RnVP8YH7JPpHR6+LMn/1A6tkx1tQFZDFYSl1HBVs+/cbKpNc2PcCiMbRMTrEbEN8GngqxHxXHIIG+LN5sr0uVw2/g8Cb+CX+HXwYq8WfO00tRHOhmRzEky4EJeIX8fE2qqlyGY49f1c9Ttkhs0kLd7fjYiIxNt7NzceWcr0bg3sj8vDFVH4q1jxN0U3y/cCSavh9grzA3tG71in/vBF4KSpZtwkIpWxr8CzNs/GzWufSbd3xAILyNSPdJTMvp8yTtTfydpeuAW9v0/jSDSCf+OM5CNY3b9HRFRBwdeBO1JAnMWaxzNC/4x7ja3R3wdqe/hqZHTgg561cBJWsq4SERen93ZeTI8YnT46xfxnyyHri2UkLYjLYxdDn9LZXNhJvZoc1izRMbKiaahXtVIfHRO4SexBjRjVBSlLcwZujjkEGCPp5OhfrTWKzOwHiIhXcWm4B5KWx8qtX6ZL0zEFX+BJRSqxro1bnxwSEUt13J8PeDHxZ7bHGZGskBxsZ7uZhTG5/ELos+E3jSfwYeN24F5Jc0ZSyUHP7zI+8bSWjYjHmjJ0IISHVdd5s0PwOhqKuanQ2xg5N3xA0j5YXDNP4gCTnrvw73FtWjNfATZpztT+ERGPSjq8vq7lvo1r4P57OeEyrNY+GLhc0nY4k/owNW6epM8Af87oXe1BOlT3ZEnT+7kacErHAXuKrPlWZZmQHvTHcJ14Jzyv8k7cc+wOeUzLeRFxi6Qv4wV2WXMW/2dQZkIEeVbY6rh+/2l8EhyN+72MT6WevSLDNiNpU5+A4KkMlaHyoPOP4wa88+BxIPfhuZUh6cfAN9LfzwJ2iIg3m7P4vUMZChEkXY1LT8vhIO1sLKl/Pt1fEYtV/tScld3R3zNN3Li5I+N5p9CTLV0E+A1ui/IkVpsfhfvXbR8R26Ry5Z4RcWhjxnZBR5avurYQDjDvbMisCZB4V8LjBXfDTafXxgrFp9LPZ0bEW6l8/Er9gJILujzvmfE+P8WVuG1A1oFExN4Mv7zL4uzSO7hUUnEmPgA8nWOE3x/UOyh1VuAnEbFrw/bMjtVxv4g0liJdnx731jkI2/nzdL1IObcyU4am739fnJF8BfPc3sSn2n9ExJZy36PFw+OIskc9KJa0BfBS0welxAkbgcsfM0TE84kOsSXmziwNLJ2y7dn1YnovUIbq0E6kjMxNONOxOQ4U3gIOjIgLVZtM0ZyV7w3Vnpj2mGejYWWopL3xfnINbvVTqf2XxPMr3x8R327OwknDxHyNprAytA3IEupfhHq7gc+Js2azA8/EFOzQOzmgNKKnn+v1yfUzRsQxDZhXt2c6rGLZCauargVOjlpzw9qGk1UHcFnBOlNE/H2Az1TP+0jg1Mho6G8dcg+jlTD34+qIeLBhk7pC0pBuTqe2Vi4Dtmya+5Y4j/viYcRj8PDzs+v0h4h4Kde9RNJCEfHkAPfre+XVwKdy+z3Ul6y/OPB8JP5bOnQsnPN6nxhqv9/vMbesUTFComqsjZX8S+NGwn/GjZqfr30uuzUvaQM8caLfYKj2rFfBVYQvTClbsiRjTm3UNvRhkk4DjpB0KU69PhIR54UJxdmdZFWgECEixkfE8bi/zgVY7v9zSU9I+oXc4Lb6bDbBWMJ7UYZ+ggyUoeoVSWwi6VhJf5V0NA7Oz4iIEyvn1M/ayQXFCBEi4vcRsSYmLc+PW1o8IWm0pDVzDsYSSleH1nE8cAJwm6SLZPXfvBHxYK5rvbKr2/6izJShyZ67cQD2Jh4sPg4Pnb9M0r5Kyu7c1ooyU4a2AVlfbI9r3gdiDtncwEmSzmzUqoGxjKTPAsdignMfIYKk2WsOKxshQipbHgrsExGjImJx4BfARsBHk805bpjFKUNrNhyKu67vjEs2p0p6Jp0Qq89mkzKXNFTSZ1Ip8pCIeKTj/nzqbeGRhRChtg6+ArwVEWtHxBK4VPYR/MyzDcY0CNSh0EPcnw9YLiJWBxbDwo8NgZslLZPTWq/QUTIrShkKfA1P+Tg2Ig7A9JNH8HSNXNujvBdl6KpMYWVoq7KkjxN6AzdSfVjSWNzXa0H8pfWUopqxckKkl3Q2POtsdeCm5LweCo8zORAroW7B2ZofNGVrP1gYkz1nBl5N147Da/LXkFdwAGUqQ2vZ3+G4fHB6unVbur8+Fq/kyNWbBT/nvYDpJf0vNSECHmvyjfTZJfFosKZRPb8lMPcUSUMj4kZJhwEzp2Ahq72khuLVobWAd3ngEUnzhdW5o4HRSpSUZq3sihKVodWafxD4vKQ7gUcj4h+SngMqDq3IT5GblTK05ZAlSFoCb0K3YlVflryf/qCChAiq8dwkHYDnER4P3IDH4rw/TC7POYtQjDK0FpDtgU+s5wCnAC9HP3zD3KCChAj1gFZWnR2LD0Pn4YPdscDeEXFthsFvH6hwdSiApP1wwHIfbnL7AHB/eLZslvuL3psydI+IOKwxY+nlQ6a/T4cPRQH8E6+dpYA1IuKd5qzsH8pQGdoGZDXII1m2wmWzpzAZ99QceCn9ocMBZC9EkPvnfAaXOVaIiNtTuXV3YFb8vH8bEQ/kYnMFFa4MlflXm+EA8g2cpr8BuC0yVsh1QhkLEVIG7zpMIH9W0rLAftgpjQGeiojGs6bdoEGgDq0dQKo/P4T3nIp/+ApweLiHYLZQIcpQSVcAX8CTSi6NiNclbYJLxC8Bd0fEX3PbzyFPZWgbkCVI2gp3Pr4v/bwRLoeMjojseBK1DWcYJhrOhJt+Xg9ckJOjqiBpbdx0r+LpPYw7gV8PvJ1TANMJFagMrdnzQZw5vSY50zXxJr8S8D85Oif1KlU3wUHkqjiAPCU6+i/lEvzKA+bvwNSA5YBzcVPV1+oZglzs7YQKV4dCnzW/Ow5mTsIOdyimbSwWESc2aGJXqDBlaMowzYqzeJcDK+NnfWREXNWgaZMEZagMbQMyQB5w/X3gNeA54C7MU3mpUcMGQG3j2Q+Xz/bBp5J18Ub094j4UpM2dkLS5Tj7uA4uQS0MfAgHk69iZ9u4KnEgSNoUj9GYE29AM+LZoT8AxmbqaDfBz/0VXH66IiLuStymcbkGCACS7sXTEO7FJPJ1MeF8h3ow3DRk7uZI3Hj349jGT2DH+jSmQpyUQ6A+MchK80XwwWMmXDI7OSKuyzkYq0Mehv4ZvBfOjblCl0TGzWxrAdkZmEO2CF43F2Oy/BM5vauSzscUiNsiYmyqzuwKbIttPyMidm7Sxm6oPeuV8GSeii++PLAQ8CscWE7V/m7TfEBW+2LmBFbBTvbT2NEeHRHnDfgfaBiS/g8YExFXJ+7SjCQhQnK6uWRrPoad1dfwTLyvpZPgonjzWRE4PiYcjZMNUtnyZmDVKqsk6duYf7B7RJyf04ZZIfGwlsGnwMVxoPAkcHBkMky5jtphYzhWV+7UcX994M5Uos/ieUs6CTvP6TE/71fydISFcYA2b0Qc3KCJA6KWkfwKnuO3S7q+OlY/zwIsWUIwVkEW4cyF9/UDcJZsnfCMziwhK0P/GBEj5XFno4BNSX4pIv7WqIE1SDod8/JG4f5cf6jdWxr4UERclIsPqqPm90/BlYPT0/V5gBOBeyLi4Km9v7QqS5whCzf6vBwrLc7Bm9AT6X4Wm34nZCHCYcCtkl4MCxHewWREoHmOQQ13Y5Lw7cB96iWDjsFqxasjw1EaHShGGVrbcGaNiNdxsHBr2nAAFgCyU8h1YAtg5ZQF7hEi1Df+XJ43/v53xcHurpJmwVMD/pWyfEMg372E8tWh9aByJOYuvQ28AFwiaQFgujChP7vvQIUpQ9P6/j4+ZC8AjEzB5N+AvyXqz32QlQ+qI0tlaJshc9+dI0llSkwqHyfpz8BamS6mHqgAIULK0KwZEX+QdBUmey6PszTnABeGCcTZbZRQtjJU0mjsZEcD50bEy5JGAStHxN452lxBhQgRZJXzQ5jcvBtWmL2JuZHXAA9GxGuNGTgR1N87la8OnR4fPmYD/ojX/X3ARbjsek7Ov4MKUYamffBM4MOY3/k0FoS8g6k/N0amClxlrAxtAzL301kOWCH9sxpWs9wUEXtmfiIsQoggt1xYMyI2k7RkRDyUyglfBL6Mn/+IyJNcXqwyFKy+xbyrDXDZ8mVcVjswIq7KzeZaubIYIYKkWzAZ/lbcOf1Jmbe3PvBRHAj/qEkbB4IKV4dWqK9lWSm3LZ5O8iTmBu/YhJOdGGprvghlaKL33Ij37SHV4Si9sytiTvMlEXFNjsGvMlaGtgGZeR5DgflwdP8uMC+uIWerKFJBQoT0ApwQEb/ucn/OlLnJ7lmrQGVobWOfHpOyZ8fcwvHAp3Dj4BuatHFiUCFChJTF+3VEjOxyfxjuVP5YjusbyleHQp81vxQO5OfFarkx8pSSpyJ1i8/td6jZXoQyVNIuwKiI+GSX+1m05OgPylwZOk0GZDWuwRdwuW9ZrGR5KGo9pnKFChIiJId1G25s+BRWy93bGTjmuFFC2crQVP7YFPM6ngYujogbm7Vq0qBChAiSvouzvCfgwP3aqEnmc4cGgTq0FtDMjykQY/HInuVwBSGHCQ4ThQpRhkq6Fq+TlzDX6vSIuLlZqyYNylwZOk0GZBUkjcHd7B/Dm9E3cMDw9ci4i3mqey8UFiJU1z6IhQjfDhNxswhwJB2EHevFeOOfHvgXzjSNwfyIbPhAdahAZWgtWF8F89w2xs51JJ73+H8RcX2TNnaDJhQiVNfnwVy9BYCdcljXFSTdBJyFs48jcND+OJbR3xoRbzRo3kShwtWh0OeA/R1g7kQ1mR0H89/E3LGLm7Vy0qDMlaGykOysiFgl7TGb4l5e7+I9/ryIeLhJGweCMleGTrMqS0kLYgXO42mxXwpcKo8MmQvI+ZS7CnCkpLoQ4VG5eeAtkJX67IvAhqlkczXm6X0YBwifxKWR3zdn3oAoThlaK4mNxPyrp3Bm8jpJb2MuWZYBWc3249LGXwkR/iHpPmC2lAnJovSXeG1ExNEpiFkE866WwgrRHSTtHxFPN2jmxFC6OrReGvsn5v+S+Fa3SHqYdCDM8XdQecrQdUj7dUTcgp/xbHj9bA6cLWnVHN7PTqgAZeg0mSGTNCTxCQ7FKqITsONdGPhBRKzUqIETgQoRIqRM3hrh8U19NpTkcNfAQcPjGW04wKBQhi4PnIazN39MXJoTccPgw3IJavqDChEipDX8wYi4rOP6MByULRgR5zRi3CRAhatDAdIhdIaIeC19H5fig9RlwJ3AycBWEXFPxu9qMcrQtLbHhTm/E9gkaY7ITBFaQQUoQ6epgEzSzPjk+li46/QCwC7AMNx5fWbcMiLLZnYVVKgQAfI+adehApWh8sDnD+C5bH+TtAbmv82Gy0934YHEL+X2PdR4QEUKEbo4p6yecSdUuDoUQNK+6a8XRsQjKQuyBQ7o5wN+HhEXNWbgRKBClaGlQYUoQ6e1gGwXYC1g16iRyiV9FAdjN0em3DEVLkTohCRBVqXVPlCBylBZgHAKLmG/mYKbxXCQNhb4Zy5clG5QQUKE3AOugaDBoQ6dk97JGS+na1VgP0cBa71YZejEIGn2zA6rRShDp2vyf94AtsUnppdkVL//GKxqGdKcaQOjtlAOBg7BysorgVGSjpU0Y1O2/SdIG4wAJH0xJ/uTs/oo8GFJO0laQ9Jc9c/UUvZZOCq5d9E8EXF2RLwJXjNhgu2bmJydpYOq3kOZJLwFDshOxTzO4yV9okHzumJSnKSkmeTxQ7lhW2AuSftJ2jwFYD2IiOcj4rH09yzWeD/YHpeZXq451Oo7+XzaV7L0cbVgbH48qmdjXKn5tsw7HBtpjmIpwVj1HcjcyrUaNqcTWwLDJd2c/OWq9Ztpr2y8IpblYp0SSOXJGUikd5wdHJ9Of2/hlzt37lgfIUJEXJoi/mWwECE7VJmw/pCe/1zA9zLLTG6NuRwPYS7Q5sAekraWtII8Yy63jfKzOFtQlebrz/4NYO+BvosmEf0IESLiuog4CiuHN2jOuv8MlXPClIgPN2lLF6wHHAG8jp3n9yQdIGmtVPYrAe/HfDGAGdIhu3ru44CNMg4mK9+7M57Luh2eGPNTYFVJGzZl2KSin/2k2g/3whnuLCBzC2eOiKWwWvtV4GhJN6Q1v0SzFvZimgjI0sJ5DrgJn757nEAKCoYAS2KpepaQhQhPYRuPkLSqpBGS1gPmiEx7H3ULWmob5+bA4VPPoknCF4FvRsSZwM8wQfgtHDD8L3ZmueGvwKzpgPFmujY0/fkJ4NV0Is/5nb8RWFvSPpKWS9dWwIRbcrFd0jySZq2t4fq9yklVgcB6pDmnuUA1dSjwK9yI9Cp8YN0COCEdYHPHRcAGkuaOiHEde80WmOBPf99T04i+ytBn0rVXw8rFShk64IF2aqPTlup5V9Wm6O2NOU9k1CuQDmVoROyPeanfw+2Lzs5mb8nrkD9lIWk7PPLmQuDyiLg5fRHfARaPiC8pMzK/ChQi1F7O9+FAd37gyYi4rfaZKmV/CfDliHixKXvrUKHK0FTyPRcT+H8GXB0Rb6Xv4BLcn+4KZcYHUoFCBEmXAkcDl6V1Pj0wLNLwZ/XyPRcHjoqIrLIdKlwdWiG9qz/AB6gzcDZ1PD50fxVYKTIkxKtgZaiszJ0fJ3PG4nYR42v3dwHGR8QvmrFwQqggZeg0FZABSPokLoHMCXwQmAMPLD4lvQCNfyl1qEAhQi0gOxJnaTbAHdZPlbQQHdOB3AAADoBJREFU8HS6vwRwZkSs1qjBE0Fum2I3pFLqHvh0/QHg37hlxNMRsVeTtnWDChMiyFy9szCR/F1ZpfVTnOl4HLfNeSd99nDg+ojItc9ekerQTkhaF5f+1sGK0XuA30TEX3I5pNahwpShtf18PWBPzEldEPdnfBaXJ3+T3oczgd1zOWCXhmkmIKtvMmkTXRgvrJki4tpGjRsAkm4E9o6IG1LKuOK+zYTLIVdGrat5LkiZvTsiYoSkG4AdIuI+SWcAP4uIO+R+U3NFGo6eO6qUfY7OqrZpzoqzHLPidPyYiLg1fSYrR5uCm3OjH6WfpNXwSfuWCf/N5iDpW8DsEbF/yhZ8DTvRk3A7lFMjdYWXtC1wdoYHpqzWwX+KLsHkohExtiGTJgoVqAyt7S2/xjy3VXD/rjswX+yIiDguZSxXiIg7GjT3P4YyUIZmUTedGqi/uBHxaERcExE3R8S1OXIMoHghwgjgCnk491spGJsd93y5GyAiniklGIO8laHRy4l8PSJuj4jrI+LUKhhL93JzwiUKEZ4FFkkZ6oOxnbtFxG/x2LU1qg9GxOjcgjEoXh3ag9oBWxUHKDyfsMevpT0nJxSnDI1envXbOAjbEPhuRByPedn3VJ8rLRhTX2Xomg2bM+0EZHX0s8lXL3Y2TjbZWLIQYQwmpx4D3JqyIXvjrNm7uQbBUKwydALUf49MHWyJQoTTgAexuGNB4DDgiXRvDeBPAJKKHEun/NWhEyCMCWgmyrP9QsnK0MOwjbcAB6bnuxqm/GQlQOiGbr4fZ/oaTw7ktNFNUdS/iM4TYo5ONm0y7wK3AftLOlCpd0pyUAdiuXSWwU24h84JwNm44eGRwCw4qwC9L0J26JZBUN7K0AmQgpmcHezNmLz/J0nrS5opeoUIX8IjqrJCeie/jwc/rxoRz2LHuibu9XZN+lyOZPKi1aHdMEAgkFX7hYQilaFpP78PVwiOxoHlgcDhlQ/KMANfnDJ0muGQdYN6FVE74u/rpKZt6oQKESLUnuVCwGeAf0XEBZKGRhpVkStUoDJU0jyYB/lWdBCXa7ZWf14KfKlpmzuhgoQIktYCPoJ7d10QEf9M12cA/gcrua5QhkRyKF8dWqE/7lh1nV5ax5x4FM7Hp76F3aGClKG1vWNe3Iz8TVyyvxMnCv5VD3ByDMigLGVokWn1ScWkOFl6T4Sfx6TcbFAt8vB8rb/TRYiQQzAGfXrrXIBf2I9L+iVwsaSzIuKPuTqrGg6ipgwFbtOEytD3ZxLYnE5ysNBzqh6WuHnR4WDHZ2JzD9L7OU7SsWQuRJBnPB6EB28H3jdOkrQSniU7Pal0k+P6TpSBYbjdz3jV1KGS+qhDsWLxxIZMnSjS2u7mZKu1siV+P7JCsnE/eTTbzritS6UM3S0i3slojxR+njvgZ30xsABuS7MxLr3+EvLjp2oiylBJPcpQ3F5n9+as7cWgDshqKMnJ9qC+yFM6tSelmtFLC/Q5TY0AXo+I3dL1hYDdcPO9lcOjfLJDWgczA5+JXmXoTen2D3BvrztwdmS7ZqzsxWBwsFETIuCNEhzw1D+Ty0Y/Cvh6OhytCPxE0uY4EFsEuDQifteohQNjc+CKFKBX6tAh2Ml+GWe0L06fvZfEhcsJpTrZOmqH7MuBy9O1PsrQXPb12kF/CN5PbpXV/ctivuQTkM+hqQt2whSDShl6D73K0HdTxvKnufj+Qc0h63CyuwJP0dfJrpD+/jpWv2SLbmRE5SNEqOxbGnhe0sqSZouIJyPigIiYO9dgrIZJVYbe36SRCZ0O9lv0OtgVsYOtkKWD7Q/1da5MhAgpw7458CJARNyJW11cAuwcEUtiTllVksoRxatDa6ic7F/we3kPDiqH5ehk66iX+JSxMrSyJ5V+X8AzZT8WEW9FxG3hKQ9/gqwOTT2IQpWhuW4ekxPFtl+oO6fORR+ZCRFqp6lVceZmV2ATSatLWiRtQLmrcEpShg4mB9uDqtSafsxFiPAynm96uqQHJB2H+WJHR8Qj0JPly4Y+0A9Oo3B1aKlOtj+kLFnOytBqr/4JbgdxD3CBpLGSjpHnKpeAopShg57Un17grwE74uzBycBWeHTINrmV/iYFylCIUE9bS5oNZ2jWxbygscAhYaVO1kgp+X2weGJ+HKSdEBGPKhPxBPTwxQ7C5bIP455ez6WA5kocqF8jaYbIhCQM5QsRZILzVlhhdhcm9x+fedkG6BFQvB94PnGVhgCrA8dExPLNWjdpSDYvBdwPHArMhScnnAyMSPtiCd9F51i2qhz7W2CvyEDxByDpQmDbSA1TU0BzMHB+Ces+7ZPTYaX/UVgYd35E/DJH3z/oAzIox8lCsWq/yuYV8dDWZYBzIuLyVE4bERFn5fjyqmxlaHEOVoNE6Qc9PL6D8bSBbEbddEKFq0M7UZqThbKUoTX/MgJXC+4ERgP35uQr+0PN9iKVoYMyICvcyRY5B1Lm6t2IX9whOIvwIvDZ6G36mS0k3UxShgIL4WxqlsrQUh2sBtkcyBKgCdWh90REXR26MOYivtGgmQOidCdbQQW1XwCQ+Zufw/vhk/iZPw7cEBHPNWlbN9T85/6YyF8pQ4fhMuzdEfHLJm0cCNnyBf4bRMHtF+K9qf0aFyLUMoybA/dHxBHp1uGSDsal4qObsm8gdJwEi1CG9uNgS2q/ULzSr0CUrg6Ftv1CI4iIG4Eb5RF+6wGL41F9D+MpMtkhCleGDrqArEQn2w8mSYgAPNOgjSQ7qhfgZeDljoX+Ci5f1gO3nFBt9D3KUOC+iHgSq+YOaNK4LijZwT4LLCcLEQ7Cp+3dIuLvKUBbgxSQRcTo5swcHFCvOvQwsDpU0nw4eLkkIh6Rh9Hn+n4C5TvZhCLaL9SqS2vgA9JOwL4RcVqiSCwREfc2aWM3qG/n/UoZuntE3IATM7elEnF2gXuFwaiyHAztF4pQ+0maTSbwkzg0cwBjJO0n6Ys4eDirSRsHQhSmDFX57RdOo3ClX2EoXh2qtv3C1Ea1Dg7Dh6MbgLnTtc1whi9XFK8MHZQcMgBJP8SnkbHA1TjAeRr4O+T58tahAoQIkrbGz/g24MaIeChl9bbH2bHfRMSVTdo4EOonahWgDE2B+BbAfsDMwJXAKhHx0UYNew9QgUKEwQAVqg6tZWx+id/Lcfg9HYcDhh9gXm3Ov0NRylBJw/Ec2bUxL3jjiHhO0nU4W3ZzowZOBCpYGTroArLSnGwFFShEkPQZYDlgOA4QnsHNGq+rkbOzXPwqWBkK5TlYFSpEGIxQIerQOkp2slCWMjRl4rfH9i4RETtLWhaLyEY2a13/6KAqFaUMrWNQBWSlO1koS+0HPSWF9+Gy39LALHge4V3AsRHxVoPmDQgVrgytkLuD1SBQ+rWY+ijZydZsL1IZKml7/M6+CpyKm9XeEBGH5+iHKqhAZWgdgyoggzKdbMfGc1xErJOuV0KEXYGshAiSlk5/nRcHY49hvsoiWI3zSER8pCHzBkQtcN8G2CAiRtXuHQy8mLgpLSYDJJ0LHB81IQK9itBKiLBHkza2yBclOlkV2H6hkwqTKkw7Yr7nScCjEfHvHAPITqivMnRR4PCIuKdRoyYBg4Y8q4LbL1CY2k/S3MBfccB7MLb9I8ApOED7C4l4nuNpKspWhhYFDRKlX4vmEG37hamCFEAKxwUREa8BR0haBFgx0gzfXOytoIKVoZ0YNAFZyU42+lf7XS0pVyHCK5hjsAfuJfVtXJ58pfODuQVj6lWFvhYRF0kahZWhp2OO4SgspmgxeVBX+lVChHH1DGRkrvRrMfVRspNVYe0Xkp2HA0eGZzq/na7PnKpKPwOuS9eyCSBrqCtD98O85roy9Kb+/qUckaM0/j1DhbdfqL2c++NGh5diDlk1g3OGnF6CiHg3Is6LiDWAFXBW7CFJN0jaGXp/pwzxOeAwSdtIWjIitsRtGJYFPgF8MyKugzZAmBxIa+XsiFgRr+kH8FD0KyTtClmvlRbNoW2/MPXwOn6+Z0l6UNIBkuapUXxWImP/meg+w/FzvxWXhs9Jt3fB/qkIDAoOmQpuvzAYhAgVJC2JCaxZksuhbGXoYELuQoQWzUNt+4WpDknzANsB++Im5P8A5o2IT+dYXaqgApWh/WGwBGRFO9kShQglo2RlaIsW0wpKdLIlK0M7kQ5N+wGXRcS5OfKB6yhVGVrHoAjIoEwn26r9pi5KVoa2aDEtolQnW6IytEQMJmUoDAJSfxcnuwa9Tna5iPhpQ+YNiJKFCKWhdGVoixbTCur7XUScKul8ep3s/sCj6aPZ7oklKkNLRKnK0G4oOkOWnOyT9HWys+PyX4+TjYgXcnOydbVf+vlsYHmgUvvtDewTEde1Adl/D7lT9mZYGbooVob+uj9laIsWLZpFh5OtaCeVk82ScziAMvT03JWhpaEfZWh1feaIeFPSBZiydFQp2TEoX2VZtV+4FSsSnwN+GBG/j4hTIuKBiHgB8mu/QKv2m6ooXBnaosWgh6Q5JZ0oaekw3g7PO505feRn+DCV67tasjK0NBStDO2GojNkdSR1yA7AXsAjwOiIODHX6Lh0IcJgQQnK0BYtpgXIc023BvbEs4dPA34REf9I98fiiSUv5Lo3lq4MLRGlKkP7w6AJyOooxcmWKERo0aJFiymNUp1sicrQwYTSlKGdGJQBWe5o1X4tWrRoMWkozcmWqgxt0TzagGwqo2QhQosWLVq0mBCDrf1Ci2bQBmRTGa3ar0WLFi0GH0pUhrbIC6WrLItDq/Zr0aJFi8GBQaAMbZER2gxZJihFiNCiRYsWLYzBoAxtkQ/agKxFixYtWrT4L1GqMrRFPmgDshYtWrRo0WIyojRlaIs80AZkLVq0aNGiRYsWDaMl9bdo0aJFixYtWjSMNiBr0aJFixYtWrRoGG1A1qJFixYtWrRo0TDagKxFixYtWrRo0aJhtAFZixYtWrRo0aJFw/h/1zmfZc37tuoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table_lr_l1 = experiment_pipeline(LogisticRegression(class_weight='balanced', penalty='l1'),X, y, X_remove_stopword, X_lemmatized)\n",
    "summary_bar(table_lr_l1, 'Logistic Regression with L1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3e0c61e78d9c4c21b3b3a5c71fbd828b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=12), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table_500_lr_l1 = experiment_pipeline(LogisticRegression(class_weight='balanced', penalty='l1'),X, y, X_500_remove_stopword, X_500_lemmatized)\n",
    "summary_bar(table_500_lr_l1, 'Logistic Regression with 500 cutoff and L1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "11b2f25b49084118bedfaa01c86e2b4d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=12), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table_lr_l2 = experiment_pipeline(LogisticRegression(class_weight='balanced', penalty='l2', random_state=42),X, y, X_remove_stopword, X_lemmatized)\n",
    "summary_bar(table_lr_l2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "50cd20bf417341579506e01e1ebefaed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=12), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "table_rf = experiment_pipeline(RandomForestClassifier(random_state=42),X, y, X_remove_stopword, X_lemmatized)\n",
    "summary_bar(table_rf, 'Random Forest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Combination</th>\n",
       "      <th>test_neg</th>\n",
       "      <th>test_pos</th>\n",
       "      <th>train_neg</th>\n",
       "      <th>train_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None_Count_None</td>\n",
       "      <td>0.562212</td>\n",
       "      <td>0.907856</td>\n",
       "      <td>0.968801</td>\n",
       "      <td>0.991728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None_Count_Smote</td>\n",
       "      <td>0.516605</td>\n",
       "      <td>0.865916</td>\n",
       "      <td>0.993001</td>\n",
       "      <td>0.993001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None_Tfidf_None</td>\n",
       "      <td>0.548077</td>\n",
       "      <td>0.909615</td>\n",
       "      <td>0.968903</td>\n",
       "      <td>0.991721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None_Tfidf_Smote</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>0.881023</td>\n",
       "      <td>0.990794</td>\n",
       "      <td>0.990834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RS_Count_None</td>\n",
       "      <td>0.566210</td>\n",
       "      <td>0.907677</td>\n",
       "      <td>0.968903</td>\n",
       "      <td>0.991721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RS_Count_Smote</td>\n",
       "      <td>0.519298</td>\n",
       "      <td>0.857736</td>\n",
       "      <td>0.990376</td>\n",
       "      <td>0.990376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RS_Tfidf_None</td>\n",
       "      <td>0.521327</td>\n",
       "      <td>0.902604</td>\n",
       "      <td>0.964052</td>\n",
       "      <td>0.990410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>RS_Tfidf_Smote</td>\n",
       "      <td>0.524345</td>\n",
       "      <td>0.870540</td>\n",
       "      <td>0.992119</td>\n",
       "      <td>0.992133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lema_Count_None</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.896947</td>\n",
       "      <td>0.961983</td>\n",
       "      <td>0.990004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lema_Count_Smote</td>\n",
       "      <td>0.471831</td>\n",
       "      <td>0.844398</td>\n",
       "      <td>0.991663</td>\n",
       "      <td>0.991714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Lema_Tfidf_None</td>\n",
       "      <td>0.527363</td>\n",
       "      <td>0.909265</td>\n",
       "      <td>0.968699</td>\n",
       "      <td>0.991736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lema_Tfidf_Smote</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.894260</td>\n",
       "      <td>0.992119</td>\n",
       "      <td>0.992133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Combination  test_neg  test_pos  train_neg  train_pos\n",
       "0    None_Count_None  0.562212  0.907856   0.968801   0.991728\n",
       "1   None_Count_Smote  0.516605  0.865916   0.993001   0.993001\n",
       "2    None_Tfidf_None  0.548077  0.909615   0.968903   0.991721\n",
       "3   None_Tfidf_Smote  0.476190  0.881023   0.990794   0.990834\n",
       "4      RS_Count_None  0.566210  0.907677   0.968903   0.991721\n",
       "5     RS_Count_Smote  0.519298  0.857736   0.990376   0.990376\n",
       "6      RS_Tfidf_None  0.521327  0.902604   0.964052   0.990410\n",
       "7     RS_Tfidf_Smote  0.524345  0.870540   0.992119   0.992133\n",
       "8    Lema_Count_None  0.460000  0.896947   0.961983   0.990004\n",
       "9   Lema_Count_Smote  0.471831  0.844398   0.991663   0.991714\n",
       "10   Lema_Tfidf_None  0.527363  0.909265   0.968699   0.991736\n",
       "11  Lema_Tfidf_Smote  0.588235  0.894260   0.992119   0.992133"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ea5b7a052f14d1abcb641e210ccd236",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=12), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "table_rf_500 = experiment_pipeline(RandomForestClassifier(random_state=42),X, y, X_500_remove_stopword, X_500_lemmatized)\n",
    "summary_bar(table_rf_500, 'Random Forest with 500 word cutoff')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b06bf675af74796a3c78510affa8dab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=12), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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ipoRMREREJGLqsoxSIs3EkJpNxWriFhERKTVqIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmK6yFBER+bXS+ap5SQlqIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmK6yFJHk0tVnIiJxqYVMREREJGJJTcjMrLOZLTKzJWY2oJgyF5jZQjP7zMxGJzMeERERkVSUtC5LM8sAngA6AsuBmWY21t0XxpQ5ArgNONXdfzSz/ZMVj4iIiEiqSmYL2UnAEndf6u5bgRygW6EylwNPuPuPAO6+MonxiIiIiKQkc/fk7NisO9DZ3S8Ll/sAJ7v7tTFlXgMWA6cCGcAgd/93EfvqB/QDqFevXvOcnJyCBVasiB9Q1apxi6yp5GypsSVuuWpUi1umNrXjx5RI3FBqsZda3KA6R3UOqnNAdY7qHFCdozqHouNu27btbHfPivfcuF2WZlYNuAmo7+6Xh92MR7n7m/GeWsS6wtlfReAIIBs4GJhiZpnu/lOBJ7k/DTwNkJWV5dnZ2QX3kshVXAlcwfVC3e0syl4Ut1xTmsYtk012/JhK8eqzRGIvtbhBdY7qHFTngOoc1TmgOkd1DrtR50VIpMvyWWAL0CJcXg7cl8DzlgOHxCwfDHxXRJnX3X2bu/8HWESQoImIiIiUG4kkZIe7+/3ANgB330zRrV+FzQSOMLPDzKwy0BMYW6jMa0BbADPbFzgSWJpg7CIiIiJlQiIJ2VYz24uwu9HMDidoMSuRu+cC1wITgM+Bl9z9MzO7x8y6hsUmAKvNbCHwHnCLu6/+Be9DREREJG0lMu3FXcC/gUPMbBTBAPyLE9m5u48HxhdaNzDmsQM3hv9EREREyqUSEzIzM+AL4FzgFIKuyj+4+6o9EJuIiIhIuVBiQububmavuXtzYNweiklERESkXElkDNnHZnZi0iMRERERKacSGUPWFrjCzL4BNhJ0W7q7H5/UyERERETKiUQSstOTHoWIiIhIORa3y9LdvwHqAF3Cf3XCdSIiIiJSCuImZGb2B2AUsH/4759mdl2yAxMREREpLxLpsuxLcFPwjQBm9n/AR8BjyQxMREREpLxI5CpLA7bHLG8nsVsniYiIiEgCEmkhexaYbmZjwuWzgeHJC0lERESkfImbkLn7X83sfaAVQcvYJe4+N9mBiYiIiJQXcRMyMzsF+Mzd54TLNc3sZHefnvToRERERMqBRMaQPQVsiFneGK4TERERkVKQ0KB+d/e8BXffQWJjz0REREQkAYkkZEvN7HozqxT++wOwNNmBiYiIiJQXiSRkVwItgf+G/04G+iUzKBEREZHyJJGrLFcCPfdALCIiIiLlUrEtZGZ2uZkdET42MxthZmvN7BMza7bnQhQREREp20rqsvwD8HX4uBdwAtAQuBF4JLlhiYiIiJQfJSVkue6+LXx8FvC8u69290lA9eSHJiIiIlI+lJSQ7TCzA8ysKtAemBSzba/khiUiIiJSfpQ0qH8gMAvIAMa6+2cAZtYGTXshIiIiUmqKTcjc/U0zOxSo6e4/xmyaBfRIemQiIiIi5USJ0164ey7wY6F1G5MakYiIiEg5k8jEsCIiIiKSRErIRERERCL2ixIyMzu6tAMRERERKa9+aQvZ26UahYiIiEg5VuygfjN7tLhNQJ3khCMiIiJS/pR0leUlwE3AliK29UpOOCIiIiLlT0kJ2UxggbtPK7zBzAYlLSIRERGRcqakhKw78HNRG9z9sOSEIyIiIlL+lDSov4a7b9pjkYiIiIiUUyUlZK/lPTCzV/ZALCIiIiLlUkkJmcU8bpjsQERERETKq5ISMi/msYiIiIiUopIG9Z9gZusIWsr2Ch8TLru710p6dCIiIiLlQLEJmbtn7MlARERERMor3VxcREREJGJKyEREREQipoRMREREJGJKyEREREQipoRMREREJGJJTcjMrLOZLTKzJWY2oIRy3c3MzSwrmfGIiIiIpKKkJWRmlgE8AZwONAZ6mVnjIsrVBK4HpicrFhEREZFUlswWspOAJe6+1N23AjlAtyLK3QvcD/ycxFhEREREUpa5J+euSGbWHejs7peFy32Ak9392pgyTYE73P08M3sfuNndZxWxr35AP4B69eo1z8nJKVhgxYr4AVWtGrfImkrOlhpb4parRrW4ZWpTO35MicQNpRZ7qcUNqnNU56A6B1TnqM4B1Tmqcyg67rZt285297hDskq6ddKvZUWsy8/+zKwCMBS4ON6O3P1p4GmArKwsz87OLlhg0KD40TTepbd0Fy/U3c6i7EVxyzWladwy2WTHjymRuKHUYi+1uEF1juocVOeA6hzVOaA6R3UOu1HnRUhml+Vy4JCY5YOB72KWawKZwPtm9jVwCjBWA/tFRESkvElmQjYTOMLMDjOzykBPYGzeRndf6+77unsDd28AfAx0LarLUkRERKQsS1pC5u65wLXABOBz4CV3/8zM7jGzrsl6XREREZF0k8wxZLj7eGB8oXUDiymbncxYRERERFKVZuoXERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiZgSMhEREZGIVYw6gNKwrXJlljdrxs+1axdfqFKluPs5IgMafN4gbrnKVI5b5nM+j1uGTp3il4FSi73U4obEYi8h7qpbtnDw998D2xN7PRERkTKsTCRky5s1o2bDhjSoXh0zK7pQ1apx97O6krOl5pa45apRLW6ZOtSJW4bvvotfBkot9lKLGxKLvZi43Z3V69ezHGDbssReT0REpAwrE12WP9euzT4lJWOSUsyMfWrW5OcqVaIORUREJCWUiYQMUDKWZvT3EhER2anMJGQiIiIi6apMjCHbxUMP7bquYvy3ulcFp3KVXQeZrx90U4nPW7N6Dd3adwNg5fcrycjIYP/99gdgxowZVK4cfzA9wIicHM5o147f7L//Ltt6X3EFk6dN46vZs6lcuTLf/+9/tDrzTJbMmpXQvhP16quv0rhxY44++mgAbr/9djp06EDbtm1L9XVERERkp7KZkO1he++zN1PmTQFgyKAhVK9RnTtvvnO39zMiJ4dmmZlFJmQQdPM9l5PD5Rdd9KviLcmrr75KhQoV8hOyP//5z0l7LREREQmoyzLJnnvuOU466SSaNGnC1VdfzY4dO8jNzaVPnz4c1749me3a8ejw4bz4+uvM++wzelx1FU06dmTr1q277Kv/VVfx4OOPs337rq14j/z1Ec486Uw6HN+BofcMzV//4F0P0vro1vTs2JNLe1zKUw8/BcCIYSNod2I7Wp3QiovPv5jNmzczbco0xo8fT//+/WnSpAlff/01vXv35rXXXuONN97gwgsvzN/vpA8+4Jy+fQF46913adGlC806daLHlVeycdOm0q5GERGRMk0JWRItWLCAMWPGMG3aNObNm0dubi45OTnMnj2bVatW8ek777Dg3Xe5qHt3enTrRpNjj+XFp55i3sSJRXZzHla/Pic3b87ol18usH78xIks/3Y5b05/k7fnvc2sabOYOW0msz+ezaQ3JzFx/kT+/srfmTNzTv5zzj7/bN6d+S5T50+lweENGP2P0bQ8rSVnnHEGQ4cOZd68eTRo0CC/fKdOnZgyZQqbN28G4MU33qBH166sXLWKIU88wTsvvcScCRM4/phjeOSZZ5JToSIiImWUuiyTaNKkScycOZOsrCwANm/ezCGHHEKnTp1YtGgRfxg4kDPateO3bdokvM8/9e9P90suoX3r1vnr3n7vPd6Z9A7Tm04HYNOGTSxdvJQfV/1Ip7M7UaVKFapUqUKns3ZO5vrZJ58xeOBg1v60lg3rNxTYVpTKlSvTsWNHxo0bR7du3Zjw/vs8fPfdTPzgAxYuXkzLrl0B2LptG61OOinh9yMiIiJKyJLK3bn00ku59957d9n2ySef8NaoUTw6fDivjB/P0/ffn9A+jz7ySBofdRSvvvlmgde58ZYbOf+a8wuUfeqBp4rdz5UXXcm/3voXjTMb8/wzzzPr4/gXB/To0YPhw4dTrVo1WjRvTvVq1XB3OmdnM/KxxxKKX0RERHalLssk6tChAy+99BKrVq0CYPXq1SxbtowffvgBd+f8Ll24++abmfPppwDUrFGD9Rs3xt3v7TfeyAOPP56/3KldO0Y9P4pNG4OxW98t/441q9ZwYqsTmTh2Ilu2bGHD+g1MHD8x/zmbNm6i3m/qsW3bNl4evbMLtGbNmqxfv77I123fvj3Tp09n+PDh9AhbxFpmZTH5449Z+s03AGzctIkvly7dnWoSEREp98pmC9lNRUxTkcDthzYneOukRB133HHcdddddOjQgR07dlCpUiWGDRtGRkYGffv2xbduxcz4v9tvB+CSCy7gsptvZq+qVZkxblyx02WckJnJCZmZLFy0CIAzOnZk9tLFdDmlCwDVa1bnidFPkNUii+zO2XQ4vgOHNDiEpic2pVbtWgDcds9ttD+pPQfXP5hjMo9hy8/B++7VqxdXXHEFDz30EK+99lqB161YsSKnn346o0ePZtQDDwBQb7/9GP7QQ/S46qr8CxEGDxjAEQ0bllo9ioiIlHVJTcjMrDPwCJABPOPuQwptvxG4DMgFfgAudfdvkhlTsg0YNKDA8oUXXljg6sQ8c+fO3eV+kBd07coFYctTYf/8298KLI8dNarA8tXXXk3f2/ru8rxr/ngNt957K5s2buLcVufS/7b+APS7th/9ru23S/nWrVvz+ec7bzD+z3/+s8D2YcOGMWzYsAKxd2zdmo4xY9pERERk9yQtITOzDOAJoCOwHJhpZmPdfWFMsblAlrtvMrOrgPuBHsmKqTy6qe9NfLXoK7b8vIU+l/Yh8/jMqEMSERGRQpLZQnYSsMTdlwKYWQ7QDchPyNz9vZjyHwO9kxhPuTTsxWH5j6tRLcJIREREpDjm7snZsVl3oLO7XxYu9wFOdvdriyn/OPC9u99XxLZ+QD+AevXqNc/JySmwvXaNGjQ67LCSA6oQ//qFXIMdGTvilssgo1TKUMTkr0UqpdhLLW5ILPY4cS9ZupT/rPiGLTXij9tLJJmsTe34Ma1YEb8MJDTmcE0ljxt7qcUNicVeSnGD6hxQnaM6B9U5oDrnl8fdtm3b2e6eFe+5yWwhsyLWFZn9mVlvIAsockIud38aeBogKyvLs7OzC2z//KOPqLklzkGQwB9kdcXEBvUn8kepSc24ZQqPIStWKcVeanFDYrHHibuqBx+ARdmL4u6qKU3jlskmO35MgwbFLwPQuHHcIi/U3R439lKLGxKLvZTiBtU5oDpHdQ6qc0B1TinXeRGSmZAtBw6JWT4Y2OVb3Mw6ALcDbdy99C5xFBEREUkTyZyHbCZwhJkdZmaVgZ7A2NgCZtYU+BvQ1d1XJjEWERERkZSVtBYyd881s2uBCQTTXoxw98/M7B5glruPBR4AagD/MjOAZe5e9LwPu+Ott3ZdVzH+W92rglO5yq437l5/weklPm/N6jV0a98NgJXfryQjI4P999sfgBkzZhQ7n1hhI3JyOKNdO36z//67bOt9xRV8OGMGtWvVIiMjgyfvv5+Ts+J2SRfw5pg3WbpkKdffcj1vvPoGRzU+iiOPPhKAe2+/l+wO2XRp22W39ikiIiK/XlLnIXP38cD4QusGxjzukMzX31P23mdvpsybAsCQQUOoXqM6d958527vZ0RODs0yM4tMyACG3ncfZ595JuMnTuSqW25hznvvFVmuOGedc1b+4zdefYMKFSrkJ2R3/nn34xUREZHSoVsnJdlzzz3HSSedRJMmTbj66qvZsWMHubm59OnTh+PatyezXTseHT6cF19/nXmffUaPq66iSceO+bPeF6V1ixYsCW9PNGf+fE7u2JHWLVpiVvs3AAAgAElEQVRz+XmXs27tOgCeHvo02Y2z6XBCB67rfR0Azz/zPLfdcBvTpkxj4viJ/Kn/nzityWks+3oZ/Xr3Y9xr43jjjTcKTGQ7adIkzjnnHADeeustWrRoQbNmzehx5ZVs3LQpWdUmIiJSrighS6IFCxYwZswYpk2bxrx588jNzSUnJ4fZs2ezatUqPn3nHRa8+y4Xde9Oj27daHLssbz41FPMmzixxG7ONyZM4LjwqpHeV17JQ/feywcffUDDoxry8L0PA/DU/U/x9ry3mTR/Evc9XnAmkZantaTjGR0ZPHQwU+ZNoX6D+vnbOnXqxJQpU9i8eTMAL774Ij169GDlypUMGTKEd955hzlz5nD8McfwyDPPlHaViYiIlEtKyJJo0qRJzJw5k6ysLJo0acLkyZP56quvaNSoEYsWLeIPAwcy4f33qV2rVkL763/HHTRp04ZnR4/m70OHsnrNGn7esoVWp5wCwPm/P5/pH0wH4Mhjj+S63tfx6qhXqVSpUsIxV65cmY4dOzJu3Di2bdvGhAkT6NKlC9OmTWPhwoW0bNmSJk2aMGrMGL5evnz3K0VERER2UTZvLp4i3J1LL72Ue++9d5dtn3zyCW+NGsWjw4fzyvjxPH3//XH3lzeGLM+q1auLLTt6wmg+mvwRb7/+No/c9wjvLng34bh79OjB8OHDqVatGi1atKB69eq4O507d2bkyJFBoUTnUBMREZG41EKWRB06dOCll15i1apVAKxevZply5bxww8/4O6c36ULd998M3M+/RSAmjVqsH7jxoT3v+8++7BX1apMmzEDgFdGvsIpbU5h+/btrFi+glbtWnHHA3ew+ofVbN60ucBza9Sswfr164vcb/v27Zk+fTrDhw+nR4/g1qItW7Zk8uTJLA3Hrm3ctIkvw8ciIiLy65TNFrLTi5imIoHZ7jdXSmym/kQdd9xx3HXXXXTo0IEdO3ZQqVIlhg0bRkZGBn379sW3bsXM+L/bbwfgkgsu4LKbb2avqlWZMW5cQtNljHzqKa66+WY2bPmZQ488lL8++1dyc3O55sJr2Lh+Izt27OCaP15DjZo1CjzvvF7nceMVN/LEQ08w6rVRBbZVrFiR008/ndGjRzNqVLCtXr16+Qna1q1bYds2Bg8YwBENG5ZSbYmIiJRfZTMhi9CAQQMKLF944YUFrlrMM3fu3F26/S7o2pULuhY9Dds///a3Itc3O+EEpk+cyOpCyeTrH76+S9mLLrso//GprU9l+ufT85ef/ufTBcoOGzaMYcOGFVjXsWNHOnbsGCyoy1JERKTUqMtSREREJGJKyEREREQipoRMREREJGJKyEREREQipoRMREREJGJKyEREREQiVianvRhU86FdV1aM/1Y3VXC2s32X9TdxU4nPW7N6Dd3adwNg5fcrycjIYP/99gdgxowZCc0ndkn//gy45hqOatQoblmAZ0aOpF///nw6dSrHHn00AK2Pbk3OpBwOPPjAhPaRiDlz5rBy5Uo6d+4MwJgxY1iyZAm3/O53pfYaIiIi5V2ZTMj2tL332Zsp86YAMGTQEKrXqM6dN99ZoIy74+5UqFB0o+SzQ4fu9usefOCBDB46lFHFzFFWGubMmcOCBQvyE7Jzzjkn2KB5yEREREqNuiyTaMmSJWRmZnLllVfSrFkzVqxYQb9+/cjKyuLYY4/lnpgkrNXZZzNvwQJyc3Opc8wxDBg8mBM6dKBFly6s/OGHIvd/9hlnMGf+fJYUcQujd996ly4tutCpWSeu7HElmzZuAmD82PGceNSJnH7a6dx63a387uygpWvmxzP5bYvf0rRpU0499VS+/PJLNm/ezD333MOoUaNo0qQJL7/8Ms888ww33HADa378kcNOOQV3B2DDxo3UP/FEcnNz+XLpUjpdeCHN27Wj9VlnsXjJktKuWhERkTJFCVmSLVy4kL59+zJ37lwOOugghgwZwqxZs5g/fz4TP/iAhYsX7/KctevW0eaUU5g/aRItmjdnxKhRRewZKlSowC3XXcdfHn64wPpVK1fxxJAneOmdl5gwZwLHHH8MzzzyDJs2beKWq2/hlbdfYfwH41n5/cr85xx5zJG8NfUt5s6dy5133skdd9zBXnvtxcCBA/nd737HvHnz6N69e375vevWpfERRzA1vI/m6xMmcEa7dlSsWJF+t97Kk4MHM/vdd/nLnXdy7R//WBpVKSIiUmapyzLJDj/8cE488cT85RdeeIHhw4eTm5vLd8uXs3DxYhofeWSB5+xVtSqnt2sHQPPjj2fK7NnF7r/PBRfwl4cfZvm3y/PXzZo2i8ULF9O1ZXAbpm1bt3FSq5NYtHARjY5qRP1D6wPB/Sxzns8BYO1Pa7nqoqtY9tWyhN9bj65deXHsWE47+WRyXn+dG/v146e1a/l47lzOu/xyCLtnc3NzE96niIhIeaSELMmqV6+e//jLL7/kkUceYcaMGdSpU4fe553Hz1t2vZl57EUAGRkZJSY0lSpVov+VV/LYw4/lr3N3sjtn89jIxwqU/WLmF8Xu577b76Ndp3bcfPXNLFmyJH/MWEnO7tyZgQ8+yN033cSnX3xBmxYtWLtuHfvWrcu8iRMTuqG7iIiIqMtyj1q3bh01a9akVq1arFixggnvv18q++3buzfvTHyHn9b8BEBWyyw+nvwx3yz9BoBNGzex9MulHH3s0SxZtITl3y7H3Rnz4pidsa1dx4EHBVdn/uMf/8hfX7NmTdavX1/k69aqWZOmmZnccNdddP3tb6lQoQJ169ThgHr1GPPWWwDs2LGD+QsWlMr7FBERKavKZAvZoPVFTFORQGvN6krOliq7tliVlmbNmtG4cWMyMzNp2LAhp8Z0Zf4aVapU4dJ+lzLwTwMB2K/efjw0/CGu6nEVW7duBWDA4AFkHpHJ/Y/fzzkdzmGf/fah2YnN+HHNjwD84Y9/4LpLr+OJ+5+gbdu2+ftu164dDzzwAE2bNuX222/f5bV7dO1Kr6uvZuprr+Wvy3nySa667TYGDR3K1q1b6X3++ZyQmVkq71VERKQsKpMJWZQGDBqQ/7hRo0bMmzcvf9nMGDly5M7CMVNHxCY0P33+ef7jnt260bNHj11e57I+fQosX33t1fS9rW/+cuuOrWndsfUuz2vToQ0zF83E3el/RX+aZjUFoEWrFsxaPIs61AHgvvvuA2C//fZj1qxZu77RMPae3brRs1u3ApsaHnooE0aPVpeliIhIgtRlWc6MeGoEpzU5jVMan8LmzZu56PKLog5JRESk3FMLWTlz/S3Xc/0t10cdhoiIiMRQC5mIiIhIxJSQiYiIiERMCZmIiIhIxJSQiYiIiESsTA7qf73mu7uurBj/rW6uALnsOit+O9qV+Lw1q9fQrX0w9cPK71eSkZHB/vvtD8CMGTMKzLxfnEv692fANddwVKNGccsCPDNyJLfdey8HHXAAW7Zs4aobrqX71d3jPzHG8m+XM/DmgYx4cQTz58znh5U/0L1zsI8xY8awZMkSbrnllt3ap4iIiOy+MpmQ7Wl777M3U+ZNAWDIoCFUr1GdO2++s0AZd8fdqVCh6EbJZ4cO3e3X/V337jw8eDDf/+9/HNuqFe0uaMfe++6d8PMPPuRgRrw4AoD5c+bz+YLP8xOyc845Z7fjERERkV9GXZZJtGTJEjIzM7nyyitp1qwZK1asoF+/fmRlZXHsscdyT0wS1urss5m3YAG5ubnUOeYYBgwezAkdOtCiSxdW/vBDia/zm3r1OOTQQ/jvsv+yZtUaLu56MR2O70DXll35YkFw/8qp706l1QmtOK3JabRp1oaNGzeydMlSTmtyGps3b+b+e+7nX6P+RZMmTXj55Zd55plnuOGGG1izZg2HHXYY7g7Ahg0bqF+/Prm5uXy5dCmdLryQ5p070/rcc1n81VfJq0wREZEyTAlZki1cuJC+ffsyd+5cDjroIIYMGcKsWbOYP38+Ez/4gIWLF+/ynLXr1tHmlFOYP2kSLZo3Z8SoUSW+xpKlS1m+bDn1G9bn/jvvp+nJTZn0ySRuGnQT/S/uD8CwB4bx8NMPM2XeFMZ9MI6qMbPo77XXXtw68FbO/935zJs3j+7dd3Z97r333jRu3JipU6cC8Prrr3PGGWdQsWJF+t16K08OHszsf/+bv9x2G9fecUdpVJmIiEi5oy7LJDv88MM5MeaelS+88ALDhw8nNzeX75YvZ+HixTQ+8sgCz9mralVObxeMW2t+/PFMmT27yH2PevllJk+bRuVKlRj6+FBq16nNzKkzeX7c8wC0+W0b+l/cn00bN5F1ahZ/uuFPdL+wO13O60KNGjUSfg89evTgxRdf5LTTTiMnJ4cbb7yRn9au5eO5cznv8svzy+Vu357wPkVERGQnJWRJVr169fzHX375JY888ggzZsygTp069D7vPH7esuvNzGMvAsjIyCA3d9cLDWDnGDIIb4zOlvyuxTx5yzfccQNdu3bl7XFv0/7E9rzx/huYWULv4eyzz2bgwIHcfffdfPrpp7Rp04a1X3zBvnXrMm/ixIT2ISIiIsVTl+UetG7dOmrWrEmtWrVYsWIFE95/v9Rf4+TWJ/PqqFcB+GDSBxxw8AFUq16Nr7/6mszjM7nxths5rulxfLnoywLPq1GzBhvWbyhyn7Vq1aJp06bccEOQ1FWoUIG6depwQL16jHnrLQB27NjB/M8+K/X3IyIiUh6UyRaybuuLmKYiZsxUcVZXcrZU2bXFqrQ0a9aMxo0bk5mZScOGDTk1piuztNxyzy30v6Q/HY7vQLUa1fjrs38FYNiDw5g5ZSYVKlTg2OOPpd1v27F82fL857Vu15rHHniMpk2bcvvtt++y3x49etCrV6/8sWQAOU8+yVW33caghx5i67Zt9D73XE449thSf08iIiJlXZlMyKI0YNCA/MeNGjVi3rx5+ctmxsiRI3cW/u67/IdTX3st//FPn3+e/7hnt2707NFjl9e5rE+fIl9/73335rk3nttl/ZCnhlCNagXWNWzUMH+6jn3325f3Zr1HHeoUud+ePXvSs2fPgs8/9FAmjB5dZHkRERFJnLosRURERCKmhExEREQkYmUmISt8daGkNv29REREdioTCVnVtWtZvXGjvuTThLuzev16qhYx5YeIiEh5VCYG9R88Zw7LgR9q1y6+UKVKcfezMQO2Vd0Wt1xl4t8svPAA+iL99FP8MlBqsZda3JBY7CXEXXXLFg7+/nvYJ7GXExERKcvKREJWaetWDvv445ILNW4cdz8v7L+dRU0XxS3XlKZxy3SjW9wyDBoUvwyUWuylFjckFnsCcYuIiEiSuyzNrLOZLTKzJWY2oIjtVczsxXD7dDNrkMx4RERERFJR0hIyM8sAngBOBxoDvcyscJNJX+BHd28EDAX+L1nxiIiIiKSqZLaQnQQscfel7r4VyIFd+sO6AXmzmL4MtLdEb7AoIiIiUkYkcwzZQcC3McvLgZOLK+PuuWa2lmCY96rYQmbWD+gXLm4ws/gDvX6ZfQu/dhpJ19jTNW5I39jTNW5I39jTNW5I39jTNW5I39jTNW5IbuyHJlIomQlZUS1dheelSKQM7v408HRpBFUSM5vl7lnJfp1kSNfY0zVuSN/Y0zVuSN/Y0zVuSN/Y0zVuSN/Y0zVuSI3Yk9lluRw4JGb5YOC74sqYWUWgNrAmiTGJiIiIpJxkJmQzgSPM7DAzqwz0BMYWKjMW+H34uDvwrmt2VxERESlnktZlGY4JuxaYAGQAI9z9MzO7B5jl7mOB4cBIM1tC0DLWM1nxJCjp3aJJlK6xp2vckL6xp2vckL6xp2vckL6xp2vckL6xp2vckAKxmxqkRERERKJVJu5lKSIiIpLOlJCJiIiIREwJmYiIiEjElJCJiIhIJHR3np2UkJVT+hDseelc52amc4VIGki384ymutpJJ9lSYGaHxC+VWtzdzexoM7vNzK4MJ+ZNSXnJgJntZWZZZnaLme0fdVy7K6zzelHHsTvMrBKAu++IWZeyJ/x0PFby6tPMqprZmWZ2VFHb04GZ7RV1DL9EzN+gmpkdFZ4bf1N4ezpIpwQnPN6HmNn1ZlYz6nh2h5lVN7PGZlbXzDJi1v/iY0UJ2S8Q8+Hd28wGAiPMbLKZVY79w6SimC+sdsCDQBXg4nDeuEZmltA9tyLyCNAeuBroC2Bmh6dynefFZmZdzOxW4HUzuzp2W6qJibkNcJeZLTez8/K2p8kJP22OlTBRrwyMBs4DFprZAeH5pGKa1Ddm1gt41MymmNmBUcezm/K+RO8G7gQ+Ao4HMLPKqfw3iPm8nmxmD5rZG2aWsrcvivkOOp3gs1kZ6Ofu683sQDNrEGF4JYr57t8PGAqMBKYAlfO2/ZpjRQnZL5NXb5cBNYC/AevcfSvQ1sy6RxZZ4voCjwM/Ah+G604C+kQWUTHcfUf4hdWM4EPwFfBGuPkWoElUscXj7tvDhzcB84CfgJXhup5mdngkgZUgJuaHgBeBb4BKAGbWx8zqRxVbPOl2rMR0BV9AcGu5PwEfuPsKglgfSeXWmZgvqOOBawnOhQcA/zOzhmZ2dl4rayoLj5vfAJ3cvTfBbf3mhz0Hj5vZwdFGWLyYz+tw4BWC83gVADM7IwVbLfMSlrMJfjh9ALwdrjsXuC+KoBKU93ntS3BevBxY5u6bga5mdm9p7Fx2Q8wHoCPBAdUBeD1c1xbIhNRs5o7pevoRmA90Bh4L13UnvN9oCo4ZOgGYChwK7HD3BeGJvi3waaSRxWFmRwPb3f1toB4wLtz0B4K7WKQcMzsGWAV8QXByHxNuujpcTmV5x0p9UvxYifk8nkhQx9cB/w7XNQVqhC1oqfZ5LKwP8HfgIOCj8BxZH7jI3bdFGlniTgbeCnsPvnH3/xHcg/kkd18ebWhFi0mITwM+c/ePgP+4+4dmVh0YQtBokDJiWpBWEJzPbwf+Ea47jeDuPinZgxDz3Z8JvANcCbwcrssCqsIv//5M9Q95qnuOIIk50d2fCde1Y+eXV8olZDGeJ2gZywJqmFkroBEwCgqOGUoF7j4T2EbQPPx5uHoQ8KG7b03FDy/knzC/B6aZ2cPAdHffbGatgZ/dfXG0ERZrIzARuBV4x923mFkLAHf/MtLISmBmFh4rWwl+eS8MNw0itY+VRwlaB3oTtIrVA3oRdImkrJgv148Ijpl+7Gzh6AFMh9T8ci3CVIIW7AcJhhZUAa4AJkNqvoeY+t8KzAzPMRPDdS2BFe7+Q4om9E8T/EjKAPY1sz8TJPQvQoHkJxU9DpxJkMSPCltXf0v4/flLpexA7jTxEUG3SGMzGwmsB7519/mQekmNmWW4+3YzuwZ4kuALoBdBcrYIuC384q2QarGH7gOWAZeb2WcErZJ/Dbel5BiP8IT5k5ktAAYC68zsUWB/4CXY+XeJMMxduPsyM9sG/BH4Ihz3dibwLKRezDHHbNUwcbwfWAv0Cet+DKl9rHxN8MV/MkEr3iyClqZJkHrnklhh6+MUgi+jDsACM/ueoMt1UFgsZePP4+6rzewdoDnB+MMuwP/YmWCm4nGTlyh+StAY0BUYbmZXETQW/C2vWEThFSn84fSdmf2JoNW9F0EX4C3hj6ZU/Q4iHJ+3gaBOqxC0DFcBJrv7PPjln1fdy/JXMLMawBaCZu2zCLp3PnT3Tal2QIXN14cDewE5wOHhuImK7p4bbXRFi0kgGwGHALUIEsclQC13XxNpgCUwsyphctsZ6OHul5jZAQRdUy2BEcBXKZbUWN4v7pjWgOoErR77AS8AC8OxkiklHONTCbgG2MfdL4nZdpi7/yey4OKw4CrtY4CV7j4vfC8/u/uqcHv+3yWVmFktd19nZg8Cw939czM7Ffg9QSIzwt3/k6rxw85E3szqEoy9+t7d55vZEcDPwPIUjr26u280s7ZAA3d/1szOIRiPuAQYC8xOle+hvOPAgqspewGdgDnAs+7+XbTRlSzs6cgEfiBowcsO38tBBOf0z919UV7ZX3rMqIVsN8R8eE8B7gDWEHRHfQGMJ2ge3gQp+Yu2LkF3yIXAaqC5mX3v7t+a2ZHAH929b6QRFhKTrDxL0Pr4H4JfrmuB/5jZBHf/b1TxxdEyHOh8FvBuuG61u481s4kEVxal2jFSAdhuZjcDhxEkY0sJTuxfuPvPKdr1AXAUwRWKFwBPmlkmsDFMxE42s8PzWptSQcyX0wkEP5AmAxXDbrLZwNxwXUpe1RrG2dqCqynPJfiBgbt/CHxoZvXdfVm4LuXizxNznh5LkMQ0Cb98/wW8Hp4fU6o1OEZzM2tJkNw8BeDuY4AxFkyd8l2KfQ8ZQStjf+BUgm6/s4D3zGwD8Jy7PxphfCWpSzA29RLgQCDbzD4Pv3/+a2b9zGypu2/7Nce7ErLdk1fRlxNk9lOAhsARBIMRPwSeKfqp0QoHpd4aHvg1CboSKpjZuwQH2mZIna6omC+swwh+tZ5vZg2BxgRj3bIIkuBU9TnBRR/NAQ9P8pPNbDpBgva4u/+q8QalLWyNrEzQynQbwTFxVLj8GzO7zd0XRBljcdz9HTPbRNCStw9B/F+H3ZWPEPwaT8XWpmyCIQP3E0yzcBhBS81vCROyFGXAduAiglb3rmFymXfRxHNmdnKqtr5DgXNMC2BrXqtqOL6zDzDOzJq5++pIAy1C+MPoe4JB5I0JkrN7CBL51wmm7hhKkNynhJjksA5wl7t/DLxhZlUJfkxVhgLDD1LJWuB94HyCK7dPJ7hKfhHBhVqZ7v70rz2/qMtyN1lwGfRTQH933xCua0CQICxw9y9S7aRfqCuqLpDh7qvM7DiCgbffAmPcfWWqfBhiWiNvBloBPd3953BbBnCYuy+JNMgEmFl74L8EJ/hWBJfT1wXOTqWuv5ju4eZAB3f/v3D9fgRd8ocBr6XCsVFYoeP7YHdfHnb7dSP4NbvE3Z+NNMhimNkI4N/unjeesAJBfW9z9xWpdi4pzMyaAXsTxNweyCXoOt7q7pemyg+8osScY3oR/Cj9M7A5L4k0s0ruvi2V/wbhcX4awXnlVIKhHQcCR7r7sVHGVpTwu/JJgottHiToNUiXq3DzhT8+jgOOJLhS9CV3H/drj3clZLsp7K6cDHxCkOWncisNUODL9vfAKQRjyfYl+GX+GMHUACl5IJjZ5QRzM+0A3gT+6cFVdKnY2gEU+OV9ENAGqE1w5dN/CAY6r/BgQGvKxW9mbxEMDr7J3R+PWZ9ysRZmZjcStC6dRDCv0V8I5ghKybjDL9OpBMnMGODhsMsvLVgwmP83BInYDHdfGH5RVQIWezC+LCV+4BUnTIDfJJiiYxzBPF5fEcwrmfKJQvg3aEdwsRMEvTiVgA0ejN9LqYQ4HB7zB+BogqEzkwk+A1/lNXCkGis4lvk8glbtl4CpXspXnCsh+wXCsUFnElyyW4dgPq/BnsIDhwHMbAYwmODq0HrAzcB4d89J5S/c8KRzPEGLR2eC7pI2eeP1UklMMrY/8BrwMUHXzmkEV+H8MdIA4zCz2gSteVcQJArjgGHuPjUVj5GYVo4OBLOsXwZsIjjpHwNc6O4/RhljUQq16h1EcOFEb4IE52Z3fyrK+EpiBa/Wbg+sI/iS+gm4zt2nRBlfomKTRQvm3fsdQSvTamCiu/+tpOdHJeaYP4ZgnrF9CaYEWgb8y93fKHEHEQt7mfLOiT2B1sCNHszTmHJi6nsSwRyBGwgmnm5H8L0/otReK8XOryktHAe0N8GYiToEE2c2IBhTNszdZ6bilxaAmTUFnnD3ljHrWhIkaGel0q+TmA9AJsE4rJ8J5jiaQDCu6fgwQUi5X98xsQ8ADnL368L1hxKM23vZ3ceVtI89LSaJrEAwsL+Wu68JE4UBQFt3z4w2yqLFxP4owVWK98VsexD4Ia/7NZWE55KWBONPKxDMzr/EzDoSdJtNTbXWjcLMbAowIK9Vz8wuIhgzOcCDmctTVsxxcxjBVCP7E/z4+JogSdjH3R9NxfN5zDnmQeBHd/9zOA6rN8EVrr3d/Ztoo9ypUALZg6B7uDbBNCkjCLpZV6XiD+w8ZrYPMMHds2LWNSeYyPl6d19XGq+jQf0JiDkxng1cSpCUfUhwafcTHnN1Yqp9eGMsAL40s78A97n7RoKT0AZ335BKJ57ww1uFYKzBIoJLjSGc18jdp+aViyjEYsXE9DPB2LG89d+Y2WqCX1bjUqy+8+K4g3A8ipltJxgXcV1euVRMgGNifxm41MwOcfdvw3X1CQY5p0zsMeeS8whaxVYSDIT/o5m94e5j88qmeDK2D8GPpPzWR3d/Puw2PpCg2y8lFWrF/ifB5LXbCRKEAq3YqfIZjRVzHNcGvg/fz8/AMxZM8N2OcL7AFJE3B9otBMfLnwkuRrgKqOnuD1vqXr2dpyqw1MwuI2iFXEuQvLcsrWQMlJAlJObEeC9BQvYdwdVn/YFDzezW/2/vzOM+Hcs2/j0wxr6EYRAVMpYwyZYiijdCIRpUSNbeF1kiSRQtWixZsw6yRslWlmxlzT6yM7JTsjNhzveP47qf535+8/yeGTUz93U9cx+fj8/Mc98/nM/9u+7rPK/zPI7zTC9EtgiTU3+GX4q7Jb2IZd4npY9MhzelRlELVDbBGY/t0/Wl8PP/CnB0gyZOKi7AY1g+jceCVKrLrGaF1pzTiliC/hXc720JYC9Jb0bE9elzjQc0A+BmXM6+U9J9mOM5N6n5bka2V3bsRsouyV2+1wP+T9JjEZHVeKf+EG6iei4OAo7EyuENMfcq22AsodrrvgbcHhF7QG8WW9KGuZf9Eo7G/NpNJD2MA+Hl0rVseJ+pvD0EZyJXDvdOEz5oHy3pyshUvV0hIp6SdAIelbSgzWclHNBPtu4EbUA2EXScpp4B7gyr454ErpI7O8+FJchZocb1mA9zxubDc8NexgqRv0bEOMjqNF5tlq8AQ1Mgdn+46eRFWKmYzWbTDeFO9x/FgeXWuC8OD2UAACAASURBVIHwwRFxd7qfhe01Oz6GswMPpJ9vTWt+U+D6XOyto/ZuzoH5YkfiLN9nsNrvlshMJZfsnQEPml8EN5J+FjhF0lcwHSL79Q0QESdLeh5zUb+D29AcAvm0z+kPNbvG0X8WewXcjiHb7yDZdqekY3Cn++nwnnlqZCgYSu/hhcAewA/Se/A45r89MPC/3Qxq+4swt/NP+KC3CT60Hgnckj4+WQ58bUA2ADoW9Wu4tHChpCNwSn4YMCxtqDmisv2U9PfH8YiH53AftdnxppQNapvlVZgw/A1cap0bB2OHpvtVk8HsIGlvXE4YjzM3X8g9g4p77Owu6VXMc/sblnRXw+azcrC1zXI4VsndCgzH7+klOBjLqhRfISLekfR74CeyKvFpTIgfFhG3pM9kZTNMoDZbE1gGi1Y2wJMFenqO5bRW6khcq3+njOmZuCnp2jiLfT8ZZrE7kcp7G0taD7gMc96mjwxVoelQ+mREPI+f92Epm/cgXvd/ScFaVvtLQpUc+AamcqyBVaG/jF7epGDyva8tqX8ApJd3nXr6WtJumMi/Iu7Uf2JEXJzpgkLSXHjUw0aYPLkEdrQrAD+JiPsbNK8P5MG4iwCHRMRt6doWuAfWcKx8+v0A/4nGUAsQ1sGtRA7FAe9iwGw4u3pQ5NV7bHHg1Yh4Lv28ErAxft4rYjn63qk8lVVgU3veX8NtLnbGNIKPYsXfrBGxVYMmToBU2rsEZxzfkLupr4Ozk+PwXnJrxntJRc7+HQ58rwZG4v3wNZyd+UODJk4UKQs5C1bG35Eub4kJ8a8Bp0fEbxoyb0DUnv9OWBF6Fs6+L4nf1V/ltD+mEvDBONB9CPOun8F+aD3ctPbuyHt+MimTtxHu17kLfvZLAhvGZBZotQHZAJC0AnAdJq9eDhyW0sTz0JuifCknR1Wh5rAWxnXv0ZF6pqQSz8IpC5IN5B41e2Euyr/xqfWIyHhmZSckjQJmiIgzUlZvYZxJmD4ifp1TYJPKHeviHmkX4k39fbiP0VB8sp1shNUpAUmfwj2NTkyZp0oJPVPifWSx0UuaBTunVXBl4lqcMXg8MmzL0Q0pO/Mz3IPxVUnvw40x18LZjptzWuN1pLXxTXwoFRZU3AXcFVa4Zml3J1IG/t5IPTAlzYu5wUMjYveM1vxQPCFjGTxd5W3Mpb0P7zXjcjx4QB//OSPuonBW3Q9JWgb3OHx1cq6bNiCbBKSHvwOuHb+OR1Jk1dm+GyT9GAc4s+EJA+dGxKPNWjVxyCTz3YAv4tr9RRFxfM6bZtrwR+EXeO8qy5fuzZhTdqyCpBE4Q/Bp7KTOAf4UGRPLa5mCD2Khx3pYkftH7KiyDnBSGeeLuJfh3ThTc170qkOzQ+2Zvx+XcMYBR0YaKyRphsh4TFId8nDrJXEmdRHcvmg8Lr9eleP+UgsQhuJ39cu4ZcSdkYbQd362CTu7Ie2NWwKrYd7YYniPvKZJu7qhVp7fAb+r/8YHqAcwp/n1KeH724BsIpA0a3r4c+IvZT9MYAVYP8cUfe3lXRpnxlaS9Dk8GHUF3PjwfyLipUYNrUHSrPgl/QAOej+OU90b4s3nb5FvL6zKWW2LN5038Xy5sbgdw7k5ZvnqpbGU+fg8zn7MjzkeK+a2scMEfZjeBC7Cz31ZHCicHRGnN2ljJyTNhoOAf+Jy8KtYWPNDnJHcOyJ+nqMzhT57ylmYH7kwbqVzE3ADcE8kgVCukNWs47FwYgXMYdoZ7zUfxsHYOs1Z2B21Nb8H3g+fxpmmV3Ep7faIuKtJG+tI5cr18PMejp/xzfgQMhRniTcJT3fIdc3PiA9Mh+P5uHOnW28Bx0+JA1RL6u+CxE1ZDVhMHlr8Au7ifBpwPH4RrmzOwgExPVaZrUZSgaRa9yWSZgY+l1kwNht+vo/hNXk5Poksj7N6J5NGg2TKr6k2k5WB70bETYl/OArYFQdnuzZlXH+Q9E28tufDG/r6uDz/J1xe+F1ywNk979qpdEHMN7wXuCWt7VE445FT77GdgO9jjtJYnMlbMf25Dd5LrmjIvImiFozNC8wZEesnZ/V57HS3ww0/s+GjdiIFCJUi/lQcFMyLeXAPYz7T/emzOa/5D2Le2OO4TLwkFju9iMuvueCXWOzxD9w89VKcgf85MG/UWqPkFozVsr3LY17kcen6CMxRXYyaOney/r8zexbZQNLZuMHeuVgBdTZwL+YCvZPLZj8QJO2HN/wb8Gb0l6pcmZP9abP8Dm6+dzsOcK7EQ6GzPnVXSGWQ8/HmfljUZpxV5cpcnrmkJfBw34dwgHAeLlXOERHPysPbq/5BuZ5eF8Hv5nBcrvx9RNzXrFX9QxamfBV4FPNorsUl+FJKfDOkPW8z4NvATpHUoOn+sLCKLlskPvCROCB7BTeYzvVA3Qe1gHgEDiYPiojLaveXBZ6IiJdzeV/lIeKfTv8sjQ/Zv42IGxs06z1Bnuu7Fh7rdEzt+hQL2NuArAtkBdpIXFYYgiWwD+M08UORlGm5Idn9WnKsS2CHtSIuMcyBTyzfy43PlDJKy+Ms08KY8/YCVuVcE739sbJE2oC+ikshL6R/7sC2/7M5yyZEIpiviMnw78MnvueAv+LSR85cpuER8Uz6HT6AT6wjceZAWGl22QD/iakOue/YQnh9fxCXx4biYHgMcHEJwZmsIP5f3JPpfpxRvSzc9yq7rFJ/SO/p+ri/3jy4jPabiLgil2CmG1LgdQZ+b/8MHBsR5zdr1cCQG8Iui/nXawMz4iz8t3M4nHYi2RvpADIH5o5vj1tcXYpHJF4/pQ7XbUA2EaTMx3LY0S6CWxmMjYhDB/wXG4KkC3CX+HPC/V1mwDYPxyeVmcIKwGw3n1RGG4lf5GWAoyLijlwyTHVI2h/4dUQ8lnhwswGrYqc7Ep8Kz2vSxm5IQfAHcUA2AgcNC2LFYnYlNEkfAk4Hto6Ih2vXP4yf+6fIX+k3Bw4kP4yb2Q4Ddkv8oOxslrQy5hRegg+lQ7BKcSRe50sDX4p8ezH2i5QFXh6XXUcBW0XEX5u1akKkd3Rd3LT55XRtTswj2wYfrDaIpLjMGYlSsCawZEQckXirOU3RQNL3cfXgd1HrHSn3O/wWboM1xbjMbUDWBf1tjilQWA33brq6Gcu6Q1adXRARI9PPS2PO22u4ZPljYHyuJ9kuz/wDuDVAdgtV0sb4BPWFcC+dWfH4nqdx5mNx4JGIeCUnZ9vlOVeBwkeAP+SW1QOQdBDuL7ZX4jB9AXPzbsTCiVsbNXAAdHnm8+NZfg93+dcah6TzcRbp56mEPQIf8MDcw+ERcUfX/0ABkDRLZDrYWtLOwHoRsVH6eT58cFI6pC6Ny5WTtf3CtApJdwNbRsSYFEDuhcvcD0bE8bXPTZHkQEvq74L6wq4WekS8APy+dn2hiJgi5L7/EJvjjuVV24jdSc4Kk4ofjIhzmjNvYHR55mPTz+8H5s/sFLs5Jn2OS897Z8w5GI8VWztVH8xpo+zynF/BiqK70/XsspGYj1INO98W9zi6Fu9jB0v6WmbvYw/6+/4T7aFqyvtJPF0gG85kCtIXi4hN08+fAY7CBPLXge9EjUtWMN4CkHs2Phd5dbzfDLd2QR4jdwjOWD4h6dLwQPeK85nNHgMTb78h6WDggFz2GUkb4sP/GLm/3q5YvX0UsK6kpyOiGqk1RWzOfcJ6FuhwYNOlP2fFc/Nywh3Aa/IMwp/gmZVHRMT1uIz5KejpCdM4JA1L5OwJUD3zarMB9sSE6Cwg9wMajtPb4Jf3bWCViFgCWEDuN5U1OoOz2vP+oqR1GzJrAiTO2O3A0vJEgT2ASyLiOxGxD25lMH+TNnYiOfiB7tffw++T0fpOWBtzOJH0EdylfDRWm18IbJc4N4MFx+Iu8lkgZcOG4kM1uOXS3zGP7xRgC0mL51TxkLR+ta77C8Zq/nNlYEQuwVjCnMAbcuP3HXFJe5uIOBKLEjaDKRv4tgFZF6SSSL+30p9bYdVlTvgLlnJfhrtQ/6BWDtkI98SC3t+haXwPWFLSQikDNgFqm80auAyYBVIm4xTge5J+iEfg/DR6mzQuwRSSRv+36OZE00ZTbZBfx60AskAqKZ2HM2N74/FDJ0EPh2yWiLi9QRP7w68lzSRpucTl7IQAJG0E3JCZcwKTrx+StAsOVl7GGeHAgoRFIg1vb9DGAVHZ1m1/UW9/r9mA90UmTbOT3S9jtflRkkbjAOHEiLgjIv6IS5ePN2hmH0haE/cxlKTtu30s/flleucS54Iz8Yzqn+ID9kn0Dg9fhuR/aofWyY42IKuhCsJSariq2XduNtWmuRFuhZENIuL1iPgq8Bng6xHxXHIIG+DN5qr0uVw2/g8Bb+CX+HXwYq8WfO00tSHOhmRzEky4AJeIX8fE2qqlyKY49f1c9Ttkhk0lLdbfjYiIxNt7NzceWcr0bgXsi8vDFVH461jxN0U3y/cCSavi9goLALtH71in/vAl4MSpZtwkIpWxr8SzNs/CzWufSbe3xwILyNSPdJTMfpgyTtTfydpeuDm9v0/jSDSCf+OM5CNY3b9bRFRBwTeBO1JAnMWaxzNC/4x7ja3e3wdqe/iqZHTgg561cCJWsq4cERen93Y+TI8YnT46xfxnyyHri6UlLYTLYxdDn9LZXNhJvZoc1izRMbKiaahXtVIfHRO4SewBjRjVBSlLczpujjkEGCPppOhfrTWKzOwHiIhXcWm4B5KWw8qtE9Kl6ZiCL/CkIpVY18KtTw6KiCU77s8PvJj4M9vijEhWSA62s93MIphcfgH02fCbxhP4sHE7cK+kOSOp5KDndxmfeFrLRMRjTRk6EMLDquu82SF4HQ3F3FTobYycG94vaS8srpk3cYBJz13497g2rZmvARs3Z2r/iIhHJR1aX9dy38bVcf+9nHA5VmsfCFwhaRucSX2YGjdP0meBP2f0rvYgHap7sqTp/VwVOLnjgD1F1nyrskxID/rjuE68A55XeSfuOXaHPKbl3Ii4RdJX8AK7vDmL/zMoMyGCPCtsNVy//ww+CY7G/V7Gp1LPHpFhm5G0qU9A8FSGylB50PkncAPeefE4kPvw3MqQ9FPgW+nvZwLbRcSbzVn83qEMhQiSrsalp2VxkHYWltQ/n+6vgMUqf2zOyu7o75kmbtzckfG8U+jJli4K/Ba3RXkSq82PwP3rto2Ir6Zy5e4RcXBjxnZBR5avurYwDjDvbMisCZB4V8LjBXfBTafXwgrFp9LPZ0TEW6l8/Er9gJILujzvmfE+P8WVuG1A1oFExN4Uv7zL4OzSO7hUUnEm3g88nWOE3x/UOyh1VuBnEbFzw/bMjtVxv4o0liJdnx731jkA2/nLdL1IObcyU4am739vnJF8BfPc3sSn2n9ExBZy36PFwuOIskc9KJa0OfBS0welxAkbgcsfM0TE84kOsQXmziwFLJWy7dn1YnovUIbq0E6kjMxNONOxGQ4U3gL2j4gLVJtM0ZyV7w3Vnpj2mGejYWWopD3xfnINbvVTqf2XwPMr54mI7zZn4aRhYr5GU1gZ2gZkCfUvQr3dwOfEWbPZgWdiCnbonRxQGtHTz/X65PoZI+KoBsyr2zMdVrHsgFVN1wInRa25YW3DyaoDuKxgnSki/j7AZ6rnfThwSmQ09LcOuYfRipj7cXVEPNiwSV0haUg3p1NbK5cDWzTNfUucx73xMOIxePj5WXX6Q0S8lOteImnhiHhygPv1vfJq4NO5/R7qS9ZfDHg+Ev8tHToWyXm9Twy13+8izC1rVIyQqBprYSX/UriR8J9xo+bna5/Lbs1LWh9PnOg3GKo965VxFeGLU8qWLMmYUxu1DX2YpFOBwyRdilOvj0TEuWFCcXYnWRUoRIiI8RFxLO6vcz6W+/9S0hOSfiU3uK0+m00wlvBelKGfJANlqHpFEhtLOlrSXyUdiYPz0yPi+Mo59bN2ckExQoSIuCgi1sCk5QVwS4snJI2WtEbOwVhC6erQOo4FjgNuk3ShrP6bLyIezHWtV3Z121+UmTI02XM3DsDexIPFx+Gh85dL2ltJ2Z3bWlFmytA2IOuLbXHNe3/MIZsbOFHSGY1aNTCWlvQ54GhMcO4jRJA0e81hZSNESGXLg4G9ImJURCwG/ArYEPhYsjnHDbM4ZWjNhoNx1/UdccnmFEnPpBNi9dlsUuaShkr6bCpFHhQRj3Tcn1+9LTyyECLU1sHXgLciYq2IWByXyj6Kn3m2wZgGgToUeoj78wPLRsRqwAex8GMD4GZJS+e01it0lMyKUoYC38BTPo6OiP0w/eQRPF0j1/Yo70UZugpTWBnaqizp44TewI1UH5Y0Fvf1Wgh/aT2lqGasnBDpJZ0NzzpbDbgpOa+HwuNM9sdKqFtwtuZHTdnaDxbBZM+ZgVfTtWPwmvwN5BUcQJnK0Fr2dzguH5yWbt2W7q+HxSs5cvVmwc95D2B6Sf9LTYiAx5p8K312CTwarGlUz29xzD1F0tCIuFHSIcDMKVjIai+poXh1aC3gXQ54RNL8YXXuaGC0EiWlWSu7okRlaLXmHwS+IOlO4NGI+Iek54CKQyvyU+RmpQxtOWQJkhbHm9CtWNWXJe+nP6ggIYJqPDdJ++F5hMcCN+CxOPOEyeU5ZxGKUYbWArLd8In1bOBk4OXoh2+YG1SQEKEe0Mqqs6PxYehcfLA7GtgzIq7NMPjtAxWuDgWQtA8OWO7DTW4fAO4Pz5bNcn/Re1OG7hYRhzRmLL18yPT36fChKIB/4rWzJLB6RLzTnJX9QxkqQ9uArAZ5JMuWuGz2FCbjnpIDL6U/dDiA7IUIcv+cz+Iyx/IRcXsqt+4KzIqf9+8i4oFcbK6gwpWhMv9qUxxAvoHT9DcAt0XGCrlOKGMhQsrgXYcJ5M9KWgbYBzulMcBTEdF41rQbNAjUobUDSPXnh/GeU/EPXwEODfcQzBYqRBkq6Urgi3hSyaUR8bqkjXGJ+CXg7oj4a277OeSpDG0DsgRJW+LOx/elnzfE5ZDREZEdT6K24QzDRMOZcNPP64Hzc3JUFSSthZvuVTy9h3En8OuBt3MKYDqhApWhNXs+hDOn1yRnugbe5FcE/idH56ReperGOIhcBQeQJ0dH/6Vcgl95wPwdmBqwLHAObqr6Wj1DkIu9nVDh6lDos+Z3xcHMidjhDsW0jQ9GxPENmtgVKkwZmjJMs+Is3hXASvhZHx4Rf2rQtEmCMlSGtgEZIA+4/iHwGvAccBfmqbzUqGEDoLbx7IPLZ3vhU8k6eCP6e0R8uUkbOyHpCpx9XBuXoBYBPoyDyVexs21clTgQJG2Cx2jMiTegGfHs0B8BYzN1tBvj5/4KLj9dGRF3JW7TuFwDBABJ9+JpCPdiEvk6mHC+XT0Ybhoyd3Mkbrz7CWzjJ7FjfRpTIU7MIVCfGGSl+aL44DETLpmdFBHX5RyM1SEPQ/8s3gvnxlyhSyLjZra1gOx0zCFbFK+bizFZ/omc3lVJ52EKxG0RMTZVZ3YGtsa2nx4ROzZpYzfUnvWKeDJPxRdfDlgY+DUOLKdqf7dpPiCrfTFzAitjJ/sZ7GiPjIhzB/wPNAxJ/weMiYirE3dpRpIQITndXLI1H8fO6ht4Jt430knwA3jzWQE4NiYcjZMNUtnyZmCVKqsk6buYf7BrRJyX04ZZIfGwlsanwMVwoPAkcGBkMky5jtphYzhWV+7QcX894M5Uos/ieUs6ETvP6TE/79fydIRFcIA2X0Qc2KCJA6KWkfwanuO3U7q+GlY/zwIsUUIwVkEW4cyF9/X9cJZs7fCMziwhK0P/EBEj5XFno4BNSH4pIv7WqIE1SDoN8/JG4f5cl9XuLQV8OCIuzMUH1VHz+yfjysFp6fq8wPHAPRFx4NTeX1qVJc6QhRt9XoGVFmfjTeiJdD+LTb8TshDhEOBWSS+GhQjvYDIi0DzHoIa7MUn4duA+9ZJBx2C14tWR4SiNDhSjDK1tOLNGxOs4WLg1bTgACwLZKeQ6sDmwUsoC9wgR6ht/Ls8bf/8742B3Z0mz4KkB/0pZviGQ715C+erQelA5EnOX3gZeAC6RtCAwXZjQn913oMKUoWl9/xAfshcERqZg8m/A3xL15z7IygfVkaUytM2Que/O4aQyJSaVj5P0Z2DNTBdTD1SAECFlaNaIiMsk/QmTPZfDWZqzgQvCBOLsNkooWxkqaTR2sqOBcyLiZUmjgJUiYs8cba6gQoQIssr5IUxu3gUrzN7E3MhrgAcj4rXGDJwI6u+dyleHTo8PH7MBf8Dr/j7gQlx2PTvn30GFKEPTPngG8BHM73waC0LewdSfGyNTBa4yVoa2AZn76SwLLJ/+WRWrWW6KiN0zPxEWIUSQWy6sERGbSloiIh5K5YQvAV/Bz39E5EkuL1YZClbfYt7V+rhs+TIuq+0fEX/KzeZaubIYIYKkWzAZ/lbcOf1Jmbe3HvAxHAj/pEkbB4IKV4dWqK9lWSm3NZ5O8iTmBm/fhJOdGGprvghlaKL33Ij37SHV4Si9sytgTvMlEXFNjsGvMlaGtgGZeR5DgflxdP8uMB+uIWerKFJBQoT0AhwXEb/pcn/OlLnJ7lmrQGVobWOfHpOyZ8fcwvHAp3Hj4BuatHFiUCFChJTF+01EjOxyfxjuVP5YjusbyleHQp81vyQO5OfDarkx8pSSpyJ1i8/td6jZXoQyVNJOwKiI+FSX+1m05OgPylwZOk0GZDWuwRdxuW8ZrGR5KGo9pnKFChIiJId1G25s+BRWy93bGTjmuFFC2crQVP7YBPM6ngYujogbm7Vq0qBChAiSvo+zvMfhwP3aqEnmc4cGgTq0FtAsgCkQY/HInmVxBSGHCQ4ThQpRhkq6Fq+TlzDX6rSIuLlZqyYNylwZOk0GZBUkjcHd7B/Dm9G3cMDwzci4i3mqey8cFiJU1z6EhQjfDRNxswhwJB2AHevFeOOfHvgXzjSNwfyIbPhAdahAZWgtWF8Z89w2ws51JJ73+H8RcX2TNnaDJhQiVNfnxVy9BYEdcljXFSTdBJyJs48jcND+OJbR3xoRbzRo3kShwtWh0OeA/T1g7kQ1mR0H89/G3LGLm7Vy0qDMlaGykOzMiFg57TGb4F5e7+I9/tyIeLhJGweCMleGTrMqS0kLYQXO42mxXwpcKo8MmQvI+ZS7MnC4pLoQ4VG5eeAtkJX67EvABqlkczXm6X0EBwifwqWRi5ozb0AUpwytlcRGYv7VUzgzeZ2ktzGXLMuArGb7MWnjr4QI/5B0HzBbyoRkUfpLvDYi4sgUxCyKeVdLYoXodpL2jYinGzRzYihdHVovjf0T839JfKtbJD1MOhDm+DuoPGXo2qT9OiJuwc94Nrx+NgPOkrRKDu9nJ1SAMnSazJBJGpL4BAdjFdFx2PEuAvwoIlZs1MCJQIUIEVImb/Xw+KY+G0pyuKvjoOHxjDYcYFAoQ5cDTsXZmz8kLs3xuGHwIbkENf1BhQgR0hr+UERc3nF9GA7KFoqIsxsxbhKgwtWhAOkQOkNEvJa+j0vxQepy4E7gJGDLiLgn43e1GGVoWtvjwpzfCWySNEdkpgitoAKUodNUQCZpZnxyfSzcdXpBYCdgGO68PjNuGZFlM7sKKlSIAHmftOtQgcpQeeDz+/Fctr9JWh3z32bD5ae78EDil3L7Hmo8oCKFCF2cU1bPuBMqXB0KIGnv9NcLIuKRlAXZHAf08wO/jIgLGzNwIlChytDSoEKUodNaQLYTsCawc9RI5ZI+hoOxmyNT7pgKFyJ0QpIgq9JqH6hAZagsQDgZl7DfTMHNB3GQNhb4Zy5clG5QQUKE3AOugaDBoQ6dk97JGS+na1VgP0cBa71YZejEIGn2zA6rRShDp2vyf94AtsYnppdkVL//GKxqGdKcaQOjtlAOBA7CysqrgFGSjpY0Y1O2/SdIG4wAJH0pJ/uTs/oY8BFJO0haXdJc9c/UUvZZOCq5d9G8EXFWRLwJXjNhgu2bmJydpYOq3kOZJLw5DshOwTzOYyV9skHzumJSnKSkmeTxQ7lha2AuSftI2iwFYD2IiOcj4rH09yzWeD/YFpeZXq451Oo7+ULaV7L0cbVgbAE8qmcjXKn5rsw7HBtpjmIpwVj1HcjcyjUbNqcTWwDDJd2c/OUq9Ztpr2y8IpblYp0SSOXJGUikd5wdHJ9Of2/hlzt37lgfIUJEXJoi/qWxECE7VJmw/pCe/1zADzLLTG6FuRwPYS7QZsBukraStLw8Yy63jfJzOFtQlebrz/4NYM+BvosmEf0IESLiuog4AiuH12/Ouv8MlXPClIiPNGlLF6wLHAa8jp3nDyTtJ2nNVPYrAfNgvhjADOmQXT33ccCGGQeTle/dEc9l3QZPjPk5sIqkDZoybFLRz35S7Yd74Ax3FpC5hTNHxJJYrf0qcKSkG9KaX7xZC3sxTQRkaeE8B9yET989TiAFBUOAJbBUPUvIQoSnsI2HSVpF0ghJ6wJzRKa9j7oFLbWNczPg0Kln0SThS8C3I+IM4BeYIPwWDhj+Fzuz3PBXYNZ0wHgzXRua/vwk8Go6kef8zt8IrCVpL0nLpmvLY8ItudguaV5Js9bWcP1e5aSqQGBd0pzTXKCaOhT4NW5E+id8YN0cOC4dYHPHhcD6kuaOiHEde83mmOBPf99T04i+ytBn0rVXw8rFShk64IF2aqPTlup5V9Wm6O2NOW9k1CuQDmVoROyLeak/wO2Lzspmb8nrkD9lIWkbPPLmAuCKiLg5fRHfAxaLiC8rMzK/ChQi1F7O9+FAdwHgyYi4rfaZKmV/CfCViHixKXvrUKHK0FTyPQcT+H8BXB0Rb6Xv4BLcn+5KZcYHUoFCBEmXAkcCl6d1Pj0wLNLwZ/XyPRcDjoiIrLIdKlwdWiG9qz/CB6jTcTZ1PD50fx1YMTIkxKtgZaiszF0AJ3PG4nYR42v3dwLGR8SvmrFwQqggZeg0FZABSPoULoHMCXwImAMPLD45vQCNfyl1TJg+OAAADpJJREFUqEAhQi0gOxxnadbHHdZPkbQw8HS6vzhwRkSs2qjBE0Fum2I3pFLqbvh0/X7g37hlxNMRsUeTtnWDChMiyFy9MzGR/F1ZpfVznOl4HLfNeSd99lDg+ojItc9ekerQTkhaB5f+1saK0XuA30bEX3I5pNahwpShtf18XWB3zEldCPdnfBaXJ3+b3oczgF1zOWCXhmkmIKtvMmkTXQQvrJki4tpGjRsAkm4E9oyIG1LKuOK+zYTLIVdFrat5LkiZvTsiYoSkG4DtIuI+SacDv4iIO+R+U3NFGo6eO6qUfY7OqrZpzoqzHLPidPyYiLg1fSYrR5uCm3OiH6WfpFXxSfuWCf/N5iDpO8DsEbFvyhZ8AzvRE3E7lFMidYWXtDVwVoYHpqzWwX+KLsHkByJibEMmTRQqUBla21t+g3luK+P+XXdgvthhEXFMylguHxF3NGjufwxloAzNom46NVB/cSPi0Yi4JiJujohrc+QYQPFChBHAlfJw7rdSMDY77vlyN0BEPFNKMAZ5K0OjlxP5ekTcHhHXR8QpVTCW7uXmhEsUIjwLLJoy1AdiO3eJiN/hsWurVx+MiNG5BWNQvDq0B7UDtioOUHg+YY9fS3tOTihOGRq9POu3cRC2AfD9iDgW87LvqT5XWjCmvsrQNRo2Z9oJyOroZ5OvXuxsnGyysWQhwhhMTj0KuDVlQ/bEWbN3cw2CoVhl6ASo/x6ZOtgShQinAg9iccdCwCHAE+ne6sAfASQVOZZO+atDJ0AYE9BMlGf7hZKVoYdgG28B9k/Pd1VM+clKgNAN3Xw/zvQ1nhzIaaOboqh/EZ0nxBydbNpk3gVuA/aVtL9S75TkoPbHcuksg5twD53jgLNww8PDgVlwVgF6X4Ts0C2DoLyVoRMgBTM5O9ibMXn/j5LWkzRT9AoRvoxHVGWF9E7+EA9+XiUinsWOdQ3c6+2a9LkcyeRFq0O7YYBAIKv2CwlFKkPTfn4frhAciQPL/YFDKx+UYQa+OGXoNMMh6wb1KqK2x9/XiU3b1AkVIkSoPcuFgc8C/4qI8yUNjTSqIleoQGWopHkxD/Kt6CAu12yt/rwU+HLTNndCBQkRJK0JfBT37jo/Iv6Zrs8A/A9Wcl2pDInkUL46tEJ/3LHqOr20jjnxKJxPTH0Lu0MFKUNre8d8uBn5m7hkfydOFPyrHuDkGJBBWcrQItPqk4pJcbL0ngi/gEm52aBa5OH5Wn+nixAhh2AM+vTWOR+/sJ+QdAJwsaQzI+IPuTqrGg6gpgwFbtOEytB5MglsTiM5WOg5VQ9L3LzocLDjM7G5B+n9HCfpaDIXIsgzHg/Ag7cD7xsnSloRz5KdnlS6yXF9J8rAMNzuZ7xq6lBJfdShWLF4fEOmThRpbXdzstVa2QK/H1kh2biPPJptR9zWpVKG7hIR72S0Rwo/z+3ws74YWBC3pdkIl15PgPz4qZqIMlRSjzIUt9fZtTlrezGoA7IaSnKyPagv8pRO7UmpZvTSAn1OUyOA1yNil3R9YWAX3HxvpfAon+yQ1sHMwGejVxl6U7r9I9zb6w6cHdmmGSt7MRgcbNSECHijBAc89c/kstGPAr6ZDkcrAD+TtBkOxBYFLo2I3zdq4cDYDLgyBeiVOnQIdrJfwRnti9Nn7yVx4XJCqU62jtoh+wrginStjzI0l329dtAfgveTW2V1/zKYL/kE5HNo6oIdMMWgUobeQ68y9N2Usfx5Lr5/UHPIOpzszsBT9HWyy6e/v47VL9miGxlR+QgRKvuWAp6XtJKk2SLiyYjYLyLmzjUYq2FSlaH3N2lkQqeD/Q69DnYF7GArZOlg+0N9nSsTIULKsG8GvAgQEXfiVheXADtGxBKYU1aVpHJE8erQGion+xf8Xt6Dg8phOTrZOuolPmWsDK3sSaXfF/BM2Y9HxFsRcVt4ysMfIatDUw+iUGVorpvH5ESx7Rfqzqlz0UdmQoTaaWoVnLnZGdhY0mqSFk0bUO4qnJKUoYPJwfagKrWmH3MRIryM55ueJukBScdgvtiREfEI9GT5sqEP9INTKVwdWqqT7Q8pS5azMrTaq3+G20HcA5wvaayko+S5yiWgKGXooCf1pxf4G8D2OHtwErAlHh3y1dxKf5MCZShEqKetJc2GMzTrYF7QWOCgsFIna6SU/F5YPLEADtKOi4hHlYl4Anr4YgfgctlHcE+v51JAcxUO1K+RNENkQhKG8oUIMsF5S6wwuwuT+4/NvGwD9Ago5gGeT1ylIcBqwFERsVyz1k0aks1LAvcDBwNz4ckJJwEj0r5YwnfROZatKsf+DtgjMlD8AUi6ANg6UsPUFNAcCJxXwrpP++R0WOl/BBbGnRcRJ+To+wd9QAblOFkoVu1X2bwCHtq6NHB2RFyRymkjIuLMHF9ela0MLc7BapAo/aCHx3cgnjaQzaibTqhwdWgnSnOyUJYytOZfRuBqwZ3AaODenHxlf6jZXqQydFAGZIU72SLnQMpcvRvxizsEZxFeBD4XvU0/s4Wkm0nKUGBhnE3NUhlaqoPVIJsDWQI0oTr0noioq0MXwVzENxo0c0CU7mQrqKD2CwAyf/PzeD98Ej/zx4EbIuK5Jm3rhpr/3BcT+Stl6DBchr07Ik5o0saBkC1f4L9BFNx+Id6b2q9xIUItw7gZcH9EHJZuHSrpQFwqPrIp+wZCx0mwCGVoPw62pPYLxSv9CkTp6lBo2y80goi4EbhRHuG3LrAYHtX3MJ4ikx2icGXooAvISnSy/WCShAjAMw3aSLKjegFeBl7uWOiv4PJlPXDLCdVG36MMBe6LiCexam6/Jo3rgpId7LPAsrIQ4QB82t4lIv6eArTVSQFZRIxuzszBAfWqQw8Bq0MlzY+Dl0si4hF5GH2u7ydQvpNNKKL9Qq26tDo+IO0A7B0RpyaKxOIRcW+TNnaD+nber5Shu0bEDTgxc1sqEWcXuFcYjCrLwdB+oQi1n6TZZAI/iUMzBzBG0j6SvoSDhzObtHEgRGHKUJXffuFUClf6FYbi1aFq2y9MbVTr4BB8OLoBmDtd2xRn+HJF8crQQckhA5D0Y3waGQtcjQOcp4G/Q54vbx0qQIggaSv8jG8DboyIh1JWb1ucHfttRFzVpI0DoX6iVgHK0BSIbw7sA8wMXAWsHBEfa9Sw9wAVKEQYDFCh6tBaxuYE/F6Ow+/pOBww/AjzanP+HYpShkoajufIroV5wRtFxHOSrsPZspsbNXAiUMHK0EEXkJXmZCuoQCGCpM8CywLDcYDwDG7WeF2NnJ3l4lfBylAoz8GqUCHCYIQKUYfWUbKThbKUoSkTvy22d/GI2FHSMlhENrJZ6/pHB1WpKGVoHYMqICvdyUJZaj/oKSm8D5f9lgJmwfMI7wKOjoi3GjRvQKhwZWiF3B2sBoHSr8XUR8lOtmZ7kcpQSdvid/ZV4BTcrPaGiDg0Rz9UQQUqQ+sYVAEZlOlkOzaeYyJi7XS9EiLsDGQlRJC0VPrrfDgYewzzVRbFapxHIuKjDZk3IGqB+1eB9SNiVO3egcCLiZvSYjJA0jnAsVETItCrCK2ECLs1aWOLfFGik1WB7Rc6qTCpwrQ95nueCDwaEf/OMYDshPoqQz8AHBoR9zRq1CRg0JBnVXD7BQpT+0maG/grDngPxLZ/FDgZB2h/IRHPczxNRdnK0KKgQaL0a9Ecom2/MFWQAkjhuCAi4jXgMEmLAitEmuGbi70VVLAytBODJiAr2clG/2q/qyXlKkR4BXMMdsO9pL6Ly5OvdH4wt2BMvarQ1yLiQkmjsDL0NMwxHIXFFC0mD+pKv0qIMK6egYzMlX4tpj5KdrIqrP1CsvNQ4PDwTOe30/WZU1XpF8B16Vo2AWQNdWXoPpjXXFeG3tTfv5QjcpTGv2eo8PYLtZdzX9zo8FLMIatmcM6Q00sQEe9GxLkRsTqwPM6KPSTpBkk7Qu/vlCE+Dxwi6auSloiILXAbhmWATwLfjojroA0QJgfSWjkrIlbAa/oBPBT9Skk7Q9ZrpUVzaNsvTD28jp/vmZIelLSfpHlrFJ8Vydh/JrrPcPzcb8Wl4bPT7Z2wfyoCg4JDpoLbLwwGIUIFSUtgAmuW5HIoWxk6mJC7EKFF81DbfmGqQ9K8wDbA3rgJ+T+A+SLiMzlWlyqoQGVofxgsAVnRTrZEIULJKFkZ2qLFtIISnWzJytBOpEPTPsDlEXFOjnzgOkpVhtYxKAIyKNPJtmq/qYuSlaEtWkyLKNXJlqgMLRGDSRkKg4DU38XJrk6vk102In7ekHkDomQhQmkoXRnaosW0gvp+FxGnSDqPXie7L/Bo+mi2e2KJytASUaoytBuKzpAlJ/skfZ3s7Lj81+NkI+KF3JxsXe2Xfj4LWA6o1H57AntFxHVtQPbfQ+6UvSlWhn4AK0N/058ytEWLFs2iw8lWtJPKyWbJORxAGXpa7srQ0tCPMrS6PnNEvCnpfExZOqKU7BiUr7Ks2i/cihWJzwE/joiLIuLkiHggIl6A/Nov0Kr9pioKV4a2aDHoIWlOScdLWiqMt8PzTmdOH/kFPkzl+q6WrAwtDUUrQ7uh6AxZHUkdsh2wB/AIMDoijs81Oi5diDBYUIIytEWLaQHyXNOtgN3x7OFTgV9FxD/S/bF4YskLue6NpStDS0SpytD+MGgCsjpKcbIlChFatGjRYkqjVCdbojJ0MKE0ZWgnBmVAljtatV+LFi1aTBpKc7KlKkNbNI82IJvKKFmI0KJFixYtJsRga7/Qohm0AdlURqv2a9GiRYvBhxKVoS3yQukqy+LQqv1atGjRYnBgEChDW2SENkOWCUoRIrRo0aJFC2MwKENb5IM2IGvRokWLFi3+S5SqDG2RD9qArEWLFi1atJiMKE0Z2iIPtAFZixYtWrRo0aJFw2hJ/S1atGjRokWLFg2jDchatGjRokWLFi0aRhuQtWjRokWLFi1aNIw2IGvRokWLFi1atGgYbUDWokWLFi1atGjRMP4fNwKWQ4BKEJAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "table_svm_500 = experiment_pipeline(SVC(kernel='linear', probability=True, random_state=42),X, y, X_500_remove_stopword, X_500_lemmatized)\n",
    "summary_bar(table_svm_500, 'SVM with 500 word cutoff')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Combination</th>\n",
       "      <th>test_neg</th>\n",
       "      <th>test_pos</th>\n",
       "      <th>train_neg</th>\n",
       "      <th>train_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None_Count_None</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>0.975845</td>\n",
       "      <td>0.993435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None_Count_Smote</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.911854</td>\n",
       "      <td>0.993453</td>\n",
       "      <td>0.993424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None_Tfidf_None</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.932563</td>\n",
       "      <td>0.905009</td>\n",
       "      <td>0.976364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None_Tfidf_Smote</td>\n",
       "      <td>0.748031</td>\n",
       "      <td>0.935614</td>\n",
       "      <td>0.988266</td>\n",
       "      <td>0.988111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RS_Count_None</td>\n",
       "      <td>0.719101</td>\n",
       "      <td>0.923547</td>\n",
       "      <td>0.975845</td>\n",
       "      <td>0.993435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RS_Count_Smote</td>\n",
       "      <td>0.618321</td>\n",
       "      <td>0.898580</td>\n",
       "      <td>0.993435</td>\n",
       "      <td>0.993441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RS_Tfidf_None</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.984389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>RS_Tfidf_Smote</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.945783</td>\n",
       "      <td>0.989996</td>\n",
       "      <td>0.989881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lema_Count_None</td>\n",
       "      <td>0.700389</td>\n",
       "      <td>0.922301</td>\n",
       "      <td>0.975845</td>\n",
       "      <td>0.993435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lema_Count_Smote</td>\n",
       "      <td>0.640927</td>\n",
       "      <td>0.905966</td>\n",
       "      <td>0.993447</td>\n",
       "      <td>0.993430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Lema_Tfidf_None</td>\n",
       "      <td>0.675926</td>\n",
       "      <td>0.932171</td>\n",
       "      <td>0.927973</td>\n",
       "      <td>0.981377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lema_Tfidf_Smote</td>\n",
       "      <td>0.768000</td>\n",
       "      <td>0.941884</td>\n",
       "      <td>0.987826</td>\n",
       "      <td>0.987676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Combination  test_neg  test_pos  train_neg  train_pos\n",
       "0    None_Count_None  0.727273  0.926829   0.975845   0.993435\n",
       "1   None_Count_Smote  0.666667  0.911854   0.993453   0.993424\n",
       "2    None_Tfidf_None  0.666667  0.932563   0.905009   0.976364\n",
       "3   None_Tfidf_Smote  0.748031  0.935614   0.988266   0.988111\n",
       "4      RS_Count_None  0.719101  0.923547   0.975845   0.993435\n",
       "5     RS_Count_Smote  0.618321  0.898580   0.993435   0.993441\n",
       "6      RS_Tfidf_None  0.705882  0.936709   0.940000   0.984389\n",
       "7     RS_Tfidf_Smote  0.785714  0.945783   0.989996   0.989881\n",
       "8    Lema_Count_None  0.700389  0.922301   0.975845   0.993435\n",
       "9   Lema_Count_Smote  0.640927  0.905966   0.993447   0.993430\n",
       "10   Lema_Tfidf_None  0.675926  0.932171   0.927973   0.981377\n",
       "11  Lema_Tfidf_Smote  0.768000  0.941884   0.987826   0.987676"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_svm_500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 2 artists>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar([1,2],[390,34],tick_label=['SVM','Logistic Regression'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean Embedding\n",
    "\n",
    "1. Just use Mean Embedding\n",
    "2. Stack Mean Embedding after Bag of Words vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\gensim\\utils.py:1197: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
      "  warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4805a0c5a56a499e92e3f5c5845eeef9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=14), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "   Combination  test_neg  test_pos  train_neg  train_pos\n",
      "0         C=10  0.728745  0.933067   0.971008   0.970367\n",
      "1          C=9  0.741036  0.934804   0.970169   0.969456\n",
      "2          C=8  0.733068  0.932798   0.968494   0.967627\n",
      "3          C=7  0.724409  0.929577   0.967213   0.966282\n",
      "4          C=6  0.726563  0.929435   0.966321   0.965426\n",
      "5          C=5  0.718750  0.927419   0.965487   0.964508\n",
      "6          C=4  0.733591  0.930233   0.962036   0.960958\n",
      "7          C=3  0.727969  0.928065   0.956522   0.955091\n",
      "8          C=2  0.726592  0.925586   0.948552   0.947322\n",
      "9          C=1  0.724014  0.920537   0.928634   0.927880\n",
      "10       C=1/2  0.691275  0.903158   0.896251   0.895522\n",
      "11       C=1/3  0.668919  0.897059   0.875274   0.875383\n",
      "12       C=1/4  0.645161  0.882729   0.854403   0.853380\n",
      "13       C=1/5  0.634615  0.878205   0.843805   0.842982\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 7min 3s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%aimport mean_embedding_vectorizer\n",
    "pretrained = './nlpword2vecembeddingspretrained/glove.6B.300d.txt'\n",
    "# mevec = mean_embedding_vectorizer.MeanEmbeddingVectorizer()\n",
    "# mevec.load(pretrained)\n",
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
    "tfidf = TfidfVectorizer()\n",
    "stack_emb_vec = mean_embedding_vectorizer.StackedEmbeddingVectorizer(tfidf)\n",
    "stack_emb_vec.load(pretrained)\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "%aimport Pipeline_V1\n",
    "Pipeline = Pipeline_V1.Pipeline\n",
    "count = CountVectorizer()\n",
    "tfidf = TfidfVectorizer()\n",
    "smote = SMOTE(random_state=42)\n",
    "\n",
    "stack_emb_vec.set_pca(True)\n",
    "# stack_emb_vec.set_vectorizer(TfidfVectorizer(min_df=0.1))\n",
    "model = LogisticRegression()\n",
    "p = Pipeline(X_lemmatized, y, vectorizer=stack_emb_vec, model=model, sampler=smote)\n",
    "p.exec(silent=True)\n",
    "\n",
    "def tune_C_experiment():\n",
    "    experiment_in = {\n",
    "        'C=10': Pipeline(X_lemmatized, y, vectorizer=stack_emb_vec, model=LogisticRegression(penalty='l1', C=10.0), sampler=smote),\n",
    "        'C=9': Pipeline(X_lemmatized, y, vectorizer=stack_emb_vec, model=LogisticRegression(penalty='l1', C=9.0), sampler=smote),\n",
    "        'C=8': Pipeline(X_lemmatized, y, vectorizer=stack_emb_vec, model=LogisticRegression(penalty='l1', C=8.0), sampler=smote),\n",
    "        'C=7': Pipeline(X_lemmatized, y, vectorizer=stack_emb_vec, model=LogisticRegression(penalty='l1', C=7.0), sampler=smote),\n",
    "        'C=6': Pipeline(X_lemmatized, y, vectorizer=stack_emb_vec, model=LogisticRegression(penalty='l1', C=6.0), sampler=smote),\n",
    "        'C=5': Pipeline(X_lemmatized, y, vectorizer=stack_emb_vec, model=LogisticRegression(penalty='l1', C=5.0), sampler=smote),\n",
    "        'C=5': Pipeline(X_lemmatized, y, vectorizer=stack_emb_vec, model=LogisticRegression(penalty='l1', C=5.0), sampler=smote),\n",
    "        'C=4': Pipeline(X_lemmatized, y, vectorizer=stack_emb_vec, model=LogisticRegression(penalty='l1', C=4.0), sampler=smote),\n",
    "        'C=3': Pipeline(X_lemmatized, y, vectorizer=stack_emb_vec, model=LogisticRegression(penalty='l1', C=3.0), sampler=smote),\n",
    "        'C=2': Pipeline(X_lemmatized, y, vectorizer=stack_emb_vec, model=LogisticRegression(penalty='l1', C=2.0), sampler=smote),\n",
    "        'C=1': Pipeline(X_lemmatized, y, vectorizer=stack_emb_vec, model=LogisticRegression(penalty='l1', C=1.0), sampler=smote),\n",
    "        'C=1/2': Pipeline(X_lemmatized, y, vectorizer=stack_emb_vec, model=LogisticRegression(penalty='l1', C=1/2), sampler=smote),\n",
    "        'C=1/3': Pipeline(X_lemmatized, y, vectorizer=stack_emb_vec, model=LogisticRegression(penalty='l1', C=1/3), sampler=smote),\n",
    "        'C=1/4': Pipeline(X_lemmatized, y, vectorizer=stack_emb_vec, model=LogisticRegression(penalty='l1', C=1/4), sampler=smote),\n",
    "        'C=1/5': Pipeline(X_lemmatized, y, vectorizer=stack_emb_vec, model=LogisticRegression(penalty='l1', C=1/5), sampler=smote)\n",
    "    }\n",
    "    \n",
    "    experiment_out = []\n",
    "    for key, pipeline in tqdm(experiment_in.items()):\n",
    "        report = pipeline.exec(silent=True)\n",
    "        experiment_out.append([key, report['test_f1_neg'], report['test_f1_pos'], report['train_f1_neg'], report['train_f1_pos']])\n",
    "    \n",
    "    experiment_table = pd.DataFrame(experiment_out)\n",
    "    experiment_table.columns = ['Combination', 'test_neg', 'test_pos', 'train_neg', 'train_pos']\n",
    "    return experiment_table\n",
    "\n",
    "tune_C_table = tune_C_experiment()\n",
    "print(tune_C_table)\n",
    "summary_bar(tune_C_table)"
   ]
  }
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