PersonalizedCancerDiagnosis CountVectorizer(ngram_range=(1,2)).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-size:36px;text-align:center\"> <b>Personalized cancer diagnosis</b> </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>1. Business Problem</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>1.1. Description</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p> Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment/ </p>\n",
    "<p> Data: Memorial Sloan Kettering Cancer Center (MSKCC)</p>\n",
    "<p> Download training_variants.zip and training_text.zip from Kaggle.</p> \n",
    "\n",
    "<h6> Context:</h6>\n",
    "<p> Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment/discussion/35336#198462</p>\n",
    "\n",
    "<h6> Problem statement : </h6>\n",
    "<p> Classify the given genetic variations/mutations based on evidence from text-based clinical literature. </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>1.2. Source/Useful Links</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Some articles and reference blogs about the problem statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. https://www.forbes.com/sites/matthewherper/2017/06/03/a-new-cancer-drug-helped-almost-everyone-who-took-it-almost-heres-what-it-teaches-us/#2a44ee2f6b25\n",
    "2. https://www.youtube.com/watch?v=UwbuW7oK8rk \n",
    "3. https://www.youtube.com/watch?v=qxXRKVompI8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>1.3. Real-world/Business objectives and constraints.</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* No low-latency requirement.\n",
    "* Interpretability is important.\n",
    "* Errors can be very costly.\n",
    "* Probability of a data-point belonging to each class is needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>2. Machine Learning Problem Formulation</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>2.1. Data</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>2.1.1. Data Overview</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment/data\n",
    "- We have two data files: one conatins the information about the genetic mutations and the other contains the clinical evidence (text) that  human experts/pathologists use to classify the genetic mutations. \n",
    "- Both these data files are have a common column called ID\n",
    "- <p> \n",
    "    Data file's information:\n",
    "    <ul> \n",
    "        <li>\n",
    "        training_variants (ID , Gene, Variations, Class)\n",
    "        </li>\n",
    "        <li>\n",
    "        training_text (ID, Text)\n",
    "        </li>\n",
    "    </ul>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>2.1.2. Example Data Point</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h6>training_variants</h6>\n",
    "<hr>\n",
    "ID,Gene,Variation,Class<br>\n",
    "0,FAM58A,Truncating Mutations,1 <br>\n",
    "1,CBL,W802*,2 <br>\n",
    "2,CBL,Q249E,2 <br>\n",
    "...\n",
    "\n",
    "<h6> training_text</h6>\n",
    "<hr>\n",
    "ID,Text <br>\n",
    "0||Cyclin-dependent kinases (CDKs) regulate a variety of fundamental cellular processes. CDK10 stands out as one of the last orphan CDKs for which no activating cyclin has been identified and no kinase activity revealed. Previous work has shown that CDK10 silencing increases ETS2 (v-ets erythroblastosis virus E26 oncogene homolog 2)-driven activation of the MAPK pathway, which confers tamoxifen resistance to breast cancer cells. The precise mechanisms by which CDK10 modulates ETS2 activity, and more generally the functions of CDK10, remain elusive. Here we demonstrate that CDK10 is a cyclin-dependent kinase by identifying cyclin M as an activating cyclin. Cyclin M, an orphan cyclin, is the product of FAM58A, whose mutations cause STAR syndrome, a human developmental anomaly whose features include toe syndactyly, telecanthus, and anogenital and renal malformations. We show that STAR syndrome-associated cyclin M mutants are unable to interact with CDK10. Cyclin M silencing phenocopies CDK10 silencing in increasing c-Raf and in conferring tamoxifen resistance to breast cancer cells. CDK10/cyclin M phosphorylates ETS2 in vitro, and in cells it positively controls ETS2 degradation by the proteasome. ETS2 protein levels are increased in cells derived from a STAR patient, and this increase is attributable to decreased cyclin M levels. Altogether, our results reveal an additional regulatory mechanism for ETS2, which plays key roles in cancer and development. They also shed light on the molecular mechanisms underlying STAR syndrome.Cyclin-dependent kinases (CDKs) play a pivotal role in the control of a number of fundamental cellular processes (1). The human genome contains 21 genes encoding proteins that can be considered as members of the CDK family owing to their sequence similarity with bona fide CDKs, those known to be activated by cyclins (2). Although discovered almost 20 y ago (3, 4), CDK10 remains one of the two CDKs without an identified cyclin partner. This knowledge gap has largely impeded the exploration of its biological functions. CDK10 can act as a positive cell cycle regulator in some cells (5, 6) or as a tumor suppressor in others (7, 8). CDK10 interacts with the ETS2 (v-ets erythroblastosis virus E26 oncogene homolog 2) transcription factor and inhibits its transcriptional activity through an unknown mechanism (9). CDK10 knockdown derepresses ETS2, which increases the expression of the c-Raf protein kinase, activates the MAPK pathway, and induces resistance of MCF7 cells to tamoxifen (6). ... "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>2.2. Mapping the real-world problem to an ML problem</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>2.2.1. Type of Machine Learning Problem</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "    \n",
    "            There are nine different classes a genetic mutation can be classified into => Multi class classification problem\n",
    "   \n",
    "      \n",
    "    \n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>2.2.2. Performance Metric</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment#evaluation\n",
    "\n",
    "Metric(s): \n",
    "* Multi class log-loss \n",
    "* Confusion matrix \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>2.2.3. Machine Learing Objectives and Constraints</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p> Objective: Predict the probability of each data-point belonging to each of the nine classes.\n",
    "</p>\n",
    "<p> Constraints:\n",
    "</p>\n",
    "* Interpretability\n",
    "* Class probabilities are needed.\n",
    "* Penalize the errors in class probabilites => Metric is Log-loss.\n",
    "* No Latency constraints."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>2.3. Train, CV and Test Datasets</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Split the dataset randomly into three parts train, cross validation and test with 64%,16%, 20% of data respectively"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>3. Exploratory Data Analysis</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import time\n",
    "import warnings\n",
    "import numpy as np\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.manifold import TSNE\n",
    "import seaborn as sns\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics.classification import accuracy_score, log_loss\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from collections import Counter\n",
    "from scipy.sparse import hstack\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# from sklearn.cross_validation import StratifiedKFold \n",
    "from sklearn.model_selection import StratifiedKFold \n",
    " \n",
    "from collections import Counter, defaultdict\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import math\n",
    "from sklearn.metrics import normalized_mutual_info_score\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from mlxtend.classifier import StackingClassifier\n",
    "\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LogisticRegression\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>3.1. Reading Data</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>3.1.1. Reading Gene and Variation Data</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points :  3321\n",
      "Number of features :  4\n",
      "Features :  ['ID' 'Gene' 'Variation' 'Class']\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gene</th>\n",
       "      <th>Variation</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>FAM58A</td>\n",
       "      <td>Truncating Mutations</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CBL</td>\n",
       "      <td>W802*</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>CBL</td>\n",
       "      <td>Q249E</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>CBL</td>\n",
       "      <td>N454D</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>CBL</td>\n",
       "      <td>L399V</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID    Gene             Variation  Class\n",
       "0   0  FAM58A  Truncating Mutations      1\n",
       "1   1     CBL                 W802*      2\n",
       "2   2     CBL                 Q249E      2\n",
       "3   3     CBL                 N454D      3\n",
       "4   4     CBL                 L399V      4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('training_variants')\n",
    "print('Number of data points : ', data.shape[0])\n",
    "print('Number of features : ', data.shape[1])\n",
    "print('Features : ', data.columns.values)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "    training/training_variants is a comma separated file containing the description of the genetic mutations used for training. <br>\n",
    "    Fields are \n",
    "    <ul>\n",
    "        <li><b>ID : </b>the id of the row used to link the mutation to the clinical evidence</li>\n",
    "        <li><b>Gene : </b>the gene where this genetic mutation is located </li>\n",
    "        <li><b>Variation : </b>the aminoacid change for this mutations </li>\n",
    "        <li><b>Class :</b> 1-9 the class this genetic mutation has been classified on</li>\n",
    "    </ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>3.1.2. Reading Text Data</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points :  3321\n",
      "Number of features :  2\n",
      "Features :  ['ID' 'TEXT']\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>TEXT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Cyclin-dependent kinases (CDKs) regulate a var...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Abstract Background  Non-small cell lung canc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Abstract Background  Non-small cell lung canc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Recent evidence has demonstrated that acquired...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Oncogenic mutations in the monomeric Casitas B...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID                                               TEXT\n",
       "0   0  Cyclin-dependent kinases (CDKs) regulate a var...\n",
       "1   1   Abstract Background  Non-small cell lung canc...\n",
       "2   2   Abstract Background  Non-small cell lung canc...\n",
       "3   3  Recent evidence has demonstrated that acquired...\n",
       "4   4  Oncogenic mutations in the monomeric Casitas B..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# note the seprator in this file\n",
    "data_text =pd.read_csv(\"training_text\",sep=\"\\|\\|\",engine=\"python\",names=[\"ID\",\"TEXT\"],skiprows=1)\n",
    "print('Number of data points : ', data_text.shape[0])\n",
    "print('Number of features : ', data_text.shape[1])\n",
    "print('Features : ', data_text.columns.values)\n",
    "data_text.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>3.1.3. Preprocessing of text</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading stop words from nltk library\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "\n",
    "def nlp_preprocessing(total_text, index, column):\n",
    "    if type(total_text) is not int:\n",
    "        string = \"\"\n",
    "        # replace every special char with space\n",
    "        total_text = re.sub('[^a-zA-Z0-9\\n]', ' ', total_text)\n",
    "        # replace multiple spaces with single space\n",
    "        total_text = re.sub('\\s+',' ', total_text)\n",
    "        # converting all the chars into lower-case.\n",
    "        total_text = total_text.lower()\n",
    "        \n",
    "        for word in total_text.split():\n",
    "        # if the word is a not a stop word then retain that word from the data\n",
    "            if not word in stop_words:\n",
    "                string += word + \" \"\n",
    "        \n",
    "        data_text[column][index] = string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there is no text description for id: 1109\n",
      "there is no text description for id: 1277\n",
      "there is no text description for id: 1407\n",
      "there is no text description for id: 1639\n",
      "there is no text description for id: 2755\n",
      "Time took for preprocessing the text : 245.9123242 seconds\n"
     ]
    }
   ],
   "source": [
    "#text processing stage.\n",
    "start_time = time.clock()\n",
    "for index, row in data_text.iterrows():\n",
    "    if type(row['TEXT']) is str:\n",
    "        nlp_preprocessing(row['TEXT'], index, 'TEXT')\n",
    "    else:\n",
    "        print(\"there is no text description for id:\",index)\n",
    "print('Time took for preprocessing the text :',time.clock() - start_time, \"seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gene</th>\n",
       "      <th>Variation</th>\n",
       "      <th>Class</th>\n",
       "      <th>TEXT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>FAM58A</td>\n",
       "      <td>Truncating Mutations</td>\n",
       "      <td>1</td>\n",
       "      <td>cyclin dependent kinases cdks regulate variety...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CBL</td>\n",
       "      <td>W802*</td>\n",
       "      <td>2</td>\n",
       "      <td>abstract background non small cell lung cancer...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>CBL</td>\n",
       "      <td>Q249E</td>\n",
       "      <td>2</td>\n",
       "      <td>abstract background non small cell lung cancer...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>CBL</td>\n",
       "      <td>N454D</td>\n",
       "      <td>3</td>\n",
       "      <td>recent evidence demonstrated acquired uniparen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>CBL</td>\n",
       "      <td>L399V</td>\n",
       "      <td>4</td>\n",
       "      <td>oncogenic mutations monomeric casitas b lineag...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   ID    Gene             Variation  Class  \\\n",
       "0   0  FAM58A  Truncating Mutations      1   \n",
       "1   1     CBL                 W802*      2   \n",
       "2   2     CBL                 Q249E      2   \n",
       "3   3     CBL                 N454D      3   \n",
       "4   4     CBL                 L399V      4   \n",
       "\n",
       "                                                TEXT  \n",
       "0  cyclin dependent kinases cdks regulate variety...  \n",
       "1  abstract background non small cell lung cancer...  \n",
       "2  abstract background non small cell lung cancer...  \n",
       "3  recent evidence demonstrated acquired uniparen...  \n",
       "4  oncogenic mutations monomeric casitas b lineag...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merging both gene_variations and text data based on ID\n",
    "result = pd.merge(data, data_text,on='ID', how='left')\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gene</th>\n",
       "      <th>Variation</th>\n",
       "      <th>Class</th>\n",
       "      <th>TEXT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>1109</td>\n",
       "      <td>FANCA</td>\n",
       "      <td>S1088F</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>1277</td>\n",
       "      <td>ARID5B</td>\n",
       "      <td>Truncating Mutations</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1407</th>\n",
       "      <td>1407</td>\n",
       "      <td>FGFR3</td>\n",
       "      <td>K508M</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1639</th>\n",
       "      <td>1639</td>\n",
       "      <td>FLT1</td>\n",
       "      <td>Amplification</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2755</th>\n",
       "      <td>2755</td>\n",
       "      <td>BRAF</td>\n",
       "      <td>G596C</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID    Gene             Variation  Class TEXT\n",
       "1109  1109   FANCA                S1088F      1  NaN\n",
       "1277  1277  ARID5B  Truncating Mutations      1  NaN\n",
       "1407  1407   FGFR3                 K508M      6  NaN\n",
       "1639  1639    FLT1         Amplification      6  NaN\n",
       "2755  2755    BRAF                 G596C      7  NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[result.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.loc[result['TEXT'].isnull(),'TEXT'] = result['Gene'] +' '+result['Variation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gene</th>\n",
       "      <th>Variation</th>\n",
       "      <th>Class</th>\n",
       "      <th>TEXT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>1109</td>\n",
       "      <td>FANCA</td>\n",
       "      <td>S1088F</td>\n",
       "      <td>1</td>\n",
       "      <td>FANCA S1088F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID   Gene Variation  Class          TEXT\n",
       "1109  1109  FANCA    S1088F      1  FANCA S1088F"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[result['ID']==1109]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>3.1.4. Test, Train and Cross Validation Split</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>3.1.4.1. Splitting data into train, test and cross validation (64:20:16)</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = result['Class'].values\n",
    "result.Gene      = result.Gene.str.replace('\\s+', '_')\n",
    "result.Variation = result.Variation.str.replace('\\s+', '_')\n",
    "\n",
    "# split the data into test and train by maintaining same distribution of output varaible 'y_true' [stratify=y_true]\n",
    "X_train, test_df, y_train, y_test = train_test_split(result, y_true, stratify=y_true, test_size=0.2)\n",
    "# split the train data into train and cross validation by maintaining same distribution of output varaible 'y_train' [stratify=y_train]\n",
    "train_df, cv_df, y_train, y_cv = train_test_split(X_train, y_train, stratify=y_train, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p> We split the data into train, test and cross validation data sets, preserving the ratio of class distribution in the original data set  </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in train data: 2124\n",
      "Number of data points in test data: 665\n",
      "Number of data points in cross validation data: 532\n"
     ]
    }
   ],
   "source": [
    "print('Number of data points in train data:', train_df.shape[0])\n",
    "print('Number of data points in test data:', test_df.shape[0])\n",
    "print('Number of data points in cross validation data:', cv_df.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>3.1.4.2. Distribution of y_i's in Train, Test and Cross Validation datasets</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in class 7 : 609 ( 28.672 %)\n",
      "Number of data points in class 4 : 439 ( 20.669 %)\n",
      "Number of data points in class 1 : 363 ( 17.09 %)\n",
      "Number of data points in class 2 : 289 ( 13.606 %)\n",
      "Number of data points in class 6 : 176 ( 8.286 %)\n",
      "Number of data points in class 5 : 155 ( 7.298 %)\n",
      "Number of data points in class 3 : 57 ( 2.684 %)\n",
      "Number of data points in class 9 : 24 ( 1.13 %)\n",
      "Number of data points in class 8 : 12 ( 0.565 %)\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in class 7 : 191 ( 28.722 %)\n",
      "Number of data points in class 4 : 137 ( 20.602 %)\n",
      "Number of data points in class 1 : 114 ( 17.143 %)\n",
      "Number of data points in class 2 : 91 ( 13.684 %)\n",
      "Number of data points in class 6 : 55 ( 8.271 %)\n",
      "Number of data points in class 5 : 48 ( 7.218 %)\n",
      "Number of data points in class 3 : 18 ( 2.707 %)\n",
      "Number of data points in class 9 : 7 ( 1.053 %)\n",
      "Number of data points in class 8 : 4 ( 0.602 %)\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in class 7 : 153 ( 28.759 %)\n",
      "Number of data points in class 4 : 110 ( 20.677 %)\n",
      "Number of data points in class 1 : 91 ( 17.105 %)\n",
      "Number of data points in class 2 : 72 ( 13.534 %)\n",
      "Number of data points in class 6 : 44 ( 8.271 %)\n",
      "Number of data points in class 5 : 39 ( 7.331 %)\n",
      "Number of data points in class 3 : 14 ( 2.632 %)\n",
      "Number of data points in class 9 : 6 ( 1.128 %)\n",
      "Number of data points in class 8 : 3 ( 0.564 %)\n"
     ]
    }
   ],
   "source": [
    "# it returns a dict, keys as class labels and values as the number of data points in that class\n",
    "train_class_distribution = train_df['Class'].value_counts().sortlevel()\n",
    "test_class_distribution = test_df['Class'].value_counts().sortlevel()\n",
    "cv_class_distribution = cv_df['Class'].value_counts().sortlevel()\n",
    "\n",
    "my_colors = 'rgbkymc'\n",
    "train_class_distribution.plot(kind='bar')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Data points per Class')\n",
    "plt.title('Distribution of yi in train data')\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "# ref: argsort https://docs.scipy.org/doc/numpy/reference/generated/numpy.argsort.html\n",
    "# -(train_class_distribution.values): the minus sign will give us in decreasing order\n",
    "sorted_yi = np.argsort(-train_class_distribution.values)\n",
    "for i in sorted_yi:\n",
    "    print('Number of data points in class', i+1, ':',train_class_distribution.values[i], '(', np.round((train_class_distribution.values[i]/train_df.shape[0]*100), 3), '%)')\n",
    "\n",
    "    \n",
    "print('-'*80)\n",
    "my_colors = 'rgbkymc'\n",
    "test_class_distribution.plot(kind='bar')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Data points per Class')\n",
    "plt.title('Distribution of yi in test data')\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "# ref: argsort https://docs.scipy.org/doc/numpy/reference/generated/numpy.argsort.html\n",
    "# -(train_class_distribution.values): the minus sign will give us in decreasing order\n",
    "sorted_yi = np.argsort(-test_class_distribution.values)\n",
    "for i in sorted_yi:\n",
    "    print('Number of data points in class', i+1, ':',test_class_distribution.values[i], '(', np.round((test_class_distribution.values[i]/test_df.shape[0]*100), 3), '%)')\n",
    "\n",
    "print('-'*80)\n",
    "my_colors = 'rgbkymc'\n",
    "cv_class_distribution.plot(kind='bar')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Data points per Class')\n",
    "plt.title('Distribution of yi in cross validation data')\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "# ref: argsort https://docs.scipy.org/doc/numpy/reference/generated/numpy.argsort.html\n",
    "# -(train_class_distribution.values): the minus sign will give us in decreasing order\n",
    "sorted_yi = np.argsort(-train_class_distribution.values)\n",
    "for i in sorted_yi:\n",
    "    print('Number of data points in class', i+1, ':',cv_class_distribution.values[i], '(', np.round((cv_class_distribution.values[i]/cv_df.shape[0]*100), 3), '%)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>3.2 Prediction using a 'Random' Model</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-size:16px\"> In a 'Random' Model, we generate the NINE class probabilites randomly such that they sum to 1. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function plots the confusion matrices given y_i, y_i_hat.\n",
    "def plot_confusion_matrix(test_y, predict_y):\n",
    "    C = confusion_matrix(test_y, predict_y)\n",
    "    # C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j\n",
    "    \n",
    "    A =(((C.T)/(C.sum(axis=1))).T)\n",
    "    #divid each element of the confusion matrix with the sum of elements in that column\n",
    "    \n",
    "    # C = [[1, 2],\n",
    "    #     [3, 4]]\n",
    "    # C.T = [[1, 3],\n",
    "    #        [2, 4]]\n",
    "    # C.sum(axis = 1)  axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array\n",
    "    # C.sum(axix =1) = [[3, 7]]\n",
    "    # ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7]\n",
    "    #                           [2/3, 4/7]]\n",
    "\n",
    "    # ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3]\n",
    "    #                           [3/7, 4/7]]\n",
    "    # sum of row elements = 1\n",
    "    \n",
    "    B =(C/C.sum(axis=0))\n",
    "    #divid each element of the confusion matrix with the sum of elements in that row\n",
    "    # C = [[1, 2],\n",
    "    #     [3, 4]]\n",
    "    # C.sum(axis = 0)  axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array\n",
    "    # C.sum(axix =0) = [[4, 6]]\n",
    "    # (C/C.sum(axis=0)) = [[1/4, 2/6],\n",
    "    #                      [3/4, 4/6]] \n",
    "    \n",
    "    labels = [1,2,3,4,5,6,7,8,9]\n",
    "    # representing A in heatmap format\n",
    "    print(\"-\"*20, \"Confusion matrix\", \"-\"*20)\n",
    "    plt.figure(figsize=(20,7))\n",
    "    sns.heatmap(C, annot=True, cmap=\"YlGnBu\", fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.show()\n",
    "\n",
    "    print(\"-\"*20, \"Precision matrix (Columm Sum=1)\", \"-\"*20)\n",
    "    plt.figure(figsize=(20,7))\n",
    "    sns.heatmap(B, annot=True, cmap=\"YlGnBu\", fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.show()\n",
    "    \n",
    "    # representing B in heatmap format\n",
    "    print(\"-\"*20, \"Recall matrix (Row sum=1)\", \"-\"*20)\n",
    "    plt.figure(figsize=(20,7))\n",
    "    sns.heatmap(A, annot=True, cmap=\"YlGnBu\", fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss on Cross Validation Data using Random Model 2.507263105659845\n",
      "Log loss on Test Data using Random Model 2.4377219953413842\n",
      "-------------------- Confusion matrix --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Precision matrix (Columm Sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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Y6/ERCDGhtUg+XHaB6W6NI7inX2OuGfc7+fbjI3JSjhTdd+1Lz2bpjkO0qB1KboGdeuGB/PpEXwD8fbz55Ym+9H5xQQXXRkqrjlMS3KWivtFGAxcAJ465NIAaGRarVbce+SnJ5B9Kxcdq48jKv4i7+Y5/dYwjK/4icqBW2D5TcU0SSD+QSkZSGiHhoWxYtIrBj950Rsds3KkFD/3vhZLXLw/9r4IILtqYfpQ6wbWIC/QjJSefAQmRPPXnFpf29TIg2MfC4fxCGoUG0Dg0kGeSii4bQT4WEoL92Xs0hy4xVnYf0SKYp7Mp4yh1gvyJDfAjNSef/nUiGfWXi20BBPlaOFLcFo1CA1mWvBW7CRf/cHz1518GdVUQwQW2BnU5lpRCVsoh/MOs7Fu6kk73uDazLzstA7/gQLx9fcnPyiZt606aXFR0I5h7+Ci1QoPJz8pmx7xFdLn/toqshkfYeuQotQP8ifb3Iy03n54xkby61rXz4h+9YiL4dNuektfLD2Vww2/Hz4vpfboqiOCCgLr1yEtJIa/4Xipj+XLq3Xa7a/vWq09hdjYFR4/iExzM0S2bCahbD4CCw5n4hFoxTZPDf6+mVlztCqyF5whtUJes5BSyUw9Ry2YlcekK2t11q0v7ls63b/GfHN61h6ZXD8Zht1OYnYNvcBCOQjspa9YR0UILwp7O2n2Z1IsIJD4sgOTDOVzWrjYPfLHKKU/z2iG8cEUbbv5wKWnHjgdDQ/x9yM23k293YAv0pUO9MD74ZTvbk4/R6bnjC8CvH3uxgghVQIGEf+8HIMg0zTUnJhiG8WsFvWeFMry9ib7qOva9/xY4HIR27YZfXG1Sf/iOWgn1CG7dlpw9uzgwcRz27CyOrf+bQ7Nn0WDU8wDkpx2iMCOdgEZNnI6b/st80uf/TOGRw+we+yyBLVoRe/3NVVDDmsPL25sL77qCr0aNw+Fw0LZ/FyLrxvLr57OJa5xAky6tSNy6h2ljJpF7LIdtf63nt//9yH/GPwnAp4+9Rdq+ZPJz83n7xlFc+sB1NOzQrIprVXPZTXh15Q7e7dUSby+DWTuT2Xkkmztb1mVT+lEWJabTPCyIV7s3J8TXQo+4MIa3SuDqH1dhMQw+7NsGgKyCQkYt3YK9eCzSmL+28Uq3ZjhMk6MFhTy/TNMaTsduwmtrdvBOj5Z4GfD97mR2HclmePMENmUcY/HBdJrZgnilazOCfS30iA3jjuYJXDtvNRYvg4nnFz3pzioo5Jm/tpa0hfx7Xt7etL35aha//B6mw0G9Xl0JjY9jw/TvsdWvS1yH1qTv2M2fb04kPzubg6vXsXHGbAa8MoqjiUn8/r8ZRdPnTJMml/QjNKHoi9Hfn08jc0/RqJHmQy4mOPakg/ykmMOE8Zt3MLp90Xkx70Aye7OyuaFhAtuOHGNZajqNQ4J4qm0zgnwsdIoM4/qGCdz9R9FCllG1/Iio5ce6f7HGhZTP8PYm/urr2PHOW5gOk/DzuuEfV5uDs2YSULcuoW3akrV7F7smjMOenc3hdWtJ+mEmzZ55HsPLi9pDr2T7W6+DCQEJCYR37wHA7smTKDx6DDDxj69DnetuqNqK1hBe3t60vPEa/nrlXUzTQXzP8wiOj2PLjO+x1k8gun0bMnfuZuXbH1CQlU3y6nVs/fYHer148gctjsJClr36DqbdgelwENGiKQnnd6/EWtVMdofJM9+s47PhXfAyDKb9tZdtyUd56IJzWLc/k/kbknni0hYE+ll4/8aikVD//Mxjo+ggXriiDaZpYhgGExZud/q1B5GKYlTXBVCq29SGs9kV9fQkuLp4e0VAVRdBinlbNKS8OulXTwtJVRdr0rWSfnUR7HPib+VIVYkLKKzqIkixb6fqS3Z1suv1yz36hiqm+eNu+06btPHlavVZabK+iIiIiIiIiNt57tQGz62ZiIiIiIiIiLidRiSIiIiIiIiIuJkWWxQRERERERERl3lyIMFzayYiIiIiIiIibqcRCSIiIiIiIiJuZnjwc3sFEkRERERERETczJOnNiiQICIiIiIiIuJmhmFUdREqjOeGSERERERERETE7TQiQURERERERMTNNLVBRERERERERFzmyYstem7NRERERERERMTtNCJBRERERERExM00tUFEREREREREXObJgQTPrZmIiIiIiIjIWcAwjAsNw9hiGMZ2wzBGlJP+H8Mw1hmGscYwjCWGYTQv3t7fMIyVxWkrDcPo48r7aUSCiIiIiIiIiJtV1mKLhmF4A+8D/YH9wHLDMGaZprmxVLYvTdOcUJz/cuAN4ELgEHCZaZqJhmG0BH4Gap/uPattIGFU2yNVXQQp9nuyT1UXQYo91imrqosgxWbt86/qIkgpUf72qi6CFLuinq5T1YVF406rjdnqM6oNr/Tcqi6CnE0qb2pDJ2C7aZo7AQzD+BoYCJQEEkzTLP0FOxAwi7evLrV9A1DLMAw/0zTzTvWG1TaQICIiIiIiIiKnVRvYV+r1fqDziZkMw7gHeBjwBcqbwjAUWH26IAJojQQRERERERERtzMMLzf+GcMNw1hR6m946bcq5+3NMhtM833TNBsCjwNPOZfVaAG8DNzpSt00IkFERERERETEzQyjvO/3/z+maU4EJp4keT9Qp9TreCDxFIf7Ghj/zwvDMOKBb4EbTdPc4Up5NCJBREREREREpOZaDjQ2DKO+YRi+wDXArNIZDMNoXOrlJcC24u1WYDbwhGmav7v6hhqRICIiIiIiIuJmlfWrDaZpFhqGcS9Fv7jgDUw2TXODYRjPAytM05wF3GsYRj+gAMgAbire/V6gETDKMIxRxdsGmKaZcqr3VCBBRERERERExM2MyvvVBkzTnAPMOWHb06X+/4GT7DcGGPNv309TG0RERERERETEZRqRICIiIiIiIuJublxssbpRIEFERERERETE3Tx4/L8HV01ERERERERE3E0jEkRERERERETcTVMbRERERERERMRlHhxI0NQGEREREREREXGZRiSIiIiIiIiIuJsHP7ZXIEFERERERETEzUxNbRARERERERER0YiEf+XvpZv4/K3vcDgcnH9ZFy4f1tcpvSC/kPGjv2T3ln0EhQZy3/M3EhkbRmGhnUkvTmHX1v047A66X9iRgTf2A+CBoaOpFeCHl5cX3t5ejJn8cFVUrcbZu2ojSybPwOFw0LxfV9oPGeCUnrhhO0smzyBtTyIDHr6Zhue1A+DQrv389sEU8nNyMby86DB0AI27dwBgwbufk7hhO74B/gD0ve8GIurHV27FaqhtKzYxe8I3mA4HHS7sQs+r+jul7163nTkffEvyrkSuHHETLXu0LUn79Knx7N+8h4QW9Rn23J0l2zOS0pj60qdkH80irlEdhv73Biw+umSdzuH169k7ZQqmw0Fk9+7EXnSRU/rRrVvZO2UK2QcO0PCOOwjr0KEkLS8tjd2ffUZ+RgYYBk3uuw+/iAiSFy4kecEC8lJTafv66/gEB1d2tWokXaeqj50rNzL/w29wOBy06d+Vrlc6X6P2rt/Ogg+/IWV3IgMfu4mm3dqVpE15ZhyJW/YQ36wBVz5z/BplmiaLPp/N5t9X4+XlRbuLutPx8l6VVqeaaseKjfw8sai/aDugK91O6C/2rN/OvInfkLwrkSGP30Sz7sfb4stR4ziwZQ91mjfgmmfvPPHQ/DR+On/PX8rjM16r8Hp4iiMb1rN/6teYDgfh3XoQc6Fzn3Fs21b2T51CzoH91LttOLZSfUZ+ehp7P/+M/Ix0DAwa3Hs/fhERJen7vv6S9D//oM3b71VafWqyni1jGHVdW7y9DKYs2sUHczY7pd86oAlX9ayP3WGSfjSPxycvJzEtG4DYsABevKUjsWEBmKbJbW8u5kBaNl2bRTHi6jb4enuxfk8GIyYvx+4wq6J6Zy/PHZCgQIKrHHYHn7z+DU+89R/CokIZdfubtO/egvj6MSV5fv1hGYHB/rwxdSR/zl/NV+N+4P7RN7Js4RoKCgp5+fPHyMvN57HrX+a8/u2JjA0D4Kl37ybYGlRVVatxHHYHiz6cxmXP3ENQuJXpj71KvXNbEVYntiRPUKSNPvfdwJqZC5z2tfj50vf+YVjjoshKP8y0/75CQrtm+AUGAHDejYNKbubFNQ67g+/fn8bNY+8mJMLKhAdep2nnVkTVPX5uhEbZGPLIdSyZ8UuZ/bsP7UNBXgHLf/zdafvPk2fRddD5tD6/PbPencKqn5fS6dLuFV6fmsx0ONjz5Zc0eeghfG02No4di7VNG/zj4kry+IaFUf+WW0iaO7fM/rs+/pjYiy8mtHlz7Lm5JSsNBzVqhLV1aza//nql1aWm03Wq+nDYHcydMI1rRt9DcLiVTx5+jcadWxKRcLwtQiJtXPLg9Sz7dmGZ/TsP6UtBXj5rfvzDafu6Bcs4ciiD4eNHYnh5kZV5tMLrUtM57A5+HD+N68fcQ0iElY8eeo0mXVoSWaotQiNtXPbQ9Sz9pmxbdB1a1BarTmgLgMRte8nNyq7Q8nsa0+Fg31df0uiBh/Cx2djy4guEtnbuM3xsYdS96RaS5/1cZv89H08m+qJLCCnuMwyv49+Ysvfsxp6TUyn18ARehsGzw9pz02u/kZSew7dP92PBmkS2Jx4pybNxbwaDnt9Bbr6d63o3ZMRVrbl//FIAXrujE+O+38TvG5MJ8LPgME0MA169vRM3vPIru5OP8eCgFgzpVo9pi3dVVTXPTl6eG0mosKkNhmE0NQyjr2EYQSdsv7Ci3rMi7di0l+j4CKJqh2PxsdClbztWLl7vlGfl4vX0vPhcADqd35oNK7dhmiaGYZCXm4+90E5+XgEWHwv+gX5VUQ2PkLJ9D6GxEYTGRODtY6FR9w7s+mudU56QqHAi6tV26tQArHFRWOOiAAgMC8U/NIicw8cqreyeaP/WPYTHRRIWG4HFx0KrXu3ZtNS5PWzR4cTUr41XOfPEGrY7B78A5/PBNE12/b2NFj3aANC2Xyc2/bmuzL7iLGvXLvyioqgVGYmXxULYueeS8fffTnn8IiIIiI8v83NEOYmJmHY7oc2bA+BdqxbefkXtEpiQ4PSUSU5P16nq4+C2PdhiI7EWt0Xznu3Ztsy5LazR4UTVr41RzjWqXptz8PWvVWb76jlL6H7NhRheRbdSgVaN1DmdxK17CIuLxBZb1BYterZn69KybRF9krao37b8tnDYHSz46Dv63jqwwsruibJ378IvKhK/4j7Ddu65HF67ximPX0QE/vHxZdojJzER0+EgpFSf4eVb1GeYDgcHZkyn9pChlVMRD9CmQRh7Uo6xLzWLAruDH/7aS792cU55lm5OJTffDsCaHWnE2IqCy43iQrB4Gfy+MRmA7LxCcvPt2IL8yC+wszu5qP9YsiGZCztqBJu4T4WMSDAM437gHmAT8JFhGA+YpjmzOHks8FNFvG9FSk89THiUteR1WJSVHRv2OOXJSD1MWHEeb4s3AYG1OHY4i06927By8XruGfgs+bkF3HD/QIJCAgEwDIOXHvoADIO+A7vSZ2DXyqtUDZWVlklQuK3kdVC4leRtu//1cZK37cZeaCc05vgXpGVf/sDyaT8R36oJXYddjrePjzuK7NGOHDpMaOTxcyM0wsr+LXtOscfpZR/JolagP97e3iXHPJKWeUbHPBvkZ2biGxZW8trXaiVrl2tPHnKTk/EOCGDb+PHkHzpESLNmxA8ZUvIlSf4dXaeqj6NpmQRHHL9GBYdbSdx6ZtcogIykQ2xavIqtS9cSEBJEvzuHElYcAJLyHU3LJKR0W0RYSTzD/gJgxQ+LaNK5FcFhoWd8rLNJfkYmvrbSfYbN5T4jLyUZ7wB/dk4YR37aIYKbNiNu8FAMLy9Sf1lIaOs2+IRaT38gASDa5s/B9OMjapLSc2jTMOyk+a/sWZ/f1h0EoH50EEeyCxh373nERwTyx8ZkXpm2jvSjeVi8vWhVz8a63RlcdG48sWH+FV4XOYEWW/zX7gA6mKY5CDgfGGUYxgPFaTXz0zTLzic6MTprlpMHw2DHxr14eXnx3sxneXP6SOZ89SspB9IAeGb8fbzw8SM89vodzPtmCZvW7KiQ4nuS8mZ2Gf/yn1VW+mEWvP05fe69vuSLUpcsLgFTAAAgAElEQVTrL+fad5/iylf+S96xbFZ9O98NpT0bVMBcu3IbuWZeOirVSa5BLu3qcHBs2zbqXHEFzZ98krzUVA79UXb4sLhG16lqpIKuJ/aCQrx9fbj5zUdpc8F5zHn7yzM+pqcr7xJ1preFR9MOs3HJGs69vOcZHefsVF6f4eKedgfHtm2n9tArOWfESPIOHSL9z98pyMwkc9VKInv3cW9RPVy5H/tJbq8Gdk2gVb0wPvxxCwDe3l6c2ySCF6f8zeDn51MnMoih3esB8MCEpYy8ti3fjOpLVm4hhXatj1DpDDf+VTMVFUjwNk3zGIBpmrspCiZcZBjGG5ziYzAMY7hhGCsMw1jxzWfVa9BCWJSVtJTjT0TTUzKxRoSUyZNenMdeaCc7K5egkAD+mLeK1l2aYrF4E2oLpknr+uzcvA8AW2RR9DzUFkzHnq3YuXFvJdWo5goKt3IsLaPk9bG0TAL+xVOI/OwcZr8wgU7XXUrMOfVLtgeGhWIYBt4+PjTt04WUbWf+lORsEBJh5XDq8XPj8KFMgsPP7KlQQGgguVk52O32kmOG6EnTafnabOSnp5e8zs/MxMfq2hMhX5uNgIQEakVGYnh7Y23bluy9uh79f+k6VX0ER1g5euj4NepoWibBYSGn2MPF44ZbOee8oulXTbq2JnV34hkf09OFRFg5UrotDmUSHH5mbZG0Yz8Ziam8f/to3r3lWQryCnj/9ufPtKhnBV+bjfyM0n1Gxr/oM6wE1KmD3z99RpuiPiN7317yUlPYOGokG54cgSM/nw2jnqyoKniMpIwcYsMCSl7HhPmTnFl2jYnzmkdx96XNufPtJeQXOor2Tc9mw95M9qVmYXeYzFt1gBZ1i0bErd6RxjUv/sKQ0Qv4a0tqyTQHEXeoqEBCkmEYJcuyFwcVLgUigFYn28k0zYmmaXY0TbPjkBur11IKDZrWIWl/KimJaRQWFLJ0wWo6dG/plKd99xYsmrMcgL9+XUuLDo0wDIOIaCsbi9dLyM3JY9uGPcTVjSI3J4+crFwAcnPyWPfXVuIbxJR5b3EW1SiBwwdTOZJ8CHtBIduXrKT+uSf9Z+XEXlDIjy9P4pzzO9HohMXKstIPA8Xz85etJazU4k9ycrWbJJCWmEpGUtG5se63VTTt0vL0O56CYRjUb92YDYuL5vevmf8XTbue2THPBoH16pGXkkLeoUM4CgtJX74cW5s2Lu9bmJ1NwdGiBeOObtlCrVidA/9fuk5VH7GNE0hPTCUzKQ17QSEbF62iUSfX2uJUmnRpzZ61W4GiX32waVrDacU1SSD9QFF/YS8oZMOiVTTpfGZt0bhTCx763wvc9/Gz3Pfxs/j4+XDPpKfdVGLPFlD3nz4jFUdhIRnLlxPa2rU+I6Be/RP6jM3Uio0jtFVrWr3yOi3GvkSLsS/h5etLi9FjK7IaHmHtrnTqRQURHxGIj7cXl3ZKYMFq5+Bk8wQrY27qyJ3vLCHtaF6pfTMIDfAlLLhojYquzaJKFmkML97ma/Hizoub8tWvGvlc6bwM9/1VM0a5w/HP9KCGEQ8UmqaZVE5aN9M0fy9nNycrDs2udmNv1vyxkc/fmYnD7qDXpZ0YdFN/pn/4I/Wb1qFDj5bk5xUwfvSX7Nm6n8CQAO577kaiaoeTm53HB2O/5sCuJEyg18Xncun1fUg5kMabT04GwF7o4LwB7Rl0U/9TF6IK/J5c/ebf7lm5gSWTZ2A6TJr27ULHKy7gr69mE9kwgfqdWpG8bQ8/vTyJvKxsvH0sBNhCuPbtkWz5bTm/vPcFtlIrp//z82kzn36HnCPHwITw+rU5/85r8PGvXotixvo7qroI5dr61wbmTPwWh91B+wFdOP/aASz4bA5xTerQrEsr9m/Zw1ejPyLnWA4WXwtBthDu/+AJACb9921S9yWTn5tPQHAAgx66lsYdmpF+8BBTX/qUnKPZxDaM54pHh2HxrT4/NDNrX/WcZ5i5bh17p0wBh4OIbt2Iu+QSDsycSUDdutjatuXY7t1sHzcOe3Y2ho8PPiEhtHruOQAOb9zIvmnTwDQJqFuXesOG4WWxkLxgAQd//pmCI0fwCQ4mtFUr6t94YxXX1Nm5EXmnz1TJztbrVIhPteu+2bFiA/M/LPrJwdb9unDe1Rew6IvZxDZOoHHnVhzcuodvxk4i91gO3r4Wgqwh3D6u6CnqF4+/Rdr+ZApy8/EPDuCi+6+jQftm5B7L5vvXP+NIagY+tfy44J6ria5fu4pr6sxSDZc42b58A3MnFv0UZ9v+Xeh+zQX8+vls4hon0KRLKxK37mHamKK2sPhaCLSF8J/xRW3x6WNvkVbcX/gHB3DpA9fRsEMzp+O/PPS/1fLnH2dX0z7j8Lp1HJj2NabDJPy8bsRcfAkHZxX1GaFt2pK1exe7Jjj3Gc2eKRrxcWTjRg7MmAomBCQkUOeGG/GyOPfTfz9wb7X7+cfln5X5elItnN86hqeubYeXl8H0xbsY98MmHhzUgnW7M1iwJpHP/tuLc+JDSTlcNFIhMS2bO98p+krVrXk0T17TBsOA9bszGPnJSgrsDkZc1ZrebeLwMuB/v+zgk3nbqrKK5drx8VXV7xuyGzXu/5HbOsVt826rVp9VhQQS3KE6BhLOVtUxkHC2qq6BhLNRdQ0knK2qYyDhbFUdAwlnq+oYSDhbVddAwtmougYSzlYKJLiuugUSqs/jPRERERERERFPUa2++ruXAgkiIiIiIiIi7lYN1zZwFw16ExERERERERGXaUSCiIiIiIiIiLt57oAEBRJERERERERE3M00PDeSoKkNIiIiIiIiIuIyjUgQERERERERcTcPXmxRgQQRERERERERd/PcOIKmNoiIiIiIiIiI6zQiQURERERERMTdPHixRQUSRERERERERNzNg9dI0NQGEREREREREXGZRiSIiIiIiIiIuJvnDkhQIEFERERERETE7Tx4jQRNbRARERERERERl2lEgoiIiIiIiIi7efCIhGobSGhutVZ1EaRYk9Ccqi6CFCt05FV1EaTYxXV8q7oIUoph1KrqIkixQEtMVRdBihU4squ6CFLsqvoFVV0EKZZ1nn9VF0HOJh48/t+DqyYiIiIiIiIi7lZtRySIiIiIiIiI1Fia2iAiIiIiIiIiLvPcOIICCSIiIiIiIiLuZnp5biRBaySIiIiIiIiIiMs0IkFERERERETE3bRGgoiIiIiIiIi4zHPjCJraICIiIiIiIiKu04gEEREREREREXfz4MUWFUgQERERERERcTcPXiNBUxtERERERERExGUakSAiIiIiIiLibp47IEGBBBERERERERG38+A1EjS1QURERERERERcphEJIiIiIiIiIu7mwSMSFEgQERERERERcTPTc+MICiT8G78vXserL32Jw24yaGgPbr3jEqf0/PwCRj0xiU0b9hBqDeTl1+8irnYES//YwDtvTqegoBAfHwsPPnIVnbo0c9r3gXve4cD+VKbPHF2ZVarR/liygddfmo7D7mDg0G7cfPsAp/T8/AKeeeIzNm/cS6g1kLGv3UZc7XA2rNvNC89+WZTJhDvuvpje/dqW7Ge3O7jx6peJirLy5ri7KrNKNdafSzbz1sszsTscXD6kMzfe1scpPT+/kOdHfsXmjfsJDQ1gzKvDiK0dVpKedDCD6wa9ym13DeD6m88v2W63O7jl2reIjArl9fduq6zq1Gh/LNnAay9NxW43GTS0G7fcfoFTen5+AU8/8Smbis+Ll167nbja4axft5sXnv0fAKZpMvzuS+nTry1JB9N5+slPSTt0BC8vg8FXdOe6YX3Ke2s5we+L1/PaS19jtzsYPLQHt9xxkVN6UZ8xmU0b9mC1BvHS68OL+4yNvPPmDAoL7Fh8vHnwkStK+oyfZi9j8oc/ggGRkVbGvHwbNltwVVSvRlm0aCUvvPAhDoeDK6/sz/DhVzql5+cX8Nhjb7Bhww6s1mDefPMx4uOjAfjgg2lMnz4PLy8vnnpqOD16tHfpmFK+JYvX8NLYT7A7HAy9og+33zHIKT0/v4AnHn+fjRt3YrUG89obD1C7dhSZGUd56ME3WL9+B4MGnc/IUbeW7HPnHWNJTc3AXuigfcemPDXqNry9NXvXFUsW/83LL36Ow+5gyBXnc9sdlzul5+cXMHLEBDZu2EWoNZhX37iX2rUjycw8yiMPvsP6dTsZOLgnTz51U8k+c2b/waSJszAMg8goKy++fLeuUy6oiHupwRe+QECAH97eXnh7e/Hx1w9WZpXEw+kq6yK73cFLL3zBexMeYsasMfw0Zxk7th9wyvPdjMUEhwQy66eXuP7GAbz9xjQArLYg3nr/fqZ9N5rnx97GU0986LTfgnkrCQjwq7S6eAK73cErY6by9vh7mDprFHPnrGDnjoNOeWZ+8ychIQF8++NzXDesD+++8R0ADRvF8dmUx/lyxpO888E9vPj8VxQW2kv2+/qLX6jfIKZS61OT2e0OXh/7LW+Mv52vvnuUeT+uZteOJKc833+zjOAQf6bPfoJrhvXk/bdmO6W//cosunRvWubYU/+3mHr1oyu0/J7Ebnfw0piveWf8vUyf9TQ/z1le5rz47ps/CAkJYOaPz3P9sD6888a3QNF58fmUEXw1YyTvfnAfY5//H4WFdrwt3jz06FBmfP8Mn3z5GNO+/q3MMaUsu93Byy98ybsTHmDGrOf5ac5f7Nye6JTnuxlLCAkJYNZPY7n+xn68/cYMoKjPePv9+5j63bM8P/ZWRj0xGYDCQjuvvjSFDz5+hKnfPkvjJvFM+fKXSq9bTWO323n++QlMmvQss2e/zw8/LGL79r1OeaZNm0tISBDz5k3k5psH8tprnwCwffteZs9exOzZ7zNp0rM899x47Ha7S8eUsux2B2NGT2b8xCeY9f0bzJn9Ozu273fK8830hYSEBvLjz+8w7MaLeeO1osC/r58P991/Nf99dFiZ477+5oN8892rfPf9a2SkH+Hnn/6slPrUdHa7g7FjPmX8B4/x3fev8OOcpWXubb+Z8SshIYHM/vkNht10IW+9/jUAvr4+3HPfFTzy6HVO+QsL7bz84hd89MlIZnz3Ik2aJPDV/+ZWWp1qqoq8l3r/o7v4bNrDCiJUFS/DfX/VjAIJLlq/bid16kQRXycKH18LF1zcmV9/WeOU59eFq7ls4HkA9BvQkb+WbsI0TZo2q0tUlA2Aho1qk59XQH5+AQDZWbl88enP3H7npZVboRpuw7rd1EmIJL5OBD4+Fvpf1IHfFq51yrNo4VouGdgZgD4D2rF82RZM06SWvy8WizcAeXkFGKV+lyU5KYMli9YzcOh5lVeZGm7j+r3EJ4RTOz4cHx8L/S5sy6JfNjjlWfzrBi6+vCMAvfu3ZsWybZimCcBvC9cTFx9Gg4bOAYOUpEx+X7SJy4d0qpyKeIDj50UkPj4WBlzUkV8X/u2U57eFf3PpwC4A9B3Qnr+WbcY0TfxLnRf5pc6LyMhQmjVPACAwsBb1G8SQkpxZibWqmdav20V8neK28LVwwcXnltNnrOHS4j6j74AOLF+6ubjPSCAyygoUBXj+6TNM08Q0TXJy8jFNk6ysHCIjrZVet5pm7dpt1K0bS506Mfj6+nDJJT1ZsGCZU56FC5cxeHBfAC64oBt//vk3pmmyYMEyLrmkJ76+PtSpE0PdurGsXbvNpWNKWevWbichIZo6daLx8bVw0cXnsXDhcqc8CxeuYODAXgAMuKALy5auxzRNAgJq0b5DU/z8fMocNygoACj6EltQUIhhVL8b7upo/bodJCREl9zbXnhRF35ZuNIpz68LV3H5oB4A9B/QiWVLN5Rqj3PKtIdpmmCa5GTnFV2njuWU3APLyVXUvZRUA4bhvr9qpsICCYZhdDIM49zi/29uGMbDhmFcXFHvV9FSkjOJjj0+fCg62kZqcoZznpRMYmKK8lgs3gQF+5OZecwpz/y5KzmnWQK+vkUX3nHvfsuwmy/A318jEv6N1JRMomOOd0zR0VZSU5y/3KSUymOxeBMU5M/hzCwA1q/dxVUDR3Pt4BcY8fQ1JV+g3nh5Ovc/PBivaniyVlepyYeJij7+ZSYq2kpqyuEyeaKL8xxvi2xysvP4YvIv3HaX87QUgLdemcm9D1+KVzWMwFZXKWXOC1uZ8yK1nPMis/i8WLd2F1cOfJ6rB4/hiaevKzkv/pF4II3Nm/bRsnW9iq2IB0hNziSmVJ8RFW0rE4BJTckkpnRblNNnLJi7qqTP8PGx8OSoG7h60LNccP6j7NxxkEFDu1d8ZWq45OQ0YmIiSl5HR4eTnJxWJk9sbFEei8Wb4OBAMjKOlLNvBMnJaS4dU8pKSUknJia85HV0dDgpJ95LJacTE1uUp+i8CCAz8+hpjz389hfo1X04gYH+DLigi3sL7qGSkzOIjil1bxsTRkpKxknzHG8P5+tUaT4+FkY+fQtDB42gb6972bHjAIOHnl8h5fckFXUvZQAP3DmRm69+k++mL63QOsjZp0ICCYZhPAO8A4w3DONF4D0gCBhhGMbIinjPimeW3XTCl81/ooLOWY7n2bH9AO+8OY2nnimaR7Zl01727U2hT78O7i3qWaCcj7rME4jy2uOfwQctW9dn6sxRfPr143wyaS55eQUs/nUdtrBgmrVIqIASe65yPuWybVFuHvhw3FyuHtajzNSeJb9txBYWRNPm8e4r6FngdNegojxl9/snS6vW9Zk282k+//pxPpn0E3l5BSV5srNzefShD/jv41cSFOTv1nJ7IrOcf/Unxidd6zNmMPKZGwAoKChk2pRf+XL6KH7+9VUaN4nn4w/nuLfgHsi186L8POVvd+2YUtbJPk/nPGX3Kz1y8GQmThrJL4smkJ9fwLKl6/+/RTy7lNceruQ5RXMUFBQy9ev5TJ3xAgt+e48m5yTw0YezzqycZ4GKuJcC+OCze/l06kO8Me52Znz9O6tX7HBPgcV1Hjy1oaIWW7wCaAv4AUlAvGmaRwzDeBVYBrxQ3k6GYQwHhgO8O+5Rbr1jYAUV79+LiraRfDC95HVyckbJ0NN/REfbSEpKJzomjMJCO8eO5hAaGliUPymdh+9/j9Fjb6dOQhQAf/+9g40bd3Nx/0ex2x2kpx3h9ptfZtInj1dexWqoqGgryUnHo+bJyZlERIY65YmOtpGclEF0jK2oPY4db49/1G8Yg7+/Lzu2JfL36p0s/nUdfyzeQF5eAVlZuYx6/BNGv3xzZVSpxoqKDnV60pqSnElEZEiZPMnJmUTFWEvaIiQ0gI3r9vLL/LW8/+Zsjh3NwTAMfP0spKYcYfGvG/ljyWby8wrJysrl2Se+5NkXrzvx7aWUf/7N/yM5OaPMefHPuXPq8yKWWv5+7NiWSPOWdSkosPPogxO56JJO9OnfrlLqUtNFRdtIKtVnpJTTZ0RF20hKyjhpn/HI/eN4fuytJX3G1s37AEpe97+wI59M+rEyqlOjxcREkJR0qOR1cnIaUVFhZfIcPHiImJgICgvtHD2ahdUaXM6+h4iKKnpafrpjSlnR0eEkJR0fuZGcnEbkCcPeo2PCSDqYRkxMePF5kU2oNcil4/v5+dK7T0d+WbiC87q1dmvZPVF0TBjJSaXubZPSy22P5KR05/YIPXl7bNm8B4A6CUVD7Adc2JnJH35fAaX3LBVxL3Xltd2JjCq6BwgLD6ZXn5ZsXL+Pdh0bVmrdznoevJBARVWt0DRNu2ma2cAO0zSPAJimmQM4TraTaZoTTdPsaJpmx+oURABo0bI+e/cmc2B/KgX5hfw8Zxnn927rlKdX77Z8P/MPAObPXcG5nZtiGAZHj2Rz311vcd+DQ2nbvnFJ/quu6c28X99kzrxX+fjzJ6hbL0ZBBBc1b1mXvXtTOLD/EAUFhcz7cSU9e7dyytOjdytmzyyas7pw7mrO7dwEwzA4sP9QyeKKBxPT2LM7hbja4dz70EBmL3iBWXNHM/bVWzm30zkKIrigWYs67NtziMT9aRQUFDL/pzX0OL+FU57u57dgzqwVAPwyby0dOjXCMAwmfHoP3/40km9/GsnV1/fgptv7cuW13bn7gYuZNX8U3/40ktGvXE+HTo0URHBB85Z12VfqvJj74wp69Xa+me7VuzU/zCwa3rhg7irO7XzOSc6LZGJrh2OaJqOf/pz6DWK44aZ+lV6nmqpFy3rFbfFPn7GcXr3bOOXp1bstPxT3GQvmrixpi6NHsrn/rne578EhtG3fqCR/VLSNXTsOkpFeNMx72R8bqd8gtvIqVUO1atWY3bsT2bcvifz8AmbPXkSfPs5rr/Tp05lvv10AwM8//06XLq0xDIM+fToxe/Yi8vML2Lcvid27E2ndurFLx5SyWrZqyN49Sezfn0JBfiE/zvmD3r07OuXp3bsjM2f+BsDcn5fSuUuLU472yM7KJbV4OH5hoZ1Fv62mfoO4iquEB2nRsgF7SrXHTz8u5fze7Z3ynN+7PbO+WwzAvLl/0alz81O2R1R0GDt3HCA9/QgAS/9YRwO1x2lVxL1UTnYeWVm5AORk57Hsz600aKTFxMV9KmpEQr5hGAHFgYSScfuGYYRyikBCdWaxePP4yBu4e/gbOBwOBg7uTsNGtRn37rc0b1GP8/u0Y9DQnjw14kMuv3AEIaGBvPTanQB8/eUC9u1L4cMJ3/PhhKKo7PgPHyEsPORUbymnYLF489iTV3H/ne9jtzu4fHBXGjaKY8J7P9CsRQK9erdm4JDzeOaJTxl80TOEhAbywqtFPxX196odfPLRXCwWb7y8vHj8qaux2lx72iFlWSzePPLkYB6860McdpNLB51Lg0YxTHz/J5o1r0OP3i24bHAnnnvyK6645EVCQgMY/coNVV1sj1R0XlzDvXe+i93uYODg82jYKI7x731P8xYJ9OrdhoFDujHqiU8YeNHThIYGMPbVop/VXLNqB5989DMWizeGl8GIp67BZgti9artzP5+GY0a1+baoUWDye55YCDde7asyqpWe0V9xnXcM/wtHA6Tywd3o2Gj2ox/dybNW9SlV5+2DBranVEjPuLyC58kNDSQF18bDsCULxcW9xk/8OGEHwAY9+FDREZZGX73pdx20ytYLN7Exobz3NhbqrKaNYLF4s3TT/+H229/BrvdwdCh/WjcuC5vv/0FLVs2pm/fzlxxRX8effQN+vcfTmhoEG+++RgAjRvX5aKLunPxxXfj7V10HG/vorVDyjumnJrF4s2TT93KnbePxe5wMHjI+TRqXIf33plKi5YN6N2nI0Ou6M0Tj7/HRRfcT2hoEK++/kDJ/gP63suxrGwKCgpZuGA5EyeNJNQaxL33vEJ+fiEOu4POXVpw1dX9q7CWNYfF4s2TI2/irjtewe5wMGhwLxo1juf9d6fTvEV9evfpwOChvXjy8QlccsHDhFqDeOW1e0v2v7Dfgxw7llPcHiv44MMRNGxUm//cPYRbbhxTdJ2Ki2DM2OFVWMuaoSLupdLTjzHiwU+Aol+FGHBRO7qW86sOUsE8eNqbUe488jM9qGH4maaZV872CCDWNM11pztGduHv7i+Y/L8UmjlVXQQpVuhQW1QXvl6Bp88klcYwKiouLv9WoEVPvKqLAkd2VRdBijnMgtNnkkqRVZh0+kxSacL8LvPcb9pAg/u/c9t32p3vDKpWn1WF3HmVF0Qo3n4IOFRemoiIiIiIiIhUf3qEIyIiIiIiIuJmpgdPbVAgQURERERERMTd9KsNIiIiIiIiIiIakSAiIiIiIiLifl6a2iAiIiIiIiIirvLgNRI0tUFEREREREREXKYRCSIiIiIiIiLupqkNIiIiIiIiIuIyz40jaGqDiIiIiIiIiLhOIxJERERERERE3MzU1AYRERERERERcZkHBxI0tUFEREREREREXKYRCSIiIiIiIiLuZmhEgoiIiIiIiIi4ysuNf6dhGMaFhmFsMQxju2EYI8pJf9gwjI2GYaw1DGOBYRh1T0gPMQzjgGEY77laNRERERERERGpgQzD8AbeBy4CmgPXGobR/IRsq4GOpmm2BqYDr5yQPhr4zdX3VCBBRERERERExN0Mw31/p9YJ2G6a5k7TNPOBr4GBpTOYpvmLaZrZxS+XAvHHi2l0AKKBua5WrdqukdDqjgNVXQQpVtA6qqqLIMXOaxta1UWQYut2V3UJpLRmdU+fRyrHigXbq7oIUszILazqIkixen3Dq7oIUuzaBr5VXQQpZXjTqi5BBau8X22oDewr9Xo/0PkU+W8DfgQwDMMLeB0YBvR19Q2rbSBBRERERERERMAwjOHA8FKbJpqmOfGf5HJ2MU9ynBuAjkCv4k13A3NM09xn/IvFIRVIEBEREREREXE3N45IKA4aTDxJ8n6gTqnX8UDiiZkMw+gHjAR6maaZV7y5K9DDMIy7gSDA1zCMY6ZpllmwsTQFEkRERERERETczKy8n39cDjQ2DKM+cAC4BriudAbDMNoBHwAXmqaZUlJG07y+VJ6bKVqQ8ZRBBNBiiyIiIiIiIiI1lmmahcC9wM/AJmCqaZobDMN43jCMy4uzvUrRiINphmGsMQxj1pm8p0YkiIiIiIiIiLhbJT62N01zDjDnhG1Pl/r/fi4c4xPgE1feT4EEEREREREREXervKkNlU5TG0RERERERETEZRqRICIiIiIiIuJubvzVhupGgQQRERERERERd/PgQIKmNoiIiIiIiPwfe/cdHlWxPnD8O+l903sCoZNA6L1X8YqCvaDY8f6EexU7dhRsgNhARUXFglhQQIr0rvSWhE5ISCE9IT3Z3fP7Y5ckmwQSr0lIwvt5nn0ge2ZPZnYyM7vvmZkjhKg1mZEghBBCCCGEEELUteY7IUECCUIIIYQQQgghRF3TZGmDEEIIIYQQQgghhMxIEEIIIYQQQggh6p5qvjMSJJAghBBCCCGEEELUtWa8tEECCUIIIYQQQgghRF1rvnEECST8HXqQ26IAACAASURBVIM7+fPSXV2xtlIs2RrLp6uOWRx/YHQ7bhschsGokZlbzLML95CUUQBAgKcTb97fkwBPJzRN48G520jMKKB/R1+evb0LVgoKivQ888Ue4lLzrkTxmpQhLTx5dWgbrK0UP0QlM39PvMXxh7oHc2enAPRGjczCUp5ae4zE3GLCfVyYObwtrvY2GIwaH+2OY8WJNAB+vq0rzramJuHtZMvB87k8vCKqwcvWFF2IjiLhxx/QjEa8BgzCf8y1FsfzTp4g4cclFCYm0PLBSXj06FF2rCQzg/hvFlGSlYlC0WrKf7H39ibuq4XknTyBtaMjAKH33o9TSGiDlqspGhjkwXN9WmOtFL+cOM/nR85ZHL83Ioib2/mjN2pkFZXy4vYTJOcXA/DpqE5E+rixPzWHyeujy14T5OLA7KEd0NnbEpORy7Stxyk1ag1arqYoNzqKpB9/AM2Ix4BB+F5j2S7yT54g6aclFCUmEPrgJHTdLdtF4reLKM3KBBQtp/wXOy9vTs9+G2NxEQD63FycWobR4t+TG7JYTdKQtt68fF24afzee46Pt56xOP7ggDDu6BlsGjPyS3hm6WESs4vKjrvY27D+8cH8EXOeV1bEAGBrrZh+fQR9w7zQNI1Z606wJvp8g5arKRrc3odXxnfGykqxZFccn2w8ZXH8wcGtuL1PCwxGjYz8Yp5dcpDErEIATs26nuPJFwBIyi7k4YW7AXjrti5EhrijUMSm5fHUDwcoKDE0bMGaqF7e7kwJb4W1gpXnUlh8JtHieKSHG5PDw2jt6sxrB4+z9XxG2bFH2regr68nSsG+9Gw+jInF3sqKV7u3J9DJAaMGO1Mz+ex4XEMXq0mK3R/Dps+WohmNdBrVjz63jLI4nhB9ik2fLyXtbBJjn7qXdgO6lR375dX5JJ+II6hjK2586ZGy5+MPn2DLl79h0Bvwax3CNf+5Eytr6wYrk2jeJJBQS1ZK8eo93bl39hbOZxby68sj2XAwiVNJF8rSxMRnMf610xSVGLhrWGueuy2S/378FwCzH+7N/BVH2RGTgpO9DUbN9CH8tYk9eOSD7ZxOzmXCsNZMvr4jz3yx54qUsamwUjBjeFsmLD1Ecm4xK+7qwbrT6ZzMLChLE52ax3Xf76NIb+TuyECeH9SayatiKCw1MPWPY5zNLsTP2Y6VE3qwJS6LC8V6bvnxYNnrPxkbwbrT6VeieE2OZjRybvH3tHlsKrYeHhx/cya6yC44BgaWpbH18KTFvfeTsu6PKq+P+3Ihftdeh1t4OIaiIlSFKWCBN91qEXQQl2el4IW+bXj4jyOkFBSz5PpubIrP4HROeds4mpHHbcsPUGQwcnv7AJ7sFcZTm01B0YVRCTjaWHFr+wCL8z7RM4xF0Ymsjk3j5X5tuKmtP0uOJzdo2ZoazWgk6YfvCfvvVGw8PDj91kzcIrvgEFChXXh6EjzxftLXV20XCV8txOfa63DtaNkuWj/1bFmauE8/xq1Ll/ovTBNnpeC16yO4+8vdnL9QxPL/G8C6o6mcSisP2sck5XD9/DiKSo3c3TuUadd0YMqS8jHhyZFt2RWbaXHeKUPbkJFXwvC5W1AK3B1tG6xMTZWVgtduiuSeT//kfE4hyx4fzPro85xKKa+L6MQcbnhvK0WlBib0a8lzY8P5zzf7ACgqNXDdu1uqnHfGsmjyivUAvHBDBBMHhlUJUIiqrIDHIlrx9O5o0opK+GRAF3amZhKXV1iWJqWomLcPn+T2sCCL10a4u9LJw40Htx0A4IN+neni6cax7DyWnEniYGYONkoxp08EvX3c2Z2W3ZBFa3KMBiMbPv2JW6ZPxtXLne+emk2b3p3wCi0fj129PRjz2AT2/rqxyut73jgCfXEJh//YWfacZjSy+r1vufX1KXgG+bLju5VEb9xN51H9GqRMwsSqGd/aoMGKppRa1FC/qz50aeVJXGoe59LyKTUY+X13PCO7BVqk+etYGkXmCPjB0xn4ezgB0CbQDRsrxY6YFAAKivVl6TRNw8X84cPVyZaUCldARPW6+rtxNruQ+JwiSo0aK46nMrq1t0WaPxOyKdIbATiQfIEAV3sAYrMLOZttGiBT8ktILyjFs9KHP2dbawaEuPOHBBJqpeBsLPa+Ptj7+GBlY4NHr17kHD5okcbe2xvH4GBUpQ1nCpOS0IxG3MLDAbB2cMDKzr7B8t7cdPZ25VxuIQl5prax6kwaw0K9LNLsPp9DkcHUNg6lXcDfqfz93pWcTX5p1at4fQLcWXvWNHNn2akURrTwqpJGWCo4G4udjw925nah69mLC4cs24Wdl6ldVN6IqSjZ1C5cO166XRiKisg7fgy3Lt0Ql9c12J24zALOZRVSatBYcTiZ0R39LNL8GZtJUal5zDiXjb/OoexYp0A3vF3s2XYyzeI1t3YPZv6W0wBoGmQVlNZzSZq+LqEexGXkcy6zwFQXBxIZFeFvkeav0xkUmfuhA/GZ+OscazzvxSACgIOtNZpMmKqVDu6uJBUUkVxYjF7T2JicxgA/T4s0KYXFnMktwIjlm6oBdtZW2FhZYWtl+jeruJRio5GDmTkA6DWNkzn5+DjIuF6T8yfjcPf3wd3fG2tbG9oP6s6p3Ucs0uj8vPBpGWRxweWiFl3aY+foYPFcYW4+NrY2eAb5mtJ07cDJPw/VXyFEtZSqu0djUy8zEpRSyys/BQxTSrkDaJp2Q3383vrk5+FIcoUr3uczC+nS2vOS6W8dHMaWI6YrdmF+LlwoKGX+lP4EezuzMyaFd346glHTmPblXr6YOoiiEgN5haXcMmNDvZelqfN3sScpt7js5+S8Yrr6u10y/e2dAthU6UoSQBc/V2ytFHHZhRbPj2njzY5z2eTJtMhaKcnKxs6jvC3YuXuQHxtbq9cWp6Zg7eTImU/mU5KRjmuHjgTeeDPKHL5NXv4r51etwLV9RwJvvAkrW7nidzl+TvZlyxQAUgqKifRxvWT6m9v5sy0x67LndLe3IbdEj0G7eM4SfJ3kQ2FN9NnZ2FZoF7YeHhTUtl2kpGDt6Ejcp/MpSU/HpUNH/Cu0C4ALB/fj0qFD2dIfcWl+bg4k5ZQH6ZMvFNI1xP2S6W/rGcxm85I3peDFazsy9edDDGhVHkBzczB9fHpyZDv6tvIkLqOAV1ZEk55fUk+laB78dQ4kVxhzz+cU0TXU45Lpb+/dgi3HUsp+trexYtnjgzEYjXy88RTrosqXkrxze1eGdfTjZEouM5dHV3c6UYm3gx2pReV/s2mFJXR0v/SYUVFMdi4HMnL4ZUQvAH6LSyY+3/LzlLONNf38PPnlbFLdZbqZysvIxtW7vF9y9XIn+cQ/WxLi6OaCwWDg/Ml4/NuGcmLnQXLTLz/mC/F31NeMhGDgAvAuMMf8yK3w/2oppSYppfYqpfZeOL6+nrL2v6k2CHSJiPe4fqF0bunJZ6uPA2BtbUWvdt68ueQQN762nhAfF24e2BKAB65px4NztzHwyd/5ZftZnr+za73kvzmpri60S1x+uLGDH5F+rny6z3IPBV9nO94b05Gn1h6vUo03tPdlWYUPLqIm1bz3tYyaagYjeSdPEXTzrbR/7gWK09PJ/HMHAIE33kTHV1+n/XMvYCjIJ2XtmjrMczNVzft+qStzY1v5EuHlysJKeyhUPWXVk8rFvlqo5o2v9dUEo5H8U6cIuOlW2jz3AiXp6WSZ28VF2Xv24N6zdx1ktPmr7n2/VLsY3yWQyEAdC7aZgj739GnBphNpJOdYzha0tlIEujuyNz6LsfN2sP9cNs9f27Gus97sVDt+X6JHGd89mM4hOhZsOl323IAZ6xj33lYe+3Y/L4/rRKiXU9mxZ5YcpM/0PziVksvYroHVnVJU8nfqo7JAJwdauDhy68Y93LpxD928dER6lF/UsVLwUtf2LD2bRHJh8WXOJOAS4+o/vAStlGLsU/exeeFSvntqNnaO9hYBadEwmvOMhPr6a+oJ7ANeAHI0TdsMFGqatkXTtKqL28w0TVugaVpPTdN6urUfWU9Z+9+czyokwLN8wPL3dCSl0pVsgP7hvjw6NpxH3t9OiXlq/fnMAqLjszmXlo/BqLFufyIRLTzwdLWnQ4g7h86Yrpb/vvsc3VvLlOGaJOcVE+hafkU0wMWe1GquAg0M9WBK71AeXHaEEkN5F+1iZ82X4zoze2csB85fsHiNu4MNXf3d2FjNDAZRPTsPD0qyyt+vkuwsbN0vfbXP8rXuOIWEYO/jg7K2xr1LVwriTUEfW507SimsbG3x7DeAgrO1u5p7NUvJLybAubxt+DnZk1pQtW30DXBnUpdQpmyIrnHTxKziUlztbLBWF89pR1qBfCisiY2Hh3mjRJPSrCxsdLVrF7bu7jiGhGBnbhduXbtSGF8eDNXn5VEYF4tr58g6z3dzdD6niMAKSxUC3BxJvVD1b3hAay+mDG3DQ9/uo8S8/Kd7iDsT+7Zg+1NDef7ajtzUNYhnR7cnq6CUghI9f8SYroivikqmU+ClZ8YJk+ScIgLcy2fR+OscSMmpuqRzQFtvJo9sy8MLd5fVBVBWb+cyC/jrdDoRQTqL1xk1WHkwiTGRlvu8iOqlFZXg62BX9rOPox0ZxbWbVTPIz4uY7FyKDEaKDEZ2p2UT7lE+m+GpTm1ILCjkl7Oyn05tuHq5k5tevo9EbkY2Lp7/vE8J7BDGHW8+zoTZTxEc0RqPQJ9/fE7x9yil6uzR2NRLIEHTNKOmaXOB+4EXlFIf0cQ3djwcm0lLXxeCvZ2xtbZibO9QNhywnKoVHurOjHt78sgH28moMPX+cGwWOic7PM1ffvt19OVU0gVy8ktwdbSlpZ8LAAMj/DidnNtwhWqiDp3PJczDkRA3B2ytFNe392XdGcv9DCJ8XHhzRDseXB5FRmH5ulVbK8Vn13di6dEUVlZa7wowtp0vG2IzKK7wwUVcnlOLlhSnplKcnoZRrydrzx50kbXbAM6pZRj6ggJKc01/97nHj5VtRleaYxpQNU0j59ABHAKDLnkeYRKVnkuomyNBLqa28a9WPmw6l2GRpoOnM6/0b8uUDVFkFtVuTffu5GxGtzR9+BjXxo+N8Rk1vEJcbBcl5naRs3cPbrVsF44twzAUFKA3t4v8Cu0CIGf/Xlw7RcpSn1o6lJhDSy9ngj0csbVWXB8ZwLpKs84iAtx4Y1wnHvp2LxkVAtOP/3SIAbM2MXD2Zt5YfZSlBxN5e61ptuGGY6n0DTMF/we08uKk3HGpRofPZdPS25lgTydTXXQLYn20ZV2EB7kx85YuPLxwNxl55XXh5miLnbXpY6uHsx09WnpyMsXURlp4OZelGxHhxxmpi1o5lpNLkLMj/o722CjF8AAfdqbU7kJKalExXTx1WCmwVoounm7E5ZmWAD/QLhRnW2s+ipELALXl3zaU7OQ0clIyMJTqOb5tP617d/7H5y3INrURfWkpe5ZuoMuYgf/4nEJcpC41JbxOf4lS1wEDNE17vravaX3/j41u9uzQSH9evLMbVlaKn7fFMv/3ozw+PoIjZ7PYcDCJRU8NoX2wjtQc00yFpIwCHvnANB11QLgfz9/RBaUg6mwWL3y1j1KDkdHdg3j8xgiMRsgpKOG5hXs4l5Z/JYtZRWmk75XOQhXDWnryytA2WCvFkuhkPtodzxP9WnIkJZd1ZzL4/uYutPdyLpupkJRbxIPLo7ixgx+zR7fnREb5fhdPrj1GjHn37iW3dGX+nni2xDXOGQn9uzbOKWk5R46Q+NMPaEYNr/4D8P/XdSQvX4ZTixbounQl/2wssZ/Mx1BQgLK1xdbNjY6vvAbAhZgYEn/5ETRwCg0l5O6JWNnYcHLubPS5eYCGY3AIIXfdjbWDw+Uz0oCOnL3SOajeoGAPnuvdGiul+PXkeRYcPseUbi2ITs9l07lMPr+mM209nEk3z1RIzi9mygbTeuJF13YhzN0RJxtrsov1vLz9BDuSsgiucPvHoxl5PLv1WKO7/WPHFlc6B1VdiDpC8k8/gFHDo/8AfK+9jpQVy3AMbYFbl64UnI0l7lNTu7CytcXGzY12L5vaRe7RGM7/8iOaBo6hoQRNMLULgDPvzsLnmmtxjeh0JYt3SXs3NL4vcUPb+Zhu/6jgx/0JzNt8mqkj2nIkMYf1x1L59v7etPd3Jc18ESAxu5CHv91ncY5bugXROVhXdvvHIHcH3r2lK26ONmTml/D0L4ct9mJoDFSRvuZEDWxoB19eHt8JK6X4aXc88zacZOo17TmSkM366BS+eaQfHQLcSL1gei8v3uaxe0sPZt7SBU3TUErx5dYz/Lg7HqXgx8kDcXGwQQFHky/w0s+HLTZgbAxajmicM077+HgwOTwMK2B1QirfnU7g/rahHM/JY2dqJu11LrzevQMutjaUGI1kFZdy/7YDWAGPd2pNpKcbmgZ70rOYf/Qs3g52/DS8F3F5BZQaTRdlfj17nlUJjWfJ6J2tGl8fBXBmbzSbv1iK0Wik04i+9L3tGnZ8txK/NqG06dOZ8yfjWPbm5xTlFWJjZ4Ozuxv3fWT6avXDtPfITEihtKgEB1cnrplyFy27d2TLl79xZm80mlGjy7UD6HHDsCtcyqomdbim8V1qr0NtPtlaZx+YTv17cKN6rxokkPC/aIyBhKtVYwwkXK0aayDhatRYAwlXq8YYSLhaNcZAwtWqMQYSrlaNNZBwNWqsgYSrVXMPJLT9tO4CCScfaVyBBPlWIoQQQgghhBBCiFpr0vsWCCGEEEIIIYQQjZFqxpftJZAghBBCCCGEEELUsUZ4s4U604xjJEIIIYQQQgghhKhrNc5IUEo5A4WaphmVUu2ADsBqTdNqd98wIYQQQgghhBDiKmN1lc9I2Ao4KKWCgA3A/cBX9ZkpIYQQQgghhBCiKVOq7h6NTW0CCUrTtALgJuBDTdNuBMLrN1tCCCGEEEIIIYRojGqz2aJSSvUDJgAP/o3XCSGEEEIIIYQQV6XGOJOgrtQmIPA4MA34VdO0aKVUK2BT/WZLCCGEEEIIIYRoulQzjiTUGEjQNG0LsAVAKWUFpGua9t/6zpgQQgghhBBCCCEanxr3SFBKfa+UcjPfvSEGOK6Uerr+syaEEEIIIYQQQjRNyqruHo1NbbIUrmnaBWA8sAoIBe6p11wJIYQQQgghhBBN2NV+1wZbpZQtpkDCMk3TSgGtfrMlhBBCCCGEEEKIxqg2gYRPgbOAM7BVKdUCuFCfmRJCCCGEEEIIIZqy5jwjoTabLX4AfFDhqTil1LD6y5IQQgghhBBCCNG0NcYAQF2pze0fUUpdB0QADhWefq1eciSEEEIIIYQQQohGq8ZAglLqE8AJGAZ8DtwC7K7nfLFklnN9/wpRSzrbvCudBWFWbLzSORAXOfWUrWIak92ptlc6C8Ls9aelo2osjFoj3Ob7KhXiUnClsyDM9MaSK50FcRWxasYzEmozwvTXNG0ikKVp2nSgHxBSv9kSQgghhBBCCCGarua8R0JtAgmF5n8LlFKBQCkQVn9ZEkIIIYQQQgghRGNVmz0SfldKuQOzgP2Ybv34eb3mSgghhBBCCCGEaMIa40yCulKbuza8bv7vL0qp3wEHTdNy6jdbQgghhBBCCCFE06Wa8SYJlwwkKKVuuswxNE1bWj9ZEkIIIYQQQgghRGN1uRkJ11/mmAZIIEEIIYQQQgghhKjGVbm0QdO0+xsyI0IIIYQQQgghRHPRnAMJl7xrg1LqCaXUg9U8/x+l1OP1my0hhBBCCCGEEEI0Rpdb2vAA0L2a5xcAe4D36iVHQgghhBBCCCFEE9ecZyRcLpCgaZpWUs2TxUo157dECCGEEEIIIYT4Z5rxTRsuvbQBQCnlV5vnhBBCCCGEEEIIcXW4XCBhFrBSKTVEKeVqfgwFVgCzGyR3QgghhBBCCCFEE6RU3T0am8vdtWGRUioNeA3ohOmWj9HAK5qmrW6g/AkhhBBCCCGEEE2Ouuz8/6btcnskYA4YSNBACCGEEEIIIYQQQA2BBCGEEEIIIYQQQvx9jXFJQl2RQIIQQgghhBBCCFHHmvPNDiWQ8Dcc+uso37z3G0ajkaHX9+WGe0ZYHC8t0fPx699z9vg5XHTO/Oe1ifgEeKLXG/j8zSXEnkjAaDAycExPxk0cCcBjN7+Og5M9VlZWWFtbMWPhE1eiaE3Ovj+PsWCOqS5Gj+vDrfdWrYt3X/2eU8cScNU58+zMe/AL9AQg9mQSH735M4X5RSgrxdyvHsfO3pYtf+znx682oJTC09uNJ1+7C527y5UoXpNz4M9jLJxrqo8RN/ThpolV6+OD6d9z5ngCrm7OPDHjHnwDPdm6Zh/Lvttcli7uVDKzvp6Kf5AXL/57XtnzGanZDB7Tgwemjm+oIjVZe3ce45PZyzAajYwZ34fb7htucbykRM+cVxZz8mgCbjonpr1p2TY+eOMXCvKLsFKK9xc9hp29Lc9Mmk9mei72DrYAzPzoYdw9XRu8bE3Nyb1HWf3pUjSjke7X9GXQbaMsjp89coo1C34lJTaJW567l4iBXcuOffPSxyQciyM0PIwJ0x8pe37Xiq389dsWMpPTeWbxTJx10kfVxv4/j/H5u6Y+atQNfbi5mjHjvenfc9o8Zjw1w9QutqzZx6/fbi5LF3cqmTmLphIU6sM70xZxPjEdKysreg0KZ+LksQ1cqqZpf4XxYuQlxov3K4wXT5rHiy3VjBezv55KWLsgSkv1fD77V6L2n8LKSnHXI/+i3/DIBi5Z07Rzewxz3voZo8HIuJv7c99Doy2Ol5SU8sq0bzgWE4/O3Zk3Zj9AYJAX0UfOMvPVxaZEGjz86L8YNrILALkXCpjxyvecPpWMAl56fQKRXVs1cMmanj+3H+O9t5dhMBq54aY+THyw6vj92guLORaTgE7nxIxZ9xAQ5Fl2/HxyFneNn8WD/zeaCfcNBeDGMTNxcrLH2tr0PePLHx5vyCKJZu6SgQSl1GW/0Wqa9m7dZ6fxMhqMfDVnKdPe+zeevjpeemgu3QdGEBzmX5Zm8++7cHZ15N0fX+DP9QdYPP93/vv6RHZtPEhpqZ63v3mG4qISnpnwNv1HdccnwNT4X/zwUVzlC2utGQxGPn5nKTM+egQvXx1T732PPoMiCG1VXhdrl+/C2dWJz5Y+z5a1B/jqo9959o2JGPQG5rzyPU+8ehet2gVyITsfaxtrDHoDC95dxvwlT6Nzd2HhByv4/ccdTJh0zRUsadNgMBj5bPZSXv7AVB/P3v8evQZFEFKhbWxYvgsXNyfm/fw829cd4Jt5v/PkzIkMHtODwWN6AKYPhW89s5CwdkEAzPnmybLXP33vXPoM7dywBWuCDAYj897+lTfmTcLbT8djE9+nz+BwWlRsG8t24eLqyMLfprH5jwMs/HAl0968B4PewDsvLebp1+60aBsXPTPjLtqFh1yJYjVJRoORlfN/YuLMR3HzdmfB43No37czvqHldaHz9WD8E3ex85dNVV4/4ObhlBaXsnfVDovnQ8Nb0a53BF89+1G9l6G5MBiMfDprKdM/NPVRT9/3Hr0HRRBSoV2sW74LF1cnPvnlebatPcCieb/z9MyJDBnTgyHmPursqWTefHohrdoFUVxUwvgJQ+ncsw2lpXpenvwJ+3YepUf/jleqmE3CxfHiFfN48Uw148V683gx3zxeLJr3O09VqovK48UvX61H5+HCvJ+mYTQaybtQcEXK19QYDEbemfEjH302BT9/d+69fRaDh3WmVeuAsjTLlv6Jm5sjv65+lbWr9vLhu8t4c84DtG4TyKIlz2BjY016Wg533fwmg4Z2wsbGmjlv/Uy/AeG8PfchSkv1FBWWXMFSNg0Gg5E5b/zK+wsm4eun44E732fQ0HDCWpe3jRVLd+Hq5sjPK6exbvUB5r23khmz7ik7/v47y+k7sEOVc8/74v9w93BukHKIqprxhITL3v7RtYZHrSmlBiqlnlBKja45deN0+mg8fsHe+AZ5YWNrQ98R3di3Lcoizb5tUQz+Vy8Aeg+NJHrfSTRNQylFcVEJBr2BkuJSbGxtcHS2vxLFaBZORMcTEOyFf5AXtrY2DB7djb+2Rluk+WtLFCOu6wnAwOGRHNpjqov9u07Qsk0ArdoFAuDm7oy1tRUaoGkaxYUlaJpGQX4xXj5uDV20JulUTDz+Fepj4Khu7KlUH7u3RTH0X6b66DcskiN7TfVR0fZ1Bxg4qluV8yfFp5GTlUu4XM2o0YnoeAJDvAgINtXFkNFd+WuLZV38uSWakWNNdTFoRCQHd5vqYt9fJwhrW7VtiP9N4ok4PAN98AzwxsbWhk6Du3PszyMWaTz8vPAPC0JZVf2U0apre+wcq44TAa2D8fDzqrd8N0cnYyzHjIGjurGrch+1NYph5jGj//BIDu+p2kdtW3uAQaNNfZS9gx2de7YBwNbWhtbtg8lIzWmA0jRtp6qpi92V6mLPtiiG1TBebKs0XmxYsZub7jVdvbWyssJNLs7USvSRs4SEehMc4o2trQ2jru3Olo2HLdJs3XiY68b1AWD46G7s2XUcTdNwcLTDxhxsLi4uRWHqx/LyCjmw7zTjbu4HmNqHq5tTA5aqaYqJiic41Isg8/g9ckxXtm6ybBvbNkfzrxtMbWPYqEj27ipvG1s2RhEY7Emr1n4NnndxeVfr7R+n/68nVUrt1jStt/n/DwOTgV+BV5RS3TVNe+t/PfeVkpmWg5eve9nPnr7unI6Os0iTlZaDpzmNtY01Ts4O5OXk03tYF/Zti2LyuFcpKSrl7v+Ow8XNFBlUSvHW1E9BKUaM68fwcf0arlBNVEZaDj5+5XXh7avjeHR8pTQXytJY21jj5OLIhZx8kuLTUErx0n8+5UJ2PoNGdeWWicOxsbHm0WdvZvJds3FwsCMwxIf/e+amBi1XU5WZloO3RdvQcbJSfWSmXcC7Un3k5uRbfNjbsf4gz71zf5Xzb193gAEjg0NeUwAAIABJREFUuzbrNWZ1JT21cttw53iUZT+VkZpTpS4u5BSQGJ+GAl6YsoCcrHyGjO7KrfcOK3vd3OlLsLK2YsDwztz54EipjxpcyMhB511eFzpvdxKOx13mFaK+ZFb4mwfwulQf5Xv5Pmr7+oM8P6tqH5WXW8ie7dGMvWNQPZWg+cio9FmqurrISLuA198YL/JzCwFY/Okaovafxj/Yi4efvAl3L1l+VZO01Bz8/D3Kfvbz8yDqyFmLNKkV0tjYWOPi4khOdj7uHi5EHT7Lay99y/mkTKa/eS82NtYkJmTg7uHC9Be/5eTxRDqGh/Dkc7fg6CQX0C4nLSUH3wr9lK+fO9FH4qqk8TOnKa+LAuztbfh24SbeXzCJ77/abPEaBTz2yAKUgvG39mP8LX3ruyjiKlLj5SallINSarJSar5SauHFRw0vs63w/0nAKHNgYjQw4R/k98qpFA2HqptnVI6YmxNxOiYeKysrPlr2KnN/foFVizeTmpgBwCsf/4eZXz7JM3MeZt3S7Rw9eLpest+sVPc2V0lTTX2hMBgMxByM5anXJ/D2Z1P4c3MUB3efQK83sOqXnXzwzRMsWvUKLdsG8NNXG+ol+81NtX/2VdJcvv2ciIrD3sGW0ArTKS/ase4gA0dXnakgaqlyP1VdEkzTKqMPxfLMjAnM/mIyOzdHcWD3SQCemTGBj5c8xazPHiXqQCwbVu6r/3w3dbVpGKJBVPc3X7kuLjV+X3Sxj2pRqY8y6A28+9K3XHfbIPyDZKZIjaqtjMppajdeXKwLg8FARmoOHSLDmLPoCdp3asnXH66oqxw3a9WPzTWnudh+OkW25MdlL/L1D8/w1edrKS4uxaA3cPzoOW65fRDf/fwcDo72fPXFunrIffNS7dhcm/FbwWfz13L7PYNwqiZY8+miKXz941Tenf8Qv/ywgwN75XtGQ2vOMxJqM2/1G8AfuAbYAgQDuTWdVynloZTyApSmaWkAmqblA/pLvUgpNUkptVcptXfpojW1KkBD8fR1JyM1u+znzNRs3L3dqqTJNKcx6A0U5Bfh4ubEznX7iezbARsba3QerrSLDOPMsXMAePjoANB5uNJzcGfOxFhG5kVVXr460lLK6yI9NQdP8/tYXRqD3kBBXiGuOie8fN3p1L0VOncXHBzs6DmgI6ePJ3LmRCIAAcHeKKUYNKILRytF5UX1vHx1pFu0jerrI71SfbhUmOq4Y/3Bapc1nD2ZhMFgoHUHWZtfG95V2kZ2lSU63tXUhavOCW9fHZ27t0bn7oyDgx29BnTg9LGEstcAODk7MGxMN05ESz9VEzdvd3LSy+siJz0bV0/dZV4h6kvF/gdMs3I8vavpo1IrtYsKfdS2dQfLljVUNP/NnwgI8eaGOwfXU+6bFy9fncVnqYxLjBcZlxkvtlcaL1x1ztg72NFnaCcA+o+I5MzxhPosRrPh6+dOyvmssp9TUrLwrlQffhXS6PUG8vIK0eks19uHtfbH0dGO0yeT8PX3wNfPnU6RLQEYMborx2PO1W9BmgFfPx2pFfqp1JRsvCuN375+OlLMaS7WhZvOiZgj8cybu5Ibx8xkyXfb+PrzDfy0eDsAPubx29PLlSHDOxETJXXR0KxU3T0am9oEEtpomvYSkK9p2tfAdUBNu57pgH3AXsBTKeUPoJRy4TLXZDRNW6BpWk9N03reNHFMrQrQUFp1COF8QhqpSRnoS/X8teEAPQZ2skjTfWAEW1ftAWD35sNE9GiDUgpvP3dizPslFBUWczI6jsAWvhQVFlOYXwRAUWExR3afILjC5k+ieu3CQ0g6l875xAxKS/VsXXuAPoMiLNL0GRzBhpV7Adi+8TCRPduilKJH3/acPZVMkXnPiqj9pwkN88PLR8e52BRysvIAOLD7BCEtZZ1ZbbTpGELyuXRSkkz1sX3dAXpWqo9egyLYvMpUH39uOkwnc30AGI1Gdm44xIBqAgnb1u6X2Qh/Q+W2sWXtQfoOtqyLvoMjWP+7qS62bThMl16mfqpHv/bEnixvG0f2nyG0lR8GvYGc7HzA9MFl17YYWrSWfqomge1CyUxKI+u8acyI2rqfDn071fxCUefaVtNH9a7ULnoPimCTeczYufEwnavpowZV6qO++2Q1+XlFPDh1XMMUpBmobrzoVc14sanCeFFdXVQMJCil6DkwnOj9piuth/ecJDhMxu/aCO/Ugvj4NBIT0ikt1bNu9X4GD7O828WgYZ1ZuWwXABvXHqBXn3YopUhMSEevNwCQnJRJ3NkUAoO88PZ2w8/fg7OxKQDs+eu4xYaBonodI0I4F5dOUoKpbaxfc5BBQy3bxsChEaxabmobm9Ydpkdv0/j9ydeT+XXNC/y65gVunzCIex8awa13DqSwoJh88/eMwoJidv15glZtpC5E3VHVTlmqmMC834FSaivwKHAe2K1p2t/e+Uwp5QT4aZoWW1PavekrazMBrkEd3BnDNx8sw2gwMmRsb8bfO4qfP1tNWIcQegzqRElxKR+//j1xJxJwdnPiP9Mn4hvkRVFBMZ++8QOJsefRgCH/6sXYCcNJTcxg7vOmVSIGvZH+o7sz/t5Rl8/EFaCzbXRVwZ4dR/ns3d8wGjVGXd+b2x8YybefrqFtx2D6DDbVxZxXvufMiURc3Jx4duY9ZdNON63eZ1q2oBQ9+3fggf9eD8CqX3ayfMk2bGys8fH3YOrLd+Dm3rh2uS02XukcVG/fzqN8OddUH8PH9uaW+0eyeMEa2nQIppe5Pj6Y/j2x5vqY+np5fUTtO8W381fy1hePVTnv/900kxfefYjgRhjUcbJpfO0CYPf2oyx4dxkGg8boG3px54MjWfTJGtp1DKHvkAhKikuZ9fJiTh9PxNXNiefeuJuAYFNdbFy1jyVfbUQBvQZ05MHHxlJUWMzTD89HrzdiNBrp1rstD0+9odFtxLg71bbmRA3sxJ5o1nz6q+l9G92XIXeMZuM3qwhsG0KHvp1JPBHHD69/QWFeITZ2Nrh4uDHlk2kAfPH0+6SfS6GkqAQnVyfGPX4nbXp05K9lW9jx8wbysnJxdnehbc9wxj1+5xUuqaUuXpeceHjF7N1xlIVzf8Ng1Bh5fW9uvX8k33+6hjYdg+lt7qPee9U0Zri6OfHkjPI+6si+U3wzbyXvLCzvo9JTsnnohtcJbumLja1pu6nrbh3AqHGNa/2xsRF2U/t2HjXf/lFjRIXxonWH8rp4v8J48USl8eKb+St5u9J4kZqcyQfTF5OfW4ibhzNTXrwDnwpr/xuDEJfGuUfAjq3RvPv2zxgMGjfc2JcHHhnDJx/9TseIUIYMi6S4uJRXpi3i+NFzuOmcmTnrfoJDvFm1fDdffbEWGxtrrKwUD/37WoaOMN3+8fixBGa+/B2lpQaCQrx5+fW7cdM1ng0X9caiK52Fau3cdpT33lmG0aAxdnwv7ps0kgXz1tAxPIRBwyIoLi5l+vOLOXEsETedE6+/czdBwZZLqj6f/weOTvZMuG8oiQkZPPf4V4Bp+eLoa7tx36SRV6Bkl+dpf30jvNZed0at2VFnPfG6MQMa1XtVm0DCQ8AvQCTwJeACvKxp2if1mbHGGEi4WjXGQMLVqrEGEq5GjTWQcLVqjIGEq1VjDCRcrRpjIOFq1VgDCVejxhpIuFo190DCNX9sr7Oe+I9rBjaq9+qSd224SNO0z83/3QLI/deEEEIIIYQQQogaNMa9DepKjYEEpZQ9cDPQsmJ6TdNeq79sCSGEEEIIIYQQojGqMZAALANyMG2eWFy/2RFCCCGEEEIIIZq+xrWjVN2qTSAhWNO0xnULBSGEEEIIIYQQohGzUs13s5raBEl2KqVqut2jEEIIIYQQQgghrgK1mZEwELhPKRWLaWmDAjRN0yIv/zIhhBBCCCGEEOLqdFVvtghcW++5EEIIIYQQQgghmpGrco8EpZSbpmkXgNwGzI8QQgghhBBCCCEascvNSPgeGIvpbg0apiUNF2lAq3rMlxBCCCGEEEII0WRdlUsbNE0ba/43rOGyI4QQQgghhBBCNH2qGd+1ocY9EpRS3at5OgeI0zRNX/dZEkIIIYQQQgghRGNVm80W5wPdgcOYljd0Bg4BXkqpf2uatrYe8yeEEEIIIYQQQjQ5zXlpQ202kjwLdNM0raemaT2ArkAUMBJ4px7zJoQQQgghhBBCNElWdfhobGqTpw6apkVf/EHTtBhMgYUz9ZctIYQQQgghhBBCNEa1CSQcV0p9rJQaYn7MB04opeyB0nrOnxBCCCGEEEII0eRYKa3OHjVRSo1RSh1XSp1SSj1XzfHBSqn9Sim9UuqWSsdClVJrlVJHlVIxSqmWNf2+2uyRcB/wKPA4pj0StgNPYQoiDKvF64UQQgghhBBCiKtKQ+2RoJSyBuYBo4AEYI9Sarl5NcFF8Zi+2z9VzSkWATM1TVunlHIBjDX9zhoDCZqmFQJzzI/K8mp6/f/Kx6HGvIsGkl3SjHcJaWJWxttf6SwIsztaF13pLIgKBgfIBLnGIkfGjEZjf4btlc6CMFuTcKVzIC56pIPrlc6CEPWhN3Dq4vYDSqkfgHFAWSBB07Sz5mMWX7SVUuGAjaZp68zpavUd/5KBBKXUj5qm3aaUOgJUmUuhaVpkbX6BEEIIIYQQQghxtanLTRKVUpOASRWeWqBp2gLz/4OAcxWOJQB9annqdkC2UmopEAasB57TNM1wuRddbkbCY+Z/x9YyA0IIIYQQQgghhKBulzaYgwYLLnG4ut9U88YKJjbAIKAbpuUPSzAtgfjici+6ZJBE07Rk81qLLzRNi6v8qGWmhBBCCCGEEEIIUX8SgJAKPwcDSX/jtQc0TTujaZoe+A3oXtOLLjvbwjydoUAppatlJoQQQgghhBBCiKteA961YQ/QVikVppSyA+4Altcym3sAD6WUj/nn4VTYW+FSanPXhiLgiFJqHZB/8UlN0/5by4wJIYQQQgghhBBXlYa6a4OmaXql1BTgD8AaWKhpWrRS6jVgr6Zpy5VSvYBfAQ/geqXUdE3TIjRNMyilngI2KKUUsA/4rKbfWZtAwkrzQwghhBBCCCGEEI2MpmmrgFWVnnu5wv/3YFryUN1r1wF/62YKtQkkLAHaYNqs4bSmaXLPMyGEEEIIIYQQ4jLq8q4Njc3lbv9oA7wBPADEYXofgpVSXwIvaJomN+0WQgghhBBCCCGqUYu9DZqsywVJZgGeQJimaT00TesGtAbcgdkNkTkhhBBCCCGEEEI0Lpdb2jAWaKdpWlkYRdO0C0qp/wOOAY/Vd+aEEEIIIYQQQoimqKE2W7wSLhdI0CoGESo8aVCqGc/REEIIIYQQQggh/qHmHEi43NKGGKXUxMpPKqXuxjQjQQghhBBCCCGEEFeZy81ImAwsVUo9gOlekhrQC3AEbmyAvAkhhBBCCCGEEE3SVXnXBk3TEoE+SqnhQASggNWapm1oqMwJIYQQQgghhBBNUXO+a8PlZiQAoGnaRmBjA+RFCCGEEEIIIYQQjVyNgQQhhBBCCCGEEEL8Pc15s0UJJPwNe3Ye4+PZyzAajIwZ34c77h9ucbykRM+slxdz8mgCrjonXnjrHvwDPQE4czKJ92f+QkF+EUopPvrmMezsbfly3mrWrdxL3oVClm9/40oUq8k6+OcxvnzvN4wGIyNu6MP4iSMsjpeW6Pnote85cywBV50zj8+4B98AT/R6A5+88SOxxxMwGowMvrYnN947olbnFNVLPBjD7q9+RjMaaTu8P53Hj7Y4fj7mFHu+/pms+CQGP3Y/Lft2AyAvLZNNcz5DMxoxGgx0HDOE9qMGoS8uYfPcL8hNSUdZKUJ6dKbHXeOuRNGanL07j/HJ7GUYjaZ+6rb7qvZTc14x9VNuOiemvXkPfuZ+KvZkEh+8YeqnrJTi/UWmfuqZSfPJTM/F3sEWgJkfPYy7p2uDl62p2b3jGB/NWobBaOS68X2464GqdfHmS4s5Ya6LV942jRnnkzK596Z3CGnhC0B451CeePEWAEpL9bz/1q8c2nsaZaV4cPK1DBkZ2eBla2r2/3mMz9/9DaPRyKgb+nDzvVXHi/emf89p83jx1AxTu9iyZh+/fru5LF3cqWTmLJpKUKgP70xbxPnEdKysrOg1KJyJk8c2cKmaptN7Y/hjwVI0o5Guo/sx4LZRFsfjok6xbsFSUmKTuOnZe+k4sFvZse9fmk/i8ThCwltxx6uPVDn3mo9/5tD6v3j2l9n1Xo7m4tyBGHZ+aRq/O4zoT9cbLcfv5JhT7PzyZzLjkhgx9X5a9TPVR3psAts/+4HSgiKUlRXdbr6G1gN6ALD8xbmUFhUBUJiTi0+bllzz7KSGLVgTtHN7FLPf+hGDwcj4mwdy/0NjLI6XlJTy8rQvORoTj87dmbdmP0xgkDdRR2KZ+eq3AGgaTHp0LMNHdqvVOUX9uyr3SBCWDAYjH731K2/Nn4S3n47/3PM+/YaE06KVf1maNb/twsXNka+WTWPTHwf44oOVvPDWPRj0Bt5+cTHPvH4nrdsFciE7H2sbawD6Dg7nhtsGcP+Nb12pojVJRoORL+Ys5cX3H8HLV8e0B96j56AIgsPK62Pjil04uzrx4c/Ps2PdAb6b9ztTZ0zkrw2H0JfqmfPd0xQXlfDEne8wYHQ3vH3dazynqMpoNPLXwh8Z/cIUnLzcWTltFiE9O+MeHFCWxsXbgwGP3kP0CsstVhw93PjX609gbWtLaVExy56aSUiPztg5OxExdgQBndph0OtZ+/qHJByIJrhbREMXr0kxGIzMe/tX3phn6qcem/g+fQZb9lNrl+3CxdWRhb9NY/MfB1j44UqmvWnqp955aTFPv3YnrSr1UwDPzLiLduEhV6JYTZLBYOT9t35l1seT8PHT8e8J79N/SDgtW5fXxarfduHq6sh3y6excc0BPn1/Ja+8fQ8AgcFefL7kiSrn/fbzDXh4uvDNsucwGo3k5hQ2WJmaKoPByKezljL9Q1Pf/vR979F7UAQhFdrFuuW7cHF14pNfnmfb2gMsmvc7T8+cyJAxPRgyxvTl6OypZN58eiGt2gVRXFTC+AlD6dyzDaWlel6e/An7dh6lR/+OV6qYTYLRYGT1xz8xYcZk3Lzd+WLqbNr17YRPaPl4ofPx4PqpE/hradVVtf1uHkFpcQn7V++scizpZDxF+QX1mv/mxmgwsv3zH7nu5Sk4e7rz63OzaNGzMx4hluP30Mn3cHi55fhtY2/LsP9MRBfgS35mNkufeYfgrh2xd3bihhlTy9KtnfUZLXtJsLMmBoORt2YsZv5nj+Pn78E9t7/JkGGRtGodWJbmt6U7cHNzZtnqGfyxag8fvLuUt+ZMonWbIL5Z8jw2NtakpeVw582vM3hoJEqpGs8pxD/RnIMkdep4dDyBIV4EBHtha2vDkNFd2bk52iLNn1uiGTW2JwCDR0RyYPdJNE1j318nCGsbQOt2pobr5u6MtbXpre/YuQVePm4NW5hm4FRMPP7BXvgFeWFja0P/kd3Ys9WyPvZui2Lov0z10XdYJFF7TfWBgqLCEgx6AyXFpdjYWuPk5FCrc4qq0k+dxc3PG1c/b6xtbAjr351zew5bpHHx9cKzRRCq0vwuaxsbrG1NV7kNpaVgNG1IY2NvR0CndmVpvMJCKMjMboDSNG0nqumn/tpStZ8aae6nBo2I5GClfqpVNf2U+PuORZnqItBcF8Ov6cqOSmPGjs3RXHO9qS6GjIxkv7kuLmf1st1lMxusrKzQeTjXTwGakZMx8QQEe+EfZKqLgaO6satS3757axTDrjPVRf/hkRzeU7Uutq09wKDRpqt89g52dO7ZBgBbWxtatw8mIzWnAUrTtCWdiMMz0AePAG+sbW2IGNydE38dsUjj7ueFX1gQSlWdDxzWtT12jg5VnjcajGz44jdGPCAz1/6OtFNn0fl74+Znqo/WA7pzttL47errhVfLquO3e6AfugDTrClnT3ccda4UXcizSFNSWERS1Ala9pZAQk2ij8QSEupLcIgPtrY2jL62J5s3HrJIs2XjIcaO6wvAiNHd2b3rGJqm4ehoh4058F9SXIr6G+cU9c9K1d2jsamXT4lKqT5KKTfz/x2VUtOVUiuUUm8rpXT18TvrW3pqDj5+7mU/+/i5k5Fm+aEhPa08jbWNNc4ujlzILiAhPg2lYNrkBTx611x+/HpTg+a9OcpMy8HLt7w+vHx1ZFaqj8y0C3hVqA8nF0dyc/LpO7wLDo52TLp+Oo+On8H1dw3FRedUq3OKqgoyc3D28ij72cnLg/ys2r9v+elZLH/6DX5+9CU6jRuJk6e7xfGS/ALO7TtCQKf2dZbn5qpyP+Xt617ly01Gag7eldrFhZwCEuPTUMALUxYwZcJcfqrUT82dvoTJd73L95+vq/HLrjDVhW+lMSO98piRmoOvf3lduJjHDIDziZk8fMe7PPbgfA7vPwNAXq5p9sHCeX8w6c65vPr0IjIzchuiOE1aZoW/ebj0eOHtW3W8qGj7+oNlgYSK8nIL2bM9mshebesh981LbkY2bt7ldeHq7U5uxj8fZ/f+vpV2fTrj6tkkP2JeMfmZOTh7l4/fzl4e5Gf+/fpIPXkWo16Pm5+3xfNndx0iqHN77Jwc/3Fem7vU1Gz8/Mvrws/Pg7RUywsoaanZ+PmbliLamMeM7GxTP3XkcCy3jnuV2298jWkvT8DGxrpW5xT1Tymtzh6NTX1dbloIXJxf9j6gA942P/dlPf3O+lVN3VWJllebBgx6I1EHY3luxgTe/WIyOzZFcWD3yfrJ51Wiuu8xVaqjmkRKKU5Fx2Nlpfh0xSt89MvzrFi8hZTEjFqdU1Sjuvf5b7zc2duDG2Y9z03vv8KpLbspzL5QdsxoMLD1g6/oOGYorpU+oIhaqvRHXN0wpDBNq4w+FMszMyYw+4vJ7Nxc3k89M2MCHy95ilmfPUrUgVg2rNxX//lu4qp/nyvVxSUqw9PbjR9Wv8hnPzzBo0/ewIznvyM/rwiD3khaSg6durZkweKphEe24JO5K+ol/81JtR+9ajFeVGw7J6LisHewpUXrAIskBr2Bd1/6lutuG4R/kNc/z2wzV30M8p8NtLkZOcRsP0ivGwb/o/Nclar9nPT3TlGQlcOmDxcxZPLdKCvLrxWnt++j9cAe/ySHV43//XOt6d/OkWH8tOxVvvlhGl99vobi4lL5XCvqXX0FEqw0TdOb/99T07THNU3brmnadKDVpV6klJqklNqrlNr7/cI19ZS1/423n460lPIoXlpKNp7elksSvH3L0xj0BvLzCnHVOeHtpyOye2t0Hs44ONrRa0AHTh5LaND8NzdevjoyKkRVM1Jz8PDWVU1ToT4K8gpxcXNi+9r9dO3bARsba3SerrTv3JLTR8/V6pyiKicvd/Izssp+LsjIwsnj779vTp7uuAf7k3LsdNlzfy5YjKu/D+HXDauTvDZ3FfsggPTU7CpLp7x9daRXaheuOie8fXV07t4anbszDg6mfuq0uZ/y9jXVp5OzA8PGdONEdHwDlajp8vHVkVppzKhcFz5+OlLPl9dFXl4hbjon7Oxs0Lmbliy0Dw8mMNiLhLg03NydcHCwZdDwTgAMHdWFE0cTG6hETZdXhb95MPXtntWMF+mpldqFm1PZ8W3rqp+NMP/NnwgI8eaGO+VLbG24ebtzIb28LnLTs3H1+mfLO8+fTiArKY15D73Oh/e/SmlxKfMeeu2fZvWq4OzlTn56+fid/zfH75KCQla/8TG97hiLX7swi2NFuXmknjpLaPdOdZbf5szPz52U8+V1kZKShbeP5QxNXz8PUs5nAqA3jxk6neXytrDWATg42nH6ZGKtzinqnyxt+PuilFL3m/9/SCnVE0Ap1Q4ovdSLNE1boGlaT03Tet71QOPaVbR9eAiJ59JJTsygtFTPlrUH6TfEcuO3fkMiWPf7XgC2bjhM115tUErRs197Yk8ml63LP7L/DC3C/K5EMZqN1h1DSD6XTmpSBvpSPTvXH6DnIMv66DEwgs2rTPXx16bDRPRoi1IKb38PovadQtM0igqLORkdT1BL31qdU1Tl3boFF86nkZuajkGvJ3bnfoJ71m49ZH5GFvqSEgCK8wpIO3EGXaBpzeX+H1ZQUlBI73tvrre8NzftwkNIOpfO+Qr9VN/Bln/DfQdHsN7cT23bcJgu5n6qx8V+qqi8nwpt5YdBbyDHPHVSrzewa1sMLVrLBqQ16RARQmJ8+Zix8Y+D9B9qWRf9h0TwxwpTXWxZf5hu5rrIzszDYDACkJSQQWJ8OgHBXiil6Dc4goN7TcG2/btP0rKVjCU1aWvu21OSTHWxfd0BeldqF70HRbBppakudm48TOeebctmHRqNRnZuOMSgUZaBhO8+WU1+XhEPTpV1+bUV2C6UzMQ0ss5nYCjVE711P+36dP5H52zbO4Kp383kP1++yn++fBVbe1smf/5yHeW4efNp04Kc5DQupKRjKNVzesd+WtRyY0RDqZ6173xGuyF9aNW/e5XjZ3YeILRHJ2zsbOs6281SeKeWnItPJTEhndJSPWtX72XIsC4WaYYMi+T3ZX8BsGHtfnr16YBSisSEdPR6AwDJSRnEnU0hIMi7VucU9c+qDh+NjaqPta7mfRDeBwYB6UB34Jz58V9N02rc6SMub0WjWwiye/tRPp6zDKNB45pxvbjrwZF8/fEa2oWH0G9IBCXFpbz90mJOH0/EVefE82/cTUCwaarj+lX7WPLlRlDQe0BHHn7MdJuoz97/nU1rDpCRdgEvHzfGjO/NxEeuuZLFrCK7pBGGwID9O4/y9Xu/YTRqDBvbm5vuG8mSBWto3TGYnoM6UVJcykfTvyf2RCIubk48/vo9+AV5UVRQ/P/s3Xd8VMX6x/HPpAFJIPQkQOgo0pUqKB1CFbFhA/WqyP2JggoKYsGCBVHUq6CIBSsKNiAo0qR4KaIgRUBCLymU0CGeS7ozAAAgAElEQVRld35/7BoSkpjo3WSXzffta19m98w5+8wOu3P22Zk5THp2Ovt2JWEtdOrdkqtu7ZTnMX1J3J4S3g4hV/vWbuLnaTNxOi31OrahyTU9WPvFHCrUrk71Fk04FL+bxS+/Q9qp0wQGB1GybBmufvkxDqzfzJqPvsY1tNVSP7Y9F3W9glOHU5j5f48TUSWSgGDXxWXqx3bgoi5tvVrPrG6sc9bbIeRq9fLNTHnlWxwOS/erWnLTnV358K3vueiSGNq4P6deesL9OVUmlFFZPqcWzf2Fzz9YhAFatruEO4f14eyZVEbePYmMDCdOp5NLW9Xj7geu8rmFGEsG5l+mqK1ctpk3J3yL02np2a8lt97Vlfcmfc/FDWJo19HVFs899hnbtu6nTJlQHn/hVqpUq8CSBet5f/I8AgMDCAwM4PYh3WnrTlwnHjjC8499xsmTZ4koF8YjYwcQGV0un0iK1jEf7DPW/LSZ9yZ+g8Np6dq3Fdff0ZVP3/6eupdUo1V7V3/x6thP2fGH633x0LMDM6cqbPglno/ejGP8e8Myj3co6Sh3XfUM1WpWJsj9GdX7+nZ0cy+E5ivWHPK9L3HxP2/ihylf4XQ6adatDVfcGMuPH8VRpV51LmrTmAN/7GbGs1M5e/IMQSFBhJUrw5DJjwIw7eFXObw3ibSzaZQqHUqfYTdTp3n2K2W8eO0In7z8Y/IZ3/rM/NOeXzex4n1X/31x5zZcdm0P1kyfQ8U61anZsgnJ8buZP/4dUt39d2jZMlz/6mNsW7qaH9/8mPJZrvDQ4d6BVKxVDYDZT7xKs/7dibm0gbeqlqd76vve+wJg+dINvPyi61KN/fq34857ejH5jVk0aFiDDp2akpqazuOj32Pr5r1ERITx3Et3US2mEnGzVvLBu98TFBSICTDcPaQPnbo0y/OYviY8uKPvdRoeNGbNQo99px3XootPvVaFkkjIPLgxpXFNZQgC9llrkwq6ry8mEoorX00kFEe+mkgojnw1kVBc+WIiobjyxURCceWLiYTiylcTCcWRryYSiit/TyQ8/ssCj32nfaZ5V596rYIK8+DW2hOArjMiIiIiIiIixYovrm3gKUqPioiIiIiIiEiBFeqIBBEREREREZHiyJ9HJCiRICIiIiIiIuJh/ryEk6Y2iIiIiIiIiEiBaUSCiIiIiIiIiIcFGP+9EKESCSIiIiIiIiIe5s9rJGhqg4iIiIiIiIgUmEYkiIiIiIiIiHiYP49IUCJBRERERERExMMC/TiRoKkNIiIiIiIiIlJgGpEgIiIiIiIi4mGa2iAiIiIiIiIiBabLP4qIiIiIiIhIgfnziAStkSAiIiIiIiIiBaYRCSIiIiIiIiIeFujtAAqRzyYSutxwyNshiFvylp+8HYK4VezW09shiNsnbSp5OwTJYseTk70dgrhVq93J2yGIW0bzKG+HIG7BKw54OwRx+8/Nl3g7BMli11BvR1C4NLVBRERERERERAQfHpEgIiIiIiIicqHSVRtEREREREREpMACNbVBREREREREREQjEkREREREREQ8zp8XW1QiQURERERERMTD/DmRoKkNIiIiIiIiIlJgGpEgIiIiIiIi4mH+PCJBiQQRERERERERDwv048s/amqDiIiIiIiIiBSYRiSIiIiIiIiIeJg//2qvRIKIiIiIiIiIh/nzGgn+nCQREREREREREQ/TiAQRERERERERD/PnEQlKJIiIiIiIiIh4mK7aICIiIiIiIiKCRiSIiIiIiIiIeJymNggA7ZtX5bF7WhMYYPhi3h+8PWNDtu3/6t+QG2IvIsPh5Mixs4x6dTkHkk8BMPKOFnRqWQ2AN6b/xtylOwF4flg7GtWriDGwa/9xHn5lGafPZhRtxS5AXds35MUnbiIwIIBpXyxj4lvfZdv+r5s7cPfATjgcTk6dTuX+Rz9ka3wC1atW4Of5z7BtRyIAP6/bwQOPfUypkiF8+OYQalWvhMPh5LtF6xk7/ktvVO2C1L5hJE/c0JSAAMMXy3fy1rw/sm2/s2s9bmhXE4fTcuRkKg9P+4UDR04DUKVcKZ4f1JzocqWwFv71xk/sP3w6c98nb2zKdZfXpPGwb4u0TheqK6qWY1TrOgQaw5d/JDJ1w95s229rWJVrL4oiw2lJOZvOY8v/IOFUKgBvd2tEk0pl+DX5GPcu2JS5T9XwkkzoWJ+IEsH8fvgEo5duJd3pv0P1PKVbh6ZMGDuIwMAAPpi+mAmTZmXbftetXblnUDf359RZ7h01lS3b9gPQqH513nj+TkqXDsXpdHJF38dITU1n7MgbuOXa9pSNCKPSJXd4o1oXpCtbVeOxoW0JDDR8EbeFKZ/+lm37TVddwi1XN8TpdHLqTAaPT1hK/O6jANxzczOu730xDoflmf/8l+U/78vcLyDA8PXb/Uk6dIrBo+cVaZ0uVO0vrsSTVzcmIMDw+ardvLUoPtv2O9vXZkDrGjiclsOnUnnk83XsTzkDQPxLfdmacByAA0fPcPd7qwF44YamNIkpi8Gw8+BJRkxfy+k0R9FW7AJ1ZesYxgxvR2CgYcbszUz5aF227Tde3YBbrm2I02E5fSadx15cyvZdKQDcM/BSrutbH4fD8uyry1m+yvXeGHRDY2646hIM8MWszUz7YsP5Tyu56FC9HE9cWZdAY/j89wQm/5q9/76zWTVubODqv4+cSefhRVvZfyKVBhXDeLbjRYQHB+KwljfX7GFO/MFs+45tX5fr60fRcMryoqySoESC4DpZGPt/bbhtzDwSD53mq1f7snDlHuL3Hsss8/v2w1w9bBZnUx3c3OtiHvlXS4a98CMdW1ajYd3y9B36LSHBgXw6vidLf97HyTPpjJuympNn0gF49O6WDOx7SY4EhWQXEGB4+alb6DfoFfYnpvDjN48xd8E6tsYnZJaZMWsV7326BICeXZry/JgBXHPHqwDs3H2QK/o8neO4r78zj2UrtxIcHMjsjx+iW4dGzF+ysWgqdQELMPDUTc0Y9OpyElNO883ozixYn0B8wonMMpv2HKXfkkWcTXdwS/vajLq2Efe/4zoBnHBHSyZ9t4Xlm5MJLRGI03nu2I1rlKVMqeCirtIFK8DAmDZ1uXveBpJOp/J530tZvOcw24+dS8xsPnySG2at5azDyYCLo3moZS1G/LgFgPc27qNUUADXXxyd7bgPtqjFh5v2893OgzxxeV2uqRfF51sTkLwFBBheffYOet/yHPsTDrN89jjmzP8lM1EA8Pk3PzH14wUA9O7WnBcfH0i/QS8QGBjAe6/dy53D32TD5j2ULxtOerorwTx3wa+8Ne0HNiyZ6JV6XYgCAgxjh13B7SPiSDx4ii/f6s+in3ZnJgoAZi+I57NZmwHo3LYGo++9nDsf/o66NcrSu3Mdet0+g8oVwpj2cm+6DfwcpzuRdtu1jdi++yjhYfqcKogAA09f04SBb68g8dgZvh3engWbEolPOplZZtP+Y1z16lJXf3F5TUb1acB9H/0CwNl0B71fWZLjuM9+u4mTqa73yJirGjLoilo5EhSSU0CA4ckRV3DHsDkkJp/iy3evYeGy3ZmJAoDZP2xj+je/A9D5ihqMvv9y7npwLnVqlqN31zr0uuVzIiuG8cHrfeg+YDp1apblhqsu4bo7vyI9w8G7r/Tmx//uYfe+Y3mFIbjfGx3qceu360k8mcqsGy5j/s7DxKec679/P3iSvl/8ytkMJ7c2imZ029oMnbeZMxlOHpy/hV3HzlA5LIQ5N1zG0j1HOO5OpjWuHE6ZEH3lE88rlDUSjDH3G2NiCuPY3tL0oorsPnCCvYknSc9wErd0B10vr56tzMr1iZxNdb1p1205SFTFUADqVi/L6g1JOJyWM6kZbNlxhPYtqgJkJhEASoQEYfUjX75aNK3Fjt3J7Np7iPR0B1/OWU3vbs2ylTlx8mzm32GhJbD5vLBnzqaxbOVWANLTHfy2cQ9Vosp5Png/1LRWeXYnn2LvoVOkOyxz1uyjW9Mq2cqs/OMgZ9Nd7421Ow8TVbYUAHWjSxMUaFi+ORmA06mOzHIBBkZd25gXvlQyp6AaVyzN3hNn2HfyLOlOy9wdB+lUvUK2MqsTj3HW4crW/HbwOFGhJTK3rUo4yqn0nL/itY4uyw+7XL9ufBufRJcaFXKUkexaNqvL9l2J7NqTTHq6gxmzV9Cne4tsZU6cPJP5d1ipc59TXds3YePmPWzYvAeAI0dPZn5xXb02nsTko0jBNalfid37j7E34YSr/160nS7tamYrc/L0ub44tGRQZlt0aVeTuEXbSUt3si/xBLv3H6NJ/UoARFUKo2Ob6nwRt6XI6nKha1q9HLsPn2LvkdOkOyyz1+6nW8OobGVWbj98rr/Yc4SoiFL5HvfPJAJAyeBAnUsVUJMGldm97zh7D7jfGwu20/XKmtnKnMry3ihVKhjcr23XK2sSt2A76elO9iWcYPe+4zRpUJk6Ncrx28YkzqZm4HBYVq89QLcOtYqwVhemZpFl2H3sDHuPu/rv2duS6V47e1+7Yv9Rzma4+u+1iSeICnf13zuPnmHXMVd/knwqjcNn0ilfKgRwnUs92rYOz/93RxHWRrIKMJ67+ZrCSk89A4wyxmwHPgNmWGsP5rOPT4usEErCoVOZ9xMPnabpxZXyLH997EUsWeP65WnLjiPcd0sz3vt6I6VKBNG6STTxe86dCL7wwBV0bFGN+D1HeX7q6sKrhJ+IjirHvoRz2fIDCSm0aFY7R7m7B3Zi6L+6ERwcRN9bJ2Q+XiOmIstmP8GJk2d45pVvWPHztmz7RZQuRY8uTZn8wYLCq4QfiSpbioQsGfOElDM0q1U+z/I3tKvFkk1JANSqXJrjp9OZPKQN1SqE8dOWZMZ/tQGnhUGd6rLwtwQOHj+b57Eku8jQEpnTFACSTqfSpFLpPMtfe1EUy/an5LkdoGyJIE6kZeCwfx4zjcpZkg+SuypR5dh34HDm/f0Jh2nVrG6OcvcM6sb9d/cmJDiIHjc+C0C92tFYLLM+GkXF8mWYOXsFr7w1u8hi9zdRlcJIOJil/z54iqYNKucod8vVDfjX9U0IDg5g4ANzAIisFMa635Oy7RtVKQyAMUMvZ/zbqwgL1WiEgoqKKEnC0XMJtMRjZ2lWPe+k/YBWNViy5dzrXyIogG+Ht8fhdDJ5UTzzNyZmbhs/oBmdLolkW9IJxs3alNvh5DyRlcJIzDIaJPHgSZo2iMxR7pZrGnLHTU0IDgpk0H2zM/ddtynLeyP5JJGVwti24wgP3NOKsmVKcDbVQYe21dm4+YL+ClAkIsNCOHDiXP+dcDKVZpFl8ix/Q4Moftx9JMfjTSuXJjjAsNudWLitcVUW7DzEwdNpng9aCiTQBxMAnlJYV23YAVTDlVBoDvxujPneGHObMSbPs1pjzGBjzBpjzJrje34spND+GWNy/ivI61fufp1q07heBabOdE1RWL72AD/+vI8vJvRm4iMdWLslmYws84tHTVxO24Gfs33vUXq3V9Y2P7m9H3Nri3c+WkzTTo/y5PiZjLy3DwCJB4/R8IqHubLv0zw67gvenXg3pcNLZu7jGlI8mLenLWTX3kOFVQW/Z8njvdE6hsY1yvLOD641FIICDS3rVeS5mRu4+vlFVK8YxnVta1I5oiS9mldl2uLtRRn2hS+XN0dev8z1qV2ZhhVK8955ayjkPGQun33/JLZiJvc+I2e5tz+cT8Mrh/PY858y6v7+AAQFBtC2xcXccf+bdLl2LFfFtqBju4aFHXKxkluf8ck3v9Pllum89PYq/m/gZUBe/Q10urw6h1POsOkP9RN/R66vZx6fKFdfVo3GMRFMydIPtHt2Pv1eXcqwj3/liX6NqF4hNHPbw5+vo/VT84hPOkGfZlVyO6Scp6DnU598tYmu13/GS5NW8n+3u98bufY3lu27j/LOx+t4/7U+vDuxF1u2HSbDoV4jP7m/N3J39UWVaVK5NFPOW0OhUmgIr3Srz8iFW7FA5bAQetWtxAfr9+d+IJH/UWElEqy11mmt/cFaeydQBZgE9MCVZMhrpynW2hbW2hZlqncspND+mcRDp4iuGJZ5P6piKMlHTuco17ZZNP8e0JTBTy0kLePcZO/Jn6/nqvtmcfuYHzIXVszK6bTELd1J7HnDLSWnA4kpVIs+9wtGlehyJPzFUN+Zs3+md3fX1Ie0tAyOHHX9MrVu42527jlI3Vrnsu+vPzeI7buSmfS+RiMUVOLRM0SXO3cyF12uFMlHc44iaFe/Mvf2rM/gSSsy3xsJKWfYtOcoew+dwuG0/LDuAA2rl6VhTFlqVApn8TOxLB3Xg1IhgSx6JrbI6nShSjqVSnTYudECkaElSM7lV4g20WUZ3LQ6QxduynfRxJTUdEqHBGVm1CNDQzh4OvUv9xHYn3CEalXODUutGl2BA8l5j/74YtYK+rqnPuxPOMKyVZs5nHKCM2fT+H7xOi5tpCTzP5V48BTRlbL035XCSD6Us//+05xF2+l2Rc0s+4Zn2zfp0CkuaxRJl3Y1WDz9Jl59ogttLq3KhDGdCq0O/iLh2Fmiy56bqhAVUZKkY7n0F/Uqcm/Xetz93mrSHOfOpZKPuz579h45zcrth2hYNSLbfk4LcesO0KNJ9nVeJHeJB08RFZn133f4X7434hbE07V9Tde+yaeIrpxl38rn9p05Zwv97/iSW/5vFseOp2p9hAJIPJVGldLn+u/o8BIkn8rZ17arVpahLapzV9xG0rL03+HBgbzfpxEvr9zJ2iTXGlUNK4ZTM6IUSwa2Zvmg1pQKDuDHW1sVfmUkmwBjPXbzNYWVSMiWWLPWpltrZ1lrbwKq57GPT1v/xyFqVClDtchwgoMC6N2+NgtXZs8ENqhdnmfva8s9Ty/kSJaOMSDAUNb94XBxzXLUr1me5b+6soM1os8N0Ojcujo79urDNj+/rN9F7ZqR1KhWkeDgQK7t04q5C7KvwF2n5rlhq7GdmrB9l2sOfoXy4QS4JxnVjKlInZqV2bXH9YvS4w9eTZnSpXjkmelFVBP/sH5XCjUrh1OtQijBgYY+Laqx4LcD2co0iIng2VsvZfCkFRzOMnRv/a4jRIQGUz7cNZevbf1KxCccZ/HGRFo/HEf7Md/Tfsz3nElz0PlxrYien42HTlC9TCmqhpckOMDQq3YlFu89nK1M/fJhPNm2HkMXbuTI2fQ8jpTd6oSjdK/pmsrVr24ki/YczmcPWfPbdurWiqJGTCWCgwO5vu/lxM3/JVuZOjXPzQ3v2eVS4ne5hmnPX7qeRvWrU6pkCIGBAVzZ5hI2b9MvSv/Uhq0HqVktgmpRpV39d+c6LPzv7mxlalQ9N4S4U5vq7Nrv6osX/nc3vTvXISQ4gGpRpalZLYL1Ww7y8js/c+X1n9Lpxs8Y/vRCVq7dz4hxi4u0Xhei9XuPUrNiGNXKu/qLvpdWZUGW4fEADaqWYdx1Tbn7vdUcPnkuEVqmVDAhga7T1nJhITSvWZ5t7i9MNSqcSxR1aRjJjuSTSP42bE52vTei3e+NrnVYuHxXtjI1qp1L1nRsW4Nd7vPUhct30btrHYKDA6gW7X5v/O461ypfzjXSMzoynO4dazFnfvYppJLTb0nHqRlRimqlXf1333qVmb8ze1/bsGI4z3W6iLviNnE4yxprwQGGt3s15KutSczdfm6U1OLdR2j5/gqu+HAVV3y4ijPpTjp+rCnURS3AgzdfU1hrJAzIa4O19kxe23yZw2l5avJK3n+2O4EBhhk/bGPbnqMMu/VSNm47xMJVe3nkzpaElgzmP6M7ApBw8BT3PL2QoMAApr/UC4CTp9N4aMJSHE6LMTD+oSsJDw3BAJt3HuHJN1Z4r5IXCIfDycixn/L1tOEEBgTw0Yyf2LLtAGOG9+PXDbv4buFvDB7YmY7tLiE9w8HRY6cZMuI9ANq1uogxw/uR4XDicDgZ/tjHpBw7RZWocowc2oet8Qksm/04AFM+XMyHXyzzZlUvCA6nZez0dUwbdgUBAYYZP+1iW8IJhvdtwIbdKSxcn8Doa5sQViKINwa3BuDAkdMMnrQCp4Xnv9zAxw+0xxjYsDuF6ct2erlGFy6HhXEr45nSvREBxvD1tkS2Hz3N0EtrsOnQCRbvPcKIlrUJDQ5kYscGACScSmXoQtd84g97NqVW2VKEBgWy8IbWPLH8D346kMIra3YyoWN97r+sJpsPn+TLPxL/KgzB9Tn1wOMfMPuj0QQGBjDt8x/Z/Mc+Hn/wOn7dsJO4+b/w79u70+mKxqSnZ3D02CnufnAyAEePneL1qXNZPmcc1lrmLV7H94vWAjDu0ZsZ0K8toaVCiF/1Bu9PX8y4ibpU7V9xOCxPvfYT773Uk8CAAGZ+t5X4XSkMu6M5G7YeYtF/dzOwf0PaNq9KhsPJsRNpPPz8jwDE70rhux938N0HN5DhcDL21Z8yF76Uv8/htDz51QY+HNyGAGOYsXoP25JO8EDsxWzYd5QFm5IY3achYSWCeHOQa4TOn5d5rBsZzrjrmmKtxRjDW4viiU86iTEw4aZLCS8Z5DqXSjjO4zPXe7eiFwiHw/L0K8t5d2JvAgMNM+dsJX5nCvff1YKNWw6yaPlubr2uEW1bVCUjw8mxE6k88qwrYRa/M4W5i3bw3acDyMiwPPXyssz3xhvjYikbUYKMDCdPTVjO8ROan58fh4UnlsbzYb/GBBrDF78nsu3IaR5oVZMNySdYsOswo9u5+u9JPVz99/6TZ7k7bhO961aiVZUIypUM5rr6rgT1iIVb+D3L2m4ihcHkt5q9t9Tt9b5vBlYMJW/5ydshiFvFbj29HYK4lWqT92KrUvR2PDnZ2yGIW7XaGuLvKzKaR+VfSIpE8IoD+ReSIpF28yXeDkGy2DW0gx8vRwiLDsz12HfazlV6+dRrpYuKioiIiIiIiHiYrtogIiIiIiIiIoJGJIiIiIiIiIh4nC9ebcFTlEgQERERERER8bAATW0QEREREREREdGIBBERERERERGP8+cRCUokiIiIiIiIiHiYPw//9+e6iYiIiIiIiIiHaUSCiIiIiIiIiIcZTW0QERERERERkYLy4zyCpjaIiIiIiIiISMFpRIKIiIiIiIiIh2lqg4iIiIiIiIgUmD8P//fnuomIiIiIiIj4PWNMD2PMVmNMvDFmVC7bSxhjPndvX2WMqel+PNgYM80Ys8EYs9kYM7ogz6dEgoiIiIiIiIiHGWM9dvvr5zGBwJtAT6ABcJMxpsF5xe4EUqy1dYGJwIvux68HSlhrGwPNgXv+TDL8FSUSRERERERERDzMePCWj1ZAvLV2h7U2DZgO9DuvTD9gmvvvmUAXY4wBLBBmjAkCSgFpwPH8ntBn10iIn9vO2yFIJrWFr8hwnvF2COLmJN3bIUgWIXc85e0QRETylOqI9nYI4maMfkcVv1QV2Jvl/j6gdV5lrLUZxphjQAVcSYV+QAIQCjxgrT2S3xPqnSQiIiIiIiLiYcZ48mYGG2PWZLkNzvpUuTz9+fMh8irTCnAAVYBawEPGmNr51c1nRySIiIiIiIiIXKg8efVHa+0UYEoem/cBMVnuVwMO5FFmn3saQwRwBLgZ+N5amw4kG2N+AloAO/4qHo1IEBEREREREblw/QzUM8bUMsaEADcCs84rMwu4zf33dcAia60F9gCdjUsY0AbYkt8TakSCiIiIiIiIiIcFeHJIwl9wr3kwFJgHBALvWWs3GWOeBtZYa2cB7wIfGWPicY1EuNG9+5vA+8BGXIMo3rfWrs/vOY0rCeGL/vDVwES8Rost+g4ttuhbQgLKeDsEEZE8pTqOeTsEcdNii74lJKB5EX3V9o5NKXM89p22Ybk+PvVa6Z0kIiIiIiIiIgWmqQ0iIiIiIiIiHmZ8agyBZymRICIiIiIiIuJhfpxHUCJBRERERERExNP8OZGgNRJEREREREREpMA0IkFERERERETEw4rq8o/eoESCiIiIiIiIiIf5cR5BUxtEREREREREpOA0IkFERERERETEw4yx3g6h0CiRICIiIiIiIuJhmtogIiIiIiIiIoISCX/L0qW/EBs7hG7dBjNlyowc29PS0hk+/EW6dRvM9dc/xL59SZnb3n57Bt26DSY2dgjLlv1a4GNK3tQevmPZsnX07jmMHrH38c473+TYnpaWzkMPTKRH7H3cOOBR9u9PBuBoygluv+0pWjQfyLPPvJttn9de/Ywunf5Ni+YDi6QO/mL5st/o23MEvWIfZOo7s3JsT0tLZ8QDr9Mr9kFuHvAE+/cfBFxt8a/bnqVV838x7pkPsu2TnpbB2Cem0qfHQ/TtNYL5P6wuiqpc8PQZ5TvUFr5F7eE7li/7jb69RtA79kHezaPPGPngf+gd+yA3D3jyXJ9x9AR33j6O1s3v5Llnp2XbZ27cf7mm3yiuvXo0Qwa/SErKiSKpy4XO1X8/RK/YB/Lpvx/g5gGP59J/38G4Z97Pts8dg56hb8+HuK7/aK7rP5rDh48VSV3kHGM8d/M1SiQUkMPh4Omn32Lq1LHExb3JnDlLiY/fk63MjBk/UKZMOPPnT+H22/sxYcIHAMTH7yEubilxcW8ydepYnnpqMg6Ho0DHlNypPXyHw+Fk3DPv8taUR5k1eyJz434iPn5ftjJfzlxEmYgwvp/3HwYN6s0rEz4BIKREMPfdP4CRI3MmCzp2bM70z58rkjr4C1dbfMCkKQ/z7ezxfBe3gu3ntcVXM3+kTEQYc+e9wsBBPZk44TPA1RZD77+eESNvznHcKW9/Q/nyZZjz/ct8O2c8LVpeUiT1uZDpM8p3qC18i9rDdzgcTp57dhqT336Yb2aP57u5K9kevz9bma++/JEyZcKIm/cKA2/rwasvTwcgJCSYe++7jofO6zMyMhy8+PzHvPvBGL785nkuuqg6n33yQ5HV6ULl6r/fd/ffL/Fd3H//ov+emEv/fR0jRt6S652yYTIAAB+cSURBVLFfeOleZn79PDO/fp4KFSIKvS6SXYAHb76mUGIyxoQYYwYZY7q6799sjHnDGHOvMSa4MJ6zsK1fv40aNaKJiYkiJCSY3r3bs3DhqmxlFi1aRf/+XQCIjW3HihW/Ya1l4cJV9O7dnpCQYGJioqhRI5r167cV6JiSO7WH79iwPp6Y6lHExEQSEhJEr15tWbzo52xlFi1aQ79+HQHoHtuGlSs3Yq0lNLQkzZvXJ6RESI7jNm12EZUqlyuKKviNDeu3U716JDExlQkOCaJnrzYsXvRLtjKLF/3CVf3aA9AtthWrVm7KbIvLml9MSImcH9Fff7WEuwZfBUBAQADlypUu/Mpc4PQZ5TvUFr5F7eE7Nm5w9RnV3H1Gj545+4wfF/3KVVdfCUC37jn7jBLn9RnWWrCWM6dTsdZy6uQZKqsvz9eG9fHu/jvS3X9fnkv/vYar+rnbIrY1q7KcS13WvH6u/bdIYSqs5Mb7QG9gmDHmI+B6YBXQEphaSM9ZqJKSDhMVVTHzfmRkBZKSDucoEx3tKhMUFEjp0mGkpBzPZd+KJCUdLtAxJXdqD9+RlHyE6KgKmfddr9uRbGWSk44QFe0q42qLUI4e1VBHT0tOPkJUtrYoT1JSSvYySSlERZcHXG0RXjqUo0dP5nnM48dPAfDG6zO54ZoxPDj8NQ4d0tDI/OgzyneoLXyL2sN3JCWlEBlVPvN+ZFR5kpNT8ixTkD4jODiIMU/cwbVXj6JLh6Fs376f/td2LJT4/Ulyckou/ff551Ip2c6lwgt4LvXYo29zXf/RvDXpK1eiR4qUpjb8fY2ttQOA/kB34Dpr7UfAHcClee1kjBlsjFljjFkzZcrnhRTaP5PbG8+c16J5lcn98YIdU3Kn9vAh/7Qt/HodW+/I7fygYG2RN4fDSVLiES697CK++GocTZvV4+Xxn/yPkfo/fUb5DrWFb1F7+JCC9Ad5vOZ5SU/P4IvpC/jiy3EsXPIGF11cPde1FyS7f/y+yOdc6oWX7uXrWS8y7eMn+PWXrcz+dtn/Fqj8bcaDN19TWImEAGNMCFAaCAX+nJBTAshz3I21doq1toW1tsXgwQMKKbR/JiqqIomJhzLvJyUdpnLl8jnKJCS4ymRkODhx4hRly5bOZd9DVK5coUDHlNypPXxHZGQFEhLP/fLjet2yD2OMjKpAYoKrjKstThNRNrxI4ywOIiPLk5itLY5QuXLZ7GWiypOY4PqVIyPDwcl82qJs2XBKlSpBl64tAIiNbc3m33d5Png/o88o36G28C1qD98RGVWepMRzv3onJR7JMaUwa5nMPiMi7z5j65bdAMRUj8QYQ/cerVm3dlshRO9fcu+/c7ZF1nOp/PrvP48LEBZWil592rJhw3YPRy7FWWElEt4FtgDrgDHADGPMO8DPwPRCes5C1bhxPXbtOsDevYmkpaUTF7eUzp1bZSvTuXNrvv56IQDz5v1EmzZNMMbQuXMr4uKWkpaWzt69iezadYAmTeoV6JiSO7WH72jUuA57diewb18yaWkZzJ37Xzp1apGtTKdOzfn22x8B+GHeSlq3aahfiwpBo8a12b07kX37kklPy+C7uSvp2Kl5tjIdO13GrG+XAjB/3mpa5dMWxhg6dLyUn1dvBmDlyo3Urlu18CrhJ/QZ5TvUFr5F7eE7GjbK3md8/91KOna6LFuZjp0uY9Y3rl+x5/+wmlatG/xln1E5sjw7tu/nyJHjAKz87wZq165SeJXwE40a1zmv/16RS//dnFnuEQXz563Kt//OyHCQkuJqh/T0DJb+uJZ69WIKrxKSK3+e2mAKa66MMaYKgLX2gDGmLNAV2GOtLeB1w/7wuUk8S5as4bnn3sHhcHLttV35978H8NprH9OoUT26dGlNamoaI0e+wubNO4iICGfixIeJiYkCYPLkz/nyywUEBgby6KN30aFDizyPKQVTHNsjw3nG2yHkaumSX3nh+Wk4nU76X9OJe4Zcw39e/5yGjerQuXMLUlPTGPXIG2zevJOIiHAmvDycmJhIALp1uZeTp06Tnp5BmdJhTJn6GHXrVmPCSx8zN245yckpVK5cjmuv68y9Q2/wck3PcZLu7RBytXTJOsY//xEOp5P+13Rg8JCreeP1mTRsVItOnZuTmprG6Ecms2XzbiIiwhj/8n3ExFQGILbLME6eOkN6egalS4cyZeoo6tStxoH9Bxn9yGROnDhN+fJleGbcYKKrVMwnkqIVElDG2yHkUBw/o3yV2sK3FMf2SHX45toyy5asY/wLH+NwOrm6fwcGD+nHm/+ZSYOG5/qMRx95iy2bdxFRNpzxE4ZSzd1n9Og6nJMn3X1GmVDefmcUdepW5YvpC/nk43kEBQUSXaUizz43mLJlfWeRXmN8cf17WLpkbZb+u6O7/55Bw0a1s/Tfk87rv13nUrFd7s/Sf4cxZeoooqtU5PaBT5OR4cDpcNKmbSNGPjKQwEDfqn9IQHMf/IrsOftOzfbYd9pqYX196rUqtETC/873Egki3uariYTiyFcTCcWVLyYSRET+5KuJhOLIVxMJxZUSCQXna4mEIG8HICIiIiIiIuJvAnzqq79nKZEgIiIiIiIi4mF+nEcotMUWRURERERERMQPaUSCiIiIiIiIiIcZ47/L/imRICIiIiIiIuJhmtogIiIiIiIiIoJGJIiIiIiIiIh4nPHjIQlKJIiIiIiIiIh4mB/nETS1QUREREREREQKTiMSRERERERERDzMn3+1VyJBRERERERExMP8eY0Ef06SiIiIiIiIiIiHaUSCiIiIiIiIiMf575AEJRJEREREREREPMz4cSJBUxtEREREREREpMA0IkFERERERETEw4zx39/tfTaRkO487e0QxC04INTbIYhbUEApb4cgbgfP7vZ2CJJF2ZBAb4cgbhant0MQN4PeF76iRGCEt0MQEa/Q1AYREREREREREd8dkSAiIiIiIiJyofLnxRaVSBARERERERHxOP9NJGhqg4iIiIiIiIgUmEYkiIiIiIiIiHiYrtogIiIiIiIiIn+DpjaIiIiIiIiIiGhEgoiIiIiIiIin6aoNIiIiIiIiIlJg/pxI0NQGERERERERESkwjUgQERERERER8Tj//d1eiQQRERERERERDzNGUxtERERERERERDQiQURERERERMTz/HdEghIJIiIiIiIiIh6mqzaIiIiIiIiIiKBEwt+yfNk6+vQcTs/Y+5n6zjc5tqelpfPQA6/SM/Z+bhowhv37kwE4mnKCO257ipbNBzHumfcyy585k8q/73mBvr0eoF+fh5j48qdFVhd/sHTpL8TGDqFbt8FMmTIjx/a0tHSGD3+Rbt0Gc/31D7FvX1LmtrffnkG3boOJjR3CsmW/FviYkju1he9Y+dMWbrpqPAP6vMBH7y7KsT0tLYMnRn7MgD4vcPctr5Ow/0i27YkJKXRrM4ZPp/2Y+djnHy3l1v4TGHjNBJ585BNSU9MLuxp+wdVnPEDP2GFMfefbHNvP9RnDcukznqZl89uy9RlZDf2/l7i674hCjd+fLF/2G317PkSv2AeY+s6sHNvT0tIZ8cDr9Ip9gJsHPM7+/QcBV1v867ZnadX8DsY9836ux77v/ybQv+/DhRq/P/H0uRTAPXc/xzVXj6Rfn4d4auw7OBzOIqmLP1D/7TvUFv4qwIM33+J7Efkoh8PJs8+8x+Qpo5k1+xXmxv3E9vh92cp8NXMRZSLC+G7e6wwc1ItXJrgSAyElgrnv/gGMGDkwx3Hv+FcfZs+dyMyvXmTt2q0sW7q2SOpzoXM4HDz99FtMnTqWuLg3mTNnKfHxe7KVmTHjB8qUCWf+/Cncfns/Jkz4AID4+D3ExS0lLu5Npk4dy1NPTcbhcBTomJKT2sJ3OBxOXnnuayZMupOPvx7Bgu/XsXN7UrYyc75eTekypfh8zigG3Nqeya/Ozbb9Py/NovUV9TPvH0w6xsxPl/PuZ8P46KsROJ1OFn6/rkjqcyE712eMYtbsl/PoMxZTJiKc7+a9xsBBvc/rM25gxMhbcz32/B9WExpaotDr4C8cDifjnnmfSVMe5tvZL/Fd3H9zaYsfKRMRxtx5Exk4qCcTJ3wGuNpi6P3XMWLkLbkee8EPqykVWrLQ6+AvCutc6uWJw/nqm5f4ZvYEUo4cZ973K4qkPhc69d++Q23hv4wH//M1hZZIMMbUMcaMMMa8Zox52RgzxBgTUVjPV9g2rI+nevVIYmIiCQ4Jomevtixa9HO2MosWraFfvw4AdI9tw6qVG7HWEhpaksua16dEieBs5UuVKkGr1o0ACA4J4pIGtUhKzP7roORu/fpt1KgRTUxMFCEhwfTu3Z6FC1dlK7No0Sr69+8CQGxsO1as+A1rLQsXrqJ37/aEhAQTExNFjRrRrF+/rUDHlJzUFr5j88Y9VIupSNVqFQgODqJrj2Ys/3FTtjLLF2+i51XNAejYrTG/rN6GtRaApYs2UqVaBWrVicy2j8PhJDU1nYwMB6ln0qlYqUzRVOgC5uozos7rM9ZkK+PqM9oD0D22NatWbvrLPgPg9KmzfDgtjnuGXFMk9fAHOfvvy1m86JdsZRYvWsNV/a4EoFts6xz9d0iebTGXe4ZcXST18AeFcS4FEB4eCkBGhoP09Ay/vtyaJ6n/9h1qC7kQFUoiwRhzP/AWUBJoCZQCYoAVxpiOhfGchS05+QhRURUy70dGViA5KSV7maQjREW7ygQFBRJeOpSjR08U6PjHj59iyeJfaH15I88F7ceSkg4TFVUx835kZAWSkg7nKBMd7SoTFBRI6dJhpKQcz2XfiiQlHS7QMSUntYXvOJh8nMpRZTPvV6ocwcGkY+eVOZZZJigokLDwkhw7epozp9P45P3F3DGkW7bylSIjuPG2DlwbO46ruz5DWOmStGp7ceFX5gKXs88oT3JS9kRxzj6jVL59xn9e/5zbbu9NyVIhng/aTyUnp+Roi6QcbZHyt/vv/7w+w90WGh1SUIV5LjX4rnF0uGIwYWGl6B7bxrOB+yn1375DbeG/jDEeu/mawhqRcDfQw1r7LNAVaGCtHQP0ACYW0nMWqj9/scvq/PbMpUiBhqFkZDh4eMTr3HJrD2JiIvMtL3m1hylQmbzasiDHlJzUFr6jYG2Rcz9j4N3J87jh1vY5hswfP36a5Ys38cXc0Xwz/3HOnklj3pxfch5Essn9dS5AW/xFn7Fl8y727Emia7dW/2t4xco//ozKpy327kmkS7eW/3uAxUhhnktNmTqGxUvfIi0tnVUrN/7TEIsV9d++Q23hz4wHb76lMNdI+PPSkiWA0gDW2j1AzjFpbsaYwcaYNcaYNVOnfFmIof19kZEVSEw8l8VLSjpMpcrlspeJKk9igqtMRoaDkydOE1E2PN9jj31yCtVrRDHwtt6eDdqPRUVVJDHxUOb9pKTDVK5cPkeZhARXmYwMBydOnKJs2dK57HuIypUrFOiYkpPawndUjowgOfFo5v2DyceoWLlMnmUyMhycOnmWMhGh/L5hL5NfjeO6ns8x45NlfDR1EV9+9hNrVm4jump5ypUPJyg4kPZdGrHht91FWq8LUWRk+fP6jCMF6DPO/GWfsW7dH/y+aSfduwxl0C1j2bU7gdsHPVU4FfAjubVF5f+x//5t3TZ+37ST2C73M+iWp9i1O4E7Bj1TOBXwI4V5LgVQokQInTq3YPF504gkd+q/fYfawn8ZAjx28zWFFdFU4GdjzBRgBfAGgDGmEpDnIgDW2inW2hbW2hZ3Db62kEL7Zxo1rsOe3Yns25dMeloG3839L506tchWplOnFnz77RIAfpi3ktZtGuab+Xv91emcPHGaUaNvK7TY/VHjxvXYtesAe/cmkpaWTlzcUjp3zv4rXefOrfn664UAzJv3E23aNMEYQ+fOrYiLW0paWjp79yaya9cBmjSpV6BjSk5qC99Rv2EMe/cc4sC+I6SnZ7Dg+3W069AgW5l2HRvw3SzXiIIf52/gslZ1McYw6YP/Y+Z3jzLzu0e5/pYrGXhXZ669qR2RUeXYtH4PZ8+kYa3ll1Xx1KxV2RvVu6Dk3mc0z1amU6fmfPvtUgB+mLcq3z7jxpu6s3jpZH5Y+AYffjKWmjWi+eDDJwu1Hv6gUeM67M7WFivoeF5bdOzUnFnfLgNg/rxVtMqnLQbc1I1FSycxb+HrfPjJk9SsEc37Hz5eqPXwB4VxLnX61FkOJrumR2RkOFi6ZC21alcpvEr4EfXfvkNtIRcik9uwF48c2JiGwCXARmvtlr+7f7pzXeEE9j9YumQtLz4/DYfTSf9rOnLPkGt44/UvaNioNp06tyA1NY3Rj7zB5s27iIgI56WXh2VOVejeZSgnT50mPT2DMqXDmDJ1DGHhpeja6f+oVbsKISGugRo33RzLddd38WY1cwgOCPV2CLlasmQNzz3nuszTtdd25d//HsBrr31Mo0b16NKlNampaYwc+QqbN+8gIiKciRMfJiYmCoDJkz/nyy8XEBgYyKOP3kWHDi3yPKbkrzi2xcGzf/tjrUisWLaZ18bPwul00vvqVtx2dxemvjmP+g2rcUXHhqSmpvPMmOls27KfMmVCGTv+FqpWq5DtGO9O/oFSoSHcfFtH1/1J81g47zcCAwO4qH5VHhl7PSEhQbk8u/eUDYnxdgg5ZO8zOnHPkP659BlvZukz7j+vzziTpc94lDp1q2Uee//+ZO4dMp5vZk/wVvXyZPG9S+8tXbKW8c9/lNl/Dx5yNW+8PsPdFs3dbTGJLZt3ExERxviX78tsi9gu92e2RenSYUyZOuq8tjjI0CEv8fXs8d6qXp4Mgd4OIQdPn0tFlA3n3n+/SFpaBk6Hk9ZtGvLwqNsICvKtuutcSvJTfNviIt8bs+9BqY6fPfadtkRgS596rQotkfC/8sVEQnHlq52fiDf5aiKhuPLFREJx5YuJhOLKFxMJxZXOpUTy4t+JhDTnGo99pw0JaOFTr5XvTbYQEREREREREZ/lW2NTRURERERERPyCTw0i8CglEkREREREREQ8zBevtuAp/lszEREREREREfE4jUgQERERERER8ThNbRARERERERGRAjJ+nEjQ1AYRERERERERKTCNSBARERERERHxMGP8d0SCEgkiIiIiIiIiHue/EwD8t2YiIiIiIiIi4nEakSAiIiIiIiLiYf682KISCSIiIiIiIiIe57+JBE1tEBEREREREZECUyJBRERERERExMOMMR67FeC5ehhjthpj4o0xo3LZXsIY87l7+ypjTM0s20a7H99qjIktSN2USBARERERERHxuAAP3vJmjAkE3gR6Ag2Am4wxDc4rdieQYq2tC0wEXnTv2wC4EWgI9AAmuY+Xb81ERERERERE5MLUCoi31u6w1qYB04F+55XpB0xz/z0T6GJcQx36AdOttanW2p1AvPt4f0mJBBEREREREREPMx78Lx9Vgb1Z7u9zP5ZrGWttBnAMqFDAfXPw2as2BAc084slLo0xg621U7wdh6gtfIk/tEWlkhd5OwSP8Ie28CdqD9+htvAdagvfobbwHWqLC8VFHvtOa4wZDAzO8tCULP8Gcnsee/4h8ihTkH1z0IiEwjc4/yJSRNQWvkNt4TvUFr5F7eE71Ba+Q23hO9QWvkNtUcxYa6dYa1tkuWVNJO0DYrLcrwYcOO8QmWWMMUFABHCkgPvmoESCiIiIiIiIyIXrZ6CeMaaWMSYE1+KJs84rMwu4zf33dcAia611P36j+6oOtYB6wOr8ntBnpzaIiIiIiIiIyF+z1mYYY4YC84BA4D1r7SZjzNPAGmvtLOBd4CNjTDyukQg3uvfdZIz5AvgdyADutdY68ntOJRIKn+Yu+Q61he9QW/gOtYVvUXv4DrWF71Bb+A61he9QW0g21tq5wNzzHnsiy99ngevz2HccMO7vPJ9xjWYQEREREREREcmf1kgQERERERERkQJTIqGQGGPeM8YkG2M2ejuW4s4YE2OMWWyM2WyM2WSMGebtmIorY0xJY8xqY8xv7rZ4ytsxFXfGmEBjzFpjzBxvx1KcGWN2GWM2GGPWGWPWeDue4swYU9YYM9MYs8Xdb1zu7ZiKK2PMxe73xJ+348aY4d6Oq7gyxjzg7rs3GmM+M8aU9HZMxZUxZpi7HTbpPSHeoqkNhcQY0x44CXxorW3k7XiKM2NMNBBtrf3VGFMa+AW42lr7u5dDK3aMMQYIs9aeNMYEA8uBYdbalV4OrdgyxjwItADKWGv7eDue4soYswtoYa095O1YijtjzDRgmbV2qnvl61Br7VFvx1XcGWMCgf1Aa2vtbm/HU9wYY6ri6rMbWGvPuBdmm2ut/cC7kRU/xphGwHSgFZAGfA/821q7zauBSbGjEQmFxFq7FNdqmOJl1toEa+2v7r9PAJuBqt6NqniyLifdd4PdN2UzvcQYUw3oDUz1diwivsAYUwZoj2tla6y1aUoi+IwuwHYlEbwqCCjlvv58KAW4zrwUikuAldba09baDGAJ0N/LMUkxpESCFCvGmJrApcAq70ZSfLmH0q8DkoH51lq1hfe8CjwMOL0diGCBH4wxvxhjBns7mGKsNnAQeN895WeqMSbM20EJ4LpM2WfeDqK4stbuByYAe4AE4Ji19gfvRlVsbQTaG2MqGGNCgV5AjJdjkmJIiQQpNowx4cCXwHBr7XFvx1NcWWsd1tpmQDWglXuInhQxY0wfINla+4u3YxEA2llrLwN6Ave6p8dJ0QsCLgMmW2svBU4Bo7wbkrinmFwFzPB2LMWVMaYc0A+oBVQBwowxt3o3quLJWrsZeBGYj2taw29AhleDkmJJiQQpFtzz8b8EPrHWfuXteATcw4V/BHp4OZTiqh1wlXtu/nSgszHmY++GVHxZaw+4/58MfI1r7qsUvX3AviwjpWbiSiyId/UEfrXWJnk7kGKsK7DTWnvQWpsOfAW09XJMxZa19l1r7WXW2va4plJrfQQpckokiN9zL/D3LrDZWvuKt+MpzowxlYwxZd1/l8J1YrLFu1EVT9ba0dbaatbamriGDC+y1urXJS8wxoS5F4LFPYy+O66hq1LErLWJwF5jzMXuh7oAWpjX+25C0xq8bQ/QxhgT6j6v6oJrzSnxAmNMZff/qwPXoPeHeEGQtwPwV8aYz4COQEVjzD7gSWvtu96NqthqBwwENrjn5gM8aq2d68WYiqtoYJp79e0A4AtrrS47KMVdJPC169ycIOBTa+333g2pWLsP+MQ9nH4HcIeX4ynW3HPAuwH3eDuW4sxau8oYMxP4Fdcw+rXAFO9GVax9aYypAKQD91prU7wdkBQ/uvyjiIiIiIiIiBSYpjaIiIiIiIiISIEpkSAiIiIiIiIiBaZEgoiIiIiIiIgUmBIJIiIiIiIiIlJgSiSIiIiIiIiISIEpkSAiIsWOMcZhjFlnjNlojJnhvsTcPz1WR2PMHPffVxljRv1F2bLGmP/7B88x1hgzIo9tg9z12GSM+f3PcsaYD4wx1/3d5xIRERHJjxIJIiJSHJ2x1jaz1jYC0oAhWTcal7/dR1prZ1lrX/iLImWBv51IyIsxpicwHOhurW0IXAYc89TxRURERHKjRIKIiBR3y4C6xpiaxpjNxphJwK9AjDGmuzFmhTHmV/fIhXAAY0wPY8wWY8xy4Jo/D2SMud0Y84b770hjzNfGmN/ct7bAC0Ad92iIl9zlRhpjfjbGrDfGPJXlWGOMMVuNMQuAi/OIfTQwwlp7AMBae9Za+875hYwxT7ifY6MxZooxxrgfv989imG9MWa6+7EO7vjWGWPWGmNK/4+vr4iIiPgZJRJERKTYMsYEAT2BDe6HLgY+tNZeCpwCHgO6WmsvA9YADxpjSgLvAH2BK4GoPA7/OrDEWtsU10iBTcAoYLt7NMRIY0x3oB7QCmgGNDfGtDfGNAduBC7FlahomcdzNAJ+KUBV37DWtnSPwCgF9HE/Pgq41FrbhHOjMkYA91prm7nrd6YAxxcREZFiRIkEEREpjkoZY9bhSg7sAd51P77bWrvS/XcboAHwk7vsbUANoD6w01q7zVprgY/zeI7OwGQAa63DWpvblIPu7ttaXKMg6uNKLFwJfG2tPW2tPQ7M+p9qC52MMauMMRvccTV0P74e+MQYcyuQ4X7sJ+AVY8z9QFlrbUbOw4mIiEhxFuTtAERERLzgjPsX90zu0f6nsj4EzLfW3nReuWaA9VAcBnjeWvv2ec8xvIDPsQloDizK8wlcIygmAS2stXuNMWOBku7NvYH2wFXA48aYhtbaF4wxcUAvYKUxpqu1dsvfrJeIiIj4MY1IEBERyd1KoJ0xpi6AMSbUGHMRsAWoZYyp4y53Ux77LwT+7d430BhTBjgBZF1zYB7wryxrL1Q1xlQGlgL9jTGl3GsU9M3jOZ4Hxhtjotz7l3CPJMjqz6TBIffzXOcuGwDEWGsXAw/jWggy3BhTx1q7wVr7Iq4RG/X/6kUSERGR4kcjEkRERHJhrT1ojLkd+MwYU8L98GPW2j+MMYOBOGPMIWA5rrUKzjcMmGKMuRP4/3bu0KaiIIgC6L2hACQ1UAZ90AB1UAAFkKAwNMA3WPIFCUEQNHSAwJJFvFWEhMWSc+yOmKy8mZnPJGdjjH3b+7bPSXbzTsJxkv2ciPhIcjrGeGx7k+QpyVu2g5A/9Xjb9ijJ3TygOJJcfat5b3uZ7Q7Ea5KH+XSQ5LrtYbbJiItZe972ZPb8kmT3t58DAP67buudAAAAAL+z2gAAAAAsEyQAAAAAywQJAAAAwDJBAgAAALBMkAAAAAAsEyQAAAAAywQJAAAAwDJBAgAAALDsCxe3fM+vQFDsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Recall matrix (Row sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we need to generate 9 numbers and the sum of numbers should be 1\n",
    "# one solution is to genarate 9 numbers and divide each of the numbers by their sum\n",
    "# ref: https://stackoverflow.com/a/18662466/4084039\n",
    "test_data_len = test_df.shape[0]\n",
    "cv_data_len = cv_df.shape[0]\n",
    "\n",
    "# we create a output array that has exactly same size as the CV data\n",
    "cv_predicted_y = np.zeros((cv_data_len,9))\n",
    "for i in range(cv_data_len):\n",
    "    rand_probs = np.random.rand(1,9)\n",
    "    cv_predicted_y[i] = ((rand_probs/sum(sum(rand_probs)))[0])\n",
    "print(\"Log loss on Cross Validation Data using Random Model\",log_loss(y_cv,cv_predicted_y, eps=1e-15))\n",
    "\n",
    "\n",
    "# Test-Set error.\n",
    "#we create a output array that has exactly same as the test data\n",
    "test_predicted_y = np.zeros((test_data_len,9))\n",
    "for i in range(test_data_len):\n",
    "    rand_probs = np.random.rand(1,9)\n",
    "    test_predicted_y[i] = ((rand_probs/sum(sum(rand_probs)))[0])\n",
    "print(\"Log loss on Test Data using Random Model\",log_loss(y_test,test_predicted_y, eps=1e-15))\n",
    "\n",
    "predicted_y =np.argmax(test_predicted_y, axis=1)\n",
    "plot_confusion_matrix(y_test, predicted_y+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>3.3 Univariate Analysis</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# code for response coding with Laplace smoothing.\n",
    "# alpha : used for laplace smoothing\n",
    "# feature: ['gene', 'variation']\n",
    "# df: ['train_df', 'test_df', 'cv_df']\n",
    "# algorithm\n",
    "# ----------\n",
    "# Consider all unique values and the number of occurances of given feature in train data dataframe\n",
    "# build a vector (1*9) , the first element = (number of times it occured in class1 + 10*alpha / number of time it occurred in total data+90*alpha)\n",
    "# gv_dict is like a look up table, for every gene it store a (1*9) representation of it\n",
    "# for a value of feature in df:\n",
    "# if it is in train data:\n",
    "# we add the vector that was stored in 'gv_dict' look up table to 'gv_fea'\n",
    "# if it is not there is train:\n",
    "# we add [1/9, 1/9, 1/9, 1/9,1/9, 1/9, 1/9, 1/9, 1/9] to 'gv_fea'\n",
    "# return 'gv_fea'\n",
    "# ----------------------\n",
    "\n",
    "# get_gv_fea_dict: Get Gene varaition Feature Dict\n",
    "def get_gv_fea_dict(alpha, feature, df):\n",
    "    # value_count: it contains a dict like\n",
    "    # print(train_df['Gene'].value_counts())\n",
    "    # output:\n",
    "    #        {BRCA1      174\n",
    "    #         TP53       106\n",
    "    #         EGFR        86\n",
    "    #         BRCA2       75\n",
    "    #         PTEN        69\n",
    "    #         KIT         61\n",
    "    #         BRAF        60\n",
    "    #         ERBB2       47\n",
    "    #         PDGFRA      46\n",
    "    #         ...}\n",
    "    # print(train_df['Variation'].value_counts())\n",
    "    # output:\n",
    "    # {\n",
    "    # Truncating_Mutations                     63\n",
    "    # Deletion                                 43\n",
    "    # Amplification                            43\n",
    "    # Fusions                                  22\n",
    "    # Overexpression                            3\n",
    "    # E17K                                      3\n",
    "    # Q61L                                      3\n",
    "    # S222D                                     2\n",
    "    # P130S                                     2\n",
    "    # ...\n",
    "    # }\n",
    "    value_count = train_df[feature].value_counts()\n",
    "    \n",
    "    # gv_dict : Gene Variation Dict, which contains the probability array for each gene/variation\n",
    "    gv_dict = dict()\n",
    "    \n",
    "    # denominator will contain the number of time that particular feature occured in whole data\n",
    "    for i, denominator in value_count.items():\n",
    "        # vec will contain (p(yi==1/Gi) probability of gene/variation belongs to perticular class\n",
    "        # vec is 9 diamensional vector\n",
    "        vec = []\n",
    "        for k in range(1,10):\n",
    "            # print(train_df.loc[(train_df['Class']==1) & (train_df['Gene']=='BRCA1')])\n",
    "            #         ID   Gene             Variation  Class  \n",
    "            # 2470  2470  BRCA1                S1715C      1   \n",
    "            # 2486  2486  BRCA1                S1841R      1   \n",
    "            # 2614  2614  BRCA1                   M1R      1   \n",
    "            # 2432  2432  BRCA1                L1657P      1   \n",
    "            # 2567  2567  BRCA1                T1685A      1   \n",
    "            # 2583  2583  BRCA1                E1660G      1   \n",
    "            # 2634  2634  BRCA1                W1718L      1   \n",
    "            # cls_cnt.shape[0] will return the number of rows\n",
    "\n",
    "            cls_cnt = train_df.loc[(train_df['Class']==k) & (train_df[feature]==i)]\n",
    "            \n",
    "            # cls_cnt.shape[0](numerator) will contain the number of time that particular feature occured in whole data\n",
    "            vec.append((cls_cnt.shape[0] + alpha*10)/ (denominator + 90*alpha))\n",
    "\n",
    "        # we are adding the gene/variation to the dict as key and vec as value\n",
    "        gv_dict[i]=vec\n",
    "    return gv_dict\n",
    "\n",
    "# Get Gene variation feature\n",
    "def get_gv_feature(alpha, feature, df):\n",
    "    # print(gv_dict)\n",
    "    #     {'BRCA1': [0.20075757575757575, 0.03787878787878788, 0.068181818181818177, 0.13636363636363635, 0.25, 0.19318181818181818, 0.03787878787878788, 0.03787878787878788, 0.03787878787878788], \n",
    "    #      'TP53': [0.32142857142857145, 0.061224489795918366, 0.061224489795918366, 0.27040816326530615, 0.061224489795918366, 0.066326530612244902, 0.051020408163265307, 0.051020408163265307, 0.056122448979591837], \n",
    "    #      'EGFR': [0.056818181818181816, 0.21590909090909091, 0.0625, 0.068181818181818177, 0.068181818181818177, 0.0625, 0.34659090909090912, 0.0625, 0.056818181818181816], \n",
    "    #      'BRCA2': [0.13333333333333333, 0.060606060606060608, 0.060606060606060608, 0.078787878787878782, 0.1393939393939394, 0.34545454545454546, 0.060606060606060608, 0.060606060606060608, 0.060606060606060608], \n",
    "    #      'PTEN': [0.069182389937106917, 0.062893081761006289, 0.069182389937106917, 0.46540880503144655, 0.075471698113207544, 0.062893081761006289, 0.069182389937106917, 0.062893081761006289, 0.062893081761006289], \n",
    "    #      'KIT': [0.066225165562913912, 0.25165562913907286, 0.072847682119205295, 0.072847682119205295, 0.066225165562913912, 0.066225165562913912, 0.27152317880794702, 0.066225165562913912, 0.066225165562913912], \n",
    "    #      'BRAF': [0.066666666666666666, 0.17999999999999999, 0.073333333333333334, 0.073333333333333334, 0.093333333333333338, 0.080000000000000002, 0.29999999999999999, 0.066666666666666666, 0.066666666666666666],\n",
    "    #      ...\n",
    "    #     }\n",
    "    gv_dict = get_gv_fea_dict(alpha, feature, df)\n",
    "    # value_count is similar in get_gv_fea_dict\n",
    "    value_count = train_df[feature].value_counts()\n",
    "    \n",
    "    # gv_fea: Gene_variation feature, it will contain the feature for each feature value in the data\n",
    "    gv_fea = []\n",
    "    # for every feature values in the given data frame we will check if it is there in the train data then we will add the feature to gv_fea\n",
    "    # if not we will add [1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9] to gv_fea\n",
    "    for index, row in df.iterrows():\n",
    "        if row[feature] in dict(value_count).keys():\n",
    "            gv_fea.append(gv_dict[row[feature]])\n",
    "        else:\n",
    "            gv_fea.append([1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9])\n",
    "#             gv_fea.append([-1,-1,-1,-1,-1,-1,-1,-1,-1])\n",
    "    return gv_fea"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "when we caculate the probability of a feature belongs to any particular class, we apply laplace smoothing\n",
    "<li>(numerator + 10\\*alpha) / (denominator + 90\\*alpha) </li>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>3.2.1 Univariate Analysis on Gene Feature</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-size:18px;\"> <b>Q1.</b> Gene, What type of feature it is ?</p>\n",
    "<p style=\"font-size:16px;\"><b>Ans.</b> Gene is a categorical variable </p>\n",
    "<p style=\"font-size:18px;\"> <b>Q2.</b> How many categories are there and How they are distributed?</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Unique Genes : 229\n",
      "BRCA1     174\n",
      "TP53       98\n",
      "EGFR       88\n",
      "PTEN       82\n",
      "BRCA2      79\n",
      "BRAF       65\n",
      "KIT        60\n",
      "ALK        50\n",
      "ERBB2      48\n",
      "PDGFRA     39\n",
      "Name: Gene, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "unique_genes = train_df['Gene'].value_counts()\n",
    "print('Number of Unique Genes :', unique_genes.shape[0])\n",
    "# the top 10 genes that occured most\n",
    "print(unique_genes.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ans: There are 229 different categories of genes in the train data, and they are distibuted as follows\n"
     ]
    }
   ],
   "source": [
    "print(\"Ans: There are\", unique_genes.shape[0] ,\"different categories of genes in the train data, and they are distibuted as follows\",)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = sum(unique_genes.values);\n",
    "h = unique_genes.values/s;\n",
    "plt.plot(h, label=\"Histrogram of Genes\")\n",
    "plt.xlabel('Index of a Gene')\n",
    "plt.ylabel('Number of Occurances')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = np.cumsum(h)\n",
    "plt.plot(c,label='Cumulative distribution of Genes')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-size:18px;\"> <b>Q3.</b> How to featurize this Gene feature ?</p>\n",
    "\n",
    "<p style=\"font-size:16px;\"><b>Ans.</b>there are two ways we can featurize this variable\n",
    "check out this video: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/handling-categorical-and-numerical-features/\n",
    "<ol><li>One hot Encoding</li><li>Response coding</li></ol></p>\n",
    "<p> We will choose the appropriate featurization based on the ML model we use.  For this problem of multi-class classification with categorical features, one-hot encoding is better for Logistic regression while response coding is better for Random Forests. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#response-coding of the Gene feature\n",
    "# alpha is used for laplace smoothing\n",
    "alpha = 1\n",
    "# train gene feature\n",
    "train_gene_feature_responseCoding = np.array(get_gv_feature(alpha, \"Gene\", train_df))\n",
    "# test gene feature\n",
    "test_gene_feature_responseCoding = np.array(get_gv_feature(alpha, \"Gene\", test_df))\n",
    "# cross validation gene feature\n",
    "cv_gene_feature_responseCoding = np.array(get_gv_feature(alpha, \"Gene\", cv_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_gene_feature_responseCoding is converted feature using respone coding method. The shape of gene feature: (2124, 9)\n"
     ]
    }
   ],
   "source": [
    "print(\"train_gene_feature_responseCoding is converted feature using respone coding method. The shape of gene feature:\", train_gene_feature_responseCoding.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one-hot encoding of Gene feature.\n",
    "gene_vectorizer = CountVectorizer(ngram_range=(1,2))\n",
    "train_gene_feature_onehotCoding = gene_vectorizer.fit_transform(train_df['Gene'])\n",
    "test_gene_feature_onehotCoding = gene_vectorizer.transform(test_df['Gene'])\n",
    "cv_gene_feature_onehotCoding = gene_vectorizer.transform(cv_df['Gene'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2267      PTEN\n",
       "1986    CTNNB1\n",
       "2199      PTEN\n",
       "292      CHEK2\n",
       "3049       KIT\n",
       "Name: Gene, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Gene'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['abl1',\n",
       " 'acvr1',\n",
       " 'ago2',\n",
       " 'akt1',\n",
       " 'akt2',\n",
       " 'akt3',\n",
       " 'alk',\n",
       " 'apc',\n",
       " 'ar',\n",
       " 'araf',\n",
       " 'arid1a',\n",
       " 'arid1b',\n",
       " 'arid2',\n",
       " 'arid5b',\n",
       " 'asxl2',\n",
       " 'atm',\n",
       " 'atr',\n",
       " 'atrx',\n",
       " 'aurka',\n",
       " 'aurkb',\n",
       " 'axin1',\n",
       " 'b2m',\n",
       " 'bap1',\n",
       " 'bcl10',\n",
       " 'bcl2',\n",
       " 'bcor',\n",
       " 'braf',\n",
       " 'brca1',\n",
       " 'brca2',\n",
       " 'brd4',\n",
       " 'brip1',\n",
       " 'btk',\n",
       " 'card11',\n",
       " 'carm1',\n",
       " 'casp8',\n",
       " 'cbl',\n",
       " 'ccnd1',\n",
       " 'ccnd3',\n",
       " 'ccne1',\n",
       " 'cdh1',\n",
       " 'cdk12',\n",
       " 'cdk4',\n",
       " 'cdk6',\n",
       " 'cdk8',\n",
       " 'cdkn1a',\n",
       " 'cdkn1b',\n",
       " 'cdkn2a',\n",
       " 'cdkn2b',\n",
       " 'cdkn2c',\n",
       " 'cebpa',\n",
       " 'chek2',\n",
       " 'cic',\n",
       " 'crebbp',\n",
       " 'ctcf',\n",
       " 'ctnnb1',\n",
       " 'ddr2',\n",
       " 'dicer1',\n",
       " 'dnmt3a',\n",
       " 'dnmt3b',\n",
       " 'egfr',\n",
       " 'elf3',\n",
       " 'ep300',\n",
       " 'epas1',\n",
       " 'erbb2',\n",
       " 'erbb3',\n",
       " 'erbb4',\n",
       " 'ercc2',\n",
       " 'ercc3',\n",
       " 'ercc4',\n",
       " 'erg',\n",
       " 'esr1',\n",
       " 'etv1',\n",
       " 'etv6',\n",
       " 'ewsr1',\n",
       " 'ezh2',\n",
       " 'fam58a',\n",
       " 'fanca',\n",
       " 'fat1',\n",
       " 'fbxw7',\n",
       " 'fgf3',\n",
       " 'fgf4',\n",
       " 'fgfr1',\n",
       " 'fgfr2',\n",
       " 'fgfr3',\n",
       " 'fgfr4',\n",
       " 'flt1',\n",
       " 'flt3',\n",
       " 'foxa1',\n",
       " 'foxl2',\n",
       " 'fubp1',\n",
       " 'gata3',\n",
       " 'gli1',\n",
       " 'gna11',\n",
       " 'gnaq',\n",
       " 'gnas',\n",
       " 'h3f3a',\n",
       " 'hist1h1c',\n",
       " 'hnf1a',\n",
       " 'hras',\n",
       " 'idh1',\n",
       " 'idh2',\n",
       " 'igf1r',\n",
       " 'ikzf1',\n",
       " 'il7r',\n",
       " 'inpp4b',\n",
       " 'jak1',\n",
       " 'jak2',\n",
       " 'kdm5a',\n",
       " 'kdm5c',\n",
       " 'kdm6a',\n",
       " 'kdr',\n",
       " 'keap1',\n",
       " 'kit',\n",
       " 'klf4',\n",
       " 'kmt2a',\n",
       " 'kmt2b',\n",
       " 'kmt2c',\n",
       " 'kmt2d',\n",
       " 'knstrn',\n",
       " 'kras',\n",
       " 'lats1',\n",
       " 'map2k1',\n",
       " 'map2k2',\n",
       " 'map2k4',\n",
       " 'map3k1',\n",
       " 'mapk1',\n",
       " 'med12',\n",
       " 'mef2b',\n",
       " 'men1',\n",
       " 'met',\n",
       " 'mga',\n",
       " 'mlh1',\n",
       " 'mpl',\n",
       " 'msh2',\n",
       " 'msh6',\n",
       " 'mtor',\n",
       " 'myc',\n",
       " 'mycn',\n",
       " 'myd88',\n",
       " 'nf1',\n",
       " 'nf2',\n",
       " 'nfe2l2',\n",
       " 'nfkbia',\n",
       " 'nkx2',\n",
       " 'notch1',\n",
       " 'notch2',\n",
       " 'npm1',\n",
       " 'nras',\n",
       " 'nsd1',\n",
       " 'ntrk1',\n",
       " 'ntrk2',\n",
       " 'ntrk3',\n",
       " 'nup93',\n",
       " 'pax8',\n",
       " 'pbrm1',\n",
       " 'pdgfra',\n",
       " 'pdgfrb',\n",
       " 'pik3ca',\n",
       " 'pik3cb',\n",
       " 'pik3cd',\n",
       " 'pik3r1',\n",
       " 'pik3r2',\n",
       " 'pik3r3',\n",
       " 'pim1',\n",
       " 'pms2',\n",
       " 'pole',\n",
       " 'ppp2r1a',\n",
       " 'ppp6c',\n",
       " 'prdm1',\n",
       " 'ptch1',\n",
       " 'pten',\n",
       " 'ptpn11',\n",
       " 'ptprd',\n",
       " 'ptprt',\n",
       " 'rab35',\n",
       " 'rac1',\n",
       " 'rad21',\n",
       " 'rad50',\n",
       " 'rad51b',\n",
       " 'rad51c',\n",
       " 'rad51d',\n",
       " 'rad54l',\n",
       " 'raf1',\n",
       " 'rara',\n",
       " 'rasa1',\n",
       " 'rb1',\n",
       " 'rbm10',\n",
       " 'ret',\n",
       " 'rheb',\n",
       " 'rhoa',\n",
       " 'rictor',\n",
       " 'rit1',\n",
       " 'rnf43',\n",
       " 'ros1',\n",
       " 'rras2',\n",
       " 'runx1',\n",
       " 'rxra',\n",
       " 'setd2',\n",
       " 'sf3b1',\n",
       " 'shq1',\n",
       " 'smad2',\n",
       " 'smad3',\n",
       " 'smad4',\n",
       " 'smarca4',\n",
       " 'smarcb1',\n",
       " 'smo',\n",
       " 'sos1',\n",
       " 'sox9',\n",
       " 'spop',\n",
       " 'srsf2',\n",
       " 'stat3',\n",
       " 'stk11',\n",
       " 'tcf3',\n",
       " 'tcf7l2',\n",
       " 'tert',\n",
       " 'tet1',\n",
       " 'tet2',\n",
       " 'tgfbr1',\n",
       " 'tgfbr2',\n",
       " 'tmprss2',\n",
       " 'tp53',\n",
       " 'tp53bp1',\n",
       " 'tsc1',\n",
       " 'tsc2',\n",
       " 'u2af1',\n",
       " 'vhl',\n",
       " 'whsc1l1',\n",
       " 'xrcc2',\n",
       " 'yap1']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_vectorizer.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_gene_feature_onehotCoding is converted feature using one-hot encoding method. The shape of gene feature: (2124, 229)\n"
     ]
    }
   ],
   "source": [
    "print(\"train_gene_feature_onehotCoding is converted feature using one-hot encoding method. The shape of gene feature:\", train_gene_feature_onehotCoding.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-size:18px;\"> <b>Q4.</b> How good is this gene feature  in predicting y_i?</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many ways to estimate how good a feature is, in predicting y_i. One of the good methods is to build a proper ML model using just this feature. In this case, we will build a logistic regression model using only Gene feature (one hot encoded) to predict y_i."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of alpha =  1e-05 The log loss is: 1.4215085922263508\n",
      "For values of alpha =  0.0001 The log loss is: 1.220666908507365\n",
      "For values of alpha =  0.001 The log loss is: 1.243721158285488\n",
      "For values of alpha =  0.01 The log loss is: 1.3608334388689416\n",
      "For values of alpha =  0.1 The log loss is: 1.4668299765539121\n",
      "For values of alpha =  1 The log loss is: 1.5053420050643895\n"
     ]
    },
    {
     "data": {
      "image/png": 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zmhNZO5I2EW3yTUCX17qcmmGVa86q8ixgiUpELgeOuN0Xr8V5+Pg4cBKIEZErgQM4PUfuCVScxphLV5b3f75e/nWFvrqojPzZPf19oAcQISJJwHNAVQBVfROnn/3v3C6L54G73AfDMkVkLLAQp/vjFFXd6q84jTElp6ocP3/cpwR0LuNcnuOrhVTzJJk2Ddtwy5W35JuAImpEEFIlMPd/TOD5s9dfoQMNqupknO7r+W37FPjUH3EZY4pW0P2f/Jrg8rv/UzustifJdG3a1Uk6+SSguuF1g/7+jwk8G5nCmEokLTONw6mHi0xAx84d46LmHUS7QfUGniTTOqJ1gQnI7v+Y0mSJypgK4MyFM/kmng27N/CnfX/yrDuRdiLPsVWkCo1rNiaydiRNazclLjIu395vl9e63C/P/xhTFEtUxgQpVSXlfIpP47+dzTib5/iwkDDqhdbjyrAradWgFT2u6JFvAmpYo6Hd/zFBzRKVMWUs62KWc//Hh/Hf0rPS8xxfK6yWJ8l0iexCZEz+z//UC6/Hl19+aT3gTLlnicqYUnIh84Jz/6eIBHT07NF87//Ur17fk2SubnB1gQ+g1gqrXCNNGGOJypgiFHT/x7vn26HUQ6ScT8lzbBWpQqOajTxJpnNk5wIfQK0WWi0AtTMm+FmiMpVSadz/8b76ufmKm/NNQI1qNrL7P8ZcIktUpkIp6v7PjgM7OLvhbKnc/7Hnf4wpG5aoTLlQWvd/aoTUIK55nN3/MaYcsURlAio1PdWn4XdK6/5PRR9l2piKyBJVEDh//jx9+/bliy++ICQkhISEBP70pz8B8PTTTzNixIg8x8yePZvx48fz3XffsXr1auLiip5OZtSoUXzyySc0atSILVu2FLrvt99+y/XXX8+sWbO44447ANi3bx+jR49m//79iAiffvop0dHRdOvWjTNnzgBw9OhRrr32Wqa8P4X3577PytUr6X1/7wITUGp6ap5zV61S1ZNkYhrEFHj/p2HNhoRWsR9hYyo6+18eBKZMmcLtt99OSEgIKSkpPP/886xZswYRoUuXLgwcOJB69erlOKZdu3Z8+OGH/OY3v/H5PCNHjmTs2LEMHz680P2ysrJ4/PHH6dOnT4719w2/j98/8nuu7no1e47s4dMDn5KyL4UOT3TwJKBTk07x75B/0+AvDZxZxGbDe7XfgzCoWbWmJ8l0iuxEv1r98k1A9avXt/s/xhgPS1RBYMaMGfzrX/8CnGmi4+PjqV+/PgDx8fF8/vnn3H13zjF+27RpU+zz3HzzzezduzfP+vSs9Bzjv814awbV2ldj95bdvLT8JV5KeYl9u/aR/EMyy9Yvg/U5j68XXs8Z4SCkIZnfZ/L7539PdONoImtHMnf/XG5scSP3D7uf2tVqFztmY4yxRBVg6enp7Nmzh+joaAAOHDhAs2Y/zRsZFRXFgQMHSlT22fSzee79bN+9nQOnD9B7em92H9nN6dWnOX7++E8HnQY+BEZA+KlwLj9/OW1qtqFeaD1+jPyRmok1OX34NDd0v4EXXnyBpnWaEh4aDsC0adOI6BvB/wz+H09xWbdksXLlSmrfb0nKGFMylqgCLDk5mbp163qWnSm5cvJuBlNVTqSd8CSfI6lHmLZxGjOSZ3D4bM5Rsc+kn8lTVuipUMiAUxdO0SS8CX2i++Roenv9kdf5r3f+i5/3/DmjfxzNgN4DuOOOO5gzZw73v3E/69evp3nz5tx5550kzk/k/vvv95T9/vvvM3r06Bzna9SoEQcP2gTNxpiSs0QVYNWrVyctLY2si1kcO3eMzJqZrFi7gnfWvcOh1EPM/HomNWJqMOedOZ5REC5kXfipgGTYvmo7NaJreJJNx8Yd6XtV33y7X58+cpqBiwayavSqfHvAPbD1AR64/wGn6ORkPv30U0JDQ4mKiqJTp060aNECgMGDB7Ny5UpPojp+/DirV69m3rx5OcpLS0ujevXq/vsAjTEVniWqAFq4eyFPffEU+0/uJ+y5MC6GXoRzwEL4PPpzAKqsqkLLni2JqhrFTc1vypN4xi0ax19G/oXuN3RHRDhw4ADDhw/ngyUf5HvOVMnby87bDz/84Hk/cuRIBgwYwODBg8nKyuLEiRMcO3aMhg0b8sUXX+ToaTh79mwGDBhAeHh4jvJ27txJu3btSvgJGWMMVAl0AJXZC8teIOl0ElfFXcWdte/k7z//O3NGzOHJp57kig+uoMWcFrz917fZMW4HS4YvIfzTcO6sfyeP3PAI4bvDGXbTMDau2cidQ+6kb9++ABw6dIjQ0Pz//rj77ru54YYb2LFjB1FRUfz73/8G4M033+TNN98sNNaQkBBeffVVevXqRfv27VFVfv3rX3u2z5w5M0+HD4ClS5fSv3//kn5Exhjj3POoKK8uXbpoSS1durTEx5bEzuSdynj05a9e1nXr1umwYcNKpdy///3v+tFHH/m0r7/rfPjwYb3lllv8eo7iKuvvOdAqW31Vrc7FBazRIPj9XdjLmv4CZNrGaVSRKgzrMIwmtZvQs2dPsrKyCAm5tAFMx44dW0oRXrp9+/bx17/+NdBhGGPKOUtUAXBRLzJt0zTiW8TTpHYTwBk1oqLp2rVroEMwxlQAdo8qABL3JrLv1D5GdMw7NJIxxpicLFEFQMLGBC6rdhmDWw8OdCjGGBP0LFGVsTMXzjBn2xzubHsn1ava80XGGFMUS1RlbO53czmXcc6a/YwxxkeWqMpYwsYEWtZvyc+a/SzQoRhjTLngt0QlIlNE5KiIFDrxkYh0FZEsEbnDa12WiGxwXwv8FWNZ23tyL4l7ExnRcYRNY2GMMT7yZ/f0qcBkYFpBO4hICPAysDDXpvOqGuu/0AJj2kbno7ivw30BjsQYY8oPv11RqeoyIO/84Tk9AMwFjvorjmChqkzbOI2e0T25ou4VgQ7HGGPKjYA98CsiTYEhwC1A7idDw0VkDZAJTFTV+YWUMwYYA9C4cWMSExNLFE9qamqJj/XF5lOb+f7E9wxtNNSv5ykOf9c5GFW2Ole2+oLVuULy5/hMQDSwpYBts4Hr3fdTgTu8tjVx/20B7AWu8uV8wTzW3+iPRmvNF2vqmQtn/Hqe4rAx0Sq+ylZfVatzcWFj/RUqDpjpdiqIAPqJSKaqzlfVgwCqukdEEoFOwPcBi/QSncs4x6yts7jjmjuoFVYr0OEYY0y5ErDu6ap6papGq2o0MAf4varOF5F6IlINQEQigBuBbYGKszTM3z6fM+ln7NkpY4wpAb9dUYnI+0APIEJEkoDngKoAqlrY5EdtgH+IyEWcRDpRVct1okrYmMAVda6ge3T3QIdijDHljt8SlarmnUWv4H1Her3/Gmjvj5gCIel0Eou/X8zTNz9NFbHnq40xprjsN6efvbfpPRRleMfhgQ7FGGPKJUtUfqSqJGxM4KbmN9GyfstAh2OMMeWSJSo/+vbgt2xP3m6dKIwx5hJYovKjqRumEh4aztBrhgY6FGOMKbcsUfnJhcwLzNwykyGth1AnvE6gwzHGmHLLEpWffLzzY06knWBk7MhAh2KMMeWaJSo/SdiYQNPaTel1Za9Ah2KMMeWaJSov58+fp3v37mRlZQHQt29f6taty4ABA4pVzpHUI3y26zN+duFnXNPmGlq2bMnEiRM920eOHMmVV15JbGwssbGxbNiwocgyixPLnDlzEBHWrFkDwOLFi+nSpQvt27enS5cufPHFFwCkpaXRv39/WrduTdu2bXniiSc8ZUyePJl33323WPU2xhh/sETlZcqUKdx+++2EhIQA8NhjjzF9+vRilzNj8wyysrJY+Y+VfPbZZ2zbto3333+fbdt+GmDjlVdeYcOGDWzYsIHY2KKn3vI1ljNnzjBp0iSuu+46z7qIiAg+/vhjNm/eTEJCAvfd99N8WI8++ijbt29n/fr1rFixgs8++wyAUaNGMWnSpOJU2xhj/MISlZcZM2YwaNAgz3KvXr2oXbt2nv3Wrl1L9+7d6dKlC3369OHQoUM5tidsTKBNehvatGpDixYtCAsL46677uKjjz4qcWwFxZLbM888w7hx4wgPD/es69SpE02aNAGgbdu2pKWlceHCBcLDw+nZsycAYWFhdO7cmaSkJABq1KhBdHQ0q1evLnHMxhhTGixRuTIyMtizZw/R0dFF7vfAAw8wZ84c1q5dy6hRo3jqqac82zcc3sCmI5v4WZ2f0axZM8/6qKgoDhw44Fl+6qmn6NChAw8//DAXLlwolTqsX7+e/fv3F9o8OHfuXDp16kS1atVyrD958iQff/wxvXr9dE8tLi6O5cuXl0psxhhTUoGc5iOonDp1irp16xa5344dO9iyZQvx8fEAZGVlERkZ6dk+dcNUwkLCuLHZjazYvSLHse6UJrz00ktcfvnlpKenM2bMGF5++WWeffbZS4r/4sWLPPzww0ydOrXAfbZu3crjjz/OokWLcqzPzMzk7rvv5sEHH6RFixae9Y0aNWL79u2XFJcxxlwqS1TA57s/52jWUdLS0orcV1Vp27Yt33zzTY71+/fvZ8BtA9h6dCsd+negda/WzJw207M9KSnJ0/yWndiqVavGr371K1599dVLrsOZM2fYsmULPXr0AODw4cMMHDiQBQsWEBcXR1JSEkOGDGHatGlcddVVOY4dM2YMMTExPPTQQznWp6WlUb169UuOzRhjLoU1/QG/+OAXLEtdRlZWVpHJqlWrVhw7dsyTqDIyMti6dSvNmjVjwqwJZP0mixcee4GuXbuya9cufvjhB9LT05k5cyYDBw4E8NzTUlXmz59Pu3btAFi9ejXDh5ds8No6deqQnJzM3r172bt3L9dff70nSZ08eZL+/fvz0ksvceONN+Y47umnn+bUqVO8/vrrecrcuXOnJzZjjAkUS1ReevfuzVdffeVZ7tatG0OHDmXJkiVERUWxcOFCwsLCmDNnDo8//jgdO3YkNjaWr7/+GnCa/RrVbESfq/oQGhrK5MmT6dOnD23atOGXv/wlbdu2BeDee++lffv2tG/fnuTkZJ5++mkA9u3bV+AVTH6xADz77LMsWLCg0HpNnjyZ3bt3M2HCBE+X+KNHj3Ls2DFefPFFtm3bRufOnYmNjeXtt9/2HLdixQpuvfXWkn+gxhhTCqzpz8vYsWN57bXXPL+cC+pIEBsby7Jly3KsO37uOJ/s/IQHrn2AqiFVAejXrx/9+vXLc3z2c0y5rVq1ij/84Q/5bisolhdeeCHf9YmJiZ73Tz/9tCcZemvYsCGqmu/x69evp23btkREROS73RhjyoolKi+dOnWiZ8+eZGVleZ6l8tX7W94n42IGI2JLPlL6K6+8UuJjS1tycjITJkwIdBjGGGOJKrdRo0aV6LiEjQnEXh5Lh8YdSjmiwMju1WiMMYFm96hKwdajW1lzcI3NO2WMMX5giaoUJGxMILRKKPe0vyfQoRhjTIVjieoSZV7M5L1N79Evph+NajYKdDjGGFPhFJmoRORqEVkiIlvc5Q4ikrcLWSX1nz3/4VDqIWv2M8YYP/HliuqfwJNABoCqbgLu8mdQ5cnUDVNpUL0BA64u3lQgxhhjfONLoqqhqrmH0M70RzDlzcm0k8zfPp+7291NWEhYoMMxxpgKyZdElSwiVwEKICJ3AIcKP6Ry+GDrB1zIunBJz04ZY4wpnC+J6g/AP4DWInIAeAj4rS+Fi8gUETmafX+rkP26ikiWmwSz140QkV3uKygzwdQNU7mm4TV0iewS6FCMMabCKjRRiUgVIE5VbwUaAq1V9SZV/dHH8qcCfYs4RwjwMrDQa1194DngOuBa4DkRqefjOcvEzuM7+SbpG0Z2HOmZvsMYY0zpKzRRqepFYKz7/qyqnilO4aq6DEgpYrcHgLnAUa91fYDFqpqiqieAxRSR8MratI3TqCJVGNZhWKBDMcaYCs2XIZQWi8ijwCzgbPZKVS0qARVJRJoCQ4BbgK5em5oC+72Wk9x1+ZUxBhgD0Lhx4xyDsfrqYtZF0jPSfT72ol7kn6v/SVzdOHas3cEOdhT7nMEgNTW1RJ9XeVbZ6lzZ6gtW54rIl0SVPfid97DeCrTIZ9/ieh14XFWzcjWf5deWlu8w36r6FvAWQFxcnGZPHFgcVb6uQljVMHxwAYC9AAAfX0lEQVQ9dsmeJRxddpT/GfA/9GhX/PMFi8TERJ/rXFFUtjpXtvqC1bkiKjJRqeqVfjx/HDDTTVIRQD8RycS5gurhtV8UkOjHOIolYWMCdarVYVCrQYEOxRhjKrwiE5WI5DvlrKpOu9STeydBEZkKfKKq893OFH/26kDRG+eh44A7c+EMc7+by7D2w6he1aZpN8YYf/Ol6c/73lE40AtYBxSZqETkfZwrowgRScLpyVcVQFXfLOg4VU0RkQnAt+6qF0rjnlhhNP+WxTzmfjeXcxnn7NkpY4wpI740/T3gvSwidYDpvhSuqnf7Goiqjsy1PAWY4uvxl0LyvSWWv6kbphJTP4Ybom7wY0TGGGOylWT09HNATGkHUh78cOIHvvzxS0Z0HGHPThljTBnx5R7Vx/zU464KcA3wgT+DClbTN01HEO7reF+gQzHGmErDl3tUr3q9zwR+VNUkP8UTtFSVhI0J9LyyJ83rNA90OMYYU2n40vS3Bliuql8Cx4DOIlLVv2EFn6/2fcWeE3sY2XFkoEMxxphKxZdEtQwId0eRWAL8CmcMv0olYWMCtcJqcXub2wMdijHGVCq+JCpR1XPA7cDfVXUIzn2qSuNcxjk+2PoBd1xzBzXDagY6HGOMqVR8SlQicgNwL/Bvd50v97YqjHnfzeNM+hlr9jPGmADwJVH9F86oEPNUdauItACW+jes4JKwMYHoutF0u6JboEMxxphKx5cHfpfh3KfKXt4DPOjPoIJJ0ukk/rPnPzxz8zNUkZI8dmaMMeZS+PIcVUNgHNAWZwglAFT1Fj/GFTTe2/QeijK8Y75DHhpjjPEzXy4RZgDbgSuB54G9/DQGX4WmqkzdMJVuzbtxVf2rAh2OMcZUSr4kqgaq+g6Qoapfquoo4Ho/xxUUVh9YzY7jOxjR0QagNcaYQPGl916G++8hEekPHMSZH6rCS9iYQPXQ6gxtOzTQoRhjTKXlS6L6kzti+n8DfwcuAx72a1RBIC0zjZlbZnJ7m9u5rNplgQ7HGGMqLV96/X3ivj0F9PRvOMHj4x0fcyLthDX7GWNMgBV5j0pErhaRJSKyxV3uICJP+z+0wJry7RTCpoXRvXl3ABISEoiJiSEmJoaEhIR8j0lJSSE+Pp6YmBji4+M5ceIEANu3b+eGG26gWrVqvPrqq/kem9vkyZNp2bIlIkJycnK++/z444906dKF2NhY2rZty5tv/jQXZXp6OmPGjOHqq6+mdevWzJ07F4Bly5bRuXNnQkND+fLLLz37Hzt2jL59+/oUmzHGlCVfOlP8E+eB3wwAVd0E3OXPoALtcOphFs5eSLc+3QirGkZKSgrPP/88q1atYvXq1Tz//POeJORt4sSJ9OrVi127dtGrVy8mTpwIQP369Zk0aRKPPvqozzHceOON/Oc//+GKK64ocJ/IyEi+/vprNmzYwKpVq5g4cSIHDx4E4MUXX6RRo0bs3LmTbdu20b27k3CbN2/O1KlTueeee3KU1bBhQyIjI1mxYoXPMRpjTFnwJVHVUNXVudZl+iOYYDFj0wx0k/Lk6CcBWLhwIfHx8dSvX5969eoRHx/P559/nue4jz76iBEjnKbCESNGMH/+fAAaNWpE165dqVrV90HnO3XqRHR0dKH7hIWFUa1aNQAuXLjAxYsXPdumTJnCk0868VepUoWIiAgAoqOj6dChA1Wq5P3qBw8ezIwZM3yO0RhjyoIviSpZRK7CnTxRRO4ADvk1qgBQd25IVeXdte9S9XRVenXuBcCBAwdo1qyZZ9+oqCgOHDiQp4wjR44QGRkJOFc7R48e9Xvc+/fvp0OHDjRr1ozHH3+cJk2acPLkSQCeeeYZOnfuzNChQzly5EiRZcXFxbF8+XJ/h2yMMcXiS6L6A/APoLWIHAAeAn7n16jKmPe08hsOb2Dr3q3Ur1ffs05VCz0mkJo1a8amTZvYvXs3CQkJHDlyhMzMTJKSkrjxxhtZt24dN9xwg0/Njo0aNfI0HRpjTLAoMlGp6h5VvRVoCLRW1ZtUda/fIwuQhI0JVA2vSpiGedZFRUWxf/9+z3JSUhJNmjTJc2zjxo05dMi52Dx06BCNGjXyf8CuJk2a0LZtW5YvX06DBg2oUaMGQ4YMAWDo0KGsW7euyDLS0tKoXr26v0M1xphi8aXXX10ReRCYALwoIpNEZJL/QwuMedvncVvH29CLSlpaGgB9+vRh0aJFnDhxghMnTrBo0SL69OmT59iBAwd6egQmJCQwaNCgIs/Xq1evfJsRfZGUlMT58+cBOHHiBCtWrKBVq1aICLfddhuJiYkALFmyhGuuKXoKsZ07d9KuXbsSxWKMMf7iS9Pfp0A0sBlY6/WqkFLOpxBdJ5revXvz1VdfAU6vvWeeeYauXbvStWtXnn32WerXd5oGR48ezZo1awB44oknWLx4MTExMSxevJgnnngCgMOHDxMVFcVrr73Gn/70J6Kiojh9+jQXL15k9+7dnrK8TZo0iaioKJKSkujQoQOjR48GYM2aNZ733333Hddddx0dO3ake/fuPProo7Rv3x6Al19+mfHjx9OhQwemT5/OX//6VwC+/fZboqKimD17Nq+99hpt27b1nHPp0qX079/fHx+rMcaUnKoW+gLWFbVPsLy6dOmiJVHrz7V06NtDVVU15PkQ/eN//qjr1q3TYcOGlag8X23evFkffvhhv56jMEuXLs2x3K1bN01JSQlMMGUkd50ruspWX1Wrc3EBazQIfn8X9vLlimq6iPxaRCJFpH72y7/pMzAyL2aSpVlUr1qdTp060bNnT7Kysvx2vnbt2vHaa6/5rfziOHbsGI888gj16tULdCjGGJODL2P9pQOvAE/hdlF3/23hr6AC5XyGc7+neqjToWDUqFGBDKdMNWzYkMGDBwc6DGOMycOXK6pHgJaqGq2qV7qvIpOUiEwRkaPZQy/ls32QiGwSkQ0iskZEbvLaluWu3yAiC3yvzqVJy3Q6T4SHhhexpzHGmLLiyxXVVuBcCcqeCkwGphWwfQmwQFVVRDoAHwCt3W3nVTW2BOe8JOcz3SuqqtZF2xhjgoUviSoL2CAiS4EL2StV9cHCDlLVZSISXcj2VK/FmvzUrBgwuZv+jDHGBJ4viWq++yp1IjIEeAloBHj3iw4XkTU4YwpOVNUCzy8iY4Ax4Dxwm/3sUHFkZWWRkZ7B8pXO8EG7d+wm8XjxyylvUlNTS/R5lWeVrc6Vrb5gda6IfJmPKv85LUqBqs4D5onIzTgPFN/qbmquqgdFpAXwhYhsVtXvCyjjLeAtgLi4OO3Ro0ex4wj5JoSqYVVpF9sO1kLX2K70aFn8csqbxMRESvJ5lWeVrc6Vrb5gda6IfOlM4Xequgy4SkQi3OWD7r97gESgU1nEkd30Z50pjDEmeAQsUYlIS3FHdhWRzkAYcFxE6olINXd9BHAjsK0sYsru9Wf3qIwxJngU2vQnIiE494geK27BIvI+0AOIEJEk4DmgKoCqvgn8AhguIhnAeeBOtwdgG+AfInIRJ5FOVNUySVTW688YY4JPoYlKVbNEpIuIiDvUhs9U9e4itr8MvJzP+q+B9sU5V2mx56iMMSb4+NLrbz3wkYjMBs5mr1TVD/0WVYBY93RjjAk+viSq+sBx4BavdQpUvESVaZ0pjDEm2PjSPf1XZRFIMPB0prB7VMYYEzR8mTgxSkTmueP2HRGRuSISVRbBlSVVte7pxhgThHzpnv4usABoAjQFPnbXVRiCAE7TX2iVUEKr+NIiaowxpiz4kqgaquq7qprpvqYCDf0cV0CkZaZZRwpjjAkyviSqZBEZJiIh7msYTueKCud8xnm7P2WMMUHGl0Q1CvglcBg4BNzhrqtwzmeet/tTxhgTZHwZmeIXqjqwjOIJKGv6M8aY4FPoFZWqZgGDyiiWgLMrKmOMCT6+dG9bISKTgVnkHJlind+iCpC0zDS7R2WMMUHGl0T1M/ffF7zWKTlHqqgQzmect6Y/Y4wJMkXdo6oC/J+qflBG8QTU+czzNKxRIXveG2NMuVXUPaqLwNgyiiXgrOnPGGOCjy/d0xeLyKMi0kxE6me//B5ZAFjTnzHGBB9f7lFlPzP1B691CrQo/XACy3r9GWNM8PFl9PQryyKQYGDPURljTPApsOlPRMZ5vR+aa9uf/RlUoJzPsCsqY4wJNoXdo7rL6/2Tubb19UMsAWedKYwxJvgUlqikgPf5LZd7GZqBotb0Z4wxQaawRKUFvM9vudy7cPECYJMmGmNMsCmsM0VHETmNc/VU3X2Pu1zhfpunX0wHbBp6Y4wJNgUmKlUNKctAAi09y01U1vRnjDFBxZcHfisFa/ozxpjgZIkKEBFPorKmP2OMCS6WqFzZ96jsisoYY4KLXxOViEwRkaMisqWA7YNEZJOIbBCRNSJyk9e2ESKyy32N8Gec8FPTn92jMsaY4OLvK6qpFP5w8BKgo6rG4owp+DaAO+jtc8B1wLXAcyJSz5+BWq8/Y4wJTn5NVKq6DEgpZHuqqmY/k1WTn57P6gMsVtUUVT0BLMbPo2FcyLLOFMYYE4x8GT3dr0RkCPAS0Ajo765uCuz32i3JXZff8WOAMQCNGzcmMTGx2DFkZWZxASdRbVy7keTqycUuozxKTU0t0edVnlW2Ole2+oLVuSIKeKJS1XnAPBG5GZgA3Er+QzTlOxqGqr4FvAUQFxenPXr0KHYMIStDSM9wmv563tSTJrWbFLuM8igxMZGSfF7lWWWrc2WrL1idK6Kg6fXnNhNeJSIROFdQzbw2RwEH/Xl+e47KGGOCU0ATlYi0FBFx33cGwoDjwEKgt4jUcztR9HbX+Y26F2zW688YY4KLX5v+ROR9oAcQISJJOD35qgKo6pvAL4DhIpIBnAfudDtXpIjIBOBbt6gXVLXAThmlqVpotbI4jTHGGB/5NVGp6t1FbH8ZeLmAbVOAKf6IqyDVQqpRRYKmNdQYYwxBdI8qGNgzVMYYE3wsUXmxjhTGGBN8LFF5sY4UxhgTfCxRebErKmOMCT6WqLzYPSpjjAk+lqi8WNOfMcYEH0tUXqzpzxhjgo8lKi/W9GeMMcHHEpUXa/ozxpjgY4kKEHewdmv6M8aY4GOJyotdURljTPCxROXFrqiMMSb4WKLyYp0pjDEm+FiiAi7qRcCa/owxJhhZogLSMtMAa/ozxphgZIkKyLiYAVjTnzHGBCNLVF6s6c8YY4KPJSov1vRnjDHBxxKVF2v6M8aY4GOJyotdURljTPCxROXF7lEZY0zwsUTlxZr+jDEm+Fii8mJNf8YYE3wsUXmxpj9jjAk+lqi8WNOfMcYEH78lKhGZIiJHRWRLAdvvFZFN7utrEenotW2viGwWkQ0issZfMeZmTX/GGBN8/HlFNRXoW8j2H4DuqtoBmAC8lWt7T1WNVdU4P8WXVwZ0796drKwsABISEoiJiSEmJoaEhIR8D0lJSSE+Pp6YmBji4+M5ceIEAKrKgw8+SMuWLenQoQPr1q3zHNO3b1/q1q3LgAEDfApr2bJldO7cmdDQUObMmZPvPufOnaN///60bt2atm3b8sQTT+TZZ86cOYgIa9bkzP379u2jVq1avPrqqwCkp6dz8803k5mZ6VN8xhjjT35LVKq6DEgpZPvXqnrCXVwJRPkrFl/Nmj6L22+/nZCQEFJSUnj++edZtWoVq1ev5vnnn/ckIW8TJ06kV69e7Nq1i169ejFx4kQAPvvsM3bt2sWuXbt46623+N3vfuc55rHHHmP69Ok+x9W8eXOmTp3KPffcU+h+jz76KNu3b2f9+vWsWLGCzz77zLPtzJkzTJo0ieuuuy7PcQ8//DA///nPPcthYWH06tWLWbNm+RyjMcb4S7Dco7of+MxrWYFFIrJWRMaUVRCzZs5i0KBBACxcuJD4+Hjq169PvXr1iI+P5/PPP89zzEcffcSIESMAGDFiBPPnz/esHz58OCLC9ddfz8mTJzl06BAAvXr1onbt2j7HFR0dTYcOHahSpeCvq0aNGvTs2RNwEk3nzp1JSkrybH/mmWcYN24c4eE5mzfnz59PixYtaNu2bY71gwcPZsaMGT7HaIwx/hIa6ABEpCdOorrJa/WNqnpQRBoBi0Vku3uFlt/xY4AxAI0bNyYxMbFkgWTC9u3b2bt3L3v37mXZsmVkZmZ6ysvIyGDZsmVERkbmOOzAgQPs2LGDHTt2AHDw4EESExPZtGkT7dq18xxfs2ZNFixYQKtWrQDYsGEDx48fL1a8hw8fZuvWrURERBS6X2pqKrNnz+a6664jMTGRXbt2sW7dOgYPHszJkydZu3YtqampJCcn89xzz/Hqq68ya9Ysqlev7oknKyuLr7/+uuSfZ5BKTU2tcHUqTGWrL1idKyRV9dsLiAa2FLK9A/A9cHUh+4wHHvXlfF26dNGSYDzKI2irVq086/7yl7/ohAkTPMsvvPCCvvrqq3mOrVOnTo7lunXrqqpqv379dPny5Z71t9xyi65Zs8azvHTpUu3fv3+x4hwxYoTOnj270H0yMjK0b9+++re//U1VVbOysrR79+76ww8/qKpq9+7d9dtvv1VV1V/+8pc6a9YsVVV97rnn9JVXXslRVpMmTfT06dPFijHYLV26NNAhlKnKVl9Vq3NxAWvUj3mgNF4Bu6ISkebAh8B9qrrTa31NoIqqnnHf9wZe8HtAVSEtLc2zGBUVleMvlKSkJHr06JHnsMaNG3Po0CEiIyM5dOgQjRo18hy/f//+HMc3adLEb+FnGzNmDDExMTz00EOAc29qy5YtntgPHz7MwIEDWbBgAd999x3jxo1j3LhxnDx5kipVqhAeHs7YsWMBuHDhQp6mQmOMKWv+7J7+PvAN0EpEkkTkfhH5rYj81t3lWaAB8EaubuiNga9EZCOwGvi3qua9OVTaqjvNXdnJqk+fPixatIgTJ05w4sQJFi1aRJ8+ffIcNnDgQE+PwISEBM89roEDBzJt2jRUlZUrV1KnTp08zYa5Pfnkk8ybN6/EVXj66ac5deoUr7/+umddnTp1SE5O9jRpXn/99SxYsIC4uDgmTZrkWf/QQw/xxz/+0ZOkjh8/TsOGDalatWqJ4zHGmNLgz15/d6tqpKpWVdUoVX1HVd9U1Tfd7aNVtZ46XdA93dBVdY+qdnRfbVX1RX/FmFvv3r356quvAKhfvz7PPPMMXbt2pWvXrjz77LPUr18fgNGjR3u6eD/xxBMsXryYmJgYFi9e7OkW3q9fP1q0aEHLli359a9/zRtvvOE5T7du3Rg6dChLliwhKiqKhQsXArB582Yuv/zyPHF9++23REVFMXv2bH7zm9/k6PgQGxsLOFdsL774Itu2baNz587Exsby9ttvl/izWLp0Kf369Svx8cYYU2oC3fZYmq9Lukc1Hl23bp0OGzasRGWUht69e5fp+Qpr1x4yZIhu37697IIpI5Xt/kVlq6+q1bm4KAf3qIKle3pQ6NSpEz179vQ88FvWsq+sAi09PZ3Bgwd7eigaY0wgBbx7erAZNWpUoEMIuLCwMIYPHx7oMIwxBgieB36NMcaYfFmiMsYYE9QsURljjAlqlqiMMcYENUtUxhhjgpolKi/nz58vk/moCir3qaeeolmzZtSqVcuneI8fP07Pnj2pVauWZ0SJ/Dz22GO0bt2aDh06MGTIEE6ePAnAmjVr6NKlC+3bt6dLly588cUXRcYyefJk3n33XZ/iM8aY0mCJysuUKVP8Ph9VYeXedtttrF692ud4w8PDmTBhgmfCw4LEx8ezZcsWNm3axNVXX81LL70EOMMrffzxx2zevJmEhATuu+8+zzEFxTJq1CgmTZrkc4zGGHOpLFF5mTFjht/noyqs3Ouvv77I8QC91axZk5tuuqnIgWN79+5NaGio5xzZ81TFxMR4Bspt27YtaWlpXLhwodBYatSoQXR0dLESqjHGXApLVNkyYc+ePURHRwPOPFPNmjXzbI6KiuLAgQN5Djty5IjnF3pkZCRHjx4t9Hhfy/WXKVOm5JjNN9vcuXPp1KkT1apVK7KMuLg4li9f7o/wjDEmDxuZIts5qFu3rmfRGQIrJxHxubiCjr/Uci/Fiy++SGhoKPfee2+O9Vu3buXxxx9n0aJFPpXTqFEjtm/f7o8QjTEmD7uiypbPfFS+zCeVPR8V4NN8VIGapyohIYFPPvmEGTNm5EiMSUlJDBkyhGnTpnHVVVf5VFZaWhrVq1f3V6jGGJODJapsZTQfla/leps3bx5PPvlkiav2+eef8/LLL7NgwQJq1KjhWZ+amkr//v156aWXuPHGG30ub+fOnbRr167E8RhjTHFYogLe6PcG/9fp/8pkPqrCyh03bhxRUVGcO3eOqKgoxo8fD8D333/PZZddlm/s0dHRPPLII0ydOpWoqCi2bduWJ8axY8dy5swZ4uPjiY2N5be/deaunDdvHrt372bChAnExsYSGxvrucdWUCwAK1as4NZbby2Vz94YY4oU6HlGSvNV0vmoVJ35XAI9H1VB7r33Xj169Gipl1uSOWyC9TPyVWWbq6iy1VfV6lxclIP5qKwzhRfv+ahCQkICHY7He++9F+gQPJKTk5kwYUKgwzDGVCKWqHKx+agKFx8fH+gQjDGVjN2jMsYYE9QsURljjAlqlqiMMcYENUtUxhhjgpolKmOMMUHNEpUxxpigJprPIKnllYgcA34s4eERQHIphlMeWJ0rvspWX7A6F9cVqtqwNIMpbRUqUV0KEVmjqnGBjqMsWZ0rvspWX7A6V0TW9GeMMSaoWaIyxhgT1CxR/eStQAcQAFbniq+y1ReszhWO3aMyxhgT1OyKyhhjTFCzRGWMMSaoVbpEJSJ9RWSHiOwWkSfy2V5NRGa521eJSHTZR1l6fKjvIyKyTUQ2icgSEbkiEHGWpqLq7LXfHSKiIlLuu/X6UmcR+aX7XW8VkX+VdYylzYef7eYislRE1rs/3/0CEWdpEZEpInJURLYUsF1EZJL7eWwSkc5lHaPfBHrmxrJ8ASHA90ALIAzYCFyTa5/fA2+67+8CZgU6bj/XtydQw33/u/JcX1/r7O5XG1gGrATiAh13GXzPMcB6oJ673CjQcZdBnd8Cfue+vwbYG+i4L7HONwOdgS0FbO8HfAYIcD2wKtAxl9arsl1RXQvsVtU9qpoOzAQG5dpnEJDgvp8D9BIRKcMYS1OR9VXVpap6zl1cCUSVcYylzZfvGGAC8BcgrSyD8xNf6vxr4H9V9QSAqh4t4xhLmy91VuAy930d4GAZxlfqVHUZkFLILoOAaepYCdQVkciyic6/Kluiagrs91pOctflu4+qZgKngAZlEl3p86W+3u7H+YusPCuyziLSCWimqp+UZWB+5Mv3fDVwtYisEJGVItK3zKLzD1/qPB4YJiJJwKfAA2UTWsAU9/97uVHZpqLP78ood/98X/YpL3yui4gMA+KA7n6NyP8KrbOIVAH+Bowsq4DKgC/fcyhO818PnKvm5SLSTlVP+jk2f/GlzncDU1X1ryJyAzDdrfNF/4cXEBXpd1cOle2KKglo5rUcRd7mAM8+IhKK02RQ2OV2MPOlvojIrcBTwEBVvVBGsflLUXWuDbQDEkVkL05b/oJy3qHC15/rj1Q1Q1V/AHbgJK7yypc63w98AKCq3wDhOIO3VlQ+/X8vjypbovoWiBGRK0UkDKezxIJc+ywARrjv7wC+UPdOZTlUZH3dZrB/4CSp8n7fAoqos6qeUtUIVY1W1Wic+3IDVXVNYMItFb78XM/H6TiDiETgNAXuKdMoS5cvdd4H9AIQkTY4iepYmUZZthYAw93ef9cDp1T1UKCDKg2VqulPVTNFZCywEKfX0BRV3SoiLwBrVHUB8A5OE8FunCupuwIX8aXxsb6vALWA2W6fkX2qOjBgQV8iH+tcofhY54VAbxHZBmQBj6nq8cBFfWl8rPN/A/8UkYdxmsBGluM/OhGR93GabiPc+27PAVUBVPVNnPtw/YDdwDngV4GJtPTZEErGGGOCWmVr+jPGGFPOWKIyxhgT1CxRGWOMCWqWqIwxxgQ1S1TGGGOCmiUqYwogIkPc0dVbe62LLmj06uLsU8TxA7NHAxeR8SIysqRlGVMRWKIypmB3A19Rxs/SqeoCVZ1Yluc0JphZojImHyJSC7gRZxiefBOViIwUkY9E5HN3XqTnvDaHiMg/3bmfFolIdfeYX4vItyKyUUTmikiNAsqd7C6mAufd9Q96zR02szTra0wws0RlTP4GA5+r6k4gpZBJ6K4F7gVigaFeYwbG4Eyr0RY4CfzCXf+hqnZV1Y7AdziJsECq+qqqznIXnwA6qWoH4LclrZgx5Y0lKmPydzfOHEe4/95dwH6LVfW4qp4HPgRuctf/oKob3PdrgWj3fTsRWS4im3ESXNtixLQJmOGOdJ9ZjOOMKdcq1Vh/xvhCRBoAt+AkFcUZS05FZFw+u+cegyx72XsU+iyguvt+KjBYVTe6nSR6FCO0/jizvA4EnhGRtu6cacZUaHZFZUxed+DMlHqFO8p6M+AHfrpa8hYvIvXde1CDgRVFlF0bOCQiVXGuqHzizqPVTFWXAuOAujiDCRtT4VmiMiavu4F5udbNBe7JZ9+vgOnABmCuD9OFPAOsAhYD24sRUwjwnttkuB74Wzme9NCYYrHR040pIbfpLk5VxwY6FmMqMruiMsYYE9TsisoYY0xQsysqY4wxQc0SlTHGmKBmicoYY0xQs0RljDEmqFmiMsYYE9T+H+Ep4QaMLDKZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best alpha =  0.0001 The train log loss is: 1.04374216275065\n",
      "For values of best alpha =  0.0001 The cross validation log loss is: 1.220666908507365\n",
      "For values of best alpha =  0.0001 The test log loss is: 1.214311470950666\n"
     ]
    }
   ],
   "source": [
    "alpha = [10 ** x for x in range(-5, 1)] # hyperparam for SGD classifier.\n",
    "\n",
    "# read more about SGDClassifier() at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html\n",
    "# ------------------------------\n",
    "# default parameters\n",
    "# SGDClassifier(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None, \n",
    "# shuffle=True, verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5, \n",
    "# class_weight=None, warm_start=False, average=False, n_iter=None)\n",
    "\n",
    "# some of methods\n",
    "# fit(X, y[, coef_init, intercept_init, …])\tFit linear model with Stochastic Gradient Descent.\n",
    "# predict(X)\tPredict class labels for samples in X.\n",
    "\n",
    "#-------------------------------\n",
    "# video link: \n",
    "#------------------------------\n",
    "\n",
    "\n",
    "cv_log_error_array=[]\n",
    "for i in alpha:\n",
    "    clf = SGDClassifier(alpha=i, penalty='l2', loss='log', random_state=42)\n",
    "    clf.fit(train_gene_feature_onehotCoding, y_train)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(train_gene_feature_onehotCoding, y_train)\n",
    "    predict_y = sig_clf.predict_proba(cv_gene_feature_onehotCoding)\n",
    "    cv_log_error_array.append(log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "    print('For values of alpha = ', i, \"The log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(alpha, cv_log_error_array,c='g')\n",
    "for i, txt in enumerate(np.round(cv_log_error_array,3)):\n",
    "    ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))\n",
    "plt.grid()\n",
    "plt.title(\"Cross Validation Error for each alpha\")\n",
    "plt.xlabel(\"Alpha i's\")\n",
    "plt.ylabel(\"Error measure\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "best_alpha = np.argmin(cv_log_error_array)\n",
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "clf.fit(train_gene_feature_onehotCoding, y_train)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_gene_feature_onehotCoding, y_train)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(train_gene_feature_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(cv_gene_feature_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The cross validation log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(test_gene_feature_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-size:18px;\"> <b>Q5.</b> Is the Gene feature stable across all the data sets (Test, Train, Cross validation)?</p>\n",
    "<p style=\"font-size:16px;\"> <b>Ans.</b> Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q6. How many data points in Test and CV datasets are covered by the  229  genes in train dataset?\n",
      "Ans\n",
      "1. In test data 641 out of 665 : 96.39097744360903\n",
      "2. In cross validation data 516 out of  532 : 96.99248120300751\n"
     ]
    }
   ],
   "source": [
    "print(\"Q6. How many data points in Test and CV datasets are covered by the \", unique_genes.shape[0], \" genes in train dataset?\")\n",
    "\n",
    "test_coverage=test_df[test_df['Gene'].isin(list(set(train_df['Gene'])))].shape[0]\n",
    "cv_coverage=cv_df[cv_df['Gene'].isin(list(set(train_df['Gene'])))].shape[0]\n",
    "\n",
    "print('Ans\\n1. In test data',test_coverage, 'out of',test_df.shape[0], \":\",(test_coverage/test_df.shape[0])*100)\n",
    "print('2. In cross validation data',cv_coverage, 'out of ',cv_df.shape[0],\":\" ,(cv_coverage/cv_df.shape[0])*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>3.2.2 Univariate Analysis on Variation Feature</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-size:18px;\"> <b>Q7.</b> Variation, What type of feature is it ?</p>\n",
    "<p style=\"font-size:16px;\"><b>Ans.</b> Variation is a categorical variable </p>\n",
    "<p style=\"font-size:18px;\"> <b>Q8.</b> How many categories are there?</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Unique Variations : 1920\n",
      "Truncating_Mutations    62\n",
      "Deletion                45\n",
      "Amplification           44\n",
      "Fusions                 29\n",
      "Overexpression           5\n",
      "Q61L                     3\n",
      "Q61H                     3\n",
      "E17K                     3\n",
      "G12V                     3\n",
      "T58I                     3\n",
      "Name: Variation, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "unique_variations = train_df['Variation'].value_counts()\n",
    "print('Number of Unique Variations :', unique_variations.shape[0])\n",
    "# the top 10 variations that occured most\n",
    "print(unique_variations.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ans: There are 1920 different categories of variations in the train data, and they are distibuted as follows\n"
     ]
    }
   ],
   "source": [
    "print(\"Ans: There are\", unique_variations.shape[0] ,\"different categories of variations in the train data, and they are distibuted as follows\",)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = sum(unique_variations.values);\n",
    "h = unique_variations.values/s;\n",
    "plt.plot(h, label=\"Histrogram of Variations\")\n",
    "plt.xlabel('Index of a Variation')\n",
    "plt.ylabel('Number of Occurances')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.02919021 0.05037665 0.07109228 ... 0.99905838 0.99952919 1.        ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = np.cumsum(h)\n",
    "print(c)\n",
    "plt.plot(c,label='Cumulative distribution of Variations')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-size:18px;\"> <b>Q9.</b> How to featurize this Variation feature ?</p>\n",
    "\n",
    "<p style=\"font-size:16px;\"><b>Ans.</b>There are two ways we can featurize this variable\n",
    "check out this video: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/handling-categorical-and-numerical-features/\n",
    "<ol><li>One hot Encoding</li><li>Response coding</li></ol></p>\n",
    "<p> We will be using both these methods to featurize the Variation Feature </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alpha is used for laplace smoothing\n",
    "alpha = 1\n",
    "# train gene feature\n",
    "train_variation_feature_responseCoding = np.array(get_gv_feature(alpha, \"Variation\", train_df))\n",
    "# test gene feature\n",
    "test_variation_feature_responseCoding = np.array(get_gv_feature(alpha, \"Variation\", test_df))\n",
    "# cross validation gene feature\n",
    "cv_variation_feature_responseCoding = np.array(get_gv_feature(alpha, \"Variation\", cv_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_variation_feature_responseCoding is a converted feature using the response coding method. The shape of Variation feature: (2124, 9)\n"
     ]
    }
   ],
   "source": [
    "print(\"train_variation_feature_responseCoding is a converted feature using the response coding method. The shape of Variation feature:\", train_variation_feature_responseCoding.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one-hot encoding of variation feature.\n",
    "variation_vectorizer = CountVectorizer(ngram_range=(1,2))\n",
    "train_variation_feature_onehotCoding = variation_vectorizer.fit_transform(train_df['Variation'])\n",
    "test_variation_feature_onehotCoding = variation_vectorizer.transform(test_df['Variation'])\n",
    "cv_variation_feature_onehotCoding = variation_vectorizer.transform(cv_df['Variation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_variation_feature_onehotEncoded is converted feature using the onne-hot encoding method. The shape of Variation feature: (2124, 2049)\n"
     ]
    }
   ],
   "source": [
    "print(\"train_variation_feature_onehotEncoded is converted feature using the onne-hot encoding method. The shape of Variation feature:\", train_variation_feature_onehotCoding.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-size:18px;\"> <b>Q10.</b> How good is this Variation feature  in predicting y_i?</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's build a model just like the earlier!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of alpha =  1e-05 The log loss is: 1.7065322889212182\n",
      "For values of alpha =  0.0001 The log loss is: 1.7020021130730272\n",
      "For values of alpha =  0.001 The log loss is: 1.7015341491465465\n",
      "For values of alpha =  0.01 The log loss is: 1.7137648304989415\n",
      "For values of alpha =  0.1 The log loss is: 1.7286782833561007\n",
      "For values of alpha =  1 The log loss is: 1.7295552601117234\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best alpha =  0.001 The train log loss is: 1.1098623732736854\n",
      "For values of best alpha =  0.001 The cross validation log loss is: 1.7015341491465465\n",
      "For values of best alpha =  0.001 The test log loss is: 1.7376418075579205\n"
     ]
    }
   ],
   "source": [
    "alpha = [10 ** x for x in range(-5, 1)]\n",
    "\n",
    "# read more about SGDClassifier() at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html\n",
    "# ------------------------------\n",
    "# default parameters\n",
    "# SGDClassifier(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None, \n",
    "# shuffle=True, verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5, \n",
    "# class_weight=None, warm_start=False, average=False, n_iter=None)\n",
    "\n",
    "# some of methods\n",
    "# fit(X, y[, coef_init, intercept_init, …])\tFit linear model with Stochastic Gradient Descent.\n",
    "# predict(X)\tPredict class labels for samples in X.\n",
    "\n",
    "#-------------------------------\n",
    "# video link: \n",
    "#------------------------------\n",
    "\n",
    "\n",
    "cv_log_error_array=[]\n",
    "for i in alpha:\n",
    "    clf = SGDClassifier(alpha=i, penalty='l2', loss='log', random_state=42)\n",
    "    clf.fit(train_variation_feature_onehotCoding, y_train)\n",
    "    \n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(train_variation_feature_onehotCoding, y_train)\n",
    "    predict_y = sig_clf.predict_proba(cv_variation_feature_onehotCoding)\n",
    "    \n",
    "    cv_log_error_array.append(log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "    print('For values of alpha = ', i, \"The log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(alpha, cv_log_error_array,c='g')\n",
    "for i, txt in enumerate(np.round(cv_log_error_array,3)):\n",
    "    ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))\n",
    "plt.grid()\n",
    "plt.title(\"Cross Validation Error for each alpha\")\n",
    "plt.xlabel(\"Alpha i's\")\n",
    "plt.ylabel(\"Error measure\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "best_alpha = np.argmin(cv_log_error_array)\n",
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "clf.fit(train_variation_feature_onehotCoding, y_train)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_variation_feature_onehotCoding, y_train)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(train_variation_feature_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(cv_variation_feature_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The cross validation log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(test_variation_feature_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-size:18px;\"> <b>Q11.</b> Is the Variation feature stable across all the data sets (Test, Train, Cross validation)?</p>\n",
    "<p style=\"font-size:16px;\"> <b>Ans.</b> Not sure! But lets be very sure using the below analysis. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q12. How many data points are covered by total  1920  variation in test and cross validation data sets?\n",
      "Ans\n",
      "1. In test data 58 out of 665 : 8.721804511278195\n",
      "2. In cross validation data 55 out of  532 : 10.338345864661653\n"
     ]
    }
   ],
   "source": [
    "print(\"Q12. How many data points are covered by total \", unique_variations.shape[0], \" variation in test and cross validation data sets?\")\n",
    "test_coverage=test_df[test_df['Variation'].isin(list(set(train_df['Variation'])))].shape[0]\n",
    "cv_coverage=cv_df[cv_df['Variation'].isin(list(set(train_df['Variation'])))].shape[0]\n",
    "print('Ans\\n1. In test data',test_coverage, 'out of',test_df.shape[0], \":\",(test_coverage/test_df.shape[0])*100)\n",
    "print('2. In cross validation data',cv_coverage, 'out of ',cv_df.shape[0],\":\" ,(cv_coverage/cv_df.shape[0])*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>3.2.3 Univariate Analysis on Text Feature</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. How many unique words are present in train data?\n",
    "2. How are word frequencies distributed?\n",
    "3. How to featurize text field?\n",
    "4. Is the text feature useful in predicitng y_i?\n",
    "5. Is the text feature stable across train, test and CV datasets?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cls_text is a data frame\n",
    "# for every row in data fram consider the 'TEXT'\n",
    "# split the words by space\n",
    "# make a dict with those words\n",
    "# increment its count whenever we see that word\n",
    "\n",
    "def extract_dictionary_paddle(cls_text):\n",
    "    dictionary = defaultdict(int)\n",
    "    for index, row in cls_text.iterrows():\n",
    "        for word in row['TEXT'].split():\n",
    "            dictionary[word] +=1\n",
    "    return dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "#https://stackoverflow.com/a/1602964\n",
    "def get_text_responsecoding(df):\n",
    "    text_feature_responseCoding = np.zeros((df.shape[0],9))\n",
    "    for i in range(0,9):\n",
    "        row_index = 0\n",
    "        for index, row in df.iterrows():\n",
    "            sum_prob = 0\n",
    "            for word in row['TEXT'].split():\n",
    "                sum_prob += math.log(((dict_list[i].get(word,0)+10 )/(total_dict.get(word,0)+90)))\n",
    "            text_feature_responseCoding[row_index][i] = math.exp(sum_prob/len(row['TEXT'].split()))\n",
    "            row_index += 1\n",
    "    return text_feature_responseCoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of unique words in train data : 765284\n"
     ]
    }
   ],
   "source": [
    "# building a CountVectorizer with all the words that occured minimum 3 times in train data\n",
    "text_vectorizer = CountVectorizer(min_df=3,ngram_range=(1,2))\n",
    "train_text_feature_onehotCoding = text_vectorizer.fit_transform(train_df['TEXT'])\n",
    "# getting all the feature names (words)\n",
    "train_text_features= text_vectorizer.get_feature_names()\n",
    "\n",
    "# train_text_feature_onehotCoding.sum(axis=0).A1 will sum every row and returns (1*number of features) vector\n",
    "train_text_fea_counts = train_text_feature_onehotCoding.sum(axis=0).A1\n",
    "\n",
    "# zip(list(text_features),text_fea_counts) will zip a word with its number of times it occured\n",
    "text_fea_dict = dict(zip(list(train_text_features),train_text_fea_counts))\n",
    "\n",
    "\n",
    "print(\"Total number of unique words in train data :\", len(train_text_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_list = []\n",
    "# dict_list =[] contains 9 dictoinaries each corresponds to a class\n",
    "for i in range(1,10):\n",
    "    cls_text = train_df[train_df['Class']==i]\n",
    "    # build a word dict based on the words in that class\n",
    "    dict_list.append(extract_dictionary_paddle(cls_text))\n",
    "    # append it to dict_list\n",
    "\n",
    "# dict_list[i] is build on i'th  class text data\n",
    "# total_dict is buid on whole training text data\n",
    "total_dict = extract_dictionary_paddle(train_df)\n",
    "\n",
    "\n",
    "confuse_array = []\n",
    "for i in train_text_features:\n",
    "    ratios = []\n",
    "    max_val = -1\n",
    "    for j in range(0,9):\n",
    "        ratios.append((dict_list[j][i]+10 )/(total_dict[i]+90))\n",
    "    confuse_array.append(ratios)\n",
    "confuse_array = np.array(confuse_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#response coding of text features\n",
    "train_text_feature_responseCoding  = get_text_responsecoding(train_df)\n",
    "test_text_feature_responseCoding  = get_text_responsecoding(test_df)\n",
    "cv_text_feature_responseCoding  = get_text_responsecoding(cv_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/a/16202486\n",
    "# we convert each row values such that they sum to 1  \n",
    "train_text_feature_responseCoding = (train_text_feature_responseCoding.T/train_text_feature_responseCoding.sum(axis=1)).T\n",
    "test_text_feature_responseCoding = (test_text_feature_responseCoding.T/test_text_feature_responseCoding.sum(axis=1)).T\n",
    "cv_text_feature_responseCoding = (cv_text_feature_responseCoding.T/cv_text_feature_responseCoding.sum(axis=1)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# don't forget to normalize every feature\n",
    "train_text_feature_onehotCoding = normalize(train_text_feature_onehotCoding, axis=0)\n",
    "\n",
    "# we use the same vectorizer that was trained on train data\n",
    "test_text_feature_onehotCoding = text_vectorizer.transform(test_df['TEXT'])\n",
    "# don't forget to normalize every feature\n",
    "test_text_feature_onehotCoding = normalize(test_text_feature_onehotCoding, axis=0)\n",
    "\n",
    "# we use the same vectorizer that was trained on train data\n",
    "cv_text_feature_onehotCoding = text_vectorizer.transform(cv_df['TEXT'])\n",
    "# don't forget to normalize every feature\n",
    "cv_text_feature_onehotCoding = normalize(cv_text_feature_onehotCoding, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train shape  (2124, 765284)\n",
      "cv shape  (532, 765284)\n",
      "test shape  (665, 765284)\n"
     ]
    }
   ],
   "source": [
    "print(\"train shape \",train_text_feature_onehotCoding.shape)\n",
    "print(\"cv shape \",cv_text_feature_onehotCoding.shape)\n",
    "print(\"test shape \",test_text_feature_onehotCoding.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://stackoverflow.com/a/2258273/4084039\n",
    "sorted_text_fea_dict = dict(sorted(text_fea_dict.items(), key=lambda x: x[1] , reverse=True))\n",
    "sorted_text_occur = np.array(list(sorted_text_fea_dict.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({3: 151271, 4: 95905, 5: 62943, 6: 59020, 7: 45549, 8: 42178, 9: 36980, 10: 27181, 11: 24085, 12: 17724, 15: 15150, 13: 13455, 14: 11502, 16: 10214, 21: 8173, 18: 7519, 20: 7421, 17: 7302, 22: 5623, 19: 5175, 23: 4660, 26: 4648, 24: 4363, 27: 3694, 31: 3658, 42: 3555, 25: 3362, 28: 2966, 30: 2881, 54: 2824, 29: 2494, 32: 2369, 33: 2142, 47: 1864, 34: 1777, 36: 1733, 35: 1707, 38: 1576, 40: 1564, 39: 1379, 37: 1376, 43: 1349, 71: 1288, 44: 1258, 41: 1187, 45: 1168, 48: 1129, 46: 1055, 55: 1003, 49: 937, 50: 932, 56: 891, 51: 827, 52: 823, 60: 765, 57: 763, 53: 720, 58: 699, 62: 660, 59: 624, 61: 610, 63: 605, 64: 592, 66: 570, 72: 556, 65: 516, 73: 480, 74: 462, 67: 459, 68: 456, 94: 449, 69: 441, 75: 425, 77: 417, 70: 408, 78: 407, 84: 379, 79: 376, 80: 361, 76: 358, 81: 353, 82: 342, 83: 321, 86: 312, 108: 301, 89: 296, 85: 293, 87: 291, 90: 287, 88: 284, 95: 281, 92: 273, 93: 271, 91: 267, 96: 264, 100: 240, 98: 232, 97: 226, 99: 221, 104: 215, 101: 210, 103: 209, 102: 208, 109: 206, 105: 203, 107: 186, 106: 181, 110: 178, 112: 172, 116: 170, 111: 168, 115: 157, 113: 156, 126: 155, 119: 153, 127: 151, 120: 151, 124: 150, 142: 147, 123: 147, 117: 147, 122: 142, 132: 139, 114: 139, 118: 138, 121: 137, 130: 135, 141: 129, 134: 128, 144: 126, 125: 126, 128: 125, 138: 121, 139: 115, 136: 113, 133: 112, 162: 105, 143: 105, 135: 105, 131: 105, 147: 104, 140: 104, 145: 103, 164: 101, 153: 99, 148: 97, 158: 96, 150: 96, 146: 96, 129: 95, 156: 91, 137: 91, 165: 90, 168: 89, 152: 88, 159: 87, 160: 85, 149: 84, 161: 83, 157: 83, 154: 81, 188: 80, 169: 79, 155: 78, 166: 77, 174: 73, 151: 73, 172: 72, 170: 70, 167: 70, 163: 69, 189: 67, 178: 67, 220: 66, 183: 65, 198: 64, 175: 64, 184: 63, 192: 62, 190: 62, 177: 62, 208: 61, 179: 59, 181: 58, 185: 57, 176: 57, 197: 56, 196: 56, 270: 55, 201: 55, 200: 55, 194: 55, 195: 54, 171: 54, 216: 53, 218: 52, 212: 52, 199: 52, 193: 52, 210: 51, 187: 51, 180: 51, 205: 49, 182: 49, 209: 48, 203: 48, 191: 48, 229: 47, 217: 47, 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363: 19, 348: 19, 333: 19, 331: 19, 324: 19, 317: 19, 294: 19, 281: 19, 265: 19, 365: 18, 355: 18, 338: 18, 330: 18, 314: 18, 308: 18, 300: 18, 285: 18, 408: 17, 393: 17, 377: 17, 369: 17, 360: 17, 352: 17, 349: 17, 344: 17, 343: 17, 342: 17, 340: 17, 336: 17, 328: 17, 291: 17, 449: 16, 447: 16, 442: 16, 440: 16, 436: 16, 432: 16, 430: 16, 426: 16, 398: 16, 383: 16, 379: 16, 361: 16, 359: 16, 357: 16, 347: 16, 470: 15, 454: 15, 453: 15, 420: 15, 414: 15, 413: 15, 407: 15, 396: 15, 388: 15, 380: 15, 372: 15, 366: 15, 350: 15, 337: 15, 322: 15, 321: 15, 278: 15, 428: 14, 402: 14, 382: 14, 373: 14, 353: 14, 346: 14, 339: 14, 320: 14, 312: 14, 299: 14, 645: 13, 542: 13, 471: 13, 467: 13, 463: 13, 431: 13, 404: 13, 400: 13, 385: 13, 374: 13, 362: 13, 345: 13, 315: 13, 696: 12, 575: 12, 570: 12, 564: 12, 551: 12, 508: 12, 491: 12, 487: 12, 458: 12, 441: 12, 435: 12, 424: 12, 421: 12, 411: 12, 406: 12, 403: 12, 392: 12, 376: 12, 371: 12, 370: 12, 351: 12, 335: 12, 649: 11, 565: 11, 562: 11, 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7, 751: 7, 727: 7, 718: 7, 716: 7, 689: 7, 681: 7, 677: 7, 675: 7, 670: 7, 667: 7, 662: 7, 658: 7, 644: 7, 637: 7, 634: 7, 633: 7, 630: 7, 629: 7, 615: 7, 603: 7, 601: 7, 597: 7, 594: 7, 590: 7, 588: 7, 580: 7, 567: 7, 557: 7, 548: 7, 544: 7, 540: 7, 530: 7, 523: 7, 510: 7, 507: 7, 505: 7, 486: 7, 483: 7, 482: 7, 473: 7, 466: 7, 457: 7, 443: 7, 437: 7, 399: 7, 394: 7, 1585: 6, 1267: 6, 1010: 6, 979: 6, 933: 6, 917: 6, 890: 6, 874: 6, 870: 6, 864: 6, 817: 6, 793: 6, 785: 6, 783: 6, 777: 6, 769: 6, 766: 6, 741: 6, 740: 6, 733: 6, 722: 6, 714: 6, 702: 6, 694: 6, 686: 6, 669: 6, 665: 6, 659: 6, 657: 6, 655: 6, 638: 6, 616: 6, 613: 6, 610: 6, 595: 6, 585: 6, 584: 6, 583: 6, 554: 6, 547: 6, 543: 6, 539: 6, 537: 6, 528: 6, 521: 6, 518: 6, 493: 6, 461: 6, 448: 6, 422: 6, 1733: 5, 1600: 5, 1576: 5, 1557: 5, 1371: 5, 1347: 5, 1302: 5, 1277: 5, 1188: 5, 1173: 5, 1151: 5, 1141: 5, 1124: 5, 1101: 5, 1057: 5, 1013: 5, 1012: 5, 1005: 5, 990: 5, 986: 5, 980: 5, 974: 5, 970: 5, 967: 5, 952: 5, 934: 5, 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1, 1288: 1, 1283: 1, 1279: 1, 1276: 1, 1275: 1, 1273: 1, 1266: 1, 1263: 1, 1259: 1, 1256: 1, 1252: 1, 1248: 1, 1246: 1, 1241: 1, 1237: 1, 1236: 1, 1231: 1, 1226: 1, 1225: 1, 1222: 1, 1220: 1, 1217: 1, 1216: 1, 1214: 1, 1213: 1, 1206: 1, 1204: 1, 1203: 1, 1196: 1, 1193: 1, 1191: 1, 1190: 1, 1185: 1, 1182: 1, 1178: 1, 1174: 1, 1171: 1, 1170: 1, 1169: 1, 1168: 1, 1165: 1, 1163: 1, 1162: 1, 1160: 1, 1159: 1, 1156: 1, 1154: 1, 1152: 1, 1150: 1, 1145: 1, 1143: 1, 1138: 1, 1136: 1, 1132: 1, 1123: 1, 1121: 1, 1117: 1, 1114: 1, 1112: 1, 1111: 1, 1110: 1, 1105: 1, 1100: 1, 1098: 1, 1097: 1, 1095: 1, 1090: 1, 1088: 1, 1081: 1, 1076: 1, 1074: 1, 1073: 1, 1068: 1, 1065: 1, 1055: 1, 1053: 1, 1052: 1, 1051: 1, 1050: 1, 1049: 1, 1043: 1, 1042: 1, 1036: 1, 1035: 1, 1024: 1, 1018: 1, 1017: 1, 1016: 1, 1009: 1, 1007: 1, 1002: 1, 993: 1, 978: 1, 977: 1, 975: 1, 965: 1, 962: 1, 961: 1, 957: 1, 955: 1, 954: 1, 953: 1, 950: 1, 943: 1, 939: 1, 935: 1, 929: 1, 925: 1, 896: 1, 885: 1, 878: 1, 873: 1, 869: 1, 859: 1, 854: 1, 843: 1, 837: 1, 833: 1, 832: 1, 828: 1, 820: 1, 813: 1, 810: 1, 805: 1, 792: 1, 776: 1, 770: 1, 754: 1, 752: 1, 738: 1, 734: 1, 723: 1, 688: 1, 680: 1, 678: 1, 607: 1, 536: 1})\n"
     ]
    }
   ],
   "source": [
    "# Number of words for a given frequency.\n",
    "print(Counter(sorted_text_occur))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of alpha =  1e-05 The log loss is: 1.526009058339025\n",
      "For values of alpha =  0.0001 The log loss is: 1.5200782568468476\n",
      "For values of alpha =  0.001 The log loss is: 1.4712376689145656\n",
      "For values of alpha =  0.01 The log loss is: 1.2679355628588707\n",
      "For values of alpha =  0.1 The log loss is: 1.3205590455003131\n",
      "For values of alpha =  1 The log loss is: 1.4042899383254313\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best alpha =  0.01 The train log loss is: 0.8523580155686807\n",
      "For values of best alpha =  0.01 The cross validation log loss is: 1.2679355628588707\n",
      "For values of best alpha =  0.01 The test log loss is: 1.2539922857384336\n"
     ]
    }
   ],
   "source": [
    "# Train a Logistic regression+Calibration model using text features whicha re on-hot encoded\n",
    "alpha = [10 ** x for x in range(-5, 1)]\n",
    "\n",
    "# read more about SGDClassifier() at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html\n",
    "# ------------------------------\n",
    "# default parameters\n",
    "# SGDClassifier(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None, \n",
    "# shuffle=True, verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5, \n",
    "# class_weight=None, warm_start=False, average=False, n_iter=None)\n",
    "\n",
    "# some of methods\n",
    "# fit(X, y[, coef_init, intercept_init, …])\tFit linear model with Stochastic Gradient Descent.\n",
    "# predict(X)\tPredict class labels for samples in X.\n",
    "\n",
    "#-------------------------------\n",
    "# video link: \n",
    "#------------------------------\n",
    "\n",
    "\n",
    "cv_log_error_array=[]\n",
    "for i in alpha:\n",
    "    clf = SGDClassifier(alpha=i, penalty='l2', loss='log', random_state=42)\n",
    "    clf.fit(train_text_feature_onehotCoding, y_train)\n",
    "    \n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(train_text_feature_onehotCoding, y_train)\n",
    "    predict_y = sig_clf.predict_proba(cv_text_feature_onehotCoding)\n",
    "    cv_log_error_array.append(log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "    print('For values of alpha = ', i, \"The log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(alpha, cv_log_error_array,c='g')\n",
    "for i, txt in enumerate(np.round(cv_log_error_array,3)):\n",
    "    ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))\n",
    "plt.grid()\n",
    "plt.title(\"Cross Validation Error for each alpha\")\n",
    "plt.xlabel(\"Alpha i's\")\n",
    "plt.ylabel(\"Error measure\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "best_alpha = np.argmin(cv_log_error_array)\n",
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "clf.fit(train_text_feature_onehotCoding, y_train)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_text_feature_onehotCoding, y_train)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(train_text_feature_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(cv_text_feature_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The cross validation log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(test_text_feature_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-size:18px;\"> <b>Q.</b> Is the Text feature stable across all the data sets (Test, Train, Cross validation)?</p>\n",
    "<p style=\"font-size:16px;\"> <b>Ans.</b> Yes, it seems like! </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_intersec_text(df):\n",
    "    df_text_vec = CountVectorizer(min_df=3,ngram_range=(1,2))\n",
    "    df_text_fea = df_text_vec.fit_transform(df['TEXT'])\n",
    "    df_text_features = df_text_vec.get_feature_names()\n",
    "\n",
    "    df_text_fea_counts = df_text_fea.sum(axis=0).A1\n",
    "    df_text_fea_dict = dict(zip(list(df_text_features),df_text_fea_counts))\n",
    "    len1 = len(set(df_text_features))\n",
    "    len2 = len(set(train_text_features) & set(df_text_features))\n",
    "    return len1,len2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94.594 % of word of test data appeared in train data\n",
      "94.057 % of word of Cross Validation appeared in train data\n"
     ]
    }
   ],
   "source": [
    "len1,len2 = get_intersec_text(test_df)\n",
    "print(np.round((len2/len1)*100, 3), \"% of word of test data appeared in train data\")\n",
    "len1,len2 = get_intersec_text(cv_df)\n",
    "print(np.round((len2/len1)*100, 3), \"% of word of Cross Validation appeared in train data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>4. Machine Learning Models</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data preparation for ML models.\n",
    "\n",
    "#Misc. functionns for ML models\n",
    "\n",
    "\n",
    "def predict_and_plot_confusion_matrix(train_x, train_y,test_x, test_y, clf):\n",
    "    clf.fit(train_x, train_y)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(train_x, train_y)\n",
    "    pred_y = sig_clf.predict(test_x)\n",
    "\n",
    "    # for calculating log_loss we willl provide the array of probabilities belongs to each class\n",
    "    print(\"Log loss :\",log_loss(test_y, sig_clf.predict_proba(test_x)))\n",
    "    # calculating the number of data points that are misclassified\n",
    "    print(\"Number of mis-classified points :\", np.count_nonzero((pred_y- test_y))/test_y.shape[0])\n",
    "    plot_confusion_matrix(test_y, pred_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def report_log_loss(train_x, train_y, test_x, test_y,  clf):\n",
    "    clf.fit(train_x, train_y)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(train_x, train_y)\n",
    "    sig_clf_probs = sig_clf.predict_proba(test_x)\n",
    "    return log_loss(test_y, sig_clf_probs, eps=1e-15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this function will be used just for naive bayes\n",
    "# for the given indices, we will print the name of the features\n",
    "# and we will check whether the feature present in the test point text or not\n",
    "def get_impfeature_names(indices, text, gene, var, no_features):\n",
    "    gene_count_vec = CountVectorizer(ngram_range=(1,2))\n",
    "    var_count_vec = CountVectorizer(ngram_range=(1,2))\n",
    "    text_count_vec = CountVectorizer(min_df=3,ngram_range=(1,2))\n",
    "    \n",
    "    gene_vec = gene_count_vec.fit(train_df['Gene'])\n",
    "    var_vec  = var_count_vec.fit(train_df['Variation'])\n",
    "    text_vec = text_count_vec.fit(train_df['TEXT'])\n",
    "    \n",
    "    fea1_len = len(gene_vec.get_feature_names())\n",
    "    fea2_len = len(var_count_vec.get_feature_names())\n",
    "    \n",
    "    word_present = 0\n",
    "    for i,v in enumerate(indices):\n",
    "        if (v < fea1_len):\n",
    "            word = gene_vec.get_feature_names()[v]\n",
    "            yes_no = True if word == gene else False\n",
    "            if yes_no:\n",
    "                word_present += 1\n",
    "                print(i, \"Gene feature [{}] present in test data point [{}]\".format(word,yes_no))\n",
    "        elif (v < fea1_len+fea2_len):\n",
    "            word = var_vec.get_feature_names()[v-(fea1_len)]\n",
    "            yes_no = True if word == var else False\n",
    "            if yes_no:\n",
    "                word_present += 1\n",
    "                print(i, \"variation feature [{}] present in test data point [{}]\".format(word,yes_no))\n",
    "        else:\n",
    "            word = text_vec.get_feature_names()[v-(fea1_len+fea2_len)]\n",
    "            yes_no = True if word in text.split() else False\n",
    "            if yes_no:\n",
    "                word_present += 1\n",
    "                print(i, \"Text feature [{}] present in test data point [{}]\".format(word,yes_no))\n",
    "\n",
    "    print(\"Out of the top \",no_features,\" features \", word_present, \"are present in query point\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-size:24px;text-align:Center\"> <b>Stacking the three types of features </b><p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merging gene, variance and text features\n",
    "\n",
    "# building train, test and cross validation data sets\n",
    "# a = [[1, 2], \n",
    "#      [3, 4]]\n",
    "# b = [[4, 5], \n",
    "#      [6, 7]]\n",
    "# hstack(a, b) = [[1, 2, 4, 5],\n",
    "#                [ 3, 4, 6, 7]]\n",
    "\n",
    "train_gene_var_onehotCoding = hstack((train_gene_feature_onehotCoding,train_variation_feature_onehotCoding))\n",
    "test_gene_var_onehotCoding = hstack((test_gene_feature_onehotCoding,test_variation_feature_onehotCoding))\n",
    "cv_gene_var_onehotCoding = hstack((cv_gene_feature_onehotCoding,cv_variation_feature_onehotCoding))\n",
    "\n",
    "train_x_onehotCoding = hstack((train_gene_var_onehotCoding, train_text_feature_onehotCoding)).tocsr()\n",
    "train_y = np.array(list(train_df['Class']))\n",
    "\n",
    "test_x_onehotCoding = hstack((test_gene_var_onehotCoding, test_text_feature_onehotCoding)).tocsr()\n",
    "test_y = np.array(list(test_df['Class']))\n",
    "\n",
    "cv_x_onehotCoding = hstack((cv_gene_var_onehotCoding, cv_text_feature_onehotCoding)).tocsr()\n",
    "cv_y = np.array(list(cv_df['Class']))\n",
    "\n",
    "\n",
    "train_gene_var_responseCoding = np.hstack((train_gene_feature_responseCoding,train_variation_feature_responseCoding))\n",
    "test_gene_var_responseCoding = np.hstack((test_gene_feature_responseCoding,test_variation_feature_responseCoding))\n",
    "cv_gene_var_responseCoding = np.hstack((cv_gene_feature_responseCoding,cv_variation_feature_responseCoding))\n",
    "\n",
    "train_x_responseCoding = np.hstack((train_gene_var_responseCoding, train_text_feature_responseCoding))\n",
    "test_x_responseCoding = np.hstack((test_gene_var_responseCoding, test_text_feature_responseCoding))\n",
    "cv_x_responseCoding = np.hstack((cv_gene_var_responseCoding, cv_text_feature_responseCoding))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One hot encoding features :\n",
      "(number of data points * number of features) in train data =  (2124, 767562)\n",
      "(number of data points * number of features) in test data =  (665, 767562)\n",
      "(number of data points * number of features) in cross validation data = (532, 767562)\n"
     ]
    }
   ],
   "source": [
    "print(\"One hot encoding features :\")\n",
    "print(\"(number of data points * number of features) in train data = \", train_x_onehotCoding.shape)\n",
    "print(\"(number of data points * number of features) in test data = \", test_x_onehotCoding.shape)\n",
    "print(\"(number of data points * number of features) in cross validation data =\", cv_x_onehotCoding.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Response encoding features :\n",
      "(number of data points * number of features) in train data =  (2124, 27)\n",
      "(number of data points * number of features) in test data =  (665, 27)\n",
      "(number of data points * number of features) in cross validation data = (532, 27)\n"
     ]
    }
   ],
   "source": [
    "print(\" Response encoding features :\")\n",
    "print(\"(number of data points * number of features) in train data = \", train_x_responseCoding.shape)\n",
    "print(\"(number of data points * number of features) in test data = \", test_x_responseCoding.shape)\n",
    "print(\"(number of data points * number of features) in cross validation data =\", cv_x_responseCoding.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>4.1. Logistic Regression</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>4.1.1. With Class balancing</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>4.1.1.1. Hyper paramter tuning</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for alpha = 1e-06\n",
      "Log Loss : 1.5573700583757182\n",
      "for alpha = 1e-05\n",
      "Log Loss : 1.561260385635329\n",
      "for alpha = 0.0001\n",
      "Log Loss : 1.5538741676892476\n",
      "for alpha = 0.001\n",
      "Log Loss : 1.4860702180397345\n",
      "for alpha = 0.01\n",
      "Log Loss : 1.2280287643846057\n",
      "for alpha = 0.1\n",
      "Log Loss : 1.3151662144706506\n",
      "for alpha = 1\n",
      "Log Loss : 1.3907057580948765\n",
      "for alpha = 10\n",
      "Log Loss : 1.4274398296659645\n",
      "for alpha = 100\n",
      "Log Loss : 1.4361724021153133\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best alpha =  0.01 The train log loss is: 0.819819602304508\n",
      "For values of best alpha =  0.01 The cross validation log loss is: 1.2280287643846057\n",
      "For values of best alpha =  0.01 The test log loss is: 1.1945461226274563\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# read more about SGDClassifier() at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html\n",
    "# ------------------------------\n",
    "# default parameters\n",
    "# SGDClassifier(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None, \n",
    "# shuffle=True, verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5, \n",
    "# class_weight=None, warm_start=False, average=False, n_iter=None)\n",
    "\n",
    "# some of methods\n",
    "# fit(X, y[, coef_init, intercept_init, …])\tFit linear model with Stochastic Gradient Descent.\n",
    "# predict(X)\tPredict class labels for samples in X.\n",
    "\n",
    "#-------------------------------\n",
    "# video link: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/geometric-intuition-1/\n",
    "#------------------------------\n",
    "\n",
    "\n",
    "# find more about CalibratedClassifierCV here at http://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html\n",
    "# ----------------------------\n",
    "# default paramters\n",
    "# sklearn.calibration.CalibratedClassifierCV(base_estimator=None, method=’sigmoid’, cv=3)\n",
    "#\n",
    "# some of the methods of CalibratedClassifierCV()\n",
    "# fit(X, y[, sample_weight])\tFit the calibrated model\n",
    "# get_params([deep])\tGet parameters for this estimator.\n",
    "# predict(X)\tPredict the target of new samples.\n",
    "# predict_proba(X)\tPosterior probabilities of classification\n",
    "#-------------------------------------\n",
    "# video link:\n",
    "#-------------------------------------\n",
    "\n",
    "alpha = [10 ** x for x in range(-6, 3)]\n",
    "cv_log_error_array = []\n",
    "for i in alpha:\n",
    "    print(\"for alpha =\", i)\n",
    "    clf = SGDClassifier(class_weight='balanced', alpha=i, penalty='l2', loss='log', random_state=42)\n",
    "    clf.fit(train_x_onehotCoding, train_y)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(train_x_onehotCoding, train_y)\n",
    "    sig_clf_probs = sig_clf.predict_proba(cv_x_onehotCoding)\n",
    "    cv_log_error_array.append(log_loss(cv_y, sig_clf_probs, labels=clf.classes_, eps=1e-15))\n",
    "    # to avoid rounding error while multiplying probabilites we use log-probability estimates\n",
    "    print(\"Log Loss :\",log_loss(cv_y, sig_clf_probs)) \n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(alpha, cv_log_error_array,c='g')\n",
    "for i, txt in enumerate(np.round(cv_log_error_array,3)):\n",
    "    ax.annotate((alpha[i],str(txt)), (alpha[i],cv_log_error_array[i]))\n",
    "plt.grid()\n",
    "plt.title(\"Cross Validation Error for each alpha\")\n",
    "plt.xlabel(\"Alpha i's\")\n",
    "plt.ylabel(\"Error measure\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "best_alpha = np.argmin(cv_log_error_array)\n",
    "clf = SGDClassifier(class_weight='balanced', alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "clf.fit(train_x_onehotCoding, train_y)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_x_onehotCoding, train_y)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(train_x_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(cv_x_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The cross validation log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(test_x_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>4.1.1.2. Testing the model with best hyper paramters</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss : 1.2280287643846057\n",
      "Number of mis-classified points : 0.40225563909774437\n",
      "-------------------- Confusion matrix --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Precision matrix (Columm Sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Recall matrix (Row sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read more about SGDClassifier() at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html\n",
    "# ------------------------------\n",
    "# default parameters\n",
    "# SGDClassifier(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None, \n",
    "# shuffle=True, verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5, \n",
    "# class_weight=None, warm_start=False, average=False, n_iter=None)\n",
    "\n",
    "# some of methods\n",
    "# fit(X, y[, coef_init, intercept_init, …])\tFit linear model with Stochastic Gradient Descent.\n",
    "# predict(X)\tPredict class labels for samples in X.\n",
    "\n",
    "#-------------------------------\n",
    "# video link: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/geometric-intuition-1/\n",
    "#------------------------------\n",
    "clf = SGDClassifier(class_weight='balanced', alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "predict_and_plot_confusion_matrix(train_x_onehotCoding, train_y, cv_x_onehotCoding, cv_y, clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>4.1.1.3. Feature Importance</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_imp_feature_names(text, indices, removed_ind = []):\n",
    "    word_present = 0\n",
    "    tabulte_list = []\n",
    "    incresingorder_ind = 0\n",
    "    for i in indices:\n",
    "        if i < train_gene_feature_onehotCoding.shape[1]:\n",
    "            tabulte_list.append([incresingorder_ind, \"Gene\", \"Yes\"])\n",
    "        elif i< 18:\n",
    "            tabulte_list.append([incresingorder_ind,\"Variation\", \"Yes\"])\n",
    "        if ((i > 17) & (i not in removed_ind)) :\n",
    "            word = train_text_features[i]\n",
    "            yes_no = True if word in text.split() else False\n",
    "            if yes_no:\n",
    "                word_present += 1\n",
    "            tabulte_list.append([incresingorder_ind,train_text_features[i], yes_no])\n",
    "        incresingorder_ind += 1\n",
    "    print(word_present, \"most importent features are present in our query point\")\n",
    "    print(\"-\"*50)\n",
    "    print(\"The features that are most importent of the \",predicted_cls[0],\" class:\")\n",
    "    print (tabulate(tabulte_list, headers=[\"Index\",'Feature name', 'Present or Not']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>4.1.1.3.1. Correctly Classified point</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Class : 7\n",
      "Predicted Class Probabilities: [[0.1048 0.1209 0.0176 0.0933 0.0556 0.0432 0.5522 0.005  0.0073]]\n",
      "Actual Class : 7\n",
      "--------------------------------------------------\n",
      "302 Text feature [oncogene] present in test data point [True]\n",
      "Out of the top  500  features  1 are present in query point\n"
     ]
    }
   ],
   "source": [
    "# from tabulate import tabulate\n",
    "clf = SGDClassifier(class_weight='balanced', alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "clf.fit(train_x_onehotCoding,train_y)\n",
    "test_point_index = 1\n",
    "no_feature = 500\n",
    "predicted_cls = sig_clf.predict(test_x_onehotCoding[test_point_index])\n",
    "print(\"Predicted Class :\", predicted_cls[0])\n",
    "print(\"Predicted Class Probabilities:\", np.round(sig_clf.predict_proba(test_x_onehotCoding[test_point_index]),4))\n",
    "print(\"Actual Class :\", test_y[test_point_index])\n",
    "indices = np.argsort(-clf.coef_)[predicted_cls-1][:,:no_feature]\n",
    "print(\"-\"*50)\n",
    "get_impfeature_names(indices[0], test_df['TEXT'].iloc[test_point_index],test_df['Gene'].iloc[test_point_index],test_df['Variation'].iloc[test_point_index], no_feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>4.1.1.3.2. Incorrectly Classified point</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Class : 2\n",
      "Predicted Class Probabilities: [[0.0069 0.957  0.0071 0.0037 0.0101 0.0017 0.0064 0.0045 0.0027]]\n",
      "Actual Class : 2\n",
      "--------------------------------------------------\n",
      "Out of the top  500  features  0 are present in query point\n"
     ]
    }
   ],
   "source": [
    "test_point_index = 100\n",
    "no_feature = 500\n",
    "predicted_cls = sig_clf.predict(test_x_onehotCoding[test_point_index])\n",
    "print(\"Predicted Class :\", predicted_cls[0])\n",
    "print(\"Predicted Class Probabilities:\", np.round(sig_clf.predict_proba(test_x_onehotCoding[test_point_index]),4))\n",
    "print(\"Actual Class :\", test_y[test_point_index])\n",
    "indices = np.argsort(-clf.coef_)[predicted_cls-1][:,:no_feature]\n",
    "print(\"-\"*50)\n",
    "get_impfeature_names(indices[0], test_df['TEXT'].iloc[test_point_index],test_df['Gene'].iloc[test_point_index],test_df['Variation'].iloc[test_point_index], no_feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>4.1.2. Without Class balancing</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>4.1.2.1. Hyper paramter tuning</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for alpha = 1e-06\n",
      "Log Loss : 1.5281810051707558\n",
      "for alpha = 1e-05\n",
      "Log Loss : 1.5186090436660067\n",
      "for alpha = 0.0001\n",
      "Log Loss : 1.5148208273293065\n",
      "for alpha = 0.001\n",
      "Log Loss : 1.4733755893214984\n",
      "for alpha = 0.01\n",
      "Log Loss : 1.2515878121956765\n",
      "for alpha = 0.1\n",
      "Log Loss : 1.3133804609592512\n",
      "for alpha = 1\n",
      "Log Loss : 1.3992064946411595\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best alpha =  0.01 The train log loss is: 0.8252963750359642\n",
      "For values of best alpha =  0.01 The cross validation log loss is: 1.2515878121956765\n",
      "For values of best alpha =  0.01 The test log loss is: 1.2398167233207562\n"
     ]
    }
   ],
   "source": [
    "# read more about SGDClassifier() at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html\n",
    "# ------------------------------\n",
    "# default parameters\n",
    "# SGDClassifier(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None, \n",
    "# shuffle=True, verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5, \n",
    "# class_weight=None, warm_start=False, average=False, n_iter=None)\n",
    "\n",
    "# some of methods\n",
    "# fit(X, y[, coef_init, intercept_init, …])\tFit linear model with Stochastic Gradient Descent.\n",
    "# predict(X)\tPredict class labels for samples in X.\n",
    "\n",
    "#-------------------------------\n",
    "# video link: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/geometric-intuition-1/\n",
    "#------------------------------\n",
    "\n",
    "\n",
    "\n",
    "# find more about CalibratedClassifierCV here at http://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html\n",
    "# ----------------------------\n",
    "# default paramters\n",
    "# sklearn.calibration.CalibratedClassifierCV(base_estimator=None, method=’sigmoid’, cv=3)\n",
    "#\n",
    "# some of the methods of CalibratedClassifierCV()\n",
    "# fit(X, y[, sample_weight])\tFit the calibrated model\n",
    "# get_params([deep])\tGet parameters for this estimator.\n",
    "# predict(X)\tPredict the target of new samples.\n",
    "# predict_proba(X)\tPosterior probabilities of classification\n",
    "#-------------------------------------\n",
    "# video link:\n",
    "#-------------------------------------\n",
    "\n",
    "alpha = [10 ** x for x in range(-6, 1)]\n",
    "cv_log_error_array = []\n",
    "for i in alpha:\n",
    "    print(\"for alpha =\", i)\n",
    "    clf = SGDClassifier(alpha=i, penalty='l2', loss='log', random_state=42)\n",
    "    clf.fit(train_x_onehotCoding, train_y)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(train_x_onehotCoding, train_y)\n",
    "    sig_clf_probs = sig_clf.predict_proba(cv_x_onehotCoding)\n",
    "    cv_log_error_array.append(log_loss(cv_y, sig_clf_probs, labels=clf.classes_, eps=1e-15))\n",
    "    print(\"Log Loss :\",log_loss(cv_y, sig_clf_probs)) \n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(alpha, cv_log_error_array,c='g')\n",
    "for i, txt in enumerate(np.round(cv_log_error_array,3)):\n",
    "    ax.annotate((alpha[i],str(txt)), (alpha[i],cv_log_error_array[i]))\n",
    "plt.grid()\n",
    "plt.title(\"Cross Validation Error for each alpha\")\n",
    "plt.xlabel(\"Alpha i's\")\n",
    "plt.ylabel(\"Error measure\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "best_alpha = np.argmin(cv_log_error_array)\n",
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "clf.fit(train_x_onehotCoding, train_y)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_x_onehotCoding, train_y)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(train_x_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(cv_x_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The cross validation log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(test_x_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>4.1.2.2. Testing model with best hyper parameters</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss : 1.2515878121956765\n",
      "Number of mis-classified points : 0.4041353383458647\n",
      "-------------------- Confusion matrix --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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3O41uvRmAuENH+XrGx2RlZlK3+U3c+uTdzkc1pp5h+ev/IfVkEgGhNeg74lGq+/uWW5l/99gNV5d3Fgp4/vk3+GHbXpKSUgkODmTo0AfyviW9/4GeWJbFpIlz2bBhB9WqV2Xq1Kdp1KgeAEs+X83cuYsBeHLQvfTvfysAe/YcYvSoGZw7l0mHjs156aUn3e6PIndZ7/RH69fHMHXqO2Rn59C//60MHnwfM2Z8RMOG9ejatQ3nz2cybNhb7N//KzabH9OnD6d27hNMZs9eyJIl3+Ht7c3o0U/QqVPLIo/pTuIyDpZ3FgDYvfM3nn5sFtfUq5n3+zrgHz25cCGLGa8tIyUpHT//6lx3wxW88e+BnEpI4Y2Ji3ltpvPO51s27GfmtOXk5Fj07NuKB59wtocTx3Mf1ZiaQb0brmTMlL9RpUolzp+/wNSxn3Lo4H8JCPDh5Vf/zhW1govMX1kJqVarvLOQz7FjDp4Z+iYA2Vk59Lq9HU8O6sfCz5zXhPvu7+Z8pOyk/7Bx4y6qV6vKpKmDaNjQ+UjZL5as5Z15ywAY+GQ/+t3VGYC9e3+5+KjGDk0ZPfZRt+unKnuV/3Xrjy7HPgog8fyB8s4CAC8P/5gdMb+QnHyGoCB/nhjSnTpXhzL9teVkZ+dQpUolho29i/o31WL/vmMsXbSF0ROcj5T7cuk23p+/BnA+qvH2O1sBsH/fMSbnPqrx5vb1eWHUnc5HNSafYcyLH+GITybcHsiUNx/E5gazo4Kq1i/vLBTqcm0b7ujyrYvr3esi5mLhNw5z2d+0jv1vlPu5KpXBA1dwt8GDy5k7Dh5crtx18OBy5C6DB+LkboMHlzN3HDy4XLnL4IG47+CBSPnz7MED+00jXPY3bfxPr5X7uapU3hkQERERERER8TzusdzAVTyrNCIiIiIiIiLicpp5ICIiIiIiIuJi7nKjQ1fR4IGIiIiIiIiIi3na4IFnlUZEREREREREXE4zD0RERERERERczHjYd/UaPBARERERERFxMU9btqDBAxEREREREREXM8aUdxZcyrOGQkRERERERETE5TTzQERERERERMTFtGxBRERERERERIrlaTdM9KzSiIiIiIiIiIjLaeaBiIiIiIiIiItp2YKIiIiIiIiIFMvTBg88qzQiIiIiIiIilxljzLvGmARjzN5Lwt4wxhwwxuw2xiw1xgTmhl9tjDlrjNmV+5pTkvfQ4IGIiIiIiIiIixm8XPYqgfeA2/4QthpoaFlWY+BnYNQlcb9YltU09zWoJG/gtssWHq5nL+8sSK6k8wfLOwuSK6hq/fLOguSy+1xf3lmQSxhMeWdBcuVYWeWdBcm16Ndq5Z0FyTXoxvLOgYiUizJctmBZVrQx5uo/hH17yeYW4O7/y3to5oGIiIiIiIiIGzPGDDTGxFzyGvgXD/EY8PUl23WNMTuNMeuNMR1KcgC3nXkgIiIiIiIiUlG58oaJlmXNA+b9b/kwY4As4OPcoDjgKsuyThtjWgDLjDENLMtKLe44GjwQERERERERcTFjyn9ZpTHmYeB2oKtlWRaAZVnngfO5P283xvwCXA/EFHcsLVsQERERERER8TDGmNuAEUAfy7IyLgkPNcZ45/58DVAP+PXPjqeZByIiIiIiIiIuVsKnJLjmvYz5FOgMhBhjjgPjcD5doSqwOncWxJbcJyt0BCYaY7KAbGCQZVmJf/YeGjwQERERERERcTFX3vPgz1iW9UAhwQuKSLsEWPJX30PLFkRERERERESkWJp5ICIiIiIiIuJqbnDDRFfS4IGIiIiIiIiIq3nYPH8PK46IiIiIiIiIuJpmHoiIiIiIiIi4mpYtiIiIiIiIiEixPGzwQMsWRERERERERKRYmnkgIiIiIiIi4moe9lW9Bg9EREREREREXMzSsgURERERERERuZxo5sFflJ2dw733jCQ8LIh/zxmZLy4z8wKjRsxk30+/Ehjoz5tvPcuVV4YB8M68pSxZsgZvLy9GjXmU9u2bArBhwy5enfofsnNy6H93VwYMuLPMy1RRTH55EZvX/0SNID8+XvoiAD8f+C+vT/qCzMwLeHt78+KYfjRodFWBfSOXx/DeO1EAPDKgK737tgTgwE/HmTR2IefPX6Bth/o8N6IvxhhSUjJ4adhHxJ1IouYVNZg87e8EBPiUXWErmOjo7UyZ8g45OTncc083Bg68J198ZuYFhg9/i337fiEw0J/p04dTq1Y4AHPnLubzz1fj5eXF2LED6dCheYmOKQWNHjWDdetiCA628eVXMwvEW5bFlCnvEL0+hmrVqvLKq8/SoMG1ACxdGsWc2YsAGDT4Xvr16wrA3r2xjBo1g/PnztOxU0vGjBmA8bBR9NKiduEe4uJOMXLEDE6dSsJ4eXHvvd146KE78qWxLIupUxYQHb2datWqMvWVoXltY9nSNcye8zkAgwfdzZ39IgDYt/cXRo16m/PnM+nYsQWjxzyutlGMnOwcPnnxDfyCbdw5dhALR03nwtnzAGSkpGGvV4c+owcW2G/fmq1sW7wKgNb39KBBRBsAHLFHWfX2R2RlXqBuiwZ0fqI/xhjOpZ0hctp/SE1IJCAsiN7DHqOan67fRVE/5T5UFx7Kwy4LmnnwF3344UquuebKQuOWfL6GAJsv36z6Fw891Ju3pn0MQGzscVau3MyKL99i7jtjmDxxAdnZOWRn5zBl0gLmzBvNii+nszJyE7Gxx8uyOBVK7z4tmT77iXxhs6ZH8vigbnyw+HkGPNWdWdMjC+yXkpLBu3NWM//joSz4ZCjvzllNamoGAK9P/oKR4/qz+KsRHDtyii0bDwLw4YI1tGxzHYu/GkHLNtfx4YK1pV/ACio7O5uJE+cwf/54IiNn8dVX0cTGHs2XZvHibwkI8GP16nk88khfpk17D4DY2KNERkYTGTmL+fPHM2HCbLKzs0t0TCmo311deWf++CLjo6O3c+TwCVZ9O5eJk55iwvjZACQnpzFr5mcsXDSNRYvfZNbMz0hJSQdgwvjZTJz4FKu+ncuRwyfYEL2jLIpS4alduA9vby+Gj3iEyJUzWfjZa3zy8dfExh7LlyY6egdHjpzgm1X/ZsLEwUycMBfIbRuzFrFw4WssWvQ6s2Ytutg2JsxhwsTBfLPq3xw5coING9Q2irPzq3UE5f6hA3DfK8/x93+O5O//HEnNG+py3S1NCuxzLu0MWxZ+zQOvv8ADb7zIloVfcy7def2OmruQW4c8wKOzXyY5LoHDO34CYNuS1dRufD2Pzn6Z2o2v54clq8umgBWQ+in3obrwYF7GdS83UGqDB8aY+saYrsYYvz+E31Za71na4uNPE71+B/3v7lpo/Jo1MfTt2xmA7j1uZsuWvViWxdo1P9CrV1uqVKlMrVph1L7Kzp7dsezZHUvtq+zUrh1OlSqV6NWrLWvX/FCGJapYmrW8hgBb/m8PjDGcOXMOgPS0c4SEBhTYb+umg7S6pR42mw8BAT60uqUeWzYe5NTJVM6kn6NRk6sxxtDzjhasX7sXgA1rf6JXH+fshF59WhK9Zl8pl67i2r37EHXq1KR2bTtVqlSmd++OREVtzZdmzZqted9k9+jRju+//xHLsoiK2krv3h2pUqUytWvbqVOnJrt3HyrRMaWgVq0aYrP5FRkfFbWVvnd2wRhD06b1SU09Q0JCIhs37qBtu6YEBvpjs/nRtl1TNmzYTkJCIunpGTRrVh9jDH3v7MJ3UVvKsEQVl9qF+wgLC8qbReDrV51rr62Fw3E6X5o1Udvo2/f3tnFDXtvYtHEXbds2udg22jZh44aduW3j7MW20bcLUd9tK4/iVQhpp5L4LWYfDbvdUiAu8+w5ju35mWvbNC4Qd3jnfuo0qU81f1+q+flQp0l9Du/4ifTEFDIzznFF/boYY7ixc2t+2boHgF+37eGmLs7ZCTd1acMvW3eXbuEqMPVT7kN1IRVFqQweGGOeBpYDQ4G9xpi+l0RPLY33LAuvvvIeL7z4d7yKGPlJcCRirxkMQKVK3vj7+5CcnIbDkYjdHpyXzh4ehCMhEUdCIjUvCQ8PD8bhSCzdQniYZ4f3YeZbkfTtNpl/vfUVg5/pVSDNyYQUwu2Bedth4TZOJqRwMiGFsHDbH8JTAUhMTMsbiAgJDSApMb2US1JxORynsdtD8radv8enC6SpWdOZxtk2fElKSi1k3xAcjtMlOqb8dQ7HaWraQ/O27fbg3POdSM1Lzrc9ty/6Yz3Y7SGqhxJSu3BP/z2ewP79v9GkyfX5wh2O03nXb3C2jYTf20DNS855bptJcCQSfun12666KM66BV/Q4eG+GFPwY2fslh+p3fgGqvpULxCXnpiCf8jF67dfcCDpiSmkJ6bgF/zH8GQAMpLT8AtyXtv9gmxkpKS5ujgeQ/2U+1BdeDBjXPdyA6U182AA0MKyrDuBzsBLxphncuPco+R/0bq12wkKstGgwTVFprEsq0CYwRQeboBCwyvk6Sk3Xyz6nmeG3cHy1WN5Zlgfpo5bVCBNIacZY0zh4aWQR09X+O+3KVGaotpGSY4p/4Mi2kLhfVEx6eVPqV24nzNnzvL0068xctRj+P1hDXwhv+rOuigkprhwKejXH/biY/Mj/LqC9yMCOLhhO/U7tCh850I/VxUeriv4X6d+yn2oLjyYceHLDZTW4IG3ZVnpAJZlHcY5gNDTGPMWxRTdGDPQGBNjjIl5Z97npZS1/83OnQdZtzaGbl2f4sUX/snWrXsZMfztfGnC7cHExzlH9LKysklLy8AW6IfdHkx8/MWRvnhHImGhQYSHBxN3SbjDcZqwsBplUyAPsXLFdjrf2giArt0b89PeYwXShIXbcMQn520nOFIICQ0gLNxGgiMlf3iYc7ZBUJA/p046ZyGcOplKjaCip4Jf7uz2EOLjT+VtO3+PgwqkiYtzpnG2jTMEBvoXsu8pwsKCS3RM+evC7cHExZ/M246Pd55XZ/jF8x3vuBh+aT3Ex59SPZSQ2oV7uXAhi2eefp077uhI9+4Fp87bwy9ev8HZNkLDamAPDyE+7pJz/nubCQ/Gcen1O151UZQTB37l1x/2smDAOFa++R+O7f6Zr6e/D8DZ1DPEHzpC3ZYNCt3XLziQtFMXr9/pp5PxDbI5Zxqczh/++2wDn0B/0hOd1/b0xBR8bP6lVbQKT/2U+1BdSEVRWoMH8caYpr9v5A4k3A6EAI2K2smyrHmWZbW0LKvlgIF3l1LW/jfPPf831qybw+qoWUx781natGnIa68/nS9Nly4tWL58HQDfrtpCm5sbYIyhS5eWrFy5mczMCxw/nsDRI3E0anwdDRtdy9EjcRw/nkBmZhYrV26mS5eW5VC6iiskNICdMb8CELM1ltpXhRRI06bdDWzb/DOpqRmkpmawbfPPtGl3AyGhAfj6VmXvj0ewLIuvv9xOxy7ODzDtO9/EyhUxAKxcEUOHLjeVXaEqmEaN6nH48AmOHYsnM/MCkZHRRES0zpcmIqINS5c6n3axatUmbr65McYYIiJaExkZTWbmBY4di+fw4RM0blyvRMeUvy4iojXLl63Fsix27TqAv78PYWFBtG/fnE0bd5KSkk5KSjqbNu6kffvmhIUF4etbnV27DmBZFsuXraVr1zblXYwKQe3CfViWxdixs7jm2lo88mjfQtN0iWjF8uW/t42DeW2jXfumbNq062Lb2LSLdu2bXtI2DjrbxvK1RHRVXRSm/YN9GLBgEo+/M4FeLzxK7cbX0/O5hwH4efNO6rZsSKUqlQvd9+pmN3Jk137OpWdwLj2DI7v2c3WzG/ELslGlejXiDv6GZVnsX7eNa1s7P15e07oRP611ruv+ae1Wrmld5MfOy576KfehuvBgHnbDxNJ6VONDQNalAZZlZQEPGWPmltJ7lot/vb2QBg2vJSKiJf3vjmDkiJnc1mMoNpsf0958FoDr6tXmtttuoc/tz+Pt7cXYlx7H29s5bjNm7GMMfGIKOTk59LurC9fVq12exXFrLw//mB0xv5CcfIY+t07miSHdGTXubqa/tpzs7ByqVKnEyHHOQaf9+46xdNEWRk+4B5vNh0efvJXHHnDOFHlsUDdsuTdeHDb2LibnPqrx5vb1uaV9fQAeerwLY178iC+X/kC4PZApbz4LO9nCAAAgAElEQVRYPoWuACpV8ubllwfxxBPjyM7OoX//W6lXrw4zZnxEw4b16Nq1DXff3Y1hw96iW7eB2Gx+TJ8+HIB69erQs2d7evUagre38zje3t4AhR5Tivf882/ww7a9JCWl0qnjowwd+gBZWdkA3P9ATzp1akn0+u107/Yk1apXZepU5wBoYKA/Q4bcxz13Pw/AkKfuJzDQ+W3duPGDGT1qBufOZdKhY3M6dixierHko3bhPnbs2M+K5eu4/vo69LvzOQCefe7vxMU5Z+Hcf/9tdOrUgujo7fToPtj5qMapQwFn2xg85B7uvWcYAEOG3HuxbYx7klGj3+b8uUw6dGhOx47Ny6F0FdvPG7bTqn+3fGHxsUfZ881Guv3jb1Tz96XNvbfxyYtvAHDzfT2p5u8LQMSg+/j27Y/IOn+Bq1vcyNUtnIP8re7qRuQb77Lvuy34h9Tg9uGPlW2hKhD1U+5DdeHBPGypiClsPYw7yMr50T0zdhlKvXCkvLMguYKq1i/vLEiuwtY8S/kx7rIYUMixsv48kZSJeQf0+Gd3MejGq8s7CyJu6nqPvoDX67bAZR8YD61+vNzPVWnNPBARERERERG5fJX7n/uupcEDEREREREREVdzk3sVuEpp3TBRRERERERERDyEZh6IiIiIiIiIuJpnTTzQ4IGIiIiIiIiIq1ke9rQFLVsQERERERERkWJp5oGIiIiIiIiIq3nYDRM1eCAiIiIiIiLiap41dqBlCyIiIiIiIiJSPM08EBEREREREXE1D7thogYPRERERERERFzNw+55oGULIiIiIiIiIlIszTwQERERERERcTXPmnigwQMRERERERERl/Owex5o2YKIiIiIiIiIFEszD0RERERERERczcNmHrjt4IG3V7XyzoLkCqpav7yzILlyrKzyzoLkyrbOl3cW5BKVvXzLOwuSy8u47UeLy85jNwSXdxZERC5vHjbP38OKIyIiIiIiIiKupq8HRERERERERFxNyxZEREREREREpFieNXagwQMRERERERERV7O8PGv0QPc8EBEREREREZFiaeaBiIiIiIiIiKvpngciIiIiIiIiUizPGjvQsgURERERERERKZ5mHoiIiIiIiIi4mofdMFGDByIiIiIiIiKu5mH3PNCyBREREREREREplmYeiIiIiIiIiLiaZ0080OCBiIiIiIiIiMt52D0PtGxBRERERERERIqlmQciIiIiIiIiruZhMw80eCAiIiIiIiLiYpZnjR1o2cJfMXrUDNre8iB33P6PQuMty2Ly5Hl07zaQPncMZd++X/Lili6Nokf3J+nR/UmWLo3KC9+7N5Y77hhK924DmTx5HpZllXo5PEV09HZ69BhEt24DmTdvcYH4zMwLPPvsa3TrNpB77nmB48cdeXFz5y6mW7eB9OgxiA0bdpT4mFJQXNwpHn7oJXr3+ge33/40H3zwZYE0lmUxZfJ8enQfTN8+z+ZrG8uWrqFHjyH06DGEZUvX5IXv2/sLfe54hh7dBzNl8ny1jRLq3vUf9OszjP79RnDv3aMLxFuWxdQp79GzxzP06zucn/b9lhe3fNl6evV4ll49nmX5svV54fv2/Uq/PsPo2eMZpk55T3VRQuqj3Ifqwr2kpp7h+Wf+yR29XqBP7xfZtfPnfPGWZfHKlPfp1eM57uo74g/9VDS9ezxH7x7PsXxZdF64s58aQa8ez/HKlPfVT5WQ2ob7UF1IRaDBg7+g311deWf++CLjo6O3c+TwCVZ9O5eJk55iwvjZACQnpzFr5mcsXDSNRYvfZNbMz0hJSQdgwvjZTJz4FKu+ncuRwyfYEL2jyOPLRdnZ2UycOIf588cTGTmLr76KJjb2aL40ixd/S0CAH6tXz+ORR/oybdp7AMTGHiUyMprIyFnMnz+eCRNmk52dXaJjSkHe3l4MH/EIkStnsvCz1/jk46+JjT2WL0109A6OHDnBN6v+zYSJg5k4YS6Q2zZmLWLhwtdYtOh1Zs1adLFtTJjDhImD+WbVvzly5ES+i6EU7933X2LJ0tdY9PnUAnEbondx9EgcK7/5J+MnDGDSxPkApCSnM3vWEj5dOJlPF01m9qwleXUxacICxk0YwMpv/snRI3Fs3LCrTMtTEamPch+qC/fz2tQPaNe+CV+ufJMlS1/lmmuvzBe/IXoXR47EE/nNW4yb8ASTJ74LXOynPlk4iU8WTcrXT02e8C7jJjxO5DdvceRIPBs3/Fjm5apo1Dbch+rCg3kZ173cgAYP/oJWrRpis/kVGR8VtZW+d3bBGEPTpvVJTT1DQkIiGzfuoG27pgQG+mOz+dG2XVM2bNhOQkIi6ekZNGtWH2MMfe/swndRW8qwRBXX7t2HqFOnJrVr26lSpTK9e3ckKmprvjRr1mylX7+uAPTo0Y7vv/8Ry7KIitpK794dqVKlMrVr26lTpya7dx8q0TGloLCwIBo0uBYAX7/qXHttLRyO0/nSrInaRt++v7eNG/LaxqaNu2jbtsnFttG2CRs37MxtG2cvto2+XYj6blt5FM/jrF0TQ5++HTHG0KRpPdJSMziZkMSmTT9yS9tG2AL9sNn8uKVtIzZt/JGTCUmcST9L02bXY4yhT9+OrImKKe9iuD31Ue5DdeFe0tMz2B5zgLvu7gxA5SqVCAjwzZdm7Zrt9OnboZB+anch/dRuTiYkkZ6vn+qgfqoE1Dbch+rCgxnjupcbKLXBA2NMa2NMq9yfbzLGPG+M6VVa7+cOHI7T1LSH5m3b7cE4HKdxOBKpaQ+5GB4ejMORiMNxGvul4faQAn90SeH+eO7Cw4MLnDuH4zQ1azrTVKrkjb+/L0lJqYXsG5JbT39+TCnef48nsH//bzRpcn2+cIfjNPaawXnbdnswCb+3gZqXnPPcNpPgSCTcHlwgXP6cMYaBj0/l3v6jWLzouwLxDkci9nznNghHQmLB8PAgZz+VkEh4eFCBcCme+ij3obpwL8ePJVAjyJ+xo+dyz12jGDd2HhkZ5/KlSXAkYbdf0u/Yg0hISCKhkH4qwZFIQkJSgX4qwZFU+oWp4NQ23IfqQiqKUhk8MMaMA94GZhtjXgFmAn7ASGPMmNJ4T7dQyPI6YwwUsu7OmGLSy58qbC3jH89dUWkKDy/ZMaVoZ86c5emnX2PkqMfw8/PJF1fYylNjDFYhMcWFy5/78JMJLP7iVWbPG8mnn3xLzA/788UXtgzY2S7+WrgUT32U+1BduJfs7Bz2/3SY++6/lcVfvEJ1n6oseGdFvjSF3q+gmPNeVD1J8dQ23IfqwoNp2UKJ3A20AzoCTwF3WpY1EegB3FfUTsaYgcaYGGNMzLx5C0spa6Un3B5MXPzJvO34+NOEhQXlhp+6GO64GB5/aXj8KcLCgpA/Z7eH5Dt3jtxz+sc0cXHONFlZ2aSlnSEw0L+QfU8RFhZcomNK4S5cyOKZp1/njjs60r37LQXi7eHBxMddHO2Ojz9NaFgN7OEhxMddcs5/bzPhwTjiTxcIlz/3+3kKDrbR9dZW7NkTmy/ebg8iPt+5TSQstEbBcEciYWE1sP9hpsHv4VI89VHuQ3XhXsLDgwgPD6Jxk+sA6Na9Dft/Opw/jT2I+PhL+p3cfsr5uSl/PxUaVqPAjKjfw6V4ahvuQ3Xhwbxc+HIDpZWNLMuysi3LygB+sSwrFcCyrLNATlE7WZY1z7KslpZltRw4sMgxBrcVEdGa5cvWYlkWu3YdwN/fh7CwINq3b86mjTtJSUknJSWdTRt30r59c8LCgvD1rc6uXQewLIvly9bStWub8i5GhdCoUT0OHz7BsWPxZGZeIDIymoiI1vnSRES0yXuyxapVm7j55sYYY4iIaE1kZDSZmRc4diyew4dP0LhxvRIdUwqyLIuxY2dxzbW1eOTRvoWm6RLRiuXLf28bB/PaRrv2Tdm0adfFtrFpF+3aN72kbRx0to3la4noqrr4MxkZ5zhz5mzez5s37aZevdr50nTu0oIVy6OxLIsfdx3Cz9+H0LAatGvXhM2bdufVxeZNu2nXrgmhYTXw8a3Gj7sOYVkWK5ZH0yWiZXkUr0JRH+U+VBfuJSQ0EHvNYH777QQAW7fs5drr8t8wsUuXFqxYvuGSfqp6bj/VmO837cnrp77ftId27RoTGlYDX9/ql/RTG+gS0aI8ilehqG24D9WFuIIx5l1jTIIxZu8lYUHGmNXGmEO5/9fIDTfGmLeNMbHGmN3GmOYleY9KpZT3TGOMT+7gQV7vbYyxUczggbt7/vk3+GHbXpKSUunU8VGGDn2ArKxsAO5/oCedOrUkev12und7kmrVqzJ16tMABAb6M2TIfdxz9/MADHnqfgID/QEYN34wo0fN4Ny5TDp0bE7HjrrYlUSlSt68/PIgnnhiHNnZOfTvfyv16tVhxoyPaNiwHl27tuHuu7sxbNhbdOs2EJvNj+nThwNQr14devZsT69eQ/D2dh7H29sboNBjSvF27NjPiuXruP76OvS78zkAnn3u78TFOWfh3H//bXTq1ML5uKDug6lWrSpTpw4FnG1j8JB7uPeeYQAMGXLvxbYx7klGjX6b8+cy6dChOR07lqhPu6ydPp3CM0PfBCA7K4det7ejfYemLPxsNQD33d+Njp2asSF6Fz17PEP1alWZNHUQALZAP54cfBf33+tcWTZoSH9sgc4bxL407nHGjprNufOZdOjQlA4dm5ZD6SoW9VHuQ3XhfkaNeZiRw2Zx4UIWtWqHMWnKkyz6zHmPlnvvv5UOnZoSHb2LXj2eo1q1qkye+iTwez/VjwfufQmAJ4f0u6Sfeoyxo+Zw7nwm7Ts0UT9VAmob7kN14cHKdqnIezhvF/DBJWEjgSjLsl41xozM3R4B9ATq5b7aALNz/y+WKY3n4BpjqlqWdb6Q8BCgpmVZe/7sGBYH9YBeN2FwjzU2AjlWVnlnQXJlF+zipBxV9vL980Qil5nMnLTyzoLkquLlX95ZEHFT13v0HxrXPL3MZX/T/vr2nX96rowxVwNfWZbVMHf7INDZsqw4Y0xNYJ1lWTcYY+bm/vzpH9MVd/xSWbZQ2MBBbvipkgwciIiIiIiIiMj/SfjvAwK5/4flhl8JHLsk3fHcsGKV1rIFERERERERkcuW5cJlC8aYgcDAS4LmWZY17389XCFhfzpLQoMHIiIiIiIiIq7mwnn+uQMFf3WwwGGMqXnJsoWE3PDjwKV31a4FnPizg7nJQx9ERERERERExIVWAA/n/vwwsPyS8Idyn7pwM5DyZ/c7AM08EBEREREREXE9r7K7H6Qx5lOgMxBijDkOjANeBRYZYx4HjgL35CZfCfQCYoEM4NGSvIcGD0RERERERERcrQwf1WhZ1gNFRHUtJK0FPPVX30PLFkRERERERESkWJp5ICIiIiIiIuJqZbhsoSxo8EBERERERETE1Txr7EDLFkRERERERESkeJp5ICIiIiIiIuJilpYtiIiIiIiIiEixPGzwQMsWRERERERERKRYmnkgIiIiIiIi4mrGs2YeaPBARERERERExNU8bJ6/hxVHRERERERERFxNMw9EREREREREXE3LFspGwtmD5Z0FyRVctU55Z0FyVfKqXt5ZkFz9ok6XdxbkEstu9SnvLIi4nTMX4so7C5KrSlX/8s6CiJQHPW1BRERERERERC4nbjvzQERERERERKTC8rCZBxo8EBEREREREXExy8PueaBlCyIiIiIiIiJSLM08EBEREREREXE1D/uqXoMHIiIiIiIiIq6mZQsiIiIiIiIicjnRzAMRERERERERV9PTFkRERERERESkWB42eKBlCyIiIiIiIiJSLM08EBEREREREXE1z5p4oMEDEREREREREVeztGxBRERERERERC4nmnkgIiIiIiIi4mrGs2YeaPBARERERERExNU8bNmCBg9EREREREREXM2zxg40eFAcR3wyU8d+xunTaXgZwx3923DP/+tAakoG44d/RNyJJGpeUYMJb/wd/wCfAvt/vSKGD96JAuChAV3p2aclAAd/Os7UlxeSef4CN7evz9PD+2KMKfFxxSk7O4d77xlJeFgQ/54zMl9cZuYFRo2Yyb6ffiUw0J8333qWK68MA+CdeUtZsmQN3l5ejBrzKO3bNwVgw4ZdvDr1P2Tn5ND/7q4MGHBnmZepooqO3s6UKe+Qk5PDPfd0Y+DAe/LFZ2ZeYPjwt9i37xcCA/2ZPn04tWqFAzB37mI+/3w1Xl5ejB07kA4dmpfomHJRdkYG8R+/z/m4/wJQ8++Pkrh2NZkOhzP+bAbe1X2oO3pcgX3T9+0l4fNPsXJyCGzXgeDuvQDIPHWSE+/OIzvjDNVqX8UVDz+BqVSJnAsXiPtgAeeOHsHb148rHn+SKsEhZVfYCmT0qBmsWxdDcLCNL7+aWSDesiymTHmH6PUxVKtWlVdefZYGDa4FYOnSKObMXgTAoMH30q9fVwD27o1l1KgZnD93no6dWjJmzACMh02JLA2qi/I1+eXP2LR+PzWC/Phk6TAAxgz7gKOHTwKQlnYWf//qfLj4hQL7fr/xANNfW0ZOTg597mrDQ487z/+J46cZO/wjUlMzuOHGKxk/9W9UrlyJzMwsJoz5hIM/HSfA5svkNx7kiiuDyq6wFYyu3+5DdSEVgW6YWAxvby+GvHA7Hy0dxpwP/8HShZs5/IuDj99dQ/M21/HplyNo3uY6Pnp3bYF9U1MyeG/uauZ+NJR5Hw/lvbmrSUvNAODNKV8w7KX+fLJiBMePnmLrpoMAJTquXPThhyu55porC41b8vkaAmy+fLPqXzz0UG/emvYxALGxx1m5cjMrvnyLue+MYfLEBWRn55CdncOUSQuYM280K76czsrITcTGHi/L4lRY2dnZTJw4h/nzxxMZOYuvvoomNvZovjSLF39LQIAfq1fP45FH+jJt2nsAxMYeJTIymsjIWcyfP54JE2aTnZ1domPKRY7PP8X3pgZc8/Jk6o4eTxV7Ta58fBB1R4+j7uhx+Ddtjn/T5gX2s3JycCz6mFpPPcs1L00iNWYb5+NOAHBy2RKCIrpx7fipePv4krx5AwAp32/E28eXaye8QlBEN04u+7xMy1qR9LurK+/MH19kfHT0do4cPsGqb+cycdJTTBg/G4Dk5DRmzfyMhYumsWjxm8ya+RkpKekATBg/m4kTn2LVt3M5cvgEG6J3lEVRKjzVRfnq3acV02cPyBc25Y2H+HDxC3y4+AW63NqYzl0bFdgvOzuHaVO/YPrsAXy6bDjffr2T336JB2DWPyN54MGOfP7VKAICfFjxxTYAVnyxlYAAHz6PHM0DD3Zk1j+/Kv0CVlC6frsP1YXn8vJy3csdlFk2jDEflNV7uUpIaAA33FgLAB/fatS5JoyTCSlsXPcTt93hnEVw2x0t2bh2X4F9t20+SMub6xFg88E/wIeWN9dj66aDnDqZSsaZczRscjXGGHrc3oINa/cClOi44hQff5ro9Tvof3fXQuPXrImhb9/OAHTvcTNbtuzFsizWrvmBXr3aUqVKZWrVCqP2VXb27I5lz+5Yal9lp3btcKpUqUSvXm1Zu+aHMixRxbV79yHq1KlJ7dp2qlSpTO/eHYmK2povzZo1W/O+revRox3ff/8jlmURFbWV3r07UqVKZWrXtlOnTk127z5UomOKU/bZs5yNPYStbQcATKVKePtcnLFkWRZpO2IIaNm6wL7nDv9GldAwqoSEYipVIqBFa9J378KyLDJ+PoB/sxYA2Nq0JX33LgDSd+/C1qYtAP7NWpBx8ACWZZV2MSukVq0aYrP5FRkfFbWVvnd2wRhD06b1SU09Q0JCIhs37qBtu6YEBvpjs/nRtl1TNmzYTkJCIunpGTRrVh9jDH3v7MJ3UVvKsEQVl+qifDVreS0BtsJnUlqWRdSqXXTr2axA3E97j1LrqmCurBVM5cqV6HZbM6LX7sOyLGK2HaJLt8YA9OrTkui1ewDYsG4vvXJnenbp1piYrYfURxVB12/3obrwXMa47uUOSmXwwBiz4g+vL4G7ft8ujfcsbXH/TeTQgRPc1Ogqkk6nERIaADgHGJIS0wukP5mQQpg9MG87LNzGyYQUTiWkEBpuywsPDbdxKiEVoETHFadXX3mPF178O15F3IQkwZGIvWYwAJUqeePv70NychoORyJ2e3BeOnt4EI6ERBwJidS8JDw8PBiHI7F0C+EhHI7T2O0Xp607z93pAmlq1nSmcdaHL0lJqYXsG4LDcbpExxSnC6dO4u3nR9yH/+G3VyYQ9/F75Jw/nxd/NvYQlQICqBIWXnDf5CQq1aiRt10psAYXkpPIPpOOV/XqGG9vZ3gNZ/gf9zHe3nhVr072GfVV/wuH4zQ17aF523Z7cO7vfyI1L/n9t+f2R39sF3Z7iNqFi6guys+u7b8SFOzPVXVCC8SddKQQFl7ws1RK8hn8/atTqZL3xXBHau4+qYTn7lOpkjd+ftVJST5TBiWpeHT9dh+qC6koSmvmQS0gFXgLeDP3lXbJz4Uyxgw0xsQYY2I+XLCqlLL212VknOelFz9g6LA++PpVK9lOhQxyG2MobPDbXUaSKop1a7cTFGSjQYNrikxT2LcMBlN4uIHCKkZrV0um8HNqSpSmqPooyTHFycrJ4dyxo9To0Jm6o8bhVaUqp7/9Oi8+NWYr/i0KzjookjFF9l/ONywkztPuBlRWijrPRfZTRaSX/zvVRbn59uudhc46gEJPMxReLVzsonT9KCldv92H6sJzaeZBybQEtgNjgBTLstYBZy3LWm9Z1vqidrIsa55lWS0ty2r54OM9Silrf03WhWxeeuEDuvVqRqfc9Xg1gv05ddI5wn3qZCo1ggpOhQwNt5EQn5y3neBIISQ0gNBwGycdKXnhJx0pBOfONijJcQV27jzIurUxdOv6FC++8E+2bt3LiOFv50sTbg8mPs45upqVlU1aWga2QD/s9mDi4y+OusY7EgkLDSI8PJi4S8IdjtOEhdVA/pzdHkJ8/Km8bee5CyqQJi7OmcZZH2cIDPQvZN9ThIUFl+iY4lQ5sAaVAmtQva5zMM2/WQvOHTsCgJWdTdqPOwho0arIfbOSkvK2s5KTqGwLxNvPj5yzZ7Gys53hSUlUsjm/yatc4+I+VnY2OWfP4uXrW2rl82Th9mDi4k/mbcfHO3/PneEXf//jHRfDL20X8fGn1C5cRHVRPrKyslkXtYduPZoWGh8WbiPBkf+zVGiojcAavqSlnSUrKzsvPCQsIG8fR+4+WVnZpKefLXLJxOVO12/3obrwXMYYl73cQakMHliWlWNZ1nTgUWCMMWYmFfDJDpZl8dqERdSpG8Z9D3bKC2/X6Sa++TIGgG++jKF955sK7Nu67Q388P3PpKVmkJaawQ/f/0zrtjcQEhqAj09V9u0+gmVZrPpqO+07NyjxcQWee/5vrFk3h9VRs5j25rO0adOQ115/Ol+aLl1asHz5OgC+XbWFNjc3wBhDly4tWblyM5mZFzh+PIGjR+Jo1Pg6Gja6lqNH4jh+PIHMzCxWrtxMly4ty6F0FU+jRvU4fPgEx47Fk5l5gcjIaCIi8n/THRHRhqVLnU8eWbVqEzff3BhjDBERrYmMjCYz8wLHjsVz+PAJGjeuV6JjilMlm43KNYI473DeROzMwf1UtV/h/PnAfqqE16RyjcI/LFSrczWZCQ4yT53Eysoidfs2/Bo1wRiDz/U3kLZzOwApWzfj19j54d6vURNStm4GIG3ndnyur+82F7SKJiKiNcuXrcWyLHbtOoC/vw9hYUG0b9+cTRt3kpKSTkpKOps27qR9++aEhQXh61udXbuc95lYvmwtXbu2Ke9ieATVRfn4Ycshrq4blm+Z56VubFCbY0dOceL4aS5cyGL1Nzvp0Nl5PW/R6jrWrt4NwMoVMXTo3BCADp0bsHKF87PU2tW7adm6nvqoIuj67T5UF1JRmLK4iYwxpjfQzrKs0SXdx3F2Rbnf3Wb3zt/4x6P/5pp6drxyLzwDhvbkpkZXMW74RzjikgmvGcjENx4kwObDgX3HWP75FkaMcz4GJXLZNj5asAaABx/vSq87nd/+Hdh3jFdeXsj58xdo064+z468E2MMKclnCj1ueQuuWqe8s1Ckbdv28d67X/LvOSP519sLadDwWiIiWnL+fCYjR8xk//7fsNn8mPbms9Sunfs4mzlfsPSLtXh7ezFy1CN06OicLhm9fgevvvI+OTk59LurC08Ouqs8i1aoSl7VyzsLhVq/PoapU98hOzuH/v1vZfDg+5gx4yMaNqxH165tOH8+k2HD3mL//l+x2fyYPn04tWvbAZg9eyFLlnyHt7c3o0c/QadOLYs8pjvp+52jvLOQ59yxo8R/8j5WVhaVQ0Kp+eCjePv4cuKDd6le9xpqdOicl/ZCcjLxH79H7aeeBSB9724cSxZCTg62W9oRctvtwO+PapxL9hnnoxprPvwEXpUrOx/V+P58zh07irevL1c89iRVQgquVS5ry24NK+8sFPD882/ww7a9JCWlEhwcyNChD+R9U3r/Az2xLItJE+eyYcMOqlWvytSpT9OoUT0Alny+mrlzFwPw5KB76d//VgD27DnE6FEzOHcukw4dm/PSS0/qD6MSuFzrIvn8ofLOwv9n787joq72P46/Dii5IQgKY0neFv1V7qaZe2KKS6WmLXdpLyu7Vrdui0s39zbNNnOtW7d7KzVDS8wlUEFLS80160Zdt4RBxT0VGM7vj0GEWCQb4Mvwfj4e84A53zPfOef74cwMnznn+wXgmSffY8O6Hzl06DhhYcHcNySGG25sx5iRH9C0eUNuvLlDbt19aYeZMGoOk9/0Xp3hi6TtTH5xPtkey3X9r+Kuwd7j//OeAzzz5HscOfwLjS+7gFHP/ZmgoCqcOpXJ6OHv89/vfqZ2SA3GvngbFzQIL7RdZanOeY3LuwmFqozv305VeWPR2FkvnD526bREn/1Pm4mvFgQAACAASURBVPxAl3I/VmWSPDgXTkgeiJeTkweVjVOTB5WRk5IH4szkgUh5c0ryQJybPBApf/6dPGg03XfJgx/uL//kgUOuGCkiIiIiIiIiTlXhzkMgIiIiIiIi4nTGz76qV/JARERERERExMccdiqc383PciEiIiIiIiIi4mtnnXlgjKkJnLDWZhtjGgOXAZ9ZazNLvXUiIiIiIiIiFVBAJZx5kAhUM8ZcAMQDdwHvlGajRERERERERCoyY3x3c4KSJA+MtfYX4EbgdWvtAOCK0m2WiIiIiIiIiDhFSU6YaIwx7YE/A/f8hseJiIiIiIiIVEpOmTHgKyVJAjwKDANirbXbjDEXA8tLt1kiIiIiIiIiFZfxs+zBWZMH1tqVwEoAY0wAsN9a+3BpN0xEREREREREnOGs5zwwxrxvjKmdc9WFb4HvjTFPlH7TRERERERERComE+C7mxOUpBlXWGuPAP2BRcCFwG2l2ioRERERERGRCqwyXm2hqjGmKt7kwQJrbSZgS7dZIiIiIiIiIuIUJUkeTAd2ADWBRGNMQ+BIaTZKREREREREpCLzt5kHJTlh4mvAa3mKdhpjupVek0REREREREQqNqf80+8rJblUI8aYvkAToFqe4jGl0iIRERERERERcZSzJg+MMdOAGkA3YBYwCPiqlNtFaFD90n4KKSFjAsu7CZLD6nQjjvGfrp7yboLkccpzsLybIDnOC6xT3k2QHDWqRJZ3E0REKrUAP5t5UJJzHnSw1t4OHLTWjgbaA1Gl2ywRERERERGRisvfznlQkuTBiZyfvxhjzgcygYtKr0kiIiIiIiIi4iQlOefBQmNMKPASsAHvZRpnlWqrRERERERERCowp8wY8JWSXG1hbM6v84wxC4Fq1trDpdssERERERERkYrL+NlJD4pMHhhjbixmG9baj0unSSIiIiIiIiLiJMXNPLi+mG0WUPJAREREREREpBBluWzBGPN/wOw8RRcD/wBCgfuAfTnlw621i87lOYpMHlhr7zqXHYqIiIiIiIhUdmWZPLDWfg+09D6vCQR+BmKBu4DJ1tqJv/c5irzagjHmMWPMPYWUDzXGPPp7n1hEREREREREfK478KO1dqcvd1rcpRrvBt4rpHxGzjYRERERERERKYQxvryZwcaYdXlug4t56luBD/Lc/6sxZrMx5m1jTJ1z7U9xyQNrrc0opPAU4F+njRQRERERERHxoQDju5u1doa1tk2e24zCntMYEwTcAMzNKZoKXIJ3SUMKMOmc+1PcRmNMZEnKRERERERERKTc9QY2WGvdANZat7XWY63NBmYCV53rjotLHrwExBljuhpjgnNu1wCfAr/7ZAsiIiIiIiIi/sqXyxZ+gz+SZ8mCMaZ+nm0DgK3n2p/irrbwL2PMPmAM0BTv5Rm3Ac9aaz871ycUERERERER8Xem2Hn+pfB8xtQAegD35yl+0RjTEu//8zt+te03KTJ5AJCTJFCiQERERERERMTBrLW/AOG/KrvNV/svNnkgIiIiIiIiIr/db1xu4HhKHoiIiIiIiIj4mPGz7IGSByX0v//t5cnH3si9v2dPGkOGDuK223vllllreWHCeyQlbqRa9fMYO2EwV1xxEQAL5icyc9oCAO57oB/9+ncB4Ntt/2Pk8OmcOplB5y4teWr4bX73R1YaTp3K4Pa/PENGRiZZHg89e7Zn6MO35quTkZHJ00+9xrZtPxEaGszLLz/GBQ0iAJgx/WPmzYsnMCCA4SPuplPnVgAkJX3Dc+PfxpOdzaBB3blv8I1l3reKZviwV1mxYh3h4SF8uvCNAtuttYwfP5PEleuoVu08nnv+UZo0uQSA2Nh4pk2dA8ADD97MgAHdAdi6NZlhw17l1MlTdOnahhEj7tO4KMLoke+SlLiFsLBg5sx/FoBXJn5E4srNVK1ShQZR9Rg17g6Ca9co8NgvVm1l4vNz8Hiy6T+wE3fd6309+3nPfoY9MZMjh3/hssujGPv83VStWoWMjEz+MeyfbP92FyGhNXl+4n2cf0HdMu1vRfLeu4v5+KOVGGNo1LgBY8bfy3nnBeVuz8jIZMTTM9i+bQchobV48eUhXHBBPQDemvEpsfMSCQgM4Knhf6Fjp2YArE7azAvP/YdsTzYDBnXlnvuuK5e+VTR6nXIOfZ5ylsTE9YwfP5Ps7GxuuqkHgwfflG97RkYmTz75Mtu2/UhoaDCTJz9JgwbeC69Nnz6Xjz5aRkBAACNHDqZz59Yl2qcUTrGQiqDIUzgYYx4r7laWjXSCiy46n7mxE5gbO4EPPxpHtWrn0b17m3x1ViVuYufOVBYunsQ/Rt/DuNHvAHD40DGmvRnLfz4czfuzxzDtzViOHD4OwLgx/+TZ0fewcPEkdu5MZVXS5rLuWoUUFFSVt98ZReyCl/k4dhKrVm1k08b/5qsz76N4ateuxZKlU7jjjuuYNOk9AJKTd/PZolV8uvAVZswaydgxM/F4PHg8HsaNmcn0mSP4dOErLIpbRXLy7vLoXoUy4MbuzJw1qsjtiYnr2bljL0uWTmfM2IcYPWoqAIcOHWXKGx8ye85E5sydxJQ3PuTw4WMAjB41lTFjHmLJ0uns3LGXpMQNZdGVCun6/u15fdrD+cratb+CObHPMjv2HzT8QwT/nFXw1DUeTzbPj/uA16YO5aNPRrFk0df89ONeAF6b/DF/vu1a5i8aS+3aNZk/bzUA8z9eTe3aNVnw2Tj+fNu1vPbyx6XfwQrK7U7n/X8v44O5o/n4kwlke7JZvGhtvjqx8xKpXbsmC5e8xF/uiOGVSd5/UH9M/pnFn63l408n8OaMvzNh7Lt4PNl4PNlMGPcv3pz+OLGfPsfiRWv4Mfnn8uhehaPXKefQ5ynn8Hg8jBkzjVmzRhEXN4WFCxNJTt6Vr87cuUupXbsWy5bN4M47+zFx4jsAJCfvIi4ukbi4KcyaNYrRo6fmfpY62z6lIMXCf5XT1RZKTXHnfww+y63EjDGdcpIOPc+1oU6yds02oi6MKPCN2/KE9VzfrxPGGFq0uJSjR4+zb99BVq/eTPv2TQkJrUXtkJq0b9+UVas2sW/fQY4dO0GLlo0wxnB9v04sj19XTr2qWIwx1KxZHYCsLA9ZWVnwq0GVEP8V/ftfA0DPmPas+XIL1loS4r+md59OBAVVpUGDSC680MWWzcls2ZzMhRe6iIpyERRUld59OpEQ/3UZ96ziadu2KSEhtYrcHh+/ln79u2GMoWXLyzhy5DhpaemsWrWBDh1bEhoaTEhILTp0bElS0nrS0tI5duwXWrW6DGMM/fp34/P4NWXYo4qldZvGhITkn1XQvuMVVKkSCEDT5hfjdh8q8LhtW/5H1IURNIiqR9WqVejZuw0rEjZhreXrtd/Rvaf3W4vr+l3NioSNAKxM2MR1/a4GoHvP1ny19justaXZvQrN48nm1MkMsrI8nDiZQb2I0Hzblyds4Ib+nQDo0bMtX635FmstKxI20Kt3u5zXqHpEXRjJ1i0/sXXLT0RdGEmDqAiqBlWhV+92rEjQP6wlodcpZ9LnqfK1efMPNGxYP/dzT9++XYiPz5/kTEhYmzvbJiamI19+6X2fiI9fS9++XQgKqkpUlIuGDeuzefMPJdqnFKRY+C9/Sx4Ud6nG0ee6U2PMV9baq3J+vw94CIgFnjXGtLbWPn+u+3aCxYu+pHef9gXK09IO4nKdObllZGQYae6DpLkP4qqfp9x1pjwyMix//bSDpdt4P+LxeBg08El27UrlT3/qRYsWjfNtd6el46rv/UBSpUogwcE1OHToKGnuAzRveaZupCsctzsdILc+gMsVxuZNP5RBT/yb232A+q56ufddrnDc7gO43enUd+U53pHeOLjdB3DlLXfVxe0+UKZt9iefxK6mZ682BcrT0g4R6aqTez8ysg5bt/yPQ4eOExxcIzf5EBFZh31p3uTDvrRDRLq8r1lVqgRSq1Z1Dh06Tp06Rf9TVllFRoZxx129ien+GNWqBdG+Q1M6dGyWr06a+yCuvMczuDqHDh3DnXaQ5s0vybevNLf3veF0fYAIVxhbNv9YBr3xf3qdKh/6PFW+fv13HBkZzubN/y1Qp36+z1I1OXjwCG73AVq0+L88jz0zBs62TylIsZCK4qxXnjTGVDPGPGSMedMY8/bp21keVjXP74OBHjnJiJ7An39He8tdZkYWK5ZvoGdMuwLbCvsGzhjzm8odk1aqAAIDA4mdP4nlK2awZfMP/PDf/FOxCj3uGAr7ntQYsIVtUTx+v0IPq4FCx0Ux9eU3e2v6IgIDA+l9XWGvVwXre49/YRtOP6aImEkBRw4fZ3nCBhYtm8iyFa9w4sQpFn6yOl+dot4binopKuo1TXxAr1NlTp+nyl+Rr0ElqFPU+0FJ9ikFKRb+y99mHpw1eQC8B7iAGGAl0AA4erb9GmPqGGPCAWOt3QdgrT0OZBX1IGPMYGPMOmPMulkzY0vUgbK2KmkTl1/xB8LrhhTYFhkZRmrqmW8e3O506kWEEukKIzUlT3nqmfLT33ifrh9RL/+0Vjm72rVr0vaqpiQlfZOv3BUZTmrKfsC7tOHo0V8ICa1FZGT4r+JxgIiIsHz1AVJT04mICEN+n0hXOCmp+3Lvp+Ycb295nuPtPlOemrc8db/icA4+XfAlSYmbGffCPYV+WIiMDMWdeuabObf7IHXrhRJapxZHj/5CVpYH8H47Xi/ndSkisg7uVO9rVlaWh2PHThASUrMMelPxrPlyGxdcUI+wsNpUrVqF7j2uZNPG5Hx1Il1hpOY9nke9xzMyz3GG0+8ldfLVB0hLTSciQu8ZvqDXqbKnz1Plz+Wqm+/v2J3z9/3rOin5PksdJzQ0uJDH7iciIrxE+5SCFAv/FWB8d3OCkiQPLrXWPgMct9a+C/QFmp3lMSHAemAdEGaMcQEYY2pRYGX6GdbaGdbaNtbaNvfeN6BEHShrnxUxxQ7gmujWfLpgFdZaNm1KJji4BvXq1aFjx+Z88cVWjhw+zpHDx/nii6107NicevXqULNmNTZtSsZay6cLVtEt+soy7lHFlJ5+mCNHvCdJOnnyFF9+uZmLL74gX51u0W2ZP38FAEuXfEm7q5tijKFbdBs+W7SKjIxM9uxxs3NnCs2aX0rTZpeyc2cKe/a4ycjI5LNFq+gWXXC6t/w20dFXsWD+cqy1bNz4HcHBNYiICKNTp9asXvUNhw8f4/DhY6xe9Q2dOrUmIiKMmjWrs3Gjdz39gvnL6d694DdTUrQvVm3l3beWMPn1h6hePajQOlc0/QO7d6Xx8579ZGZmsfSzdXTt1gJjDG2u+j/il3rX0i9csIau0S0A6NqtOQsXeNd1xy/dQNt2l+lbjCK46oezeVMyJ06cwlrL2jXfctHF5+erc023VnwyfxUAy5Z+zVXtLscYQ9durVj82dqc16h97Nrppmmzi2nS9CJ27XSzZ88+MjOyWPzZWrp2a1Ue3fM7ep0qe/o8Vf6aNWvEjh172b07lYyMTOLiEomOvipfnejodsTGxgOwZMlqrr66OcYYoqOvIi4ukYyMTHbvTmXHjr00b96oRPuUghQLqSjM2U52dfr8BcaYRGAIkAp8Za29+Dc/mTE1gEhr7f/OVveU52vHnYXrxIlT9Ix+hEVLXyY42HuCsjkfegfxzbd2x1rLhHHvsnrVZqpVC2Ls+ME0aeo9TLHzVjJrRs6lhe7vR/8buwKwbetPjBw+g1OnMujUuQXDRtzuuA/jVQKql3cTCvj++x0Me/oNsj0esq2lV68ODHnoZl5/7QOaNL2U6Oi2nDqVwVNPvsb27f8jNKQWE1/+G1FRLgCmTfuI2HkJBAYG8vTwu+jSxXtyuJUr1/P8hH+SnZ3NgIHRPPDAoPLsZgEBpurZK5Wxxx57ia+/2srBg0cIDw9l6NA/5n5rfesfe2OtZeyY6SQlbaBa9fOYMOFhmjVrBMC8j5YxffpcAO5/4GYGDrwWgC1bfmD4sFc5eTKDzl1a88wz9ztuXBzPTCnvJgAw/IlZrPv6ew4dOkZ4eG3uH3I9/5y1mMyMLEJCvbMCmjW/mOHP/pl9aYcY++x7vDZ1KACrErcw6QXvpRr7DejIPff3AWDP7n0Mf2IWhw8f5/8uj2Lc83cTFFSVU6cyeWbY23y/fTchITWZ8NK9NIiqV2TbylKVgGrl3YQC3nz9Y5Ys/orAwAAuu7who8bezczpn9KkyR+4Jro1p05lMOKpGXy3fSe1Q2vy4sQhNIjyXk525rRPmB+bSGBgIE8+/Sc6dfEmcJJWbuLF5/9DdnY2/Qd04b4HbijPLhbqvMA6Z69Uxirr61SG50h5N6FQlfHz1HmBBWdYOMHKleuYMGEmHk82Awdey4MP3sKrr/6bpk0b0b17O06dyuCJJ15m+/afCAmpxeTJT+Z+lpo6dTbz5n1OYGAgw4ffS9eubYrcp5xd5Y1FY+cM1FLQY/Fqn/1Pu6xXx3I/ViVJHtwLzAOaA/8EagH/sNZOK82GOTF5UFk5MXlQWTkxeVBZOSV5IF5OTB5UVk5MHlRWTk0eVEZOTR6IlD//Th7ELFnls/9pl8R0KvdjVeTVFk6z1s7K+XUl8JtnG4iIiIiIiIhUNk45V4GvnDV5YIw5DxgI/CFvfWvtmNJrloiIiIiIiIg4xVmTB8AC4DDeEyCeKt3miIiIiIiIiFR8Jbk6QUVSkuRBA2ttr1JviYiIiIiIiIifCDD+dRq/kiRDvjDGnO3SjCIiIiIiIiLip0oy86ATcKcx5n94ly0YwFprm5dqy0REREREREQqqEp3wkSgd6m3QkRERERERMSPVJpzHhhjaltrjwBHy7A9IiIiIiIiIuIwxc08eB+4Du9VFize5QqnWeDiUmyXiIiIiIiISIVVaZYtWGuvy/l5Udk1R0RERERERKTiM352tYWznvPAGNO6kOLDwE5rbZbvmyQiIiIiIiIiTlKSEya+CbQGNuNdutAM2ASEG2MesNYuLcX2iYiIiIiIiFQ4/rZsoSQngNwBtLLWtrHWXgm0BLYC1wIvlmLbRERERERERCqkAB/enKAk7bjMWrvt9B1r7bd4kwk/lV6zRERERERERMQpSrJs4XtjzFTgw5z7twD/NcacB2SWWstEREREREREKqiAynbCROBOYAjwKN5zHqwC/o43cdCt1FomIiIiIiIiUkH52zkPzpo8sNaeACbl3H7tmM9blCPQBJXWruU3UixECqpZtX55N0HysNZT3k2QHBsP/FDeTZAcTetcUN5NEBERP1Jk8sAYM8dae7MxZgtQYL6FtbZ5qbZMREREREREpIJyyokOfaW4mQeP5Py8riwaIiIiIiIiIuIvKs2yBWttijEmEHjLWnttGbZJRERERERERByk2HMeWGs9xphfjDEh1trDZdUoERERERERkYqsMl5t4SSwxRizDDh+utBa+3CptUpERERERESkAqs0yxbyiMu5iYiIiIiIiEglVJLkwWzgUrxXXPjRWnuydJskIiIiIiIiUrFVmqstGGOqABOAu4GdePvewBjzT2CEtTazbJooIiIiIiIiUrH42zkPikuGvASEARdZa6+01rYCLgFCgYll0TgRERERERERKX/FLVu4Dmhsrc1Nl1hrjxhjHgS+Ax4p7caJiIiIiIiIVESV6YSJNm/iIE+hxxg/m38hIiIiIiIi4kP+ljwobtnCt8aY239daIz5C96ZByIiIiIiIiJSCRQ38+Ah4GNjzN3AerxXW2gLVAcGlEHbRERERERERCqkSnO1BWvtz0A7Y0w00AQwwGfW2viyapyIiIiIiIhIReRvV1sobuYBANbaBCChDNoiIiIiIiIiIg501uSBiIiIiIiIiPw2/nbCRCUPfiOPJ5ubb3qayIgw3pz2dL5tGRmZDHvqDbZ9+xOhocFMevlRLrggAoCZM2KZNy+BwIAAho24i06dWgKQlLSR5yf8E092NgMHdee++/qXeZ8qqsTE9YwfP5Ps7GxuuqkHgwfflG97RkYmTz75Mtu2/UhoaDCTJz9JgwaRAEyfPpePPlpGQEAAI0cOpnPn1iXapxROsXCG4cNeZcWKdYSHh/DpwjcKbLfWMn78TBJXrqNatfN47vlHadLkEgBiY+OZNnUOAA88eDMDBnQHYOvWZIYNe5VTJ0/RpWsbRoy4D2P87J2wFKSk7Ofpp15l//6DmIAAbr65B7fffn2+OtZaJox/i8TE9VSrdh4TnhuaG4/5sQlMnfYRAA8+MIj+A6IB2Lb1R4YNe41TpzLo0uVKho+4R/EoRMapTEYPmUJmZhbZnmzadWvOTff24vVR/+an7/YQGBjIpVdEce9TN1GlSmCBx69c9DWx73wOwIA7r6Vrn7YA/PTdbqaO+5CMU5m0an85d/ytP8YYjh35hVef+Rf7Ug5Sr34dHhl7O7Vq1yjTPlckPbv/lZo1qxMQGEBgYCBzPpqQb7u1lucmvEtS4jdUq3Ye4yc8yBVNLgJgwfyVTJ8aC8D9Dw6gX/+uAGzb9hMjh03l5KkMOndpxbDhd2hslIDev51DsfBP/nbOA3/rT6l7771FXHzxBYVum/dRArVDarJ4yevcfntfXp74HwCSk/ewaNEXfPLpy0yfOYJxY97C48nG48lm/Ni3mDZjOJ98OplFcatJTt5Tlt2psDweD2PGTGPWrFHExU1h4cJEkpN35aszd+5SateuxbJlM7jzzn5MnPgOAMnJu4iLSyQubgqzZo1i9OipeDyeEu1TClIsnGPAjd2ZOWtUkdsTE9ezc8deliydzpixDzF61FQADh06ypQ3PmT2nInMmTuJKW98yOHDxwAYPWoqY8Y8xJKl09m5Yy9JiRvKoisVXmBgAE8+dSdxi95g9ocv8P5/PiM5eXe+OomJG9i5cy+Ll7zJ6DEPMmb0dCAnHlPmMHv2C8yZ8yJTpsw5E4/R0xg95kEWL3mTnTv3kpSkeBSmalAVnnn9QV781995/t3H2bjme37YupNOPa/k5Q+e4qV//52MU5kkfLK2wGOPHfmFeW8vZdysRxg36xHmvb2UY0d+AeCtl+Zx31M38cqcYaTs2c/GNd6LTy14L56mVzbilTnDaHplIxa8p9WeZ/P2u88wL/aFAokDgKTEjezamcKixa8wavR9jB0zC4DDh44xdco8Ppg9jg/mjGPqlHm5Y2Ps6Ld4dvR9LFr8Crt2prAqaWOZ9qci0vu3cygWUlEoefAbpKYeIHHlBgYO6l7o9oSEdfTrdw0APWOuZs2arVhrWZ7wNX36dCAoqCoNGkQQdaGLLZuT2bI5magLXURFRRIUVIU+fTqwPOHrMuxRxbV58w80bFifqCgXQUFV6du3C/Hx+T8EJiSszf32NCamI19+uQlrLfHxa+nbtwtBQVWJinLRsGF9Nm/+oUT7lIIUC+do27YpISG1itweH7+Wfv27YYyhZcvLOHLkOGlp6axatYEOHVsSGhpMSEgtOnRsSVLSetLS0jl27BdatboMYwz9+nfj8/g1ZdijiisiIix3FkHNWtW55JIGuN0H8tVJiP+Kfv1Ox+P/cuOxetVGOnRocSYeHVqwKumbnHicOBOPft2I//yr8uie4xljqFbjPAA8WR48WR4w0KrD5RhjMMZwyRUXkp52qMBjN635jmZtG1Ordg1q1a5Bs7aN2bTmOw7uP8KJ4ydp3OwPGGPo0utK1iVuBWBd0ja65MxO6NKnLeuStpZdZ/3Q8oR13NCvC8YYWrRsxNEjv7Av7SCrV2+ifYdmhITWIiSkFu07NGP1qk3sSzvI8WMnaNmqMcYYbujXhYT4deXdDcfT+7dzKBb+K8D47uYEpZI8MMa0M8bUzvm9ujFmtDHmU2PMC8aYkNJ4zrLw/HPv8Pjf/0JAEdFLc6fjqh8OQJUqgQQH1+DQoaO43em4XOG59VyRYbjT0nGnpVM/T3lkZDhud3rpdsJPuN0HcLnq5t73HrsDBerUr++t441HTQ4ePFLIY+vidh8o0T6lIMWi4nC7D1DfVS/3vssVnnO806mf53i7cl6Lfh0Hl6uu4nAOft6Txvbt/6NFi8b5yt3uA7nvGeCNR9rp414/z99/TpzS3OlE5n3PcGlcFCfbk81Td0xicN9nada2MY2aNMzdlpXlIWnxelpcfVmBx6XvP0x4RGju/bCIUNL3HyZ932HCfl2+7zAAh9OPUqdubQDq1K3NkYPHSqtbfsEYw+B7JnDzwGHMnfN5ge2//twU6cr53PTr8sgw72tVWjqRkWEFyqV4ev92DsXCfxljfXZzgtKaefA28EvO768CIcALOWX/LKXnLFUrlq8nLCyEJk0uLrKOtQWDajCFlxug0HKHpJUcrvBjakpUp6h4lGSfUpBiUYEU8r5jvAe8kPJi6kuJHT9+gocffoGnh91NrVr518AX9jHAGIMtZEtx5VK4gMAAXnj3cd6c/w9+3L6L3T+m5G57+6V5XN7yYi5vWch7emF/9xQ1TnT8z8V7749m7sfPM3XG03zw/lLWfb093/ZCDnXOe8ZvK5fi6f3bORQLqShKK3kQYK3Nyvm9jbX2UWvtKmvtaKDI/76NMYONMeuMMetmzviolJp2br755ntWLF9Hj+4P8ffHX2Ht2q089eRr+epEusJJTfFm9LKyPBw9+gshobVwucJJTT2T6Ut1pxNRL4zIyHBS8pS73QeIiKhTNh2q4FyuuqSm7s+97z12YQXqpKR463jjcZzQ0OBCHrufiIjwEu1TClIsKo5IVzgpqfty76emeo+rt/zM8U51nynPG4fU1P2Kw2+QmZnFIw+/yPXXd6Fnz/YFtrsiz7xngDce9SLq4IqsS2pKnr//03GKDMed9z0jVeOiJGoGV+eKVpewca33/AQfvbWEI4eOcdvDNxRaP6xeCAfyLGdITztEnbq1vTMNCikHCAkL5uD+IwAc3H+E2nWKXj4k5P7dhoeH0P3atmzZkpxvu8sVYresKwAAIABJREFUlu9zkzs1nYh6dQqWu9OJiKjjndGZZ6bB6XIpnt6/nUOx8F9atlAyW40xd+X8vskY0wbAGNMYyCzqQdbaGdbaNtbaNvcNHlRKTTs3f3vsTySsmMay+ClMnPQo7do15YUXH85Xp1u3K1mwYAUAS5esod3VTTDG0K1bGxYt+oKMjEz27Elj184UmjW/lKbNLmHXzhT27EkjIyOLRYu+oFu3NuXQu4qnWbNG7Nixl927U8nIyCQuLpHo6Kvy1YmObkdsbDwAS5as5uqrm2OMITr6KuLiEsnIyGT37lR27NhL8+aNSrRPKUixqDiio69iwfzlWGvZuPE7goNrEBERRqdOrVm96hsOHz7G4cPHWL3qGzp1ak1ERBg1a1Zn48bvsNayYP5yundvV97dqBCstYwcOYWLL2nAnXf1K7ROt+i2LFhwOh7f58ajY6eWrF698Uw8Vm+kY6eWeeLxvTceC5YT3V3jojBHDh7j+NETgPfKC1vW/cD5DSNJ+GQNm9Z+z8NjbiMgoPCPQC2uvozNX/2XY0d+4diRX9j81X9pcfVl1Klbm2o1qvHD1p1Ya0lcvJ42nZsCcGWnJiQu8p6zKHHR17Tp3KRsOloB/fLLSY4fP5H7+xerN9OoUVS+Otd0u5JPFiRirWXTxh+oFVyDehF16NixBV+s3pw7Nr5YvZmOHVtQL6IONWpWY9PGH7DW8smCRLpF6/PU2ej92zkUC/8V4MObE5TWpRrvBV41xowE9gNfGmN2A7tztvmN11+bTZOmlxAd3YaBg6J5+qk36BUzlJCQWkyc9CgAlzaKolev9txw3WMEBgYw8pl7CAz0/gmMGHk3g+8dT3Z2NgNu7Malv3oDlcJVqRLIP/7xAPfe+yweTzYDB15Lo0YNefXVf9O0aSO6d2/HoEE9eOKJl+nRYzAhIbWYPPlJABo1akjv3p3o02cIgYHe/QQGei/VVdg+pXiKhXM89thLfP3VVg4ePELXLncxdOgfycryAHDrH3vTtWsbEleup2eP+6lW/TwmTPAmQENDgxky5BZuGvQYAEMeupXQ0GAAnh31IMOHvcrJkxl07tKaLl2uLJ/OVTAbNmznkwUraNy4IQP6/w2AR//2F1JSvDM/br21F127Xkli4npiej7ovVTjhKGANx4PDrmJm296AoAhQ24+E49n72fY8Nc4dTKDzp1b06VL63LonfMdPHCEqWM/IDvbkp1tad+9BVd2vII/dX6CupF1eGawd+bgVV2bMfDunvy4fTefz/+C+4fdQq3aNbjxrmsZcc8rAAy8q0fuZRfveWJg7qUaW7a/jJbtvedM6HdbNK+M/BfLF35FeGQofxt/R/l0vAI4cOAwjwydBIAnK5s+13WkU+eWzP5wGQC33NqDLl1bkZS4kd4xj1C92nmMnfAAACGhtbj/wRu59eYRADwwZCAhod5ZHs88e8+ZSzV2bknnLi3LoXcVi96/nUOxkIrCFLYexmc7NyYY7zKFKsAea627pI/Nyt7kjLNCCFUCqpd3E0Qcp7D151J+rPWUdxMkx6b0n8q7CZKjaZ3CLy0tZa9qQM3yboKIQzV2yIT80vHM+s999oFx7JXXlvuxKq2ZBwBYa48Cm0rzOUREREREREScxinnKvAVpyyfEBERERERERGHKtWZByIiIiIiIiKVkb/NPFDyQERERERERMTHAsu7AT6mZQsiIiIiIiIiUizNPBARERERERHxsQBTtlfnMsbsAI4CHiDLWtvGGBMGzAb+AOwAbrbWHjyX/WvmgYiIiIiIiIiPBRjf3X6DbtbaltbaNjn3nwbirbWNgPic++fWn3N9oIiIiIiIiIg4Wj/g3Zzf3wX6n+uOlDwQERERERER8bFymHlggaXGmPXGmME5ZZHW2hSAnJ8R59ofnfNARERERERExMcCfXipxpxkwOA8RTOstTN+Va2jtXavMSYCWGaM+c53LVDyQERERERERMTRchIFv04W/LrO3pyfacaYWOAqwG2MqW+tTTHG1AfSzrUNWrYgIiIiIiIi4mNluWzBGFPTGBN8+negJ7AV+AS4I6faHcCCc+2PZh6IiIiIiIiI+FgZX6oxEog1xoD3//z3rbWLjTFfA3OMMfcAu4CbzvUJlDwQERERERER8bHfeInF38Va+xPQopDyA0B3XzyHli2IiIiIiIiISLE080BERERERETExwLLuwE+5tjkQZWA6uXdBMlx0pNe3k2QHNUCw8q7CZLDUIbz0OSsPDazvJsgOVqFNy7vJkiO6hc+W95NkBwndo0u7yaISDkoy2ULZUHLFkRERERERESkWI6deSAiIiIiIiJSUZXx1RZKnZIHIiIiIiIiIj4WqGULIiIiIiIiIlKZaOaBiIiIiIiIiI/52wkTlTwQERERERER8TF/Sx5o2YKIiIiIiIiIFEszD0RERERERER8zN9mHih5ICIiIiIiIuJjgX52qUYtWxARERERERGRYmnmgYiIiIiIiIiP+ds39UoeiIiIiIiIiPiYv53zwN+SISIiIiIiIiLiY5p5ICIiIiIiIuJj/jbzQMkDERERERERER/T1RZEREREREREpFLRzAMRERERERERH/O3ZQuaefAbJCauJybmAXr0GMyMGXMLbM/IyOTRR1+gR4/B3HTT4+zZ487dNn36XHr0GExMzAMkJW0o8T6laO+9u5gB1w/jxhuG89Tf3+TUqYx82zMyMnnisSlcF/MEf75lND//vC9321szPuW6mCe4oc9TrF61Jbd8ddJmbujzFNfFPMFbMxeWWV8qOo0N51AsnMXjyWbgjU8y5IHnC2zLyMjk8b9NplfMUG69ZTg//5yWu23mjFh6xQylb+9HWLVqY255UtJG+vZ+hF4xQ5k5c36Z9MEfaFyUvWkv3c/ODdNYt+zFAtseHdyXE7s+ILxOMACNLzmfFbGjOfTDv3h0cN8i99kwqh6JC8ayZeXLvDflYapWDQQgKKgK7015mK2Jk0lcMJYLG9TNfczfH+rH1sTJbFo+iWu7NPdxLys+jQ3nUCz8U4Dx3c0JlDwoIY/Hw5gx05g1axRxcVNYuDCR5ORd+erMnbuU2rVrsWzZDO68sx8TJ74DQHLyLuLiEomLm8KsWaMYPXoqHo+nRPuUwrnd6bz/72V8MHc0H38ygWxPNosXrc1XJ3ZeIrVr12Thkpf4yx0xvDJpDgA/Jv/M4s/W8vGnE3hzxt+ZMPZdPJ5sPJ5sJoz7F29Of5zYT59j8aI1/Jj8c3l0r0LR2HAOxcJ53ntvERdffEGh2+Z9lEDtkJosXvI6t9/el5cn/geA5OQ9LFr0BZ98+jLTZ45g3Ji3cl+jxo99i2kzhvPJp5NZFLea5OQ9ZdmdCknjony8N3cl/W4vmDRrUD+M6M7N2LXnTEL/4KFjPP7su7wyo/ik/fhhf+L1WYto1vUxDh4+zp23dAPgzlu6cfDwcZp2+Ruvz1rE+GF/AuCyRhdw0/XtaX3tE9xw+/O8Ov5uApzyCdwBNDacQ7GQiqJUkgfGmIeNMVGlse/ysnnzDzRsWJ+oKBdBQVXp27cL8fH5/1lNSFjLgAHdAYiJ6ciXX27CWkt8/Fr69u1CUFBVoqJcNGxYn82bfyjRPqVoHk82p05mkJXl4cTJDOpFhObbvjxhAzf07wRAj55t+WrNt1hrWZGwgV692xEUVJUGDeoRdWEkW7f8xNYtPxF1YSQNoiKoGlSFXr3bsSJhQ2FPLXlobDiHYuEsqakHSFy5gYGDuhe6PSFhHf36XQNAz5irWbNmK9Zalid8TZ8+HXJeoyKIutDFls3JbNmcTNSFLqKiIgkKqkKfPh1YnvB1GfaoYtK4KB+rv/qO9EPHCpS/+OztjJjwPjbPOcT2HTjC+s0/kZnlKXafXTs04eOcLwr+81Ei18e0AeC6nlfyn48SAfh40Vqu6dg0p7wNcz/9koyMLHbu3sePO1Jp2/JSX3TPL2hsOIdi4b8086BkxgJrjTFJxpghxph6pfQ8ZcbtPoDLdWYaXGRkOG73gQJ16tf31qlSJZDg4JocPHikkMfWxe0+UKJ9SuEiI8O4467exHR/jGu7PkJwrRp06NgsX50090FcrjDAG49awdU5dOgY7rSDROaUn95XmvtgvvoAEa4w3GkHy6ZDFZjGhnMoFs7y/HPv8Pjf/1LkN51p7nRc9cOB07GowaFDR3G703G5wnPruSLDcKel405Lp36ecm8s0ku3E35A48I5+va4kr2p6WzZ/tu//QyvE8zhI8fxeLIB+DnlAOfnvGef7wpjz17v8fd4sjly9BfC6wRzQWSd3HLvY9I531XHBz3xDxobzqFY+K9A47ubE5RW8uAnoAHeJMKVwLfGmMXGmDuMMcFFPcgYM9gYs84Ys27GjNml1LRzY23By2wYY0pUp/Dyku1TCnfk8HGWJ2xg0bKJLFvxCidOnGLhJ6vz1Sny+BZyxZQi44HicTYaG86hWDjHiuXrCQsLoUmTi4usU9RrTlGxQLE4JxoXzlC9WhBP/bU/Yyad27rrwg7v6TgUduyttYU+qJDQVVoaG86hWEhFUVpXW7DW2mxgKbDUGFMV6A38EZgIFDoTwVo7A5jhvfdfR728u1x1SU3dn3vf7T5ARERYgTopKftxueqSleXh6NHjhIYGF/LY/UREeL89Ots+pXBrvtzGBRfUIyysNgDde1zJpo3JXHdDx9w6ka4wUlPTiXSFkZXl4djRE4SE1CQysg7u1DPf1rnd6dSL8H4TkZqnPC01nYhfLYWQgjQ2nEOxcI5vvvmeFcvXkZT4DacyMjh+7ARPPfkaL7z4cG6dSFc4qSkHcLnCc2LxCyGhtXC5wklNPfPtUKo7nYh63mOekqfcGwt9i3o2GhfOcHHDSBpG1eOrxS8AcEH9ML5cNIHON4zEve/wWR+/P/0oIbVrEhgYgMeTzQX1w0lxe2cH/pxygAbnh/NzajqBgQHUDq5B+qFj/JyaToPzz8zWuaB+WO5jRGPDSRQL/xVgHPUv7e9WWjMP8qW1rLWZ1tpPrLV/BC4specsVc2aNWLHjr3s3p1KRkYmcXGJREdfla9OdHQ7YmPjAViyZDVXX90cYwzR0VcRF5dIRkYmu3ensmPHXpo3b1SifUrhXPXD2bwpmRMnTmGtZe2ab7no4vPz1bmmWys+mb8KgGVLv+aqdpdjjKFrt1Ys/mwtGRmZ7Nmzj1073TRtdjFNml7Erp1u9uzZR2ZGFos/W0vXbq3Ko3sVisaGcygWzvG3x/5EwoppLIufwsRJj9KuXdN8iQOAbt2uZMGCFQAsXbKGdlc3wRhDt25tWLToi5zXqDR27UyhWfNLadrsEnbtTGHPnjQyMrJYtOgLunVrUw69q1g0Lpxh2/e7adj6AS7r+DCXdXyYn1PSad9neIkSB6clfrmNG/u0A+DPg7qwcOl6AOKWrefPg7oAcGOfdqz8Yltu+U3XtycoqAoNo+px6UUuvt6Y7OOeVVwaG86hWPivAB/enKC0Zh7cUtQGa+2JUnrOUlWlSiD/+McD3Hvvs95Lbw28lkaNGvLqq/+madNGdO/ejkGDevDEEy/To8dgQkJqMXnykwA0atSQ3r070afPEAIDvfsJDPReXqiwfcrZNW9xCT16tuXWQc8SGBjAZZc3ZNDN1zDl9Y9p0uQPXBPdmgEDuzDiqRlcF/MEtUNr8uLEIQBc2qgBPWOuYsD1wwgMDGT4yNsIDPQOyWEjbuPB+14iOzub/gO6cGmjBuXZzQpBY8M5FAvne/212TRpegnR0W0YOCiap596g14xQwkJqcXESY8CcGmjKHr1as8N1z1GYGAAI5+5J/c1asTIuxl873iys7MZcGM3Lm3kV+cmLhUaF+Xj3deH0rn95dStE0zy2jcY+/JHvDt7RaF1I+uFsHrheIJrVSc72/LXe3rTqvsTHD12gth3nmTIUzNJcR9kxHMf8N4bQ3n2iZvZtG0H78xeDsA7s1fw9itD2Jo4mYOHjnHbX18HYPt/9zBv4Rq+iZ9IVpaHR0f+k+xs//oW8PfQ2HAOxUIqClPYehhncNayhcrspEcn5HKKaoGabiZSmKzsCpmX9ktVAqqXdxMkR/ULny3vJkiOE7tGl3cTRByqsV+fiCFh7yKf/U8bfX6fcj9WpTXzQERERERERKTScspVEnzFKcsnRERERERERMShNPNARERERERExMf87WoLSh6IiIiIiIiI+FiAli2IiIiIiIiISGWimQciIiIiIiIiPuZvMw+UPBARERERERHxMX+b5u9v/RERERERERERH9PMAxEREREREREfM1q2ICIiIiIiIiLF8bPcgZYtiIiIiIiIiEjxNPNARERERERExMe0bEFEREREREREiuVv0/z9rT8iIiIiIiIi4mOaeSAiIiIiIiLiY8bY8m6CTyl5ICIiIiIiIuJjfnbKAyUP5OyqBYaVdxNERIpVJaB6eTdBxHGO73ymvJsg4jgW//omuKLzt3+u/Z2SByIiIiIiIiI+pqstiIiIiIiIiEix/Cx3oKstiIiIiIiIiEjxNPNARERERERExMcC/GzqgZIHIiIiIiIiIj7mZ7kDLVsQERERERERkeJp5oGIiIiIiIiIj+lqCyIiIiIiIiJSLD/LHSh5ICIiIiIiIuJr/pY80DkPRERERERERKRYmnkgIiIiIiIi4mO6VKOIiIiIiIiIFMvPcgdatiAiIiIiIiIixVPyQERERERERMTHjLE+u539uUyUMWa5MWa7MWabMeaRnPJRxpifjTEbc259zrU/WrYgIiIiIiIi4mNlvGwhC3jcWrvBGBMMrDfGLMvZNtlaO/H3PoGSByIiIiIiIiIVmLU2BUjJ+f2oMWY7cIEvn0PLFn6DxMT1xMQ8QI8eg5kxY26B7RkZmTz66Av06DGYm256nD173Lnbpk+fS48eg4mJeYCkpA0l3qcUTfFwDsXCORQL51AsnEOxcI6UlP3ccfsz9O3zV6677mH+9a9PC9Sx1jJ+3Cxiej5IvxseZdu2H3O3zY9NICZmCDExQ5gfm5Bbvm3rj9xw/SPE9HyQ8eNmYe3Zp/iKxoZTDB/2Kh3a38b11/210O3WWsaNm0HPHoO54fqh+cZEbGw8MT3vJ6bn/cTGxueWb92azPXXD6Vnj8GMGzdDY6KcGOPLmxlsjFmX5za46Oc1fwBaAWtziv5qjNlsjHnbGFPnXPuj5EEJeTwexoyZxqxZo4iLm8LChYkkJ+/KV2fu3KXUrl2LZctmcOed/Zg48R0AkpN3EReXSFzcFGbNGsXo0VPxeDwl2qcUTvFwDsXCORQL51AsnEOxcJbAwACefOpO4ha9wewPX+D9/3xGcvLufHUSEzewc+deFi95k9FjHmTM6OkAHDp0lClT5jB79gvMmfMiU6bM4fDhYwCMHj2N0WMeZPGSN9m5c2++f2alcBobzjHgxu7MnDWqyO2JievZuWMvS5ZOZ8zYhxg9aiqQMybe+JDZcyYyZ+4kprzx4ZkxMWoqY8Y8xJKl09m5Yy9JiRoT5SHAhzdr7QxrbZs8txmFPacxphYwD3jUWnsEmApcArTEOzNh0u/pj88ZY4KMMbcbY67Nuf8nY8wbxpiHjDFVS+M5S9vmzT/QsGF9oqJcBAVVpW/fLsTHr81XJyFhLQMGdAcgJqYjX365CWst8fFr6du3C0FBVYmKctGwYX02b/6hRPuUwikezqFYOIdi4RyKhXMoFs4SERFGkyaXAFCzVnUuuaQBbveBfHUS4r+iX79uGGNo2fL/OHLkOGlp6axetZEOHVoQGhpMSEgtOnRowaqkb0hLS+fYsRO0anUZxhj69etG/OdflUf3KhSNDedo27YpISG1itweH7+Wfv1Pj4nLcsfEqlUb6NCx5Zkx0bElSUnrc8bEL2fGRP9ufB6/pgx7JOUl53/tecB/rLUfA1hr3dZaj7U2G5gJXHWu+y+tmQf/BPoCjxhj3gNuwjtloi0wq5Ses1S53Qdwuerm3o+MDC/wZud2H6B+fW+dKlUCCQ6uycGDRwp5bF3c7gMl2qcUTvFwDsXCORQL51AsnEOxcK6f96Sxffv/aNGicb5yt/sArvrhufddrnDS3Ok55XmOu8t73NPc6US6wguUS/E0NioOt/sA9V31cu+7cv7G3e506uc53q7IcNynx0reclddxaGc+HLZwtmfyxjgLWC7tfblPOX181QbAGw91/6U1gkTm1lrmxtjqgA/A+dbaz3GmH8Dm4p6UM66jcEA06ePYfDgW0qpeb9dYeuEzK+iWFSdwsshO/vs+5TCKR7OoVg4h2LhHIqFcygWznT8+AkefvgFnh52N7Vq1ci3rbCV2cYYbCFbiiuX4mlsVCCFDApjDBQRhyLrS5kr46PeEbgN2GKM2ZhTNhz4ozGmJd6/jB3A/ef6BKWVPAgwxgQBNYEaQAiQDpwHFLlsIWfdRs7ajf866qweLlddUlP35953uw8QERFWoE5Kyn5crrpkZXk4evQ4oaHBhTx2PxER3gz52fYphVM8nEOxcA7FwjkUC+dQLJwnMzOLRx5+keuv70LPnu0LbHdFhpOacuZb0tTUA9SLqIMrsi5ffXXmCzN36gGuuqqp99vt1AP5yhWPs9PYqDgiXeGkpO7LvZ+a8zce6QrPNyZS3TljwhWeLw6pqfsVh0rAWruKwvMVi3z1HKW1bOEt4DtgIzACmGuMmQl8DXxYSs9Zqpo1a8SOHXvZvTuVjIxM4uISiY7Ov1wkOrpd7llOlyxZzdVXN8cYQ3T0VcTFJZKRkcnu3ans2LGX5s0blWifUjjFwzkUC+dQLJxDsXAOxcJZrLWMHDmFiy9pwJ139Su0TrfotixYsBxrLRs3fk9wcA0iIsLo2Kklq1dv5PDhYxw+fIzVqzfSsVNLIiLCqFmzOhs3fo+1lgULlhPdXfE4G42NiiM6+ioWzD89Jr7LHROdOrVm9apvzoyJVd/QqVPrPGPiO++YmL+c7t3blXc3KqWyXLZQFkxpXbbDGHM+gLV2rzEmFLgW2GWtLeEZbJw18wBg5cp1TJgwE48nm4EDr+XBB2/h1Vf/TdOmjejevR2nTmXwxBMvs337T4SE1GLy5CeJinIBMHXqbObN+5zAwECGD7+Xrl3bFLlPKRnFwzkUC+dQLJxDsXCOyhqLbJtV3k0oYP36b/nLn0fQuHFDAgK8n4Yf/dtfSEnxfqt66629sNYyduwMViV9Q7Vq5zFhwlCaNrsUgHnzPmfG9HkA3H//IG4c6D2Z39YtyQwb/hqnTmbQuXNrRj5zn6OmaQeY0prs+/tUxrFR2DKX8vbYYy/x9VdbOXjwCOHhoQwd+keysjwA3PrH3t4xMWY6SUkbqFb9PCZMeJhmzRoBMO+jZUyf7r0k5v0P3MzAgdcCsGXLDwwf9ionT2bQuUtrnnnmfkeNidMM/+e8RvnQnuOf+uwPrkHN68v9WJVa8uD3c17yQERERKSicGLyoLJyavKgMnJi8qAyU/Kg5JyQPNArmYiIiIiIiIiPBZT7v/u+peSBiIiIiIiIiI/5We6g1E6YKCIiIiIiIiJ+QjMPRERERERERHzMGP86x4aSByIiIiIiIiI+pmULIiIiIiIiIlKpaOaBiIiIiIiIiI8ZP5t6oOSBiIiIiIiIiI/5We5AyxZEREREREREpHiaeSAiIiIiIiLiY/72Tb2SByIiIiIiIiI+5m/nPPC3ZIiIiIiIiIiI+JhmHoiIiIiIiIj4nH9NPVDyQERERERERMTHjJ8lD7RsQURERERERESKpZkHIiIiIiIiIj5mjH99V6/kgYiIiIgfsmSXdxNEHCfbZpZ3EySPQP+a1V8I/+qgf6VCRERERERERMTnNPNARERERERExMf87YSJSh6IiIiIiIiI+Jx/JQ+0bEFEREREREREiqWZByIiIiIiIiI+pqstiIiIiIiIiMhZaNmCiIiIiIiIiFQimnkgIiIiIiIi4mO62oKIiIiIiIiIFMvfkgdatiAiIiIiIiIixdLMAxERERERERGf86/v6pU8EBEREREREfExY7RsQeT/27vzKKuqK4/j358FKIJAByNFK86zOCGgUYNayOAsUWNcy25NupvW0I5Rg0N3q8ky2u0cjVqgMWltNYjYhsI4gAq4kDCIgIJKFKQUChXngYJi9x/vgoVUlYW+qnt59/dx3cWr+867Z9+3Pauqdp1znpmZmZmZmeWIZx6YmZmZmZmZFV1pzTxw8cDMzMzMzMysyPxpC2ZmZmZmZmaWKy4ebICJE2cwaNBZDBgwlMrKUes9X1u7kvPPv44BA4Zyyim/oLq6Zu1zd901igEDhjJo0FlMmjSz2de0xjkf2eFcZIdzkR3ORXY4F9mxYkUtp57yS4accCHHHXsev731wfXa1Nau5MILbmDQwGGc+uPhvF29bO1zlXc9wqCBwzh68DlMnvTi2vOTJr3I0YPPYdDAYYyofKRV7qUUeGxkg8dFKdukiEf6shHFRqCuro6rr76TkSOvpKrqdsaOnciCBW+t02bUqCfp1KkjTz1VyZlnnsD1198LwIIFb1FVNZGqqtsZOfJKrrrqDurq6pp1TWuY85EdzkV2OBfZ4Vxkh3ORLe3ateWee69kzP/dyCNjbmDy5Fm8NOu1ddqMfng8nTp15Iknb+eMM47lhhv+B4AFCxbz+LjJ/HnszVSOvIJfXT1ibT5+ffUI7hpxOX8eezPjqiazYMHiNG5vo+KxkR0eF6VLRfwvC1qseCBpJ0kXSbpF0g2SzpLUuaX6a2mzZ7/Odtt1p0ePctq1a8sxx/Rj/Pip67SZMGEqQ4b0B2DQoEOYMuUlIoLx46dyzDH9aNeuLT16lLPddt2ZPfv1Zl3TGuZ8ZIdzkR3ORXY4F9nhXGSLJDp0aA/AqlV1rFq1ar39xCaM/ysnnng4AAMH/YAXpswhIpgwfhpHHX0o7dq1ZZtturHttuXMmb2hkk6GAAANs0lEQVSAObMXsO225WvzcdTRhzJh/LRWvrONj8dGdnhc2MaiRYoHks4F7gQ2A/oA7YEewBRJh7dEny2tpuZ9ysu3XPt1t25dqal5f7023bsX2rRpU8YWW3Tggw8+buC1W1JT836zrmkNcz6yw7nIDuciO5yL7HAusqeuro4hJ/6CQw/5GQcfvC/77rvrOs/XLFtO+Tr52JwPP/yEZTXvU96969p23cq7UlOznJqar9oDlJd/j2XOxzfy2MgWj4vSJKloRxa01MyDfwEGR8SvgSOBPSPicmAwcFML9dmiImK9c19PYmNtGj7fvGtaw5yP7HAussO5yA7nIjuci+wpKytjzKM38MyzlcyZ/Tqvv7butPYG31/E+meTfDT0jPPxjTw2ssXjolSpiEf6WnLPgzUfA7kpsAVARLwFtG3sBZKGSpouaXpl5UMtGNqGKy/fkqVL31v7dU3N+2y11ffWa7NkSaHNqlV1fPLJZ3TpskUDr32Prbbq2qxrWsOcj+xwLrLDucgO5yI7nIvs6tSpA3369mRSvQ3eAMq7dWXpOvn4nM5dOtKtW1eWLvnqL6c1Swvve/32AEuXLnc+msFjI5s8LkqL2KRoRxa0VBQjgWmSKoEpwG0Akr4PLG/sRRFRGRG9I6L30KGntlBo387ee+/CwoXvsHjxUmprV1JVNZGKir7rtKmoOJAxY8YD8MQTz3PQQfsgiYqKvlRVTaS2diWLFy9l4cJ32GefXZp1TWuY85EdzkV2OBfZ4Vxkh3ORLcuXf8THH38GwJdfrmDKlNnsuOPW67Q5oqIPjz76LABPPjGFAw/qiSSOqOjN4+MmU1u7kurqGhYtWsLe++xMz713ZtGiJVRX11Bbu5LHx03miIrerX1rGx2PjezwuLCNhRqaAlOUC0t7AXsAcyNi/oZf4bWWCew7eO656VxzzQjq6lZz0klHcvbZp3LLLffRs+cu9O9/ICtW1HLxxTcyb94bdO7ckZtuuoQePcoBuOOOhxg9+mnKysq47LJ/5rDDejd6TWse5yM7nIvscC6yw7nIjrzmoi5q0w5hPa++upBLh9/G6ro6VkcwePDB/HzYj/ntrQ+wV8+dqajow4oVtfzykluZN+9NunTuyPU3XrA2H3fe+TBjRk+grKyM4Zf9lH79egHw3HMzuPaa37N69WqGnFTBWWednOZtrqdM7dIOoUF5HBseF9lSpp7ZmI/fQlbUTSva77SblvVJ/b1qseLBd5e94oGZmZnZxiKLvyTlVVaLB3nkcZEtpV48qF09vWi/07bbpHfq71U2Fk+YmZmZmZmZWWa1+eYmZmZmZmZmZrZhUp8sUFQuHpiZmZmZmZkVWVY+JaFYSutuzMzMzMzMzKzoPPPAzMzMzMzMrOi8bMHMzMzMzMzMmqASKx542YKZmZmZmZmZNckzD8zMzMzMzMyKTCqtmQcuHpiZmZmZmZkVXWlN9C+tuzEzMzMzMzOzovPMAzMzMzMzM7MiK7UNE108MDMzMzMzMyu60ioeeNmCmZmZmZmZmTXJxQMzMzMzMzOzIpNUtKMZfQ2W9KqkBZKGt8T9eNmCmZmZmZmZWdG1zt/qJZUBtwMDgGpgmqTHIuKVYvbjmQdmZmZmZmZmG6++wIKIeCMiaoEHgROK3YlnHpiZmZmZmZkVWSt+2sLWwOJ6X1cDBxa7kwwXD3Ytia0pJQ2NiMq04zDnIkuci+xwLrLF+ciOUshFWUn8JFUauSgVpZALjwtrXcX7nVbSUGBovVOV9f4faKifKFbfa3jZQssb+s1NrJU4F9nhXGSHc5Etzkd2OBfZ4Vxkh3ORHc5FzkREZUT0rnfULx5VAz3qfb0N8E6xY3DxwMzMzMzMzGzjNQ3YRdIOktoBPwEeK3YnGV62YGZmZmZmZmZNiYhVkv4NeAIoA+6JiJeL3Y+LBy3Pa5Gyw7nIDuciO5yLbHE+ssO5yA7nIjuci+xwLmwdETEOGNeSfSii6PsomJmZmZmZmVkJ8Z4HZmZmZmZmZtYkFw9aiKR7JC2TNDftWPJOUg9Jz0iaJ+llSeelHVNeSdpM0l8lvZTk4qq0Y8o7SWWSXpQ0Nu1Y8kzSQklzJM2SND3tePJMUhdJD0uan3zf+EHaMeWVpN2SMbHm+FjS+WnHlVeSLki+d8+V9ICkzdKOKa8knZfk4WWPCWtNXrbQQiT1Az4F/hgRPdOOJ88kdQe6R8RMSVsAM4ATI+KVlEPLHUkCOkTEp5LaApOB8yLihZRDyy1JFwK9gU4RcWza8eSVpIVA74h4L+1Y8k7SH4BJETEy2bF684j4MO248k5SGfA2cGBELEo7nryRtDWF79l7RsQXkv4EjIuIe9ONLH8k9QQeBPoCtcBfgLMj4vVUA7Nc8MyDFhIRE4HlacdhEBFLImJm8vgTYB6wdbpR5VMUfJp82TY5XMFMiaRtgGOAkWnHYpYFkjoB/YC7ASKi1oWDzOgP/M2Fg1S1AdpLagNsTgt8hrw1yx7ACxHxeUSsAp4DhqQck+WEiweWK5K2B/YHpqYbSX4l0+RnAcuApyLCuUjPzcAlwOq0AzECeFLSDElD0w4mx3YE3gV+nyznGSmpQ9pBGVD4zPIH0g4iryLibeB64C1gCfBRRDyZblS5NRfoJ6mrpM2Bo4EeKcdkOeHigeWGpI7AaOD8iPg47XjyKiLqImI/YBugbzL9zlqZpGOBZRExI+1YDIBDIqIXcBQwLFn6Zq2vDdALuCMi9gc+A4anG5Ily0eOB0alHUteSfo74ARgB+DvgQ6STk83qnyKiHnAdcBTFJYsvASsSjUoyw0XDywXkvX1o4H7I+KRtOMxSKYCPwsMTjmUvDoEOD5Za/8gUCHpvnRDyq+IeCf5dxkwhsJaVmt91UB1vRlRD1MoJli6jgJmRkRN2oHk2JHAmxHxbkSsBB4BDk45ptyKiLsjoldE9KOwTNr7HVircPHASl6ySd/dwLyIuDHtePJM0vcldUket6fww8j8dKPKp4i4NCK2iYjtKUwHnhAR/itSCiR1SDZzJZkiP5DCtFRrZRGxFFgsabfkVH/Am+um7zS8ZCFtbwEHSdo8+bmqP4U9pCwFkrZK/t0W+BEeH9ZK2qQdQKmS9ABwOLClpGrgPyPi7nSjyq1DgH8A5iRr7QEui4hxKcaUV92BPyS7Zm8C/Cki/BGBlnfdgDGFn8dpA/xvRPwl3ZBy7Rzg/mSq/BvAT1OOJ9eSNd0DgH9NO5Y8i4ipkh4GZlKYIv8iUJluVLk2WlJXYCUwLCI+SDsgywd/VKOZmZmZmZmZNcnLFszMzMzMzMysSS4emJmZmZmZmVmTXDwwMzMzMzMzsya5eGBmZmZmZmZmTXLxwMzMzMzMzMya5OKBmZnljqQ6SbMkzZU0Kvk4uG97rcMljU0eHy9peBNtu0j6+bfo40pJFzXy3D8m9/GypFfWtJN0r6STN7QvMzMzs4a4eGBmZnn0RUTsFxE9gVrgrPpPqmCDv0dGxGMRcW0TTboAG1w8aIyko4DzgYERsRfQC/ioWNc3MzMzW8PFAzMzy7tJwM6Stpc0T9LvgJlAD0kDJU2RNDOZodARQNJgSfMlTQZ+tOZCks6UdFvyuJukMZJeSo6DgWuBnZJZD/+dtLtY0jRJsyVdVe9al0t6VdLTwG6NxH4pcFFEvAMQEV9GxIivN5L0H0kfcyVVSlJy/txktsJsSQ8m5w5L4psl6UVJW3zH99fMzMxKgIsHZmaWW5LaAEcBc5JTuwF/jIj9gc+AK4AjI6IXMB24UNJmwAjgOOCHQHkjl78VeC4i9qUwI+BlYDjwt2TWw8WSBgK7AH2B/YADJPWTdADwE2B/CsWJPo300ROY0YxbvS0i+iQzLdoDxybnhwP7R8Q+fDX74iJgWETsl9zfF824vpmZmZU4Fw/MzCyP2kuaRaEg8BZwd3J+UUS8kDw+CNgTeD5pewawHbA78GZEvB4RAdzXSB8VwB0AEVEXEQ0tJxiYHC9SmO2wO4Viwg+BMRHxeUR8DDz2ne4WjpA0VdKcJK69kvOzgfslnQ6sSs49D9wo6VygS0SsWv9yZmZmljdt0g7AzMwsBV8kf1lfK5nJ/1n9U8BTEXHa19rtB0SR4hDwm4i462t9nN/MPl4GDgAmNNpBYabE74DeEbFY0pXAZsnTxwD9gOOBf5e0V0RcK6kKOBp4QdKRETF/A+/LzMzMSoxnHpiZmTXsBeAQSTsDSNpc0q7AfGAHSTsl7U5r5PXjgbOT15ZJ6gR8AtTfQ+AJ4Gf19lLYWtJWwERgiKT2yZ4DxzXSx2+A/5JUnrx+02TGQH1rCgXvJf2cnLTdBOgREc8Al1DYzLGjpJ0iYk5EXEdhZsbuTb1JZmZmlg+eeWBmZtaAiHhX0pnAA5I2TU5fERGvSRoKVEl6D5hMYe+BrzsPqJT0T0AdcHZETJH0vKS5wOPJvgd7AFOSmQ+fAqdHxExJDwGzgEUUNnVsKMZxkroBTyebIAZwz9fafChpBIV9HRYC05KnyoD7JHWmMAPipqTtryQdkcT8CvD4hr1zZmZmVopUWK5pZmZmZmZmZtYwL1swMzMzMzMzsya5eGBmZmZmZmZmTXLxwMzMzMzMzMya5OKBmZmZmZmZmTXJxQMzMzMzMzMza5KLB2ZmZmZmZmbWJBcPzMzMzMzMzKxJLh6YmZmZmZmZWZP+H88ITsl5sJQJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Precision matrix (Columm Sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Recall matrix (Row sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read more about SGDClassifier() at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html\n",
    "# ------------------------------\n",
    "# default parameters\n",
    "# SGDClassifier(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None, \n",
    "# shuffle=True, verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5, \n",
    "# class_weight=None, warm_start=False, average=False, n_iter=None)\n",
    "\n",
    "# some of methods\n",
    "# fit(X, y[, coef_init, intercept_init, …])\tFit linear model with Stochastic Gradient Descent.\n",
    "# predict(X)\tPredict class labels for samples in X.\n",
    "\n",
    "#-------------------------------\n",
    "# video link: \n",
    "#------------------------------\n",
    "\n",
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "predict_and_plot_confusion_matrix(train_x_onehotCoding, train_y, cv_x_onehotCoding, cv_y, clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>4.1.2.3. Feature Importance, Correctly Classified point</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Class : 7\n",
      "Predicted Class Probabilities: [[0.1109 0.1273 0.0186 0.0985 0.0609 0.0466 0.5241 0.0059 0.0073]]\n",
      "Actual Class : 7\n",
      "--------------------------------------------------\n",
      "Out of the top  500  features  0 are present in query point\n"
     ]
    }
   ],
   "source": [
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "clf.fit(train_x_onehotCoding,train_y)\n",
    "test_point_index = 1\n",
    "no_feature = 500\n",
    "predicted_cls = sig_clf.predict(test_x_onehotCoding[test_point_index])\n",
    "print(\"Predicted Class :\", predicted_cls[0])\n",
    "print(\"Predicted Class Probabilities:\", np.round(sig_clf.predict_proba(test_x_onehotCoding[test_point_index]),4))\n",
    "print(\"Actual Class :\", test_y[test_point_index])\n",
    "indices = np.argsort(-clf.coef_)[predicted_cls-1][:,:no_feature]\n",
    "print(\"-\"*50)\n",
    "get_impfeature_names(indices[0], test_df['TEXT'].iloc[test_point_index],test_df['Gene'].iloc[test_point_index],test_df['Variation'].iloc[test_point_index], no_feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>4.1.2.4. Feature Importance, Inorrectly Classified point</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Class : 2\n",
      "Predicted Class Probabilities: [[1.020e-02 9.593e-01 1.000e-03 5.100e-03 8.300e-03 1.200e-03 9.700e-03\n",
      "  5.100e-03 1.000e-04]]\n",
      "Actual Class : 2\n",
      "--------------------------------------------------\n",
      "Out of the top  500  features  0 are present in query point\n"
     ]
    }
   ],
   "source": [
    "test_point_index = 100\n",
    "no_feature = 500\n",
    "predicted_cls = sig_clf.predict(test_x_onehotCoding[test_point_index])\n",
    "print(\"Predicted Class :\", predicted_cls[0])\n",
    "print(\"Predicted Class Probabilities:\", np.round(sig_clf.predict_proba(test_x_onehotCoding[test_point_index]),4))\n",
    "print(\"Actual Class :\", test_y[test_point_index])\n",
    "indices = np.argsort(-clf.coef_)[predicted_cls-1][:,:no_feature]\n",
    "print(\"-\"*50)\n",
    "get_impfeature_names(indices[0], test_df['TEXT'].iloc[test_point_index],test_df['Gene'].iloc[test_point_index],test_df['Variation'].iloc[test_point_index], no_feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion \n",
    "- What ever we have implemented in pervious notebook we will do the same thing just with minor changes in order to see where we can impove the model performance or not so and to do that what we will apply Logistic regression with CountVectorizer Features, including both unigrams and bigrams insteed of TFIDF what we have applied in pervious notebook and as we know when we will use CountVectorizer Features, including both unigrams and bigrams we know we will have the data with huge dim so instead of apply this on all the models we will only apply this on Logistic regression because as we know logisti reg works well on high dim data     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mPerformance Table of Models with TF-IDF \u001b[0m\n",
      "+-------------------------------------+-------+-------+-------+------------------+\n",
      "|                Models               | Train |   CV  |  Test | Misclassified(%) |\n",
      "+-------------------------------------+-------+-------+-------+------------------+\n",
      "|     Naive Bayes (One hot coding)    |  0.73 |  1.16 |  1.18 |      0.377       |\n",
      "|            KNN (Response)           |  0.47 | 1.008 |  1.11 |      0.3421      |\n",
      "|  LR(Class balanced) one hot coding  |  0.58 |  1.02 | 1.044 |      0.3515      |\n",
      "| LR(Class unbalanced) one hot coding |  0.57 |  1.08 | 1.088 |      0.3646      |\n",
      "|       Lr SVM one hot encoding       |  0.67 |  1.07 |  1.11 |       0.34       |\n",
      "|     Random Forest one hot coding    |  0.84 |  1.18 |  1.22 |       0.4        |\n",
      "|    Random Forest Response coding    |  0.05 |  1.25 |  1.32 |       0.47       |\n",
      "|         Stacking classifier         |  0.79 |  1.12 |  1.14 |       0.37       |\n",
      "|      Maximum Voting Classifier      |  0.94 |  1.16 |  1.19 |       0.37       |\n",
      "+-------------------------------------+-------+-------+-------+------------------+\n",
      "\n",
      "\n",
      "\u001b[32mPerformance Table of Models with CountVectorizer(ngram_range=(1,2))\u001b[0m\n",
      "+-------------------------------------+--------+------+------+------------------+\n",
      "|                Models               | Train  |  CV  | Test | Misclassified(%) |\n",
      "+-------------------------------------+--------+------+------+------------------+\n",
      "|  LR(Class balanced) one hot coding  | 0.8198 | 1.22 | 1.19 |      0.4022      |\n",
      "| LR(Class unbalanced) one hot coding | 0.8252 | 1.25 | 1.23 |     0.40041      |\n",
      "+-------------------------------------+--------+------+------+------------------+\n"
     ]
    }
   ],
   "source": [
    "from prettytable import PrettyTable\n",
    "from termcolor import colored\n",
    "print(colored('Performance Table of Models with TF-IDF ','red'))\n",
    "x = PrettyTable()\n",
    "x.field_names =[\"Models\",\"Train\",\"CV\",\"Test\",\"Misclassified(%)\"]\n",
    "\n",
    "x.add_row([\"Naive Bayes (One hot coding)\",0.73,1.16,1.18,0.377])\n",
    "x.add_row([\"KNN (Response)\",0.47,1.008,1.11,0.3421])\n",
    "x.add_row([\"LR(Class balanced) one hot coding\",0.58,1.02,1.044,0.3515])\n",
    "x.add_row([\"LR(Class unbalanced) one hot coding\",0.57,1.08,1.088,0.3646])\n",
    "x.add_row([\"Lr SVM one hot encoding\",0.67,1.07,1.11,0.34])\n",
    "x.add_row([\"Random Forest one hot coding\",0.84,1.18,1.22,0.40])\n",
    "x.add_row([\"Random Forest Response coding\",0.05,1.25,1.32,0.47])\n",
    "x.add_row([\"Stacking classifier\",0.79,1.12,1.14,0.37])\n",
    "x.add_row([\"Maximum Voting Classifier\",0.94,1.16,1.19,0.37])\n",
    "\n",
    "print(x)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(colored('Performance Table of Models with CountVectorizer(ngram_range=(1,2))','green'))\n",
    "x = PrettyTable()\n",
    "x.field_names =[\"Models\",\"Train\",\"CV\",\"Test\",\"Misclassified(%)\"]\n",
    "\n",
    "x.add_row([\"LR(Class balanced) one hot coding\",0.8198,1.22,1.19,0.4022])\n",
    "x.add_row([\"LR(Class unbalanced) one hot coding\",0.8252,1.25,1.23,0.40041])\n",
    "\n",
    "print(x)"
   ]
  }
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