spam_detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d23e4aca",
   "metadata": {},
   "source": [
    "# Importing Packages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05f3dc4b",
   "metadata": {},
   "source": [
    "Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "303d3da2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:17.454286Z",
     "start_time": "2022-03-29T16:21:15.843722Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tqdm\n",
    "import nltk\n",
    "import time\n",
    "import random\n",
    "import seaborn as sns\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "import matplotlib.pyplot as plt\n",
    "from operator import itemgetter\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c82c01ab",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:21.726531Z",
     "start_time": "2022-03-29T16:21:17.457272Z"
    }
   },
   "outputs": [],
   "source": [
    "import keras as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, roc_curve, roc_auc_score, f1_score, recall_score, precision_score\n",
    "from tensorflow.keras.layers import Embedding, LSTM, Dropout, Dense, Bidirectional\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.metrics import Recall, Precision\n",
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "%load_ext tensorboard"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb16430",
   "metadata": {},
   "source": [
    "# Load Data and Preprocess"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aadf0e4d",
   "metadata": {},
   "source": [
    "Load the messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a4dc4772",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:21.747842Z",
     "start_time": "2022-03-29T16:21:21.728990Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('messages.csv')\n",
    "df.head()\n",
    "df_orig = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7af259fd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:21.759886Z",
     "start_time": "2022-03-29T16:21:21.750401Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ccd12caa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:21.769348Z",
     "start_time": "2022-03-29T16:21:21.761888Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Category  \\\n",
      "0      ham   \n",
      "1      ham   \n",
      "2     spam   \n",
      "3      ham   \n",
      "4      ham   \n",
      "\n",
      "                                                                                                                                                       Message  \n",
      "0                                              Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...  \n",
      "1                                                                                                                                Ok lar... Joking wif u oni...  \n",
      "2  Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's  \n",
      "3                                                                                                            U dun say so early hor... U c already then say...  \n",
      "4                                                                                                Nah I don't think he goes to usf, he lives around here though  \n"
     ]
    }
   ],
   "source": [
    "pd.set_option('display.max_colwidth', None)\n",
    "\n",
    "print(df_orig.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1e5e18e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:21.779278Z",
     "start_time": "2022-03-29T16:21:21.771743Z"
    }
   },
   "outputs": [],
   "source": [
    "df_orig.drop_duplicates(subset=['Message'], keep='first', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6e637418",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:21.786051Z",
     "start_time": "2022-03-29T16:21:21.781163Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5157, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b291455a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:21.796770Z",
     "start_time": "2022-03-29T16:21:21.787883Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category\n",
       "ham     0.875703\n",
       "spam    0.124297\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig.value_counts('Category', normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57a44a95",
   "metadata": {},
   "source": [
    "Create a binary target where 1 = Spam and 0 = Ham. Rename the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8b30e31f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:21.814533Z",
     "start_time": "2022-03-29T16:21:21.799404Z"
    }
   },
   "outputs": [],
   "source": [
    "df['target'] = [1 if x=='spam' else 0 for  x in df.Category]\n",
    "df.drop('Category', axis=1, inplace=True)\n",
    "df.columns = ['message','target']\n",
    "df.drop_duplicates(subset=['message'], keep='first', inplace=True)\n",
    "df_spam = df[df.target==1]\n",
    "df_ham = df[df.target==0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b35c707",
   "metadata": {},
   "source": [
    "Observe the baseline accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dc669cd6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:21.828204Z",
     "start_time": "2022-03-29T16:21:21.819310Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.875703\n",
       "1    0.124297\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.target.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "653adf41",
   "metadata": {},
   "source": [
    "Baseline is 86.6%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc9fd054",
   "metadata": {},
   "source": [
    "Define X and y datasets, or predictors and target datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d771a757",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:21.833375Z",
     "start_time": "2022-03-29T16:21:21.830304Z"
    }
   },
   "outputs": [],
   "source": [
    "X= df.message\n",
    "y= df.target"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d26a1f0",
   "metadata": {},
   "source": [
    "I have left punctuation and non-alphanumeric characters in here for now because they are harmless. The Tensorflow Tokenizer can deal with them. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e72e396",
   "metadata": {},
   "source": [
    "# EDA Plots and Word Clouds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82fb7fc6",
   "metadata": {},
   "source": [
    "## EDA Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b08750bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:21.848359Z",
     "start_time": "2022-03-29T16:21:21.835618Z"
    }
   },
   "outputs": [],
   "source": [
    "df_orig['word_count'] = df_orig.Message.apply(lambda x: len(x.split(' ')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "92c2a882",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:21.866888Z",
     "start_time": "2022-03-29T16:21:21.851147Z"
    }
   },
   "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",
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       "    }\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ham</th>\n",
       "      <td>4516</td>\n",
       "      <td>14.328388</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spam</th>\n",
       "      <td>641</td>\n",
       "      <td>23.659906</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count       mean  median\n",
       "Category                          \n",
       "ham        4516  14.328388    11.0\n",
       "spam        641  23.659906    25.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig.groupby('Category')['word_count'].agg(['count','mean','median'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "61c6757d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:21.879788Z",
     "start_time": "2022-03-29T16:21:21.869380Z"
    }
   },
   "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",
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       "    .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>Category</th>\n",
       "      <th>Message</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2965</th>\n",
       "      <td>spam</td>\n",
       "      <td>Do you ever notice that when you're driving, anyone going slower than you is an idiot and everyone driving faster than you is a maniac?</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5411</th>\n",
       "      <td>ham</td>\n",
       "      <td>I ask if u meeting da ge tmr nite...</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>spam</td>\n",
       "      <td>okmail: Dear Dave this is your final notice to collect your 4* Tenerife Holiday or #5000 CASH award! Call 09061743806 from landline. TCs SAE Box326 CW25WX 150ppm</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>ham</td>\n",
       "      <td>Goodmorning sleeping ga.</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2586</th>\n",
       "      <td>ham</td>\n",
       "      <td>I will be outside office take all from there</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Category  \\\n",
       "2965     spam   \n",
       "5411      ham   \n",
       "471      spam   \n",
       "204       ham   \n",
       "2586      ham   \n",
       "\n",
       "                                                                                                                                                                Message  \\\n",
       "2965                            Do you ever notice that when you're driving, anyone going slower than you is an idiot and everyone driving faster than you is a maniac?   \n",
       "5411                                                                                                                               I ask if u meeting da ge tmr nite...   \n",
       "471   okmail: Dear Dave this is your final notice to collect your 4* Tenerife Holiday or #5000 CASH award! Call 09061743806 from landline. TCs SAE Box326 CW25WX 150ppm   \n",
       "204                                                                                                                                            Goodmorning sleeping ga.   \n",
       "2586                                                                                                                       I will be outside office take all from there   \n",
       "\n",
       "      word_count  \n",
       "2965          25  \n",
       "5411           9  \n",
       "471           27  \n",
       "204            3  \n",
       "2586           9  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "391ebaf8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:23.794992Z",
     "start_time": "2022-03-29T16:21:21.881549Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot message length by spam/ham\n",
    "\n",
    "df_orig['Category'] = pd.Categorical(df_orig['Category'], ['spam','ham'])\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "fig, ax = plt.subplots(ncols=2, nrows=1, figsize=(16,5))\n",
    "sns.histplot(data=df_orig, \n",
    "             x=\"word_count\",\n",
    "             hue=\"Category\",\n",
    "             palette='husl',\n",
    "             bins=75,\n",
    "             ax=ax[0],\n",
    "             legend=True).set_title('Distribution of message length by Spam/Ham')\n",
    "ax[0].set_xlabel('Message word count')\n",
    "ax[0].set_ylabel('Count')\n",
    "colors = sns.color_palette('husl')\n",
    "\n",
    "ax[1].pie(df_orig.Category.value_counts(normalize=True), labels = ['Ham','Spam'], autopct='%0.f%%')\n",
    "ax[1].set_title('Dataset message composition by Spam/Ham')\n",
    "plt.savefig('images/eda.png',bbox_inches='tight',dpi=400, pad_inches=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e7e364f8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:23.808434Z",
     "start_time": "2022-03-29T16:21:23.797251Z"
    }
   },
   "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>Category</th>\n",
       "      <th>Message</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1085</th>\n",
       "      <td>ham</td>\n",
       "      <td>For me the love should start with attraction.i should feel that I need her every time around me.she should be the first thing which comes in my thoughts.I would start the day and end it with her.she should be there every time I dream.love will be then when my every breath has her name.my life should happen around her.my life will be named to her.I would cry for her.will give all my happiness and take all her sorrows.I will be ready to fight with anyone for her.I will be in love when I will be doing the craziest things for her.love will be when I don't have to proove anyone that my girl is the most beautiful lady on the whole planet.I will always be singing praises for her.love will be when I start up making chicken curry and end up makiing sambar.life will be the most beautiful then.will get every morning and thank god for the day because she is with me.I would like to say a lot..will tell later..</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>ham</td>\n",
       "      <td>The last thing i ever wanted to do was hurt you. And i didn't think it would have. You'd laugh, be embarassed, delete the tag and keep going. But as far as i knew, it wasn't even up. The fact that you even felt like i would do it to hurt you shows you really don't know me at all. It was messy wednesday, but it wasn't bad. The problem i have with it is you HAVE the time to clean it, but you choose not to. You skype, you take pictures, you sleep, you want to go out. I don't mind a few things here and there, but when you don't make the bed, when you throw laundry on top of it, when i can't have a friend in the house because i'm embarassed that there's underwear and bras strewn on the bed, pillows on the floor, that's something else. You used to be good about at least making the bed.</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158</th>\n",
       "      <td>ham</td>\n",
       "      <td>Sad story of a Man - Last week was my b'day. My Wife did'nt wish me. My Parents forgot n so did my Kids . I went to work. Even my Colleagues did not wish. As I entered my cabin my PA said, '' Happy B'day Boss !!''. I felt special. She askd me 4 lunch. After lunch she invited me to her apartment. We went there. She said,'' do u mind if I go into the bedroom for a minute ? '' ''OK'', I sed in a sexy mood. She came out 5 minuts latr wid a cake...n My Wife, My Parents, My Kidz, My Friends n My Colleagues. All screaming.. SURPRISE !! and I was waiting on the sofa.. ... ..... ' NAKED...!</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1579</th>\n",
       "      <td>ham</td>\n",
       "      <td>How to Make a girl Happy? It's not at all difficult to make girls happy. U only need to be... 1. A friend 2. Companion 3. Lover 4. Chef . . .  &amp;lt;#&amp;gt; . Good listener  &amp;lt;#&amp;gt; . Organizer  &amp;lt;#&amp;gt; . Good boyfriend  &amp;lt;#&amp;gt; . Very clean  &amp;lt;#&amp;gt; . Sympathetic  &amp;lt;#&amp;gt; . Athletic  &amp;lt;#&amp;gt; . Warm . . .  &amp;lt;#&amp;gt; . Courageous  &amp;lt;#&amp;gt; . Determined  &amp;lt;#&amp;gt; . True  &amp;lt;#&amp;gt; . Dependable  &amp;lt;#&amp;gt; . Intelligent . . .  &amp;lt;#&amp;gt; . Psychologist  &amp;lt;#&amp;gt; . Pest exterminator  &amp;lt;#&amp;gt; . Psychiatrist  &amp;lt;#&amp;gt; . Healer . .  &amp;lt;#&amp;gt; . Stylist  &amp;lt;#&amp;gt; . Driver . . Aaniye pudunga venaam..</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2434</th>\n",
       "      <td>ham</td>\n",
       "      <td>Indians r poor but India is not a poor country. Says one of the swiss bank directors. He says that \" &amp;lt;#&amp;gt;  lac crore\" of Indian money is deposited in swiss banks which can be used for 'taxless' budget for  &amp;lt;#&amp;gt;  yrs. Can give  &amp;lt;#&amp;gt;  crore jobs to all Indians. From any village to Delhi 4 lane roads. Forever free power suply to more than  &amp;lt;#&amp;gt;  social projects. Every citizen can get monthly  &amp;lt;#&amp;gt; /- for  &amp;lt;#&amp;gt;  yrs. No need of World Bank &amp;amp; IMF loan. Think how our money is blocked by rich politicians. We have full rights against corrupt politicians. Itna forward karo ki pura INDIA padhe.g.m.\"</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>ham</td>\n",
       "      <td>Okie...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>ham</td>\n",
       "      <td>ALRITE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2508</th>\n",
       "      <td>ham</td>\n",
       "      <td>Yup...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>ham</td>\n",
       "      <td>Beerage?</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2602</th>\n",
       "      <td>ham</td>\n",
       "      <td>Okie</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5157 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Category  \\\n",
       "1085      ham   \n",
       "1863      ham   \n",
       "2158      ham   \n",
       "1579      ham   \n",
       "2434      ham   \n",
       "...       ...   \n",
       "284       ham   \n",
       "2908      ham   \n",
       "2508      ham   \n",
       "783       ham   \n",
       "2602      ham   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Message  \\\n",
       "1085  For me the love should start with attraction.i should feel that I need her every time around me.she should be the first thing which comes in my thoughts.I would start the day and end it with her.she should be there every time I dream.love will be then when my every breath has her name.my life should happen around her.my life will be named to her.I would cry for her.will give all my happiness and take all her sorrows.I will be ready to fight with anyone for her.I will be in love when I will be doing the craziest things for her.love will be when I don't have to proove anyone that my girl is the most beautiful lady on the whole planet.I will always be singing praises for her.love will be when I start up making chicken curry and end up makiing sambar.life will be the most beautiful then.will get every morning and thank god for the day because she is with me.I would like to say a lot..will tell later..   \n",
       "1863                                                                                                                          The last thing i ever wanted to do was hurt you. And i didn't think it would have. You'd laugh, be embarassed, delete the tag and keep going. But as far as i knew, it wasn't even up. The fact that you even felt like i would do it to hurt you shows you really don't know me at all. It was messy wednesday, but it wasn't bad. The problem i have with it is you HAVE the time to clean it, but you choose not to. You skype, you take pictures, you sleep, you want to go out. I don't mind a few things here and there, but when you don't make the bed, when you throw laundry on top of it, when i can't have a friend in the house because i'm embarassed that there's underwear and bras strewn on the bed, pillows on the floor, that's something else. You used to be good about at least making the bed.   \n",
       "2158                                                                                                                                                                                                                                                                                                                                    Sad story of a Man - Last week was my b'day. My Wife did'nt wish me. My Parents forgot n so did my Kids . I went to work. Even my Colleagues did not wish. As I entered my cabin my PA said, '' Happy B'day Boss !!''. I felt special. She askd me 4 lunch. After lunch she invited me to her apartment. We went there. She said,'' do u mind if I go into the bedroom for a minute ? '' ''OK'', I sed in a sexy mood. She came out 5 minuts latr wid a cake...n My Wife, My Parents, My Kidz, My Friends n My Colleagues. All screaming.. SURPRISE !! and I was waiting on the sofa.. ... ..... ' NAKED...!   \n",
       "1579                                                                                                                                                                                                                                                                                                             How to Make a girl Happy? It's not at all difficult to make girls happy. U only need to be... 1. A friend 2. Companion 3. Lover 4. Chef . . .  &lt;#&gt; . Good listener  &lt;#&gt; . Organizer  &lt;#&gt; . Good boyfriend  &lt;#&gt; . Very clean  &lt;#&gt; . Sympathetic  &lt;#&gt; . Athletic  &lt;#&gt; . Warm . . .  &lt;#&gt; . Courageous  &lt;#&gt; . Determined  &lt;#&gt; . True  &lt;#&gt; . Dependable  &lt;#&gt; . Intelligent . . .  &lt;#&gt; . Psychologist  &lt;#&gt; . Pest exterminator  &lt;#&gt; . Psychiatrist  &lt;#&gt; . Healer . .  &lt;#&gt; . Stylist  &lt;#&gt; . Driver . . Aaniye pudunga venaam..   \n",
       "2434                                                                                                                                                                                                                                                                                           Indians r poor but India is not a poor country. Says one of the swiss bank directors. He says that \" &lt;#&gt;  lac crore\" of Indian money is deposited in swiss banks which can be used for 'taxless' budget for  &lt;#&gt;  yrs. Can give  &lt;#&gt;  crore jobs to all Indians. From any village to Delhi 4 lane roads. Forever free power suply to more than  &lt;#&gt;  social projects. Every citizen can get monthly  &lt;#&gt; /- for  &lt;#&gt;  yrs. No need of World Bank &amp; IMF loan. Think how our money is blocked by rich politicians. We have full rights against corrupt politicians. Itna forward karo ki pura INDIA padhe.g.m.\"   \n",
       "...                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              ...   \n",
       "284                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Okie...   \n",
       "2908                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          ALRITE   \n",
       "2508                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Yup...   \n",
       "783                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         Beerage?   \n",
       "2602                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Okie   \n",
       "\n",
       "      word_count  \n",
       "1085         171  \n",
       "1863         162  \n",
       "2158         125  \n",
       "1579         121  \n",
       "2434         119  \n",
       "...          ...  \n",
       "284            1  \n",
       "2908           1  \n",
       "2508           1  \n",
       "783            1  \n",
       "2602           1  \n",
       "\n",
       "[5157 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig.sort_values(by='word_count', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77bb6078",
   "metadata": {},
   "source": [
    "## Word Clouds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5f4ce213",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:23.815457Z",
     "start_time": "2022-03-29T16:21:23.810801Z"
    }
   },
   "outputs": [],
   "source": [
    "# count word appearances\n",
    "def word_counter(mat):\n",
    "    count = np.array(mat.sum(axis=0))[0]\n",
    "    return count\n",
    "\n",
    "# sort words by frequency\n",
    "def word_frequency_sorter(words, counts):\n",
    "    sort_result = sorted(zip(words, list(counts)),\n",
    "                         key=itemgetter(1), reverse=True)\n",
    "    return sort_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "87a7a1e8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:24.106580Z",
     "start_time": "2022-03-29T16:21:23.821137Z"
    }
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stop = stopwords.words('english')\n",
    "\n",
    "# Series text column\n",
    "tvec = TfidfVectorizer(stop_words = stop,\n",
    "                       ngram_range = (1, 2),\n",
    "                       max_features = 1000,\n",
    "                       token_pattern = '[A-Za-z0-9]+(?=\\\\s+)',\n",
    "                        )\n",
    "\n",
    "# Fit vectorizer on text data\n",
    "tvec.fit(df.message)\n",
    "\n",
    "# Transform data\n",
    "tvec_mat = tvec.transform(df.message)\n",
    "\n",
    "# words occuring\n",
    "words = tvec.get_feature_names_out()\n",
    "\n",
    "# count word appearances\n",
    "csum= word_counter(tvec_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "12ba6253",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:24.119679Z",
     "start_time": "2022-03-29T16:21:24.109797Z"
    }
   },
   "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>frequency_series</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>u</th>\n",
       "      <td>187.755719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>call</th>\n",
       "      <td>109.487576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>get</th>\n",
       "      <td>108.333458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91.910420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ur</th>\n",
       "      <td>81.290887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>got</th>\n",
       "      <td>78.853555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>like</th>\n",
       "      <td>78.550733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>come</th>\n",
       "      <td>77.469296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>go</th>\n",
       "      <td>74.703036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>know</th>\n",
       "      <td>68.392965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>67.684492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>going</th>\n",
       "      <td>64.618320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>want</th>\n",
       "      <td>61.138508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ok</th>\n",
       "      <td>60.599661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>still</th>\n",
       "      <td>59.964543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58.781663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>send</th>\n",
       "      <td>56.903585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>need</th>\n",
       "      <td>55.439239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>see</th>\n",
       "      <td>54.492491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>text</th>\n",
       "      <td>52.445365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       frequency_series\n",
       "u            187.755719\n",
       "call         109.487576\n",
       "get          108.333458\n",
       "2             91.910420\n",
       "ur            81.290887\n",
       "got           78.853555\n",
       "like          78.550733\n",
       "come          77.469296\n",
       "go            74.703036\n",
       "know          68.392965\n",
       "good          67.684492\n",
       "going         64.618320\n",
       "want          61.138508\n",
       "ok            60.599661\n",
       "still         59.964543\n",
       "4             58.781663\n",
       "send          56.903585\n",
       "need          55.439239\n",
       "see           54.492491\n",
       "text          52.445365"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort words by frequency\n",
    "df_word_frequency = pd.DataFrame(word_frequency_sorter(\n",
    "    words, csum), columns=[\"word\", \"frequency_series\"])\n",
    "df_word_frequency.set_index('word', inplace=True)\n",
    "df_word_frequency.index.name = None\n",
    "df_word_frequency.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "88755b94",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:24.633251Z",
     "start_time": "2022-03-29T16:21:24.121436Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 413095 unique words in all the descriptions.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<wordcloud.wordcloud.WordCloud at 0x7fbd17770790>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all description text\n",
    "wrds_cnt = \" \".join(wrds for wrds in df.message)\n",
    "print (\"There are {} unique words in all the descriptions.\".format(len(wrds_cnt)))\n",
    "\n",
    "# create stopword list:\n",
    "stopwords = set(STOPWORDS)\n",
    "\n",
    "# create word cloud\n",
    "wc_all = WordCloud(stopwords=stopwords,\n",
    "                                  max_words=200,\n",
    "                                  background_color=\"white\")\n",
    "\n",
    "# generate a word cloud image\n",
    "wc_all.generate(wrds_cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "31b42de4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:27.953086Z",
     "start_time": "2022-03-29T16:21:24.634755Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 324561 unique words in ham the descriptions.\n"
     ]
    }
   ],
   "source": [
    "# ham description text\n",
    "wrds_cnt = \" \".join(wrds for wrds in df_ham.message)\n",
    "print (\"There are {} unique words in ham the descriptions.\".format(len(wrds_cnt)))\n",
    "\n",
    "# create word mask\n",
    "mask_ham = np.array(Image.open('images/ham2.png'))\n",
    "\n",
    "# create word cloud\n",
    "wc_ham = WordCloud(stopwords=STOPWORDS,\n",
    "               mask=mask_ham, background_color=\"white\",\n",
    "               max_words=2000, max_font_size=256,\n",
    "               random_state=42, width=mask_ham.shape[1],\n",
    "               height=mask_ham.shape[0])\n",
    "wc_ham.generate(wrds_cnt);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0c92bbba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:29.989381Z",
     "start_time": "2022-03-29T16:21:27.956187Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 88533 unique words in spam the descriptions.\n"
     ]
    }
   ],
   "source": [
    "# spam description text\n",
    "wrds_cnt = \" \".join(wrds for wrds in df_spam.message)\n",
    "print (\"There are {} unique words in spam the descriptions.\".format(len(wrds_cnt)))\n",
    "\n",
    "# create word mask\n",
    "mask_spam = np.array(Image.open('images/spam2.png'))\n",
    "\n",
    "# create word cloud\n",
    "wc_spam = WordCloud(stopwords=STOPWORDS,\n",
    "               mask=mask_spam, background_color=\"white\",\n",
    "               max_words=2000, max_font_size=256,\n",
    "               random_state=42, width=mask_spam.shape[1],\n",
    "               height=mask_spam.shape[0])\n",
    "wc_spam.generate(wrds_cnt);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "600a4ba2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:33.570603Z",
     "start_time": "2022-03-29T16:21:29.991691Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# combine wordcloud plots\n",
    "sns.set_style(\"white\")\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(14,8))\n",
    "ax[0].imshow(wc_spam, interpolation=\"bilinear\")\n",
    "ax[0].set_title('Spam word cloud')\n",
    "ax[1].imshow(wc_ham, interpolation=\"bilinear\")\n",
    "ax[1].set_title('Ham word cloud')\n",
    "fig.set_facecolor(\"white\")\n",
    "ax[0].axis('off')\n",
    "ax[1].axis('off')\n",
    "plt.savefig('images/cloud.png',bbox_inches='tight',dpi=400, pad_inches=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd1e8854",
   "metadata": {},
   "source": [
    "# Tensorflow Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "89521956",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:33.574219Z",
     "start_time": "2022-03-29T16:21:33.572118Z"
    }
   },
   "outputs": [],
   "source": [
    "# https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1ca39067",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:33.579666Z",
     "start_time": "2022-03-29T16:21:33.576539Z"
    }
   },
   "outputs": [],
   "source": [
    "SEQUENCE_LENGTH = 50 # the length of all sequences (number of words per sample)\n",
    "EMBEDDING_SIZE = 100  # Using 100-Dimensional GloVe embedding vectors\n",
    "TEST_SIZE = 0.25 # ratio of testing set\n",
    "\n",
    "BATCH_SIZE = 64\n",
    "EPOCHS = 20 # number of epochs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b96c113",
   "metadata": {},
   "source": [
    "## Tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d7ca9a65",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:33.795492Z",
     "start_time": "2022-03-29T16:21:33.580900Z"
    }
   },
   "outputs": [],
   "source": [
    "# Text tokenization - we have to do this before obtaining the word embeddings \n",
    "# https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/\n",
    "\n",
    "# vectorizing text, turning each text into sequence of integers. \n",
    "# Default behaviour is to filter all punctuation, plus tabs and line breaks, minus the ' character.\n",
    "tokenizer = Tokenizer()\n",
    "tokenizer.fit_on_texts(X)\n",
    "# convert to sequence of integers\n",
    "X = tokenizer.texts_to_sequences(X)\n",
    "\n",
    "# Save this dictionary for later\n",
    "word_index = tokenizer.word_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1e514f86",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:33.802324Z",
     "start_time": "2022-03-29T16:21:33.798214Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[49, 304, 1364, 435, 6, 1766]\n"
     ]
    }
   ],
   "source": [
    "print(X[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "957f9431",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:33.832678Z",
     "start_time": "2022-03-29T16:21:33.810249Z"
    }
   },
   "outputs": [],
   "source": [
    "# pad sequences at the beginning of each sequence with 0's\n",
    "# for example if SEQUENCE_LENGTH=4:\n",
    "# [[5, 3, 2], [5, 1, 2, 3], [3, 4]]\n",
    "# will be transformed to:\n",
    "# [[0, 5, 3, 2], [5, 1, 2, 3], [0, 0, 3, 4]]\n",
    "X = pad_sequences(sequences=X, \n",
    "                  maxlen=SEQUENCE_LENGTH, \n",
    "                  padding='pre', \n",
    "                  truncating='post')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "542436a3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:33.838577Z",
     "start_time": "2022-03-29T16:21:33.834687Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5157, 50)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4292bc7",
   "metadata": {},
   "source": [
    "Discuss what this 100 term is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f6acfc2f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:33.848311Z",
     "start_time": "2022-03-29T16:21:33.842799Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0,    0,    0, ...,   56, 4005,  137],\n",
       "       [   0,    0,    0, ...,  435,    6, 1766],\n",
       "       [   0,    0,    0, ...,  707,  437, 4006],\n",
       "       ...,\n",
       "       [   0,    0,    0, ...,  100,  240, 9003],\n",
       "       [   0,    0,    0, ...,  192,   12,   53],\n",
       "       [   0,    0,    0, ...,    2,   63,  284]], dtype=int32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e497fe80",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:33.861646Z",
     "start_time": "2022-03-29T16:21:33.852276Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       0\n",
       "2       1\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "5567    1\n",
       "5568    0\n",
       "5569    0\n",
       "5570    0\n",
       "5571    0\n",
       "Name: target, Length: 5157, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8293fe14",
   "metadata": {},
   "source": [
    "## Train-Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f8381025",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:33.873094Z",
     "start_time": "2022-03-29T16:21:33.863327Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape: (3867, 50)\n",
      "X_test.shape: (1290, 50)\n",
      "y_train.shape: (3867,)\n",
      "y_test.shape: (1290,)\n"
     ]
    }
   ],
   "source": [
    "# split and shuffle\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=TEST_SIZE, random_state=5)\n",
    "# print our data shapes\n",
    "print(\"X_train.shape:\", X_train.shape)\n",
    "print(\"X_test.shape:\", X_test.shape)\n",
    "print(\"y_train.shape:\", y_train.shape)\n",
    "print(\"y_test.shape:\", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c06c1906",
   "metadata": {},
   "source": [
    "## Word Embedding Using GloVe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f1131a2",
   "metadata": {},
   "source": [
    "I'm using a pre-trained [GloVe word embedding](https://nlp.stanford.edu/projects/glove/) dataset in 100 dimensional space, saved outside of this repository. \n",
    "\n",
    "This function opens the GloVe file and maps each of the tokenized words to the corresponding embedded vector. This process works to encode meanings of words in a way that causes words of comparable meaning to be expressed similarly in the defined vector space. More can be read about word embedding [here](https://machinelearningmastery.com/what-are-word-embeddings/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "242fccba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:33.881433Z",
     "start_time": "2022-03-29T16:21:33.875814Z"
    }
   },
   "outputs": [],
   "source": [
    "# https://machinelearningmastery.com/what-are-word-embeddings/\n",
    "# https://nlp.stanford.edu/projects/glove/\n",
    "\n",
    "def get_embedding_vectors(tokenizer, dim=100):\n",
    "    embedding_index = {}\n",
    "    with open(f\"../data/glove.6B.{dim}d.txt\", encoding='utf8') as f:\n",
    "        for line in tqdm.tqdm(f, \"Reading GloVe\"):\n",
    "            values = line.split()\n",
    "            word = values[0]\n",
    "            vectors = np.asarray(values[1:], dtype='float32')\n",
    "            embedding_index[word] = vectors\n",
    "\n",
    "    word_index = tokenizer.word_index\n",
    "    embedding_matrix = np.zeros((len(word_index)+1, dim))\n",
    "    for word, i in word_index.items():\n",
    "        embedding_vector = embedding_index.get(word)\n",
    "        if embedding_vector is not None:\n",
    "            # words not found will be 0s\n",
    "            embedding_matrix[i] = embedding_vector\n",
    "            \n",
    "    return embedding_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13826bab",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T13:33:17.081125Z",
     "start_time": "2022-03-23T13:33:17.076234Z"
    },
    "code_folding": []
   },
   "source": [
    "## BiLSTM Model Definition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc31d549",
   "metadata": {},
   "source": [
    "Define a list of metrics to be used later in the model definition. These will be calculated and saved after each epoch in the model build for visibility on the modelling process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "58004b2f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:33.925467Z",
     "start_time": "2022-03-29T16:21:33.885068Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-03-29 17:21:33.894996: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "METRICS = [\n",
    "      keras.metrics.TruePositives(name='tp'),\n",
    "      keras.metrics.FalsePositives(name='fp'),\n",
    "      keras.metrics.TrueNegatives(name='tn'),\n",
    "      keras.metrics.FalseNegatives(name='fn'), \n",
    "      keras.metrics.BinaryAccuracy(name='accuracy'),\n",
    "      keras.metrics.Precision(name='precision'),\n",
    "      keras.metrics.Recall(name='recall'),\n",
    "      keras.metrics.AUC(name='auc'),\n",
    "      keras.metrics.AUC(name='prc', curve='PR')] # precision-recall curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fef16d59",
   "metadata": {},
   "source": [
    "Now to define the Keras model. I'm going to do this using the [sequential model API](https://machinelearningmastery.com/keras-functional-api-deep-learning/) where Sequential class is created and model layers are then added to it. The steps I use are as follows:\n",
    "* Embed the tokenized words\n",
    "* Add a user defined number of Bi-directional Long Short-Term Memory units \n",
    "* Add a Dropout feature of 0.2 to combat over-fitting\n",
    "* Output one dense layer with a sigmoid activation function for 1/0 classification\n",
    "* Compile the model using [binary crossentropy](https://machinelearningmastery.com/how-to-choose-loss-functions-when-training-deep-learning-neural-networks/) as the loss function and rmsprop optimizer, reporting the extensive list of metrics after each epoch \n",
    "\n",
    "With training the model through back-propagation, RNNs famously suffer from the vanishing gradient problem. This means that the RNN would struggle to learn long-range dependencies from the early layers and is often described as a short-term memory problem. To combat this we used specialised units within the hidden layers - Long Short-Term Memory units or LSTMs. These LSTMs are able to learn long-range dependencies through a series of gated tensor operations which dictate what information to add or remove from the hidden state at each unit. \n",
    "<br>\n",
    "<br>\n",
    "<img src=\"images/LSTM.png\" width=400 height=400 />\n",
    "<center><sub>This is a graphical representation of a single LSTM unit. The sigma and tanh cells represent sigmoid and hyperbolic tangent functions respectively. X_t and h_t are the input vector and output value of the LSTM unit. </sub></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "83537248",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:33.933725Z",
     "start_time": "2022-03-29T16:21:33.928009Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def get_bidirectional_model(tokenizer, lstm_units):\n",
    "    \"\"\"\n",
    "    Constructs the model,\n",
    "    Embedding vectors => Bi-LSTM => 1 output Fully-Connected neuron with sigmoid activation\n",
    "    \"\"\"\n",
    "    # get the GloVe embedding vectors\n",
    "    embedding_matrix = get_embedding_vectors(tokenizer)\n",
    "    # we're going to define the model sequentially https://machinelearningmastery.com/keras-functional-api-deep-learning\n",
    "    model = Sequential()\n",
    "    # First, embed the words using loaded GloVe\n",
    "    model.add(Embedding(len(tokenizer.word_index)+1,\n",
    "              EMBEDDING_SIZE,\n",
    "              weights=[embedding_matrix],\n",
    "              trainable=True,\n",
    "              mask_zero=True,\n",
    "              input_length=SEQUENCE_LENGTH))\n",
    "    # Add bidirectional long short-term memory units\n",
    "    model.add(Bidirectional(LSTM(lstm_units, recurrent_dropout=0.2)))\n",
    "    # Add dropout to combat overfitting\n",
    "    model.add(Dropout(0.2))\n",
    "    # Add output dense layer with sigmoid for 1/0 classification\n",
    "    model.add(Dense(1, activation=\"sigmoid\"))\n",
    "    # compile as rmsprop optimizer\n",
    "    # aswell as with recall metric\n",
    "    model.compile(optimizer=\"rmsprop\", \n",
    "                  loss=\"binary_crossentropy\",\n",
    "                  metrics=[METRICS])\n",
    "\n",
    "    model.summary()\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2384db2f",
   "metadata": {},
   "source": [
    "Call the `get_model` function to generate the skeleton - not trained yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bbbe25e8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:21:42.663050Z",
     "start_time": "2022-03-29T16:21:33.936582Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reading GloVe: 400001it [00:08, 48521.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, 50, 100)           900500    \n",
      "                                                                 \n",
      " bidirectional (Bidirectiona  (None, 256)              234496    \n",
      " l)                                                              \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 256)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 257       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,135,253\n",
      "Trainable params: 1,135,253\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# constructs the bidirectional-model with 128 LSTM units\n",
    "bimodel = get_bidirectional_model(tokenizer=tokenizer, lstm_units=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cc47304",
   "metadata": {},
   "source": [
    "Initialise required TensorBoard callbacks, best model_checkpoints and early stopping. Then fit the model using a batch size and number of epochs defined at the start of section 3. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f555ac89",
   "metadata": {},
   "source": [
    "[Intersting link on explaining feature importance, although I don't know how it will work with the word embedder](https://github.com/slundberg/shap#deep-learning-example-with-deepexplainer-tensorflowkeras-models)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c5a6e38",
   "metadata": {},
   "source": [
    "## BiLSTM Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "476371bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:24:33.431912Z",
     "start_time": "2022-03-29T16:21:42.665617Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ERROR: Could not find `tensorboard`. Please ensure that your PATH\n",
       "contains an executable `tensorboard` program, or explicitly specify\n",
       "the path to a TensorBoard binary by setting the `TENSORBOARD_BINARY`\n",
       "environment variable."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 0.1601 - tp: 322.0000 - fp: 52.0000 - tn: 3330.0000 - fn: 163.0000 - accuracy: 0.9444 - precision: 0.8610 - recall: 0.6639 - auc: 0.9509 - prc: 0.8381\n",
      "Epoch 1: val_loss improved from inf to 0.07070, saving model to ../results/spam_classifier_0.07.h5\n",
      "61/61 [==============================] - 15s 159ms/step - loss: 0.1601 - tp: 322.0000 - fp: 52.0000 - tn: 3330.0000 - fn: 163.0000 - accuracy: 0.9444 - precision: 0.8610 - recall: 0.6639 - auc: 0.9509 - prc: 0.8381 - val_loss: 0.0707 - val_tp: 137.0000 - val_fp: 9.0000 - val_tn: 1125.0000 - val_fn: 19.0000 - val_accuracy: 0.9783 - val_precision: 0.9384 - val_recall: 0.8782 - val_auc: 0.9828 - val_prc: 0.9577\n",
      "Epoch 2/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 0.0710 - tp: 426.0000 - fp: 29.0000 - tn: 3353.0000 - fn: 59.0000 - accuracy: 0.9772 - precision: 0.9363 - recall: 0.8784 - auc: 0.9873 - prc: 0.9602\n",
      "Epoch 2: val_loss improved from 0.07070 to 0.06883, saving model to ../results/spam_classifier_0.07.h5\n",
      "61/61 [==============================] - 9s 142ms/step - loss: 0.0710 - tp: 426.0000 - fp: 29.0000 - tn: 3353.0000 - fn: 59.0000 - accuracy: 0.9772 - precision: 0.9363 - recall: 0.8784 - auc: 0.9873 - prc: 0.9602 - val_loss: 0.0688 - val_tp: 130.0000 - val_fp: 3.0000 - val_tn: 1131.0000 - val_fn: 26.0000 - val_accuracy: 0.9775 - val_precision: 0.9774 - val_recall: 0.8333 - val_auc: 0.9846 - val_prc: 0.9626\n",
      "Epoch 3/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 0.0471 - tp: 452.0000 - fp: 20.0000 - tn: 3362.0000 - fn: 33.0000 - accuracy: 0.9863 - precision: 0.9576 - recall: 0.9320 - auc: 0.9931 - prc: 0.9794\n",
      "Epoch 3: val_loss improved from 0.06883 to 0.05711, saving model to ../results/spam_classifier_0.06.h5\n",
      "61/61 [==============================] - 8s 138ms/step - loss: 0.0471 - tp: 452.0000 - fp: 20.0000 - tn: 3362.0000 - fn: 33.0000 - accuracy: 0.9863 - precision: 0.9576 - recall: 0.9320 - auc: 0.9931 - prc: 0.9794 - val_loss: 0.0571 - val_tp: 138.0000 - val_fp: 3.0000 - val_tn: 1131.0000 - val_fn: 18.0000 - val_accuracy: 0.9837 - val_precision: 0.9787 - val_recall: 0.8846 - val_auc: 0.9869 - val_prc: 0.9714\n",
      "Epoch 4/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 0.0340 - tp: 459.0000 - fp: 9.0000 - tn: 3373.0000 - fn: 26.0000 - accuracy: 0.9909 - precision: 0.9808 - recall: 0.9464 - auc: 0.9944 - prc: 0.9861\n",
      "Epoch 4: val_loss did not improve from 0.05711\n",
      "61/61 [==============================] - 9s 139ms/step - loss: 0.0340 - tp: 459.0000 - fp: 9.0000 - tn: 3373.0000 - fn: 26.0000 - accuracy: 0.9909 - precision: 0.9808 - recall: 0.9464 - auc: 0.9944 - prc: 0.9861 - val_loss: 0.0594 - val_tp: 135.0000 - val_fp: 1.0000 - val_tn: 1133.0000 - val_fn: 21.0000 - val_accuracy: 0.9829 - val_precision: 0.9926 - val_recall: 0.8654 - val_auc: 0.9873 - val_prc: 0.9736\n",
      "Epoch 5/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 0.0258 - tp: 467.0000 - fp: 10.0000 - tn: 3372.0000 - fn: 18.0000 - accuracy: 0.9928 - precision: 0.9790 - recall: 0.9629 - auc: 0.9987 - prc: 0.9932\n",
      "Epoch 5: val_loss improved from 0.05711 to 0.05522, saving model to ../results/spam_classifier_0.06.h5\n",
      "61/61 [==============================] - 9s 140ms/step - loss: 0.0258 - tp: 467.0000 - fp: 10.0000 - tn: 3372.0000 - fn: 18.0000 - accuracy: 0.9928 - precision: 0.9790 - recall: 0.9629 - auc: 0.9987 - prc: 0.9932 - val_loss: 0.0552 - val_tp: 144.0000 - val_fp: 5.0000 - val_tn: 1129.0000 - val_fn: 12.0000 - val_accuracy: 0.9868 - val_precision: 0.9664 - val_recall: 0.9231 - val_auc: 0.9875 - val_prc: 0.9644\n",
      "Epoch 6/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 0.0141 - tp: 476.0000 - fp: 3.0000 - tn: 3379.0000 - fn: 9.0000 - accuracy: 0.9969 - precision: 0.9937 - recall: 0.9814 - auc: 0.9977 - prc: 0.9961\n",
      "Epoch 6: val_loss did not improve from 0.05522\n",
      "61/61 [==============================] - 8s 139ms/step - loss: 0.0141 - tp: 476.0000 - fp: 3.0000 - tn: 3379.0000 - fn: 9.0000 - accuracy: 0.9969 - precision: 0.9937 - recall: 0.9814 - auc: 0.9977 - prc: 0.9961 - val_loss: 0.0780 - val_tp: 147.0000 - val_fp: 8.0000 - val_tn: 1126.0000 - val_fn: 9.0000 - val_accuracy: 0.9868 - val_precision: 0.9484 - val_recall: 0.9423 - val_auc: 0.9826 - val_prc: 0.9364\n",
      "Epoch 7/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 0.0129 - tp: 474.0000 - fp: 3.0000 - tn: 3379.0000 - fn: 11.0000 - accuracy: 0.9964 - precision: 0.9937 - recall: 0.9773 - auc: 0.9986 - prc: 0.9968\n",
      "Epoch 7: val_loss improved from 0.05522 to 0.04715, saving model to ../results/spam_classifier_0.05.h5\n",
      "61/61 [==============================] - 8s 137ms/step - loss: 0.0129 - tp: 474.0000 - fp: 3.0000 - tn: 3379.0000 - fn: 11.0000 - accuracy: 0.9964 - precision: 0.9937 - recall: 0.9773 - auc: 0.9986 - prc: 0.9968 - val_loss: 0.0471 - val_tp: 143.0000 - val_fp: 5.0000 - val_tn: 1129.0000 - val_fn: 13.0000 - val_accuracy: 0.9860 - val_precision: 0.9662 - val_recall: 0.9167 - val_auc: 0.9884 - val_prc: 0.9723\n",
      "Epoch 8/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 0.0112 - tp: 477.0000 - fp: 4.0000 - tn: 3378.0000 - fn: 8.0000 - accuracy: 0.9969 - precision: 0.9917 - recall: 0.9835 - auc: 0.9988 - prc: 0.9977\n",
      "Epoch 8: val_loss did not improve from 0.04715\n",
      "61/61 [==============================] - 8s 136ms/step - loss: 0.0112 - tp: 477.0000 - fp: 4.0000 - tn: 3378.0000 - fn: 8.0000 - accuracy: 0.9969 - precision: 0.9917 - recall: 0.9835 - auc: 0.9988 - prc: 0.9977 - val_loss: 0.0608 - val_tp: 145.0000 - val_fp: 4.0000 - val_tn: 1130.0000 - val_fn: 11.0000 - val_accuracy: 0.9884 - val_precision: 0.9732 - val_recall: 0.9295 - val_auc: 0.9792 - val_prc: 0.9616\n",
      "Epoch 9/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 0.0047 - tp: 483.0000 - fp: 2.0000 - tn: 3380.0000 - fn: 2.0000 - accuracy: 0.9990 - precision: 0.9959 - recall: 0.9959 - auc: 0.9999 - prc: 0.9996\n",
      "Epoch 9: val_loss did not improve from 0.04715\n",
      "61/61 [==============================] - 8s 136ms/step - loss: 0.0047 - tp: 483.0000 - fp: 2.0000 - tn: 3380.0000 - fn: 2.0000 - accuracy: 0.9990 - precision: 0.9959 - recall: 0.9959 - auc: 0.9999 - prc: 0.9996 - val_loss: 0.0622 - val_tp: 142.0000 - val_fp: 6.0000 - val_tn: 1128.0000 - val_fn: 14.0000 - val_accuracy: 0.9845 - val_precision: 0.9595 - val_recall: 0.9103 - val_auc: 0.9830 - val_prc: 0.9722\n",
      "Epoch 10/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 0.0050 - tp: 482.0000 - fp: 4.0000 - tn: 3378.0000 - fn: 3.0000 - accuracy: 0.9982 - precision: 0.9918 - recall: 0.9938 - auc: 1.0000 - prc: 0.9998\n",
      "Epoch 10: val_loss did not improve from 0.04715\n",
      "61/61 [==============================] - 8s 134ms/step - loss: 0.0050 - tp: 482.0000 - fp: 4.0000 - tn: 3378.0000 - fn: 3.0000 - accuracy: 0.9982 - precision: 0.9918 - recall: 0.9938 - auc: 1.0000 - prc: 0.9998 - val_loss: 0.0524 - val_tp: 145.0000 - val_fp: 3.0000 - val_tn: 1131.0000 - val_fn: 11.0000 - val_accuracy: 0.9891 - val_precision: 0.9797 - val_recall: 0.9295 - val_auc: 0.9850 - val_prc: 0.9630\n",
      "Epoch 11/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 0.0069 - tp: 485.0000 - fp: 6.0000 - tn: 3376.0000 - fn: 0.0000e+00 - accuracy: 0.9984 - precision: 0.9878 - recall: 1.0000 - auc: 0.9998 - prc: 0.9977\n",
      "Epoch 11: val_loss did not improve from 0.04715\n",
      "61/61 [==============================] - 8s 134ms/step - loss: 0.0069 - tp: 485.0000 - fp: 6.0000 - tn: 3376.0000 - fn: 0.0000e+00 - accuracy: 0.9984 - precision: 0.9878 - recall: 1.0000 - auc: 0.9998 - prc: 0.9977 - val_loss: 0.0663 - val_tp: 143.0000 - val_fp: 1.0000 - val_tn: 1133.0000 - val_fn: 13.0000 - val_accuracy: 0.9891 - val_precision: 0.9931 - val_recall: 0.9167 - val_auc: 0.9763 - val_prc: 0.9587\n",
      "Epoch 12/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 8.3626e-04 - tp: 485.0000 - fp: 0.0000e+00 - tn: 3382.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - prc: 1.0000\n",
      "Epoch 12: val_loss did not improve from 0.04715\n",
      "61/61 [==============================] - 8s 135ms/step - loss: 8.3626e-04 - tp: 485.0000 - fp: 0.0000e+00 - tn: 3382.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - prc: 1.0000 - val_loss: 0.1128 - val_tp: 142.0000 - val_fp: 3.0000 - val_tn: 1131.0000 - val_fn: 14.0000 - val_accuracy: 0.9868 - val_precision: 0.9793 - val_recall: 0.9103 - val_auc: 0.9695 - val_prc: 0.9379\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 0.0017 - tp: 484.0000 - fp: 1.0000 - tn: 3381.0000 - fn: 1.0000 - accuracy: 0.9995 - precision: 0.9979 - recall: 0.9979 - auc: 1.0000 - prc: 1.0000\n",
      "Epoch 13: val_loss did not improve from 0.04715\n",
      "61/61 [==============================] - 9s 141ms/step - loss: 0.0017 - tp: 484.0000 - fp: 1.0000 - tn: 3381.0000 - fn: 1.0000 - accuracy: 0.9995 - precision: 0.9979 - recall: 0.9979 - auc: 1.0000 - prc: 1.0000 - val_loss: 0.0643 - val_tp: 143.0000 - val_fp: 4.0000 - val_tn: 1130.0000 - val_fn: 13.0000 - val_accuracy: 0.9868 - val_precision: 0.9728 - val_recall: 0.9167 - val_auc: 0.9797 - val_prc: 0.9629\n",
      "Epoch 14/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 5.3643e-04 - tp: 485.0000 - fp: 0.0000e+00 - tn: 3382.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - prc: 1.0000\n",
      "Epoch 14: val_loss did not improve from 0.04715\n",
      "61/61 [==============================] - 8s 129ms/step - loss: 5.3643e-04 - tp: 485.0000 - fp: 0.0000e+00 - tn: 3382.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - prc: 1.0000 - val_loss: 0.1038 - val_tp: 143.0000 - val_fp: 4.0000 - val_tn: 1130.0000 - val_fn: 13.0000 - val_accuracy: 0.9868 - val_precision: 0.9728 - val_recall: 0.9167 - val_auc: 0.9704 - val_prc: 0.9510\n",
      "Epoch 15/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 4.3488e-04 - tp: 485.0000 - fp: 0.0000e+00 - tn: 3382.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - prc: 1.0000\n",
      "Epoch 15: val_loss did not improve from 0.04715\n",
      "61/61 [==============================] - 8s 128ms/step - loss: 4.3488e-04 - tp: 485.0000 - fp: 0.0000e+00 - tn: 3382.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - prc: 1.0000 - val_loss: 0.1364 - val_tp: 142.0000 - val_fp: 3.0000 - val_tn: 1131.0000 - val_fn: 14.0000 - val_accuracy: 0.9868 - val_precision: 0.9793 - val_recall: 0.9103 - val_auc: 0.9673 - val_prc: 0.9469\n",
      "Epoch 16/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 0.0038 - tp: 485.0000 - fp: 1.0000 - tn: 3381.0000 - fn: 0.0000e+00 - accuracy: 0.9997 - precision: 0.9979 - recall: 1.0000 - auc: 0.9999 - prc: 0.9979\n",
      "Epoch 16: val_loss did not improve from 0.04715\n",
      "61/61 [==============================] - 8s 128ms/step - loss: 0.0038 - tp: 485.0000 - fp: 1.0000 - tn: 3381.0000 - fn: 0.0000e+00 - accuracy: 0.9997 - precision: 0.9979 - recall: 1.0000 - auc: 0.9999 - prc: 0.9979 - val_loss: 0.1230 - val_tp: 141.0000 - val_fp: 2.0000 - val_tn: 1132.0000 - val_fn: 15.0000 - val_accuracy: 0.9868 - val_precision: 0.9860 - val_recall: 0.9038 - val_auc: 0.9608 - val_prc: 0.9372\n",
      "Epoch 17/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 0.0029 - tp: 483.0000 - fp: 0.0000e+00 - tn: 3382.0000 - fn: 2.0000 - accuracy: 0.9995 - precision: 1.0000 - recall: 0.9959 - auc: 0.9990 - prc: 0.9985\n",
      "Epoch 17: val_loss did not improve from 0.04715\n",
      "61/61 [==============================] - 8s 129ms/step - loss: 0.0029 - tp: 483.0000 - fp: 0.0000e+00 - tn: 3382.0000 - fn: 2.0000 - accuracy: 0.9995 - precision: 1.0000 - recall: 0.9959 - auc: 0.9990 - prc: 0.9985 - val_loss: 0.1260 - val_tp: 145.0000 - val_fp: 13.0000 - val_tn: 1121.0000 - val_fn: 11.0000 - val_accuracy: 0.9814 - val_precision: 0.9177 - val_recall: 0.9295 - val_auc: 0.9806 - val_prc: 0.9356\n",
      "Epoch 18/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 0.0059 - tp: 485.0000 - fp: 2.0000 - tn: 3380.0000 - fn: 0.0000e+00 - accuracy: 0.9995 - precision: 0.9959 - recall: 1.0000 - auc: 0.9998 - prc: 0.9979\n",
      "Epoch 18: val_loss did not improve from 0.04715\n",
      "61/61 [==============================] - 8s 129ms/step - loss: 0.0059 - tp: 485.0000 - fp: 2.0000 - tn: 3380.0000 - fn: 0.0000e+00 - accuracy: 0.9995 - precision: 0.9959 - recall: 1.0000 - auc: 0.9998 - prc: 0.9979 - val_loss: 0.0965 - val_tp: 145.0000 - val_fp: 6.0000 - val_tn: 1128.0000 - val_fn: 11.0000 - val_accuracy: 0.9868 - val_precision: 0.9603 - val_recall: 0.9295 - val_auc: 0.9766 - val_prc: 0.9588\n",
      "Epoch 19/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 0.0016 - tp: 484.0000 - fp: 1.0000 - tn: 3381.0000 - fn: 1.0000 - accuracy: 0.9995 - precision: 0.9979 - recall: 0.9979 - auc: 1.0000 - prc: 1.0000\n",
      "Epoch 19: val_loss did not improve from 0.04715\n",
      "61/61 [==============================] - 8s 129ms/step - loss: 0.0016 - tp: 484.0000 - fp: 1.0000 - tn: 3381.0000 - fn: 1.0000 - accuracy: 0.9995 - precision: 0.9979 - recall: 0.9979 - auc: 1.0000 - prc: 1.0000 - val_loss: 0.0736 - val_tp: 143.0000 - val_fp: 3.0000 - val_tn: 1131.0000 - val_fn: 13.0000 - val_accuracy: 0.9876 - val_precision: 0.9795 - val_recall: 0.9167 - val_auc: 0.9767 - val_prc: 0.9598\n",
      "Epoch 20/20\n",
      "61/61 [==============================] - ETA: 0s - loss: 3.4334e-04 - tp: 485.0000 - fp: 0.0000e+00 - tn: 3382.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - prc: 1.0000\n",
      "Epoch 20: val_loss did not improve from 0.04715\n",
      "61/61 [==============================] - 8s 129ms/step - loss: 3.4334e-04 - tp: 485.0000 - fp: 0.0000e+00 - tn: 3382.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - prc: 1.0000 - val_loss: 0.0979 - val_tp: 144.0000 - val_fp: 3.0000 - val_tn: 1131.0000 - val_fn: 12.0000 - val_accuracy: 0.9884 - val_precision: 0.9796 - val_recall: 0.9231 - val_auc: 0.9764 - val_prc: 0.9535\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ERROR: Could not find `tensorboard`. Please ensure that your PATH\n",
       "contains an executable `tensorboard` program, or explicitly specify\n",
       "the path to a TensorBoard binary by setting the `TENSORBOARD_BINARY`\n",
       "environment variable."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# initialize our ModelCheckpoint and TensorBoard callbacks\n",
    "# model checkpoint for saving best weights\n",
    "model_checkpoint = ModelCheckpoint(\"../results/spam_classifier_{val_loss:.2f}.h5\", save_best_only=True,\n",
    "                                    verbose=1)\n",
    "# for better visualization\n",
    "tensorboard = TensorBoard(log_dir=f\"logs/spam_classifier_{time.time()}\", histogram_freq=1)\n",
    "%tensorboard --logdir ./logs\n",
    "\n",
    "# define model stopping criteria\n",
    "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=20, \n",
    "                                              restore_best_weights=True)\n",
    "# train the model\n",
    "history = bimodel.fit(X_train, y_train, validation_data=(X_test, y_test),\n",
    "          batch_size=BATCH_SIZE, #64\n",
    "          epochs=EPOCHS, #20\n",
    "          callbacks=[tensorboard, model_checkpoint, early_stop],\n",
    "          verbose=1)\n",
    "%tensorboard --logdir logs/fit\n",
    "\n",
    "# My tensorboard source https://www.youtube.com/watch?v=-9-Hy5dWKLE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d28bba1d",
   "metadata": {},
   "source": [
    "# Model Scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7b83cfde",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:24:36.159451Z",
     "start_time": "2022-03-29T16:24:33.435900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fY8eOZfXq1emGgQghhBDWcvbsWUJDQ/njjz9wdHS0djgij5MeLCFElhk5ciS7du1i6tSpklwJIYTIEb744guWLl3K//73P0muRLaQHiwhhBBCCCGEyCIWKXJhMpl4//336d69O6GhoQ9UW1uwYAGdO3emS5cu5gXmkpKSePPNN+nZsycDBw40T84UQgghssN///1nnq9xrw0bNtC5c2e6d+/O0qVLAWmzhBBCPJxFEqx169aRkpLCkiVLGDp0KFOmTDHvi42NZdGiRSxevJgffviBqVOnopRi0aJF+Pv7s3DhQjp27PjQYgFCCCFEVvvmm28YM2YMycnJ6banpqYyefJkvv/+e+bPn8+SJUuIjo6WNksIIcRDWWQO1t69ewkKCgK0xVbvLfPp6enJr7/+io2NDefPn8fe3h6dTsfevXvNaxk0atQoU41V3bp1KV68uCVeghBCiGx2/vz5DCuWZQcfHx9mzpzJiBEj0m2PiIjAx8eHAgUKAFCzZk12794tbZYQ2SjVqEhMNZKYYtSuU42kGTNeTkUHGPS6ey56DDodBv1923V3b+t1OkxKYTTdvtx726RIu31tum+7tefY6HU6dDptXTG9DnTcvtZlfG0+PpPH6XU6dEBOm1JtVKT7d0h3Mf8bmTCaMN/PaEaUX0EXnOwMGZwh8x7WblkkwYqPj8fFxcV8/85aO3cWgLOxseHnn39m5syZ5uEY8fHxuLq6AuDs7ExcXNxjz1O8eHFWrFhhgVcghBAiu3Xq1Mlq527ZsiVRUVEPbL+3bQKtfYqPj5c2SwgLUEpx4UYSh87f4ND5Gxy8fR0dry06rNdB2UIuVC5egMrFClCxqBtKKW4kpj70cvPe20lpGE2ZT4sMeh0FHG0p4GiL2+1r7WJzz+27+13sbdBnQTZiNCmS00wkpxlJSn30dfIj9ielGtM9T6L5/sPX+sytdDpwtbehgJNthv82bg7ptxVys6dCEbdnPu/D2i2LJFguLi4kJCSY75tMpgdW1+7duzfdunVj4MCB7NixI91jEhISzIuWCiGEENZyf3uWkJCAq6urtFkix7uZlMqOiBi2R8Sw7WQ0cUlpFHKzp5CrPYXcHCjkak/h29eFXB0o7GaPl4s9Bn32dFcopTh/PdGcSB08f5PD528Qk6AlUwa9jnKFXGhSvhBVihegcnE3KhZ1w8nu6b+6KqVISDFqCdetu4lXQnIazvb3JE23v6Q72xnyZEVck0mRYjQ9kJwlp91Nyu69TnuCpDQ7ONs9mOC6Otigz6a/3cywSIIVEBDAxo0bCQ4OJiwsDH9/f/O+U6dO8emnnzJz5kxsbW2xs7NDr9cTEBDA5s2bqVq1Klu2bKFmzZqWCE0IIYTIND8/P86cOcP169dxcnJiz5499O/fnwsXLkibJXKU5DQj+85cZ9vJaLZFRPPfueuYFDjaGqhT2pOCrvZciUsm6loi+85eJ/Z2InMvvQ68Xewp5GZPYVcHCrnZU/B28lXIVUvGbAy6dF/AkzP4Qq59cc/4y3pymonEFCMnrsRx7VYqADZ6HeUKu9K8opZMVSpegOeKuuFg+2zDt+6n0+lwsbfBxd6G4u75t1y7Xq/DQW+4/f7aWjucPMkiCVaLFi3Ytm0bISEhKKWYNGkS8+bNw8fHh+bNm1OhQgW6d++OTqcjKCiIOnXqUKVKFUaOHEmPHj2wtbVl+vTplghNCCGEeKxVq1Zx69YtunfvzqhRo+jfvz9KKTp37kzhwoXp0aOHtFnCqkwmxZGLN28nVDHsOh1DUqoJg15HtRIFeKNpWRqU9aaGjzv2Ng8mKilpJq7GJ3PlZhKXbyZzNS6JK3HJXL6pXV+4kcR/UdeJSUjhaRb00evAwVb7Em9vozdf29++blmpCJWKF6BK8QJUKOKa5cmUENaUq9fB6tSp0wPj2VNTU4mKiiIpKclKUVmeg4MDJUqUwNZWfnUQQuQdGX2m5yV5/fUJyzsbc4t/Tkaz7WQ02yOizT1A5Qq5EFjWm4ZlvalbxhNXh6z7fpBqNBEdn8yVm1rypUBLlGwMONjec21rwMHm7rWNwSKFqoXIUR72uW6RHixrioqKwtXVlVKlSuXJcbNKKWJiYoiKiqJ06dLWDkcIIYTIM9KMJk5ejcfJ1sZq8zqUUiSlmszzg45fjjMP+zsXmwhAETcHmlUoTGBZLwLLelPYzcFi8dga9BQt4EjRAvl3SJ0QTyrPJVhJSUl5NrkCbfywl5cXV69etXYoQgghRJ6glOKPQ5f4+K9wTl29W9REpwMX+wcn1GdcWS79xc5Gn2Elu4zu372kcTMxlZT7SpC7OthQv4wXAxqWIbCsN34FnfPs9xwh8oI8l2ABef5DJ6+/PiGEECK7/HMimml/HuNA1A3KFnJhaucq6HU6cyJ0MyktXRJ04kq8+XbKU5a7zqikdJECDhkmbSU8nKhczE2G3AmRi+TJBEsIIYQQ4lHCzl1n2h/H2B4RQ3F3Rz7uUpVOASWeqEx5UqoxfQ/UPaW/U4ymh/Z6udrnrJLSQoisJQlWFpsyZQqHDx/m6tWrJCUlUbJkSTw8PJgxY8YjHzd37lzq1atH1apVsylSIYQQIv85cTmOT/4K58/Dl/FytuP9ts/Rq55PhpX2HudOlTxLzoESQuQ+kmBlsVGjRgGwYsUKTp06xbBhwzL1uEGDBlkyLCGEECJfi7p2i8/XnWDFviic7GwY8rw//YNK42IvX4WEEFkrT3+qLN8bxdI957L0ObvVKknnmiWe6DGjRo3i+vXrXL9+ndmzZ/PJJ59w6dIlrly5QrNmzRgyZAijRo0iODiY6OhoNm/eTFJSEmfPnmXgwIF06tQpS1+DEEIIkV/ExCfz1cYIft5xBnTQL7A0rzUti6eznbVDE0LkUXk6wcpJ6tWrR9++fYmKiqJ69ep07dqV5ORkGjVqxJAhQ9IdGx8fz3fffUdkZCSvvPKKJFhCCCHEE4pLSuXbraf5duspElONdK1ZkrefL0cxdyk3LoSwrDydYHWuWeKJe5ss5c6aVe7u7hw8eJAdO3bg4uJCSkrKA8dWqFABgKJFi2a4XwghhBAZS0o18vOOM3y18STXbqUSXKUI77YoT9lCLtYOTQiRT+TpBCsnuVNafcWKFbi6ujJ+/HjOnDnD0qVLUUpleKwQQgghMifNaGLFvvN8vu44F24kEVTOm+Ety1O1hLu1QxNC5DOSYGWz+vXrM3ToUMLCwrCzs8PX15crV65YOywhhBAi17mWkMLeM9fYfSaWv49c5tTVBKqVdOeTrtVoUNbb2uEJIfIpSbAs5N55U1OmTDHfLleuHL///vsDx997zB329vZs2LDBMgEKIYQQuYhSisiYW+yJjGVP5DX2nIkl4moCALYGHdVKuDOidwVaViosI0GEEFYlCZYQQgghcpyUNBOHLtxgb+Q1dkfGsu/sNaLjtXnJBRxtqenrQeeaJajl60nVEgVwsH3ydayEEMISJMESQgghhNXduJXK3rN3eqeu8d+56ySnmQDw9XKikX9Bavl6UruUB34FXdDrpZdKCJEzSYIlhBBCCKu4cD2RrzdHsONUDMcvxwNgo9dRqZgbvev5UsvXg5qlPCjk6mDlSIUQIvMkwRJCCCFEtkpOM/Lt1tN8ueEkRqWoX8aLdlWLUbOUB9VLuuNkJ19PhBC5l3yCCSGEECLbbAq/woerjnA6OoGWlQozps1zlPR0snZYQgiRZSTBEkIIIYTFnYu9xfjVR/j7yGVKezvzY786NPYvaO2whBAiy0mClcWmTJnC4cOHuXr1KklJSZQsWRIPDw9mzJjx2MeGh4dz8+ZNateunQ2RCiGEEJaXlGrk680RzN4UgUGvY2SrCvRrWAp7G6n6l6uYTHB4BVyLfPbn0umgSDUo1RBsZX6dyHskwcpio0aNAmDFihWcOnWKYcOGZfqxf/31F97e3pJgCSGEyPWUUqw7eoXxqw9zLjaRtlWL8r82FSlawNHaoYkndX4vrBkGF/Zl7fPaOkGZplC+FZR7AVyLZO3zC2EleTvBClsE+3/O2ues0Ruq98j04ampqXzwwQecOXMGk8nEO++8Q926dfnss8/YuXMnaWlpvPDCC3To0IGVK1dia2tLpUqVqFq1atbGLYQQQmSTyOgExq06zKbwq5Qr5MLCAXVpUNbb2mHlHmkpcHY7HP9Tu1/vVXD3yf44EmJg/TjYNx9cCkGnb+C5DsAzlsg3psDZf+H4HxD+B4Sv0bYXqwH+rcC/JRStrvV0CfG0jGmQcBXiLkL8Ze067jLEX4KUW/D8OChQ3CKnztsJVg7wyy+/4OHhwaRJk7h27Rq9e/dmzZo1rFq1ip9++olChQqxYsUKChcuzIsvvoi3t7ckV0IIIXKlWylpzNoYwdwtp7Cz0TOmTUX6NCiFrUFv7dByvoRoOPG3lnREbIDkm2CwB2WCXXOhek8IGgoepSwfi8kIe76HDRMhJR7qvw6NR4KDW9Y8v40dlGuhXYI/gStHIPz/tIRy0xTYNBlci2q9Wv6toExjsHPOmnOL3M+YCvFXIO6SlizdmzjF3XNJuAqoBx/v5K39YGFMtliIeTvBqt7jiXqbLOH48ePs3buXAwcOAJCWlkZsbCwff/wx06dPJzo6mqCgIKvGKIQQQjwLpRR/HLrEhNVHuHAjiRdrFGd06woUcpP5NQ+llJZY3OnFidoNKHApDJU6gn9rLbFIvAb/fA77foSwhVAtBIKGgWdpy8R1diesHQqXDkLpRtD6YyhUwTLnAq2XqnAl7dJo2D2J5v/BoRXa67Zx0GLxbwnlWoJ7ScvFI6wnLeV2T9Ol9MmS+fbtXqhbMTyYOOnAuSC4FtaS86LVtGvXwuBS5O5t50Jagm9heTvBygHKlClDkSJFeOWVV0hKSmL27Nm4uLjwxx9/8OmnnwIQHBxMmzZt0Ol0mEwmK0cshBBCZN7JK3GM+/0I/5yMpkIRVz4PqUGd0p7ZG4RScP2sNvTsWekN2pcwe5dnf677pSZB5D9a8nD8T7hxTttetDo0GaUlEEWqgf6eHj87Z2jzCQS9qyVae3/QpkBUC9F6tLz8sia2uMuw7gP4bxG4FYeuP8BzHbN/mJ6z990fyO8dKhn+f3DiL2AoFK6s9WyVawFOXtkbX27iUjjreh2fVeI1uHr8vsTpnmF7cRchMfbBx+n02v9H18LacL7iAfclTrcvzoXAkHPSmpwTSR4VEhLCmDFj6N27N/Hx8fTs2RM7OzsKFChAt27dcHBwIDAwkGLFilG5cmWmTZuGn58f9erVs3boQgghxEPFJ6cxc/0JvvvnNI52Bj5sX4ledX2wyc7hgErBqU2weao2pycr2bloX1DvfJlzLZrxfXvXRychcZe0BOH4n3BqI6TeulvcodFwbRicW9HHx+NWDIKnQcMhsO0L2DsP/lsMVbtpz/O0iZYxFXZ9ow3LS02Ehu9qiZslEswnZWMHZZpol5aTIPqE1uN3/E/45zPY+om1I8zZ7AtAm+lQpYv15rMppSXta0dAStzd7TrD7f9PRcDDF0rWyeD/WhGtV0qf+yqO6pRSGQxOzB06derEihUr0m07evQoFStWtFJE2Se/vE4hRP6R0Wd6XpKXXt+F64l0/fpfzl9PpFutEoxoVQFvF/vsC0ApbZ7S5qlwbie4FtMKQbgVe/bnNt4ZpnT5wcnxaYkPHm/rpH0RvPfXdNci2iT6E3/Chf3acW4ltGp5/q2gVNCzlyePuwTbZmhzpYzJUKWrlmh5l8v8c5zeCv83Qhuq6NccWk8D77LPFld2SbwGZ7ZrSaF4kDLB7m+1/x+VXoQ2n4JTNvcsJ8TA6rfh6CrwDYTAt28nUUW0eVD63D8382Gf69KDJYQQQohMi0tKpd8Pu7mZmMqyV+pTq1Q2fmlTCk6uh81TtDlLbsW1Igk1Qi2/npJSWuGJeyfRx19Kn4hdDIPjlyE1AdBBidrQbCyUbw2FnsvaXgTXItBqkvaldfsM2P0dHPwFKnfREq2C/g9/7I3z8PdYOLRcm+wfshDKB+euqn2OHlChjbWjyNkqd4Ztn8PGSXB2B3T4Eso+nz3nPv4n/PYGJF2HFhO0Qim5sCfqaUmCJYQQQohMSTWaeH3hfk5eieeHl+tkX3KllFb4YPNUOL9H6w1q86m2dIpNNvWc6XTgUEC7FCz/6GOT47RKfI7ulo/LtTC0/CiDRKszNB6RPta0FNjxFWz+GExp0HgUNHwHbGVtsjxJb9CGe5Z9HlYMgp87Q51B8PyHYOdkmXMmx8NfY7QhrIUqQehKKFLZMufKwfJkgqWUQpebfoV5Qrl4VKcQQohcSinF+78dYsvxq0zrXJWG5bJhXSultF/CN0/RhtoV8IG2n0P1XtlSCeyp2btm/zldCsELE6HB2/DvTNj1rdZDVelFLdG6eUEbDhhzUuutajnJcpUIRc5StBoM2gTrx8OOWRCxETrNgeI1s/Y853bDykEQexoavAXNxmTfDyA5TJ5LsBwcHIiJicHLyytPJllKKWJiYnBwkNK3Qgghss/Xm0+xaNc5Xm/qR7faFi6TrZRWNW7zVG3YnbsPtJsB1Xrk7MQqJ3ApCC3G35NofQOHb88R8SgNPX8B/xesG6PIfraO0GqyVqny19fg2xba2mZBQ5+9+p4xVfu/unW61rvcdw2UCsyauHMpiyRYJpOJcePGER4ejp2dHRMnTsTX19e8/4cffmDNGm3V7saNG/PGG2+glKJRo0aUKlUKgOrVqzN06NAnPneJEiWIiori6tWrWfJaciIHBwdKlChh7TCEEELkE6sPXGDqH8doV60YQ1vcHnJmTNUqgWXlRHWl4Nga7cvapQPaorrtv9RKkhtss+48+YGzFzw/TutJ2P2d9gW79gDLz1UTOVuZJvDqdq03c9MkrRDLi3OfvrjJ1XBt+OHFMK1nudWUnFMa3ooskmCtW7eOlJQUlixZQlhYGFOmTGH27NkAnDt3jt9//51ffvkFvV5Pjx49eP7553F0dKRSpUp8/fXXz3RuW1tbSpeWLm8hhBAiK+w9E8u7S/+jlq8HH3epil6vg2Nr4ddXtPkWLoUfXJPm/qp6jyu1bDLBsVXa3KDLB7Welg6ztBLkklg9GydPaDzc2lGInMTRHTrN1Sparh4Cc4LghQlQq3/mC52YTLBrrrZ2mp0zdP8ZKrazaNi5iUUSrL179xIUFARoPVGHDh0y7ytSpAjffvstBoP2QZuWloa9vT2HDx/m8uXLhIaG4uDgwOjRoylTpowlwhNCCCFEJpyJSWDgT3spVsCBuS/VwsGggw0fwZZpUKwG+DW7W0XvxjmI2gW3Yh58onsXC713jRvXIlov2M45cOUwePpBx6+1kuM5aNFQIfKkyp3Apx789jqsGQrhf2iVBl2LPPpxN87Db69pa9CVawntZ2r/t4WZRT694uPjcXG5u0CdwWAgLS0NGxsbbG1t8fT0RCnFtGnTeO655yhdujTR0dEMGjSI1q1bs2fPHoYPH87y5cstEZ4QQgghHuNaQgovz9uNUop5L9fBU38LFg2EE39p1fuCp2c83CwtBRKu3FPO/L61pG6ch/N7ISEauF20yaucNkypcmdJrITITm7FoPcKbc2sv8bCrHrQ7gt4rkPGxx9cBmve1YYIt/0cavbNXeX9s4lFPsVcXFxISEgw3zeZTNjY3D1VcnIy7733Hs7OznzwwQcAVK5c2dyrVatWLa5cuZLnqwEKIYQQOVFympHB8/cSdS2RBQPrUtp4Bub2hBtRWnn0Wv0e/qXKxg4KlNAuj2JMhfgr2jo5BSvkqzVyhMhRdDqoM1Cbn7ViECx9CaqGQPA0bVkC0BZ2XjNUq0xZoja8OAe8/Kwadk5mkSWUAwIC2LJlCwBhYWH4+99d7E4pxWuvvUb58uUZP368Oan68ssv+fHHHwE4duwYRYsWleRKCCGEyGZKKUYsO8CuyFg+6VaN2vGb4NvmkJqoVQer/QTzNB7FYAsFikPhSpJcCZETeJeD/n9p66Md/AVmB8LprVpZ91kN4MhvWun1l/+Q5OoxLNKD1aJFC7Zt20ZISAhKKSZNmsS8efPw8fHBZDKxa9cuUlJS2Lp1KwDvvvsugwYNYvjw4WzevBmDwcDkyZMtEZoQQgghHuGzv4/zW9gFRrzgR/vLs7XFa0vWhW4/PX5uhhAidzPYQtPRUO4FWDEQfmwHKPD2hx4LtbmX4rEskmDp9XrGjx+fbpuf391M9+DBgxk+bu7cuZYIRwghhBCZ8Muec8zYcJKXq7vy6rkRcHqzVtq75WRZf0qI/KRETXhlK2ycpPUwNxmtlfoXmSIzSYUQQuRrj1u7ce7cuaxZswYXFxcGDBhA06ZNuXDhAiNGjEApRYECBZg+fTqOjrn7y8f2k9GMXnGQ3j7XeP/iSHTxV7RS6TV6WTs0IYQ12DlDy4+sHUWuZJE5WEIIIURuce/ajUOHDmXKlCnmfeHh4axevZqlS5fy/fffM2PGDBITE/nhhx9o3bo1CxYsoFy5cixbtsyKr+DZnbgcx+Cf9zKowE4mxA5FpxT0+0OSKyGEeArSgyWEECJfe9TajREREdSpUwd7e3sAfH19CQ8Pp2LFily6dAnQliYpUiT3zk26GpfMgHn/8j/9D4QkroVSQdD1B3D2tnZoQgiRK0kPlhBCiHztYWs3ApQvX549e/YQHx/PtWvX2L9/P4mJiRQpUoQFCxbQpk0btmzZQqtWrawV/jNJTDEybN6fTE8cS4hpLdR/A0J/leRKCCGegfRgCSGEyNcetXajn58fvXr1YsCAARQrVoxq1arh4eHB6NGjmTx5MkFBQWzatImRI0fmukJNRpPiix8XMDXmfQraJELH76BKF2uHJYQQuZ4kWEIIIfK1gIAANm7cSHBw8ANrN8bGxpKQkMDixYuJi4ujX79+lCtXDjc3N1xdXQEoVKgQN2/etHygR1fDyb/BpYhWLt21CLgUBtei4FwQDE/WpP/x42TejfqEZKciGPqsgiJVLBS4EELkL5JgCSGEyNcetXZjs2bNOHXqFJ07d8bW1pYRI0ZgMBgYO3Ys48ePx2QyoZTi/ffft3yg0cfh2BpIuJrBTp2WZLnem3wVAdfbCdidpMylECgTJ+a9QpvzKzhZoC5lX1kMTp6Wj18IIfIJSbCEEELka49bu/H+fQBly5blp59+snhs6QS9q12MqRB/BeIvQdw9l/hLEHcZ4i7Cxf+0Y1D3PYmONBtHyqXdYk2BnrR6cybYyFcBIYTISvKpKoQQQuQmBlsoUFy7PIoxTevtuicRU3GX+HXLPg4512P4a29jkORKCCGynHyyCiGEEHmRwQbcimqX205ejmPYn1uYFlwVZ3v5CiCEEJYgZdqFEEKIfGJXZCwAtUvLnCshhLAUSbCEEEKIfGL36Vi8Xewp5eVk7VCEECLPkgRLCCGEyCd2R16jTmkPdDqdtUMRQog8SxIsIYQQIh84fz2R89cTqV1KhgcKIYQlSYIlhBBC5AN77sy/kgRLCCEsShIsIYQQIh/YdToWF3sbKhZ1s3YoQgiRp0mCJYQQQuQDuyNjCfD1wKCX+VdCCGFJkmAJIYQQedy1hBSOX46nTikPa4cihBB5niRYQgghRB6358w1QOZfCSFEdpAESwghhMjjdkfGYmfQU62ku7VDEUKIPE8SLCGEECKP23U6lqolCuBga7B2KEIIkedJgiWEEELkYbdS0jh0/ga1S8vwQCGEyA6SYAkhhBB5WNjZ66SZFHVk/pUQQmQLSbCEEEKIPGxXZCw6HQT4SgVBIYTIDpJgCSGEEHnYnshrVCjiRgFHW2uHIoQQ+YIkWEIIIUQelWY0se/sNVn/SgghspEkWEIIIUQedfjCTW6lGKXAhRBCZCNJsIQQQog8andkLCALDAshRHaSBEsIIYTIo3adjsXH04nCbg7WDkUIIfINSbCEEEKIPEgpxZ4z16T3SgghspmNJZ7UZDIxbtw4wsPDsbOzY+LEifj6+pr3//DDD6xZswaAxo0b88Ybb5CUlMTw4cOJiYnB2dmZqVOn4ukpjYIQQgjxNCKuxhObkEKd0lLgQgghspNFerDWrVtHSkoKS5YsYejQoUyZMsW879y5c/z+++8sXryYpUuX8s8//3Ds2DEWLVqEv78/CxcupGPHjsyaNcsSoQkhhBD5wq7T1wCZfyWEENnNIgnW3r17CQoKAqB69eocOnTIvK9IkSJ8++23GAwGdDodaWlp2Nvbp3tMo0aN+Pfffy0RmhBCCJEv7I6MxdvFjtLeztYORQgh8hWLJFjx8fG4uLiY7xsMBtLS0gCwtbXF09MTpRRTp07lueeeo3Tp0sTHx+Pq6gqAs7MzcXFxlghNCCGEyBd2nY6ldilPdDqdtUMRQoh8xSIJlouLCwkJCeb7JpMJG5u7072Sk5MZNmwYCQkJfPDBBw88JiEhATc3N0uEJoQQQuR5F64ncv56ogwPFEIIK7BIghUQEMCWLVsACAsLw9/f37xPKcVrr71G+fLlGT9+PAaDwfyYzZs3A7BlyxZq1qxpidCEEEKIPO/O+ld1ZIFhIYTIdhapItiiRQu2bdtGSEgISikmTZrEvHnz8PHxwWQysWvXLlJSUti6dSsA7777Lj169GDkyJH06NEDW1tbpk+fbonQhBBCiDxvd2QsLvY2VCwqo0GEECK7WSTB0uv1jB8/Pt02Pz8/8+2DBw9m+LgZM2ZYIhwhhBAiX9l9+hoBvh4Y9DL/SgghspssNCyEEELkIddvpRB+OY46pWT9KyGEsAZJsIQQQog8ZE+krH8lhBDWJAmWEEIIkYfsjozF1qCjWkl3a4cihBD5kiRYQgghRB6yKzKWqiXccbA1WDsUIYTIlyTBEkIIIfKIxBQjB6NuyPBAIYSwIkmwhBBCiDxi/7lrpJkUdUpLgQshhLAWSbCEEEKIPGL36WvodFDTV3qwhBDCWiTBEkIIka+ZTCbef/99unfvTmhoKGfOnEm3f+7cuXTo0IFevXqxceNGAG7dusWIESPo2bMnXbt25cCBA9YI/QF7zsRSvrArBRxtrR2KEELkWxZZaFgIIYTILdatW0dKSgpLliwhLCyMKVOmMHv2bADCw8NZvXo1v/zyCwAhISHUq1eP7777jnLlyjFt2jSOHTvGsWPHqFq1qjVfBmlGE/vOXKNzzRJWjUMIIfI76cESQgiRr+3du5egoCAAqlevzqFDh8z7IiIiqFOnDvb29tjb2+Pr60t4eDj//PMPtra29O/fn1mzZpkfb01HLt4kIcUoBS6EEMLKJMESQgiRr8XHx+Pi4mK+bzAYSEtLA6B8+fLs2bOH+Ph4rl27xv79+0lMTOTatWvcvHmT7777jmbNmjF16lRrhW+263QsAHVKS4IlhBDWJEMEhRBC5GsuLi4kJCSY75tMJmxstObRz8+PXr16MWDAAIoVK0a1atXw8PDA3d2dZs2aAdC0aVPmzp1rldjvtTsyFh9PJwq7OVg7FCGEyNekB0sIIUS+FhAQwJYtWwAICwvD39/fvC82NpaEhAQWL17Mhx9+yMWLFylXrhw1a9Zk8+bNAOzevZuyZctaJfY7lFLsibwmwwOFECIHkB4sIYQQeUpKSgp2dnaZPr5FixZs27aNkJAQlFJMmjSJefPm4ePjQ7NmzTh16hSdO3fG1taWESNGYDAYGDx4MGPGjKF79+7Y2NhYfYhgxNUEYhJSqF1K1r8SQghrkwRLCCFEntK5c2fq1atH165d0/VGPYxer2f8+PHptvn5+Zlv378PwN3dnS+//PLZg80iuyO1+Ve1Zf6VEEJYnSRYQggh8pTffvuNrVu38uWXX3Lt2jXat29PcHAwzs7O1g7NYnafjsXbxY4y3nn3NQohRG4hc7CEEELkKXq9nkaNGtG5c2fc3d2ZP38+/fv35+eff7Z2aBazKzKWWr6e6HQ6a4cihBD5nvRgCSGEyFOmTZvG+vXrqVOnDgMHDqRq1aqYTCY6depE7969rR1elrt4I5Goa4m8HFja2qEIIYRAEiwhhBB5TKlSpVi5ciVOTk6kpqYCWq9WTpozlZV2R14DoI5UEBRCiBxBhggKIYTIU5RSzJw5E4DBgwfz66+/AlCiRAkrRmU5u0/H4mxnoGJRV2uHIoQQAkmwhBBC5DGLFy9m6NChAMyZM4dFixZZOSLL2h0ZS4CvBzYGadKFECInkE9jIYQQeYper8fGRhsBb2trm6cLP9y4lUr45TgZHiiEEDmIzMESQgiRpzRv3pyePXtStWpVDh8+TLNmzawdksXsOROLUrL+lRBC5CSSYAkhhMhTXnvtNZo2bcrp06fp2LEjFSpUsHZIFrMrMhZbg47qJd2tHYoQQojb8u0QwTSjiY//PMb1WynWDkUIIUQWOnPmDFu2bOHUqVOsW7eO999/39ohWczu07FULeGOg63B2qEIIYS4LVMJ1vbt29myZQubN2/m+eefZ9WqVZaOy+Jib6Xw1cYIlu87b+1QhBBCZKE7BS727dtHVFQU169ft25AFpKUauTg+RvUKuVh7VCEEELcI1MJ1meffUapUqX46aefWLRoEYsXL7Z0XBZXyNWB0t7ObD8Zbe1QhBBCZCEnJycGDx5M4cKFmTJlCtHRefNzfv/Z66QalRS4EEKIHCZTCZaDgwNeXl7Y2NhQsGDBPFORqYGfFztPx5JqNFk7FCGEEFlEp9Nx9epVEhISuHXrFrdu3bJ2SBaxOzIWnQ5q+UqCJYQQOUmmEiwXFxcGDBhA69atWbBgAZ6eeePDPLCsN/HJaRyIum7tUIQQQmSRN954g3Xr1tGhQweef/556tevb+2QLGJ3ZCzlC7tSwMnW2qEIIYS4R6aqCH7xxRecPXuWsmXLcuLECbp27WrpuLJF/TJe6HSw7WQMNeUXQCGEyBMOHDhA//79Aa1ke16UZjSx78w1OgWUsHYoQggh7pOpHqxTp04RFxfHf//9x8SJE9m7d+8jjzeZTLz//vt0796d0NBQzpw588AxsbGxtGzZkuTkZACUUgQFBREaGkpoaCjTp09/ipfzZDyc7ahUzI1/ZB6WEELkGZs3b8ZoNFo7DIs6ejGOhBSjrH8lhBA5UKZ6sMaNG8fYsWOZOXMmQ4YM4eOPP37kkIt169aRkpLCkiVLCAsLY8qUKcyePdu8f+vWrUyfPp2rV6+at509e5ZKlSrx9ddfP8PLeXKBft58v+00t1LScLKTZcGEECK3u3btGkFBQZQoUQKdTodOp8sTxZnutSsyFkAKXAghRA6UqYzCzs6OcuXKkZqaSvXq1dHrH93xtXfvXoKCggCoXr06hw4dSrdfr9czb948OnfubN52+PBhLl++TGhoKA4ODowePZoyZco86et5YoFlvZmz5RS7I6/R2L+gxc8nhBDCsrL7hzpr2H06lpKejhQp4GDtUIQQQtwnUwmWTqdjxIgRNGrUiLVr12Jr++gJtfHx8bi4uJjvGwwG0tLSsLHRThcYGPjAYwoWLMigQYNo3bo1e/bsYfjw4SxfvvxJXstTqV3KEzuDnm0noyXBEkKIPGDlypUPbHvjjTesEIllKKXYHRlL4/LSZgkhRE6UqQTrs88+4+DBgzRu3JidO3fy6aefPvJ4FxcXEhISzPdNJpM5uXqYypUrYzBoK9HXqlWLK1euoJSyeEl4RzsDNXzc2SbzsIQQIk/w9vYGtETkyJEjmEx5aymOU9EJxCSkyPBAIYTIoTI9RHDHjh0sWLCAUqVKUb58+UceHxAQwMaNGwkODiYsLAx/f//HnuPLL7/E3d2dgQMHcuzYMYoWLZpt6201LOvNp+uOE5uQgqezXbacUwghhGWEhISkuz9gwAArRWIZu09r86+kwIUQQuRMmaoi+N5771GsWDGGDBlC8eLFGTVq1COPb9GiBXZ2doSEhDB58mRGjx7NvHnzWL9+/UMfM2jQIHbv3k3v3r2ZPHkykydPfrJX8gwalPVGKfg3IibbzimEEMIyTp8+bb7s2rWLCxcuWDukLLUrMhYvZzvKeDtbOxQhhBAZyFQP1rVr1wgNDQWgYsWK/Pnnn488Xq/XM378+HTb/Pz8Hjhuw4YN5tsFChRg7ty5mQkny1UrUQAXexu2RUTTpmpRq8QghBAia7z//vvodDqUUjg4ODBy5Ehrh5SldkfGUquUR7aN8hBCCPFkMpVgJScnc/XqVQoWLEh0dHSeG89uY9BTr4wn22UelhBC5HrffvstERERPPfcc6xbt44GDRpYO6Qsc+lGEudiE+lTv5S1QxFCCPEQmRoi+PbbbxMSEkLHjh0JCQlJV149r2jg501kzC2irt2ydihCCCGewfDhwzl69CigDRd83LD23GT3nfWvZP6VEELkWJlKsAIDA1m/fj3ff/89f//9N0uWLLF0XNkusKxWdWr7SZmHJYQQudnly5fNPwQOHDiQK1euWDmirLM7MhZnOwPPFXWzdihCCCEeIlMJ1h2enp7mce15jX9hF7xd7NkWIcMEhRAiN9PpdJw+fRqAs2fP5qlh7btOxxLg64GN4YmabyGEENkoU3Ow7pcXJ9bqdDoCy3qx7WRMtqy/JYTIJmGLwKUQlG1u7UhENhk9ejRDhgwhOjqaQoUK8eGHH1o7pCxxIzGV8MtxBFeRYkxCCJGTPTLBevfddx9INJRSnDt3zqJBWUugnze/hV3g+OV4yhdxtXY4QohndeR3+PUV7Xa5F+CFj6Dg49flE7lbxYoVmTRpkrnIRYUKFawdUpbYeyYWpaC2LDAshBA52iMTrPsXa3zc9twusJw2D2vbyWhJsITI7W6ch9/fhGI1oFIn2PIxzK4PtQdC4xHgJF9S86phw4bRuHFjnnvuOU6fPs3//d//MX36dGuH9cx2nb6GrUFHDR93a4cihBDiER6ZYNWpUye74sgRirs7UsrLiW0no+nXsLS1wxFCPC2TEVYOBmMqdP4OvPygWg/YOBF2zYEDi6Hp/6Dmy2B4qpHSIge7v8jFnXUcc7uTV+KoXtIdB1uDtUMRQgjxCDJL9j4Nynqz83Qsaca8MylaiHxn2+cQuRWCp2nJFYBLQWj3BQzeAoUrw9ph8HUgnFxv1VBF1ru3yMWZM2fyTJGL/7V5jk+7Vbd2GEIIIR5DEqz7NCzrTXxyGv9F3bB2KEKIpxG1FzZOgkovQvVeD+4vUgX6rILuCyAtGX7uBAu7Q/SJ7I9VWMSdIhcNGzakf//+eWah4dLezpT0dLJ2GEIIIR5DEqz71C/jhU6nzcMSQuQyyXGwvD+4FoW2n8HDqoHqdFCxLby+E1qMh8htMKse/PEeJF7L3phFlqtWrRrjx4+nQYMGJCYmEhMj6xsKIYTIPjL54D4eznY8V9SNbSejeat5OWuHI4R4EmtHwPUz0HcNOHo8/ngbewh8W5uftWEi7JgF/y2CZv+DgL4yPyuXSUlJYc2aNSxYsAA7Ozvi4+NZv349Dg4O1g5NCCFEPiI9WBkILOvN/rPXuZWSZu1QhBCZdXAZ/LcQGg0H3yccEuZSCNrPuD0/qxKsGQpzgiBi47PFpBQkxMD5fXD4V9j2hZbIxV1+tucVGWrWrBnh4eF88sknLFy4kEKFCklyJYQQItvJz7MZCCzrzdwtp9gdeY3G/gWtHY4Q4nGunYHV70KJOtBoxNM/T9Gq2vyso6vgrzEwvyP4t4aWH90tlnG/xOtw/azWc3b9rBbLvfdT4h98zKlNWi+bjf3Txyoe0KdPH1atWsX58+fp0qULSilrhySEECIfkgQrA7VLeWBr0LH9ZLQkWELkdMY0WDEIUND5m2cf1qfTwXPttYWJd86GLZ/AV3WhzkBw970nebqdQCXdVxDHzkU7zqMUlG6k3Xb3AQ9fKFBSS65+6QP/NxLaff5ssYp0Bg4cyMCBA9m1axe//PILhw4d4uOPP6ZDhw74+8sC00IIIbKHJFgZcLKzoYaPB9sipNCFEDne1k/g3A7o9K2W1GQVWwdoOASq9YQNE2DHbECBrZOWMLn7QMl6d5Mndx8tmXL0eHhxDYBKHeHiEPjnMyhWHWr2zbqYBaCt4VinTh1u3rzJb7/9xogRI/j1118ferzJZGLcuHGEh4djZ2fHxIkT8fX1Ne+fO3cua9aswcXFhQEDBtC0aVPzvl27djF8+HA2b95syZckhBAiF5EE6yEalvXms3XHuZaQgoeznbXDEUJk5OwO2DwVqoZA1a6WOYdrYejwpbYwsd4GnL0fnUBlRrOxcPE/WDtcW5OrRK2siVWk4+bmRmho6GMXGl63bh0pKSksWbKEsLAwpkyZwuzZswEIDw9n9erV/PLLLwCEhIRQr149HB0duXjxIvPmzSMtTebrCiGEuEuKXDxEYFkvlIJ/T0l5XyFypKQbsHyg1nMU/LHlz+dWVFus+FmTKwC9ATp/p5WTXxIqRS+sbO/evQQFBQFQvXp1Dh06ZN4XERFBnTp1sLe3x97eHl9fX8LDw0lOTuaDDz5g3LhxVopaCCFETiUJ1kNULeGOs51B1sMSIidSClYPgZvntUTFwc3aET05J08IWaCtu/VLH0hLsXZE+VZ8fDwuLi7m+waDwdwrVb58efbs2UN8fDzXrl1j//79JCYmMn78ePr160fhwoWtFbYQQogcShKsh7A16KlXxksSLCFyov8Ww6Hl0HR07h5eV6SKNvzw7L/w1/+sHU2+5eLiQkJCgvm+yWTCxkYbQe/n50evXr0YMGAAEyZMoFq1ahgMBvbs2cNXX31FaGgoN27cYMiQIdYKXwghRA4jc7AeoUFZb9Yfu8L564kUd3e0djhCCICYCFg7DHwDoeG71o7m2VXpAhf2w79fQrEaUL2ntSPKdwICAti4cSPBwcGEhYWlqzgYGxtLQkICixcvJi4ujn79+lGzZk3+/PNP8zGBgYF89tln1ghdCCFEDiQJ1iMElvUCYNvJaLrVKmnlaIQQGFNhxUBtDlOnudp1XvD8h3DpAKx6BwpV1BItkW1atGjBtm3bCAkJQSnFpEmTmDdvHj4+PjRr1oxTp07RuXNnbG1tGTFiBAZDHvm7E0IIYRGSYD1C+cKueLvYSYIl8oeLB+DCvpxdNnzTZDi/F7r+CAVKWDuarGOwgS7zYG4TrejFoE1atUKRLfR6PePHj0+3zc/v7sLS9++737Zt2ywSlxBCiNxJ5mA9gk6no4GfN9sjYlBKWTscISxHKfjtNVj1tlY+PCc6vRW2fgoBL2lrSeU1zt7QfT7EX4Ff+moLKAshhBAi15EE6zECy3pxNS6ZE1firR2KEJZzbDVcOgjoYMsn1o7mQbdiYcUg8PKDVlOsHY3lFKsB7T6HyK2w7gNrRyOEEEKIpyAJ1mMEltWG6fxzQqoJijzKZIJNU8CrLAS+DUdXwZVj1o7qLqVg1VuQcBU6fwt2ztaOyLKq94Q6g7SiFweXWTsaIYQQQjwhSbAeo4SHE75eTmyPkARL5FHHVsHlQ9B4JDR4C2wd4Z9PrR3VXft+0pK+5u/nn+IPLSeBTwP47Y3bPYtCCCGEyC0kwcqEBn7e7DgVS5rRZO1QhMhad3qvvP2hcmdw9oJa/eDgLxB7ytrRwdXj8McoKNME6r9h7Wiyj8EWuv4Aju6wuJc2RFIIIYQQuYIkWJnQsKw38clp/Bd1w9qhCJG1jv4GV45ovVd3Sp43eBP0tvDP51YNjbRkWN5f61F7cQ7o89nHlWth6DYf4i5q74PJaO2IhBBCCJEJ+ewby9Op76eth7X9pAwTFHmIyQSbpoJ3eaj04t3trkUgIBTCFsKNKOvFt2GitjZU+y+1mPKjkrUh+BOI2AAbJlg7GiGEEEJkgkUSLJPJxPvvv0/37t0JDQ3lzJkzDxwTGxtLy5YtSU5OBiApKYk333yTnj17MnDgQGJjc86QGE9nO54r6sY2mYcl8pIjK+HqUWgy8sEFewPfBhRsm2GV0Lh0SCvyULMvVAi2Tgw5Rc0+2vvwz2dw+FdrRyOEEEKIx7BIgrVu3TpSUlJYsmQJQ4cOZcqU9GWVt27dSr9+/bh69ap526JFi/D392fhwoV07NiRWbNmWSK0p9awnDf7zlwnMUWG6Yg8wGTUeq8KVoTnXnxwv7sPVA2BfT9q6zJlJ6Vg7XBw9IDmUqocgNbToERt+PU1uHLU2tEIIYQQ4hEskmDt3buXoKAgAKpXr86hQ4fSn1SvZ968ebi7u2f4mEaNGvHvv/9aIrSn1sDPixSjid2ROadnTYindnglRIff7r16yMdAwyFgTNF6krLTwWVwdruWXDl5Zu+5cyobe20+lp0zLO4JidetHZEQQgghHsIiCVZ8fDwuLi7m+waDgbS0NPP9wMBAPDw8HniMq6srAM7OzsTFxVkitKdWp7QntgadDBMUuZ/JCJunQqFKULHDw4/zLguVOsHu77Kvil1yHPw1BooFQI3Q7DlnbuFWFLr9BNfPaosum3JQVVOTSZI+IYQQ4jaLJFguLi4kJCSY75tMJmxsbDL9mISEBNzc3CwR2lNzsrOhho8H26TQhcjtDi2H6OOP7r26I2gopMTDzq+zJ7bNUyH+MrT5JP9VDcwM3/rQagqc+BM2T3n88dkh5Rb82A5+aGvtSIQQQogcwSLfYAICAtiyZQsAYWFh+Pv7Z+oxmzdvBmDLli3UrFnTEqE9k0A/bw5fuMn1WynWDkWIp2NM05KYwpWhQrvHH1/4OajQVkuwkm5aNrar4bBjtlbBsHjO+/+fY9QeANV7af+Oe3+0bixpybCkF5zZBg3fsW4sQgghRA5hkQSrRYsW2NnZERISwuTJkxk9ejTz5s1j/fr1D31Mjx49OHHiBD169GDJkiW88UbOW1Q0sKwXSsG/ETHWDkWIp3NoGcSchCajMt9DFDQUkm7A7m8tF5dS8H8jtDlGUtji0XQ6aPMplG0Bq96y7L/LoxhT4Ze+Wgn59jOhShfrxCGEEELkMI8et/eU9Ho948ePT7fNz8/vgeM2bNhgvu3o6MiMGVYqCZ1J1Uq642xn4J+T0bSuUtTa4QjxZO70XhWpovVKZVbxACj7vFbsou5gLQnKakd/h1ObtDWfnL2z/vnzGlsHCFkAS/vAmqHav229V7Lv/MY0WD4Awtdq/2YBMl9OCCGEuEMmOTwBW4OeumW82C49WCI3OrgUYk9Bk9FaL8iTCBoGt2IsMyQtJQH+eE9L/Gr1y/rnz6ts7LWiFxXbwR8jYfvM7DmvyQS/vwFHfoUWE6DOwOw5rxBCCJFLSIL1hBr4eXE6OoHz1xOtHYoQmWdMg83ToGg1KP8UC/f61gffhrB9hjbvJitt/RRuRmk9IfcveCwezcYOusyDSi9q1Re3Trfs+ZSCNe/Cf4ug6f8g8C3Lnk8IIYTIhSTBekINy2nDl6SaoMhVDiyGa6efrvfqjkbDIO4ihC3IurhiIrSkrWoI+NTLuufNTwy20OlbqNIN1o/XFpC2BKXgj9Gwd562Rlqj4ZY5jxBCCJHLSYL1hMoXdsXbxY7tkmCJ3MKYqvVeFasB/q2e/nnKNNGq+/3zmfacWeGP0WCwhxYfZs3z5VcGG3jxa6264KZJsGGilhBlpQ0TYOdsqPuKVojkaRN1IYQQIo+TBOsJ6XQ66vt5sy0iBpXVX2CEsIT/FsH1M8/WewXaYxsN1xa6Pbjs2eMK/z9tPacmo8C1yLM/X36nN0D7LyHgJdjyMaz7IOuSrM0fa8MPa/bV1uGS5EoIIYR4KEmwnkLDsl5cjUvmxJV4a4cixKOlpWhftovXhHIvPPvz+beCwlW0L9sm49M/T2oS/N9IKFhBq0wosoZeD22/gFr9YdsX8Of/nj3J2j4TNk7UhnG2+UySKyGEEOIxJMF6Cg38ZB6WyCX+W6j1OD1r79UdOh00GgoxJ+DIb0//PNtnaL1qradpc4hE1tHroc10qPsq7PhKW1/saZOsXd9oxTOe6wgdvsr82mlCCCFEPiat5VMo6emEj6cT205KuXaRg6WlwJZPoHgtbR2rrFKxPXiV03qxnuaL+7Uz2mMrvQhlGmddXOIunQ5aTYYGb8KuubB6iFZe/UnsXwBrh4F/a+j0jTbPSwghhBCPJQnWUwos683OUzGkGZ/wS4sQ2SXsZ7hxLut6r+7QGyBoKFw+BMf/ePLH//ke6PTwwsSsi0k8SKfT1qkKGqpV/lv1ZuaHdR5cpq11VaYpdP1BKwcvhBBCiEyRBOspBZb1Ii45jQPnb1g7FCEelJYMW6ZDidpQtnnWP3+VLuDuq83vepJerJPr4NhqrVhGgRJZH5dIT6eDZmOh8SjY/zP8+trjk6yjq2DFIPCpDyELwdYhe2IVQggh8ghJsJ5S/TJeAFKu/XGUyvpy0eLx9s/XFu/N6t6rOwy20PAdOL8XTm3M3GPSUrTCFp5+UP/1rI9JZEyng6ajodkYbT20FQO1haczcmId/PKyVtK/5xKwc8reWIUQQog8QBKsp+TlYs9zRd34RxKsh4u/Ct82h2UvP/n8D/H00pJh66dQsi74NbPcear3Atei2jyvzNgxC2JOaoUtbOwtF5fIWKPh8PyHcGg5LO/34FpmpzbDkl5QqCL0Xg72rtaJUwghhMjlJMF6BoFlvdh35jqJKc9QrjqvirsEP7SBC2FweKVWNU5kj30/wc3zluu9usPGHgLfhjPb4Mz2Rx9747y22HH5NlAuCwtuiCfT8B1oOVmrALm0j5aMA5zdAYt6gEdpCP0VHN2tGKQQQgiRu0mC9QwalPUmxWhiz5lYa4eSs9w4D/OC4UYU9Pldqxa3fjxEbrN2ZHlfapJWoc+nPpRpYvnzBfQBJ+/H92L9PRaUEVpNsnxM4tHqvwbBn0D4GlgSqiVXC7pqiz2/9Bs4e1k7QiGEECJXkwTrGdQp5YmNXifDBO917QzMaw0JVyF0JZRqCO1mgEcpWNZPGzYoLGffTxB30fK9V3fYOWnzqSLWa/OxMnJ6qzYsreEQ7e9AWF+dgdD2czjxJ3zfUuux6vM7uBa2dmRCCCFEricJ1jNwtrchwMeD7bIelib2lDYsMOk6vPQr+NTVtju4QbeftO3L+2e+VLR4MqlJ8M+n4BsIpRtl33lrDwCHAtq8r/sZU7WFbt19tOGEIueo9TJ0/Frr7eyzSqo6CiGEEFkk/yZYJqO21kvYQjj+F0TthWuRkBz3RFXvGpT14tCFG1y8kWi5WHOD6BPasMCUBO3LWvGa6fcXqawNSzq9WZuLI7Le3h+yt/fqDgc3qPuqVn798uH0+3Z9A1eOQKspYOuYfTGJzKneA/r9IT2LQgghRBaysXYAVpN8E1a9AylxD+4z2IOTl3Zxvn3t5H372hOcvc3bOpR14LvNipe+28XiQfXwcsmH1dEuH4GfOgAK+q6Bws9lfFxAKJz9FzZPhZJ1LLM+U36Vmqj1XpUKgtJB2X/+uoPh3y+1+V9dvte2xV2GTZOhbAsoH5z9MQkhhBBCWEH+TbAcPWBYOMRfhluxkBANt2Lg1p3rGEi4fX39nLY96cFFhUsD/9naseB6U96aG8uXg9vg4WyX/a/HWi4d1JIrva3Wc1XQ/9HHB38CF/Zra/G88g+4FcueOPO6PfO0v+Uu86xzfidPqN0fts2AJu+Bd1lYNw7SkqD11OztURNCCCGEsKL8m2AB2DmDZxntkhnGVEi8dk8ypiVk+vP76fXfYrrd2MCfM1rRpN8k3Ar7WDb2nOD8Ppj/ovY+9lkFXn6Pf4ydE3T9Eb5pqi1o2ne1tmiteHopt+Cfz7R5V6UCrRdH/Tdg5xwtloCX4L+F0PDdzP1dCCGEEELkEfk7wXpSBltwKaRd7lUb9I2HE7P6I1qfXI6a/QfJNfti3/jdvNtDc24X/Nz5dvWxVU82h6OgP7T7Qit4sX48vDDBUlHmfVeOwdphkHAFmvxk3VhcCmll2/d8B1G7wK04NBpm3ZiEEEIIIbJZ/i1ykdU8SlEs9Bt2tvmLlaaGGPZ+j/qiOqwdATcvWDu6rBW5Teu5cvaGl//v6SbIV+kCtfprCxAfW5vlIeZ5STfgj/fg60BtmGa7L8C3vrWjgsC3AB1EH4eWH2m9m0IIIYQQ+YgkWFmsYZ1auHf/mudTPmW9XVPUnu8gLyVapzbBgi5az1zftc9W2rnVZChaHX59RavgKB7PZNIqX86sBTtmQY3e8OY+qNnX2pFpCpSAJqO0uJ7raO1ohBBCCCGynQwRtICWlYpg7NGSwYsK0ap4CF8UX4/Nnu+0Mto1+0LDd3Ln0MGT62BxL23O2ku/PThU8knZ2EPXH2BOY/ilL/T7U9uWHVKTtEqSSTch+YZWnj/p5j3b4m7fvqFd37/fmAz+rbQ1oIrXzJ4iDhfCYO1wbfhdidrQaykUq2H58z4pGRYochmTycS4ceMIDw/Hzs6OiRMn4uvra94/d+5c1qxZg4uLCwMGDKBp06ZcuHCB9957D6PRiFKK8ePHU6ZMJufzCiGEyNMkwbKQ4CpFSTMp3lm8n2t2vfl+8BAcdnymzU/JjYlW+P/B0pegYHkI/U0rX58VPEtDx1mwpBf8+T9o80nWPO/9lIJDy2HjJLhxDowpj3+MrTPYu2rrPNm7adcFimu3TWlwdBX8t0jrhaszECp3tsxaT7ditblqe3/QhmV2mAXVeoBeOqCFyArr1q0jJSWFJUuWEBYWxpQpU5g9ezYA4eHhrF69ml9++QWAkJAQ6tWrxxdffEHv3r15/vnn2bp1K59++ilffvmlNV+GEEKIHEISLAtqX60YRpOJd5f+x8DVOr556QscgoZqawU9baKllFbJMO4SxF/S1hq6/1qZoFAFKFwZCj2nrUvl6PH0L+TIb7CsHxSpCqErnu25MlKxrVaB7t8vtXlElTtn7fNf/A/+b6S2BlfRalDvtbtJ053Eyd4tfTJl7waGx/z3CP4Y/lsMu7+F317XEsQavaFWv6ypnGcywt55sH6C1oNW7zVoMhIcCjz7cwshzPbu3UtQkLZ+XPXq1Tl06JB5X0REBHXq1MHeXutd9/X1JTw8nJEjR+Lq6gqA0Wg07xdCCCEkwbKwF2uUINWoGLHsAK/8vJc5oTWx7/Al3Em0dn97N9Gq3R9Sb2WcNJmvL4Mp9cET2bmAS2FwLQLo4fCv2vPe4VZCS7QKV4JClbRr73KPL5F+cBmsGAQlakGvXyz35f75cRC1G35/S0vkvMs9+3MmRMOGCbD3R21h6HYztARIb3j25wYtIaszUBsmeGYb7PoGdn6tJYpln9e2l3vh6c53dodWHfDSQW3x4OCPoVDFrIlbCJFOfHw8Li4u5vsGg4G0tDRsbGwoX748c+fOJT4+ntTUVPbv30/37t3x9PQE4NSpU0ydOpWvvvrKWuELIYTIYSTBygbdapXEaFKMXnGQ1xfsY1avmth5lob7E61dcx58sKOnljS5FAKvcuBaGFyK3HNdREus7F3SP04piLsIlw/D5UNw+Yh2O2Lj3QRNb6sN+St0O/G6c3Etqs0pCluo9cz41IeeS7SEwlIMttoiuV83hKV9YMA6bc2sp2FMhd3fwaZJkJIA9V6FxiO1kvKWoNNBqYba5eZF2PeT1vO0KAQK+ECtl7V1oZy9H/9ccZfg7w/gwGKtzHmXeVDpRVmoVwgLcnFxISEhwXzfZDJhY6M1j35+fvTq1YsBAwZQrFgxqlWrhoeH1ou/Y8cOPvzwQ6ZNmybzr4QQQphJgpVNetTxIc1oYuxvh3lz0T6+7BmArUGvzUG6k2id3qL1tNxJmlwKPX3RB51OG3boVgzKtbi7PS0FYk7cTrgOwZUjWu/LwaV3j3Fw1xKvc7ugTGMIWfT0yc6TKFAcOn8DP3fRijl0fIpfhE9tgv8bBVePQpmm0GqKNlwyu7gV1YbxBb0L4Wu1Xq31H8KmyVqiVHug1ht4f8KUlqL1fm2ephXQCBqqXaTMuRAWFxAQwMaNGwkODiYsLAx/f3/zvtjYWBISEli8eDFxcXH069ePcuXKsWPHDj766CO+/fZbihcvbsXohRBC5DSSYGWj0PqlSDMpPlx1hHcWh/FFSHVsDLcLFXiW1i6WZmN3t6eKrne3J17Tkq4rR+72eFXvCW0+BVsHy8d1R9nnodFw2DJNm49Vo3fmHnftDPz1P63whLsvhCyE8sHW6/kx2MJzHbTLlWPanLuwRXBgiTYEss5AqNxFS1wjNmhzxKKPQ7mWWvn6rJjDJYTIlBYtWrBt2zZCQkJQSjFp0iTmzZuHj48PzZo149SpU3Tu3BlbW1tGjBiBwWBg0qRJpKamMmrUKABKly7N+PHjrfxKhBBC5AQ6pZTK6id9XMnbpUuXsnjxYmxsbHj11Vdp2rQp169fp2XLluZfDp9//nn69OnzyPN06tSJFStWZHX4FvfNllN8tPYoHaoX49Nu1THoZfhXOiYj/NQBovbAwPW3k8GHSEmAfz6HbV9oc52ChmoFM7IzKcys5Dg4sFQbDnrliDafrUhViNwKHqW13rbyrawdpRBWk1s/0zMrr78+IYTIbx72uW6RHqxHlby9evUq8+fPZ/ny5SQnJ9OzZ08CAwM5cuQIbdu2ZezYsZYIKUcZ2KgMqSYT0/4Ix6DX8XGXapJk3UtvgM7fwZwgrTT8oE0Pzv9SCg6vgL/Gws3zUKUrPP+hNswwp7J31QqZ1OqnVTTc9Y123WwM1H8zZyaFQgghhBDiiVgkwXpUydsDBw5Qo0YN7OzssLOzw8fHh2PHjnHo0CEOHz5M79698fT0ZMyYMRQq9IwL2eZgrzUpS5pR8enfx7HR65jSqSp6SbLuci0MXb6HH9tplQW7fH93uN+lg9qQujPbtB6gzt9pwwlzC50OfBtoFyGEEEIIkadYJMF6VMnb+Ph489ohAM7OzsTHx1OmTBkqV65MgwYN+P3335k4cSIzZsywRHg5xlvNy5FmNDFjw0lsDHo+6lgZnVSLu6tUQ613Z/14LRmp1Ak2TtTKzzt6QLsvoEZo1pVdF0IIIYQQ4hlZJMF6VMnb+/clJCTg6upK1apVcXR0BLQJx3k9ubpjSAt/Uk2K2Zsi0AFj2z6Hg60kDGaBQ7Q1of58DzZM1OYx1RmsVerL6gWPhRBCCCGEeEYWSbAeVfK2atWqfP755yQnJ5OSkkJERAT+/v6MHDmSF154geDgYP79918qVXpEYYM8RKfTMaJleUwmxZwtp9h8/CojWlWgXdWi0psFoNfDi3Pg+5baulCtJsuCu0KIPCM1NZWoqCiSkpKsHYpFOTg4UKJECWxtH7O4vRBC5AEWSbAeVfK2efPmhIaG0rNnT5RSDBkyBHt7e4YOHcp7773HokWLcHR0ZOLEiZYILUfS6XSMDq5IULmCfLT2KG8t2s/3/5xmTJuK1Crlae3wrM/JE17fJYvtCiHynKioKFxdXSlVqlSe/VFNKUVMTAxRUVGULp0Ny5EIIYSVWSTB0uv1D6wH4ud3d12fbt260a1bt3T7S5Ysyfz58y0RTq7RsJw3q99syPJ9UUz/K5wuX/9L68pFGNW6Ar5e+XzB2Tz6xUMIkb8lJSXl6eQKtB8Rvby8uHr1qrVDEUKIbCELDecwBr2ObrVK0rZqUb7Zcpo5WyJYd/QyofVK8Vbzsrg72Vk7RCGEEFkoLydXd+SH1yiEEHforR2AyJiTnQ1vP1+OTcOa0DmgBD9sP03jjzfx7dZTpKSZrB2eEEIIIYQQIgPSg5XDFXJzYErnqvQNLMVHa44ycc1R5u84w8hWFWhduYj8KiiEEOKpTZkyhcOHD3P16lWSkpIoWbIkHh4ej63kO3fuXOrVq0fVqlWzKVIhhMg9JMHKJSoUcWN+/7psPn6VSWuO8tqCfdTy9eB/bSpSw0fKlQshhHhyo0aNAmDFihWcOnWKYcOGZepxgwYNsmRYQgiRq0mClcs09i9IoJ8Xy/ZGMf3v47w4azttqxZlZKsKlPR0snZ4QgghntLyvVEs3XMuS5+zW62SdK5Z4okeM2rUKK5fv87169eZPXs2n3zyCZcuXeLKlSs0a9aMIUOGMGrUKIKDg4mOjmbz5s0kJSVx9uxZBg4cSKdOnbL0NQghRG4jc7ByIRuDnpA6Pmwa1oS3mpVl3dHLNJ++mclrj3IjMdXa4QkhhMjl6tWrx+LFi0lISKB69ep89913LFu2jMWLFz9wbHx8PHPmzGH27NnMnTvXCtEKIUTOIj1YuZizvQ3vvlCennV9+eSvcOZuPcXSPed4tYkfL9YoQUFXe2uHKIQQIpM61yzxxL1NlnJnvSp3d3cOHjzIjh07cHFxISUl5YFjK1SoAEDRokUz3C+EEPmN9GDlAUUKOPBJ12qsfrMhzxVzY9LaY9SbvJ6Xvt/F8r1RxCenWTtEIYQQucidAkorVqzA1dWV6dOn069fP5KSklBKZXisEEIIjfRg5SGVihVgwYB6nLgcx29hF/jtv/MM/eU/3lt5kOefK0zH6sVp7F8QOxvJq4UQQjxe/fr1GTp0KGFhYdjZ2eHr68uVK1esHZYQQuRokmDlQeUKuzKsZXmGvuDPvrPX+C3sAqsPXGTNgYsUcLQluEpROlYvRu1Snuj18sujEELkd/cWppgyZYr5drly5fj9998fOP7eY+6wt7dnw4YNlglQCCFyEUmw8jCdTkdNX09q+noytu1z/HMymt/2n+e3sPMs2nWWYgUcaFe9GB2qFadiUVcZ5iGEEEIIIcQzkgQrn7A16GlavhBNyxfiVkoafx+5zG9hF/hu62nmbD6Ff2EXOlQvTvtqxaTcuxBCCCGEEE9JEqx8yMnOhg7Vi9OhenFiE1JYc/Aiv4ed5+M/w/n4z3Bq+nrQsXox2lYthoeznbXDFUIIIYQQIteQBCuf83S2I7SeL6H1fDkXe4tVBy7w2/4LjP3tMJPWHiO0vi8Dg8pIyXchhBBCCCEyQRIsYVbS04nXmpTltSZlOXLhJt9sPcW3W0/x07+R9K7ry6DGZSjk6mDtMIUQQgghhMixpF63yNBzxdz4rHt11r3bmOAqRfl+22mCpm5k/KojXLmZZO3whBBCCCGEyJGkB0s8UpmCLnzarTpvNSvHlxtP8uO/kSzYeYYedXx4tYkfhd2kR0sIIXKrKVOmcPjwYa5evUpSUhIlS5bEw8ODGTNmPPax4eHh3Lx5k9q1a2dDpEIIkXtIgiUypZS3M590rcabzcry1caTzN9xhoW7ztKjdkleaeJH0QKO1g5RCCHEExo1ahQAK1as4NSpUwwbNizTj/3rr7/w9vaWBEsIIe4jCZZ4Ir5ezkzrUo03m5Xjq40nWbDzLIt2naN77ZK82sSPYu6SaAkhxFMJWwT7f87a56zRG6r3yPThqampfPDBB5w5cwaTycQ777xD3bp1+eyzz9i5cydpaWm88MILdOjQgZUrV2Jra0ulSpWoWrVq1sYthBC5mCRY4qmU9HRiSueqvN60LLM2RbBo11kW7z5Lt1olea1pWYpLoiWEELnOL7/8goeHB5MmTeLatWv07t2bNWvWsGrVKn766ScKFSrEihUrKFy4MC+++CLe3t6SXAkhxH0kwRLPpKSnE5M7VeH1pn7M2hTB0j3nWLrnHF1qluS1Jn6yaLEQQmRW9R5P1NtkCcePH2fv3r0cOHAAgLS0NGJjY/n444+ZPn060dHRBAUFWTVGIYTI6STBElmihIcTk16swutNyzJ700mW7o7ilz3n6FKzBK2rFMXF3ka7ONjgYmeDs70BG4MUsRRCiJykTJkyFClShFdeeYWkpCRmz56Ni4sLf/zxB59++ikAwcHBtGnTBp1Oh8lksnLEQgiR80iCJbJUcXdHJna8k2hFsHjXORbvPpfhsfY2enPS5WynJWDO9gac7W1wvb3N2f7OdhsKONri4WRLASdbPJzs8HCyw9HOkM2vUAgh8q6QkBDGjBlD7969iY+Pp2fPntjZ2VGgQAG6deuGg4MDgYGBFCtWjMqVKzNt2jT8/PyoV6+etUMXQogcQxIsYRFFCzgyvkNl3mpejtPRCcQnp5Fw+xKfbCQ+KY2ElLT7tqdxNT6ZyJhb5u23UoyPPI+9jR732wmXu5Mt7o52eDjb4u5kh7vjPdud7PBwssXD2Q4vZzt0Ol02vRNCCJHzderUyXx72rRpD+x/4403eOONN9Jta9KkCU2aNLF0aEIIketIgiUsytvFHm8X+6d+vNGkuHU7EbuRmMr1W6lcv5XCtVv33tbu37iVSsTVeK6d0banmVSGz1nc3ZFG/t40KleQBmW9KeBo+9TxCSGEEEIIcS9JsESOZtDrcHWwxdXB9onW2lJKkZBi5FpCipaIJWpJ2NW4ZHadjmH1fxdZtOsceh1UL+lOI/+CNPIvSLUS7hj00rslhBBCCCGejiRYIk/S6XTmwholPdPv69+wNKlGE2HnrrPl+FW2nIjmi/Un+HzdCdwcbGhYTuvdauRfUNb1EkJYnFIqzw9bVirjEQVCCJEXSYIl8iVbg57apTypXcqToS+U51pCCv+cjL6dcF1l7cFLAPgVdDb3btUr7ZXtRTVMJsXNpFRiElKITUghJj5Zux2fQkxCyu3tyaQZFbVKeRDo502ArwcOtlL8Q4jcwMHBgZiYGLy8vPJskqWUIiYmBgcHB2uHIoQQ2UISLCEAD2c72lUrRrtqxVBKceJKPFuOX2Xz8ass3HmWedsisTPoqV3ag0blChJY1htnexuMJoVSCqNSmExgUgqjSWFSdy7cvW9CO04pTCZtX6rRROz9ydOd+7evjQ+ZS+bqYIOXsx2eznYYFXy9+RRfbYzAzkZPTR8PAst6Ud/Pm6olCmArJfFzHaNJcelmEudibxF1LZFzsbc4d027HRV7i5iEFEp7O/NcUTcqmi+ueD3DnEeR/UqUKEFUVBRXr161digW5eDgQIkSJawdhhBCZAtJsIS4j06nw7+wK/6FXRkQVIakVCO7Tseae7cm/98xi5zX1cEGbxd7PJ3tKOnpRA0fdzyd7fB0tsfL2Q4vFy2Z8nK2x8PZFnub9L1U8clp7Dodw/aTMWyPiOGTv44Dx3Gxt6FOaU8a+HlR38+LikXc0Ms8s0xRSnE1PpkzMbc4F3sLAAdbA462BhxsDTjY6nG0u/e+dtvWoHtsb4TJpIiOT+bctVuci00k6s71de36wvXEdIVadDoo4uZACQ9H6pXxwt3JjlPR8WyLiGbF/vPm4wq52qdLuJ4r6kZpb2dZdy6HsrW1pXTp0tYOQwghRBaSBEuIx3CwNZiHCQJcvJHInshrpJlM6HU688Wg557bOvR6HXodGHTal22DXjtGp9NhMN/X4emsrellZ/NsX4Bd7G1oVqEwzSoUBiA2IYUdp2LYHhHN9pMxbDh2BQAPJ1vq+2m9W4F+XpT2ds6zQ5MyQynFlbhkIqMTOBNzi9MxCZyJSSAy+hZnYhJIeMxSARnR68DR1oCjnQF7G8M9SZgeG72ey3FJnL+WSHJa+kVavV3sKeHhSLWS7rStWpQSHk6U9HSkhIcTxdwdHkiq74hNSOHoxZscvXiTIxdvcvRiHNsjTpFq1BI0exs9/oVdqVjU9Z7ky00qaAohhBAWYJEEy2QyMW7cOMLDw7Gzs2PixIn4+vqa9y9dupTFixdjY2PDq6++StOmTYmNjWXYsGEkJSVRqFAhJk+ejKOjFBgQOU/RAo60q5bz/zY9ne0IrlKU4CpFAS0x/Dcihm0ntaTrzjyzIm4ONPDzokFZbyoUcSXVaCIx1UhyqomkVCOJqUaS7rmdnGokKe32/ZS7t+9etKTByc6Ai70NTvY2uNgbcDIvHK3ddrG3eeAYZ3sb7Tg7Q5b2uJhMWhJ1Ovp28hRzi8joBCJjtKQqMfVuEmWj11HS04lSXk7UKe1JKS8nfL2d8fV0wqDXmd+PxBTjPe+Pdp2YYiQ5TduXeM8+83uVaiLFaKJ8YVeer1iYEh6OlLydRBV3d3rqOX6eznYElvUmsKy3eVtKmomIq/HmxOvoxTjWH73C0j1R5mOKuzviV8gFN4eMF/d2tjdkuN3VwQZ7G32eScwf12bNnTuXNWvW4OLiwoABA6TNEkII8UgWSbDWrVtHSkoKS5YsISwsjClTpjB79mwArl69yvz581m+fDnJycn07NmTwMBAZs2aRdu2benUqRNz585lyZIl9O3b1xLhCZEvFS3gSKeAEnQKKIFSijMxt9gWEc32iBg2Hb+abpjZ4zjY6tMNlbO3uXvf28UGBdxKNnLpZpK2kHSKMVMLR9/L3kaPnY0eHVqvn07H3dsPbAMd9x2j07YDXI1LNid+ALaGO0mUMw38vCnl7YSvlzOlvZwp5u6QJ4bT2dnozT1VdyiluBqXbO7lOnrxJqejEzgXm/nFve8w6HU4306Qne216psftKtkqZdjUY9qs8LDw1m9ejW//PILACEhIdSrV0/aLCGEEA9lkQRr7969BAUFAVC9enUOHTpk3nfgwAFq1KiBnZ0ddnZ2+Pj4cOzYMfbu3cvgwYMBaNSoEZ9++uljG6vz58+nW31eCPF0St2+PKvk25c77G9fPDM+PNsUfMj260DY7Ut+ZQt43L48i//+gU7znu05zp/PfJKflR7VZkVERFCnTh3s7bXiIb6+voSHh0ubJYQQ4qHtlkUSrPj4eFxcXMz3DQYDaWlp2NjYEB8fj6urq3mfs7Mz8fHx6bY7OzsTFxf32PPs3Lkz64MXQgiRrzyqzSpfvjxz584lPj6e1NRU9u/fT/fu3aXNEkII8VAWSbBcXFxISEgw3zeZTNjY2GS4LyEhAVdXV/N2BwcHEhIScHNze+B5hRBCiKz2qDbLz8+PXr16MWDAAIoVK0a1atXw8PCQNksIIcRDWWSiQUBAAFu2bAEgLCwMf39/876qVauyd+9ekpOTiYuLIyIiAn9/fwICAti8eTMAW7ZsoWbNmpYITQghhEjnUW1WbGwsCQkJLF68mA8//JCLFy9Srlw5abOEEEI8lE4plfEqps/gTkWm48ePo5Ri0qRJbNmyBR8fH5o3b87SpUtZsmQJSikGDx5My5YtiY6OZuTIkSQkJODh4cH06dNxcnLK6tCEEEKIdB7VZjVr1owPPviAw4cPY2try9ChQ6ldu7a0WUIIIR7KIgmWEEIIIYQQQuRHub8WsRBCCCGEEELkEJJgCSGEEEIIIUQWsUgVwZzmzvj68PBw7OzsmDhxIr6+vub9S5cuZfHixdjY2PDqq6/StGlTK0arSU1N5b333uP8+fOkpKTw6quv0rx5c/P+H374gV9++QVPT22FoQ8//JAyZcpYK1yzF1980VzuuESJEkyePNm8L6e9zytWrGDlypUAJCcnc/ToUbZt22auBjZx4kT27duHs7MzALNmzUq3xEB2+++///jkk0+YP38+Z86cYdSoUeh0OsqVK8cHH3yAXn/395KkpCSGDx9OTEwMzs7OTJ061fy3Yo14jx49yoQJEzAYDNjZ2TF16lS8vb3THf+ovx1rxHzkyBEGDx5MqVKlAOjRowfBwcHmY3PCe3x/zEOGDCE6OhrQ1uaoVq0an332mflYpRSNGjUyv6bq1aszdOjQbI9ZPJq0WdknN7VZkLvardzWZt0fs7Rblo83T7dZKh/4888/1ciRI5VSSu3fv1+98sor5n1XrlxRbdu2VcnJyermzZvm29a2bNkyNXHiRKWUUteuXVONGzdOt3/o0KHq4MGDVojs4ZKSklSHDh0y3JdT3+c7xo0bpxYvXpxuW0hIiIqJibFSROnNnTtXtW3bVnXt2lUppdTgwYPVjh07lFJKjR07Vv3111/pjv/+++/VjBkzlFJKrV69Wk2YMMGq8fbq1UsdOXJEKaXUokWL1KRJk9Id/6i/nexyf8xLly5V33333UOPt/Z7rNSDMd9x/fp11b59e3X58uV02yMjI9XgwYOzM0TxFKTNyh65uc1SKme3W7mtzVJK2q3skJ/arHwxRHDv3r0EBQUBWvZ76NAh874DBw5Qo0YN7OzscHV1xcfHh2PHjlkrVLNWrVrx9ttvA1oGbzAY0u0/fPgwc+fOpUePHsyZM8caIT7g2LFjJCYm0q9fP1566SXCwsLM+3Lq+wxw8OBBTp48Sffu3c3bTCYTZ86c4f333yckJIRly5ZZMULw8fFh5syZ5vuHDx+mTp06ADRq1Ijt27enO/7ev/lGjRrx77//Zl+wPBjvp59+SsWKFQEwGo3Y29unO/5RfzvZ5f6YDx06xKZNm+jVqxfvvfce8fHx6Y639nsMD8Z8x8yZM+nduzeFChVKt/3w4cNcvnyZ0NBQBg4cyKlTp7IrVPEEpM3KHrm1zYKc327ltjYLpN2yRrx35MU2K18kWPHx8eYuXACDwUBaWpp5373d587Ozg/8QVqDs7MzLi4uxMfH89Zbb/HOO++k29+mTRvGjRvHjz/+yN69e9m4caN1Ar2Hg4MD/fv357vvvuPDDz9k2LBhOf59BpgzZw6vv/56um23bt2id+/efPzxx3z77bcsXLjQqo1ry5YtzQufgvYFRqfTAdp7GRcXl+74e9/vjPZb2v3x3vnQ3LdvHz///DN9+/ZNd/yj/nayy/0xV61alREjRrBgwQJKlizJV199le54a7/H8GDMADExMfz777906tTpgeMLFizIoEGDmD9/PoMHD2b48OHZFap4AtJmZY/c2mZBzm+3clubBdJuWSNeyLttVr5IsFxcXEhISDDfN5lM5n/g+/clJCRYdZ7NvS5evMhLL71Ehw4daNeunXm7Uoo+ffrg6emJnZ0djRs35siRI1aMVFO6dGnat2+PTqejdOnSuLu7c/XqVSDnvs83b97k9OnT1KtXL912R0dHXnrpJRwdHXFxcaFevXo56tfLe8euJyQkmMff33Hv+53RfmtYu3YtH3zwAXPnzn1gzPej/naspUWLFlSuXNl8+/7/YznxPQb4448/aNu27QM9CACVK1c2z4upVasWV65cQclKHTmOtFnZIze2WZA7263c2GaBtFvZIa+2WfkiwQoICGDLli0AhIWF4e/vb95XtWpV9u7dS3JyMnFxcURERKTbby3R0dH069eP4cOH06VLl3T74uPjadu2LQkJCSil2Llzp/k/lDUtW7aMKVOmAHD58mXi4+MpWLAgkHPf5927d1O/fv0HtkdGRtKjRw+MRiOpqans27ePSpUqWSHCjD333HPs3LkTgC1btlCrVq10+wMCAti8ebN5f82aNbM9xnv99ttv/Pzzz8yfP5+SJUs+sP9RfzvW0r9/fw4cOADAv//++8C/f057j+/4999/adSoUYb7vvzyS3788UdAG95StGhR86/KIueQNit75MY2C3Jnu5Xb2iyQdiu75NU2K19UEWzRogXbtm0jJCQEpRSTJk1i3rx5+Pj40Lx5c0JDQ+nZsydKKYYMGfLAOFtr+Prrr7l58yazZs1i1qxZAHTt2pXExES6d+/OkCFDeOmll7Czs6N+/fo0btzYyhFDly5dGD16ND169ECn0zFp0iTmz5+fo9/n06dPU6JECfP9e/8uOnToQLdu3bC1taVDhw6UK1fOipGmN3LkSMaOHcunn35KmTJlaNmyJQD9+vXj66+/pkePHowcOZIePXpga2vL9OnTrRar0Wjko48+omjRorz55psA1K5dm7feeosRI0bwzjvvZPi3c/8wguw2btw4JkyYgK2tLd7e3kyYMAHIme/xvU6fPv3Al4E7MQ8aNIjhw4ezefNmDAaDVSpeiceTNit75MY2C3Jnu5Wb2iyQdis75dU2S6dyS1+bEEIIIYQQQuRw+WKIoBBCCCGEEEJkB0mwhBBCCCGEECKLSIIlhBBCCCGEEFlEEiwhhBBCCCGEyCKSYAkhhBBCCCFEFskXZdqFyCl27tzJO++8Q9myZc3bPDw8mDFjxjM976hRowgODn7oWhJCCCHEk5I2S4inIwmWENmsXr16fPbZZ9YOQwghhHgsabOEeHKSYAmRA4SGhlK6dGlOnz6NUorPPvuMggULMmXKFPbu3QtA27Zt6dOnD5GRkYwZM4bU1FQcHBzMDd+SJUv49ttviY+PZ9y4cVStWtWaL0kIIUQeJW2WEI8mCZYQ2WzHjh2Ehoaa7zdu3BiAgIAAxo8fz4IFC5gzZw6BgYFERUWxdOlS0tLS6NmzJ/Xq1ePzzz9n0KBBNGrUiPXr13PkyBEAKlWqxGuvvcaKFStYsWKFNFZCCCGembRZQjw5SbCEyGYZDbfYvHkz9erVA7RGa8OGDRQpUoRatWqh0+mwtbWlWrVqREREcPr0aWrUqAFA8+bNAVi9ejWVKlUCwNvbm6SkpGx8RUIIIfIqabOEeHJSRVCIHOLQoUMA7Nu3j7Jly+Ln52ceapGamsr+/fvx9fXFz8+PgwcPAvD7778zf/58AHQ6nXUCF0IIke9ImyXEw0kPlhDZ7P7hFgBJSUmsXLmSH374AUdHR6ZNm4aHhwe7du2ie/fupKam0qpVKypVqsSIESN4//33mT17Ng4ODnz88cccPnzYSq9GCCFEXiZtlhBPTqeUUtYOQoj8LjQ0lHHjxuHn52ftUIQQQohHkjZLiEeTIYJCCCGEEEIIkUWkB0sIIYQQQgghsoj0YAkhhBBCCCFEFpEESwghhBBCCCGyiCRYQgghhBBCCJFFJMESQgghhBBCiCwiCZYQQgghhBBCZBFJsIQQQgghhBAii0iCJYQQQgghhBBZRBIsIYQQQgghhMgikmAJIYQQQgghRBaRBEsIIYQQQgghsogkWCJPiIqKonz58vTq1euBfaNHj6Z8+fLExsY+0XMOHjyYFStWPPKYnTt30rZt2yd63qzQrFkzDh48+FTnv3jxIm3btqV9+/bs37/fQhEKIUT+lN/ao4EDB3Ly5MmH7j948CBvvfWWxeMwGo28+uqrtGzZkp9//tni5xPiUWysHYAQWcXe3p7IyEjOnz9P8eLFAbh16xZ79+61cmQ5y86dO/H29uaHH36wdihCCJEn5af26Jtvvnnk/ipVqjBjxgyLx3H58mX++ecfwsLCMBgMFj+fEI8iCZbIMwwGA61bt2bVqlW88sorAPz11180b96c77//3nzckiVLmD9/Pnq9Hm9vb8aOHUvp0qW5fPkyo0aN4sqVKxQrVoyYmBjzYyIiIvjoo4+4fv06RqOR0NBQunTp8tBYdu7cyUcffYSTkxO3bt1i2bJl/PPPP8yePZvU1FQcHBwYOXIkNWrUIC0tjY8//phNmzZhMBioUaMGH3zwATdv3uT9998nJiaGq1evUrx4cT7//HO8vLwy9X5k9DovX77M559/TlxcHKGhocyfPz/dYxYuXMjixYuxtbXF3t6e8ePHU7ZsWZo1a8bzzz/Pnj17iIuL4+WXX6Znz56YTCYmTZrEf//9R0JCAkopJk6cSM2aNRk1ahT29vYcPHiQ6OhoWrdujaenJxs3buTq1atMnDiR+vXrP8k/sRBC5Ao5rT2aNm0ahQsX5ty5czg4ODBlyhT8/PwYNWoU169f59y5czRp0oS3336bTz75hN27d2M0GnnuuecYM2YMLi4unD59mvfff5/Y2Fj0ej2vvvoqwcHBNGvWjC+++IIyZcowevRozpw5g16vp1KlSowfP57du3czYcIEVq9eTVxcHB9++CHHjh1Dp9MRFBTEu+++i42NDVWqVGHQoEFs27aNK1eu8NJLL9G3b98HXs+ePXuYNm0aiYmJ2Nra8s477xAQEMCAAQNIS0ujU6dOzJw5Ex8fn3SPmTJlCiaTCdB6BFu2bMmoUaPQ6XREREQQGxtLYGAgY8aMwdbWlmXLlrFkyRJSU1O5ceMGAwcOpGfPnqxYsYK//vqLpKQkzp8/T9GiRenVqxc///wzkZGRvPzyy/Tr1+9Z/4REbqeEyAPOnTunqlevrg4ePKhat25t3t6nTx8VHh6u/P39VUxMjNq+fbt6/vnnVUxMjFJKqeXLl6vWrVsrk8mkXnvtNfXZZ58ppZSKjIxU1atXV8uXL1epqakqODhYHTp0SCml1M2bN1Xr1q3V/v371Y4dO1SbNm0eiGfHjh2qQoUKKioqSiml1OnTp1Xbtm1VbGysUkqp48ePq8DAQJWQkKB+/PFH1atXL5WYmKiMRqN6++231cqVK9UPP/yg5syZo5RSymQyqQEDBqjvvvtOKaVU06ZN1YEDBx56/ke9zuXLl6tBgwY98Ji0tDRVqVIldfnyZaWUUitXrlSLFy82n2/s2LHKZDKpixcvqrp166pjx46pffv2qTfffFMZjUallFJz5sxRgwcPVkopNXLkSNW1a1eVkpKirly5ovz9/dVPP/2klFLqhx9+UC+//HLm/nGFECIXyant0e7du5VSSi1cuFC9+OKLSintc7pPnz7mY2fOnKmmTJmiTCaTUkqp6dOnqw8++EAppVTHjh3Vzz//rJRS6sKFC6p58+YqLi7O3B6tXLlS9evXTymltSf/+9//VGRkZLq4RowYoSZMmKBMJpNKTk5W/fr1M7dz/v7+av78+UoppQ4ePKgqV66skpKS0r2W2NhYVb9+fRUWFqaU0trSOnXqqLNnz5rf94y89NJLavXq1UoppY4eParGjRtnfv0dO3ZU8fHxKjk5WfXq1UvNnz9fxcfHq27dupnb7P3795ufe/ny5apmzZrqwoULymg0quDgYHM7ePToUVWlShVzmyjyL+nBEnlK5cqV0ev1HDp0CC8vLxISEvD39zfv37p1K8HBwXh6egLQqVMnPvroI6Kioti+fTsjR44EwNfXl7p16wIQGRnJ2bNnee+998zPk5SUxJEjR/Dz83toLEWLFjUPDbnzi9y9v8bpdDrOnj3L9u3b6dChAw4ODgB8/vnn5mP27NnDvHnziIyM5MSJE1SrVi1T78OjXufDGAwGWrVqRUhICE2aNCEwMJB27dqZ9/fs2ROdTkeRIkUICgpi27Zt9OvXjwIFCrB48WLOnTvHzp07cXZ2Nj+madOm2NraUrBgQZycnAgKCgLAx8eH69evZ+q1CCFEbpST2qMKFSpQq1YtADp37sz48eO5du0aADVr1jQft2nTJuLi4ti+fTsAqampeHl5cf36dY4dO0bXrl0BrX1bt25dunPUrFmTzz77jNDQUBo0aECfPn3w9fXl0qVL5mO2bNnCokWL0Ol02NnZERISwo8//sigQYMAaN68OQCVKlUiJSWFW7duYW9vb378gQMH8PHxMbeF5cqVIyAggF27dpnfo4y0bt2a8ePHs2HDBho0aMC7775r3vfiiy+a260OHTqwfv16evfuzddff83mzZuJjIzk2LFj3Lp1y/yYKlWqULRoUQBKlChBw4YN0ev1lCxZkuTkZBITE9O1hSL/kQRL5Dnt27fn999/x9PTkw4dOqTbp5R64HilFGlpaeh0unT7bWy0/x5GoxE3Nzd+++03877o6GhcXV0JCwt7aBxOTk7m2yaTifr166dLni5evEihQoXM57n3uU0mEz/++CMHDhygc+fO1K1bl7S0tAzjz8ijXuejfPLJJxw/fpzt27fzzTffsGzZMmbPng2QLk6TyYRer2fTpk189NFHvPzyyzRv3pwyZcrw+++/m4+zs7NL9/z3v1YhhMjLckp7dP+cJKWUedv9bdV7771H48aNAUhISCA5Odl8fp1OZz721KlTFCtWzHy/ZMmS/P333+zcuZMdO3bw8ssvM2bMGDw8PNI9/71MJlO6dulOMnXnPPe/R/c//s4xj2vbQkJCaNq0Kdu2bWPr1q18+eWX5rbq3vdGKYVer+fSpUt0796dbt26UbNmTVq1asXGjRvNx0nbJh5HqgiKPKdDhw788ccfrF279oGKSg0bNmTt2rXmCk7Lly/H3d0dX19fgoKCWLJkCQAXLlxg586dAJQuXRp7e3tzg3anCt+hQ4cyHVO9evXYtm0bERERAGzevJn27duTnJxM/fr1Wb16NSkpKZhMJsaNG8eaNWv4559/6NOnDx07dsTLy4vt27djNBozdb5Hvc6HiY2NpXHjxri7u9O3b1/eeecdwsPDzft//fVX83uzbds2GjVqxLZt22jatCk9e/akSpUqrFu3LtMxCiFEXpdT2qNjx45x7NgxQJv3FRAQgJub2wPHNWzYkAULFpjbo7Fjx/Lpp5/i4uJCpUqVzO3AxYsX6dGjB3FxcebHLly4kNGjR9OwYUOGDx9Ow4YNOXHiRIbPr5QiJSWFpUuX0qBBg8y+nVSrVo3Tp09z4MABAE6cOMHu3bupU6fOIx8XEhLC0aNH6dSpExMmTODmzZvcuHEDgP/7v/8jJSWF5ORkVq5cSdOmTTl06BCenp689tprBAUFmZMrad9EZknKLfKcwoUL4+fnh6urK+7u7un2BQYG0rdvX/r06YPJZMLT05M5c+ag1+v54IMPGD16NK1bt6ZIkSJUqFAB0H6pmjVrFh999BHffvstaWlpvP3229SsWdPc6D1OuXLlGD9+PO+++y5KKWxsbJg9ezZOTk6EhIRw/vx5OnXqhFKKOnXqEBoaSvHixZk2bRqzZs3CYDAQEBDA2bNnM3W+R73Oh/H09OTVV1+lb9++ODg4YDAYmDhxonl/VFQUnTp1IikpiTFjxlCmTBlCQkIYNmwY7dq1w2AwUKtWLf76668Mf2UUQoj8Jqe0R97e3nz++eecP38eT09Ppk2bluFxr732GlOnTuXFF1/EaDRSsWJFRo0aBcD06dP58MMPmT9/Pjqdjo8++oiCBQuaH9uxY0d27dpFcHAwjo6OFCtWjJdeesmc2AGMGTOGiRMn0q5dO1JTUwkKCjIXAckMT09PvvjiCyZMmEBSUhI6nY7JkydTunTpRw6BHzZsGJMmTeLzzz9Hr9fzxhtvUKJECQAcHBzo2bMnN2/epGXLlnTu3Jnk5GSWLVtGq1atcHR0pGrVqnh6enLmzJlMxyryN53K7JgjIUS+dadKVJUqVawdihBCiCewc+dOcxU/kd6oUaMoV64c/fv3t3YoIo+RIYJCCCGEEEIIkUWyvQfrv//+45NPPnlg/Z0NGzbw1VdfYWNjQ+fOnenWrVt2hiWEECKfeJJ2KCkpieHDhxMTE4OzszNTp07F09NT2iwhhBAPla1zsL755ht+//13HB0d021PTU1l8uTJLFu2DEdHR3r06EGzZs3w9vbOzvCEEELkcU/aDq1atQp/f3/efPNN1qxZw6xZsxg5cqS0WUIIIR4qW4cI+vj4MHPmzAe2R0RE4OPjQ4ECBbCzs6NmzZrs3r07O0MTQgiRDzxpO7R3717z+m2NGjXi33//lTZLCCHEI2VrD1bLli0zrPISHx+Pq6ur+b6zszPx8fGPfb66deuaF3IVIq9RCpLTTCSnGUlONZGUZiQ5zURKmgnTveuj6HXY2WTNbyUKMJkUJgVGk0p3nkfR6cCg06HX6dDrtfVLTEphMimMKuP1XjJy5/EGnXb7niVXcoQ0kyLVqDJ8PTZ6HbYGPbYGPTaGu7dtb9+20esw6O++IJOCVKOJVKOJNKO6fVu7TjPdvX0/HWBr0GPQ61AKjEr7dzKaMj/a26DX/q0Mt99jk9L+nUxP9G9++9/p9nO52ttQpIBDpmPIyPnz5zNdmfNpPWk7dO92Z2dn4uLipM0SwsriktI4d+0WKO0z6P7PSr1Oaxf/v707j4uq6h84/hkGhm0AQURFARXXVEQ0l0xNjSdTS3ML10otbbPMXJ6enjIzs3psL8tfaanlmm22aFpquSaKBCIlrigqqCzDDnN/fxwZxRV0mBng+369eMHMnbnzveM4537vOed7DHqn0r/P/12WtqXkOzq/yExhkZmCYtX+Fpz/+9LvypLve2swa1BYZKb4ktcoae8Nev1Fx6Puc9GX7TygyKxZjuHi4ykoMl/2Pup0Ogx6HUXmC22Mm7Meo5sznq7OGF31ODlAQ62h/q0uPZ6Svy9tH/VOOlyd9QT7uZf5fbuaq7VbDlGm3Wg0kp2dbbmdnZ1dqvG6mnr16rF69eqKDE0IC7NZY8+xc6yNP8WWA2k46XQYXdWXjJebc6m/PQ16jG4uGF31GF1d8HTVq/tdzz/O4GxJRM5lF3Ag1UTSaRMHTpvU36kmks/lUvLd6qSDID8PwmoZaRxgJLSW5/nfRmp4GK4R9c0fc3ZBEdn5xZjyCzHlF2PKK8KUr36y8y/8bcpTt7Py1YLIRldny5ewl+uFYy95n4xupW97GvQ43+QXnS1omkZ6TiEnM/M4mZnHqYzzvzPzOJmRx8nMfE5l5nEqu+Cy57q5OFHT05WM3EJM+ZcvjOntppKU2t5u1PF2u+LfNT0Nls/OpXHlFZrJyi9U/16X/Dtllfx7XXJ/fpHZ0lB6Gi7/d7n49sX3Wyupv9jAgQOtvs+yulo7dPH92dnZeHt7S5slhJ2YzRrvbPiHdzb8wy11vfloZDuCa3qQV1hM8rkcjp7N4eiZHI6dy+Xo2RyOnVX35RSUXr+qlpcrwX4eBPm6E+znQS1vN05n5l30nFzSTPmlnuNp0BPk50FwyU9ND4J8PQjy86C+rztuLqUXc75ZmqZxLqfQcgwlsR07f5wn0vNKJQ4ueh31argT5OdhidOgd+LYuQvvw7GzueQWln4vanu70thXPb7keSW/A7xccXLSYTZr7EvJZMuBNLYknWHnoTOkFJpx0kFY/Rp0aVyTLqH+RIT4Wv19KHkvzmQXXHQc6lhK3peUjFwuzqEMeifq+5a8F+6Wf7OS98bbzcVqsV2t3XKIBCs0NJQjR46Qnp6Oh4cHu3btkpKZwiEUFJnZdvAMa+NP8su+U6Rm5eOi19E+xA9XFydMeUUkn8shu+DCiWthcdl6ADwNevROOjLzLpxou7k40cjfSHiQL4MjgggNUIlUg5qeFfKldT1OTjq83FzwcnMBbq53oqrQ6XT4ehrw9TTQou7lC3WWyC8q5nRmvkrEMi4kYGeyC/Bxd6GOj0qcaluSJ1c8DDf+lazT6XA36HE36OH65/riEldrh06cOMGmTZsICwtj8+bNtGvXTtosIewgPaeAp5fHsDExlUER9XnlvlaWdtHNRU/jAC8aB1z+5Xetk/M/D5/ju70nMGvqQmZgDXUyfmeLgFKJSrCfB74eLuhs2Fuj0+nw8zTg52mgTVCNy7YXFZtJycgrlXyV/P7prxTO5RQCFxLDkJqedG1S66Jkw536vh5lOrdwctLRqp4Prer5ML57KPlFxew5ms7WA2n8cSCNjzYd5IPfknB1duLWBn7cdj7halXPp9TIjWvJKSgi+Vzu+QT5QkJYklReKUkO8nXn1ga+BPvVK5Uc1vZ2K/PrVhS7Jljff/89OTk53H///UyfPp2xY8eiaRqDBg2idu3a9gxNVGM5BUVsSkxlbfxJNuw/TVZeER4GPXc0q8VdLetwR7MAfNyvfvUjv6i4zD0IhcVmgv08CA0w0riWkXo13K/YOyEqH1dnvaWBFo7reu3QsGHDmDZtGsOGDcPFxYW5c+fi4uIibZYQNhR3PIMJS6I5lZnHK/e1YniH4DInOzqdDn+jK/5GV9oG+162vaDIzNnsAmoaDTc9XMyWnPVOljamyxW2Z+UVUlBkxs/TYPXE0NVZT6dGNenUqCbP/KsZWXmF7Dx0li0HzrDlQBqv/5wIJOLt5kzn0Jp0aezPbaH+uLk4cfRsDsnnE9yLE6lLeww9DHpLD+FtjWue73H0sPRK3cwFSVuo1AsNDxw4UIZbCKs4l13A+oRTrI0/xe//pJJfZMbXw4U7W9TmrpZ1uL2Jv116kISoTqr6d3pVPz7hWFIycvnjH9XDsOXAGZx0cNv5k90ujf0JrOF+/Z04gJW7jvH8N3H4eRr4cETEFZMk4VhSs/LZmpTG1gNn+ONAGsfTcy97jN5JR10fN0viFFxTDbUs6YWqWQGJYUW42ve6Y6d/QlSgE+m5rIs/ydr4U+w8fJZis0agjxvDOgRzV8s63NrAt1LMCRJCCCGy8grZcfAsfxxI4/d/UklKVfME/Y0GujT2x6zB7/+k8U3MCQAa+nta5s50Dq1ZofN5b0R+UTEvfb+PL3cc5bbQmrw7rC3+Rld7hyXKoJaXK/3D69E/vB6apnH0bA7bks6ggSWhqlvDrVL1GJaXJFii2igoMpOUauLX/adZG3+S2OQMABoHGJnQvRF3taxD63o+leKKiRBCCOs7ciabL3ccpeB8NTUdqj24uFko+bPkvpI2Q3fJA9yc9dTzdbdcna9j5XkhhcVm9h5L548DafzxTxoxx9IpMmu4uTjRoWFNom4N5vYm/jSv42WJ0WzWSDyVxZYDaWxNOsPXu4+zZPtRdDpoGeiterdC/bm1gZ+az2knJ9JzefSL3ew9ls6E7qE8+6+mcsGzktLpdITU9CSkpqe9Q7EpSbBEhTqZkcfZ7ALq+LjZbIJoVl4hSanZqipfqqrMl5Rq4uiZHIrOl5lpE1SDqb2bcVfLOoTWMlZ4TEIIIa5A0+DEHqjbBpzsOwx7/8lMRn6yk4zcAjUk/KIJFCV/lsyquHC7ZLtW6jZAQbG51G0XvY7AGu6l5pGU/F2WIgqappGUms2WA2n8/k8a2w+ewZRfhE4HYfV8eKRbI25v4k+7EF9cna/8Xjo56WhR15sWdb0Z17WRJUnbcuAMW5LSWPDHIT7edBCD3om2wTW4vbE/tzX2p019H+slOFmn4Odp0LwftB582eYtB9J4cukeCorMfDQygt6t6lrndYWwIUmwRIX4+1QW8zYm8d3eE5YyogZnJ2p7u16onHZxGerzFdUCvF2v2jBcTNM0TmflW5Kni3+fyrwwUdLZSUcDf0+aBnhxd6s6NA4w0rmR/02v1yOEEOImFRfBj5Mh+jPoNhV6/sduoew5eo4HF/6Ju4uen57qesVqdOVVUGTmRHru+TLZuZYJ/clnc1gbf5KzlyzlUFLtrb7vhdLSQb4eZBcUWeZSpWTkAWqY1T1tAunaxJ/bbmJ4n4veifYN/GjfwI+n7mxCTkEROw+dZWuSKlbw5vq/mfvL3xhdnenY0E8VK2hck6YBXjdWkOlUPHx5P2Qcg/iv4dxh6DoZdDo0TeOjTQd5Y+1+QmsZ+WhUO7kAKiotSbCEVe05eo4PNybxy75TeBj0PHRbAyJCfC+UqT5fqjrueAbrE06RV3j5Qqp+nobzCZirJQGr7e3GuZyC84lUNgdPm8i6aB0hL1dnGgUYub1xLVXavJaR0AAjwX4eVXqMrxBCVEoFObBqDPz9E/g2gC3vQPgw8Gtk81C2Hkhj3KJd1PJyZcnYjlar/GlwdqKBvycN/K88NMqUr5b5KFm36djZnPNrOaleqovXK/Jxd6FL45o80difro1rEVyzYqqTehicuaNZAHc0CwDgbHYB25JU79bWA2ls2H8agJqeBlVFLrQmt4XWpJG/5/VHqPyzHlY+CK5GGLseds6HX1+GjGNk9ZrDs1/Fszb+FH3D6vL6oDA8XeUUVVRe8ukVN03TNLYmneGD3w6wNekMPu4uPNWrCQ/e1gBfz6tfVdM0jczcossWbC35OyUjj9jkDM5cdJWvjrcboQGeDIyoZyltHhpgJMDLVeZOCSFEZZB9BpbeD8m7oM//1FCx99vDT9NhxAqbhvLLvlM8/uVuGtT0YMnYjgR42250g9HVmeZ1vGle5/L19C5eu8nZyYlbAr3tsq6Pn6eBvmF16Rumhukln1PFCrYlnWHbwTP88FcKAAFernQ+n2x1buRPkJ976TZ55//BT1OhdksYthx86kG9duBTH/54k/iYv9ia+wTP9w1n7O0NpT0XlZ4kWOKGmc0avySc4sPfDrA3OYMAL1f+06cFwzoGYyzDlSedToePhws+Hi40q3P14Rj5RcWkZuXj416y4K0QQohK6dxhWDIIMpLh/sXQ4h51/x3TYd3zkPgzNOttk1C+jTnOMyv20irQm88e6nDNC4K2dvHaTY6kvq8HQ9p7MKR9EJqmceRMDlvPJ1tbDpzh2/MVCuvVcKdzaE06N6zBXcnvYYz5P2h6Nwz6RPVgATg58X2th9ltzuR5FrCt7lyM4atLVxQRopKSBEuUW2Gxme/3nmDexiT+OW0i2M+D2fe1ZmBEvQpZK8rVWU99X1msVQghKrUTMfDFECgugNHfQnCnC9s6ToDdi1Xxg0Z3gEvF9iQt2X6E/34bR4cGfnz64K1luigoStPpdJYhkMM7BqNpGgdOm9h2UPVwbdl3mN6xb2HU72Gl8z3scZ1Mp/2ZdG7kQg0PF+b8tJ9P/zhEu5BBZHbqhu+PE+DTO2HEV1Crqb0PT4ibIt8ooszyCotZuesYH28+SPK5XJrV9uKdqHD6tq4r5VOFEEJc3YENsGI0uPvCg2ugVrPS2/Uu0OcNWHSvmo91x7QKC2XexiRe+3k/PZsH8OGICFlE3kp0Oh1NanvRpLYXo29xQVv6FJyKZ2vTf7O2oBc7/jrNl3+qHi4/TwNnswt48LYGPNenBQZnJwioD18MhU8jYdgyCOls5yMS4sZJgiWuKyuvkC92HOWT3w+RZsonIrgGM+5pSc/mATdWRUgIIUT1EbMUvnsCajWHEavA+ypltxt1h5b3wR9vQpv7VfELK9I0jTfWJvLhxiTuaRPIm0PbSBGkinBiD3wZha4gG4av5LYmd3IbUGzWiD+RwbakM+xNTueulnXoH17vwvPqtYNx6+GLwbCoPwz8WH0ehKiEJMESV3XGlM9nWw/z+dbDZOYV0bWJP4/3aEvHhn4yAVUIIcS1aRr88RZseAkadoP7l4Cbz7Wf869Z8PdaWPsfiPrCaqGYzRozvo9n0bYjDOsQzKwBrexSNKLK2/8DfDUOPGrC2LWqqMV5eicdYfVrEFa/xtWf79cQxv4CS6Ng5UOQeQI6P17xcVc16Udh1wLVY9zlKXtHUy1JgiUuc+C0iUXbDrNi1zHyi8z0blmHx+5oTOv612kYhRBCCABzMfw0Df78P2g1GAbMA+cyFJHwqQ/dpqik7J/10OTOmw6lqNjM1FWxrN5znPHdGjH97uZykdDaNA22faAKlQS2VUP8vGrf2L48/NQcvdWPwNrnIP0Y3PWK3ReidniaBof/gJ0fq0RXM4PeVc1vdHasYinVgSRYAlBd9xsSTrFo2xH+OJCGQe/EveGBTOgeSuMAWehPCCFEGRXmql6M/WvgtifhzpngVI6heJ2fgJgv4Kcp0HD7TZ0c5hcV8+SXe1i37xTP/qspj/doLMmVtRUXwo9TIHohtLgX7vsYDDdZmMrFHYZ8phK27R9CZjIM/D91vyitIAf+Wgk7PobT8Rd6rYy14efpkBILQbfaO8pqRxKsau5sdgHL/zzGku1HOJ6eS6CPG1PuakbUrUHUdLDysEIIIRxczllYOgyO7YC7XoXOj5V/H84GuPt1WDIQtr0PXSffUCjZ+UWMXxzNHwfSmHHPLTzYpeEN7UdcQ16GWjw46Ve4fRL0fKF8yfS1OOmh96uqV3Ptf9S8rKil4FnTOvuv7NKPwZ+fwO7PIfcc1G4F974HrYeoRDTrlEqwjm2XBMsOJMGqpuKOZ/DZ1sN8t/cEBUVmOjeqyX/7teDOFrWlIqAQQojySz8KSwbDuUMweAG0Gnjj+2rcSy1AvPl/0Hoo1Agq19Mzcgp56LOdxBxL539D2jC4Xf0bj0Vc2bkj8OVQOHMA7n0fIkZVzOt0fhy866khg59Gwsiv1Fyt6kjT4MgW2PGRGgYI6v9JxwkQclvpNcS8aqtCMcd2AE/aI9pqTRKsaqSgyMxPcSl8vvUwu4+m42HQM7R9fUZ3bkDT2ldf6FcIIYS4ppN/qeSqMBdGfQ0Nbr/5ffZ+Fd7vAOv+A0MXlflpqVn5jF6wkwOns/hwRAS9W12laqG4ccf+hGXD1Jpmo75WRUwqUssBasjbsmEqyRq+XFUdrC4Kcy8MAzwVp4YB3jYRbh137YsPQR0h6TeVmMnQWJuSBKsaOJmRx5c7jvDlzmOkmfJp6O/Ji/fcwqB29fF2c7F3eEIIISqzg5tg2Qhw9YIxP0PtW8r81IycQv7zzV8Y9E7U8XGjro8bdX3cz/9dG7+uz6D77RV1khja47r7O56ey6hPdnAiI5dPH7iVbk1r3cyRiSuJWw3fPApedWD4j7ZbFDiks6owuGQgfNYPBi+EZr1t89r2kn4Mdn0K0Z9deRjg9QR1hNjlcO5w9e31sxNJsKooTdPYeegsi7Yd4ef4k5g1jV7NAxjduQG3N/aX9auEEELcvL9WwdcToGZjGLlKzZcph5e+j+enuJPU8XbjVGYeRWat1HajczN+dqmD7ssneavJQmrV8LIkYXV93Kjj40ZNTwM6nY6DqSZGfrKDrLwiloztSPsGftY8UgFqEehfXoCgThD1pe3nQ/k3gbHr1dDEZcOgz//g1rG2jaGiaRoc2aqqASasAbTzwwDHQ0iX8vVEBXdSv4/tqDoJVkay+jEXq0qJmhm0kr819ftq2y6+38UNmt9TtuqmN0ASrComO7+I7/ae4POth9l/MgsfdxfG3t6QUZ1CCPK7yao+QgghqidNgwITZKdBzhn1O3kn/D5XnfRFfaGGLZXDuviTrN5znIm9mvBMZFPMZo00Uz4pGXmkZOSSkpHHyYw81hx/ignH/03DA4t4K+fuy5Kwkt6v9JwCXPROLH2kE63qybIiVrfvO5VctbwPBnykTlDtwas2PPgDrBoDPzwDcV+poXIt7gF9JR6Vk2+Cv1bAn59eNAzwSZVA1gi+sX3Wag6u3nB0O7SJsm689nB0O3x+jxqaetN0qke0ggqASIJVBZjNGtsPnmHV7mR+jjtJTkExLep689qg1tzbph7uBlk7QgghxEU0DfLSIfsM5KSdT5zSSidQl94uzr98P7cMUGW5y3myfTa7gOe+/otb6nrzRI/GADg56QjwdiPA2402QTUuenQL+HIzjx9ezaNTp5Omr0lKep4lETuZof7OLypmyl3NZWmRipB2AL55TM17uu9j+6+r5GpUPWg7PoKd82HVQ2CsA+0eVD/elWjeXdo/qhpgzJeQnwl1WsM976phgDdb7t5JD/VvhWM7rROrPWUkw/KR4BMEfV4HJxfQOV34cdKf/1sHOv1Vtl304+IOxoAKC1cSrEosKdXE6t3JfL37OCcy8vBydebeNoEMblefdiG+jrHWx+n9aly+Tz17RyKEEELT1ByWQ5vBXHTlx7h4qqFfHv5qnk3tVhdue/pf+O1ZS11Zv4G25r/fxpGRW8iScR0xOJehcm3vV+GDjjitf4GAwZ8S4OVGm/IVFhQ3qiAHVoxWvUNDPrd/clVC7wy3PQGdHoUD62Hn/8Gm12DzG9CiH9z6sCq24gjnQpcqLoK/f1IxH9oEeoO6WNHhYZUQWTPmoI6w8VXITQf3Gtbbry0V5MCy4VCYp3ovazWzd0TXJQlWJZOeU8D3sSl8FZ1MzLF0nHTQrWktpvdpwb9uqY2bi4P0Vh3bqb7oDqwHZzfo8R9ValVWYhdC2JHZbGbGjBkkJiZiMBiYNWsWISEhlu3z58/nhx9+wGg0Mm7cOHr06MErr7zC/v37AUhNTcXb25sVK1Ywa9Ysdu/ejaenJwAffvghXl4OXpH16Ha1ZlGrwVAv4nyydHHyVLPCF3NdE3uCH2JTmHJXM5rX8S7bk/wawu1Pq3al3YPQsGtFhihKaBqsmQSn96k5duUsl28TTnpoepf6OXsQdi2APUtg37dQq4UaYtcmSl3stTfTabVu1a7P1OLJ3vWh538h4gEwVlBBluCOgAbHd0HjOyvmNSqSpsF3T6oFk4ctqxTJFUiCVSkUFpvZmJjK6t3JbEg4TUGxmeZ1vPhPnxb0Dw8kwNtO46Cv5Mg21QAe/A3c/aDn83B8D/zyX9j3DfT/AAJa2DtKIUQ1tX79egoKCli+fDkxMTHMmTOHefPmAZCYmMiaNWtYuXIlAFFRUXTq1In//Oc/ABQWFjJ8+HBefvllAOLj4/nkk0/w86tExRSiF4LBC+59FwyeNn/51Kx8/vtNHG3q+zC+W6PyPfn2SbB3Kfw0FcZvrtzzbSqL6IUQuwzu+HflODn3awT/mqUu6sZ9pXqIfnwW1r+kkqxbx0FAc9vGpGmqyMTO/1NJn7kQGvWAu1+Dpr1VT1xFqtdODYk7uqNy/BteasvbELcKer1QqapGSoLloDRNI/5EJl/tTua7mBOcyS6gpqeBkZ1CGBhRj5aB3o4xBBDUl8fhP1Ridfh3NWwk8mVoP0aNk9Y0iF8NP06Bj7pC92nqSqQ0jkIIG4uOjqZrV9X7ER4eTlxcnGVbUlISHTp0wNVVDYEKCQkhMTGR8PBwAJYsWUKXLl1o1qwZZrOZI0eO8MILL5CWlsbgwYMZPHiwzY+nXHLOQvw30HakXZIrTdN47uu/yC4oZu7QNuVf1N7FHe56FZaPUCernR+rmECFcnw3/DRNnZR3m2rvaMrHxV19zsNHqOP48/9g9yL1u0FXlWg171ux5yEF2Wrtqp2fwKm/wNVHve6tY1U1RFtx9VLDfI9tt91rWsvf61Ry3HIg3P6MvaMpF0mwHMzpzDy+3nOc1buPk3gqC4PeiTtvCWBQRH26Na2FS3kbpIqkaXBwI2x6HY5uVYsA3vWqGr5x8cRMnQ5aDYKG3dWVx99mQcK3qjerbht7RS+EqIZMJhNG44UiCHq9nqKiIpydnWnWrBnz58/HZDJRWFjInj17uP/++wEoKChg2bJlrFq1CoCcnBxGjhzJQw89RHFxMaNHj6ZVq1Y0b27jq+PlEbtcFapo/5BdXv6bmOP8su8Uz/VpTuOAGxyu1byvOuHf+KpqV7xqWzdIR2SPRWJzzsKKB1S7PvD/wMmBzj3KQ6eD+u3Uz79egT2L4M8FsPIB8KoL7R6Cdg+ouYbWknZArV215wvIz1DJTb+3IWyoXS5sAKpc+54v1Nyviu4xs5a0f+CrsaroR/8PHHMu3TVUkne56juYauKl7/fx+z+pmDVoG1yDWQNacU9YID4eDtbTo2lwYIPqsUreCV6BcPcbEDHq2mP3Pf1h8AJ1JeKHZ2B+DzXko/tUx5k0K4So0oxGI9nZ2ZbbZrMZZ2fVFIaGhjJixAjGjRtHYGAgbdq0wddXlR7ftm0bt956q2WOlbu7O6NHj8bdXX3nderUif379ztugqVpsGuhGi5Up7XNX/5kRh4vfhtP+xBfxt5ezqGBF9Pp4O7X4cNOsP5FuO8j6wXpSIryVe/H9nmqGMmwZbZbx8hshtWPQFYKjFkLHpVoCOy1eNZU5xy3TYR/1qnKfRtnw+bXocld6hzlZp07rIpWOLnALf1V0YqgjvZPDoI6qmqLp+IgMNy+sZRFbjosjVLFP6K+vPlqinYgCZYDyC0o5pHF0aSZ8nnsjsYMjKhHo1oOWGZW0+DvtSqxOrFblcrs+6bqhi9PgtSiH4TcBmv/A7//D/avUVcn6revuNhBrYh+cKOq0GPrMdhXkpGsEtXgzlCrqb2jEbZQkK3mkORn3fy+nN0gtGelmfDrKCIiIvjtt9/o06cPMTExNG164f/e2bNnyc7OZtmyZWRlZTFmzBiaNFFDebZu3Uq3bt0sjz18+DBPP/0033zzDWazmd27d3PffffZ/HjK7Oh2SEuEe9+z+Utrmsb01bEUFJt5Y0gb9De70H3NULU+0O9z1YiJksVUq4LsNLUO0p+fQPZpCGgJWSfhkzth+ArVE1PRfp8LB36BvnNt83q25qSHZnernzNJqihGwndQZIW1lVy9oMfzEDHasXpXgzqq38d2On6CZS6Gr8apZPWB7x2zsEoZSILlAGauiScp1cTiMR25vYkVrqBYm9kMiT+qxOpkLNQIUWs0tBl24ytge/jBffOg1UD4/in4NBI6PaYmplrzSsXZQ+qLc9+3cDz6/J06aDkAuk2B2i2t91plde4I/PGm6q43F6r7arVQV7tu6a+KgNj7apewvoObVCWk9CPW3W+t5hd9dm6Rz851REZGsmXLFqKiotA0jdmzZ7Nw4UKCg4Pp2bMnBw8eZNCgQbi4uDB16lT0elX59NChQwwYMMCyn9DQUPr378/QoUNxcXGhf//+lmTMIUV/popbtBpk85desesYGxNTmXHPLTT0t9IQqa6TYe9y+OFZeGRj5Rn2dDWn98P2D2HvMjWMs8m/VOXdht3VUKkvBsNnfdUokOZ9Ki6OpF/ht1eg9VBoP7biXsdR1AyFu15RP1WZT3012ujYduj4iL2jubYNL6kEv99b6mJ8JaXTNE27/sMc08CBA1m9erW9w7gpP8Sm8PiXu3n0jlCm9XaAXpWLmc0qOdn8hupW9msEXZ9V44itOTE0L1OtDh+9UL3Gve9Dgy43vr+0A2qO175vIWWvui+wrToBbdRDHdOO+VCQpVZ+7zYV6oZZ51iu5exBdWVw7zJV0SdiNLQdpa4o7fsWjmwBNKjZ5MIJc53WcsJc2V32+X5PDdO6WTln1YWPks+OZoaajS/67IRVus9OVfhOvxa7HV/OWZjbXI026PemTV86+VwOvd/+ndb1fPhiXEecbrb36mLx36i5NHe/4fgnjVeiaari7rYPLixp0maYWtfp0p5p02n4cqhq0+5+XQ09s7aMZPi4G3gGwMMb7DdfSFSMlQ9C8i6YFHfdh9pN7EpYPU4l9zb+rrpRV/ter+SXfCq3Y2dzmL46lvCgGjwT6WBDxA79rqr+pSaoE/775qsrnxVxldDNG+55G1rep67wf9ZHLRB45wxVhbAsUhPViea+b1UyCGoo4L9mQYt7wffCOjcEhkPnJ9QK8Ns/goTvoVkf1aNVL8LKB4dK+H7/H8SuUInpreOgy1PgHXghno6PQNYpNVxy37eqh+v3/4FvwwsnzIFtK90Jc7X3zy+qhzYrRQ1puuM56/XQ+tRTJ1kdHlYnX5bPztsqkfdtcNFnJ8L6nx1NA9MpSN2v/v951YVb7rXuawjrKClu0e5Bm76s2awx7atYNE3j9cFh1k2u4PxFsztU4aSW91XcOkLWVph3fn7Vh2p9KWNttaRJuzFqntCVGAPUAqurxqiy4xnHoNcM6xWfKCpQJ+BF+XD/YkmuqqKgThD/NWQcV+2Hozm+G757AkK6QO859o7mpkmCZSeFxWaeWrYHNHhvWFvHqg6YeUKVwXX3g0GfqobLFgsEN+oOj22DDS+r5OfvtWqtltAelz9W01TDVJJUpe4HdGosfu85qmfKp/7VX8vDD3o8p4Yl7pyvriD+Xw81LKPbVAi69eaPJzURNv9Prd+gd1VXJW978urVirxqq/Ktt45V4/D3/6CObdv7ah0In2B1AnvLANUDUlmrOlUHOWfVHMO9X6ohfEMXVewcQ2OAWhah/RjIPgOJJZ+dD2DLO2q+ZEmyVa99+T47mgaZxy8kUhf/zsu48LjQnpJgOSJNU8MD67WzTU/9Rb7YeZQtB87wyn2tCPKrgEnqJQUv5t0GG2aoubyOzJSqqsv9+Qlkp0Lt1jBgnrp4WZZ5zAZPuP8LVY13yztqXvGAeeBihbUw1z0PyX/CkM9tW0Jc2E5QB/X72Hbwsf1Q4WvKOgXLR6plfoYuuvHpJw5EEiw7eWf9P+w+ms57w9pWTMNzozQNvpsIxYUw8is1PtmWDJ5w9xw1R+rbx2HxADWM7l+zwM1HzQErSarOHFBD7UK6nF/Toh941y3f67nXUFUMO05Q62NsfR8+vVOdLHafdmOTp0/tU8Mq478GFw+VVHV+snxXVz39VenYdg+cHwr2kzrmHR+rhMu7nuqZu6W/mrwqyZbjSFijqmRmp6le0W5TbFsl07OmGn4aMRpyz1347Oycrz47XoHnE/WSz875iydmM2QcvTyJSk2EAtOF/Xv4q6Sx1WD1u1Yz9dsYYLtjFGV3dLv6d7RxcYujZ3J49ccEujbxZ3iH4Ip7oVrN1IWyre9CxIPWuThmbacT1MWO2BXn51fddX5+Vbfy9yzrnVXxiRrBqopi1kmI+uLmKv39tQp2fqzex5YDbnw/wrHVaa3OSY7ttMtczKsqyocVo1R7NWatdao5OgBJsOxg64E0Pth4gKHt63NPm0B7h1Pa7kVqcuHdb9g+ubpYcCeY8AdsnKMazn9+UVfpzh0GnR4adlUNVPN+1jmxc/NWk6Y7jFdXGLe8CwvuUg1g92nQ4Pbr7yMlViVWCd+pyeRdn4FOj199yEdZefhB2xHqJzdd9ezt+1ZVPtoxD4x1oOld6suzVnP14+lv2+GEhblqInbJSXlOmnX2W3IyX6uZuqp6rWUA7C07TQ2rjV+t/i1GrLT/Om/uvhA+XP3kZUDiz+c/OwtVL7GxtrqqmX4UUv+GotwLzzXWUe97+IgLSVStZlWm8as27FDcwmzWeHbVXvQ6Ha8NCkNX0d9F3aeqIXc/ToaHf7PNiIvr0TRVMGLbB5C0AZzd1Xd4x0dvvmqsTge3P61GaXzzKHz6Lxi5Sg0LLq/URHVRNagTRM68ubiEY9O7qJ7sow604LCmwQ+T4dgOGPKZzXvZK5IkWDZ2NruAp5fH0NDfkxn32qGC3bWcOwJrn7uwyrm9ubhD5Evqavu6/6oJwF0nQ7O+N5+0XI2rUc2PunWcOjHZ8o6q3BTSRTXiDbtfnric2AOb3lDDslx9VELWcULFrB3iXgPa3K9+8rMuJFvxX8Puzy96nG/p3oWS3151by7xyjdB2t+X93KcOwycr5ej04NHzZtP8DQNcs+qNWDUjtUJxKXH5d+07HP1KoKmQdxXathOfpaaS9HlaesWgrEGN58rf3ZOxoJfKLS//aL3tan6DInKLees+m5oO9Kmc2oWbj3MzkNneWNwGIE1bHBRxNVLjXL4aqzq1fnXrIp/zWvRNPj2CYhZoi5U9PyvGr5r7Tah9WD1nb5sGHwSCcOXl28ecb4Jlo9S80KHLHS87yxhfUEd4Y+31JIhjjDPbuf/wZ7FqoBaSwde5uIGSIJlQ5qmMWXlXtJzCln40K14GBzo7Teb1ZA8dGocuyMNOavXDh760bavafBUPWTtx6hevT/egkXnh1R1nwqhvVTZ902vwz9r1cnrHc9Bx/EqCbIFVy/VwLYerBr0rJTLh3bFfwN56Rc9x/v8SXSzC71dtZqBd/3S/+Z5GapHI3X/RftMVEPISji5qF6lwHBoE3Vhn36h1hs/XVSgqi9eelxJG6D4ojVLfIKvcFxN1b9LRco6CWueUcl1vXbq/05Ai4p9TWu4+LMjqi47FLdISjXx+s/76dU8gMHtrjEP1tpaDVJXwbe+p5YSqYgqe2W18VWVXN0+SbULFTmfpEEXGPsLLCkp474QmvW+/vM0Db6fCGf+gVHfXCi6JKq2oI6gFavzl4bdrv/4inRoM/w8HZrerZboqWIc6Ay/6vts62E27D/NjHtuoWVgBZ/4ldefn8Dh39X6VhdX3KvuXNxV0hTxgGowf38LlgxSJ/QZR9VV/p7/hQ6PqGGG9qLTqQbSO1DNHyuhaWoy9aUJyt9rYc+SC49z8VQJiau3GuqXdeLCNmc3lUgFd4Raoy8kML4NK37tGWeDWhT60oWhi4tUr1mpBHC/+gwX5V14nFfg5b14tZrd/JVkTYOYL2Htv9X48X/NUvMXHGFokhBgl+IWxWaNZ1fuxc1Fz6sDW1f80MCL6XSqwFH6UdWbXCNYDZ22td2L1ZqR4SOh14u2GapdqxmMWw9fDlG9WX3+p4olXcvO/1M9771eUAWmRPVQMkfx6A77JljnDsOKB9S5xcD5jnVR30okwbKRuOMZvPrjfu5sEcADtzWwdzilnUlSwyoaR6qJ8eJyLm5q2GDb0aoy3F+rLlT8c/Wyd3RXp9OpOWrGgMu/TLPPQFpi6R6q/EzV2F6cjNQIcbzEQe8M/o3VT4t+F+43F5+fT3TxcSWoXsjC7AuP8wy4QuJVxrlr6cdU6fWkDRB8G/R/377zFYW4kmM7bF7cYv7mg+w5ms47UeEEeFuhsl15OelV5dvP+sDKh2DMT7adB3lgvfpuCO2plh6xZYLpVRse/BFWPaSK7KQfVQnelU5cj/2ppgM07Q1dJtkuRmF/7r5Qq4X6frCXfBMsHa560qK+tO/F6QokCZYNZOcXMXHpHnw9XXh9cBvbXtW7HnOxmiSrd1El0R0pNkfkbFDDbWy8nkyF8KwJnrdV6pXSL+OkB7+G6ufiYTJm8/lS44mle71iV0D+RaXGrzV3TdPUYsG/vKD+7vM/tRhiFbzyJqqAXQtVcYuWA23ycn+fyuKtX/6md8s63GvP4k2uRhi+Av6vF3wxVC2Ye60lO6wlJVZdkQ+4RZU6t8d8JlcjRC1VxT62vK0WDh7wYekqptlpanFm70C47yP5/qqOgjrAvm9Uu2jrf3+zGb6ZoC58jlhVpS9O2izBMpvNzJgxg8TERAwGA7NmzSIk5MJQtPnz5/PDDz9gNBoZN24cPXpcYe2jSmrGd/EcOpPNF+M64ufpYLX9t32grmTcN1/GYIuqy8kJagSpnyZ3Xrhf09Q8qkuHUO77FnI/u/A4V281rPDcYbWwqQylFY7s4uIWNigAU1hsZvKKvRjdnJl1Xyv7X0T0qgMjVsCC3irJGvNzxV4lTz8GXwxRcz5HrLDvFXm9M/R7Ww2R3DDzfBn3JerikblYFQLJToNxv0ghm+oquJMqipWWaPs5w5vfgITv4a7Z0LiXbV/bxmyWYK1fv56CggKWL19OTEwMc+bMYd68eQAkJiayZs0aVq5cCUBUVBSdOnXC3d2BSzKX0bcxx1kZncyTPRtzW6iDlTc+vR9+naVKnYcNtXc0QtieTqfWTvOuW3pBa01TJyEX93adO6yqWLYdJT29wrGVrLdko572eRuT+Ot4BvNGROBvtOGab9dSuyUM/VwlPisfUL1aFdGrlJuuXqMwRyVyjnChUqdT31U+wefLuN+lyrjvXgwHN6oLRPZeQkLYT1BH9fvodtsmWAnfw8bZ0GaYmrNcxdkswYqOjqZr164AhIeHExcXZ9mWlJREhw4dcHVVX8whISEkJiYSHh5uq/AqxNEzOfzn6zjahfjyVC8HWxm9uEh107oaod9bcsIoxMV0OrUwtLGWWnNNiMqiZChrYIRNilvEn8jg3Q3/cG+bQO5uXc6F3itaaE/Vvn33pJqXdI+Vh8EXFcDykWrR+5FfqaTOkYQNUb15y0bAx93VshfhI2WudXXn10itMXlsJ7R/yDavWVSg5icGRqge1mpwzmmzwZcmkwmj8cJQBb1eT1GRWt+mWbNm7Nq1C5PJxLlz59izZw+5ublX21WlUFhs5slle3DSwTtR4TjrHWyc8x9vqfWb+r5pnYV6hRBC2F9JcQsbnDgVFKmhgb6eBmb2d7DkokTEaLXGTslyG9aiafDdE6pyaf/3HbcSX8OuMHatWnqkbhvo+79qcXIrrkGnU71Yx2y44PA/6yDnDNzxb1U0rBqwWQ+W0WgkO/tCFS+z2Yyzs3r50NBQRowYwbhx4wgMDKRNmzb4+lbuscFz1/3N3mPpfDgigvq+HvYOp7SUWNg0R60b0nKAvaMRQghhLdGf2ay4xbsb/mH/ySw+Gd2eGh4ONr/4Yj2fV0N8N7yk5iZZY/23315R64z1fF6tA+jIAlrAk9Hqb2cHGcIp7Cu4o1q/0XTaNhfZ9y5V1XsvXkamirNZt0pERASbN28GICYmhqZNm1q2nT17luzsbJYtW8ZLL71ESkoKTZo42JC6cvj9n1Q+2pTEsA7B9HG0IRNFBWpMtkdNVQVNCCFE1ZB7ThW3CBtS4cUtdh0+y7xNSQyKqM+dt9Su0Ne6aTqdqqYXfBt88xgc2XZz+4v+TE3WL+kdqwycXSW5EhcEdVK/j+2s+NfKOavW3gwbWvFrZzoQmx1pZGQkW7ZsISoqCk3TmD17NgsXLiQ4OJiePXty8OBBBg0ahIuLC1OnTkWvd7B1d8oozZTPpOV7aRJg5IV+t9g7nMtteg1OxcGw5Te/2KoQQgjHsXe5Wmi7XcUOD8zIKeSpZTHUq+HOjHsdsJ27EmdXiPoCPrlTLcY7bsONlYj+5xdY8ww0vlMNsZfhdqIyqtsG9AY1TPDitSQrQtxXYC50/J5eK7NZguXk5MTMmTNL3RcaeuHL7dJtlZHZrDF5xV6y8gpZMq4D7gYHSxKTo+GPNyF8ROk1goQQQlRuNipuoWka076K5VRmHl89ehtebnZY7+lGefjBiJXwaSR8MRjGrlfrAZbViRi11lXtljDkM/usdSWENbi4QWBb2/Rg7V0GtVtBndYV/1oOxMEqL1Run/5xiE1/p/J8v1toXsfBVqYuzFVVA70Cofer9o5GCCGENZUUt6jg0uxLdhzl5/iTTO3djDZBNSr0tSpEzVAYtgwyjquerMK8sj0v/Sh8OVQlacNXgKtXxcYpREUL6qCKnZX1/8CNSPsHju+qdr1XIAmW1cQmp/P62v3c1bI2IzsGl38HMUvh/Vth+zyVDFnbr7Mg7W/o/55aDFEIIUTVUVLcotWgCnuJ/SczeXnNPro3rcW42xtV2OtUuKAOMPBjlZR+8yiYzdd+fO45WDJYnYiOWKnWzROisgvqBMUFkBJTca+xdxnonKD1kIp7DQclCZYVmPKLmLh0D7WMrrw2KKz8q9in7FXrA2Snwc/T4e0w2PoeFGRf/7llcWQrbPsA2o+pVhVchBCiWrBBcYvcgmKe+HIPPu4uzB3aBienSj73qOV9EDkT4lfDr9eYolCUD8tGwtmDag6XLRdmFaIilSw4fGxHxezfbFaVNkN7qvXYqhlJsKzghW/iOHo2h7ej2pa/VG1ehhrT7VETnvgTHvwRat8C656Ht1urdTvys248uHyTukJXIxgiX77x/QghhHBMluIWD1bYS8xcE09Sqom3hobjb6wi1ehum6gKgvzxluoBvJTZfL7q4B8wYJ4sOi6qFmMttejw0QpKsI5sgYxj0GZYxezfwVXfBCs3Hd5tq6763YRtSWdYvec4E3s1oUPDclbl0zT15Z1xTE2Y9fSHBl1g9LcwZp2agLh+hkq0Nr+hkrHyWv8inDuiStRWcNleIYRwdGazmRdeeIH777+fUaNGceTIkVLb58+fT//+/RkxYgS//fYbAOnp6XTs2JFRo0YxatQoPv/8cwBWrFjBwIEDGTp0qOWxNqdpKjkIjFCVwSrA93tPsHTnMSZ0D+X2Jv4V8hp2odOp5Uoa36kqAx5YX3r7ry9D3Cro9aLqHRSiqgnqpHqwNM36+967TA1bbtbH+vuuBKpPQfpLuXqBsQ6sfkT9Dul8Q7uJPnIWgHFdb2A8+vYPYf8a+NcratG3iwV3hJFfqcp/m19Xc6i2vgedHoOOE8C9xvX3n/Qb/PmJek6D28sfnxBCVDHr16+noKCA5cuXExMTw5w5c5g3bx4AiYmJrFmzhpUrVwIQFRVFp06d2LdvH/369eO///2vZT+pqaksXryYr776ivz8fIYPH06XLl0wGGy84O6xHZCaAPe8WzG7P5vDc6v/om1wDZ6JbHr9J1Q2emd1gXPB3bDiQRjzM9RpBbsWqKq77R6C2yfZO0ohKkZQB9j7pRoCeyPLFlxNQQ7s+wZaDgCDh/X2W4lU3x4sJ70aT10jWFUSSvvnhnaTkJJFsJ8HRtdy5qpHd8AvL0DzftD58as/rn47GL4cHtkEDbrCxldVj9avr6jF264mLwO+fQJqNoZeL5QvNiGEqKKio6Pp2lUN9QoPDycuLs6yLSkpiQ4dOuDq6oqrqyshISEkJiYSFxdHfHw8I0eOZOLEiZw+fZrY2Fjatm2LwWDAy8uL4OBg9u/fb4cD+qzCilsUFpt5cuke0MG7UW1x0VfRUwZXL9XOunqpSoHRn8MPk6HJXaqHS9a6ElVVcMmCw1YeJrj/BygwVdvhgVCdEyw4vybGKtDp1ZoYptRy7yLhZCbN65SzXGv2GVj1EPjUh/4flO3LOzBcJYQT/oDQHqpX6+3WsP4ltb9LrX0Osk7AgI/Axb188QkhRBVlMpkwGi8Ml9br9RQVFQHQrFkzdu3ahclk4ty5c+zZs4fc3FwaNWrExIkTWbJkCXfeeSezZs3CZDLh5XXhu9/T0xOTyWTbg6ng4hZv/vI3McfSmTMwjCC/Kn4V2qcejFihLk5+PxHqhMHgBaqHS4iqyr+Zqix9dLt197t3KfgEQ/Bt1t1vJVK9EywAv4bqylXWKVgapbo1yyi3oJjDadm0qFuONa/MZlj9sKoYOHRR2Yb6XaxOa/W8R7dB07vU5Ny3W8O6/4LptHrM32thzxLo8hQE3Vq+/QshRBVmNBrJzr5QodVsNuPsrE6iQ0NDGTFiBOPGjePll1+mTZs2+Pr60qlTJzp2VMO4IyMj2bdv32X7yc7OLpVw2UQFFrf4/Z9U5m1MYliHIPqGVZOy5HVaw/1LoFnf82tdybxlUcU5OUH9DtbtwcpMgYO/QZv71f6rqep75Ber3x4GfQLHo1XyYy4u09MST2Vh1ihfgvX7/yBpA9z92s1NSK59i7q69vhOaNEPtr2vyrv/NB2+mwgBt8Ad/77x/QshRBUUERHB5s2bAYiJiaFp0wvzis6ePUt2djbLli3jpZdeIiUlhSZNmvD888+zdu1aALZt20bLli0JCwsjOjqa/Px8srKySEpKKrWvCmcpbtHW6sUtUrPymbR8L00CjLzQr6VV9+3wQnvAsC/Bq7a9IxHCNoI7qkXKc89ZZ39/rQDNDGHVb3Hhi0nfd4kW/aD3q2odqnXPq7+vY39Kpnpq3TJetTy4EX6bDWH3W++KY62mMHA+dJ8Gv8+FnfPVkMPhy8G5ipTSFUIIK4mMjGTLli1ERUWhaRqzZ89m4cKFBAcH07NnTw4ePMigQYNwcXFh6tSp6PV6Jk+ezHPPPcfSpUtxd3dn1qxZ1KpVi1GjRjF8+HA0TWPSpEm4utrwO/fYzgopbmE2a0xeuZesvEK+GNcRd4PeqvsXQjgYy3pYf0LTf93cvjQNYpZC/VvBv/HNx1aJSYJ1sU6PqpLm2z+EGiHQacI1H56QkomnQU+QbxnGpmemwFfjoFYz6PeW9SfN1gxVpdi7T4WcM2rOlhBCiFKcnJyYObP0wrKhoReqZ126DSAoKIjFixdfdv/QoUMZOnSo9YMsi+iFFVLc4pM/DrL571RmDWhFs/LOLxZCVD712qlaBMd23HyCdTJWXfjpO9c6sVVikmBd6q5X1LpUP0+HGkHQvO9VH5qQkkXzut7XX9G+uAhWjVHzux78AQyeVg76Ir4N1I8QQoiqqaS4Rfhwq84TijmWzus/J3J3qzqM6Bhstf0KIRyYwVPNP7TGPKy9y8DJBVoOvPl9VXIyB+tSTnoY+H9QLwJWjVXrUF2BpmllryD460w4uhXueUf1YAkhhBA3KnaF1YtbZOUVMnHpHmp7uzFnYBg6KU0uRPUR3AmSd0Fx4Y3vo7gQ/loJzXqrKt3VnCRYV2LwgGHLwRgAS++Hc4cve8jx9Fyy8oquX+Bi/4+w5R1oP0ZWghdCCHFzNA12LbRqcQtN0/jP13EcT8/l3WHh+Hi4WGW/QohKIqgjFOXCyb9ufB9Jv0J2arVe++pikmBdjbEWjPxKZeRLBl+2qO/+lCzgOgUuzh2GbyaoRvCu6xfNEEIIIa6ppLhFu4estsuV0cl8t/cEk+5sQrsQufIsRLVjKXRxE8ME9y4Fj5rQONI6MVVykmBdi38TGLYU0o/A8pFQlG/ZlHC+gmCzOlfpwSrMgxUPgAYM+Rxc3GwQsBBCiCot+jMwGK1W3OLA6Sxe/Dae20Jr8ugd1bvqlxDVlk898Am68QQrN12N2Go1GJwNVg2tspIE63pCboMB8+DIFvjmMbVQMJBwMpOQmh4YXa9SJ2Ttc5ASA/fNU4sZCyGEEDcj9xzEr4bWQ6xS3CKvsJgnvtyDu0HPW/eHo79ewSYhRNUV1AGO7lDDkMtr3zdQnA9tqvfaVxeTBKssWg+GXi9C3Cr49WVADRG8aoGL2JWw61O47clrViEUQgghyqykuEV76wwPfPXHBPafzGLukDbU9pZRFkJUa0GdIOsEZCSX/7l7l4F/MzU3VABSpr3sbp+khgr+8Sb5XvU5dKYO94YHXv641ET4/ikI7qySMiGEEMIaDv8O9dpbpbjF2viTfL7tCONub0iP5gFWCE4IUakFdVC/j+1QyxSV1dmDcHSbOueV6qMWkmCVlU4HfeZCxnEMP0+hu24yLeq2K/2YgmxYMRpc3GHwAtBLJSYhhBBW0u8dq5zAnEjPZeqqWFrX82Fq7+ZWCEwIUenVbgUunnB0uxq5VVaxKwAdhNlp0XUHJUMEy0PvDEMWku7VhPdd3iVMf/TCNk2DNc+oHqxBn4D3FXq3hBBCiBvlWdMq68tM+yqWomIz7w1ri8FZTgOEEKhz3Prty1foQtNU9cCG3cCnfsXFVgnJN2t5uXrxSfAcTHhS54fRF8aq7v4cYpfBHdMhtId9YxRCCCGuwJRfxB8H0hh7e0Ma+HvaOxwhhCMJ6gin4iA/q2yPP7ZDLUkka19dRhKsG7AzzZXXa76MriAbvhgCh/+AH6dCox7QbYq9wxNCCCGuKO54BpoGbUN87R2KEMLRBHcEzQzHo8v2+L1LwcUDWtxTsXFVQpJglZOmaexPycIjOAyGLoK0v+GzvmpxtUGfgJPe3iEKIYQQV7T3WDoAberXsGscQggHVP9WQKfKtV9PYR7EfQ0t7rXKshFVjSRY5ZR8Lpes/CJa1PVWQwHvfQ88a8GQheDpb+/whBBCiKuKTc6gvq87fp6yGKgQ4hJuPhBwCxzbfv3H/v0T5GfI2ldXIVUEyykhJROA5nW81R3hw9XYUylNKYQQwsHtTU6X3ishxNUFd4S/VoG5+NqjsvYuA69AVeBCXEZ6sMpp/8ksdDpKLzIsyZUQQggHd8aUT/K5XMLq+9g7FCGEowrqCPmZcDrh6o8xpcI/v6jS7DI15ookwSqnhJRMQvw88HSVzj8hhBCVR+zxDADaBNWwbyBCCMcV1FH9vla59rhVoBXL8MBrkASrnPafzLowPFAIIYSoJGKPZaDTQat60oMlhLgK3wbgGXDtBGvvUqgbDgEtbBVVpSMJVjnkFBRx+Ey2KnAhhBBCVCKxyek0rmXEKCMwhBBXo9OpeVhXS7BO7YOUvbL21XVIglUO+09moWnQoq7X9R8shBBCOAhN09ibnE6YFLgQQlxPUEe1gHDWqcu3xS4DJ2doNcjmYVUmchmrHPanqJWtpQdLCCEqH7PZzIwZM0hMTMRgMDBr1ixCQkIs2+fPn88PP/yA0Whk3Lhx9OjRgxMnTvDcc89RXFyMpmnMnDmTRo0a8dlnn7Fy5Ur8/PwAeOmll2jUqJG9Du26TmTkkWYqoE2QDA8UQlxHUCf1+9h2uKX/hfvNxRC7AhpHgrGWfWKrJCTBKoeElEy8XJ2p7+tu71CEEEKU0/r16ykoKGD58uXExMQwZ84c5s2bB0BiYiJr1qxh5cqVAERFRdGpUyfeeecdRo4cyZ133snvv//Om2++yfvvv09cXByvvfYarVq1suchlVns+QWGpQdLCHFddduA3hWO7SydYB3cCFkp0HuO3UKrLCTBKoeElEya1/VCJ2XZhRCi0omOjqZr164AhIeHExcXZ9mWlJREhw4dcHV1BSAkJITExESmTZuGl5caFl5cXGzZHh8fz/z580lNTeWOO+5g/PjxNj6a8tmbnIGLXidD3IUQ1+dsgHoRcPSSBYf3LlOLETe72z5xVSIyB6uMNE2TCoJCCFGJmUwmjEaj5bZer6eoqAiAZs2asWvXLkwmE+fOnWPPnj3k5ubi5+eHi4sLBw8e5LXXXuPxxx8HoG/fvsyYMYPPP/+c6OhofvvtN7scU1nFJqfTvI43rs6yZo0QogyCOqpiFoW56nZ+FiR8r+ZeObvaN7ZKQBKsMko+l4spv0jmXwkhRCVlNBrJzs623DabzTg7q4EcoaGhjBgxgnHjxvHyyy/Tpk0bfH19Adi+fTuPP/44r7/+Oo0aNULTNB544AH8/PwwGAx0796dffv22eWYysJs1vgrOUPmXwkhyi6oI5gL4cQedXvfd1CUK9UDy0gSrDLal5IJQHMZXiGEEJVSREQEmzdvBiAmJoamTZtatp09e5bs7GyWLVvGSy+9REpKCk2aNGH79u288sorfPLJJ7Ru3RpQPWH9+vUjOzsbTdPYsWOHQ8/FOnQmm6z8Ipl/JYQou5IFh0uGCe5dCn6NoP6t9oupErHZHKzrVW9asGABa9asQafTMWHCBCIjI20VWpnsT8lCp4PmdSTBEkKIyigyMpItW7YQFRWFpmnMnj2bhQsXEhwcTM+ePTl48CCDBg3CxcWFqVOnotfrmT17NoWFhUyfPh2Ahg0bMnPmTCZNmsTo0aMxGAx07tyZ7t272/norm7v+QIXbSTBEkKUlWdNqNlEFbpIPwqHf4ce/1HrZInrslmCda3qTZmZmSxatIh169aRm5vLgAEDHC7BSkjJpEFNTzwMUhdECCEqIycnJ2bOnFnqvtDQUMvfl24D+O677664rwEDBjBgwACrxldRYpMz8DDoaRxgvP6DhRCiRFBHSPwRYper22FD7RtPJWKzIYLXqt7k7u5OYGAgubm55ObmOmSVvv0nM6X3SgghRKWzNzmdVoE+6J0cr20VQjiw4I6Qexa2fQAhXcC3gb0jqjRs1h1ztepNJROM69atS9++fSkuLna4crfZ+UUcOZvDwIj69g5FCCGEKLPCYjP7TmQyqlPI9R8shBAXK5mHlXsO2kTZN5ZKxmY9WNeq3rR582ZOnz7Nhg0b2LhxI+vXryc2NtZWoV3X/pNZaBpSQVAIIUSlkngyi/wiM2FBNewdihCisqnZBNx9wdmt9ILD4rpslmBdq3qTj48Pbm5uGAwGXF1d8fLyIjMz01ahXdf+k+crCMoQQSGEEJVIbHIGAOFS4EIIUV5OTtB+DNz2pFpgWJSZzYYIXqt6U69evdi6dStDhw7FycmJiIgIunTpYqvQrishJRMvN2fq+7rbOxQhhBCizGKT0/H1cCHIT9ovIcQN6PWCvSOolGyWYF2vetPEiROZOHGircIpl4SULFrU8XbI4htCCCHE1exNzqB1/RrSfgkhhA1JzfHrMJs1Ek9mMTCinr1DEUIIcYmEhASWL19Ofn6+5b5XX33VjhE5jtyCYv4+lcWdLQLsHYoQQlQrkmBdR/K5XEz5RVLgQgghHND06dMZOXIkderUsXcoDif+RAbFZo0wmX8lhBA2JQnWdexLkQIXQgjhqPz9/RkyZIi9w3BIe88XuGhTXyanCyGELUmCdR37T2ai00EzSbCEEMLh1KtXj/nz59OiRQvLPKPbb7/dzlE5htjkdOp4uxHg7WbvUIQQolqRBOs6ElIyaVjTEw+DvFVCCOFoCgsLOXToEIcOHbLcJwmWEpucQZj0XgkhhM1J1nAdCSlZtKon86+EEMIRvfrqq/z9998cOHCAhg0b0qJFC3uH5BAycgs5lJbN4Hb17R2KEEJUO+VOsAoKCq66zWAw3FQwjsaUX8TRszkMkQZKCCEc0uLFi1mzZg1hYWEsWLCAu+++m7Fjx9o7LLv7yzL/qoZ9AxFCiGqo3AlW79690el0aJpW6n6dTseGDRusFpgjSDypClxIBUEhhHBMa9as4YsvvsDZ2ZnCwkKioqIkwQL2JqcD0FqGCAohhM2VO8H69ddfKyIOh5SQkgVA87pS4EIIIRyRpmk4O6umzMXFBRcXFztH5Bj2Hkunob8nPu7yfgghhK2VO8G6//77r7oi/LJly246IEeSkJKJt5sz9Wq42zsUIYQQV9CuXTsmTpxIu3btiI6Opm3btvYOySHEJmfQsZGfvcMQQohqqdwJ1ptvvlkRcTikhJRMmtf1vmpCKYQQwr6mTZvGxo0bSUpKYtCgQXTv3t3eIdnd6cw8TmbmyQLDQghhJ07lfUK9evWoV68eRUVFrFmzhq+//pqvv/6ajz/+uCLisxuzWSPxZBYtZP0rIYRwOL/99hsAy5cv59SpUxiNRk6ePMny5cvtHJn9yQLDQghhXzdcpn3y5MlERkaye/duAgICyMnJsWZcdnfsXA7ZBcVS4EIIIRxQeno6AKmpqfYNxAHFJqejd9LRMlASLCGEsIcbTrA8PDwYP348hw8f5tVXX2X48OHWjMvuElKkgqAQQjiq++67D4AnnniCrKwsdDod69evp0ePHnaOzP72JmfQtLYX7ga9vUMRQohq6YYTLJ1OR2pqKtnZ2eTk5FS5HqyElCycdNC0tgwRFEIIRzVp0iTuuOMO9uzZg9ls5pdffuGDDz6wd1h2o2kascnp9G5Zx96hCCFEtVXuOVglnnjiCdavX0///v2588476dy5szXjsruElEwa+HvKFUAhhHBgp0+fpn///iQlJTFz5kyys7Ov+Xiz2cwLL7zA/fffz6hRozhy5Eip7fPnz6d///6MGDHCMs/r7NmzjBkzhuHDh/P000+Tm5sLwIoVKxg4cCBDhw61PNbejp7NIT2nUApcCCGEHd1wD1abNm0wGo20aNECTdOqXOWmhJOZhNWrYe8whBBCXENhYSHr1q2jcePGnD179roJ1vr16ykoKGD58uXExMQwZ84c5s2bB0BiYiJr1qxh5cqVAERFRdGpUyc+/PBD+vXrx8CBA5k/fz7Lly+nb9++LF68mK+++or8/HyGDx9Oly5dMBgMFX7M11JS4CJMClwIIYTd3HAP1rPPPsu+ffsAOHToENOnT7daUPaWlVfIsbO5tJAFhoUQwqGNGzeOH374gfHjx7N48WIee+yxaz4+Ojqarl27AhAeHk5cXJxlW1JSEh06dMDV1RVXV1dCQkJITEws9Zxu3bqxdetWYmNjadu2LQaDAS8vL4KDg9m/f3/FHWgZxR5Lx9XZiWZSAVcIIezmhhOsU6dOMWjQIAAefvhhTp8+bbWg7C3xZBYgBS6EEMJRFRUVAXDHHXfwxhtv4Ofnx6OPPkqXLl2u+TyTyYTRaLTc1uv1ln01a9aMXbt2YTKZOHfuHHv27CE3NxeTyYSXl0pYPD09ycrKKnVfyf0mk8nah1lusckZ3BLojYv+hpt3IYQQN+mmilwcOnSIhg0bcvToUcxmszXjsquE8wlWc0mwhBDCIU2bNo25c+fSu3fvyxaD37Bhw1WfZzQaSw0jNJvNODurpjA0NJQRI0Ywbtw4AgMDadOmDb6+vpbnuLm5kZ2djbe392X7yc7OLpVw2UOxWSPuRAZD2wfZNQ4hhKjubjjB+ve//82kSZNIS0sjICCAl156yZpx2VVCSibebs4E+rjZOxQhhBBXMHfuXEAlUydPnqRu3brExsYSFhZ2zedFRETw22+/0adPH2JiYmjatKllW8kcrmXLlpGVlcWYMWNo0qQJERERbNq0iYEDB7J582batWtHWFgYb7/9Nvn5+RQUFJCUlFRqX/Zw4LSJnIJi2gTJ/CshhLCnmypysXjxYo4fP05QUBCenp7WjMuuElIyaVHX+7KrokIIIRzLiy++SEhICGPHjuW7777j+++/5z//+c9VHx8ZGcmWLVuIiopC0zRmz57NwoULCQ4OpmfPnhw8eJBBgwbh4uLC1KlT0ev1PProo0ybNo0VK1bg6+vL3Llz8fDwYNSoUQwfPhxN05g0aRKurq42PPLL7U1OB5AKgkIIYWc3nGCtXbuWefPmUVxcbBmicb3JxZWB2ayReDJLhlgIIUQlsG/fPmbOnAnA888/z4gRI675eCcnJ8vjS4SGhlr+vnQbgL+/P59++ull9w8dOpShQ4feSNgVYu+xdLxcnWlYs+pc8BRCiMrohmfBLly4kBUrVlCjRg0ee+wx1q9fb8247Obo2RxyCoqlgqAQQlQS586dAyAzM5Pi4mI7R2M/sckZtK7vg5OTjL4QQgh7uuEeLCcnJwwGAzqdDp1Oh7u7uzXjspuElExAKggKIURl8PjjjzNo0CB8fHzIysrixRdftHdIdpFfVMz+k5mMvb2RvUMRQohq74YTrPbt2/PMM89w6tQpXnjhhetOLK4sEk5m4aSDprWlB0sIIRxdjx496Natm6XgUnWdO5uQkkVhsUYbWWBYCCHsrtwJVlFREb/++iu33XYbBQUF3HLLLfj7+7Nx48YKCM/2ElIyaejviZuL3t6hCCGEuI6dO3cyc+ZMy3zgwMBAhgwZYu+wbC62pMBFUA27xiGEEOIG5mA9++yzlgIXR48eJTQ0lDfeeIMWLVpURHw2l5CSKetfCSFEJfHOO++wZMkS/P39mTBhAkuXLrV3SHax91gG/kaDLC8ihBAOoNw9WEePHmX16tUUFBRYStkuWrSoVBWmyiozr5Dkc7kM6xBs71CEEEKUgZOTEzVq1ECn0+Hq6lqllgwpj9jkdNrUr1Fth0gKIYQjKXcPltFoBMBgMGA2m1mwYEGVSK4AEk9mAUgFQSGEqCSCg4OZO3cu6enpzJ8/n8DAQHuHZHOm/CIOpJpk/SshhHAQN1ymHaBmzZrUqFHDSqHYX0kFweZ1ZIigEEJUBi+++CKBgYG0a9cOd3d3Xn75ZXuHZHN/JWegaRAWJAUuhBDCEZR7iOCBAweYPHkymqZZ/i4xd+5cqwZnawkpWfi4u1BXxrALIUSlMGHCBBYsWGDvMOyqpMBFG+nBEkIIh1DuBOvtt9+2/B0VFWXNWOwuISWTFnW9ZAy7EEJUEt7e3qxfv56GDRvi5KQGZTRs2NDOUdlWbHIG9X3d8fM02DsUIYQQ3ECC1aFDh4qIw+7MZo3Ek1ncf2uQvUMRQghRBiaTiWPHjvH5559b7tPpdCxatMiOUdne3vMFLoQQQjiGG15ouKo5cjaH3MJibpES7UII4fCWLFnCggUL0Ov1PPXUU3Tr1s3eIdnFGVM+yedyGdUpxN6hCCGEOO+milxUJSUFLlpIgiWEEA5vzZo1/Pzzzyxfvrza9VhdLPZ4BoBUEBRCCAciCdZ5+1MycdJBk9pGe4cihBDiOgwGAwaDAT8/PwoLC+0djt3sPZaOTget60sFQSGEcBSSYJ23LyWLRrWMuLno7R2KEEKIctA0zd4h2E1scgaNaxkxusqIfyGEcBQ2+0Y2m83MmDGDxMREDAYDs2bNIiREjRlPSEhg9uzZlsfGxMTwwQcf2HRMfUJKJm2Da9js9YQQQty4qrxkSFlpmkZscjrdmwbYOxQhhBAXsVmCtX79egoKCli+fDkxMTHMmTOHefPmAdCiRQsWL14MwE8//URAQIBNk6vMvEKOp+cyvGOwzV5TCCHEjavKS4aU1YmMPNJMBbSRBYaFEMKh2CzBio6OpmvXrgCEh4cTFxd32WNycnJ47733WLJkia3CAmB/ShaAVBAUQohKoqouGVIescfSASlwIYQQjsZmc7BMJhNG44UCEnq9nqKiolKPWbVqFb1798bPz89WYQEXKgg2r+tl09cVQgghbtTe5Axc9DpaSNslhBAOxWYJltFoJDs723LbbDbj7Fy6A+37779nyJAhtgrJYv/JTGp4uFDH283mry2EEELciNjkdJrX8cbVWYozCSGEI7HZEMGIiAh+++03+vTpQ0xMDE2bNi21PSsri4KCAurWrWurkCz2pWTRoo43Op3O5q8thBDCNq5VbAlgwYIFrFmzBp1Ox4QJE4iMjGT+/Pn8/vvvAGRmZpKWlsaWLVv47LPPWLlypWXExUsvvUSjRo1seCwafyVn0L9toM1eUwghRNnYLMGKjIxky5YtREVFoWkas2fPZuHChQQHB9OrVy8OHTpEvXr1bBWORbFZ4++TWUR1CLL5awshhLCdaxVbyszMZNGiRaxbt47c3FwGDBhAZGQkjzzyCI888ggA48ePZ8qUKQDExcXx2muv0apVK7scy8G0bLLyi2T+lRBCOCCbJVhOTk7MnDmz1H2hoaGWv8PCwvjwww9tFY7FkTPZ5BYW00IKXAghRJV2rWJL7u7uBAYGkpubS25u7mUjGtatW4e3tze33347APHx8cyfP5/U1FTuuOMOxo8fb7sDQQ0PBGgjCZYQQjicar8yYYJUEBRCiGrhasWWSuYD161bl759+1JcXHxZwvTxxx/z5ptvWm737duX4cOHYzQaeeKJJ/jtt9/o0aOHbQ4EtcCwh0FP4wDj9R8shBDCpmxW5MJR7T+Zid5JJ42UEEJUcdcqtrR582ZOnz7Nhg0b2LhxI+vXryc2NhZQixp7e3tb5mtpmsYDDzyAn58fBoOB7t27s2/fPpsey97kdFoF+qB3krnDQgjhaKp9gpWQkkkjf0/cXKQKkxBCVGURERFs3rwZ4LJiSz4+Pri5uWEwGHB1dcXLy4vMTLWEx9atW+nWrZvlsSaTiX79+pGdnY2maezYscOmc7EKi83sO5FJWH1ZYFgIIRyRDBFMySIixNfeYQghhKhg1yu2tHXrVoYOHYqTkxMRERF06dIFgEOHDln+BvDy8mLSpEmMHj0ag8FA586d6d69u82OI/FkFvlFZsKCatjsNYUQQpRdtU6wMnILOZ6ey4hOwfYORQghRAW7XrGliRMnMnHixMue9+KLL15234ABAxgwYIDVYyyL2OQMANpID5YQQjikaj1EcH+KGv4hFQSFEEJUFnuPpVPDw4VgPw97hyKEEOIKqnWClVCSYNWRBEsIIUTlsDc5nbD6NS4rJS+EEMIxVOsEa//JLHw9XKjt7WrvUIQQQojryi0o5p/TJhkeKIQQDqxaJ1gJKZm0qOstVwGFEEJUCvEnMig2a4TJAsNCCOGwqm2CVWzWSDyVRXMZHiiEEKKS2CsFLoQQwuFV2wQrJSOXvEIzLep62TsUIYQQokxik9Op4+1GgLebvUMRQghxFdU2wQr0cWdm/5bc0ybQ3qEIIYQQZRKbnCELDAshhIOrtgmWk5OO0Z0b4Oait3coQgghxHVl5BRyKC2bNrLAsBBCOLRqm2AJIYQQlUns8XQA2kiBCyGEcGiSYAkhhBCVQOz5AhetZYigEEI4NEmwhBBCiEogNjmdhv6e+Li72DsUIYQQ1+Bs7wCEEEIIcX2dG9Xk9ia17B2GEEKI65AESwghhKgEHuzS0N4hCCGEKAMZIiiEEEIIIYQQViIJlhBCCCGEEEJYiSRYQgghhBBCCGElkmAJIYQQQgghhJVIgiWEEEIIIYQQViIJlhBCCCGEEEJYiSRYQgghhBBCCGElkmAJIYQQQgghhJXIQsNCCCGqBbPZzIwZM0hMTMRgMDBr1ixCQkIs2xcsWMCaNWvQ6XRMmDCByMhINE2jW7duNGjQAIDw8HAmT57Mr7/+ygcffICzszODBg1i6NChdjoqIYQQjkYSLCGEENXC+vXrKSgoYPny5cTExDBnzhzmzZsHQGZmJosWLWLdunXk5uYyYMAAIiMjOXr0KC1btuSjjz6y7KewsJBXX32VVatW4e7uzrBhw+jZsyf+/v72OjQhhBAORIYICiGEqBaio6Pp2rUroHqi4uLiLNvc3d0JDAwkNzeX3NxcdDodAPHx8Zw6dYpRo0bx8MMPc/DgQZKSkggODsbHxweDwUC7du34888/7XJMQgghHI/0YAkhhKgWTCYTRqPRcluv11NUVISzs2oK69atS9++fSkuLmb8+PEA1KpVi0ceeYS7776bXbt2MWXKFP7973/j5eVl2Y+npycmk8m2ByOEEMJhSYIlhBCiWjAajWRnZ1tum81mS3K1efNmTp8+zYYNGwAYO3YsERERtGrVCr1eD0D79u05ffr0ZfvJzs4ulXAJIYSo3mSIoBBCiGohIiKCzZs3AxATE0PTpk0t23x8fHBzc8NgMODq6oqXlxeZmZm8//77fP755wDs37+funXrEhoaypEjR0hPT6egoIBdu3bRtm1buxyTEEIIxyM9WEIIIaqFyMhItmzZQlRUFJqmMXv2bBYuXEhwcDC9evVi69atDB06FCcnJyIiIujSpQutW7dmypQpbNq0Cb1ez6uvvoqLiwvTp09n7NixaJrGoEGDqF27tr0PTwghhIOQBEsIIUS14OTkxMyZM0vdFxoaavl74sSJTJw4sdR2Hx8f5s+ff9m+evbsSc+ePSsmUCGEEJWaDBEUQgghhBBCCCuRBEsIIYQQQgghrEQSLCGEEEIIIYSwEpvNwTKbzcyYMYPExEQMBgOzZs0iJCTEsn3Tpk188MEHaJpGy5YtefHFFy0LPQohhBBCCCFEZWCzHqz169dTUFDA8uXLmTx5MnPmzLFsM5lMvPHGG3z00UesXLmSevXqce7cOVuFJoQQQgghhBBWYbMEKzo6mq5duwIQHh5OXFycZduePXto2rQpr732GsOHD8ff3x8/Pz9bhSaEEEIIIYQQVmGzIYImkwmj0Wi5rdfrKSoqwtnZmXPnzrFjxw6++eYbPDw8GDFiBOHh4TRs2NBW4QkhhBBCCCHETbNZD5bRaCQ7O9ty22w24+ys8rsaNWrQunVratWqhaenJ+3btychIcFWoQkhhBBCCCGEVdgswYqIiGDz5s0AxMTE0LRpU8u2li1b8vfff3P27FmKiorYu3cvjRs3tlVoQgghhBBCCGEVNhsiGBkZyZYtW4iKikLTNGbPns3ChQsJDg6mV69eTJ48mXHjxgHQu3fvUgmYEEIIIYQQQlQGNkuwnJycmDlzZqn7QkNDLX/37duXvn372iocIYQQQgghhLA6WWhYCCGEEEIIIaxEEiwhhBBCCCGEsBJJsIQQQgghhBDCSiTBEkIIIYQQQggrkQRLCCGEEEIIIazEZlUEbaWwsJDk5GTy8vLsHUqFcXNzo379+ri4uNg7FCGEEDehOrRZIO2WEKJ6qXIJVnJyMl5eXjRo0ACdTmfvcKxO0zTOnDlDcnIyDRs2tHc4QgghbkJVb7NA2i0hRPVT5YYI5uXlUbNmzSrbUOl0OmrWrFnlr3YKIUR1UNXbLJB2SwhR/VS5BAuo0g0VVP3jE0KI6qQ6fKdXh2MUQogSVTLBEkIIIYQQQgh7qHJzsOxtzpw5xMfHk5qaSl5eHkFBQfj6+vLuu+9e83nz58+nU6dOhIWF2ShSIYSoXsxmMzNmzCAxMRGDwcCsWbMICQmxbF+wYAFr1qxBp9MxYcIEIiMjycrKYsqUKZhMJgoLC5k+fTpt27bll19+4bXXXqNu3boAPPnkk3To0MFeh3bDpM0SQgjrq9IJ1lfRyazYdcyq+xzaPohB7epfdfv06dMBWL16NQcPHuTZZ58t034feeQRq8QnhBDiytavX09BQQHLly8nJiaGOXPmMG/ePAAyMzNZtGgR69atIzc3lwEDBhAZGcnChQvp1KkTDz74IAcPHmTy5Ml8/fXXxMXFMWXKFO666y6rxSdtlhBCVA1VOsFyFNOnTyc9PZ309HTmzZvH//73P06ePMnp06fp2bMnkyZNYvr06fTp04e0tDQ2bdpEXl4eR48e5eGHH2bgwIH2PgQhhKj0oqOj6dq1KwDh4eHExcVZtrm7uxMYGEhubi65ubmWOUMPPvggBoMBgOLiYlxdXQGIj48nISGBzz//nLCwMJ599lmcnatGkyptlhBC3Jyq0RpcxaB29a955c6WSq6AJicnEx4ezpAhQ8jPz6dbt25MmjSp1GNNJhOffvophw8fZsKECdJYCSGEFZhMJoxGo+W2Xq+nqKjIkhjVrVuXvn37UlxczPjx4wHw9vYGIDU1lSlTpvDcc88B0KVLF+68807q16/Piy++yLJlyxg5cuRNxSdtlhBCVA1VOsFyJCVrf9SoUYO//vqL7du3YzQaKSgouOyxzZs3B1Rjf6XtQgghys9oNJKdnW25bTabLcnV5s2bOX36NBs2bABg7NixREREEBYWRmJiIs888wxTp061zLMaNGiQJfnq1asXa9eutfHRVCxps4QQ4sZJFUEbKRlusnr1ary8vJg7dy5jxowhLy8PTdOu+FghhBDWExERwebNmwGIiYmhadOmlm0+Pj64ublhMBhwdXXFy8uLzMxMDhw4wFNPPcXcuXPp3r07oBbOvffeezl58iQA27Zto2XLlrY/oAokbZYQQtw46cGysc6dOzN58mRiYmIwGAyEhIRw+vRpe4clhBBVXmRkJFu2bCEqKgpN05g9ezYLFy4kODiYXr16sXXrVoYOHYqTkxMRERF06dKFxx57jIKCAl555RVA9YLNmzePWbNm8cQTT+Dm5kZoaChDhw6189FVDGmzhBCi/HTapZeiKpGBAweyevXqUvclJCTQokULO0VkO9XlOIUQ1ceVvtOrkurcZkH1OlYhRPVwtXZLhggKIYQQQgghhJVIgiWEEEIIIYQQViIJlhBCCCGEEEJYiSRYQgghhBBCCGElkmAJIYQQQgghhJVIgiWEEEIIIYQQViLrYFnZnDlziI+PJzU1lby8PIKCgvD19eXdd9+97nMTExPJzMzk1ltvtUGkQgghqjtps4QQwvqqdoIVsxT2LLHuPtuOhPBhV908ffp0AFavXs3Bgwd59tlny7zrdevW4e/vL42VEEJUR9JmCSFElVC1EywHUFhYyIsvvsiRI0cwm808/fTTdOzYkbfeeosdO3ZQVFTEv/71L/r378/XX3+Ni4sLLVu2JCwszN6hCyGEqGakzRJCiJtXtROs8GHXvHJnCytXrsTX15fZs2dz7tw5Ro4cyQ8//MD333/PokWLCAgIYPXq1dSuXZv77rsPf39/aaiEEKI6kjZLCCGqhKqdYDmAv//+m+joaGJjYwEoKiri7NmzvPHGG8ydO5e0tDS6du1q5yiFEEIIabOEEMIaJMGqYI0aNaJOnTpMmDCBvLw85s2bh9Fo5Oeff+bNN98EoE+fPvTt2xedTofZbLZzxEIIIaorabOEEOLmSZn2ChYVFcXBgwcZOXIkUVFR1KtXD4PBgI+PD0OHDmX06NF06dKFwMBAWrVqxRdffMH27dvtHbYQQohqSNosIYS4eTpN0zR7B3GjBg4cyOrVq0vdl5CQQIsWLewUke1Ul+MUQlQfV/pOr0qqc5sF1etYhRDVw9XaLenBEkIIIYQQQggrkQRLCCGEEEIIIaykSiZYlXjUY5lU9eMTQojqpDp8p1eHYxRCiBJVLsFyc3PjzJkzVfbLXNM0zpw5g5ubm71DEUIIcZOqepsF0m4JIaqfKlemvX79+iQnJ5OammrvUCqMm5sb9evXt3cYQgghblJ1aLNA2i0hRPVS5RIsFxcXGjZsaO8whBBCiOuSNksIIaoemyVYZrOZGTNmkJiYiMFgYNasWYSEhFi2z5o1i927d+Pp6QnAhx9+iJeXl63CE0IIUcVdrx1asGABa9asQafTMWHCBCIjI8nLy2PKlCmcOXMGT09PXnvtNfz8/Pj111/54IMPcHZ2ZtCgQQwdOtSORyaEEMKR2CzBWr9+PQUFBSxfvpyYmBjmzJnDvHnzLNvj4+P55JNP8PPzs1VIQgghqpFrtUOZmZksWrSIdevWkZuby4ABA4iMjGTp0qU0bdqUJ598kh9++IEPP/yQadOm8eqrr7Jq1Src3d0ZNmwYPXv2xN/f385HKIQQwhHYrMhFdHQ0Xbt2BSA8PJy4uDjLNrPZzJEjR3jhhReIiopi1apVtgpLCCFENXGtdsjd3Z3AwEByc3PJzc1Fp9Nd9pxu3bqxbds2kpKSCA4OxsfHB4PBQLt27fjzzz9tf0BCCCEcks16sEwmE0aj0XJbr9dTVFSEs7MzOTk5jBw5koceeoji4mJGjx5Nq1ataN68+TX3efz4cQYOHFjRoQshhLCB48ePV+j+r9UOAdStW5e+fftSXFzM+PHjLc8pGa7u6elJVlZWqftK7jeZTNd9fWmzhBCiarlau2WzBMtoNJKdnW25bTabLY2au7s7o0ePxt3dHYBOnTqxf//+6yZYO3bsqLiAhRBCVCnXaoc2b97M6dOn2bBhAwBjx44lIiKi1HOys7Px9va+bD/Z2dllmjMsbZYQQlQPNhsiGBERwebNmwGIiYmhadOmlm2HDx9m2LBhFBcXU1hYyO7du2nZsqWtQhNCCFENXKsd8vHxwc3NDYPBgKurK15eXmRmZhIREcGmTZsAlYS1a9eO0NBQjhw5Qnp6OgUFBezatYu2bdva5ZiEEEI4Hp1mo9UNS6o3/f3332iaxuzZs9m8eTPBwcH06tWLTz75hJ9++gkXFxf69+/PsGHDbBGWEEKIauJ67dC7777L77//jpOTExEREUydOpW8vDymTZtGamoqLi4uzJ07l1q1almqCGqaxqBBgxgxYoS9D08IIYSDsFmCJYQQQgghhBBVnc2GCAohhBBCCCFEVScJlhBCCCGEEEJYiSRYQgghhBBCCGElNivTbk8lE5sTExMxGAzMmjWLkJAQy/YVK1awbNkynJ2defTRR+nRo4cdo1UKCwt57rnnOH78OAUFBTz66KP06tXLsv2zzz5j5cqV+Pn5AfDSSy/RqFEje4Vrcd9991nWmalfvz6vvvqqZZujvc+rV6/m66+/BiA/P5+EhAS2bNmCt7c3ALNmzWL37t14enoC8OGHH5apFHNF2bt3L//73/9YvHgxR44cYfr06eh0Opo0acKLL76Ik9OF6yV5eXlMmTKFM2fO4OnpyWuvvWb5rNgj3oSEBF5++WX0ej0Gg4HXXnsNf3//Uo+/1mfHHjHv27eP8ePH06BBAwCGDRtGnz59LI91hPf40pgnTZpEWloaoNbmaNOmDW+99ZblsZqm0a1bN8sxhYeHM3nyZJvHLK5N2izbqUxtFlSudquytVmXxiztVsXHW6XbLK0aWLt2rTZt2jRN0zRtz5492oQJEyzbTp8+rfXr10/Lz8/XMjMzLX/b26pVq7RZs2ZpmqZp586d07p3715q++TJk7W//vrLDpFdXV5enta/f/8rbnPU97nEjBkztGXLlpW6LyoqSjtz5oydIipt/vz5Wr9+/bQhQ4ZomqZp48eP17Zv365pmqb997//1datW1fq8QsWLNDeffddTdM0bc2aNdrLL79s13hHjBih7du3T9M0TVu6dKk2e/bsUo+/1mfHVi6NecWKFdqnn3561cfb+z3WtMtjLpGenq7de++92qlTp0rdf/jwYW38+PG2DFHcAGmzbKMyt1ma5tjtVmVrszRN2i1bqE5tVrUYIhgdHU3Xrl0Blf3GxcVZtsXGxtK2bVsMBgNeXl4EBwezf/9+e4Vq0bt3b5566ilAZfB6vb7U9vj4eObPn8+wYcP4+OOP7RHiZfbv309ubi5jxoxh9OjRxMTEWLY56vsM8Ndff3HgwAHuv/9+y31ms5kjR47wwgsvEBUVxapVq+wYIQQHB/Pee+9ZbsfHx9OhQwcAunXrxtatW0s9/uLPfLdu3di2bZvtguXyeN98801atGgBQHFxMa6urqUef63Pjq1cGnNcXBwbN25kxIgRPPfcc5hMplKPt/d7DJfHXOK9995j5MiRBAQElLo/Pj6eU6dOMWrUKB5++GEOHjxoq1BFOUibZRuVtc0Cx2+3KlubBdJu2SPeElWxzaoWCZbJZLJ04QLo9XqKioos2y7uPvf09LzsA2kPnp6eGI1GTCYTEydO5Omnny61vW/fvsyYMYPPP/+c6OhofvvtN/sEehE3NzfGjh3Lp59+yksvvcSzzz7r8O8zwMcff8zjjz9e6r6cnBxGjhzJG2+8wSeffMKXX35p18b1rrvuwtn5woheTdPQ6XSAei+zsrJKPf7i9/tK2yvapfGWfGnu3r2bJUuW8OCDD5Z6/LU+O7ZyacxhYWFMnTqVL774gqCgID744INSj7f3ewyXxwxw5swZtm3bxsCBAy97fK1atXjkkUdYvHgx48ePZ8qUKbYKVZSDtFm2UVnbLHD8dquytVkg7ZY94oWq22ZViwTLaDSSnZ1tuW02my3/wJduy87Otus8m4ulpKQwevRo+vfvzz333GO5X9M0HnjgAfz8/DAYDHTv3p19+/bZMVKlYcOG3Hvvveh0Oho2bEiNGjVITU0FHPd9zszM5NChQ3Tq1KnU/e7u7owePRp3d3eMRiOdOnVyqKuXF49dz87Otoy/L3Hx+32l7fbw448/8uKLLzJ//vzLxnxf67NjL5GRkbRq1cry96X/xxzxPQb4+eef6dev32U9CACtWrWyzItp3749p0+fRpOlEB2OtFm2URnbLKic7VZlbLNA2i1bqKptVrVIsCIiIti8eTMAMTExNG3a1LItLCyM6Oho8vPzycrKIikpqdR2e0lLS2PMmDFMmTKFwYMHl9pmMpno168f2dnZaJrGjh07LP+h7GnVqlXMmTMHgFOnTmEymahVqxbguO/zn3/+SefOnS+7//DhwwwbNozi4mIKCwvZvXs3LVu2tEOEV3bLLbewY8cOADZv3kz79u1LbY+IiGDTpk2W7e3atbN5jBf79ttvWbJkCYsXLyYoKOiy7df67NjL2LFjiY2NBWDbtm2X/fs72ntcYtu2bXTr1u2K295//30+//xzQA1vqVu3ruWqsnAc0mbZRmVss6BytluVrc0Cabdspaq2WdWiimBkZCRbtmwhKioKTdOYPXs2CxcuJDg4mF69ejFq1CiGDx+OpmlMmjTpsnG29vDRRx+RmZnJhx9+yIcffgjAkCFDyM3N5f7772fSpEmMHj0ag8FA586d6d69u50jhsGDB/Pvf/+bYcOGodPpmD17NosXL3bo9/nQoUPUr1/fcvviz0X//v0ZOnQoLi4u9O/fnyZNmtgx0tKmTZvGf//7X958800aNWrEXXfdBcCYMWP46KOPGDZsGNOmTWPYsGG4uLgwd+5cu8VaXFzMK6+8Qt26dXnyyScBuPXWW5k4cSJTp07l6aefvuJn59JhBLY2Y8YMXn75ZVxcXPD39+fll18GHPM9vtihQ4cuOxkoifmRRx5hypQpbNq0Cb1eb5eKV+L6pM2yjcrYZkHlbLcqU5sF0m7ZUlVts3RaZelrE0IIIYQQQggHVy2GCAohhBBCCCGELUiCJYQQQgghhBBWIgmWEEIIIYQQQliJJFhCCCGEEEIIYSWSYAkhhBBCCCGElVSLMu1COIodO3bw9NNP07hxY8t9vr6+vPvuuze13+nTp9OnT5+rriUhhBBClJe0WULcGEmwhLCxTp068dZbb9k7DCGEEOK6pM0SovwkwRLCAYwaNYqGDRty6NAhNE3jrbfeolatWsyZM4fo6GgA+vXrxwMPPMDhw4d5/vnnKSwsxM3NzdLwLV++nE8++QSTycSMGTMICwuz5yEJIYSooqTNEuLaJMESwsa2b9/OqFGjLLe7d+8OQEREBDNnzuSLL77g448/pkuXLiQnJ7NixQqKiooYPnw4nTp14u233+aRRx6hW7dubNiwgX379gHQsmVLHnvsMVavXs3q1aulsRJCCHHTpM0SovwkwRLCxq403GLTpk106tQJUI3Wr7/+Sp06dWjfvj06nQ4XFxfatGlDUlIShw4dom3btgD06tULgDVr1tCyZUsA/P39ycvLs+ERCSGEqKqkzRKi/KSKoBAOIi4uDoDdu3fTuHFjQkNDLUMtCgsL2bNnDyEhIYSGhvLXX38B8N1337F48WIAdDqdfQIXQghR7UibJcTVSQ+WEDZ26XALgLy8PL7++ms+++wz3N3def311/H19WXnzp3cf//9FBYW0rt3b1q2bMnUqVN54YUXmDdvHm5ubrzxxhvEx8fb6WiEEEJUZdJmCVF+Ok3TNHsHIUR1N2rUKGbMmEFoaKi9QxFCCCGuSdosIa5NhggKIYQQQgghhJVID5YQQgghhBBCWIn0YAkhhBBCCCGElUiCJYQQQgghhBBWIgmWEEIIIYQQQliJJFhCCCGEEEIIYSWSYAkhhBBCCCGElfw/h1OO7tKXQXoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot classification model performance metrics\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2,ncols=2, figsize=(12,8))\n",
    "ax[0,0].plot(history.history['loss'], label='Train')\n",
    "ax[0,0].plot(history.history['val_loss'], label='Test')\n",
    "ax[0,0].set_title('Binomial Cross-Entropy Loss')\n",
    "ax[0,0].set_ylabel('Loss')\n",
    "ax[0,0].set_ylim(0,0.3)\n",
    "ax[0,0].set_xlabel('Epoch')\n",
    "ax[0,0].legend(loc='upper left')\n",
    "ax[0,1].plot(history.history['accuracy'], label='Train')\n",
    "ax[0,1].plot(history.history['val_accuracy'], label='Test')\n",
    "ax[0,1].set_title('Model Accuracy')\n",
    "ax[0,1].set_ylabel('Accuracy')\n",
    "ax[0,1].set_ylim(0.9,1)\n",
    "ax[0,1].set_xlabel('Epoch')\n",
    "ax[0,1].legend(loc='lower left')\n",
    "ax[1,0].plot(history.history['recall'], label='Train')\n",
    "ax[1,0].plot(history.history['val_recall'], label='Test')\n",
    "ax[1,0].set_title('Model recall of spam')\n",
    "ax[1,0].set_ylabel('Recall')\n",
    "ax[1,0].set_ylim(0.5,1)\n",
    "ax[1,0].set_xlabel('Epoch')\n",
    "ax[1,0].legend(loc='lower left')\n",
    "ax[1,1].plot(history.history['precision'], label='Train')\n",
    "ax[1,1].plot(history.history['val_precision'], label='Test')\n",
    "ax[1,1].set_title('Model precision of spam')\n",
    "ax[1,1].set_ylabel('Precision')\n",
    "ax[1,1].set_ylim(0.8,1)\n",
    "ax[1,1].set_xlabel('Epoch')\n",
    "ax[1,1].legend(loc='lower left')\n",
    "plt.tight_layout()\n",
    "plt.savefig('images/progression.png',bbox_inches='tight',dpi=400, pad_inches=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0ba88847",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:24:37.635300Z",
     "start_time": "2022-03-29T16:24:36.161547Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "y_pred = (bimodel.predict(X_test) > 0.5).astype(\"int32\")\n",
    "\n",
    "plot_confusion_matrix(confusion_matrix(y_test, y_pred), class_names=['Ham','Spam'])\n",
    "plt.title('Test data confusion matrix with classification threshold at 0.5')\n",
    "plt.savefig('images/confusion50.png',bbox_inches='tight',dpi=400, pad_inches=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "010796f5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:24:38.332702Z",
     "start_time": "2022-03-29T16:24:37.637323Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41/41 [==============================] - 1s 15ms/step - loss: 0.0979 - tp: 144.0000 - fp: 3.0000 - tn: 1131.0000 - fn: 12.0000 - accuracy: 0.9884 - precision: 0.9796 - recall: 0.9231 - auc: 0.9764 - prc: 0.9535\n",
      "Accuracy:    98.84%\n",
      "Precision:   97.96%\n",
      "Recall:      92.31%\n",
      "F1 Score:    95.05%\n"
     ]
    }
   ],
   "source": [
    "# get the loss and metrics\n",
    "result = bimodel.evaluate(X_test, y_test)\n",
    "# extract those\n",
    "loss = result[0]\n",
    "accuracy = result[5]\n",
    "precision = result[6]\n",
    "recall = result[7]\n",
    "f1 = (2*precision*recall)/(precision+recall)\n",
    "\n",
    "print(f\"Accuracy:    {accuracy*100:.2f}%\")\n",
    "print(f\"Precision:   {precision*100:.2f}%\")\n",
    "print(f\"Recall:      {recall*100:.2f}%\")\n",
    "print(f\"F1 Score:    {f1*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "77919bfc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:24:38.909601Z",
     "start_time": "2022-03-29T16:24:38.334826Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1131    3]\n",
      " [  12  144]]\n",
      "Accuracy   98.84%\n",
      "Precision  97.96%\n",
      "Recall     92.31%\n",
      "F1-score   95.05%\n"
     ]
    }
   ],
   "source": [
    "# Sklearn metrics\n",
    "probs = list(bimodel.predict(X_test))\n",
    "outcome = [0 if x[0]<0.5 else 1 for x in list(probs)]\n",
    "dfpred = pd.DataFrame({'true': y_test,'pred':outcome})\n",
    "\n",
    "print(confusion_matrix(dfpred.true, dfpred.pred))\n",
    "print(f'Accuracy   {accuracy_score(dfpred.true, dfpred.pred)*100:.2f}%')\n",
    "print(f'Precision  {precision_score(dfpred.true, dfpred.pred)*100:.2f}%')\n",
    "print(f'Recall     {recall_score(dfpred.true, dfpred.pred)*100:.2f}%')\n",
    "print(f'F1-score   {f1_score(dfpred.true, dfpred.pred)*100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "43416748",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:24:38.915090Z",
     "start_time": "2022-03-29T16:24:38.911578Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_predictions(text):\n",
    "    sequence = tokenizer.texts_to_sequences([text])\n",
    "    # pad the sequence\n",
    "    sequence = pad_sequences(sequence, maxlen=SEQUENCE_LENGTH)\n",
    "    # get the prediction\n",
    "    prediction = bimodel.predict(sequence)\n",
    "    if prediction >0.5:\n",
    "        return 'Spam'\n",
    "    else:\n",
    "        return 'Ham'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6f3b188d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:24:38.978135Z",
     "start_time": "2022-03-29T16:24:38.916878Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spam\n"
     ]
    }
   ],
   "source": [
    "# Spam test\n",
    "text = \"Final Chance! Claim ur £150 worth of discount vouchers today! \"\n",
    "print(get_predictions(text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1d81fd3f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:24:39.038064Z",
     "start_time": "2022-03-29T16:24:38.980444Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ham\n"
     ]
    }
   ],
   "source": [
    "# Ham test\n",
    "text = \"Hi man, I was wondering if we can meet tomorrow.\"\n",
    "print(get_predictions(text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fe430c77",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:24:41.369103Z",
     "start_time": "2022-03-29T16:24:39.040229Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution plots of spam probabilities from bimodel\n",
    "\n",
    "probs_spam = list(bimodel.predict(X_test))\n",
    "probs_spam\n",
    "\n",
    "Y_pp = pd.DataFrame(probs_spam, columns=['prob_spam'])\n",
    "Y_pp['prob_ham']=Y_pp.prob_spam.apply(lambda x: 1-x)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(ncols=2, figsize=(14,6))\n",
    "sns.histplot(data=Y_pp, \n",
    "             x=\"prob_spam\",\n",
    "             palette='husl',\n",
    "             bins=75,\n",
    "             ax=ax[1],\n",
    "             legend=True).set_title('Distribution of Spam/Ham predicted probabilities with y-axis limited')\n",
    "sns.histplot(data=Y_pp, \n",
    "             x=\"prob_spam\",\n",
    "             palette='husl',\n",
    "             bins=75,\n",
    "             ax=ax[0],\n",
    "             legend=True).set_title('Distribution of Spam/Ham predicted probabilities')\n",
    "ax[1].set_ylim(0,10)\n",
    "ax[0].set_xlabel('Probability of Spam')\n",
    "ax[1].set_xlabel('Probability of Spam')\n",
    "plt.savefig('images/prob_dist.png',bbox_inches='tight',dpi=400, pad_inches=0.1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3462492a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:24:41.384128Z",
     "start_time": "2022-03-29T16:24:41.371105Z"
    }
   },
   "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>prob_spam</th>\n",
       "      <th>prob_ham</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.899212e-06</td>\n",
       "      <td>0.999998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.168372e-09</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.188382e-08</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.566612e-09</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.887944e-08</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>2.169811e-09</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>8.736322e-07</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>2.422741e-10</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>2.552180e-09</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1290 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         prob_spam  prob_ham\n",
       "0     1.899212e-06  0.999998\n",
       "1     2.168372e-09  1.000000\n",
       "2     6.188382e-08  1.000000\n",
       "3     9.566612e-09  1.000000\n",
       "4     1.887944e-08  1.000000\n",
       "...            ...       ...\n",
       "1285  2.169811e-09  1.000000\n",
       "1286  1.000000e+00  0.000000\n",
       "1287  8.736322e-07  0.999999\n",
       "1288  2.422741e-10  1.000000\n",
       "1289  2.552180e-09  1.000000\n",
       "\n",
       "[1290 rows x 2 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "41ff34c7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T17:05:58.631324Z",
     "start_time": "2022-03-29T17:05:56.041982Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define some functions to help, then plot different scores at different thresholds\n",
    "def predict_at_threshold(x, threshold=0.5):\n",
    "    if x >= threshold:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def scores_at_thresholds(probs, test_data):\n",
    "    predictions = [probs.apply(predict_at_threshold, threshold=i) for i in np.linspace(0, 1, 100)]\n",
    "    scores = np.array(\n",
    "    [(threshold, \n",
    "      accuracy_score(test_data, predictions[i]),\n",
    "      precision_score(test_data, predictions[i],zero_division=1),\n",
    "      recall_score(test_data, predictions[i]), \n",
    "      f1_score(test_data, predictions[i]),\n",
    "      confusion_matrix(test_data,predictions[i], normalize='all')[1,0],\n",
    "      confusion_matrix(test_data,predictions[i], normalize='all')[0,1],\n",
    "      confusion_matrix(test_data,predictions[i], normalize='all')[0,0],\n",
    "      confusion_matrix(test_data,predictions[i], normalize='all')[1,1]) \n",
    "      \n",
    "     for i, threshold in enumerate(np.linspace(0, 1, 100))])\n",
    "    return scores\n",
    "\n",
    "\n",
    "scores = scores_at_thresholds(Y_pp.prob_spam, y_test);\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16,7))\n",
    "\n",
    "# Plot data\n",
    "ax1.plot(scores[:, 0], scores[:, 1], label='accuracy')\n",
    "ax1.plot(scores[:, 0], scores[:, 2], label='precision')\n",
    "ax1.plot(scores[:, 0], scores[:, 3], label='recall')\n",
    "ax1.plot(scores[:, 0], scores[:, 4], label='f1')\n",
    "ax2.plot(scores[:, 0], scores[:, 5], label='FN')\n",
    "ax2.plot(scores[:, 0], scores[:, 6], label='FP')\n",
    "ax2.plot(scores[:, 0], scores[:, 7], label='TP')\n",
    "ax2.plot(scores[:, 0], scores[:, 8], label='TN')\n",
    "\n",
    "# Adjust legends, titles and labels\n",
    "ax1.legend(loc=[0.8, 0.25])\n",
    "ax1.set_xlabel('Threshold')\n",
    "ax1.set_title('Threshold vs Accuracy, Precision, Recall and F1 for Spam Classification')\n",
    "ax1.set_ylim(0.8,1)\n",
    "\n",
    "ax2.legend(loc=[0.8, 0.25])\n",
    "ax2.set_xlabel('Threshold')\n",
    "ax2.set_title('Threshold vs Normalised FN, FP, TN and TP for Spam Classification')\n",
    "ax2.set_ylim(0.0,0.2)\n",
    "\n",
    "# Annotations\n",
    "circle_rad = 15  # This is the radius, in points\n",
    "point=[0.5,0.9885]\n",
    "ax1.plot(point[0], point[1], 'o', ms=circle_rad * 1, mec='black', mfc='none', mew=1)\n",
    "ax1.annotate('99% Accuracy at Threshold 0.77', xy=point, xytext=(-120, -55),\n",
    "            textcoords='offset points',\n",
    "            color='black', size='large',\n",
    "            arrowprops=dict(\n",
    "                arrowstyle='simple,tail_width=0.1,head_width=0.5,head_length=0.5',\n",
    "                facecolor='black', shrinkB=circle_rad * 1))\n",
    "\n",
    "point=[0.5,0.95]\n",
    "ax1.plot(point[0], point[1], 'o', ms=circle_rad * 1, mec='black', mfc='none', mew=1)\n",
    "ax1.annotate('95% F1 score at Threshold 0.5', xy=point, xytext=(-120, -70),\n",
    "            textcoords='offset points',\n",
    "            color='black', size='large',\n",
    "            arrowprops=dict(\n",
    "                arrowstyle='simple,tail_width=0.1,head_width=0.5,head_length=0.5',\n",
    "                facecolor='black', shrinkB=circle_rad * 1))\n",
    "\n",
    "# point=[0.78,0.025]\n",
    "# ax2.plot(point[0], point[1], 'o', ms=circle_rad * 1, mec='black', mfc='none', mew=1)\n",
    "# ax2.annotate('FP=2.5% at Threshold 0.78', xy=point, xytext=(-90, 60),\n",
    "#             textcoords='offset points',\n",
    "#             color='black', size='large',\n",
    "#             arrowprops=dict(\n",
    "#                 arrowstyle='simple,tail_width=0.1,head_width=0.5,head_length=0.5',\n",
    "#                 facecolor='black', shrinkB=circle_rad * 1))\n",
    "plt.savefig('images/threshold.png',bbox_inches='tight',dpi=400, pad_inches=0.1)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e17f4797",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:24:43.952598Z",
     "start_time": "2022-03-29T16:24:43.941440Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99      1134\n",
      "           1       0.98      0.92      0.95       156\n",
      "\n",
      "    accuracy                           0.99      1290\n",
      "   macro avg       0.98      0.96      0.97      1290\n",
      "weighted avg       0.99      0.99      0.99      1290\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Y_pp['pred_class_thresh'] = Y_pp.prob_spam.apply(predict_at_threshold, \n",
    "                                                threshold=0.5)\n",
    "print(classification_report(y_test, Y_pp.pred_class_thresh))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d0c5e037",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:24:43.976471Z",
     "start_time": "2022-03-29T16:24:43.955152Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99      1134\n",
      "           1       0.98      0.92      0.95       156\n",
      "\n",
      "    accuracy                           0.99      1290\n",
      "   macro avg       0.98      0.96      0.97      1290\n",
      "weighted avg       0.99      0.99      0.99      1290\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Y_pp['pred_class_thresh'] = Y_pp.prob_spam.apply(predict_at_threshold, \n",
    "                                                threshold=0.77)\n",
    "print(classification_report(y_test, Y_pp.pred_class_thresh))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "276f8882",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:33:47.348572Z",
     "start_time": "2022-03-29T16:33:46.306187Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "y_pred = (bimodel.predict(X_test) > 0.5).astype(\"int32\")\n",
    "\n",
    "plot_confusion_matrix(confusion_matrix(y_test, y_pred), class_names=['Ham','Spam'])\n",
    "plt.title('Test data confusion matrix with classification threshold at 0.77')\n",
    "plt.savefig('images/confusion77.png',bbox_inches='tight',dpi=400, pad_inches=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "fa6bb980",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-29T16:24:45.633943Z",
     "start_time": "2022-03-29T16:24:45.065804Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1131    3]\n",
      " [  13  143]]\n",
      "Accuracy   98.76%\n",
      "Precision  97.95%\n",
      "Recall     91.67%\n",
      "F1-score   94.70%\n"
     ]
    }
   ],
   "source": [
    "# Sklearn metrics\n",
    "probs = list(bimodel.predict(X_test))\n",
    "outcome = [0 if x[0]<0.77 else 1 for x in list(probs)]\n",
    "dfpred = pd.DataFrame({'true': y_test,'pred':outcome})\n",
    "\n",
    "print(confusion_matrix(dfpred.true, dfpred.pred))\n",
    "print(f'Accuracy   {accuracy_score(dfpred.true, dfpred.pred)*100:.2f}%')\n",
    "print(f'Precision  {precision_score(dfpred.true, dfpred.pred)*100:.2f}%')\n",
    "print(f'Recall     {recall_score(dfpred.true, dfpred.pred)*100:.2f}%')\n",
    "print(f'F1-score   {f1_score(dfpred.true, dfpred.pred)*100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e37aeeee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensorflow_env",
   "language": "python",
   "name": "tensorflow_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "259.07px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}