Deception_and_Automated_Account_Detection_Interim.ipynb

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   "cell_type": "markdown",
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   "source": [
    "**Instructions to connect local runtime**\n",
    "\n",
    "Step 1: Install [Jupyter](https://jupyter.org/install) on your local machine. (one-time)\n",
    "\n",
    "Step 2: Install and enable the `jupyter_http_over_ws` jupyter extension (one-time)\n",
    "```\n",
    "> pip install jupyter_http_over_ws\n",
    "> jupyter serverextension enable --py jupyter_http_over_ws\n",
    "```\n",
    "Step 3: Start server and authenticate\n",
    "```\n",
    "> jupyter notebook --NotebookApp.allow_origin='https://colab.research.google.com' --port=8888 --NotebookApp.port_retries=0\n",
    "```\n",
    "Click the \"Connect\" button and select \"Connect to local runtime...\". Enter the port from the previous step in the dialog that appears and click the \"Connect\" button. After this, you should now be connected to your local runtime.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6y2k5RhH3TWT"
   },
   "source": [
    "**Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 220
    },
    "colab_type": "code",
    "id": "OHh5CUBtqx94",
    "outputId": "d9d31ddf-54d0-46df-d72b-739f743a4f4c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to C:\\Users\\Nishan\n",
      "[nltk_data]     Chathuranga\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to C:\\Users\\Nishan\n",
      "[nltk_data]     Chathuranga\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to C:\\Users\\Nishan\n",
      "[nltk_data]     Chathuranga\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "# Basic libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Natural Language Processing\n",
    "from sklearn.feature_extraction import stop_words\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "import re\n",
    "import string\n",
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "nltk.download('stopwords')\n",
    "nltk.download('punkt')\n",
    "nltk.download('wordnet')\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "stemmer= PorterStemmer()\n",
    "lemmatizer=WordNetLemmatizer()\n",
    "\n",
    "# Data Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "%matplotlib inline\n",
    "\n",
    "# Modeling\n",
    "import sklearn.ensemble\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score, confusion_matrix,roc_curve,auc\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TgRNcvuX3Yky"
   },
   "source": [
    "**Load Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GgA8sI2BiKYV"
   },
   "outputs": [],
   "source": [
    "# Bot Dataset From - https://www.kaggle.com/c/twitter-bot-classification\n",
    "url_bot_train = 'https://raw.githubusercontent.com/AsyncWave/Fake-NEWS-and-Automaticity-Detection/master/data/Bot/training_data_2_csv_UTF.csv'\n",
    "# url_bot_test = 'https://raw.githubusercontent.com/AsyncWave/Fake-NEWS-and-Automaticity-Detection/master/data/Bot/test_data_4_students.csv'\n",
    "\n",
    "# News Data\n",
    "url_news_credible = 'https://raw.githubusercontent.com/AsyncWave/Fake-NEWS-and-Automaticity-Detection/master/data/Fake%20News/not_onion.csv'\n",
    "url_news_not_credible = 'https://raw.githubusercontent.com/AsyncWave/Fake-NEWS-and-Automaticity-Detection/master/data/Fake%20News/the_onion.csv'\n",
    "\n",
    "df_bot_train = pd.read_csv(url_bot_train)\n",
    "# df_bot_test = pd.read_csv(url_bot_test,sep='\\t', encoding = \"ISO-8859-1\")\n",
    "df_news_credible = pd.read_csv(url_news_credible, index_col=0)\n",
    "df_news_not_credible = pd.read_csv(url_news_not_credible, index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zMo8-wNZ23x1"
   },
   "source": [
    "# **Fake News (News Credibility)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 197
    },
    "colab_type": "code",
    "id": "ixJTsdJa6FDN",
    "outputId": "9aedd43b-9909-4098-99aa-e989e98844eb"
   },
   "outputs": [
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       "      <th>subreddit</th>\n",
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       "      <th>title</th>\n",
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       "  </thead>\n",
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       "      <td>nottheonion</td>\n",
       "      <td>1554663925</td>\n",
       "      <td>Suspected rhino poacher is killed by an elepha...</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
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       "      <td>1554663558</td>\n",
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       "      <td>nottheonion</td>\n",
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       "      <td>Poacher hunting rhinos trampled to death by el...</td>\n",
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       "              author       domain  num_comments  score    subreddit  \\\n",
       "0       drak0bsidian      cnn.com             0      1  nottheonion   \n",
       "1          somefaces   nypost.com             1      1  nottheonion   \n",
       "2             Sombez  nytimes.com             1      1  nottheonion   \n",
       "3     Bayou-Maharaja     ktoo.org             0      1  nottheonion   \n",
       "4  phantommaster1999      buzz.ie             1      1  nottheonion   \n",
       "\n",
       "    timestamp                                              title  \n",
       "0  1554663925  Suspected rhino poacher is killed by an elepha...  \n",
       "1  1554663648  Taliban commander turns himself in to collect ...  \n",
       "2  1554663558  Rhino Poacher Killed by Elephant and Eaten by ...  \n",
       "3  1554662778  Young shoves female reporter on way to Violenc...  \n",
       "4  1554661988  Poacher hunting rhinos trampled to death by el...  "
      ]
     },
     "execution_count": 3,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_news_credible.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
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       "      <td>TheOnion</td>\n",
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       "      <td>3</td>\n",
       "      <td>TheOnion</td>\n",
       "      <td>1554501708</td>\n",
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       "             author               domain  num_comments  score subreddit  \\\n",
       "0            Kotaay            google.ca             1      1  TheOnion   \n",
       "1  DisastrousCandy3         theonion.com             3      1  TheOnion   \n",
       "2      SlovenianCat             youtu.be             0      1  TheOnion   \n",
       "3   MyNameIsGriffon  sports.theonion.com             0    386  TheOnion   \n",
       "4   MyNameIsGriffon         theonion.com             0      3  TheOnion   \n",
       "\n",
       "    timestamp                                              title  \n",
       "0  1554658622  Jack Nicholson Banned From Sitting Courtside A...  \n",
       "1  1554600149         NicoDerm Introduces New Nicotine Eye Patch  \n",
       "2  1554585700  Last Bastion Of U.S. Economy Succumbs To Pancr...  \n",
       "3  1554559311  Tom Izzo Calls 2019 Spartans Best Team He’s Ev...  \n",
       "4  1554501708  House Democrats Formally Request Trump’s Tax R...  "
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_news_not_credible.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CK4ZvZXpMqha"
   },
   "source": [
    "**Clean News Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M1CmocPeIt4d"
   },
   "outputs": [],
   "source": [
    "def clean_data(dataframe):\n",
    "    # Drop duplicate rows\n",
    "    dataframe.drop_duplicates(subset='title', inplace=True)\n",
    "\n",
    "    # Remove punctation\n",
    "    dataframe['title'] = dataframe['title'].str.replace('[^\\w\\s]',' ')\n",
    "\n",
    "    # Remove numbers \n",
    "    dataframe['title'] = dataframe['title'].str.replace('[^A-Za-z]',' ')\n",
    "\n",
    "    # Make sure any double-spaces are single \n",
    "    dataframe['title'] = dataframe['title'].str.replace('  ',' ')\n",
    "    dataframe['title'] = dataframe['title'].str.replace('  ',' ')\n",
    "\n",
    "    # Transform all text to lowercase\n",
    "    dataframe['title'] = dataframe['title'].str.lower()\n",
    "    \n",
    "    print(\"New shape:\", dataframe.shape)\n",
    "    return dataframe.head()"
   ]
  },
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   "execution_count": 0,
   "metadata": {
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    "id": "Xmt4UMOLNHCD",
    "outputId": "0c5f543f-7fbb-4f2a-bf6e-be27beaf5a71"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New shape: (12086, 7)\n"
     ]
    },
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       "      <th>subreddit</th>\n",
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       "      <th>0</th>\n",
       "      <td>drak0bsidian</td>\n",
       "      <td>cnn.com</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>nottheonion</td>\n",
       "      <td>1554663925</td>\n",
       "      <td>suspected rhino poacher is killed by an elepha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>somefaces</td>\n",
       "      <td>nypost.com</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>nottheonion</td>\n",
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       "      <td>taliban commander turns himself in to collect ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sombez</td>\n",
       "      <td>nytimes.com</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>nottheonion</td>\n",
       "      <td>1554663558</td>\n",
       "      <td>rhino poacher killed by elephant and eaten by ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bayou-Maharaja</td>\n",
       "      <td>ktoo.org</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>nottheonion</td>\n",
       "      <td>1554662778</td>\n",
       "      <td>young shoves female reporter on way to violenc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>phantommaster1999</td>\n",
       "      <td>buzz.ie</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>nottheonion</td>\n",
       "      <td>1554661988</td>\n",
       "      <td>poacher hunting rhinos trampled to death by el...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              author       domain  num_comments  score    subreddit  \\\n",
       "0       drak0bsidian      cnn.com             0      1  nottheonion   \n",
       "1          somefaces   nypost.com             1      1  nottheonion   \n",
       "2             Sombez  nytimes.com             1      1  nottheonion   \n",
       "3     Bayou-Maharaja     ktoo.org             0      1  nottheonion   \n",
       "4  phantommaster1999      buzz.ie             1      1  nottheonion   \n",
       "\n",
       "    timestamp                                              title  \n",
       "0  1554663925  suspected rhino poacher is killed by an elepha...  \n",
       "1  1554663648  taliban commander turns himself in to collect ...  \n",
       "2  1554663558  rhino poacher killed by elephant and eaten by ...  \n",
       "3  1554662778  young shoves female reporter on way to violenc...  \n",
       "4  1554661988  poacher hunting rhinos trampled to death by el...  "
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data(df_news_credible)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 214
    },
    "colab_type": "code",
    "id": "MqVZuyJBNbal",
    "outputId": "0519d2e7-909a-4b55-fef5-a3b9d8df5011"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New shape: (14189, 7)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author</th>\n",
       "      <th>domain</th>\n",
       "      <th>num_comments</th>\n",
       "      <th>score</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kotaay</td>\n",
       "      <td>google.ca</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>TheOnion</td>\n",
       "      <td>1554658622</td>\n",
       "      <td>jack nicholson banned from sitting courtside a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DisastrousCandy3</td>\n",
       "      <td>theonion.com</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TheOnion</td>\n",
       "      <td>1554600149</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SlovenianCat</td>\n",
       "      <td>youtu.be</td>\n",
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       "      <td>TheOnion</td>\n",
       "      <td>1554585700</td>\n",
       "      <td>last bastion of u s economy succumbs to pancre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MyNameIsGriffon</td>\n",
       "      <td>sports.theonion.com</td>\n",
       "      <td>0</td>\n",
       "      <td>386</td>\n",
       "      <td>TheOnion</td>\n",
       "      <td>1554559311</td>\n",
       "      <td>tom izzo calls  spartans best team he s ever t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MyNameIsGriffon</td>\n",
       "      <td>theonion.com</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>TheOnion</td>\n",
       "      <td>1554501708</td>\n",
       "      <td>house democrats formally request trump s tax r...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             author               domain  num_comments  score subreddit  \\\n",
       "0            Kotaay            google.ca             1      1  TheOnion   \n",
       "1  DisastrousCandy3         theonion.com             3      1  TheOnion   \n",
       "2      SlovenianCat             youtu.be             0      1  TheOnion   \n",
       "3   MyNameIsGriffon  sports.theonion.com             0    386  TheOnion   \n",
       "4   MyNameIsGriffon         theonion.com             0      3  TheOnion   \n",
       "\n",
       "    timestamp                                              title  \n",
       "0  1554658622  jack nicholson banned from sitting courtside a...  \n",
       "1  1554600149         nicoderm introduces new nicotine eye patch  \n",
       "2  1554585700  last bastion of u s economy succumbs to pancre...  \n",
       "3  1554559311  tom izzo calls  spartans best team he s ever t...  \n",
       "4  1554501708  house democrats formally request trump s tax r...  "
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data(df_news_not_credible)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 107
    },
    "colab_type": "code",
    "id": "cz4l3dRmYcPQ",
    "outputId": "802e999c-ca30-4600-af66-1aa6a1265254"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subreddit</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>nicoderm introduces new nicotine eye patch</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subreddit                                              title\n",
       "0          1  jack nicholson banned from sitting courtside a...\n",
       "1          1         nicoderm introduces new nicotine eye patch"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine df_news_not_credible & df_news_credible with only 'subreddit' (target) and 'title' (predictor) columns\n",
    "df_news = pd.concat([df_news_not_credible[['subreddit', 'title']], df_news_credible[['subreddit', 'title']]], axis=0)\n",
    "\n",
    "#Reset the index\n",
    "df_news = df_news.reset_index(drop=True)\n",
    "\n",
    "# Binarize Target subreddit\n",
    "df_news[\"subreddit\"] = df_news[\"subreddit\"].map({\"nottheonion\": 0, \"TheOnion\": 1})\n",
    "\n",
    "# Preview head of df to show 'TheOnion' titles appear\n",
    "df_news.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 107
    },
    "colab_type": "code",
    "id": "DpP0p2cgaKjv",
    "outputId": "86521eaf-3325-4a00-a00c-9de2e3b8df93"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>subreddit</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26273</th>\n",
       "      <td>0</td>\n",
       "      <td>farming simulator starts up esports with k pri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26274</th>\n",
       "      <td>0</td>\n",
       "      <td>mark zuckerberg killed goat with laser gun and...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       subreddit                                              title\n",
       "26273          0  farming simulator starts up esports with k pri...\n",
       "26274          0  mark zuckerberg killed goat with laser gun and..."
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_news.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "q4UjqcWTXasY",
    "outputId": "7f6da0a8-7f77-4665-e6d0-9af15333e09f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14189, 11906)\n"
     ]
    }
   ],
   "source": [
    "# Set variables to show TheOnion Titles\n",
    "mask_on = df_news['subreddit'] == 1\n",
    "df_news_not_credible_titles = df_news[mask_on]['title']\n",
    "\n",
    "# Instantiate a CountVectorizer\n",
    "cv1 = CountVectorizer(stop_words = 'english')\n",
    "\n",
    "news_not_credible_cvec = cv1.fit_transform(df_news_not_credible_titles)\n",
    "\n",
    "# Convert onion_cvec into a DataFrame\n",
    "news_not_credible_cvec_df = pd.DataFrame(news_not_credible_cvec.toarray(),\n",
    "                   columns=cv1.get_feature_names())\n",
    "\n",
    "# Inspect head of Onion Titles cvec\n",
    "print(news_not_credible_cvec_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "aEzbklbwfzuM",
    "outputId": "08d29297-1dc3-4c4a-8b0f-bee64aa23f40"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14189, 34896)\n"
     ]
    }
   ],
   "source": [
    "# Instantiate a CountVectorizer\n",
    "cv2 = CountVectorizer(stop_words = 'english', ngram_range=(2,2))\n",
    "\n",
    "news_not_credible_cvec_bi = cv2.fit_transform(df_news_not_credible_titles)\n",
    "\n",
    "# Convert onion_cvec into a DataFrame\n",
    "news_not_credible_cvec_df_bi = pd.DataFrame(news_not_credible_cvec_bi.toarray(),\n",
    "                   columns=cv2.get_feature_names())\n",
    "\n",
    "# Inspect head of Onion Titles cvec\n",
    "print(news_not_credible_cvec_df_bi.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "_LXlNTRpa2hj",
    "outputId": "33488dad-1dcd-4003-fa52-0b49bb65d0a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12086, 14451)\n"
     ]
    }
   ],
   "source": [
    "# Set variables to show NotTheOnion Titles\n",
    "mask_no = df_news['subreddit'] == 0\n",
    "df_news_credible_titles = df_news[mask_no]['title']\n",
    "\n",
    "# Instantiate a CountVectorizer\n",
    "cv3 = CountVectorizer(stop_words = 'english')\n",
    "\n",
    "# Fit and transform the vectorizer on our corpus\n",
    "news_credible_cvec = cv3.fit_transform(df_news_credible_titles)\n",
    "\n",
    "# Convert onion_cvec into a DataFrame\n",
    "news_credible_cvec_df = pd.DataFrame(news_credible_cvec.toarray(),\n",
    "                   columns=cv3.get_feature_names())\n",
    "\n",
    "# Inspect head of Not Onion Titles cvec\n",
    "print(news_credible_cvec_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "kK3k2JK_ljOg",
    "outputId": "77c2f150-0c92-4cd3-9a1c-f9d0cb01f236"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12086, 58973)\n"
     ]
    }
   ],
   "source": [
    "# Instantiate a CountVectorizer\n",
    "cv4 = CountVectorizer(stop_words = 'english' , ngram_range=(2,2))\n",
    "\n",
    "# Fit and transform the vectorizer on our corpus\n",
    "news_credible_cvec_bi = cv4.fit_transform(df_news_credible_titles)\n",
    "\n",
    "# Convert onion_cvec into a DataFrame\n",
    "news_credible_cvec_df_bi = pd.DataFrame(news_credible_cvec_bi.toarray(),\n",
    "                   columns=cv4.get_feature_names())\n",
    "\n",
    "# Inspect head of Not Onion Titles cvec\n",
    "print(news_credible_cvec_df_bi.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VEC-eq0_aiI9"
   },
   "outputs": [],
   "source": [
    "def bar_plot(x, y, title, color):    \n",
    "    \n",
    "    # Set up barplot \n",
    "    plt.figure(figsize=(9,5))\n",
    "    g=sns.barplot(x, y, color = color)    \n",
    "    ax=g\n",
    "\n",
    "    # Label the graph\n",
    "    plt.title(title, fontsize = 15)\n",
    "    plt.xticks(fontsize = 10)\n",
    "\n",
    "    # Enable bar values\n",
    "    # Code modified from http://robertmitchellv.com/blog-bar-chart-annotations-pandas-mpl.html\n",
    "    # create a list to collect the plt.patches data\n",
    "    totals = []\n",
    "\n",
    "    # find the values and append to list\n",
    "    for p in ax.patches:\n",
    "        totals.append(p.get_width())\n",
    "\n",
    "    # set individual bar lables using above list\n",
    "    total = sum(totals)\n",
    "\n",
    "    # set individual bar lables using above list\n",
    "    for p in ax.patches:\n",
    "        # get_width pulls left or right; get_y pushes up or down\n",
    "        ax.text(p.get_width()+.3, p.get_y()+.38, \\\n",
    "                int(p.get_width()), fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "colab_type": "code",
    "id": "pudjXIWIakVW",
    "outputId": "552220e4-9aac-4c14-87a0-e4b15729a701"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x231f8937d68>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 320,
       "width": 556
      },
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up variables to contain top 10 most used words in Onion\n",
    "news_not_credible_wc = news_not_credible_cvec_df.sum(axis = 0)\n",
    "news_not_credible_top_10 = news_not_credible_wc.sort_values(ascending=False).head(10)\n",
    "\n",
    "# Call function\n",
    "bar_plot(news_not_credible_top_10.values, news_not_credible_top_10.index, 'Top 10 unigrams on not credible news','r') \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "colab_type": "code",
    "id": "AS6JUKOZcsLH",
    "outputId": "d44020b3-45b2-4f4a-a7ca-3c6a8debd081"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x233384f1400>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 320,
       "width": 567
      },
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up variables to contain top 10 most used words in Onion\n",
    "news_credible_wc = news_credible_cvec_df.sum(axis = 0)\n",
    "news_credible_top_10 = news_credible_wc.sort_values(ascending=False).head(10)\n",
    "\n",
    "# Call function\n",
    "bar_plot(news_credible_top_10.values, news_credible_top_10.index, 'Top 10 unigrams on credible','b') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "ij1dD4GLddzm",
    "outputId": "d7d737da-e7f4-49d0-b8e3-ef99abcfb824"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'man', 'new', 'trump', 'year'}"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create list of unique words in top five\n",
    "news_credible_10_set = set(news_credible_top_10.index)\n",
    "news_not_credible_10_set = set(news_not_credible_top_10.index)\n",
    "\n",
    "# Return common words\n",
    "common_unigrams = news_not_credible_10_set.intersection(news_credible_10_set)\n",
    "common_unigrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "colab_type": "code",
    "id": "1mGzdS_QgpZF",
    "outputId": "2696eb16-6017-4561-d279-455c3d50cf4b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jwNd15b8X11I0z28USHuKohZntYXxrpM0yBkgkyR1q9XoqUk9o4301WnWaJiq8M9mKzNzbvlDek2Kfn8GWnVTkXZqZ1WnaVZTrl1Nz1edNL09Z9Xpmw4okJkvVl2bdjzUOglExM7AL5j3B2Azb2mdZJ6mrrWqa8k1S1ebdp7OyzNzVkQcTdER/FDgm8A3I+Jhilpq1wOXZ2Z/Bmqofp46/Sx20uw2akRVzmW9TuArZX22jZo0UPRRuCrFeV+B4kf9glJ9v9/doTxbPRsjqz5vXU7tqg3EVD/jrd6/L/TyGW9lQb+/RlZ9/go9TU3bUS9Q21utgqUN3x90vuyTKAJka0TEu6qCV+PK+bVV80OBd0fESpn5VEQsQ08gdXKdpukL410naZCziaUkqVtVNw2cb7S5WmVfQZUfAq2aFb7QYn31Puv12bKwVTcRHN4wVf00nWhe2JvzBfM2CW1H9Tnu7b5aadr3GEBEbELRbLASHPsbRTPbL1GMDvfxqqnSxLWd2i3t1ijqV82jzJxI0Yl+dbPNERT9gZ0C3BsRV0XE6Hrbt6FXzyLzBnQ70cS3Xf2twVUpa7v318I8zuqAbKf6Vmr1bCzfj7yH1Hyvfid0+hlvZUG/vzp5nvqiP/d9p8s+Xz9kEbEBRSC5ev11FLXBFqMn8PqBqjzr1iReCO86SYOcNcgkSd2quuZTyyBVRCxBT+e+rWpNLUPRIXEzlX128odaX1XXyBnZRvp3NNi2rxo1M6xWfY16+wO++hwvw7w1HlrtqxOOpuf/XIdm5o/qJYqIoDM/aDsuM68AroiI1SiaTW1OUWtjY4q+n7YFbo6ILet1sN9Cr55F5g0wdKIG48LyLEXAoN37a2Ee55wG+12Qqp/jH2Tmf3Yor4X9jC/o91d1+p0ys7cDiQykTpe9EvhanCJA9lN6mk0+Tdn3WGbOiYhpFM3rx1P0XzeuKp+GTe0X8LtO0iBnDTJJUrd6lJ6/jL+zjfTVaR5ukXadZivLTuYrza1a5bUwTK/6vEnDVLzRd1tldLHn6Rndsz+anq86aXp7zqrTr9UsYUQsTeeb7U0o539vFBwrvY1B/n+zzHw0My/KzEMzcwywNnBZuXpZ4Nt9yPbfVZ87/SwOJpXjHFb++G6lcpxz6UxNzWaqRxbsRL9V7ahugtnfTtGr74NW799l6OwzvqDfX508TwtbR8uemXPoGXV3XPlHhUqAbHJNP32VINj4mvkTwHwjJtfZ14J410ka5Ab1f8IkSVpQyiHaK33trBcRb2+xSfWoabe2SLtNi/Vb09OErmGn3wvRjfTUIHl/RDRrFrMtPbWh/tihjsObnq+IWAzYqvz6KvDXXuZ/W9Xn2k6ba21J5zrvJiKG0VPD5N4Wybfr1H4XlrJz8k/S05yuVyP0laqfp23bSN+bZ3EwqS5r09EFI+Ld9DQbu63Oc1b9PeinzLwHqPSLtnk5oMQCVfYfVRkgYlxE9Ke/rOpz2+r9O44OPuPAWuWAA81Uv3d6+86fXPV5MIx63BsLouyTyvkqwIb0NKGsHayjEiDbICJG0fPHn0ltDAQynw696yQNcgbIJEnd7OJyHsBhjRKVtYoOqlr0mxb57h8RzZrwHNqLvBa4coTD35Vflwb2b5K8+jxc2KEijIyIZj+ePkVPx9a/z8yW/X7V+F3V5y9GxFINU8LBvcy7lRfpGRl07UaJylqF/WliNmDK61EJdPQ68FCOmHhd+XWdZvdC2d/QTuXXR5m3r6DB7uKqz4dGRLNzdUSD7Sqqm651qrng+eV8aeCYDuXZyjnlfChwbD/y+S09z9mXyuepkUObrOur/2i0ogzOVEZvfIJejHwLkJl30xNUe3+Ld+WgsoDKXt088iB6+sSsbTZ5PcUfVIKiw/0lG6RrW3/fdZIGPwNkkqRu9lN6+vY5MCL2qk0QEUOAs+hpmveHzGxVg2kEcHa5bW1+36Sn9sg9wP/2odwLwkkUPyYAjo2ITWsTRMRh9NSEuBP4dQf3f2pEzNe0KyI2ougcueLk3mZc1lT5Q/l17XJf8/XDGhGHU3TQ3DHlqIeV++VdDe6xoRTBiXaaFy5UEXFARHyq3r1clWYHevquu72Pu/pu1efTyxpUtftZHfgVPTUYf5SZrfqaGjQy81p6ggXvAX5cL0gWEQdRDN4A8BhwZp3sZlZ9HtOhIv6QnqacX42Ir5e1N+cTEcMiolVNrXZ8n55REg+KiKObBQ4jYqWIOCIi3lu9PDP/SU8g/B3AGRGxZJ3tj6GnyXMnfSUiPllnfytS/CGhEpT/cR/v2a/TU2vw7Ij4aLPEEfHOiPhxs+d2Iep02a+n6IcMYO9y/jg1zSbLWuK31aSDnhpotftdWO86SYOYnfRLkrpWZj4SEV8CzqP4o9E5EfFpiqDVUxRBsX3o6XPrceALbWT9W2AX4G8RcSbFj9lVKJpnVDcV/Fw5OmaflH3pfKNmcXXfOhtExIk162/IzD/ULCMz/xERxwEnUDQJnBwRvwBuoqjdsTM9NXdeBL7QoeaVUJzvjwBTyn3eTPFX/82Bfen5cXlaZk7q4z6+QtF3zVsorukmEXEOcD/FtdmFIvj3V4pOyteh/6MWVvxf4Bfl57MjYkeKH2mzKfp72gd4O0Vtwu0YHCObVmxIce7mRMSVwBSK/qpeBVajCDZUBxUn9mUnmXlFRPyM4vlaGbi1vD7XA69QBJQ+D1Sa4d0A/KAv+xpgn6VoDjgM+CLwgYg4l+I+XBnYlZ4g9GvAvmW/S7Vuphg9cRmK4P5cYCo9ndPPzsybelOwzPx3GcC9hOI3wreBvSLi1xSDcbxGEfx/P7AjcCXzN2vrlcx8PCJ2oQhgDwWOA/aJiIsoAh7PUzyzawPvo2hOtwSwQ53svkrR7G04sAewcUScTdFP4qrA7sAWwB0U561hjc5eupEiuH1hRHwKuILiDy8bUNyzlXfy34Dv9GUHmXlNRHwd+B7FvfO7iLgZuBy4j+IZWQn4PxT/xmxcbnp4X/bXSZ0ue2Y+GxFTgPfS81u2UbPJaynu10q6RzPzrgZFXSjvOkmDXGY6OTk5OTm9KSaKQEOW01m92O6zFD/Essn0D2C9JnlcVpV2deBPTfJ6Hti1A8e7cosy15t+0CLPkyh+CDfa/klgxw6UfdeqPA8sp1eb7Pd8YIk2zsNFTfa5GUWQs9k1Xhv4e/n93gb5/Lhqm9FtHu9pLa7LlRQ/Hp8rv9/Wxn22XJv3RsNzUqY9vCrtTjXrTmnzvnoBOKCf98Ri5f5eb7GvS1sce8Pj6WO5ppd5PdGJtBQ1vu5vcYzPADu32Nc3mmxf9/5p83i3pRhQoNU1/1Vf7886272n6rlrNT0PvK/JuW1W9hkUwe+m16nVPVT7fFEE3p5ost/bgdX7e48Bn6H440075+nfwJJ9vAfafobaTdvJslPUOK1O/6UG6T5Yk+6CJnkutHedk5PT4J1sYilJ6nqZeQ7Fj6aTKP5q/DTFX7UfoQhcHEARCPlHm1k+R1ET6AsUNWAep6jZMRM4FdgwMy/q5DF0SmZ+k6KmxhkUncrPpajpdDtF7bINMvOKBbDfH1MEsM6jCB68RPGD8w/AxzPzM9mP2nblPm4C3kVRi+Muih86sylqln0D2CQz76WnT5un+rO/mn0fQBEUvIqe++shiuPbE9g+M59tnMOAOYyiVs4xFEHfBylqEL5KESy9keK+WC8zT+vPjjLz9cw8iKIz7dMoghnPlfu7H/glRXD2I1k0n1okZeZUilqpB1HUwHqM4n54CriF4lyvnZm/a5hJkc9EipqPl1OMjPhyh8r3J4pA8VeBP1K8B1+huA4zKWqY7Q98rhP7K/c5jaLG1e7A/1C8e56juM+epqgd9wuKmmGrZ+YtDfKZSlEr80SKGmiVZ/yvFP1QbZJFc8yOyswbKGo+/YAi0Pc8xcAnt1L0T7ZpZj7SOIe293M+RRPSg4HfU7xDXqS49o9S1Kw8meLfnzWzaOI9KHS47Ne2+F7xZ+Z9LiY1yXOhveskDV6RmQNdBkmSFnkRcRk9zS+GLco/4LtZ2aH2feXXX2Rmx4IAkiRJGrysQSZJktTjwKrPjWolSJIk6U3GAJkkSeoKEbFFixHy9gEOKb8+CVy8MMolSZKkgecolpIkqVv8GFgtIq6g6HfsUYr/C60FfJRiVLSKr2Tm3IVfREmSJA0EA2SSJKmbrA7s12T9S8BXM/PChVQeSZIkDQIGyCRJUrf4LPARYALFaGrDgWWBZ4B/AlcDP83MhwashJIkSRoQjmIpSZIkSZKkrmYn/ZIkSZIkSepqBsgkSZIkSZLU1QyQSZIkSZIkqasZIJMkSZIkSVJXcxRLqYWImAm8BZg1wEWRJEmSJEnzGgnMycxR/cnEAJnU2luGDh260vrrr7/SQBdEkiRJkiT1uPvuu5k7d26/8zFAJrU2a/31119pypQpA10OSZIkSZJUZezYsUydOnVWf/OxDzJJkiRJkiR1NQNkkiRJkiRJ6moGyCRJkiRJktTV7INMasPL06cza1S/BsSQJEmSJGlQGjlz5kAXYcBZg0ySJEmSJEldzQCZJEmSJEmSupoBMkmSJEmSJHU1A2SSJEmSJEnqagbIJEmSJEmS1NUMkEmSJEmSJKmrGSCTJEmSJElSU9dffz277LILa6yxBksttRRrrLEGH/rQh7jiiivmS5uZnH322YwbN46VVlqJoUOHMmrUKD75yU8yY8aMASh9a0sMdAEkSZIkSZI0eJ144okcddRRrLzyyuy0006sscYaPPHEE0ybNo1Jkyax4447vpH2xRdfZLfdduOyyy5jvfXWY4899mDYsGE8/PDDXH/99cyYMYN11113AI+mPgNkkiRJkiRJquvXv/41Rx11FNtuuy2/+c1vGDZs2DzrX3nllXm+H3bYYVx22WUceeSRnHjiiSy22GJN0w8WkZkDXQZpUIuIKaOHDBlz6YgRA10USZIkSZI6buTMmXWXv/7666yzzjo8+uijzJo1i1VWWaVpPvfeey/rrrsuY8eO5ZZbbiEiFkRx5zF27FimTp06NTPH9icfa5BJkiRJkiRpPjfeeCMzZ85k1113ZcUVV+Tyyy9n+vTpLL300rz3ve9ls802myf9L3/5S15//XX23ntv5syZw6WXXsqDDz7I8OHD2WabbVhnnXUG6EhaM0AmSZIkSZKk+fzlL38BYLXVVmPMmDHccccd86zfaqutuOiii96oWVZJP3v2bNZee22efPLJN9JGBF/60pc45ZRTWHzxxRfSEbTPUSwlSZIkSZI0n8ceewyAU089lblz5/KnP/2JZ599lunTp7Pddttx3XXXsdtuu82X/uijj2aTTTbhjjvu4Nlnn+Xqq69m7bXX5ic/+QknnHDCgBxLKwbIJEmSJEmSNJ/XXnsNgMzkoosuYsKECSy33HJssMEGXHLJJay55ppMnjyZm266aZ70a6yxBpdccgmjR49mueWWY5tttuGiiy5iscUW4+STT+bll18esGNqxADZAhARkyKiV6MfRERGxKQFVKR29j8rImYN1P4lSZIkSdLgsuKKKwKw1lpr8e53v3uedUOHDmW77bYD4NZbb50n/fbbb8/QoUPnSf/ud7+bUaNG8eyzz3L33Xcv6KL3mgGyQSwiji0DZ+MGuiySJEmSJKm7rLfeegCssMIKdddXAmJz587tU/rBxADZ4LE+8NmBLoQkSZIkSRIUnfAvscQS3HPPPXWbRU6fPh2AkSNHAjBhwoR5lld76aWXuOeee+ZJP5gYIBskMvPvmfnAQJdDkiRJkiQJYOWVV2b33Xdn9uzZHH/88fOsu+qqq7jyyitZfvnl2X777QHYYYcdWGuttbjyyiu56qqr5kl/wgknMHv2bLbeemtWX331hXYM7TJAViUilouIlyPizzXLh0bEi2Vzx71q1n25XL5fnfyWiIhvRMQ9EfFSRDwYEd+NiCF10s7TB1nZH9gx5ddry/VZ27dZRCwTEUdGxF8j4vmIeC4iboqIT/fxHCwTEd+PiAfKMv8zIo6IiGiQ/pMRcV1EzI6IuRFxR1mepVodY826s8r1I2uWfzQiro6If5fleTgiJkfEl+vksVJEfDsi7i7LMrvc9kN9OReSJEmSJHVreuqPAAAgAElEQVS7k08+mXXWWYeTTjqJrbbaisMPP5zddtuNHXbYgcUXX5yf/exnbzSpHDJkCGeffTZLL700O+ywA7vtthuHH344W2+9NSeddBKrrLIKp59++gAfUX1LDHQBBpPMfC4ibgXeFxHDMvPZctUHgErAZwJwbtVm25Tzq+tk+T/AlsDvgTnAjsDXgFWBfVsU57+AjwFbA2cDs2oTRMQKwDXAe4CpwC8ogp7bAf8TERtk5rda7KfaksAfgRFlmV8ty/AdYGnguJr9TwSOBJ4oj/U5YAdgIrBdRHwwM1/pxf5rj29/4DTgEeDScj+rAhtRnL+fVKV9BzAJGAlcD/wBWBbYCfhDRByQmT/ra1kkSZIkSepGq666Krfccgsnnngil1xyCTfffDPDhg3jwx/+MEceeSTvf//750m/xRZbcNttt3Hcccdx7bXX8swzz7Daaqux//77c9RRR7HmmmsO0JE0F5m9GmzxTS8ijgeOAnbKzMvLZd8G/hO4DnhnZr6tXL4Y8BgwOzPXrspjEkVgayrwwcx8qly+LHA7MAp4a2Y+UrVNApMzc1zVsmMpapGNz8xJdcp6FrA3cERmfq9q+dLAb4EPAWMy869tHPcs4B0UgbFdMnNuuXxVYEaZbJVKwCsiNgNuBB4E3ls5lohYAriEIjD1zcyc2OwY6xzLqMycVS6bAowG3paZj9WkXzkzn6j6PgnYCtgjMy+oWr4CReBsPWBkZj7a5BxMabDqXaOHDFnm0hEjGm0qSZIkSdIia+TMmQNdhD4bO3YsU6dOnZqZY/uTj00s51epCTahatkEYApwMbBmRKxbLt8YGE792mNQBK6eqnzJzOeB8ynO+yb9KWREDAf2BG6rDo6V+3kROAIIYI9eZn1QJThW5vUY8DtgeYogU0WlSemJ1YG+zHwVOAx4Hfh8L/ddz6vAfLXQaoJj76YISF5cHRwr0z1DEWRcGtilA+WRJEmSJElvMjaxnN9NwFzKAFlELA+MAb5H0ZyRct0MeppXXkN9t9VZ9mA5X7Gf5dwUWBzIsqZZrSXL+fq9yHN2Zv6zzvJ6ZR5Tzuc79sycERH/AkZFxAplkKovzgd+CNwZERcCk4E/Z+bjNek2K+fLNzgXq5TzpueiUbS5rFk2pt46SZIkSZK06DNAViMzX46IG4Bty+aFm1EEoq7OzLsj4mGKANlPy3nSIEDWIDD0ajlfvJ9FHV7ONy2nRpbrRZ6NAln1yrx8Of93g23+Dby9TNenAFlmnhwRTwBfBg4CDqEICE4G/jMzKwHIyrn4YDk10ptzIUmSJEmSuoRNLOu7hqJ54jYUQbCXgMrIltcC48tRGrcE7qztH2shmV3Of5SZ0WQav4D332hs1jVq0kERTGwUlF2h3sLMPCcz308RBPsw8HOKvsauLAOY1fs4uMW5aDUwgiRJkiRJ6kIGyOqr7odsG4pmfS9WrVsJ+BLFKImN+h/rhNfKeb3aZrdS9PO15QLcfzPTyvm42hURsQ6wJjCzphbd08Db6qRfnKI/t4Yy85nMvCIzvwCcRXENKsd+czkfqHMhSZIkSZIWYQbI6ptC0SxwZ2AD5g2CVT4fWc4b9T/WCU+W87fXrihrrZ0PbBIRR5WjR84jItaOiFELqGy/KOffiohKH1+VYNcPKO6tn9dscyvw9oj4UM3yb1GMoDmPiNi+3nEBlZpjLwCUTS2vBz4REfvVSU9EbFhV40ySJEmSJOkN9kFWR2a+XvZztXO56OqqdQ9ExL3A2hQ1vCYvwKJcS1FL7NsRMZqiBhaZeWK5/kDgncDxwF5l32mPAiMoOqTfFPg00PHxWjPzxoj4HvA1YHpEXAQ8D+wAjAZuAL5fs9kPgO2A35Wd7j8FbA6MAiYxf220C4AXy+OaRdHsdcvyuKYAf6pKuwdFsPLnEXEQcAtFkHNNYKOyTJsBA9EcVpIkSZIkDWLWIGusEhSbw/yjUVbWTcnM2SwgmXk3sDfwCEVH9SeUU2X9HGBr4KvAE8AuwKHAeOBZ4D+AqxZg+Y6gCMDdA3yWoiP9xShqhH0wM1+uSX818DHgTuBT5bHNAt4L3F9nF1+nGFV0DMXx70sxOucRwPjMfKUq738BY4FvUgQuP1OWZ3PgAeAA4I7+H7UkSZIkSXqzicwc6DJIg1pETBk9ZMiYS0eMGOiiSJIkSZLUcSNndrzh2UIzduxYpk6dOjUzx/YnH2uQSZIkSZIkqasZIJMkSZIkSVJXM0AmSZIkSZKkrmaATJIkSZIkSV3NAJkkSZIkSZK6mgEySZIkSZIkdTUDZJIkSZIkSepqBsgkSZIkSZLU1QyQSZIkSZIkqasZIJMkSZIkSVJXM0AmSZIkSZKkrmaATJIkSZIkSV3NAJkkSZIkSZK62hIDXQBpUTBk9GhGTpky0MWQJEmSJEkLgDXIJEmSJEmS1NUMkEmSJEmSJKmrGSCTJEmSJElSVzNAJkmSJEmSpK5mgEySJEmSJEldzQCZJEmSJEmSupoBMkmSJEmSJHU1A2SSJEmSJEnqagbIJEmSJEmS1NUMkEmSJEmSJKmrLTHQBZAWBS9Pn86sUaMGuhiSJEmS1DEjZ84c6CJIg4Y1yCRJkiRJktTVDJBJkiRJkiSpqxkgkyRJkiRJUlczQCZJkiRJkqSuZoBMkiRJkiRJXc0AmSRJkiRJkrqaATJJkiRJkjSPkSNHEhF1p9VXX32etPvss0/DtJVpwoQJA3QkUnuWGOgCSJIkSZKkwWf55ZfnkEMOmW/5csstN8/3j33sY4wcObJuHueeey733XcfO+yww4IootQxkZkDXQZpUIuIKaOHDBlz6YgRA10USZIkSeqYkTNnNl5XBrxmzZrV5/yfeeYZRowYwWuvvcZDDz3Eyiuv3Oe8pEbGjh3L1KlTp2bm2P7kYxNLSZIkSZLUceeeey5z587lE5/4hMExDXo2sZQkSZIkSfN56aWXOO+883jggQdYdtll2Wijjdhqq61YfPHF29r+Zz/7GQD777//giym1BEGyCRJkiRJ0nweeeQR9tprr3mWjRo1ijPPPJOtt9666bY33XQTd9xxB+uuuy7jx49fkMWUOsImlpIkSZIkaR777rsvV199NY888gjPP/88d9xxBwcccACzZs1ihx124Pbbb2+6/emnnw7AF77whYVRXKnfDJBpkRERx0ZERsS4XmwzKSIciUKSJEmSeuGYY45hm222YbXVVmOZZZZh9OjRnHrqqRx66KHMnTuXY489tuG2s2fP5le/+hVDhgxhn332WWhllvrDAJkkSZIkSWrLF7/4RQCuu+66hmnOO+88XnjhBTvn1yLFAJkkSZIkSWrLqquuCsDzzz/fME2lc/4DDjhgoZRJ6gQDZJIkSZIkqS033XQTAGuttVbd9bfccgu333476667LuPGjVuIJZP6xwDZIiIi3lX2v3VNkzR3RMQrEbF6zfLtIuKKiHgiIl6KiHsj4vsRsUKdPMZHxOkRcVdEzImIuRExPSKOiYil66R/o1+wiNgjIm6JiOciYlabx/XOiDgnIh6KiJcj4uHy+zvb2b4qn09FxJSyvI9FxLkRMaI3eUiSJEmS4M477+Spp56ab/n999/PgQceCMCee+5Zd9tK5/z777//giugtAAsMdAFUHsy8+8RcS0wPiLWzcwZ1esjYnNgNHBxZj5Stfxo4DjgKeAy4DFgI+BwYMeI2Cwz51RldQTwLuBG4HJgaeADwLHAuIjYNjNfq1PEw4APApcC1wLLtzqmiNgU+BMwDPhf4K5y358Bdo6ICZl5Wxv5/AdwMvAMcE453648htmttpckSZIk9fj1r3/Nd77zHcaPH8+oUaMYNmwY9957L5dffjkvvvgiO+64I4cffvh8282ZM4cLL7yQIUOGsPfeew9AyaW+M0C2aPkJMB7YnyLAVa0Snj+tsiAixlMEx24CdszMZ6rW7QOcWa7/j6p8vgzMzMx5Rn6MiBOAbwG7AhfWKds2wGaZOa2dA4mIoAhmvQXYMzPPr1q3O3ABcF5E/J/MfL1JPiOB7wBPA2Myc1a5/Ejg18An2imPJEmSJKkwfvx4/vGPfzBt2jRuuukmnn/+eVZYYQW22GIL9tprL/baay+Kn3TzOv/883n++ef51Kc+Zef8WuRETRxEg1hELAHcDywFvDUzXyqXrwA8XE7vrAS3IuIS4GPA6My8s05+08p8Vm1j38OBJ4AzM3O/quXHAscA/5WZ/9Fg83r5fQC4AbgpMzevs/56YAtg68y8rmZf4zNzUrnsm8CJwPGZeUxNHmsB9wCLZeb8b+/59zmlwap3jR4yZJlLR9hiU5IkSdKbx8iZMwe6CFK/jR07lqlTp07NzLH9yccaZIuQzHw1Is4AjgZ2Af6nXLUXMBQ4vabm12bAK8BuEbFbnSyHAKtExPDMfBIgIpYFDgY+DqxL0fyxOrj01gbFu7WXhzOmnDfqU+0aigDZe4DG4wf35DO5dkVm3hcRDwLv6GXZJEmSJElSFzFAtug5HfgGcAA9AbL9gZcpmkxWG05xjY+hueWAJyNiSYrA1HuB6RRNKR+nCLJR5rNUgzweabC8kUofZf9usL6yfL6BBBrk82iD9Y/QZoCsUbS5rFk2pt46SZIkSZK06DNAtojJzIci4lLg4xGxPrAiRef8F2bm4zXJZ1M0L1ypzex3pgiOnZ2Z+1SviIg1aB5o621b3Urn+as3WL9GTbpW+awGzNeMtEn+kiRJkiRJACw20AVQn/yknO9Pnc75q9wMrBgRG7SZ7zrl/OI667Zuv3htqXTmP67B+sryqS3yqayfr3xlH2Rv623BJEmSJElSdzFAtmi6GpgB7A18EpiRmdfWSfejcv6ziJivh/mIWDYi3l+1aFY5H1eTbi3gu/0sc60/A/8AtoiIXWv2tyuwFcUx3tAin/MpmoB+tRzRspLHYsD38R6XJEmSJEkt2MRyEZSZGRGnAieXi+rVHiMzr46IrwPfBu6JiCuAmRR9jr2DotbVDcD25SaXAv8EDo2IDSlqeb0d2Am4vPzcyWPYG7gKuDAifgf8HViPYuTNZ4HPZubrLfKZVR7jD4FpEXEhRbPL7Sj6L/sbsFGnyi1JkiRJkt58rF2z6DoLeB14CTi7UaLM/C5FbazLgQ8AhwC7UYxGeTrwraq0zwPbUHT+vwFwEEVw6QRgz04fQGbeAmxa7m8z4D+BzYFfApuW69vJ52RgD4rg3z7AfhSDDGwOPN3pckuSJEmSpDeXyOxt3+oaDCJiHHAtcF5m7jXAxXlTi4gpo4cMGXPpiPlaqUqSJEnSImvkzJkDXQSp38aOHcvUqVOnZubY/uRjDbJF19fK+Y8HtBSSJEmSJEmLOPsgW4SU/YLtBIwFdgAua7cZoiRJkiRJkuozQLZoGQtMBOYAvwa+PLDFkSRJkiRJWvQZIFuEZOZZFJ3zS5IkSZIkqUPsg0ySJEmSJEldzQCZJEmSJEmSupoBMkmSJEmSJHU1A2SSJEmSJEnqagbIJEmSJEmS1NUMkEmSJEmSJKmrGSCTJEmSJElSVzNAJkmSJEmSpK5mgEySJEmSJEldbYmBLoC0KBgyejQjp0wZ6GJIkiRJkqQFwBpkkiRJkiRJ6moGyCRJkiRJktTVDJBJkiRJkiSpqxkgkyRJkiRJUlczQCZJkiRJkqSuZoBMkiRJkiRJXc0AmSRJkiRJkrqaATJJkiRJkiR1NQNkkiRJkiRJ6moGyCRJkiRJktTVlhjoAkiLgpenT2fWqFEDXQxJkiSpI0bOnDnQRZCkQcUaZJIkSZIkSepqBsgkSZIkSZLU1QyQSZIkSZIkqasZIJMkSZIkSVJXM0AmSZIkSZKkrmaATJIkSZIkSV3NAJkkSZIkqaFzzz2XiCAiOOOMM+ZbP2fOHCZOnMjGG2/MiiuuyPLLL8+GG27IUUcdxeOPPz4AJZak3jNAJkmSJEmq68EHH+SrX/0qyy23XN31s2fPZtNNN+Wb3/wmSy65JPvssw/77bcfQ4YM4cQTT2TMmDE8+uijC7nUktR7BsgkSZIkSfPJTPbdd1+GDx/OF7/4xbppTj/9dGbMmMG+++7LX/7yF370ox/xox/9iClTprD33nvzr3/9i9NOO20hl1ySes8AmSRJkiRpPqeccgrXXHMNZ555Jssuu2zdNPfddx8AH/nIR+Zb99GPfhTAZpaSFgkGyCRJkiRJ87j77rv5+te/zsEHH8xWW23VMN0GG2wAwOWXXz7fussuuwyAbbfddsEUUpI6aImBLoAkSZIkafB49dVX2WuvvXj729/OxIkTm6b9/Oc/zy9/+Ut+/vOfc8cdd7DFFluQmVx//fXcddddnHTSSey8884LqeSS1HcGyCRJkiRJbzj++OOZNm0aN9xwA0OHDm2adumll+aaa67h4IMP5rTTTuPWW299Y92uu+7Kxz72sQVdXEnqCJtYSpIkSZIAuPXWW5k4cSKHHXYYm222Wcv0Tz75JNtttx2//e1vueCCC3jyySd54oknuOCCC7j++ut53/veN0/QTJIGKwNkC1FEZERMGoD9jiv3fezC3rckSZKkRUOlaeW6667LCSec0NY2hx12GJMnT+b0009n9913Z6WVVmL48OHsvvvunHbaaTz33HN87WtfW8All6T+M0CmtkTEpIjIgS6HJEmSpAXjueeeY8aMGdx9990svfTSRMQb03HHHQfAF77wBSKCQw45BOjpiH/8+PHz5VdZNmXKlIV0BJLUd/ZB1h1uBdYHnhjogkiSJEkanJZaaik+97nP1V03depUpk2bxhZbbMF66633RvPLl156CYDHH3+cYcOGzbPN448/DsCQIUMWYKklqTMMkHWBzHwB+PtAl0OSJEnS4DV06FDOOOOMuuuOPfZYpk2bxt57783nP//5N5ZvueWW/P73v+e4447jzDPPZLHFikZKr732GscccwwAEyZMWPCFl6R+WuSbWEbEchHxckT8uWb50Ih4sex7a6+adV8ul+9Xs3yliPh2RNwdEXMjYnZEXB0RH2qy/09HxLUR8XS5v7sj4lsRsVQvjuE/I+L1iPhzRKzURvosmzyOiIhzI+KxsrxTImKPOukb9kFWHvNJETE9Il4oj/n2iPhORCwbESPLppVbV+27Mk2qLVOD8p5Vrh9ZtWxkueysiFg3Ii4sj+P1iBhXU762rklEDImIgyJiank9XoiIWRHxu4jYttV5lSRJktQ73/3ud1l++eU555xz2GijjTjooIM46KCD2GijjTj//PNZeeWVmThx4kAXU5JaWuRrkGXmcxFxK/C+iBiWmc+Wqz4AVIJUE4BzqzbbppxfXVkQEe8AJgEjgeuBPwDLAjsBf4iIAzLzZ9X7joifA/sB/wJ+AzwDvB84AZgQER/MzFcblT0iFgP+C/gqcAmwR2a+2OahrwjcWO7zTGAF4JPA+RHx1sz8fqsMImIUcC3wDmAK8FOKoOm6wH8Ap5b5HwfsU6Y7riqLWW2WtZm1gVuAGcD5wFBgTlm+3l6Ts4BPA9OBc4C5wAhgC2B74E8dKK8kSZKk0oYbbsi0adP47ne/y1VXXcVpp51GRPC2t72NAw88kK9//eu89a1vHehiSlJLkbno97seEccDRwE7Zebl5bJvA/8JXAe8MzPfVi5fDHgMmJ2Za1flMQnYiiJIdUHV8hUogjTrASMz89Fy+T4UgalLgM9k5tyqbY4FjgEOycz/rlqewOTMHBcRS1MEhD4B/Bg4ODNfb/N4Kxft18CnKtuVAa8pwHLAuzLzvnL5OIpA2HGZeWxVPn8GNge+kZnfrtnHysBzlYBdeX62zsxoUqbJmTmuzrqzgL2BUZk5q1w2EphZJvl2Zn6jznaTaPOaRMTywNPAVOB9mflaTV7DM/PJemWvStOo99B3jR4yZJlLR4xotrkkSZK0yBg5c2brRJK0CBg7dixTp06dmplj+5PPIt/EslSpCVbduH0CRbDoYmDNiFi3XL4xMJx5a4+9m6IJ4cXVgRiAzHyGIti1NLBL1aqDgVeB/aqDY6UTgCeBz9QrbNmM8k/Ax4GvZ+ZX2w2OVXkNOKJ6u8ycCZwCLAns1WjDsgxjKYJjfwW+W7s+M5/oRW22/niUeWulAX26JgkE8BIw37lsFRyTJEmSJEnda5FvYlm6iaI53QSAsjbRGOB7wDVlmgkUzfgqzSuvqdp+s3K+fL1+uoBVyvn6Zf7LAO+mGBXykIi6lapeqqSvsRrwZ2AtYK/MPL/5oTX0QBkQqzWJInj0nhbbv7+cX9mH4Fwn3Z6ZL9VZ3qtrkplzIuJS4CPAXyPiYopmmbeUgxS01CjaXNYsG9NOHpIkSZIkadHzpgiQZebLEXEDsG1ErEoRXFkcuDoz746IhykCZD8t58m8AbLh5fyD5dTIcuV8RYraSqtQBKN6Y3XgLRT9ll3fy22rPdpg+SPlfPkW269Qzh/qRxk64ZEGy3t7TQB2B44A9qCnVtqLEXERcHileawkSZIkSVK1N0sTSygCXkFRQ2wCRQ2uysiW1wLjy5EltwTuzMzHqradXc4PzsxoMu1bk35ai/T1qpbdTtEf11uB6yJirT4e72oNlq9eU8ZGninnneoxM2kccF2hwfLKdvX09pqQmXMz89jMXBd4O7AncEM5v6h3hyNJkiRJkrrFmylAVt0P2TbAn6v60LoaWAn4EsUoiFfXbHtzOd+ynR1l5nPAncAGZX9ivZKZ5wGfohhh8bqq/tF64+1lR/e1xpXzaS22rxzzduXABa28BhARizdY/zTwttqFZfqN28i/Ufnauia1MvPBsvnqdsA9wBYRMbzFZpIkSZIkqQu9mQJkUyhqRe0MbMC8QbDK5yPLeXXzSjLzNormjp+IiP3qZR4RG5bNNytOBoYAvyhHVaxNv2JENOy3KjMvouhgfmVgckRs0OTY6lkc+G51cKscxfIgisEDzmu2cWZOAW6kCF4dUaf8w8uRNisqndy/vUGWt1IE7T5Us/xbwDualaVB+Xp1TSJilYh4X51kywLDKM7Jy70thyRJkiRJevN7U/RBBpCZr0fEZIoAGVQFyDLzgYi4F1iboibU5DpZ7EEROPt5RBwE3EIRcFsT2AgYTdG32WNlnr8oR4L8MnBvRFwJPEBRU20UsBVwJvDFJmW+NCJ2Bi4BJkXEtpl5e5uH/DfgfcCUiPgjRZ9ju1M0Z/xaZt7bRh57UnTqPzEidik/B/BO4EPAu4BZZdqrgd2A30TEFRSDItyfmeeW639AUVvrdxFxIfAUxSiZo8p8x7V5XNV6c03eCtwcEXcDU4EHKfp624mi2ekpmflsH8ogSZIkSZLe5N5MNcigJyg2B7itwbopmTlf/1yZ+S9gLPBNiiDaZyhqY21OEfg6ALijZpuvUIyaeBOwLXAo8FGKYNX3gf9qVeDMvBLYEVgauDYiNm21Tenpsmx3AvsC+wAzgc9k5vfbyaAcBbMy2ucw4EDgcxS1xH5IGQwsnQF8m+LYvgacUKat5HU18LGyPJ+i6GdtFvBe4P42j6m2fL25JrMoBkx4BBhPcS0+QXFO9gAO6UsZJEmSJEnSm19kNuojXYNVRCQwOTPHDXRZukFETBk9ZMiYS0eMGOiiSJIkSR0xcubMgS6CJHXE2LFjmTp16tTMHNuffN5sNcgkSZIkSZKkXjFAJkmSJEmSpK5mgEySJEmSJEld7U0zimU3ycwY6DJIkiRJkiS9WViDTJIkSZIkSV3NAJkkSZIkSZK6mgEySZIkSZIkdTUDZJIkSZIkSepqBsgkSZIkSZLU1QyQSZIkSZIkqasZIJMkSZIkSVJXM0AmSZIkSZKkrmaATJIkSZIkSV1tiYEugLQoGDJ6NCOnTBnoYkiSJEmSpAXAGmSSJEmSJEnqagbIJEmSJEmS1NUMkEmSJEmSJKmrGSCTJEmSJElSVzNAJkmSJEmSpK5mgEySJEmSJEldzQCZJEmSJEmSupoBMkmSJEmSJHU1A2SSJEmSJEnqagbIJEmSJEmS1NWWGOgCSIuCl6dPZ9aoUQNdDEmSJKkjRs6cOdBFkKRBxRpkkiRJkiRJ6moGyCRJkiRJktTVDJBJkiRJkiSpqxkgkyRJkiRJUlczQCZJkiRJkqSuZoBMkiRJkiRJXc0AmSRJkiSpoXPPPZeIICI444wz5ls/Z84cJk6cyMYbb8yKK67I8ssvz4YbbshRRx3F448/PgAllqTeM0Am6f+zd+dhV5X1/sff30Qm7ajgSKGQpWWkAienlFAwtUFJTeuUCormr5w6VmLWEbSwknMsG04qTllmp+N00JwCUZHI5CFzwBQFwwFUVBQnRL+/P9Z6cLPZz8C4gf1+XddzLfa97nWv79oP1dWH+76XJEmSVNPs2bM58cQT2XDDDWuenz9/Ph//+Mc544wzWH/99Rk6dChHH300HTt25Pvf/z79+vVj7ty5q7lqSVp2BmSSJEmSpKVkJsOGDaN79+4cf/zxNftceOGFPPLIIwwbNoy//vWvnHfeeZx33nlMnTqVo446iieffJILLrhgNVcuScvOgEySJEmStJTzzz+fCRMmcOmll7LBBhvU7PP4448D8LnPfW6pcwceeCCAyywlrRUMyCRJkiRJS5g+fTojRozg5JNPZsCAAS32++hHPwrAjTfeuNS5G264AYDBgwevmiIlaSXqUO8CJEmSJElrjkWLFnHEEUew9dZbM3r06Fb7Dh8+nN/97ndcfPHF3H///ey5555kJnfddRcPPfQQP/jBDzjooINWU+WStPwMyCRJkiRJi5111llMmzaNSZMm0aVLl1b7du7cmQkTJnDyySdzwQUXcM899yw+d+ihhzJkyJBVXa4krRQNu8QyIoZGREbE0HrXohUXEZeVv89e9a5FkiRJWlvdc889jB49mlNPPZXdd9+9zf7z5s1jv/3247rrruOqq65i3rx5PP/881x11VXcdddd7LrrrkuEZpK0pnIG2dDmFmEAACAASURBVAqIiFkAmdmrvpVIkiRJ0oppXlq53XbbcfbZZ7frmlNPPZU77riD66+/fvGm/ACHH344nTt3ZsiQIXz7299m4sSJq6hqSVo5GnYGGXAt8JHyKEmSJEkNbcGCBTzyyCNMnz6dzp07ExGLf0aNGgXAscceS0RwyimnAO9uxL/33nsvNV5z29SpU1fTE0jS8mvYGWSZOR+YX+86JEmSJGlN0KlTJ4455pia55qampg2bRp77rkn22+//eLll2+++SYAzz33HO9973uXuOa5554DoGPHjquwaklaOdaJGWQR0avcf+qyiPhwRFwXES9ExKsRMSkiPlXjmhb3IIuI90fE+RHxaES8UY51T0R8rzw/MCIS2AbYphyn+eey6ppaqHliOUZl28DympERsUtE3Fjee4m9tcr6fh4Rj0fEmxExLyL+LyI+XuM+742I70XEAxHxckS8EhGPRcTvI6J/O77b35X3/1BV+6/L9vE17vdWRNxZY6wvRcTtEfFi+b1Oj4jvRkSnFu794fJ3Ort8zrkRcWVEbN9W3RVj7BQRT5XPvm97r5MkSZIaTZcuXRg7dmzNn+blk0cddRRjx47l8MMPB2CvvfYCYNSoUbzzzjuLx3r77bc588wzARg0aNBqfhJJWnbr2gyy3sCfgQeAC4CtgMOBmyLi3zLz920NEBH/CtwCdAPuBK4BugI7ACOBs4FZwCjglPKyn1QM8beV8By7A6cDk4BLgE2BhWV9/YBby/puKevbFBgCTIqIz2fmH8u+AdwM7EHxvYwFFgE9gYHAXUBb853HA18EBgGPVrQ3z6HeIyI6Z+Yb5edPUvy9qg7OLgaOBp4sa34J2I3i+xwUEftm5qKK/vuX/dYHxgEzgPcDBwOfiYi9M7OptcIjYlA5xqvAgMxcGb8bSZIkSaUf/ehHTJ48mV//+tdMnTqVffbZB4Dx48fz0EMPsemmmzJ69Og6VylJbVvXArIBwJjM/FZzQ0T8nCIc+lVE3JSZL7d0cUR0BP5AET59OTOvrDrfEyAzZwEjm2efZebIlfsYfAo4PjMvqLp/B+B/gA2BvTPzjopzPYC/AhdHRK/MfBPoQxGOXZeZn68a6z3ARu2oZUJ5HAT8qrx2e4qw6jZgX+ATvBuIDaq6jvJ7Oppiv7cvZ+brFedGAmcCXwd+WrZtAvwOeI0i2Hqoov9Hgb9QhH39Wio6Ir5CES7OAA7IzCfaetCIaCks/HBb10qSJEmN6GMf+xjTpk3jRz/6EbfddhsXXHABEUHPnj054YQTGDFiBO973/vqXaYktWmdWGJZYT5wVmVDZt4L/BbYGPh8rYsqfA7oBfxfdThWjjV75ZTZpr9Vh2OlzwDbAj+rDMcAMvNp4MfAlrwbUjV7veozmflOZr7YViGZ+TjFjLm9yxlpVIz/H8DbVfcbRDFj6y8VbSdTzFw7ujIcK50NzAO+XNF2JMXv68zKcKys50HgIqBvROxQq+aIOA34dVnDJ9oTjkmSJElq2ciRI8lMhg8fvtS53r1786tf/YrHHnuMN998kzfeeINHH32Un/3sZ4ZjktYa69oMsqbMfKVG+0TgKKAvcHkr1+9WHm9ayXUtq3taaN+9PG5Tzryq1rxP2EeAPwIPUSz5/FJEbANcT7Fs897MXLgM9UygmAG2MzAN2Ad4JjOnlLOuBgFExGYUs9ZubR4/IroCOwHPA6e8m7Et4c2y5urn3KmF59yu4jkfqjp3HsVy06uBr1Qs/WxTZtbck618xhZnq0mSJEmSpLXbuhaQzW2hfU55bGtJ4cbl8amVU85ym9NCe/fy+IU2rt8QIDPfjoh9KGZ6HQr8qDz/SkRcDpyemQvaUc94ioBsUETcR7F/2U0V574dERtRBGfBkvuPbVK2bUaxlLI9mp/z2Db6bVijbUB5vGFZwjFJkiRJktS41rUlllu00L5leZzfxvUvlceVMQ+4+RUuLYWQG7fQDpAttDfXf1BmRis/oxYPlPliZn4jM3tSzDAbDjwMnAD8dzufpXk/scEUs8i6824INgFYj2LT/qX2H6uoeVobNUeNa3Zq45paswGHUOw7dnFEtBWwSZIkSZIkrXMBWb+IeG+N9oHlcVob108pjwe0835vU4RDtTTv79Wz+kRE/AvvLhNcFs317bUc15KZMzLzYoo3TS4ADmrndXMoljLuBexfNjeHYHdTLJEcRDGD7EUqvudyhtqDwEcjols7S12R55xNMYvsH8AFEfH15RhDkiRJkiQ1kHUtINuIYjnhYhHxrxQbwM+neItia8ZRbEh/YER8qfpkRFTPLJsHbBYRXar7lnuhPQx8onIz+YhYD/gvYKlr2uF64DHg6xHx6VodImL3ct8vIqJ3+dbHapsAnaixeX8rJgBdKTbcfzQz/wlQbrr/Z+AwihcITMzMd6qu/S+gI3BJRCw1cy4iNomIyj2+LqWYzXdmROxSo/97ImJgS4Vm5jMUIeD9wM8j4tR2P6UkSZIkSWo469oeZHcCwyNiV4qZTVsBh1MEgV/NzJdbuzgzF0bEF4BbgSsj4qsUs5k6U2wIP4glv7PxwMeBmyPiToqZVPdl5rjy/LnAxcDdEfEH4A2KpYjrA/dRbF7fbpn5VkQcDNwC3BgRkyk24X+NYqbax4EPlM/9Wjn+teUm8w8AT1PsBXZQWcOPlrpJy8ZTLMvcHLimxrmBFX+urvuSiOgPfA14LCJuAf4JdAN6U8z4uhQ4vuw/LyIOpQg0p0TEeIpZaO8AW1Ns4t+d4vdSU2Y+FxF7U3xXYyKic2b+YBmeV5IkSZIkNYh1LSCbSRGy/LA8dgKagLMy85b2DJCZ90bEzsAIiqWWewCvUOxrVb3J/Pcp9hL7HPAJiuWWl1PMRGsOhgL4d4q3aL5IMQvsOxRvWVxmmfn3iNipHPOzwDCK4OgZiqWNZ1K8MRLgXuAcitlU+1PMHHsOmAqcn5nL8rbOieV93sOSe4xBEYqdXf65+lxz3V+PiJsofi+DKb63FyiCsnOB31T1Hx8ROwLfBPajWG65kCLkm0A7vr/MfCEiBlG8UOD7ZUj2vTafVJIkSZIkNZTIbGk/+LVHRPSiCMcuz8yhdS1G65yImNqnY8d+43r0qHcpkiRJ0krRa+bMepcgSStF//79aWpqasrM/isyzrq2B5kkSZIkSZK0TAzIJEmSJEmS1NAMyCRJkiRJktTQ1olN+jNzFhD1rkOSJEmSJElrH2eQSZIkSZIkqaEZkEmSJEmSJKmhGZBJkiRJkiSpoRmQSZIkSZIkqaEZkEmSJEmSJKmhGZBJkiRJkiSpoRmQSZIkSZIkqaEZkEmSJEmSJKmhGZBJkiRJkiSpoXWodwHS2qBjnz70mjq13mVIkiRJkqRVwBlkkiRJkiRJamgGZJIkSZIkSWpoBmSSJEmSJElqaAZkkiRJkiRJamgGZJIkSZIkSWpoBmSSJEmSJElqaAZkkiRJkiRJamgGZJIkSZIkSWpoBmSSJEmSJElqaAZkkiRJkiRJamgd6l2AtDZY+MADzOrdu95lSJIkSYv1mjmz3iVI0jrDGWSSJEmSJElqaAZkkiRJkiRJamgGZJIkSZIkSWpoBmSSJEmSJElqaAZkkiRJkiRJamgGZJIkSZIkSWpoBmSSJEmS1ACuuOIKIoKIYOzYsUucGzhw4OJzLf0cc8wxdapckla9DvUuQJIkSZK0as2ePZsTTzyRDTfckAULFix1fujQoQwcOLDmtT/72c944YUXOOCAA1ZxlZJUPwZkkiRJkrQOy0yGDRtG9+7dOfjggxkzZsxSfYYOHVrz2n/84x+MGjWKLbbYgoMOOmgVVypJ9eMSS0mSJElah51//vlMmDCBSy+9lA022GCZrr3wwgsBGDZsGOuvv/6qKE+S1ggGZJIkSZK0jpo+fTojRozg5JNPZsCAAct07cKFC/n1r39NRHDssceuogolac1gQCZJkiRJ66BFixZxxBFHsPXWWzN69Ohlvv7qq6/m+eefZ/DgwXzgAx9YBRVK0prDPcgkSZIkaR101llnMW3aNCZNmkSXLl2W+frm5ZXHHXfcyi5NktY4ziCTJEmSpHXMPffcw+jRozn11FPZfffdl/n6Rx99lDvuuMPN+SU1DAOyOoqIXhGREXFZvWtZ00TEZeV302sFx5kYEblyqpIkSZLWfM1LK7fbbjvOPvvs5RrjwgsvXPz2Szfnl9QIDMgkSZIkaR2yYMECHnnkEaZPn07nzp2JiMU/o0aNAuDYY48lIjjllFOWun7hwoVcfvnlbs4vqaG4B1l9PQV8BJhf70LWQKcDP6T4jiRJkiS1U6dOnTjmmGNqnmtqamLatGnsueeebL/99jWXX1577bU899xz7Lvvvm7OL6lhGJDVUWa+BTxc7zrWRJn5DPBMveuQJEmS1jZdunRh7NixNc+NHDmSadOmcdRRRzF8+PCafZo35//qV7+6ymqUpDWNSyzbEBGHRcSdETE/Il6PiPsj4vSI6FSj76zyp2tEnBsR/4yINyNiRkScFhFR1b/FPcgiYquI+EU53sKIeC4iromI/jX6Di3HGRoRe5f7br0SES9HxI0R8ZFleN6IiKMiYnJ5zzciYnZE3BIRh9fo3z8iro6IZ8tnfSIifhkRW7Uwftfyu7i3rHFBREyPiPMjYouKfjX3ICuf8eqIeLz8fbwcEXdHxFfa+4ySJEmSapsxYwa33347W2yxBQceeGC9y5Gk1cYZZK2IiNEUS/2eB64EFgAHAKOB/SJi33IWWKX1gVuBHsBNwCJgCMVywc7AqHbctzcwqRxjAvA7oCfwBeAzEXFIZt5Q49LPAgeV9/0VsAPwaeDjEbFDZj7fjsf+QfnMM4H/oVj+uRXw8fL+v6+o87PA1UAA/ws8AfQH/h9wUER8IjNnVfTfBLgd2An4B3AJsBDYFjgauAaY20Z9/w08BNxJMcOse/mMV0TE9pn5vXY8oyRJkqQaLrroIjfnl9SQItMX/NUSEbsDk4HZwC6ZOads7wBcSxFGnZGZoyuumQVsQxFQHZKZr5ftmwOPlN02aw7VytlRM4HLM3NoxTi3AJ8CvpuZP6ho34MiGJoPbJOZC8r2ocClwNvAfpk5vuKac4ARwGmZ+eN2PPc84HVgu8x8rercps0hW0RsCMwCNgEGZuZdFf1OowgEb8vMT1W0Xwl8iSK8+3pmvlNx7r3AezJzfvn5MuAooHdVyLZtZj5WVVdHiu98ANArM5+qODcR+GRmLjF7r4Vnn9rCqQ/36dix67gePdoaQpIkSVptes2cWe8SJKnu+vfvT1NTU1NmLrXiblm4xLJlR5fH7zeHYwCZuQg4FXgHqL1oH05qDsfKa54Frgc2ArZv7aYR8X6KcOyfwBKBVmZOpphN1g04uMblV1WGY6ULy+Murd23ylsUYdsSqmagHUQxe+v3leFY6T8pwrN9I2JrWBwSHk4x6+ubleFYOfYrzeFYa6rDsbJtIfALihmRg9oaQ5IkSZIkqZIBWcv6lccJ1Scy8xHgSaB3RGxcdXp+Zs6oMd7s8rhJG/ftWx7vqrF8s7KevjXO3bsC9232W6AX8GBEnBMR+0fERjX6tfb9LKKY6VZZ58cp/r7dmZmvtrOWpUTE1uXebA9HxGvlPmVJsdQT4H3LO3Zm9q/1gy9SkCRJkiRpneYeZC1rDoVaepPiM8DWZb+XKtpfqt2dReVxvZVwX4DqYK7mvTNzUflugLbu2+wbwGMUM+hGlD+LIuKPwKkV4d+y1tl8fKpG33aJiA8A91CEfXdR7PU2n2K2Wy+KJZlLvTxBkiRJkiSpNQZkLWte7rclRWBUbauqfqvivrWsqvsCkJlvAz8Ffloui9wT+CLFBv0fjYiPZuaby1Fnc3i33DO8gH+nWNY5LDMvqzwREV+iCMgkSZIkSZKWiUssWzatPA6sPhERHwTeD8zMzJZmjK3offcsXwhQbe/y2LSS77uUzHw2M6/JzMMollJuC/QpT7f2/XSgCNYq67yHYt+2ARGxwXKW9MHyeHWNc59czjElSZIkSVKDMyBr2SXl8bsRsVlzY0SsB4yh+O4uXtk3zcwngdsolgyeUnkuInYF/g14keJNmitVRHSKiEFRrsmsaF+f4sUAAM1vtrwOeAH4UkTsVjXUKcAHgD9l5j8BMvM54CqKmWVjImKJv3sRsWELe51VmlUeB1Zdux8tvzBBkiRJkiSpVS6xbEFmTo6IHwPfBh6IiP8FXgUOoJhFNQk4dxXd/njgbuDciPgUxeb7PSmWOb5DscTwlVVw3y7An4BZEfEX4AmgM7Av8BHg/zJzOkBmLoiIo4E/AHdExB8o3rzZn+ItnHOAr1aNfwLFd3c8MDAibgEWAr2B/YADgYmt1PdLYBjwh4i4mmI/sz7A/sD/ULwlU5IkSZIkaZkYkLUiM0+LiGkUwc6RwPoU+5F9F/jPzFy4iu77eET8a3mfT1PMmHoZuBn4QWb+dVXclyIAPI1iGecewBDgFYpn/n+8O6uuuc7rI+ITwHcoAq6NKIKxXwFnZ+bTVf1fjIg9KGaYHQ4cR7HB/uxy7IdaKy4z/x4RewPfp/heOgD3AQdT7HFmQCZJkiRJkpZZZGa9a5DWaBExtU/Hjv3G9ehR71IkSZKkxXrNnFnvEiSp7vr3709TU1NTZvZfkXHcg0ySJEmSJEkNzYBMkiRJkiRJDc2ATJIkSZIkSQ3NgEySJEmSJEkNzYBMkiRJkiRJDc2ATJIkSZIkSQ3NgEySJEmSJEkNzYBMkiRJkiRJDc2ATJIkSZIkSQ3NgEySJEmSJEkNzYBMkiRJkiRJDc2ATJIkSZIkSQ2tQ70LkNYGHfv0odfUqfUuQ5IkSZIkrQLOIJMkSZIkSVJDMyCTJEmSJElSQzMgkyRJkiRJUkMzIJMkSZIkSVJDMyCTJEmSJElSQzMgkyRJkiRJUkMzIJMkSZIkSVJDMyCTJEmSJElSQzMgkyRJkiRJUkMzIJMkSZIkSVJD61DvAqS1wcIHHmBW7971LkOSJEnt0GvmzHqXIElayziDTJIkSZIkSQ3NgEySJEmSJEkNzYBMkiRJkiRJDc2ATJIkSZIkSQ3NgEySJEmSJEkNzYBMkiRJkiRJDc2ATJIkSVJDu+KKK4gIIoKxY8cuce7+++9n+PDh9O3bl80224xOnTrRs2dPBg8ezDXXXENm1qlqSdLK1KHeBUiSJElSvcyePZsTTzyRDTfckAULFix1furUqVx33XXstttu7LHHHmy00UbMmTOHcePGccghh/CVr3yFK664og6VS5JWJgMySZIkSQ0pMxk2bBjdu3fn4IMPZsyYMUv1+eIXv8jQoUOXan/55ZfZbbfd+M1vfsOJJ57ILrvsshoqliStKi6xlCRJktSQzj//fCZMmMCll17KBhtsULNP586da7b/y7/8C/vttx8Ajz766CqrUZK0ehiQSZIkSWo406dPZ8SIEZx88skMGDBgma9/7bXXmDBhAgAf+9jHVnZ5kqTVzCWWkiRJkhrKokWLOOKII9h6660ZPXp0u66ZMWMGv/nNb3j77beZO3cuN954I08//TSnn346O+644yquWJK0qhmQSZIkSWooZ511FtOmTWPSpEl06dKlXdfMmDGDUaNGLf7csWNHzj33XE499dRVVaYkaTVyiaUkSZKkhnHPPfcwevRoTj31VHbfffd2X7f//vuTmSxcuJAZM2Zwxhln8J3vfIcDDzyQhQsXrsKKJUmrgwHZOioiZkXErFV8j5Mi4qGIeD0iMiJOKdszIiau4nuPLO8zcFXeR5IkSeuO5qWV2223HWefffZyjbH++uuz7bbb8h//8R+cddZZ3HDDDZx//vkruVJJ0upmQLaaRMTEiMh617GyRMQXgZ8CbwA/AUYBU+palCRJktSKBQsW8MgjjzB9+nQ6d+5MRCz+aV4+eeyxxxIRnHLKKW2Od8ABBwAwceLEVVm2JGk1cA8yLa/PNh8z8+mqcx8BXlvN9UiSJEmt6tSpE8ccc0zNc01NTUybNo0999yT7bffvl3LL5966ikAOnTw/1ZJ0trO/ybX8uoBUCMcIzMfXv3lSJIkSa3r0qULY8eOrXlu5MiRTJs2jaOOOorhw4cvbp80aRK77ror66+//hL9n3vuOUaMGAHAZz7zmVVXtCRptXCJ5QqKiAMjYnxEPBMRb0bE0xFxR0R8rTzfq1xa+cnyc1b8TKwYp8V9uyLisvJ8r6r2iIgTIuLBiHgjIp6KiJ9HxEY1xji+HOM/WrjHlhHxVkTc38bzjiyfZ+/q52ntWSr3DIuIQyPinoh4LSJeiIirIuJ9Ldyvf0TcHBGvRMTLEfGniGjxn/MiYq+IGBcRT5a/jzkRMSUizmztuSRJkqRaTjjhBHr27MkhhxzCN77xDb75zW9y6KGHss0223D//fczZMgQjj766HqXKUlaQc4gWwERcRxwATAHGAc8D2wO7AgMA34JvESxP9dQYJvyz81mrWAJPwFOAp4BLgTeAg4CdgU6ApWv0/kN8CNgeET8IDPfrhrraIq/Dxe0cc+J5XEoSz9Pe3wNOBD4P+COstbDgZ0iYufMfLO5Y0TsAfypfJZrgBnAzmUNE6oHjoj9gRuBl8vxnwK6USz5/Npy1CpJkqQGd+qpp3Ldddcxbdo0brnlFhYuXMimm27KPvvswxFHHMFhhx1GRNS7TEnSCorMdWbf+NUuIqYCfYCemfls1blNM/P5is8TgU9mZs3/9SxnYN2RmQNrnLsMOAronZmzyrY9gLuBx4BdMvOFsr0zcDuwG/BEZvaqGOfnwNeBz2XmDRXtUY6zBdAjM+e349lbfJ5azxIRI4EzgVeAT2Tm/RXnrgS+BByemf9TUdN0YHtgSGZeX9H/ZIpwEGDvzJxYtl8NHAzsnJn3VdW0xO+jhWea2sKpD/fp2LHruB49WrtckiRJa4heM2fWuwRJ0mrSv39/mpqamjKz/4qM4xLLFbeIYubWEtoKY1aCYeXxB83hWHnfN4DTW7jmv8vjV6vaPwX0Bn7fnnBsBZ1fGY6VLiqPu1S07UERjt1ZGY6Vfk4R6LXk9eqG1fD7kCRJkiRJaykDshXzW6Ar8GBEnBcRQyJis9V0737l8Y4a5+6iCO6WkJkPAncCB0REz4pTx5XHX63UCmu7t0bb7PK4SUVbi89XLg+dVGOc35bHv0TEryLi8Ih4f3sLy8z+tX4AXzogSZIkSdI6zIBsBWTmf1EsffwnxV5g1wJzI+L2iPjXVXz75o3459ao621gXgvX/RJYDxgOxeb8FHuC/S0z71kFdVZ7qUZbc5i3XkVbi89XmlPdkJnXAJ8FplHsqXYVMDsi7o2IfZevXEmSJEmStK4zIFtBmfnrzNwN6A58BrgYGADcEhGbL8tQtPzShI1rtDUvhdyi+kRErFfWU8s1FKHTMWW/9m7Ov7q1+HylLWs1ZuaNmbkPxWy0QcB5wEeBGyJih5VepSRJkiRJWusZkK0kmflSZv4xM48FLqN4e+JeFV3ehsXhVS0vAj2rG8v+O9fo31QeP1nj3F60ELZl5lvAWOB9wOcoZpIt4N3liWuKFp+v/E72bO3izHw1Mydk5r8DoynehHnASq9SkiRJkiSt9QzIVkBE7B8RtYKo5pljr1W0NS953LqF4e4Bto6IT1W1fxfYpkb/y8rjGRHRraKmzsA5rdUNXEgR2P2cYnP+KzPzlTauWd0mA/8ABkTEQVXnTgC2rb4gIgZFRJcaYzXPQnutxjlJkiRJktTgWlrSp/a5CngjIiYBs4CgmL31cWAq8KeKvuOBLwDXRMQfKd60+ERmXlGeHwPsB1wfEb8HXqB4k2NvYCIwsPLGmXl3RPwMOBF4ICL+l+JtmgdRzEZ7pqWiM/OfEXEjxd5jsOYtryQzMyKOAW4Dro6Ia4AZwE7AYOBmYP+qy/4T6BUREyl+HwuB/sA+wBMUvy9JkiRJkqQlOINsxYwA/kzxxsWvAcOA9YHTgL3L5YzNxlLM7NoI+DZwNnBM88nMHA8MAR4Evkix+f8sYBeKcKeWkykCsvnAV4EvAbdQBEgL26j9kvJ4b2Y2tdqzTjLzborA8U8UyyNPBDpRhIV/qXHJaOAmij3HhgPHU8weGw18PDNfXPVVS5IkSZKktU1kZr1rUB1ExEjgTGB4Zl5c53LWaBExtU/Hjv3G9ehR71IkSZLUDr1mzqx3CZKk1aR///40NTU1ZWb/FRnHGWQNKCLeSzG76gXgd3UuR5IkSZIkqa7cg6yBRMRnKJaDfo5i6eE3M9ON6yVJkiRJUkMzIGssX6DY22wuxX5o59W3HEmSJEmSpPozIGsgmTkUGFrnMiRJkiRJktYo7kEmSZIkSZKkhmZAJkmSJEmSpIZmQCZJkiRJkqSGZkAmSZIkSZKkhmZAJkmSJEmSpIZmQCZJkiRJkqSGZkAmSZIkSZKkhmZAJkmSJEmSpIZmQCZJkiRJkqSG1qHeBUhrg459+tBr6tR6lyFJkiRJklYBZ5BJkiRJkiSpoRmQSZIkSZIkqaEZkEmSJEmSJKmhGZBJkiRJkiSpoRmQSZIkSZIkqaEZkEmSJEmSJKmhGZBJkiRJkiSpoRmQSZIkSZIkqaEZkEmSJEmSJKmhGZBJkiRJkiSpoXWodwHS2mDhAw8wq3fvepchSZJWoV4zZ9a7BEmSVCfOIJMkSZIkSVJDMyCTJEmSJElSQzMgkyRJkiRJUkMzIJMkSZIkSVJDMyCTJEmSJElSQzMgkyRJkiRJUkMzIJMkSZJacdpppzFo0CB69uxJly5d6NatG3379mXUqFHMmzdvib6zZs0iIlr8+eIXv1inp5AkSa3pUO8CJEmSpDXZeeedR79+/dh3333ZfPPNefXVV5kyZQojR47kwgsvZMqUKfTs2XOJa3baaSeGDBmy1Fh9+vRZXWVLkqRlYEAmSZIkteLll1+mc+fOS7WfccYZjB49mnPOOYdfnJo9yAAAIABJREFU/vKXS5zbeeedGTly5GqqUJIkrSiXWEqSJEmtqBWOARx22GEAPProo6uzHEmStAo4g0ySJElaDuPGjQNgxx13XOrc008/zQUXXMC8efPo3r07u+++e81+kiRpzWBAJkmSJLXDmDFjWLBgAfPnz+fee+9l0qRJ7LjjjowYMWKpvrfddhu33XbbEm0DBw7k8ssvZ+utt15dJUuSpHYyIJMkSZLaYcyYMcydO3fx5/3335/LLruMzTbbbHFb165d+d73vseQIUP4wAc+AMDf//53Ro4cye23386gQYP429/+xgYbbLDa65ckSS1zDzKtEyJiYkRkveuQJEnrrjlz5pCZzJkzh2uuuYbHH3+cvn370tTUtLjP5ptvzllnnUW/fv3YeOON2XjjjRkwYAC33noru+66KzNmzGDs2LF1fApJklSLAZkkSZK0DLbYYgs+//nPc+uttzJv3jyOPPLINq/p0KEDw4cPB+DOO+9c1SVKkqRlZEAmSZIkLYdtttmGHXbYgQcffJDnn3++zf7NSzFfffXVVV2aJElaRgZkkiRJ0nJ6+umnAVhvvfXa7DtlyhSAxXuTSZKkNYcB2RogInpFREbEZeWfr4qI5yPijYi4NyI+28q1X4qI2yPixbL/9Ij4bkR0qur3dEQ8WeP6J8p7f6+q/dNl+1lt1L5/2e+SFs53Kp/l+cqayvYREfH3iHgtIl6OiLsi4rA2vp/tIuL3EfFsRLwTEQPbqG+fiJhfPv/OrfWVJEmq9vDDDzNnzpyl2t955x3OOOMMnn32WfbYYw822WQTAP7yl7+wcOHCpfpPmDCB8847D4CvfOUrq7ZoSZK0zHyL5ZplG+Ae4HHgCqAbcDhwfUQMzszbKztHxMXA0cCTwDXAS8BuwNnAoIjYNzMXld0nAF+OiA9n5sPl9R8Emt8zPqi8rtk+5XF8GzXfAjwGHB4R38jM+VXnDwG6A/+ZmW+W9+1YXvdJ4GHgF0BX4FDg9xGxc2Z+p8a9tgX+AjwC/BboArzcUmER8WXgEorvc//MfKKNZ5EkSVrCzTffzLe+9S0GDBjAtttuS/fu3Zk7dy533HEHjz/+OFtuuSUXXXTR4v6nnXYaDz74IAMHDuT9738/ULzFcsKECQCcffbZ7LHHHnV5FkmS1DIDsjXLQGBkZo5qboiIK4GbgW8Bt1e0D6UIx64FvpyZr1ecGwmcCXwd+GnZPAH4MkUQ9nDZNqg83gZ8MiK6ZuZrFedeB/7cWsGZmRHxK+Bc4Ajg51VdjiuPF1a0nUoRjt0EHNgc4kXEKIqA8PSIuCEzJ1eNtSdwTgvh2RIi4tvAD4HJ5T1eaMc1U1s49eG2rpUkSeumwYMHc9xxx3H33Xdz33338dJLL7HBBhuw3XbbccQRR3DSSSfRrVu3xf2POOIIrr32Wv76179y00038dZbb7HFFltw2GGHccIJJ7DXXnvV8WkkSVJLIjPrXUPDi4hewEzgCWDbzHy76vwTwAaZuWlF2zSgD7BZZr5U1X89YC7weGbuUrZtA8wCrs3Mg8u2/6EIqo4BxgH7ZeatEdEdeA74U2Z+qh31d6eYxTYjMz9W0b49RRh3e2buU9H+KMVssB2aZ7NVnDsGGAtcmplHV30/c4FtmmeiVV03sXyW9YDzKcLBayjCwzfaeoZyjBYDsj4dO3Yd16NHe4aRJElrqV4zZ9a7BEmStIz69+9PU1NTU2b2X5FxnEG2ZvlbdThWmg3s3vwhIroCOwHPA6dERK2x3gQ+0vwhM5+IiMeBvSPiPUBSzFj7E3AHsIhi1titwN5AUMw6a1NmzivDtiMjYo+KmV/Ns8d+VVH7e4EPAk9Vh2Ol5nv2rXHuvlrhWJWrgSHAz4BTMvOd9jxD+Rw1/8NUBmf92juOJEmSJElauxiQrVleaqF9EUu+UGETigBrM4qllO01HjiWIux5q7x+fGa+EhF/5d0ll4Mq+rfXL4Ejga8Ck8sN+Y8CngWuq+i3UXl8poVxmts3rnFu6R1ylzaA4vsatyzhmCRJkiRJaly+xXLt1LwR/rTMjNZ+qq5rnp01mHdDsAkVx74R0a08Nx9oam9BmfmXsv9hEbEJ727Of2lmVr7Kqbn2LVsYaquqfkvcph2l7A28CIyLiM+0o78kSZIkSWpwBmRrocxcADwIfLQMtNprAkXINIjiLZWPZ2bzZhvjKf4+HAF8CJjYwnLP1vw30JliJtlx5b0uquyQma9QvPXyfRHxoRpj7F0e2x3OVY3/d4q9yF4AromIIcszjiRJkiRJahwGZGuv/wI6ApdExFLLESNik4hYYt+szHyWIlj7BMVSxMollJOBN4DmN0S2a/+xKldSzPz6NkVIdVtmPlaj3yUUS0TPLV8o0FzzpsD3Kvosl8ycTvF8c4E/RMThyzuWJEmSJEla97kH2VoqMy+JiP7A14DHIuIW4J9AN6A3RUB0KXB81aXjKd5+2fzn5vHejIi7Wb79x5rHeC0iLgdOKpsuaKHrGOAA4CDgvoj4I9AV+AKwOfDjzJy0rPevqmVGROxFEfT9NiI6ZeavV2RMSZIkSZK0bnIG2VosM78OfA74M8W+Yv8OHEixEf65wE9qXNYcfCVwewvn5mbmg8tZVvPMr2eA/2uh7oXAvsAZZdOJFBv6Pwr8W2aetpz3rr7PExRB4Qzg0og4dmWMK0mSJEmS1i3OIFsDZOYsiiWHLZ0f2Mq5G4AbluFe41q6V2aeA5zT3rFa0Lc8XpyZi1qp4w1gdPnTqra+n7LPwBbanwI+3NY9JEmSJElS43IGmVaaiOhAMYttES0vr5QkSZIkSVqjOINMKywi9qTYlH8g8DHg55n5ZF2LkiRJkiRJaicDMq0Mg4EzgReAiyjeYilJkiRJkrRWMCDTCsvMkcDIOpchSZIkSZK0XNyDTJIkSZIkSQ3NgEySJEmSJEkNzYBMkiRJkiRJDc2ATJIkSZIkSQ3NgEySJEmSJEkNzYBMkiRJkiRJDc2ATJIkSZIkSQ3NgEySJEmSJEkNzYBMkiRJkiRJDa1DvQuQ1gYd+/Sh19Sp9S5DkiRJkiStAs4gkyRJkiRJUkMzIJMkSZIkSVJDMyCTJEmSJElSQzMgkyRJkiRJUkMzIJMkSZIkSVJDMyCTJEmSJElSQzMgkyRJkiRJUkMzIJMkSZIkSVJDMyCTJEmSJElSQzMgkyRJkiRJUkPrUO8CpLXBwgceYFbv3vUuQ5Kkuug1c2a9S5AkSVqlnEEmSZIkSZKkhmZAJkmSJEmSpIZmQCZJkiRJkqSGZkAmSZIkSZKkhmZAJkmSJEmSpIZmQCZJkiRJkqSGZkAmSZKk5XLaaacxaNAgevbsSZcuXejWrRt9+/Zl1KhRzJs3b4m+b731Fj/96U8ZNmwYO++8Mx07diQiGDt2bJ2qlyRJeleHehcgSZKktdN5551Hv3792Hfffdl888159dVXmTJlCiNHjuTCCy9kypQp9OzZE4BXX32VU045BYAtttiCLbfcktmzZ9ezfEmSpMUMyCRJkrRcXn75ZTp37rxU+xlnnMHo0aM555xz+OUvfwlA165d+eMf/8jOO+/MVlttxciRIxk1atTqLlmSJKkml1hKkiRpudQKxwAOO+wwAB599NHFbR07duSAAw5gq622Wi21SZIkLQsDMkmSJK1U48aNA2DHHXescyWSJEnt4xJLSZIkrZAxY8awYMEC5s+fz7333sukSZPYcccdGTFiRL1LkyRJahcDMkmSJK2QMWPGMHfu3MWf999/fy677DI222yzOlYlSZLUfi6xlCRJ0gqZM2cOmcmcOXO45pprePzxx+nbty9NTU31Lk2SJKldDMgaUEQMjYiMiKHt7D+y7D9w1VYmSZLWZltssQWf//znufXWW5k3bx5HHnlkvUuSJElqFwMySZIkrVTbbLMNO+ywAw8++CDPP/98vcuRJElqkwGZJEmSVrqnn34agPXWW6/OlUiSJLXNgEySJEnL7OGHH2bOnDlLtb/zzjucccYZPPvss+yxxx5ssskmdahOkiRp2RiQraCI6FXuz3VZ+eerIuL5iHgjIu6NiM+2cu2XIuL2iHix7D89Ir4bEZ1a6P/h8j6zI+LNiJgbEVdGxPYt9P9gRPyhHP/ViJgcEZ9Zic++dUQ8GBELI+IrFe2zyp+uEXFuRPyzrHdGRJwWEdHCeIdFxJ0RMT8iXo+I+yPi9OrvIyL+XI63QVX7neXv4uKq9h3K9l+vrGeXJKnR3XzzzfTs2ZNBgwZx3HHHcfrpp3P00UfzoQ99iNGjR7Plllty0UUXLXHND3/4Q4YOHcrQoUO57rrrALj00ksXt40dO7YejyJJkkSHehewDtkGuAd4HLgC6AYcDlwfEYMz8/bKzmWIczTwJHAN8BKwG3A2MCgi9s3MRRX99y/7rQ+MA2YA7wcOBj4TEXtnZlNF/w8Bfwa6AzcBfwM+CFxXfl4hEbET8EfgvcCnM/NPVV3WB24FepT3WwQMAX4IdAZGVY03GjgdeB64ElgAHACMBvYrv4+3yu7jKb6rvYCby+u7AruW5wdV1bJPxXWSJGklGDx4MMcddxx333039913Hy+99BIbbLAB2223HUcccQQnnXQS3bp1W+Kam2++mTvuuGOJtsmTJzN58uTFn4cPH75a6pckSapkQLbyDARGZubi4CcirqQIcL4F3F7RPpQiHLsW+HJmvl5xbiRwJvB14Kdl2ybA74DXgAGZ+VBF/48CfwHGAv0q6vkFRTh2Smb+tKL/QRQh2XKLiMHA1cCrwF6ZeV+Nbj2A+4B9m58vIkYBjwDfiIjRzYFXROxOEY7NBnbJzDll++kU39FnKb7D0eXYE4AzKIKwm8u2vYCOwG3AvhGxbWY+Vp4bVHFda881tYVTH27tOkmSGlGfPn34xS9+sUzXTJw4cdUUI0mStIJcYrnyPAF8v7IhM28B/gnsUtX3ZIoZVUdXhmOls4F5wJcr2o4ENgbOrAzHyns8CFwE9I2IHQAi4v3AvsBM4OdV/a8Hlvyn22VQLqX8I/AUsFsL4VizkyqfLzOfBa4HNgIql4UeXR6/3xyOlf0XAacC7wCV/5w8GXiDJWeKDaL4Ts+s+ExEvIcivHw0M2e37yklSZIkSVIjcQbZyvO3zHy7RvtsYPfmD+VSwJ0olhKe0sJ2XG8CH6n43Hz9TuUMs2rblcePAA8BfcvPk1qoaSLwyZpP0bqTgYOAu4EDM/PFVvrOz8wZNdqbQ6rKHXubZ74tNcMrMx+JiCeB3hGxcWa+lJlvRMRkYO+I6J6Z8yiWUf41M/8cEXMpArILy7E3Bn7f1sNlZv9a7eXMsn61zkmSJEmSpLWfAdnK81IL7YtYcqbeJkAAm/HubKe2dC+Px7bRb8PyuFF5nNtCv6VfOdU+AyhqH99GOAatfx8Ale98b673mRaueQbYuuzXPO54ilBs74gYTxEKVi7B3Ld8GcCgiv6SJEmSJElLcYnl6je/PE7LzGjtp8Y1O7VxzeVV/bdooYYtl7P2Yyj2OzszIs5ezjFqaa63pbq2quoH7842GwzsTfF3eXzFuU0pZuoNApKKPeAkSZIkSZIqGZCtZpm5AHgQ+GhEdGurf2lKedyrnf2nlcc9I2K9GucHtnOcai9R7G12F/DdiPjxco5TrbnegdUnIuKDFG/rnJmZlbPS/gq8TBGA7QO8TvHWTng3KPs08Ang75n5/EqqVZIkSZIkrWMMyOrjvyjeuHhJRGxcfTIiNomIyj2vLqUIp86MiOoN/4mI90TEwObPmfkkxdscewMnVPU9iOXbf6x57FeA/SlCqG9FxE/buKQ9LimP342IzZoby3BvDMXf04ur6ngbuBP4IPAFiv3W3izPzQRmUeyZ1pU23l4pSZIkSZIam3uQ1UFmXhIR/YGvAY9FRPPbLrtRhFoDKEKx48v+8yLiUOBaYEq559aDFG933JpiE//uQOeK23ydYkbVTyLiU8B9FGHS54FxwOdWoP7XIuKzwNXASRHRGTg+M3M5x5tczkb7NvBARPwv8CpwANAHmAScW+PS8cBngc1Zeo+x8RRLQqlxTpIkSZIkaTEDsjrJzK9HxE0UIdhgijctvkARlJ0L/Kaq//iI2BH4JrAfxXLLhcDTFDOkrq7q/2hE7Ab8sBx/IPB3YAjFCwKWOyArx38jIj4PXAUcB3SKiKMz853lHO+0iJhGMePtSGB94DHgu8B/ZubCGpdVBl/Vs8SaA7JFFDPNJEmSJEmSaorlnPQjNYyImNqnY8d+43r0qHcpkiTVRa+ZM+tdgiRJUk39+/enqampKTP7r8g47kEmSZIkSZKkhmZAJkmSJEmSpIZmQCZJkiRJkqSGZkAmSZIkSZKkhmZAJkmSJEmSpIZmQCZJkiRJkqSGZkAmSZIkSZKkhmZAJkmSJEmSpIZmQCZJkiRJkqSGZkAmSZIkSZKkhmZAJkmSJEmSpIZmQCZJkiRJkqSG1qHeBUhrg459+tBr6tR6lyFJkiRJklYBZ5BJkiRJkiSpoRmQSZIkSZIkqaEZkEmSJEmSJKmhGZBJkiRJkiSpoRmQSZIkSZIkqaEZkEmSJEmSJKmhGZBJkiRJkiSpoRmQSZIkSZIkqaEZkEmSJEmSJKmhGZBJkiRJkiSpoXWodwHS2mDhAw8wq3fvepchSdJy6zVzZr1LkCRJWmM5g0ySJEmSJEkNzYBMkiRJkiRJDc2ATJIkSZIkSQ3NgEySJEmSJEkNzYBMkiRJkiRJDc2ATJIkSZIkSQ3NgEySJKnBnXbaaQwaNIiePXvSpUsXunXrRt++fRk1ahTz5s2rec3kyZP59Kc/Tbdu3ejatSs77rgjP/nJT3j77bdXc/WSJEkrLjKz3jVIa7SImNqnY8d+43r0qHcpkiQtt14zZ7Z4rmPHjvTr148ddtiBzTffnFdffZUpU6Zw77330qNHD6ZMmULPnj0X97/++us55JBD6Ny5M4cffjjdunVj3Lhx/OMf/+DQQw/lD3/4w+p4JOn/t3ffYXZV9f7H319TaQZCC0oJveUqEMjV0EICgpQLSBAsKHJBxRJFuAhSpElRRDp6KQI2kBAEFBBMAQIoJqH8AoSWgFwhdAipkOT7+2PvE3bOnEkhyUzCeb+eZ549Z+2111l7sp7JzGfWWluSJHr37s2YMWPGZGbvRWmn4+LqkCRJkpZNkyZNomvXri3KTzjhBM4880zOOussLr300jl1jzjiCDp06MCIESPYdtttATj99NPp378/gwcP5rrrruPggw9u03uQJElaFC6xlCRJanKNwjGAz3/+8wA8/fTTc8oGDx7Mq6++ysEHHzwnHKu1ccYZZwBw2WWXLcHeSpIkLX4GZJIkSWro1ltvBeATn/jEnLJhw4YBsMcee7Sov9NOO7H88stz//33M2PGjLbppCRJ0mLgEktJkiQBcO655zJ58mTefvttRo0axciRI/nEJz7BcccdN6fOk08+CcAmm2zS4vqOHTuy/vrr89hjjzF+/Hg233zzNuu7JEnSojAgkyRJElAEZC+//PKc13vssQdXX301q6+++pyyt99+G4Bu3bo1bKNW/tZbby3BnkqSJC1eLrGUJEkSABMnTiQzmThxIkOGDGH8+PFsvfXWjBkzZoHbqD0hPSKWVDclSZIWOwOyhRQR/SIiI+KUhbjmlPKafkuuZ0tORIyIiGzvfkiSpLax5pprsv/++3PnnXfy+uuv85WvfGXOudoMsdpMsnqTJk2aq54kSdKywICsTkT0LMOsq9u7L5IkSe1pvfXWY4sttuCxxx7jtddeA2DTTTcF4KmnnmpRf+bMmUyYMIGOHTuywQYbtGlfJUmSFoUBWdu4GNgceLC9OyJJkrQwXnzxRQA6dOgAQP/+/QG44447WtS95557mDp1Kn379qVLly5t10lJkqRFZEDWBjLztcwcl5lT27svkiRJVePGjWPixIktymfPns0JJ5zAK6+8Qt++fVlllVUAGDhwIKutthrXXXcdo0aNmlN/+vTpnHjiiQAceeSRbdN5SZKkxcSArKLcV2xC+fKr5VLL2sehDepvFRF/iYi3ImJqRNwdEX0btdtoD7KybERErBYR/xsRL0XEjIh4LCK+1kofu5TtjS/rToiIM8ryjIgRdfVXioiTImJsREyKiHci4tmIuD4ien+wr9SctvtHxNsR8WJEbFWWzVmiWn5+XUS8FhHTI2JUROw9j/s6LiIeLb+WkyLi3oj4fF29FSPi3Yi4r658ufI9MiIOqTv3rbL8sEW5X0mSPozuuOMO1llnHQYMGMDXv/51jj/+eA477DA23nhjzjzzTHr06MHll18+p/5HP/pRLr/8cmbNmkW/fv04/PDDOfbYY9lqq6144IEHGDhwIAcddFA73pEkSdLC69jeHVjKjABWBr4HPAL8qXLu4bq62wLHAg8AVwDrAgcAQyNiq8x8cgHfc2XgPuBdYDDQFRgIXBURszPzmlrFKB4HdSOwF/A0xdLNTsChwJb1DZf17wD6Vvo5E1gH6AfcC4xewH7Wt/0l4CpgPLBHZj5fV2U9iiWl44HfAN2Bg4CbI2LXzBxeaasz8FdgZ2AccAmwfPl1uL78ev4IIDMnR8SDwH9GxEqZ+U7ZzPZAbS3HgPI9a/qXx6Ef5F4lSfow23XXXfn617/OfffdxyOPPMJbb73FCiuswCabbMIhhxzCoEGD6N69+1zX7Lffftx999385Cc/4cYbb2T69OlstNFGnHfeeQwaNMgnWEqSpGWOAVlFZo6IiOcoArKHM/OUeVTfC/haZl5dK4iIbwC/LK//1gK+7SeBK4FvZOassp1fAI8CPwSuqdT9cvm+9wK7Zua7Zf2Tgb83aLsXRTj2p8zcv3oiIj4CfKDHS0XEscDZwP3Af2XmGw2q9QNOycxTK9f9niKw+x9geKXu0RTh2O1lezPL+qdShGzHR8SfM/P+sv4wikBsJ+AvZdkAYBZwT/l59T77AeMbhHj199VaWLjZvK6TJGlZ1qtXLy655JKFvm777bfntttuWwI9kiRJansusfzg7quGY6WrKGZo9VmIdqYCP6iFYwCZ+TjFrLLNI2KlSt2vlscTa+FYWf8t4PR5vMe0+oLMnJ2Zby5EP4mIj0TExcA5wE0UIV2jcAzgeeCMuvf8K/AvWn59DgOS4usws1L/Fd6/r8Mr9WszwQZUygZQzIa7EVg7IjYpy7cCVsXZY5IkSZIkqRXOIPvgRtUXZOZ7EfEysMpCtPN0Zk5qUP5CeVwZqC0j3BqYTTFzq97IBmWPUywN/UJErAfcXNYbVQ3YFsKNwH7ARcD3M3P2POo+XA39Kl4APl17UQaAGwH/zsxxDeoPK49bV8oeoAj9BpRtdAO2AX5aqT8AeIr3l1cOYz4ys+GebOXMsm3md70kSZIkSVo2OYPsg3urlfKZQIfF1A51bXUD3qjOsqp4ub6gDKj6A+dT7JF2DsXMtNci4qKIWHEh+gnFksaZwK3zCcdg3vdVHXe1ZZ4vtVK/Vr5yraAM90YC/xERa1AsoewADM3MJ4AXeX922QCK2WnzDcgkSZIkSVJzMiBbtkwCukdEo5l/aza6IDPfzMyjMnMdYGOKpYrjgO8Aly3k++8CvAncGhF7LeS1rXm7PPZo5fxadfVqhgFBEQAOAGZQhH9Q7G+2S0R0AXYEHiuXa0qSJEmSJLVgQNZSbVngwswCaysPUfyb9W1wbof5XZyZz2TmlRQb4k8G9l2YN8/MR8tr3wCGRMR+C3N9K22+AzwLfDwiNm5QZZfyOKauvLoPWX+KPeGmV851B44EVsD9xyRJkiRJ0jwYkLX0JsWSvHXbuyMNXFsez4iIzrXCcg+uk+orR8T6EbFlg3ZWAbrQYPP++SmXMO5EsaTzhog4aGHbaOAqitlgP4uIOcFkRKzG+/d1Vd01oymWce4LbMncIVjt8+PLo8srJUmSJElSq9ykv05mTo6IfwA7RsTvKDZ6nwXcUs6gak/XAgcDewBjI+IWoBNwAMVDAzal2MS/5pPATeUm82Mp9uZanSJU6kSxJ9lCy8xnImJHiuDpdxHRJTOvnd9183Au8NmyX49ExG3A8sCBwBrATzNzrocQZObsiLib92fBDa2c+1dEPAtsSPFvd/ci9E2SJEmSJH3IOYOssUOAv1AEUT8GTmcpeIphZiawP0V/OgHfpQiIrgG+XVarPhFzFHAWxf5cewBHUwRRo4E9M/O8RejL8xQzyZ4Bfh0RRyxCW+8CuwEnlEXfBb4KPA18MTN/2MqltVBsEi2fKlo7Nzoz6/cvkyRJkiRJmiOKzEXLuojYDbgTODszj59ffS24iBjdq3PnbW792MfauyuSJH1gPSdMaO8uSJIkLXa9e/dmzJgxYzKz96K04wyyZUxEtEhpImJV4Ozy5U1t2yNJkiRJkqRlm3uQLXvOi4hPAvcDrwJrUyyb7A78KjMfbM/OSZIkSZIkLWsMyJY9Q4A1gX2AlYHpwGMUT3m8oh37JUmSJEmStEwyIFvGZOYfgT+2dz8kSZIkSZI+LNyDTJIkSZIkSU3NgEySJEmSJElNzYBMkiRJkiRJTc2ATJIkSZIkSU3NgEySJEmSJElNzYBMkiRJkiRJTc2ATJIkSZIkSU3NgEySJEmSJElNzYBMkiRJkiRJTa1je3dAWhZ07tWLnqNHt3c3JEmSJEnSEuAMMkmSJEmSJDU1AzJJkiRJkiQ1NQMySZIkSZIkNTUDMkmSJEmSJDU1AzJJkiRJkiQ1NQMySZIkSZIkNTUDMkmSJEmSJDU1AzJJkiRJkiQ1NQMySZIkSZIkNTUDMkmSJEmSJDW1ju3dAWlZ8O7YsTy3/vrt3Q1Jkug5YUJ7d0GSJOlDxxlkkiRJkiRJamoGZJIkSZIkSWpqBmSSJEmSJElqagZkkiRJkiRJamoGZJIkSZIkSWpqBmSSJEmSJElqagZkkiRJy7jXX3+dK664gv3sMLwlAAAZHklEQVT335+NNtqI5ZZbjm7durHDDjtw5ZVXMnv27BbXTJ48mZNOOonNN9+crl27svLKKzNgwABuu+22drgDSZKk9tWxvTsgSZKkRXPDDTdw5JFHstZaa7HLLruw7rrr8vLLLzNkyBAOP/xwbr/9dm644QYiAoC33nqLHXfckbFjx7LlllvyjW98gylTpnDLLbew1157ccEFFzBo0KB2vitJkqS2Y0AmSZK0jNtkk03mhFsf+cj7CwTOPPNM+vTpw4033siQIUM44IADADjllFMYO3Ysn/vc57j++uvp2LH4kfDVV1+lT58+HHPMMXz2s59l4403bpf7kSRJamsusZQkSVrG9e/fn3322WeucAygR48efPOb3wRgxIgRc8qHDBkCwGmnnTYnHANYffXVOfroo3nvvff45S9/ueQ7LkmStJQwIJMkSfoQ69SpE8BcQdjEiRMB2GCDDVrUr5UNHTq0DXonSZK0dDAgkyRJ+pCaOXMm1157LQB77LHHnPLVVlsNgAkTJrS4Zvz48QCMGzeuDXooSZK0dDAgkyRJ+pA67rjjGDt2LHvuuSe77777nPK9994bKPYimzVr1pzy119/nfPOOw+AGTNmMG3atLbtsCRJUjtZpE36I6InMAG4JjMPXQz9kT6QiHgOIDN7tm9PJElaOlx44YX8/Oc/Z7PNNuM3v/nNXOdOO+007rzzTm644QaeeOIJBgwYwNSpU7n55ptZaaWVWH755Zk6dSodOnRop95LkiS1LWeQSZIkfchccsklfO9732OLLbZg+PDhdO/efa7zPXr04J///CeDBg1iypQpXHrppdx8883svffe/O1vf2PatGl069aNzp07t9MdSJIkta1FmkEmSZKkpcv555/PUUcdRa9evRg6dChrrLFGw3qrr746F1xwARdccMFc5cOHDycz2W677dqiu5IkSUsFZ5BJkiR9SJxzzjkcddRRbLXVVgwfPrzVcGxeLr/8cgC+9KUvLe7uSZIkLbUWW0AWET0j4rqIeC0ipkfEqIjYu5W6XSLiuIh4NCKmRsSkiLg3Ij7fSrsZEVdHxCYRcX1EvBIRsyOiX6XexhFxbUT8OyLejYgXy9cbN2hzpYg4KSLGlu/9TkQ8W7bdu0H9PuW5f0fEjIh4KSLubKW/n4+IeyLi7YiYFhH/LyKOj4guDeo+V350i4iLy/anR8TjETEoIqKVr99/RsTgiJhY3usLEfGriPhYg7obRMT/RsQzZX/eKPv0y4hYtVH7dde/GBH/16D8+fLf5aS68j3L8tPqypcvvw4PR8SUiJgcEQ9ExBfm8d67R8Rt5ZiaUf4b/SwiVp5fvyttfLG89omIWG9Br5MkaVlz+umnc9xxx9G7d2+GDh0650mVjcyePZvJkye3KL/iiiv4wx/+wFZbbWVAJkmSmsriWmK5HvAgMB74DdAdOAi4OSJ2zczhtYoR0Rn4K7AzMA64BFgeGAhcHxFbZeaPGrzHhsA/gKeA3wHLAZPKNrcD/gasBNwCPA5sBnwJ2DciBmTmqLJuAHcAfYEHgCuAmcA6QD/gXmB0pb9HAJcBs8q2nwbWALYFvgX8sVL3TOB44DXg98Bk4LPAmcDuEbFbZr5Xd1+dy76vDFxXvj4AuADYFPh2tXJEfA24HJhR9ucFYGPgcGCfiPhUZv6rrLsW8E/go8BtwI1AV2B94BDgYuD1Bl/rqmHAlyJis8wcV7a7EbBueX4AcHqlfv/yOLTS55XLdrYGxgBXUYSzuwO/j4gtM/PEuvs8GTgVeAP4M/AK8AngGGDPiPh0Zk6aV8cj4ljgbOB+4L8y84353KskScuka665hpNPPpkOHTqw4447cuGFF7ao07NnTw499FAApk6dypprrsluu+3GRhttBMC9997Lgw8+yIYbbshNN91Ep06d2vIWJEmS2tXiCsj6Aadk5qm1goj4PUUQ9T/A8ErdoynCsdspQouZZf1TKUK24yPiz5l5f9177ACcVR+elYHXtRQh0Jcz83eVcwdRhE6/jYgtMnM20IsiHPtTZu5f19ZHgG6V11sAl1IEcTtm5mN19deufP5pinDsBaBPZk4sy48HbgL2Lr8WZ9bd11oUwWKvzJxRXvNjimDrWxFxfWbeU5ZvAvwKeA7YOTP/XXn//sBdFMFa7b4GUoSV38/MuTYYiYgVgNnM3zCKoHEARaBJ+Tnl++0cEctn5tTKuWkU4WPN+RTh2A8z86eVPnQF/gT8KCIGZ+bDZfkuFOHYA8CemflW5ZpDgV+X549q1OHy3/EC4DvAEOBLmTl9fjcaEaNbObXZ/K6VJKk9TZgwAYBZs2Zx/vnnN6yz8847zwnIunTpwsEHH8zIkSO56667ANhwww059dRT+cEPfsCKK67YJv2WJElaWiyuJZbPA2dUCzLzr8C/gD51dQ8DEvhBLRwr67/C+zORDm/wHi9ThCL1+lIEGA9Uw7GyzeuBkRQzsXaou25afUOZOTsz36wUHUkRIp5eH46V9atLDw8rj2fUwrGyzkyKUHB2K/cFcHwtHCuveYP3vxZfq+tPJ+B71XCsvGYYxYyyfSJipbr2G93rlMxsUd5AbSbYgErZAIoZXRdSzHjbAaBcsvlJYGRmvlsp+zIwqhqOlX2YDvwQCOCLlVODyuMR1XCsvOZq4GGK0K6FMnQbTBGOXQwcuCDhmCRJy7JTTjmFzJznx4gRI+bU79SpE1deeSVPPvkkU6ZMYcqUKTz66KOcfPLJhmOSJKkpLa4ZZA9n5qwG5S8An669KIObjYB/15br1RlWHrducO6RaohUsU3dtY3a3KFs8x6K5ZcPA18o96S6mSJEG1ULdSo+VR5vb6XtBepHZj5V7uO1fkSsXBf6zKRYAlhvRHmsfi1qX8udy2Wl9dYAOgCbUCwTvYVixtolEbE7xdLW+4DHMzMX4J7IzOcjYjywSzkzKylmDP4NuLvs/wDgTmAXirCr+jXYruxTRsQpDd6itn5j87r7fA84MCIObHBNZ2D1iFg1M6tLRJejCPQ+Td1stQWRmS32n4M5M8u2aXROkiRJkiQt+xZXQPZWK+UzmXuWWm354kut1K+VN9qEfWKDsoVuMzNnlcsRT6ZYgnhOef6diLiGYjbX5Oo1wFyztRahH+uW9apfr9daCRdr99utUlbbVP9/5tOXFWFOuNUHOAXYA/hcef6FiDg3M1tuUNLYUOAIipDoPWB1YGhmvhMR/+T92WUDKvXr+7xd+THPPleu6Qj8eD79WpG591BbsezjJIowUJIkSZIkab4W21MsF9Db5bFHK+fXqqtX1dqMp4VuMzPfzMyjMnMd3t/gfhzFsrzLKtfWgqyPt9L2IvWjtFpEdGhQv9ZOtX7t826ZGfP4uLt2QWY+kZkHUYRO2wLHUfy7XxAR/70A9wXvzwjblfdDsGGV49YR0b089zbFRvz1ff7FfPq8S901b86nfmTm83X9fJVir7dOwPCI2HYB70+SJEmSJDWxNg3IMvMd4Fng4xGxcYMqtZBkTINzrXmoPPZr5XytvGGbmflMZl5J8eCAycC+ldN/L4+fXZR+lE99XBuYUL+nFsVMqb7z6PdDlbJaf3ZcgP7MJTNnZubozDwH+EJZvN8CXj6MIqAcQPGUyvGZOaE8N5RiHB1CETaOqJsR9yDF/msL0+e/A6tExJYLcQ0AmTmUYrZcR+Bv5cMTJEmSJEmSWtXWM8gArqLYp+pn1ZlTEbEacFKlzoK6D3gS2CEiBlZPlK93Ap6i2GeMiFi/leBlFaALc29ofxnFMtGTyidazqX6FMtKn0+MiNUrdToA51J8ra9s5R7OiogulWu6AyeWL39dqXcxxRLHX5RPtKzvT+eI2LHyuk9ErNng/WplUxuca6F8gMJjwPYUX8/qEsr7gelA7emiwxpc+ztg24g4KSJaLOuNiA0jYv1K0S/K4+UR8bEG9VeIiE/Vl1fe815gN4pQ786I2Hk+tyhJkiRJkprY4tqDbGGcSzEja1/gkYi4DVgeOJBik/mfZubIBW0sMzMivgrcBVwfETdTLJfclGKG1DvAVzJzdnnJJ4Gbyo3XxwIvUuyptS/F0rxzKm0/HhHfAn4JPFS2/TTvL1d8h3LWW2beHxE/BY4FxkbEYGBKea+9KAK6nzW4hZcogrmxEXFL2YeBFEsyL83Meyr9GRcRh1GEcY9FxB0U4V8niv3NdqRYZrhZeckXgW9HxN3AM8CbwIbAPsAMoPFz4BsbWt5H7fNan2ZExH003n+s5jsUs8tOAw6JiJEUTyX9GMXm/NtRzGqbULY5NCKOA84Cni7HyASKPcbWo5jtN5JiplhDmfmPcq+5u4DbImK/zLxrIe5XkiRJkiQ1iTYPyDLz3YjYDfgBRYDzXYpZWo8A38/MP3yANv9RPtXxRIp9svYBXgP+AJyemU9Wqo+iCF52pghYVqEIlUYDF2bm7XVtXx4RY4FjKJY97le2/ShwRV3dH0bEQxSB0Fcogqtny379vMFTMgHeLft8JnAwsBowHjgbuKjBvf42Ih4BjqYI5z5DEcS9CAwGrq9U/wNF+NaXYvP65SgeOHBd2Z+xDfrTmqHA9yhmZQ1vcG4A8HJmPtagz5PKWVxfp/g3PwDoShGSPQ0cRRFkVa85pwzeBlE8hXRfir3J/g38L/D7+XU4Mx+KiH4UT9y8NSIOyMy/LOgNS5IkSZKk5hCZre19ryUtIp4DyMye7dsTzUtEjO7VufM2t36sxWpPSZLaXM8JE+ZfSZIkqUn07t2bMWPGjMnM3ovSTnvsQSZJkiRJkiQtNQzIJEmSJEmS1NQMyCRJkiRJktTU2uMpliq595gkSZIkSVL7cwaZJEmSJEmSmpoBmSRJkiRJkpqaAZkkSZIkSZKamgGZJEmSJEmSmpoBmSRJkiRJkpqaAZkkSZIkSZKamgGZJEmSJEmSmpoBmSRJkiRJkpqaAZkkSZIkSZKaWsf27oC0LOjcqxc9R49u725IkiRJkqQlwBlkkiRJkiRJamoGZJIkSZIkSWpqBmSSJEmSJElqagZkkiRJkiRJamqRme3dB2mpFhGvL7fcct0333zz9u6KJEmSJEmqeOKJJ5g2bdobmbnqorRjQCbNR0TMADoAj7R3X7TM2qw8jmvXXmhZ5hjSonIMaVE5hrSoHENaVI4htaYnMCkz11+URjounr5IH2pjATKzd3t3RMumiBgNjiF9cI4hLSrHkBaVY0iLyjGkReUY0pLmHmSSJEmSJElqagZkkiRJkiRJamoGZJIkSZIkSWpqBmSSJEmSJElqagZkkiRJkiRJamqRme3dB0mSJEmSJKndOINMkiRJkiRJTc2ATJIkSZIkSU3NgEySJEmSJElNzYBMkiRJkiRJTc2ATJIkSZIkSU3NgEySJEmSJElNzYBMkiRJkiRJTc2ATGpFRKwdEVdFxIsRMSMinouI8yNilfbum5YeETEwIi6KiHsjYlJEZET8dj7X9I2I2yLijYiYGhGPRsT3I6JDW/VbS4eIWDUiDo+ImyLimYiYFhFvR8TIiPjviGj4/7RjSFURcU5EDI2IF8ox9EZEPBQRP46IVVu5xjGkVkXEIeX/ZxkRh7dSZ++IGFF+z5ocEf+IiK+2dV+1dCh/Ts5WPia2co3fh9RCROwYETdGxEvl72AvRcSdEbFng7qOIS1WkZnt3QdpqRMRGwL3A2sANwPjgD7ALsCTwPaZ+Xr79VBLi4h4GPgkMBn4P2Az4HeZ+eVW6u8L3AhMB64H3gD2ATYFBmfmgW3Rby0dIuKbwGXAS8Bw4F/AmsDngG4UY+XArPxn7RhSvYh4FxgDPA68AqwAfArYFngR+FRmvlCp7xhSqyJiHeD/AR2AFYEjMvOKujrfAS4CXqcYQ+8CA4G1gZ9n5jFt2mm1u4h4DlgZOL/B6cmZeW5dfb8PqYWIOBE4HXgN+DPFz0erAVsDwzPz2Epdx5AWOwMyqYGI+CvwGWBQZl5UKT8POAr4VWZ+s736p6VHROxCEYw9A+xMEXI0DMgi4qNlvW4UIeuosrwrMAz4NPCFzLyujbqvdhYR/SnCjL9k5uxKeQ/gQWAdYGBm3liWO4bUQkR0zczpDcp/AvwIuCwzv1WWOYbUqogI4C5gfWAIcAx1AVlE9KT4w+EUoHdmPleWrwL8E9gQ6JuZD7Rl39W+yoCMzOy5AHX9PqQWIuJA4I/A34DPZeY7dec7ZeZ75eeOIS0RLrGU6kTEBhTh2HPAJXWnf0zxA+EhEbFCG3dNS6HMHJ6ZT+eC/bVhILA6cF3tP/KyjenAieXLI5dAN7WUysxhmXlrNRwryycCvyxf9quccgyphUbhWOmP5XHjSpljSPMyCOgPfI3i551GDgO6ABfXwjGAzHwTOLN86R8RNS9+H9Jcyi0lzgGmAl+sD8cAauFYyTGkJaJje3dAWgr1L493Nvil9Z2IuI8iQPsUMLStO6dlWm1s3dHg3D0UPxT0jYgumTmj7bqlpVTtB8GZlTLHkBbGPuXx0UqZY0gNRcTmwNnABZl5TznDtZF5jaHb6+qouXSJiC8D61IErI8C92TmrLp6fh9Svb4UM1cHA29GxF5AL4rlkw82mJHqGNISYUAmtbRpeXyqlfNPUwRkm2BApoXT6tjKzJkRMQHYEtgAeKItO6alS0R0BL5Svqz+8OcYUqsi4hiKPaO6Uew/tgPFL6hnV6o5htRC+T3nNxT7IP5oPtXnNYZeiogpwNoRsXxmTl28PdVSrgfFOKqaEBFfy8y7K2V+H1K97crjyxR7av5H9WRE3EOx5cSrZZFjSEuESyyllrqVx7dbOV8rX7kN+qIPF8eWFtTZFH85vS0z/1opdwxpXo6h2Arg+xTh2B3AZyq/UIBjSI2dTLEJ9qGZOW0+dRd0DHVr5bw+nH4NDKAIyVagCDh+BfQEbo+IT1bq+n1I9dYoj98ElgN2BVai+Fnor8BOwA2V+o4hLREGZNLCi/LoEy60uDm2REQMAo6m2AT7kIW9vDw6hppQZvbIzKD4BfVzFH85fygitlmIZhxDTSYi+lDMGvv5YtpY3zHUhDLz1HJfzZczc2pmji0faHUeReBxykI05xhqPh3KY1DMFBuamZMz8zFgf4oHYu0cEZ9ewPYcQ/pADMiklub3l8+P1tWTFpRjS/MUEd8GLgAeB3bJzDfqqjiGNF/lL6g3UWwHsCpwbeW0Y0hzVJZWPgWctICXLegYmrQIXdOHR+2BMztVyvw+pHpvlsfxmflI9UQ5q7U2m75PeXQMaYkwIJNaerI8btLK+drTwFrbo0xqTatjq/wlZX2KDdnHt2WntHSIiO8DFwNjKcKxiQ2qOYa0wDLzeYqwdcuIWK0sdgypakWKsbA5MD0isvZBsVwX4PKy7Pzy9bzG0FoUy+v+z/3HVHqlPFaf/u73IdWrjYm3WjlfC9CWq6vvGNJiZUAmtTS8PH6mfOTwHBGxErA9MA34e1t3TMu8YeVxjwbndgKWB+73aTvNJyJ+CPwCeJgiHHullaqOIS2sj5XH2lPkHEOqmgFc2crHQ2WdkeXr2vLLeY2hz9bVkWpL4qpBhd+HVO8eikBr44jo3OB8r/L4XHl0DGmJMCCT6mTms8CdFJuKfrvu9KkUfwG7NjOntHHXtOwbDLwGHBwR29YKI6IrcEb58rL26JjaT0ScRLEp/2hgQGa+No/qjiHNJSI2i4geDco/EhE/odj4+P7MrP313TGkOTJzWmYe3ugDuKWsdk1Zdn35+tcUwdp3IqJnra2IWIX3n4BZW1anJhARW0ZE9wbl61HMjAb4beWU34c0l/Jnn+splkyeXD0XEbsBu1Msl6w92dsxpCUiMt23TqoXERsC91P8YnEzxeOB/xPYhWJpZd/MfL39eqilRUTsB+xXvuxB8R/4eODesuy1zDymrv5gYDpwHfAG8F8Uj6seDHw+/cbcNCLiq8DVFLN7LqLxXhnPZebVlWscQ5qjXJr7M4q/vj8LvA6sCexMsUn/RIrg9fHKNY4hzVdEnEKxzPKIzLyi7tx3gQspxtv1wLvAQGBtis3+j0FNoxwrx1GswpgAvANsCOwFdAVuA/bPzHcr1/h9SHOJiDWA+4CNKH6OfhBYj2KT/gS+mJk3VOo7hrTYGZBJrYiIdYDTKKburgq8BPwJOLXBxtlqUpVfIFrzfGb2rLtme+AEimUHXYFngKuACzNzVosW9KG1AOMH4O7M7Fd3nWNIAEREL+BIiuX/a1M80n4KxR9z/kIxJlr8n+UY0vzMKyArz+8DHANsQ7Eq5XHg4sy8pi37qfYXETsD3wS2pvhj4QoUe0k9TPEQiN80Cir8PqR65UzEEylCsY9ThK0jgbMys8X2No4hLW4GZJIkSZIkSWpq7kEmSZIkSZKkpmZAJkmSJEmSpKZmQCZJkiRJkqSmZkAmSZIkSZKkpmZAJkmSJEmSpKZmQCZJkiRJkqSmZkAmSZIkSZKkpmZAJkmSJEmSpKZmQCZJkiRJkqSmZkAmSZIkSZKkpmZAJkmSJEmSpKZmQCZJkiRJkqSmZkAmSZIkSZKkpmZAJkmSJEmSpKZmQCZJkiRJkqSmZkAmSZIkSZKkpmZAJkmSJEmSpKb2/wH3/lQRiqVd9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2348e8bde80>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 320,
       "width": 612
      },
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up variables to contain top 10 most used words in Onion\n",
    "news_not_credible_wc_bi = news_not_credible_cvec_df_bi.sum(axis = 0)\n",
    "news_not_credible_top_10_bi = news_not_credible_wc_bi.sort_values(ascending=False).head(10)\n",
    "\n",
    "# Call function\n",
    "bar_plot(news_not_credible_top_10_bi.values, news_not_credible_top_10_bi.index, 'Top 10 bigrams of not credible news','r') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "colab_type": "code",
    "id": "pu1Z1uJDlZXU",
    "outputId": "1d22d9b1-b4ec-43de-c9e4-9c86264ec213"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2348ec3a710>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 320,
       "width": 597
      },
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up variables to contain top 10 most used words in Onion\n",
    "news_credible_wc_bi = news_credible_cvec_df_bi.sum(axis = 0)\n",
    "news_credible_top_10_bi = news_credible_wc_bi.sort_values(ascending=False).head(10)\n",
    "\n",
    "# Call function\n",
    "bar_plot(news_credible_top_10_bi.values, news_credible_top_10_bi.index, 'Top 10 bigrams of credible news','b') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "tn7-TgXfprbA",
    "outputId": "1fe889c0-7610-489c-dd58-b6610e4c9970"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'white house', 'year old'}"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "news_credible_10_list = set(news_credible_top_10_bi.index)\n",
    "news_not_credible_10_list = set(news_not_credible_top_10_bi.index)\n",
    "\n",
    "# Return common words\n",
    "common_bigrams = news_credible_10_list.intersection(news_not_credible_10_list)\n",
    "common_bigrams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TMygmZHCqnvH"
   },
   "source": [
    "**Create custom stop_words to include common frequent words**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "E8PkJM-dqu-y"
   },
   "outputs": [],
   "source": [
    "# Create lists \n",
    "custom = stop_words.ENGLISH_STOP_WORDS\n",
    "custom = list(custom)\n",
    "common_unigrams = list(common_unigrams)\n",
    "common_bigrams = list(common_bigrams)\n",
    "\n",
    "# Append unigrams to list \n",
    "for i in common_unigrams:\n",
    "    custom.append(i)\n",
    "\n",
    "# Append bigrams to list \n",
    "for i in common_bigrams:\n",
    "    split_words = i.split(\" \")\n",
    "    for word in split_words:\n",
    "        custom.append(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 66
    },
    "colab_type": "code",
    "id": "Q_c-DvjPrFD0",
    "outputId": "de9f4c8d-6810-47f7-fe16-5bf12ef84e27"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.540019\n",
       "0    0.459981\n",
       "Name: subreddit, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Baseline score\n",
    "df_news['subreddit'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7KCnwphkrc3i"
   },
   "source": [
    "**Set X (predictor) and y (target) variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "grvaED9srdcL"
   },
   "outputs": [],
   "source": [
    "X = df_news['title']\n",
    "y = df_news['subreddit']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UjbXM0KcrsLv"
   },
   "source": [
    "**Train/Test Split**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "e9ErHwP6rxcu"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    random_state=42,\n",
    "                                                    stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J_iwzmHXsSJd"
   },
   "source": [
    "**CountVectorizer & MultinomialNB (Best Accuracy Score)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 83
    },
    "colab_type": "code",
    "id": "SnPNemMEsUlu",
    "outputId": "36867197-75eb-4fdc-a5ed-31edd9d2cfc9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.8878514158124429\n",
      "Train score 0.9970059880239521\n",
      "Test score 0.8972446338864363\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'cvec__ngram_range': (1, 3), 'nb__alpha': 0.36}"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline([('cvec', CountVectorizer()),    \n",
    "                 ('nb', MultinomialNB())])\n",
    "\n",
    "# Tune GridSearchCV\n",
    "pipe_params = {'cvec__ngram_range': [(1,1),(1,3)],\n",
    "               'nb__alpha': [.36, .6]}\n",
    "\n",
    "gs = GridSearchCV(pipe, param_grid=pipe_params, cv=3)\n",
    "gs.fit(X_train, y_train);\n",
    "print(\"Best score:\", gs.best_score_)\n",
    "print(\"Train score\", gs.score(X_train, y_train))\n",
    "print(\"Test score\", gs.score(X_test, y_test))\n",
    "\n",
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QFVzxvnxtdo4"
   },
   "source": [
    "**CountVectorizer & MultinomialNB: Best Params**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "ypKxqQmPtik_",
    "outputId": "5a1a9864-eee1-4de7-e766-6d6c5ddc07b1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8972446338864363\n"
     ]
    }
   ],
   "source": [
    "#Instantiate the classifier and vectorizer\n",
    "nb = MultinomialNB(alpha = 0.36)\n",
    "cvec = CountVectorizer(ngram_range= (1, 3))\n",
    "\n",
    "# Fit and transform the vectorizor\n",
    "cvec.fit(X_train)\n",
    "\n",
    "Xcvec_train = cvec.transform(X_train)\n",
    "Xcvec_test = cvec.transform(X_test)\n",
    "\n",
    "# Fit the classifier\n",
    "nb.fit(Xcvec_train,y_train)\n",
    "\n",
    "# Create the predictions for Y training data\n",
    "preds = nb.predict(Xcvec_test)\n",
    "\n",
    "print(nb.score(Xcvec_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nqhBksE8txnt"
   },
   "source": [
    "**Confusion Matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "XuICwCMrtzbN",
    "outputId": "fc9bab34-5ec8-46c5-9704-4b56122074bc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2701,  321],\n",
       "       [ 354, 3193]], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a confusion matrix\n",
    "cnf_matrix = metrics.confusion_matrix(y_test, preds)\n",
    "cnf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "colab_type": "code",
    "id": "QJOTxbWpuDWF",
    "outputId": "f2597aa2-d9b4-4875-cc8b-3a3d364d0f25"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,257.44,'Predicted label')"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23492f2edd8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 316,
       "width": 423
      },
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code from https://www.datacamp.com/community/tutorials/understanding-logistic-regression-python\n",
    "# name  of classes\n",
    "class_names=[0,1] \n",
    "\n",
    "# Set fig and axes \n",
    "fig, ax = plt.subplots()\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "\n",
    "# Create heatmap\n",
    "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"Greens\" ,fmt='g')\n",
    "ax.xaxis.set_label_position(\"top\")\n",
    "plt.tight_layout()\n",
    "plt.title('Confusion matrix', y=1.1)\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EAecCxoovqPA"
   },
   "outputs": [],
   "source": [
    "# Assign True Neg, False Pos, False Neg, True Pos variables\n",
    "cnf_matrix = np.array(cnf_matrix).tolist()\n",
    "\n",
    "tn_fp, fn_tp = cnf_matrix\n",
    "\n",
    "tn, fp = tn_fp\n",
    "fn, tp = fn_tp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LJLleLzYv3Zr"
   },
   "source": [
    "**Conclusion 1** - **Accuracy Scores**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 100
    },
    "colab_type": "code",
    "id": "D4D2JJKivvNt",
    "outputId": "0b4286db-232c-41b4-e40b-725659e59650"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 89.72 %\n",
      "Precision: 90.87 %\n",
      "Recall: 90.02 %\n",
      "Specificity: 89.38 %\n",
      "Misclassification Rate: 11.11 %\n"
     ]
    }
   ],
   "source": [
    "# Print Scores \n",
    "print(\"Accuracy:\",round(metrics.accuracy_score(y_test, preds)*100, 2),'%')\n",
    "print(\"Precision:\",round(metrics.precision_score(y_test, preds)*100, 2), '%')\n",
    "print(\"Recall:\",round(metrics.recall_score(y_test, preds)*100, 2), '%')\n",
    "print(\"Specificity:\", round((tn/(tn+fp))*100, 2), '%')\n",
    "print(\"Misclassification Rate:\", round((fp+fn)/(tn+fp+fn+tn)*100, 2), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KQZW5kxey1EI"
   },
   "source": [
    "**CountVectorizer & Logistic Regression** : Best Coefficient Interpretability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 83
    },
    "colab_type": "code",
    "id": "qu2YG6jxzDYi",
    "outputId": "8f465d16-b841-4349-972e-24d16402bca6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.8531411752765655\n",
      "Train score 0.9618897797625089\n",
      "Test score 0.8652762977622165\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'cvec__ngram_range': (1, 1), 'cvec__stop_words': None, 'lr__C': 1}"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline([('cvec', CountVectorizer()),    \n",
    "                 ('lr', LogisticRegression(solver='liblinear'))])\n",
    "\n",
    "# Tune GridSearchCV\n",
    "pipe_params = {'cvec__stop_words': [None, 'english', custom],\n",
    "               'cvec__ngram_range': [(1,1), (2,2), (1,3)],\n",
    "               'lr__C': [0.01, 1]}\n",
    "\n",
    "gs = GridSearchCV(pipe, param_grid=pipe_params, cv=3)\n",
    "gs.fit(X_train, y_train);\n",
    "print(\"Best score:\", gs.best_score_)\n",
    "print(\"Train score\", gs.score(X_train, y_train))\n",
    "print(\"Test score\", gs.score(X_test, y_test))\n",
    "\n",
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "NLdj_kpRzvhc",
    "outputId": "e23c845a-91b3-4c5c-f298-350fab4a1685"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8526411934845486\n"
     ]
    }
   ],
   "source": [
    "#Instantiate the classifier and vectorizer\n",
    "lr = LogisticRegression(C = 1.0, solver='liblinear')\n",
    "cvec2 = CountVectorizer(stop_words = custom)\n",
    "\n",
    "# Fit and transform the vectorizor\n",
    "cvec2.fit(X_train)\n",
    "\n",
    "Xcvec2_train = cvec2.transform(X_train)\n",
    "Xcvec2_test = cvec2.transform(X_test)\n",
    "\n",
    "# Fit the classifier\n",
    "lr.fit(Xcvec2_train,y_train)\n",
    "\n",
    "# Create the predictions for Y training data\n",
    "lr_preds = lr.predict(Xcvec2_test)\n",
    "\n",
    "print(lr.score(Xcvec2_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KwCCp5Ke0fFP"
   },
   "source": [
    "**Coefficient Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "colab_type": "code",
    "id": "e8fTDQtA0gbU",
    "outputId": "2f1048f2-6ea5-437a-8220-df17881262af"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23338640da0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 406,
       "width": 529
      },
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create list of logistic regression coefficients \n",
    "lr_coef = np.array(lr.coef_).tolist()\n",
    "lr_coef = lr_coef[0]\n",
    "\n",
    "# create dataframe from lasso coef\n",
    "lr_coef = pd.DataFrame(np.round_(lr_coef, decimals=3), \n",
    "cvec2.get_feature_names(), columns = [\"penalized_regression_coefficients\"])\n",
    "\n",
    "# sort the values from high to low\n",
    "lr_coef = lr_coef.sort_values(by = 'penalized_regression_coefficients', \n",
    "ascending = False)\n",
    "\n",
    "# Jasmine changing things up here on out! Top half not mine. \n",
    "# create best and worst performing lasso coef dataframes\n",
    "df_head = lr_coef.head(10)\n",
    "df_tail = lr_coef.tail(10)\n",
    "\n",
    "# merge back together\n",
    "df_merged = pd.concat([df_head, df_tail], axis=0)\n",
    "\n",
    "# plot the sorted dataframe\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(8, 6)\n",
    "fig.suptitle('Coefficients', size=14)\n",
    "ax = sns.barplot(x = 'penalized_regression_coefficients', y= df_merged.index, \n",
    "data=df_merged)\n",
    "ax.set(xlabel='Penalized Regression Coefficients')\n",
    "plt.tight_layout(pad=3, w_pad=0, h_pad=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 66
    },
    "colab_type": "code",
    "id": "7GbDHzon013p",
    "outputId": "96492d56-500b-4439-ce24-d6054d0ddbea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The word that contributes the most positively to being from r/TheOnion (Not Credible) is incredible followed by questions and ftw .\n",
      "-----------------------------------\n",
      "The word that contributes the most positively to being from r/nottheonion (Credible) is arrested followed by florida and title .\n"
     ]
    }
   ],
   "source": [
    "print(\"The word that contributes the most positively to being from r/TheOnion (Not Credible) is\", \n",
    "      df_merged.index[0], \"followed by\", \n",
    "      df_merged.index[1], \"and\",\n",
    "      df_merged.index[2],\".\")\n",
    "\n",
    "print(\"-----------------------------------\")\n",
    "\n",
    "print(\"The word that contributes the most positively to being from r/nottheonion (Credible) is\", \n",
    "      df_merged.index[-1], \"followed by\", \n",
    "      df_merged.index[-2], \"and\",\n",
    "      df_merged.index[-3],\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "colab_type": "code",
    "id": "emy4akkC1Ddb",
    "outputId": "171313b4-7fc6-46f8-f225-df8a8a240258"
   },
   "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>penalized_regression_coefficients</th>\n",
       "      <th>exp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>incredible</th>\n",
       "      <td>2.350</td>\n",
       "      <td>10.485570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>questions</th>\n",
       "      <td>2.225</td>\n",
       "      <td>9.253483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftw</th>\n",
       "      <td>2.043</td>\n",
       "      <td>7.713716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heartbreaking</th>\n",
       "      <td>2.005</td>\n",
       "      <td>7.426094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shit</th>\n",
       "      <td>1.838</td>\n",
       "      <td>6.283958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beautiful</th>\n",
       "      <td>1.809</td>\n",
       "      <td>6.104340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resistance</th>\n",
       "      <td>1.685</td>\n",
       "      <td>5.392451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kavanaugh</th>\n",
       "      <td>1.676</td>\n",
       "      <td>5.344137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pretty</th>\n",
       "      <td>1.617</td>\n",
       "      <td>5.037954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fucking</th>\n",
       "      <td>1.603</td>\n",
       "      <td>4.967914</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               penalized_regression_coefficients        exp\n",
       "incredible                                 2.350  10.485570\n",
       "questions                                  2.225   9.253483\n",
       "ftw                                        2.043   7.713716\n",
       "heartbreaking                              2.005   7.426094\n",
       "shit                                       1.838   6.283958\n",
       "beautiful                                  1.809   6.104340\n",
       "resistance                                 1.685   5.392451\n",
       "kavanaugh                                  1.676   5.344137\n",
       "pretty                                     1.617   5.037954\n",
       "fucking                                    1.603   4.967914"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show coefficients that affect r/TheOnion (Not Credible)\n",
    "df_merged_head = df_merged.head(10)\n",
    "exp = df_merged_head['penalized_regression_coefficients'].apply(lambda x: np.exp(x))\n",
    "df_merged_head.insert(1, 'exp', exp)\n",
    "df_merged_head.sort_values('exp', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Xxj9KNSl2c3F"
   },
   "source": [
    "**Conclusion 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "4hqnzFic16uG",
    "outputId": "ce2cc901-2041-46a6-d9ad-d751f8da34ad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As occurences of incredible increase by 1 in a title, that title is 10.49 times as likely to be classified as r/TheOnion (not credible).\n"
     ]
    }
   ],
   "source": [
    "print(\"As occurences of\", df_merged_head.index[0], \"increase by 1 in a title, that title is\", \n",
    "      round(df_merged_head['exp'][0],2), \"times as likely to be classified as r/TheOnion (not credible).\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "colab_type": "code",
    "id": "c5f6VQid2Hng",
    "outputId": "b51f2ba8-2475-4019-e464-a21167605746"
   },
   "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>penalized_regression_coefficients</th>\n",
       "      <th>exp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>arrested</th>\n",
       "      <td>-2.810</td>\n",
       "      <td>16.609918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>florida</th>\n",
       "      <td>-2.777</td>\n",
       "      <td>16.070736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <td>-2.733</td>\n",
       "      <td>15.378955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charged</th>\n",
       "      <td>-2.675</td>\n",
       "      <td>14.512350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>australia</th>\n",
       "      <td>-2.663</td>\n",
       "      <td>14.339242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cannabis</th>\n",
       "      <td>-2.391</td>\n",
       "      <td>10.924413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blackface</th>\n",
       "      <td>-2.342</td>\n",
       "      <td>10.402020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>police</th>\n",
       "      <td>-2.341</td>\n",
       "      <td>10.391623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cops</th>\n",
       "      <td>-2.324</td>\n",
       "      <td>10.216459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beer</th>\n",
       "      <td>-2.284</td>\n",
       "      <td>9.815865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           penalized_regression_coefficients        exp\n",
       "arrested                              -2.810  16.609918\n",
       "florida                               -2.777  16.070736\n",
       "title                                 -2.733  15.378955\n",
       "charged                               -2.675  14.512350\n",
       "australia                             -2.663  14.339242\n",
       "cannabis                              -2.391  10.924413\n",
       "blackface                             -2.342  10.402020\n",
       "police                                -2.341  10.391623\n",
       "cops                                  -2.324  10.216459\n",
       "beer                                  -2.284   9.815865"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show coefficients that affect r/nottheonion (Credible)\n",
    "df_merged_tail = df_merged.tail(10)\n",
    "exp = df_merged_tail['penalized_regression_coefficients'].apply(lambda x: np.exp(x * -1))\n",
    "df_merged_tail.insert(1, 'exp', exp)\n",
    "df_merged_tail.sort_values('exp', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nhNWtiut2YtG"
   },
   "source": [
    "**Conclusion 3**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "0zW24eKA2LI2",
    "outputId": "fd978dfb-f404-4f67-a9b8-255ea3e2131a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As occurences of arrested increase by 1 in a title, that title is 16.61 times as likely to be classified as r/nottheonion.\n"
     ]
    }
   ],
   "source": [
    "print(\"As occurences of\", df_merged_tail.index[-1], \"increase by 1 in a title, that title is\", \n",
    "      round(df_merged_tail['exp'][-1],2), \"times as likely to be classified as r/nottheonion.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "d-M0lGs52pFX"
   },
   "source": [
    "# **Automaticity Detection**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8rL00gqLSU2f"
   },
   "source": [
    "**Dataset From - https://www.kaggle.com/c/twitter-bot-classification**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 501
    },
    "colab_type": "code",
    "id": "XsNYGF3e3Dpy",
    "outputId": "a5678706-59e3-4c7a-aff6-ffdbdd31442a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>id_str</th>\n",
       "      <th>screen_name</th>\n",
       "      <th>location</th>\n",
       "      <th>description</th>\n",
       "      <th>url</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>listed_count</th>\n",
       "      <th>created_at</th>\n",
       "      <th>favourites_count</th>\n",
       "      <th>verified</th>\n",
       "      <th>statuses_count</th>\n",
       "      <th>lang</th>\n",
       "      <th>status</th>\n",
       "      <th>default_profile</th>\n",
       "      <th>default_profile_image</th>\n",
       "      <th>has_extended_profile</th>\n",
       "      <th>name</th>\n",
       "      <th>bot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.160000e+17</td>\n",
       "      <td>\"815745789754417152\"</td>\n",
       "      <td>\"HoustonPokeMap\"</td>\n",
       "      <td>\"Houston, TX\"</td>\n",
       "      <td>\"Rare and strong PokŽmon in Houston, TX. See m...</td>\n",
       "      <td>\"https://t.co/dnWuDbFRkt\"</td>\n",
       "      <td>1291</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>\"Mon Jan 02 02:25:26 +0000 2017\"</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>78554</td>\n",
       "      <td>\"en\"</td>\n",
       "      <td>{\\r      \"created_at\": \"Sun Mar 12 15:44:04 +0...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>\"Houston PokŽ Alert\"</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.843621e+09</td>\n",
       "      <td>4843621225</td>\n",
       "      <td>kernyeahx</td>\n",
       "      <td>Templeville town, MD, USA</td>\n",
       "      <td>From late 2014 Socium Marketplace will make sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>349</td>\n",
       "      <td>0</td>\n",
       "      <td>2/1/2016 7:37</td>\n",
       "      <td>38</td>\n",
       "      <td>False</td>\n",
       "      <td>31</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Keri Nelson</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.303727e+09</td>\n",
       "      <td>4303727112</td>\n",
       "      <td>mattlieberisbot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Inspired by the smart, funny folks at @replyal...</td>\n",
       "      <td>https://t.co/P1e1o0m4KC</td>\n",
       "      <td>1086</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>Fri Nov 20 18:53:22 +0000 2015</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>713</td>\n",
       "      <td>en</td>\n",
       "      <td>{'retweeted': False, 'is_quote_status': False,...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Matt Lieber Is Bot</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.063139e+09</td>\n",
       "      <td>3063139353</td>\n",
       "      <td>sc_papers</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2/25/2015 20:11</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>676</td>\n",
       "      <td>en</td>\n",
       "      <td>Construction of human anti-tetanus single-chai...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>single cell papers</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.955142e+09</td>\n",
       "      <td>2955142070</td>\n",
       "      <td>lucarivera16</td>\n",
       "      <td>Dublin, United States</td>\n",
       "      <td>Inspiring cooks everywhere since 1956.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>745</td>\n",
       "      <td>0</td>\n",
       "      <td>1/1/2015 17:44</td>\n",
       "      <td>146</td>\n",
       "      <td>False</td>\n",
       "      <td>185</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>lucarivera16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                id_str       screen_name  \\\n",
       "0  8.160000e+17  \"815745789754417152\"  \"HoustonPokeMap\"   \n",
       "1  4.843621e+09            4843621225         kernyeahx   \n",
       "2  4.303727e+09            4303727112   mattlieberisbot   \n",
       "3  3.063139e+09            3063139353         sc_papers   \n",
       "4  2.955142e+09            2955142070      lucarivera16   \n",
       "\n",
       "                    location  \\\n",
       "0              \"Houston, TX\"   \n",
       "1  Templeville town, MD, USA   \n",
       "2                        NaN   \n",
       "3                        NaN   \n",
       "4      Dublin, United States   \n",
       "\n",
       "                                         description  \\\n",
       "0  \"Rare and strong PokŽmon in Houston, TX. See m...   \n",
       "1  From late 2014 Socium Marketplace will make sh...   \n",
       "2  Inspired by the smart, funny folks at @replyal...   \n",
       "3                                                NaN   \n",
       "4             Inspiring cooks everywhere since 1956.   \n",
       "\n",
       "                         url  followers_count  friends_count  listed_count  \\\n",
       "0  \"https://t.co/dnWuDbFRkt\"             1291              0            10   \n",
       "1                        NaN                1            349             0   \n",
       "2    https://t.co/P1e1o0m4KC             1086              0            14   \n",
       "3                        NaN               33              0             8   \n",
       "4                        NaN               11            745             0   \n",
       "\n",
       "                         created_at  favourites_count  verified  \\\n",
       "0  \"Mon Jan 02 02:25:26 +0000 2017\"                 0     False   \n",
       "1                     2/1/2016 7:37                38     False   \n",
       "2    Fri Nov 20 18:53:22 +0000 2015                 0     False   \n",
       "3                   2/25/2015 20:11                 0     False   \n",
       "4                    1/1/2015 17:44               146     False   \n",
       "\n",
       "   statuses_count  lang                                             status  \\\n",
       "0           78554  \"en\"  {\\r      \"created_at\": \"Sun Mar 12 15:44:04 +0...   \n",
       "1              31    en                                                NaN   \n",
       "2             713    en  {'retweeted': False, 'is_quote_status': False,...   \n",
       "3             676    en  Construction of human anti-tetanus single-chai...   \n",
       "4             185    en                                                NaN   \n",
       "\n",
       "   default_profile  default_profile_image has_extended_profile  \\\n",
       "0             True                  False                False   \n",
       "1             True                  False                False   \n",
       "2             True                  False                False   \n",
       "3             True                   True                False   \n",
       "4            False                  False                False   \n",
       "\n",
       "                   name  bot  \n",
       "0  \"Houston PokŽ Alert\"    1  \n",
       "1           Keri Nelson    1  \n",
       "2    Matt Lieber Is Bot    1  \n",
       "3    single cell papers    1  \n",
       "4          lucarivera16    1  "
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bot_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Iio_ZDU23keq"
   },
   "outputs": [],
   "source": [
    "training_data = df_bot_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "colab_type": "code",
    "id": "f7_A4igm328u",
    "outputId": "252716c9-4708-4c71-b964-ac67ae215c1b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id',\n",
       " 'id_str',\n",
       " 'screen_name',\n",
       " 'location',\n",
       " 'description',\n",
       " 'url',\n",
       " 'followers_count',\n",
       " 'friends_count',\n",
       " 'listed_count',\n",
       " 'created_at',\n",
       " 'favourites_count',\n",
       " 'verified',\n",
       " 'statuses_count',\n",
       " 'lang',\n",
       " 'status',\n",
       " 'default_profile',\n",
       " 'default_profile_image',\n",
       " 'has_extended_profile',\n",
       " 'name',\n",
       " 'bot']"
      ]
     },
     "execution_count": 41,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(training_data.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "colab_type": "code",
    "id": "2d-DiG8c-8ss",
    "outputId": "4a985810-716a-400c-896d-5b44f616e699"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2348f034358>"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23492873e10>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 397
      },
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(training_data['bot'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 433
    },
    "colab_type": "code",
    "id": "1ieY1Y7Q_Fjj",
    "outputId": "415981ae-be46-4a82-ef85-69f6cf9b7f76"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2797 entries, 0 to 2796\n",
      "Data columns (total 20 columns):\n",
      "id                       2797 non-null float64\n",
      "id_str                   2797 non-null object\n",
      "screen_name              2797 non-null object\n",
      "location                 1777 non-null object\n",
      "description              2394 non-null object\n",
      "url                      1455 non-null object\n",
      "followers_count          2797 non-null int64\n",
      "friends_count            2797 non-null int64\n",
      "listed_count             2797 non-null int64\n",
      "created_at               2797 non-null object\n",
      "favourites_count         2797 non-null int64\n",
      "verified                 2797 non-null bool\n",
      "statuses_count           2797 non-null int64\n",
      "lang                     2797 non-null object\n",
      "status                   2508 non-null object\n",
      "default_profile          2797 non-null bool\n",
      "default_profile_image    2797 non-null bool\n",
      "has_extended_profile     2698 non-null object\n",
      "name                     2797 non-null object\n",
      "bot                      2797 non-null int64\n",
      "dtypes: bool(3), float64(1), int64(6), object(10)\n",
      "memory usage: 379.8+ KB\n"
     ]
    }
   ],
   "source": [
    "training_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 366
    },
    "colab_type": "code",
    "id": "l9cfL-2x_RCz",
    "outputId": "8b773b6e-8d97-475f-9584-d45946389cfc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                          0\n",
       "id_str                      0\n",
       "screen_name                 0\n",
       "location                 1020\n",
       "description               403\n",
       "url                      1342\n",
       "followers_count             0\n",
       "friends_count               0\n",
       "listed_count                0\n",
       "created_at                  0\n",
       "favourites_count            0\n",
       "verified                    0\n",
       "statuses_count              0\n",
       "lang                        0\n",
       "status                    289\n",
       "default_profile             0\n",
       "default_profile_image       0\n",
       "has_extended_profile       99\n",
       "name                        0\n",
       "bot                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.apply(lambda x: sum(x.isnull()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "colab_type": "code",
    "id": "DXNGNyG88qgq",
    "outputId": "5d26a87a-e8c5-4134-a606-bb1c0c3e6e95"
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>screen_name</th>\n",
       "      <th>location</th>\n",
       "      <th>description</th>\n",
       "      <th>url</th>\n",
       "      <th>created_at</th>\n",
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       "      <th>0</th>\n",
       "      <td>\"HoustonPokeMap\"</td>\n",
       "      <td>\"Houston, TX\"</td>\n",
       "      <td>\"Rare and strong PokŽmon in Houston, TX. See m...</td>\n",
       "      <td>\"https://t.co/dnWuDbFRkt\"</td>\n",
       "      <td>\"Mon Jan 02 02:25:26 +0000 2017\"</td>\n",
       "      <td>\"en\"</td>\n",
       "      <td>{\\r      \"created_at\": \"Sun Mar 12 15:44:04 +0...</td>\n",
       "      <td>False</td>\n",
       "      <td>\"Houston PokŽ Alert\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kernyeahx</td>\n",
       "      <td>Templeville town, MD, USA</td>\n",
       "      <td>From late 2014 Socium Marketplace will make sh...</td>\n",
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       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Keri Nelson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mattlieberisbot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Inspired by the smart, funny folks at @replyal...</td>\n",
       "      <td>https://t.co/P1e1o0m4KC</td>\n",
       "      <td>Fri Nov 20 18:53:22 +0000 2015</td>\n",
       "      <td>en</td>\n",
       "      <td>{'retweeted': False, 'is_quote_status': False,...</td>\n",
       "      <td>False</td>\n",
       "      <td>Matt Lieber Is Bot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sc_papers</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/25/2015 20:11</td>\n",
       "      <td>en</td>\n",
       "      <td>Construction of human anti-tetanus single-chai...</td>\n",
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       "      <td>single cell papers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lucarivera16</td>\n",
       "      <td>Dublin, United States</td>\n",
       "      <td>Inspiring cooks everywhere since 1956.</td>\n",
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       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>lucarivera16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        screen_name                   location  \\\n",
       "0  \"HoustonPokeMap\"              \"Houston, TX\"   \n",
       "1         kernyeahx  Templeville town, MD, USA   \n",
       "2   mattlieberisbot                        NaN   \n",
       "3         sc_papers                        NaN   \n",
       "4      lucarivera16      Dublin, United States   \n",
       "\n",
       "                                         description  \\\n",
       "0  \"Rare and strong PokŽmon in Houston, TX. See m...   \n",
       "1  From late 2014 Socium Marketplace will make sh...   \n",
       "2  Inspired by the smart, funny folks at @replyal...   \n",
       "3                                                NaN   \n",
       "4             Inspiring cooks everywhere since 1956.   \n",
       "\n",
       "                         url                        created_at  lang  \\\n",
       "0  \"https://t.co/dnWuDbFRkt\"  \"Mon Jan 02 02:25:26 +0000 2017\"  \"en\"   \n",
       "1                        NaN                     2/1/2016 7:37    en   \n",
       "2    https://t.co/P1e1o0m4KC    Fri Nov 20 18:53:22 +0000 2015    en   \n",
       "3                        NaN                   2/25/2015 20:11    en   \n",
       "4                        NaN                    1/1/2015 17:44    en   \n",
       "\n",
       "                                              status has_extended_profile  \\\n",
       "0  {\\r      \"created_at\": \"Sun Mar 12 15:44:04 +0...                False   \n",
       "1                                                NaN                False   \n",
       "2  {'retweeted': False, 'is_quote_status': False,...                False   \n",
       "3  Construction of human anti-tetanus single-chai...                False   \n",
       "4                                                NaN                False   \n",
       "\n",
       "                   name  \n",
       "0  \"Houston PokŽ Alert\"  \n",
       "1           Keri Nelson  \n",
       "2    Matt Lieber Is Bot  \n",
       "3    single cell papers  \n",
       "4          lucarivera16  "
      ]
     },
     "execution_count": 45,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_cols=training_data[['screen_name','location','description','url','created_at','lang','status','has_extended_profile','name']]\n",
    "text_cols.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 100
    },
    "colab_type": "code",
    "id": "FBhOFFzO_ldP",
    "outputId": "91ebe28c-db5e-442a-a796-b396f69bc573"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4238: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().rename(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "text_cols.rename(columns={'screen_name':'screen_name_processed'}, inplace=True)\n",
    "text_cols.rename(columns={'name':'name_processed'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 116
    },
    "colab_type": "code",
    "id": "UlfQv-V-_y8d",
    "outputId": "83d370e2-d174-41f5-f817-52e18b55552a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "text_cols['screen_name_processed_num_count'] = \"\"\n",
    "for index, row in text_cols.iterrows():\n",
    "    row['screen_name_processed'] = row['screen_name_processed'].lower() #Convert text to lowercase\n",
    "    row['screen_name_processed_num_count'] = (sum(c.isdigit() for c in row['screen_name_processed'])) #create new column to get number of numbers\n",
    "    row['screen_name_processed'] = re.sub(r'\\d+','', row['screen_name_processed']) #Remove numbers\n",
    "    row['screen_name_processed']= row['screen_name_processed'].translate(str.maketrans('','',string.punctuation)) #Remove punctuation\n",
    "    row['screen_name_processed'] = row['screen_name_processed'].strip() #Remove whitespaces\n",
    "    row['screen_name_processed'] = [i for i in word_tokenize(row['screen_name_processed']) if not i in stop_words] #Tokenization - REMOVE STOP WORDS\n",
    "    for word in row['screen_name_processed']:\n",
    "        row['screen_name_processed'] = lemmatizer.lemmatize(word)\n",
    "#     for word in  row['screen_name_processed']:\n",
    "#         row['screen_name_processed'] = (stemmer.stem(word))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 116
    },
    "colab_type": "code",
    "id": "BS1p0Ac9Ab0o",
    "outputId": "469c7579-5ce9-4a72-b1f2-91304f3c1a02"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "text_cols['name_processed_num_count'] = \"\"\n",
    "for index, row in text_cols.iterrows():\n",
    "    row['name_processed'] = row['name_processed'].lower() #Convert text to lowercase\n",
    "    row['name_processed_num_count'] = (sum(c.isdigit() for c in row['name_processed'])) #create new column to get number of numbers\n",
    "    row['name_processed'] = re.sub(r'\\d+','', row['name_processed']) #Remove numbers\n",
    "    row['name_processed']= row['name_processed'].translate(str.maketrans('','',string.punctuation)) #Remove punctuation\n",
    "    row['name_processed'] = row['name_processed'].strip() #Remove whitespaces\n",
    "    row['name_processed'] = [i for i in word_tokenize(row['name_processed']) if not i in stop_words] #Tokenization - REMOVE STOP WORDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 501
    },
    "colab_type": "code",
    "id": "Q8nuo6DfAlf6",
    "outputId": "86688d3d-768a-4cf5-baf4-909069735991"
   },
   "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>screen_name_processed</th>\n",
       "      <th>location</th>\n",
       "      <th>description</th>\n",
       "      <th>url</th>\n",
       "      <th>created_at</th>\n",
       "      <th>lang</th>\n",
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       "      <th>has_extended_profile</th>\n",
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       "      <th>screen_name_processed_num_count</th>\n",
       "      <th>name_processed_num_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>houstonpokemap</td>\n",
       "      <td>\"Houston, TX\"</td>\n",
       "      <td>\"Rare and strong PokŽmon in Houston, TX. See m...</td>\n",
       "      <td>\"https://t.co/dnWuDbFRkt\"</td>\n",
       "      <td>\"Mon Jan 02 02:25:26 +0000 2017\"</td>\n",
       "      <td>\"en\"</td>\n",
       "      <td>{\\r      \"created_at\": \"Sun Mar 12 15:44:04 +0...</td>\n",
       "      <td>False</td>\n",
       "      <td>[houston, pokž, alert]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kernyeahx</td>\n",
       "      <td>Templeville town, MD, USA</td>\n",
       "      <td>From late 2014 Socium Marketplace will make sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/1/2016 7:37</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>[keri, nelson]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mattlieberisbot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Inspired by the smart, funny folks at @replyal...</td>\n",
       "      <td>https://t.co/P1e1o0m4KC</td>\n",
       "      <td>Fri Nov 20 18:53:22 +0000 2015</td>\n",
       "      <td>en</td>\n",
       "      <td>{'retweeted': False, 'is_quote_status': False,...</td>\n",
       "      <td>False</td>\n",
       "      <td>[matt, lieber, bot]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>scpapers</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/25/2015 20:11</td>\n",
       "      <td>en</td>\n",
       "      <td>Construction of human anti-tetanus single-chai...</td>\n",
       "      <td>False</td>\n",
       "      <td>[single, cell, papers]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lucarivera</td>\n",
       "      <td>Dublin, United States</td>\n",
       "      <td>Inspiring cooks everywhere since 1956.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/1/2015 17:44</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>[lucarivera]</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  screen_name_processed                   location  \\\n",
       "0        houstonpokemap              \"Houston, TX\"   \n",
       "1             kernyeahx  Templeville town, MD, USA   \n",
       "2       mattlieberisbot                        NaN   \n",
       "3              scpapers                        NaN   \n",
       "4            lucarivera      Dublin, United States   \n",
       "\n",
       "                                         description  \\\n",
       "0  \"Rare and strong PokŽmon in Houston, TX. See m...   \n",
       "1  From late 2014 Socium Marketplace will make sh...   \n",
       "2  Inspired by the smart, funny folks at @replyal...   \n",
       "3                                                NaN   \n",
       "4             Inspiring cooks everywhere since 1956.   \n",
       "\n",
       "                         url                        created_at  lang  \\\n",
       "0  \"https://t.co/dnWuDbFRkt\"  \"Mon Jan 02 02:25:26 +0000 2017\"  \"en\"   \n",
       "1                        NaN                     2/1/2016 7:37    en   \n",
       "2    https://t.co/P1e1o0m4KC    Fri Nov 20 18:53:22 +0000 2015    en   \n",
       "3                        NaN                   2/25/2015 20:11    en   \n",
       "4                        NaN                    1/1/2015 17:44    en   \n",
       "\n",
       "                                              status has_extended_profile  \\\n",
       "0  {\\r      \"created_at\": \"Sun Mar 12 15:44:04 +0...                False   \n",
       "1                                                NaN                False   \n",
       "2  {'retweeted': False, 'is_quote_status': False,...                False   \n",
       "3  Construction of human anti-tetanus single-chai...                False   \n",
       "4                                                NaN                False   \n",
       "\n",
       "           name_processed screen_name_processed_num_count  \\\n",
       "0  [houston, pokž, alert]                               0   \n",
       "1          [keri, nelson]                               0   \n",
       "2     [matt, lieber, bot]                               0   \n",
       "3  [single, cell, papers]                               0   \n",
       "4            [lucarivera]                               2   \n",
       "\n",
       "  name_processed_num_count  \n",
       "0                        0  \n",
       "1                        0  \n",
       "2                        0  \n",
       "3                        0  \n",
       "4                        2  "
      ]
     },
     "execution_count": 49,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_cols.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6V8RLWYhAvvb"
   },
   "outputs": [],
   "source": [
    "def impute_na(variable):\n",
    "    for i, row in text_cols[text_cols[variable].isnull()].iterrows():\n",
    "        obs_sample = text_cols[variable].dropna().sample(1, random_state=int(row.screen_name_processed_num_count))\n",
    "        obs_sample.index = [i]\n",
    "        text_cols.at[i, variable] = obs_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VDgJUxnzAygX"
   },
   "outputs": [],
   "source": [
    "impute_na('has_extended_profile')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 416
    },
    "colab_type": "code",
    "id": "bRkIaZjsBBuF",
    "outputId": "5650cc22-c13b-44c4-b3f6-dd903c234ec3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "word_list = r'bot|b0t|cannabis|tweet me|mishear|follow me|updates every|gorilla|yes_ofc|forget' \\\n",
    "                    r'expos|kill|clit|bbb|butt|fuck|XXX|sex|truthe|fake|anony|free|virus|funky|RNA|kuck|jargon' \\\n",
    "                    r'nerd|swag|jack|bang|bonsai|chick|prison|paper|pokem|xx|freak|ffd|dunia|clone|genie|bbb' \\\n",
    "                    r'ffd|onlyman|emoji|joke|troll|droop|free|every|wow|cheese|yeah|bio|magic|wizard|face'\n",
    "            \n",
    "text_cols['screen_name_binary'] = training_data.screen_name.str.contains(word_list, case=False, na=False)\n",
    "text_cols['name_binary'] = training_data.name.str.contains(word_list, case=False, na=False)\n",
    "text_cols['description_binary'] = training_data.description.str.contains(word_list, case=False, na=False)\n",
    "text_cols['status_binary'] = training_data.status.str.contains(word_list, case=False, na=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 116
    },
    "colab_type": "code",
    "id": "du5lo986BIHQ",
    "outputId": "684acf6c-eed2-41f9-afc5-e3beae1494d9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "text_cols['listed_count_binary'] = (training_data.listed_count>20000)==False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 116
    },
    "colab_type": "code",
    "id": "4Ie4hrheBMRL",
    "outputId": "3eccb83e-b8da-43b3-a0c4-b7bb7f09c6dc"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "for column in training_data:\n",
    "    text_cols[column+'_NA'] = np.where(training_data[column].isnull(), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "colab_type": "code",
    "id": "fyGlsGwLBQ99",
    "outputId": "6a2445d7-4e8f-47ca-e730-90975f0ebc0a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id_NA',\n",
       " 'id_str_NA',\n",
       " 'screen_name_NA',\n",
       " 'location_NA',\n",
       " 'description_NA',\n",
       " 'url_NA',\n",
       " 'followers_count_NA',\n",
       " 'friends_count_NA',\n",
       " 'listed_count_NA',\n",
       " 'created_at_NA',\n",
       " 'favourites_count_NA',\n",
       " 'verified_NA',\n",
       " 'statuses_count_NA',\n",
       " 'lang_NA',\n",
       " 'status_NA',\n",
       " 'default_profile_NA',\n",
       " 'default_profile_image_NA',\n",
       " 'has_extended_profile_NA',\n",
       " 'name_NA',\n",
       " 'bot_NA']"
      ]
     },
     "execution_count": 55,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NA_cols = [col for col in text_cols.columns if 'NA' in col]\n",
    "NA_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yzBPsrp7DksL"
   },
   "outputs": [],
   "source": [
    "text_cols.has_extended_profile = text_cols.has_extended_profile.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lDwRP2qtDmnf"
   },
   "outputs": [],
   "source": [
    "text_cols.rename(columns={'has_extended_profile':'has_extended_profile_processed'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HdEmsRReBVu1"
   },
   "outputs": [],
   "source": [
    "text_cols_features = text_cols[['has_extended_profile_processed','name_processed_num_count','screen_name_processed_num_count','screen_name_binary', 'name_binary', 'description_binary', 'status_binary', 'listed_count_binary','location_NA','description_NA','url_NA','followers_count_NA','friends_count_NA','listed_count_NA','created_at_NA','favourites_count_NA','verified_NA','statuses_count_NA','lang_NA','status_NA','default_profile_NA','default_profile_image_NA','has_extended_profile_NA','name_NA']].copy()\n",
    "training_data_features = training_data[['verified', 'followers_count', 'friends_count', 'statuses_count','bot']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_T9GVJLQBuHk"
   },
   "outputs": [],
   "source": [
    "result = pd.concat([text_cols_features, training_data_features], axis=1, sort=False)\n",
    "result.to_csv('D:/processed.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "b3W3hMMHB6zn",
    "outputId": "86dceafc-e9d1-4ce8-b604-9e9e399c1487"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1321, 4638)\n"
     ]
    }
   ],
   "source": [
    "mask_on = training_data['bot'] == 1\n",
    "training_data_bot_description = training_data[mask_on]['description']\n",
    "\n",
    "# Instantiate a CountVectorizer\n",
    "cv1 = CountVectorizer(stop_words = 'english')\n",
    "\n",
    "# Fit and transform the vectorizer on our corpus\n",
    "bot_cvec = cv1.fit_transform(training_data_bot_description.values.astype('U'))\n",
    "\n",
    "# Convert onion_cvec into a DataFrame\n",
    "bot_cvec_df = pd.DataFrame(bot_cvec.toarray(),\n",
    "                   columns=cv1.get_feature_names())\n",
    "\n",
    "# Inspect head of Onion Titles cvec\n",
    "print(bot_cvec_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2WrvDmfDCBV8"
   },
   "outputs": [],
   "source": [
    "def bar_plot(x, y, title, color):    \n",
    "    \n",
    "    # Set up barplot \n",
    "    plt.figure(figsize=(9,5))\n",
    "    g=sns.barplot(x, y, color = color)    \n",
    "    ax=g\n",
    "\n",
    "    # Label the graph\n",
    "    plt.title(title, fontsize = 15)\n",
    "    plt.xticks(fontsize = 10)\n",
    "\n",
    "    # Enable bar values\n",
    "    # Code modified from http://robertmitchellv.com/blog-bar-chart-annotations-pandas-mpl.html\n",
    "    # create a list to collect the plt.patches data\n",
    "    totals = []\n",
    "\n",
    "    # find the values and append to list\n",
    "    for p in ax.patches:\n",
    "        totals.append(p.get_width())\n",
    "\n",
    "    # set individual bar lables using above list\n",
    "    total = sum(totals)\n",
    "\n",
    "    # set individual bar lables using above list\n",
    "    for p in ax.patches:\n",
    "        # get_width pulls left or right; get_y pushes up or down\n",
    "        ax.text(p.get_width()+.3, p.get_y()+.38, \\\n",
    "                int(p.get_width()), fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "colab_type": "code",
    "id": "NmEv-P9JCO8V",
    "outputId": "49c542c4-5814-4519-b6f6-deecd54d70b1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234989fd320>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 320,
       "width": 559
      },
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bot_wc = bot_cvec_df.sum(axis = 0)\n",
    "bot_top_10 = bot_wc.sort_values(ascending=False).head(10)\n",
    "\n",
    "# Call function\n",
    "bar_plot(bot_top_10.values, bot_top_10.index, 'Top 10 unigrams on Description of bots','r') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4fZ75cRQCieP"
   },
   "source": [
    "**Modling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 83
    },
    "colab_type": "code",
    "id": "Yt-jKy6-Xjm1",
    "outputId": "ef0cecbd-e35e-4a1e-8d75-a4ff6d2b814b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier Train Accuracy Score : 0.9221902017291066\n",
      "Random Forest Classifier Test Score : 0.8726190476190476\n"
     ]
    }
   ],
   "source": [
    "result_no_f = training_data_features\n",
    "training_result_no_f = result_no_f.drop('bot', axis=1)\n",
    "X = training_result_no_f.as_matrix()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, result_no_f.bot, test_size=0.3,\n",
    "                                                    random_state=0)\n",
    "ovrsmple = SMOTE(ratio='minority')\n",
    "X_train, y_train = ovrsmple.fit_sample(X_train, y_train)\n",
    "rfc = sklearn.ensemble.RandomForestClassifier(n_estimators=500,\n",
    "                                              min_samples_split=5,\n",
    "                                              criterion='gini', \n",
    "                                              max_features='auto',\n",
    "                                              max_depth = 8,\n",
    "                                              oob_score=True,\n",
    "                                              random_state=42,\n",
    "                                              n_jobs=-1)\n",
    "rfc.fit(X_train, y_train)\n",
    "\n",
    "print('Random Forest Classifier Train Accuracy Score :', rfc.score(X_train, y_train))\n",
    "print('Random Forest Classifier Test Score :', rfc.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "D8stOgFlCyy8",
    "outputId": "92a0f82b-5903-4cb7-94fb-d72815032cd2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "training = result.drop('bot', axis=1)\n",
    "X = training.as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "r52F98I7C7sP",
    "outputId": "66208e83-7246-4769-fd89-a7d302124382"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1957, 28), (840, 28))"
      ]
     },
     "execution_count": 65,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, result.bot, test_size=0.3,\n",
    "                                                    random_state=0)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 66
    },
    "colab_type": "code",
    "id": "P5ge8wwoC_6e",
    "outputId": "f077a6b2-9952-4b2c-ebe4-1c9d848041c1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1041\n",
       "1     916\n",
       "Name: bot, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 66
    },
    "colab_type": "code",
    "id": "99VNnLpWDBSs",
    "outputId": "54ef0245-f483-4c4b-c117-73a552f8eab2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    435\n",
       "1    405\n",
       "Name: bot, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ih37xe_BDORF"
   },
   "outputs": [],
   "source": [
    "ovrsmple = SMOTE(ratio='minority')\n",
    "\n",
    "X_train, y_train = ovrsmple.fit_sample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "G4ERX8QFD38n",
    "outputId": "4dcca253-5118-4e8b-fa23-cbbb3bb17ae0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1041, 1041], dtype=int64)"
      ]
     },
     "execution_count": 69,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "X4wtdsBQD9Wv"
   },
   "outputs": [],
   "source": [
    "rfc = sklearn.ensemble.RandomForestClassifier(n_estimators=500,\n",
    "                                              min_samples_split=5,\n",
    "                                              criterion='gini', \n",
    "                                              max_features='auto',\n",
    "                                              max_depth = 8,\n",
    "                                              oob_score=True,\n",
    "                                              random_state=42,\n",
    "                                              n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "FrL1w4tHEZxf",
    "outputId": "26736f92-5b13-4b34-c18f-bc3462569843"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier Train Accuracy Score : 0.9250720461095101\n",
      "Random Forest Classifier Test Score : 0.8845238095238095\n"
     ]
    }
   ],
   "source": [
    "rfc.fit(X_train, y_train)\n",
    "\n",
    "print('Random Forest Classifier Train Accuracy Score :', rfc.score(X_train, y_train))\n",
    "print('Random Forest Classifier Test Score :', rfc.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "colab_type": "code",
    "id": "AcI3dl8hEh7t",
    "outputId": "9f462f7c-02b7-4687-ecb0-39f74225ad5c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x234988c3c50>"
      ]
     },
     "execution_count": 72,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234987f4d68>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 273,
       "width": 380
      },
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\", {'axes.grid' : False})\n",
    "\n",
    "scores_train = rfc.predict_proba(X_train)\n",
    "scores_test = rfc.predict_proba(X_test)\n",
    "\n",
    "y_scores_train = []\n",
    "y_scores_test = []\n",
    "for i in range(len(scores_train)):\n",
    "    y_scores_train.append(scores_train[i][1])\n",
    "\n",
    "for i in range(len(scores_test)):\n",
    "    y_scores_test.append(scores_test[i][1])\n",
    "    \n",
    "fpr_rf_train, tpr_rf_train, _ = roc_curve(y_train, y_scores_train, pos_label=1)\n",
    "fpr_rf_test, tpr_rf_test, _ = roc_curve(y_test, y_scores_test, pos_label=1)\n",
    "\n",
    "plt.plot(fpr_rf_train, tpr_rf_train, color='darkblue', label='Train AUC: %5f' %auc(fpr_rf_train, tpr_rf_train))\n",
    "plt.plot(fpr_rf_test, tpr_rf_test, color='red', ls='--', label='Test AUC: %5f' %auc(fpr_rf_test, tpr_rf_test))\n",
    "plt.title(\"Random Forest ROC Curve for Engineered features\")\n",
    "plt.xlabel(\"False Positive Rate (FPR)\")\n",
    "plt.ylabel(\"True Positive Rate (TPR)\")\n",
    "plt.legend(loc='lower right')"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Fake News and Automaticity Detection",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}