Deception_Detection_Model_Selection.ipynb

{
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  {
   "cell_type": "markdown",
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    "id": "gEgz5kDfOxx7"
   },
   "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": 1,
   "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": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GgA8sI2BiKYV"
   },
   "outputs": [],
   "source": [
    "# News Data\n",
    "url_news = 'data/FakeNews/ira_venezuela_tweets_not_hashed.csv'\n",
    "\n",
    "df_news = pd.read_csv(url_news, index_col=0, encoding='utf8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zMo8-wNZ23x1"
   },
   "source": [
    "# **Fake News (News Credibility)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 197
    },
    "colab_type": "code",
    "id": "ixJTsdJa6FDN",
    "outputId": "9aedd43b-9909-4098-99aa-e989e98844eb"
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   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>credible</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#local #news Volunteers clean 9 tons of trash ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Doctor Is In: Increased vaping among teens...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>Tony-winning 'Cheers' actor Roger Rees dies  #...</td>\n",
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       "                                                text  credible\n",
       "0  #local #news Volunteers clean 9 tons of trash ...         0\n",
       "1  The Doctor Is In: Increased vaping among teens...         0\n",
       "2  Ex-lawmaker arraigned, governor seeks to comba...         0\n",
       "3  Tony-winning 'Cheers' actor Roger Rees dies  #...         0\n",
       "4  Manager Lloyd McClendon’s status at top of new...         0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_news.head(5)"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
    "colab": {
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    "id": "jkdVvT_96Grg",
    "outputId": "0c8e1951-208f-4bc6-d888-0e9a4613bae7"
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   "outputs": [
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       "      <th></th>\n",
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       "      <th>credible</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23995</th>\n",
       "      <td>EU escalates action on ‘unfair’ German and UK ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23996</th>\n",
       "      <td>2 dead in Christmas night shooting in Chicago ...</td>\n",
       "      <td>1</td>\n",
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       "      <th>23997</th>\n",
       "      <td>Fish rescued while trying to swim up street af...</td>\n",
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       "      <td>The way your wash your hands could be leaving ...</td>\n",
       "      <td>1</td>\n",
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       "      <th>23999</th>\n",
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      "text/plain": [
       "                                                    text  credible\n",
       "23995  EU escalates action on ‘unfair’ German and UK ...         1\n",
       "23996  2 dead in Christmas night shooting in Chicago ...         1\n",
       "23997  Fish rescued while trying to swim up street af...         1\n",
       "23998  The way your wash your hands could be leaving ...         1\n",
       "23999  After 25 years, Berkeley Breathed's 'Bloom Cou...         1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_news.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_news.rename(columns={'text': 'title'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CK4ZvZXpMqha"
   },
   "source": [
    "**Clean News Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 214
    },
    "colab_type": "code",
    "id": "Xmt4UMOLNHCD",
    "outputId": "0c5f543f-7fbb-4f2a-bf6e-be27beaf5a71"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New shape: (23113, 2)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>credible</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>local news volunteers clean tons of trash out...</td>\n",
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       "      <td>the doctor is in increased vaping among teens ...</td>\n",
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       "      <td>ex lawmaker arraigned governor seeks to combat...</td>\n",
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       "                                               title  credible\n",
       "0   local news volunteers clean tons of trash out...         0\n",
       "1  the doctor is in increased vaping among teens ...         0\n",
       "2  ex lawmaker arraigned governor seeks to combat...         0\n",
       "3  tony winning cheers actor roger rees dies ente...         0\n",
       "4  manager lloyd mcclendon s status at top of new...         0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data(df_news)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
      "(11662, 23998)\n"
     ]
    }
   ],
   "source": [
    "# Set variables to show TheOnion Titles\n",
    "mask_on = df_news['credible'] == 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.values.astype('U'))\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": 9,
   "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": [
      "(11662, 84515)\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.values.astype('U'))\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": 10,
   "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": [
      "(11451, 24551)\n"
     ]
    }
   ],
   "source": [
    "# Set variables to show NotTheOnion Titles\n",
    "mask_no = df_news['credible'] == 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.values.astype('U'))\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": 11,
   "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": [
      "(11451, 77172)\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.values.astype('U'))\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": 12,
   "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": 13,
   "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 0x20ad331feb8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 320,
       "width": 569
      },
      "needs_background": "light"
     },
     "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": 14,
   "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 0x20b59d80cf8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 320,
       "width": 560
      },
      "needs_background": "light"
     },
     "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": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "ij1dD4GLddzm",
    "outputId": "d7d737da-e7f4-49d0-b8e3-ef99abcfb824"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'http', 'new', 'trump'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "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": 16,
   "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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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20db719c278>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 320,
       "width": 593
      },
      "needs_background": "light"
     },
     "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": 17,
   "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 0x20db77e0780>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 320,
       "width": 598
      },
      "needs_background": "light"
     },
     "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": 18,
   "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": [
       "{'donald trump', 'year old'}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "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": 19,
   "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": 20,
   "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    0.504565\n",
       "0    0.495435\n",
       "Name: credible, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Baseline score\n",
    "df_news['credible'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         local news volunteers clean tons of trash out...\n",
       "1        the doctor is in increased vaping among teens ...\n",
       "2        ex lawmaker arraigned governor seeks to combat...\n",
       "3        tony winning cheers actor roger rees dies ente...\n",
       "4        manager lloyd mcclendon s status at top of new...\n",
       "                               ...                        \n",
       "23995    eu escalates action on unfair german and uk ro...\n",
       "23996     dead in christmas night shooting in chicago h...\n",
       "23997    fish rescued while trying to swim up street af...\n",
       "23998    the way your wash your hands could be leaving ...\n",
       "23999    after years berkeley breathed s bloom county c...\n",
       "Name: title, Length: 23112, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_news['title'].dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7KCnwphkrc3i"
   },
   "source": [
    "**Set X (predictor) and y (target) variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "grvaED9srdcL"
   },
   "outputs": [],
   "source": [
    "X = df_news['title']\n",
    "y = df_news['credible']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UjbXM0KcrsLv"
   },
   "source": [
    "**Train/Test Split**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 83
    },
    "colab_type": "code",
    "id": "SnPNemMEsUlu",
    "outputId": "36867197-75eb-4fdc-a5ed-31edd9d2cfc9",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.9450213453328719\n",
      "Train score 0.983327564324449\n",
      "Test score 0.9510295898944454\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'cvec__ngram_range': (1, 1), 'nb__alpha': 0.6}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "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.astype('U'), y_train.astype('U'));\n",
    "print(\"Best score:\", gs.best_score_)\n",
    "print(\"Train score\", gs.score(X_train.values.astype('U'), y_train.values.astype('U')))\n",
    "print(\"Test score\", gs.score(X_test.values.astype('U'), y_test.values.astype('U')))\n",
    "\n",
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QFVzxvnxtdo4"
   },
   "source": [
    "**CountVectorizer & MultinomialNB: Best Params**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": [
      "0.936667243467728\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.values.astype('U'))\n",
    "\n",
    "Xcvec_train = cvec.transform(X_train.values.astype('U'))\n",
    "Xcvec_test = cvec.transform(X_test.values.astype('U'))\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": 26,
   "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([[2593,  270],\n",
       "       [  96, 2820]], dtype=int64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "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": 27,
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20f629d2438>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 316,
       "width": 423
      },
      "needs_background": "light"
     },
     "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": 28,
   "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": 29,
   "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: 93.67 %\n",
      "Precision: 91.26 %\n",
      "Recall: 96.71 %\n",
      "Specificity: 90.57 %\n",
      "Misclassification Rate: 6.59 %\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": 32,
   "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: 0.9720203069112726\n",
      "Train score 0.9979808468905043\n",
      "Test score 0.9731787506489012\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'cvec__ngram_range': (1, 1), 'cvec__stop_words': None, 'lr__C': 1}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "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.values.astype('U'), y_train.values.astype('U'));\n",
    "print(\"Best score:\", gs.best_score_)\n",
    "print(\"Train score\", gs.score(X_train.values.astype('U'), y_train.values.astype('U')))\n",
    "print(\"Test score\", gs.score(X_test.values.astype('U'), y_test.values.astype('U')))\n",
    "\n",
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": [
      "0.9634884928188268\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.values.astype('U'))\n",
    "\n",
    "Xcvec2_train = cvec2.transform(X_train.values.astype('U'))\n",
    "Xcvec2_test = cvec2.transform(X_test.values.astype('U'))\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": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.9630783431406484\n",
      "Train score 0.9847121264566747\n",
      "Test score 0.9671223395051047\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'lr__C': 1,\n",
       " 'tvect__max_df': 0.75,\n",
       " 'tvect__min_df': 5,\n",
       " 'tvect__ngram_range': (1, 1)}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline([('tvect', TfidfVectorizer()),    \n",
    "                 ('lr', LogisticRegression(solver='liblinear'))])\n",
    "\n",
    "# Tune GridSearchCV\n",
    "pipe_params = {'tvect__max_df': [.75, .98, 1.0],\n",
    "               'tvect__min_df': [2, 3, 5],\n",
    "               'tvect__ngram_range': [(1,1), (1,2), (1,3)],\n",
    "               'lr__C': [1]}\n",
    "\n",
    "gs = GridSearchCV(pipe, param_grid=pipe_params, cv=3)\n",
    "gs.fit(X_train.values.astype('U'), y_train.values.astype('U'));\n",
    "print(\"Best score:\", gs.best_score_)\n",
    "print(\"Train score\", gs.score(X_train.values.astype('U'), y_train.values.astype('U')))\n",
    "print(\"Test score\", gs.score(X_test.values.astype('U'), y_test.values.astype('U')))\n",
    "\n",
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.9473289488865813\n",
      "Train score 0.9692511826468213\n",
      "Test score 0.9480879044817443\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'nb__alpha': 1,\n",
       " 'tvect__max_df': 0.75,\n",
       " 'tvect__min_df': 5,\n",
       " 'tvect__ngram_range': (1, 2)}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline([('tvect', TfidfVectorizer()),    \n",
    "                 ('nb', MultinomialNB())])\n",
    "\n",
    "# Tune GridSearchCV\n",
    "pipe_params = {'tvect__max_df': [.75, .98],\n",
    "               'tvect__min_df': [4, 5],\n",
    "               'tvect__ngram_range': [(1,2), (1,3)],\n",
    "               'nb__alpha': [0.1, 1]}\n",
    "\n",
    "gs = GridSearchCV(pipe, param_grid=pipe_params, cv=5)\n",
    "gs.fit(X_train.values.astype('U'), y_train.values.astype('U'));\n",
    "print(\"Best score:\", gs.best_score_)\n",
    "print(\"Train score\", gs.score(X_train.values.astype('U'), y_train.values.astype('U')))\n",
    "print(\"Test score\", gs.score(X_test.values.astype('U'), y_test.values.astype('U')))\n",
    "\n",
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KwCCp5Ke0fFP"
   },
   "source": [
    "**Coefficient Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "colab_type": "code",
    "id": "e8fTDQtA0gbU",
    "outputId": "2f1048f2-6ea5-437a-8220-df17881262af"
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 66
    },
    "colab_type": "code",
    "id": "7GbDHzon013p",
    "outputId": "96492d56-500b-4439-ce24-d6054d0ddbea"
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "colab_type": "code",
    "id": "emy4akkC1Ddb",
    "outputId": "171313b4-7fc6-46f8-f225-df8a8a240258"
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "4hqnzFic16uG",
    "outputId": "ce2cc901-2041-46a6-d9ad-d751f8da34ad"
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "colab_type": "code",
    "id": "c5f6VQid2Hng",
    "outputId": "b51f2ba8-2475-4019-e464-a21167605746"
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "0zW24eKA2LI2",
    "outputId": "fd978dfb-f404-4f67-a9b8-255ea3e2131a"
   },
   "outputs": [],
   "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.\")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Fake News and Automaticity Detection",
   "provenance": []
  },
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   "language": "python",
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