RandomForest_AdaBoost_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "07a4c050",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cufflinks as cf\n",
    "import plotly\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "\n",
    "from IPython.core.display import HTML\n",
    "from wordcloud import WordCloud\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from datetime import datetime\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from pandas import DataFrame\n",
    "from collections import OrderedDict \n",
    "from colorama import Fore, Back, Style\n",
    "y_ = Fore.YELLOW\n",
    "r_ = Fore.RED\n",
    "g_ = Fore.GREEN\n",
    "b_ = Fore.BLUE\n",
    "m_ = Fore.MAGENTA\n",
    "sr_ = Style.RESET_ALL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "147a9215",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "published                  2045\n",
       "title                      2045\n",
       "text                       2045\n",
       "language                   2045\n",
       "site_url                   2045\n",
       "main_img_url               2045\n",
       "type                       2045\n",
       "label                      2045\n",
       "title_without_stopwords    2045\n",
       "text_without_stopwords     2045\n",
       "hasImage                   2045\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'news_articles.csv', encoding=\"latin\", index_col=0)\n",
    "df = df.dropna()\n",
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e4b1af2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>published</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>language</th>\n",
       "      <th>site_url</th>\n",
       "      <th>main_img_url</th>\n",
       "      <th>type</th>\n",
       "      <th>label</th>\n",
       "      <th>title_without_stopwords</th>\n",
       "      <th>text_without_stopwords</th>\n",
       "      <th>hasImage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>author</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Barracuda Brigade</th>\n",
       "      <td>2016-10-26T21:41:00.000+03:00</td>\n",
       "      <td>muslims busted they stole millions in govt ben...</td>\n",
       "      <td>print they should pay all the back all the mon...</td>\n",
       "      <td>english</td>\n",
       "      <td>100percentfedup.com</td>\n",
       "      <td>http://bb4sp.com/wp-content/uploads/2016/10/Fu...</td>\n",
       "      <td>bias</td>\n",
       "      <td>Real</td>\n",
       "      <td>muslims busted stole millions govt benefits</td>\n",
       "      <td>print pay back money plus interest entire fami...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reasoning with facts</th>\n",
       "      <td>2016-10-29T08:47:11.259+03:00</td>\n",
       "      <td>re why did attorney general loretta lynch plea...</td>\n",
       "      <td>why did attorney general loretta lynch plead t...</td>\n",
       "      <td>english</td>\n",
       "      <td>100percentfedup.com</td>\n",
       "      <td>http://bb4sp.com/wp-content/uploads/2016/10/Fu...</td>\n",
       "      <td>bias</td>\n",
       "      <td>Real</td>\n",
       "      <td>attorney general loretta lynch plead fifth</td>\n",
       "      <td>attorney general loretta lynch plead fifth bar...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barracuda Brigade</th>\n",
       "      <td>2016-10-31T01:41:49.479+02:00</td>\n",
       "      <td>breaking weiner cooperating with fbi on hillar...</td>\n",
       "      <td>red state  \\nfox news sunday reported this mor...</td>\n",
       "      <td>english</td>\n",
       "      <td>100percentfedup.com</td>\n",
       "      <td>http://bb4sp.com/wp-content/uploads/2016/10/Fu...</td>\n",
       "      <td>bias</td>\n",
       "      <td>Real</td>\n",
       "      <td>breaking weiner cooperating fbi hillary email ...</td>\n",
       "      <td>red state fox news sunday reported morning ant...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fed Up</th>\n",
       "      <td>2016-11-01T05:22:00.000+02:00</td>\n",
       "      <td>pin drop speech by father of daughter kidnappe...</td>\n",
       "      <td>email kayla mueller was a prisoner and torture...</td>\n",
       "      <td>english</td>\n",
       "      <td>100percentfedup.com</td>\n",
       "      <td>http://100percentfedup.com/wp-content/uploads/...</td>\n",
       "      <td>bias</td>\n",
       "      <td>Real</td>\n",
       "      <td>pin drop speech father daughter kidnapped kill...</td>\n",
       "      <td>email kayla mueller prisoner tortured isis cha...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fed Up</th>\n",
       "      <td>2016-11-01T21:56:00.000+02:00</td>\n",
       "      <td>fantastic trumps  point plan to reform healthc...</td>\n",
       "      <td>email healthcare reform to make america great ...</td>\n",
       "      <td>english</td>\n",
       "      <td>100percentfedup.com</td>\n",
       "      <td>http://100percentfedup.com/wp-content/uploads/...</td>\n",
       "      <td>bias</td>\n",
       "      <td>Real</td>\n",
       "      <td>fantastic trumps point plan reform healthcare ...</td>\n",
       "      <td>email healthcare reform make america great sin...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          published  \\\n",
       "author                                                \n",
       "Barracuda Brigade     2016-10-26T21:41:00.000+03:00   \n",
       "reasoning with facts  2016-10-29T08:47:11.259+03:00   \n",
       "Barracuda Brigade     2016-10-31T01:41:49.479+02:00   \n",
       "Fed Up                2016-11-01T05:22:00.000+02:00   \n",
       "Fed Up                2016-11-01T21:56:00.000+02:00   \n",
       "\n",
       "                                                                  title  \\\n",
       "author                                                                    \n",
       "Barracuda Brigade     muslims busted they stole millions in govt ben...   \n",
       "reasoning with facts  re why did attorney general loretta lynch plea...   \n",
       "Barracuda Brigade     breaking weiner cooperating with fbi on hillar...   \n",
       "Fed Up                pin drop speech by father of daughter kidnappe...   \n",
       "Fed Up                fantastic trumps  point plan to reform healthc...   \n",
       "\n",
       "                                                                   text  \\\n",
       "author                                                                    \n",
       "Barracuda Brigade     print they should pay all the back all the mon...   \n",
       "reasoning with facts  why did attorney general loretta lynch plead t...   \n",
       "Barracuda Brigade     red state  \\nfox news sunday reported this mor...   \n",
       "Fed Up                email kayla mueller was a prisoner and torture...   \n",
       "Fed Up                email healthcare reform to make america great ...   \n",
       "\n",
       "                     language             site_url  \\\n",
       "author                                               \n",
       "Barracuda Brigade     english  100percentfedup.com   \n",
       "reasoning with facts  english  100percentfedup.com   \n",
       "Barracuda Brigade     english  100percentfedup.com   \n",
       "Fed Up                english  100percentfedup.com   \n",
       "Fed Up                english  100percentfedup.com   \n",
       "\n",
       "                                                           main_img_url  type  \\\n",
       "author                                                                          \n",
       "Barracuda Brigade     http://bb4sp.com/wp-content/uploads/2016/10/Fu...  bias   \n",
       "reasoning with facts  http://bb4sp.com/wp-content/uploads/2016/10/Fu...  bias   \n",
       "Barracuda Brigade     http://bb4sp.com/wp-content/uploads/2016/10/Fu...  bias   \n",
       "Fed Up                http://100percentfedup.com/wp-content/uploads/...  bias   \n",
       "Fed Up                http://100percentfedup.com/wp-content/uploads/...  bias   \n",
       "\n",
       "                     label                            title_without_stopwords  \\\n",
       "author                                                                          \n",
       "Barracuda Brigade     Real        muslims busted stole millions govt benefits   \n",
       "reasoning with facts  Real         attorney general loretta lynch plead fifth   \n",
       "Barracuda Brigade     Real  breaking weiner cooperating fbi hillary email ...   \n",
       "Fed Up                Real  pin drop speech father daughter kidnapped kill...   \n",
       "Fed Up                Real  fantastic trumps point plan reform healthcare ...   \n",
       "\n",
       "                                                 text_without_stopwords  \\\n",
       "author                                                                    \n",
       "Barracuda Brigade     print pay back money plus interest entire fami...   \n",
       "reasoning with facts  attorney general loretta lynch plead fifth bar...   \n",
       "Barracuda Brigade     red state fox news sunday reported morning ant...   \n",
       "Fed Up                email kayla mueller prisoner tortured isis cha...   \n",
       "Fed Up                email healthcare reform make america great sin...   \n",
       "\n",
       "                      hasImage  \n",
       "author                          \n",
       "Barracuda Brigade          1.0  \n",
       "reasoning with facts       1.0  \n",
       "Barracuda Brigade          1.0  \n",
       "Fed Up                     1.0  \n",
       "Fed Up                     1.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "29d14a36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['bias', 'conspiracy', 'fake', 'bs', 'satire', 'hate', 'junksci',\n",
       "       'state'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "03a0ef96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.16.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf.go_offline()\n",
    "cf.set_config_file(offline=False, world_readable=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "332a6d1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['type'].value_counts().plot.pie(figsize = (8,8), startangle = 75)\n",
    "plt.title('Types of articles', fontsize = 20)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "de83a579",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_top_n_words(corpus, n=None):\n",
    "    vec = CountVectorizer().fit(corpus)\n",
    "    bag_of_words = vec.transform(corpus)\n",
    "    sum_words = bag_of_words.sum(axis=0) \n",
    "    words_freq = [(word, sum_words[0, idx]) for word, idx in     vec.vocabulary_.items()]\n",
    "    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
    "    return words_freq[:n]\n",
    "\n",
    "def get_top_n_bigram(corpus, n=None):\n",
    "    vec = CountVectorizer(ngram_range=(2, 2)).fit(corpus)\n",
    "    bag_of_words = vec.transform(corpus)\n",
    "    sum_words = bag_of_words.sum(axis=0) \n",
    "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n",
    "    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
    "    return words_freq[:n]\n",
    "\n",
    "\n",
    "def get_top_n_trigram(corpus, n=None):\n",
    "    vec = CountVectorizer(ngram_range=(3, 3)).fit(corpus)\n",
    "    bag_of_words = vec.transform(corpus)\n",
    "    sum_words = bag_of_words.sum(axis=0) \n",
    "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n",
    "    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
    "    return words_freq[:n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cf2c4e90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "marker": {
          "color": "rgba(55, 128, 191, 0.6)",
          "line": {
           "color": "rgba(55, 128, 191, 1.0)",
           "width": 1
          }
         },
         "name": "count",
         "orientation": "v",
         "text": "",
         "type": "bar",
         "x": [
          "trump",
          "clinton",
          "one",
          "us",
          "said",
          "people",
          "would",
          "hillary",
          "also",
          "like",
          "election",
          "new",
          "time",
          "even",
          "state",
          "president",
          "get",
          "could",
          "campaign",
          "many"
         ],
         "y": [
          3382,
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         ]
        }
       ],
       "layout": {
        "legend": {
         "bgcolor": "#F5F6F9",
         "font": {
          "color": "#4D5663"
         }
        },
        "paper_bgcolor": "#F5F6F9",
        "plot_bgcolor": "#F5F6F9",
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
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   "source": [
    "common_words = get_top_n_bigram(df['text_without_stopwords'], 20)\n",
    "df3 = pd.DataFrame(common_words, columns = ['words' ,'count'])\n",
    "df3.groupby('words').sum()['count'].sort_values(ascending=False).iplot(\n",
    "    kind='bar', yTitle='Count', linecolor='black', title='Top 20 bigrams used in articles', color='blue')"
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  {
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   "id": "b06d506f",
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    {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wc = WordCloud(background_color=\"black\", max_words=100,\n",
    "               max_font_size=256,\n",
    "               random_state=42, width=1000, height=1000)\n",
    "wc.generate(' '.join(df['text_without_stopwords']))\n",
    "plt.imshow(wc, interpolation=\"bilinear\")\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "16830584",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "hasImage=%{x}<br>label=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "",
         "offsetgroup": "",
         "orientation": "h",
         "showlegend": false,
         "textposition": "auto",
         "type": "bar",
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mbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"hasImage\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"label\"}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Articles including images vs Label\"},\"barmode\":\"relative\"},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('dc5b2733-3d33-4840-b524-97f8b27a98f2');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(df, x='hasImage', y='label',title='Articles including images vs Label')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "40aa1bee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert(path):\n",
    "    return '<img src=\"'+ path + '\" width=\"80\">'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "28664a35",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sources = df[['site_url','label','main_img_url']]\n",
    "df_r = df_sources.loc[df['label']== 'Real'].iloc[6:10,:]\n",
    "df_f = df_sources.loc[df['label']== 'Fake'].head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "03a2a593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>site_url</th>\n",
       "      <th>label</th>\n",
       "      <th>main_img_url</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>author</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fed Up</th>\n",
       "      <td>100percentfedup.com</td>\n",
       "      <td>Real</td>\n",
       "      <td><img src=\"http://100percentfedup.com/wp-content/uploads/2016/11/hillary-looking-down.jpg\" width=\"80\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fed Up</th>\n",
       "      <td>100percentfedup.com</td>\n",
       "      <td>Real</td>\n",
       "      <td><img src=\"http://100percentfedup.com/wp-content/uploads/2016/11/snipes-dem.jpg\" width=\"80\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fed Up</th>\n",
       "      <td>100percentfedup.com</td>\n",
       "      <td>Real</td>\n",
       "      <td><img src=\"http://100percentfedup.com/wp-content/uploads/2016/05/hillary-comey.jpg\" width=\"80\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fed Up</th>\n",
       "      <td>100percentfedup.com</td>\n",
       "      <td>Real</td>\n",
       "      <td><img src=\"http://100percentfedup.com/wp-content/uploads/2016/11/TRUCK-TORCHED-HILLARY.jpg\" width=\"80\"></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(df_r.to_html(escape=False,formatters=dict(main_img_url=convert)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1a1874c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>site_url</th>\n",
       "      <th>label</th>\n",
       "      <th>main_img_url</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>author</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No Author</th>\n",
       "      <td>21stcenturywire.com</td>\n",
       "      <td>Fake</td>\n",
       "      <td><img src=\"http://21stcenturywire.com/wp-content/uploads/2016/10/1-Syria-No-Fly-Zone.png\" width=\"80\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No Author</th>\n",
       "      <td>21stcenturywire.com</td>\n",
       "      <td>Fake</td>\n",
       "      <td><img src=\"http://21stcenturywire.com/wp-content/uploads/2016/10/1-FBI-Comey-Hillary-Clinton-e1477728962985.jpg\" width=\"80\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shawn Helton</th>\n",
       "      <td>21stcenturywire.com</td>\n",
       "      <td>Fake</td>\n",
       "      <td><img src=\"http://21stcenturywire.com/wp-content/uploads/2016/10/Clinton-New-FBI-Probe-21WIRE-SLIDER-New.jpg\" width=\"80\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mike Rivero</th>\n",
       "      <td>21stcenturywire.com</td>\n",
       "      <td>Fake</td>\n",
       "      <td><img src=\"http://i1.wp.com/21stcenturywire.com/wp-content/uploads/2016/10/DNC-SCANDAL-PART2-21WIRE-SLIDER-SH-1.jpg?fit=620%2C415\" width=\"80\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No Author</th>\n",
       "      <td>21stcenturywire.com</td>\n",
       "      <td>Fake</td>\n",
       "      <td><img src=\"http://i0.wp.com/21stcenturywire.com/wp-content/uploads/2016/11/2-Clinton-Podesta-Email.jpg?fit=610%2C415\" width=\"80\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shawn Helton</th>\n",
       "      <td>21stcenturywire.com</td>\n",
       "      <td>Fake</td>\n",
       "      <td><img src=\"http://i2.wp.com/21stcenturywire.com/wp-content/uploads/2016/10/Clinton-Gold-21WIRE-SLIDER-SH.jpg?fit=620%2C415\" width=\"80\"></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(df_f.to_html(escape=False,formatters=dict(main_img_url=convert)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c622f4ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['100percentfedup.com', '21stcenturywire.com', 'abcnews.com.co',\n",
       "       'abeldanger.net', 'abovetopsecret.com', 'activistpost.com',\n",
       "       'addictinginfo.org', 'adobochronicles.com', 'ahtribune.com',\n",
       "       'allnewspipeline.com', 'americannews.com',\n",
       "       'americasfreedomfighters.com', 'amren.com', 'amtvmedia.com',\n",
       "       'awdnews.com', 'barenakedislam.com', 'clickhole.com',\n",
       "       'coasttocoastam.com', 'corbettreport.com', 'countercurrents.org',\n",
       "       'counterpunch.org', 'dailywire.com', 'darkmoon.me',\n",
       "       'davidduke.com', 'davidstockmanscontracorner.com',\n",
       "       'davidwolfe.com', 'dcclothesline.com', 'defenddemocracy.press',\n",
       "       'dennismichaellynch.com', 'departed.co', 'der-postillon.com',\n",
       "       'fromthetrenchesworldreport.com', 'frontpagemag.com',\n",
       "       'galacticconnection.com', 'globalresearch.ca', 'infowars.com',\n",
       "       'intrepidreport.com', 'legorafi.fr', 'madworldnews.com',\n",
       "       'naturalnews.com', 'newstarget.com', 'politicususa.com',\n",
       "       'presstv.com', 'presstv.ir', 'prisonplanet.com',\n",
       "       'projectveritas.com', 'proudemocrat.com', 'redstatewatcher.com',\n",
       "       'returnofkings.com', 'shiftfrequency.com', 'thedailysheeple.com',\n",
       "       'theeconomiccollapseblog.com', 'thefreethoughtproject.com',\n",
       "       'themindunleashed.com', 'theonion.com', 'thepeoplescube.com',\n",
       "       'therussophile.org', 'thesaker.is', 'thetruthseeker.co.uk',\n",
       "       'theunrealtimes.com', 'topinfopost.com', 'veteranstoday.com',\n",
       "       'voltairenet.org', 'washingtonsblog.com', 'westernjournalism.com',\n",
       "       'whatreallyhappened.com', 'whydontyoutrythis.com', 'wnd.com'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['site_url'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4d1d47b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "type_label = {'Real': 0, 'Fake': 1}\n",
    "df_sources.label = [type_label[item] for item in df_sources.label]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8188e947",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_real=[]\n",
    "val_fake=[]\n",
    "\n",
    "for i,row in df_sources.iterrows():\n",
    "    val = row['site_url']\n",
    "    if row['label'] == 0:\n",
    "        val_real.append(val)\n",
    "    elif row['label']== 1:\n",
    "        val_fake.append(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b584a73a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWebsites publishing real news:\u001b[32m['100percentfedup.com', 'addictinginfo.org', 'dailywire.com', 'davidduke.com', 'fromthetrenchesworldreport.com', 'frontpagemag.com', 'newstarget.com', 'politicususa.com', 'presstv.com', 'presstv.ir', 'prisonplanet.com', 'proudemocrat.com', 'redstatewatcher.com', 'returnofkings.com', 'washingtonsblog.com', 'westernjournalism.com', 'whydontyoutrythis.com', 'wnd.com']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "uniqueValues_real = list(OrderedDict.fromkeys(val_real)) \n",
    "\n",
    "print(f\"{y_}Websites publishing real news:{g_}{uniqueValues_real}\\n\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7eb3517d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWebsites publishing fake news:\u001b[31m['21stcenturywire.com', 'abcnews.com.co', 'abeldanger.net', 'abovetopsecret.com', 'activistpost.com', 'adobochronicles.com', 'ahtribune.com', 'allnewspipeline.com', 'americannews.com', 'americasfreedomfighters.com', 'amren.com', 'amtvmedia.com', 'awdnews.com', 'barenakedislam.com', 'clickhole.com', 'coasttocoastam.com', 'corbettreport.com', 'countercurrents.org', 'counterpunch.org', 'darkmoon.me', 'davidduke.com', 'davidstockmanscontracorner.com', 'davidwolfe.com', 'dcclothesline.com', 'defenddemocracy.press', 'dennismichaellynch.com', 'departed.co', 'der-postillon.com', 'fromthetrenchesworldreport.com', 'frontpagemag.com', 'galacticconnection.com', 'globalresearch.ca', 'infowars.com', 'intrepidreport.com', 'legorafi.fr', 'madworldnews.com', 'naturalnews.com', 'newstarget.com', 'presstv.ir', 'prisonplanet.com', 'projectveritas.com', 'returnofkings.com', 'shiftfrequency.com', 'thedailysheeple.com', 'theeconomiccollapseblog.com', 'thefreethoughtproject.com', 'themindunleashed.com', 'theonion.com', 'thepeoplescube.com', 'therussophile.org', 'thesaker.is', 'thetruthseeker.co.uk', 'theunrealtimes.com', 'topinfopost.com', 'veteranstoday.com', 'voltairenet.org', 'washingtonsblog.com', 'westernjournalism.com', 'whatreallyhappened.com']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "uniqueValues_fake = list(OrderedDict.fromkeys(val_fake)) \n",
    "print(f\"{y_}Websites publishing fake news:{r_}{uniqueValues_fake}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "781699ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWebsites publishing both real and fake news:\u001b[35m{'davidduke.com', 'frontpagemag.com', 'newstarget.com', 'presstv.ir', 'washingtonsblog.com', 'returnofkings.com', 'westernjournalism.com', 'fromthetrenchesworldreport.com', 'prisonplanet.com'}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "real_set = set(uniqueValues_real) \n",
    "fake_set = set(uniqueValues_fake) \n",
    "\n",
    "print(f\"{y_}Websites publishing both real and fake news:{m_}{real_set & fake_set}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fe987686",
   "metadata": {},
   "outputs": [],
   "source": [
    "type1 = {'bias': 0, 'conspiracy': 1,'fake': 2,'bs': 3,'satire': 4, 'hate': 5,'junksci': 6, 'state': 7}\n",
    "df.type = [type1[item] for item in df.type] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "17108b07",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_bar(df, feat_x, feat_y, normalize=True):\n",
    "    \"\"\" Plot with vertical bars of the requested dataframe and features\"\"\"\n",
    "    \n",
    "    ct = pd.crosstab(df[feat_x], df[feat_y])\n",
    "    if normalize == True:\n",
    "        ct = ct.div(ct.sum(axis=1), axis=0)\n",
    "    return ct.plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d3465f1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_bar(df,'type' , 'label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "32eb1270",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "branchvalues": "total",
         "domain": {
          "x": [
           0,
           1
          ],
          "y": [
           0,
           1
          ]
         },
         "hovertemplate": "labels=%{label}<br>count=%{value}<br>parent=%{parent}<br>id=%{id}<extra></extra>",
         "ids": [
          "Real/0",
          "Fake/1",
          "Fake/2",
          "Fake/3",
          "Fake/4",
          "Real/5",
          "Fake/6",
          "Real/7",
          "Fake",
          "Real"
         ],
         "labels": [
          "0",
          "1",
          "2",
          "3",
          "4",
          "5",
          "6",
          "7",
          "Fake",
          "Real"
         ],
         "name": "",
         "parents": [
          "Real",
          "Fake",
          "Fake",
          "Fake",
          "Fake",
          "Real",
          "Fake",
          "Real",
          "",
          ""
         ],
         "type": "sunburst",
         "values": [
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        }
       ],
       "layout": {
        "legend": {
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.sunburst(df, path=['label', 'type'])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fbf1e0aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_type = df[['site_url','type']]\n",
    "\n",
    "val_bias=[]\n",
    "val_conspiracy=[]\n",
    "val_fake1=[]\n",
    "val_bs=[]\n",
    "val_satire=[]\n",
    "val_hate=[]\n",
    "val_junksci=[]\n",
    "val_state=[]\n",
    "{'bias': 0, 'conspiracy': 1,'fake': 2,'bs': 3,'satire': 4, 'hate': 5,'junksci': 6, 'state': 7}\n",
    "for i,row in df_type.iterrows():\n",
    "    val = row['site_url']\n",
    "    if row['type'] == 0:\n",
    "        val_bias.append(val)\n",
    "    elif row['type']== 1:\n",
    "        val_conspiracy.append(val)\n",
    "    elif row['type']== 2:\n",
    "        val_fake1.append(val)\n",
    "    elif row['type']== 3:\n",
    "        val_bs.append(val)\n",
    "    elif row['type']== 4:\n",
    "        val_satire.append(val)\n",
    "    elif row['type']== 5:\n",
    "        val_hate.append(val)\n",
    "    elif row['type']== 6:\n",
    "        val_junksci.append(val)\n",
    "    elif row['type']== 7:\n",
    "        val_state.append(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "721ea905",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m{'bias': 0, 'conspiracy': 1, 'fake': 2, 'bs': 3, 'satire': 4, 'hate': 5, 'junksci': 6, 'state': 7}\n",
      "\n",
      "\u001b[33mSource URLs for type:\u001b[34m0\u001b[31m['100percentfedup.com', 'addictinginfo.org', 'dailywire.com', 'newstarget.com', 'politicususa.com', 'prisonplanet.com', 'proudemocrat.com', 'washingtonsblog.com', 'westernjournalism.com', 'whydontyoutrythis.com', 'wnd.com']\n",
      "\n",
      "\u001b[33mSource URLs for type:\u001b[34m1\u001b[31m['21stcenturywire.com', 'activistpost.com', 'awdnews.com', 'galacticconnection.com', 'globalresearch.ca', 'infowars.com', 'naturalnews.com', 'newstarget.com', 'presstv.ir', 'prisonplanet.com']\n",
      "\n",
      "\u001b[33mSource URLs for type:\u001b[34m2\u001b[31m['abcnews.com.co', 'adobochronicles.com']\n",
      "\n",
      "\u001b[33mSource URLs for type:\u001b[34m3\u001b[31m['abeldanger.net', 'abovetopsecret.com', 'ahtribune.com', 'allnewspipeline.com', 'americannews.com', 'americasfreedomfighters.com', 'amren.com', 'amtvmedia.com', 'barenakedislam.com', 'coasttocoastam.com', 'corbettreport.com', 'countercurrents.org', 'counterpunch.org', 'darkmoon.me', 'davidduke.com', 'davidstockmanscontracorner.com', 'davidwolfe.com', 'dcclothesline.com', 'defenddemocracy.press', 'dennismichaellynch.com', 'departed.co', 'der-postillon.com', 'fromthetrenchesworldreport.com', 'frontpagemag.com', 'galacticconnection.com', 'infowars.com', 'legorafi.fr', 'projectveritas.com', 'returnofkings.com', 'shiftfrequency.com', 'thedailysheeple.com', 'theeconomiccollapseblog.com', 'thefreethoughtproject.com', 'themindunleashed.com', 'theonion.com', 'thepeoplescube.com', 'therussophile.org', 'thesaker.is', 'thetruthseeker.co.uk', 'theunrealtimes.com', 'topinfopost.com', 'veteranstoday.com', 'voltairenet.org', 'washingtonsblog.com', 'westernjournalism.com', 'whatreallyhappened.com']\n",
      "\n",
      "\u001b[33mSource URLs for type:\u001b[34m4\u001b[31m['clickhole.com', 'intrepidreport.com', 'legorafi.fr']\n",
      "\n",
      "\u001b[33mSource URLs for type:\u001b[34m5\u001b[31m['davidduke.com', 'fromthetrenchesworldreport.com', 'frontpagemag.com', 'redstatewatcher.com', 'returnofkings.com']\n",
      "\n",
      "\u001b[33mSource URLs for type:\u001b[34m6\u001b[31m['davidstockmanscontracorner.com', 'davidwolfe.com', 'madworldnews.com', 'naturalnews.com']\n",
      "\n",
      "\u001b[33mSource URLs for type:\u001b[34m7\u001b[31m['politicususa.com', 'presstv.com', 'presstv.ir']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "uv_bias = list(OrderedDict.fromkeys(val_bias)) \n",
    "uv_conspiracy = list(OrderedDict.fromkeys(val_conspiracy)) \n",
    "uv_fake = list(OrderedDict.fromkeys(val_fake1)) \n",
    "uv_bs = list(OrderedDict.fromkeys(val_bs)) \n",
    "uv_satire = list(OrderedDict.fromkeys(val_satire)) \n",
    "uv_hate = list(OrderedDict.fromkeys(val_hate)) \n",
    "uv_junksci = list(OrderedDict.fromkeys(val_junksci)) \n",
    "uv_state = list(OrderedDict.fromkeys(val_state)) \n",
    "\n",
    "print(f\"{b_}{type1}\\n\")\n",
    "i=0\n",
    "for lst in (uv_bias,uv_conspiracy,uv_fake,uv_bs,uv_satire, uv_hate,uv_junksci,uv_state): \n",
    "    print(f\"{y_}Source URLs for type:{b_}{i}{r_}{lst}\\n\") \n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8ddefc54",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>published</th>\n",
       "      <th>title</th>\n",
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       "      <th>site_url</th>\n",
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       "    <tr>\n",
       "      <th>-NO AUTHOR-</th>\n",
       "      <td>2016-10-27T02:39:12.484+03:00</td>\n",
       "      <td>gingrich slutshames megyn kelly</td>\n",
       "      <td>obamacare tax penalty ill take it millions say...</td>\n",
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       "      <td>http://mobile.wnd.com/files/2014/02/miriam-car...</td>\n",
       "      <td>0</td>\n",
       "      <td>Real</td>\n",
       "      <td>representative government thugocracy</td>\n",
       "      <td>pope god promised land people israel statement...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Corbett</th>\n",
       "      <td>2016-11-23T17:39:44.939+02:00</td>\n",
       "      <td>reddit bans pizzagate investigation the corbet...</td>\n",
       "      <td>at  am \\nim all for a recount because it coul...</td>\n",
       "      <td>english</td>\n",
       "      <td>corbettreport.com</td>\n",
       "      <td>No Image URL</td>\n",
       "      <td>3</td>\n",
       "      <td>Fake</td>\n",
       "      <td>reddit bans pizzagate investigation corbett re...</td>\n",
       "      <td>im recount could exactly james suggests puts w...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No Author</th>\n",
       "      <td>2016-11-11T03:38:46.870+02:00</td>\n",
       "      <td>life beautiful the ceo of firestone tires surp...</td>\n",
       "      <td>email \\nwith protests erupting across the coun...</td>\n",
       "      <td>english</td>\n",
       "      <td>clickhole.com</td>\n",
       "      <td>http://images.onionstatic.com/clickhole/3459/3...</td>\n",
       "      <td>4</td>\n",
       "      <td>Fake</td>\n",
       "      <td>life beautiful ceo firestone tires surprised s...</td>\n",
       "      <td>email protests erupting across country week em...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No Author</th>\n",
       "      <td>2016-10-28T21:28:57.755+03:00</td>\n",
       "      <td>for sale hillarys state department</td>\n",
       "      <td>home  news  trump received by massive crowd in...</td>\n",
       "      <td>english</td>\n",
       "      <td>departed.co</td>\n",
       "      <td>http://i1.wp.com/departed.co/wp-content/upload...</td>\n",
       "      <td>3</td>\n",
       "      <td>Fake</td>\n",
       "      <td>breaking illegal alien causes million damage d...</td>\n",
       "      <td>home news trump tsunami incoming trump florida...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No Author</th>\n",
       "      <td>2016-10-28T02:09:26.272+03:00</td>\n",
       "      <td>uae welcomes un peace plan to end crisis in yemen</td>\n",
       "      <td>washington free beacon october   \\ndr ezekiel ...</td>\n",
       "      <td>english</td>\n",
       "      <td>presstv.ir</td>\n",
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      "text/plain": [
       "                                 published  \\\n",
       "author                                       \n",
       "-NO AUTHOR-  2016-10-27T02:39:12.484+03:00   \n",
       "Corbett      2016-11-23T17:39:44.939+02:00   \n",
       "No Author    2016-11-11T03:38:46.870+02:00   \n",
       "No Author    2016-10-28T21:28:57.755+03:00   \n",
       "No Author    2016-10-28T02:09:26.272+03:00   \n",
       "\n",
       "                                                         title  \\\n",
       "author                                                           \n",
       "-NO AUTHOR-                    gingrich slutshames megyn kelly   \n",
       "Corbett      reddit bans pizzagate investigation the corbet...   \n",
       "No Author    life beautiful the ceo of firestone tires surp...   \n",
       "No Author                   for sale hillarys state department   \n",
       "No Author    uae welcomes un peace plan to end crisis in yemen   \n",
       "\n",
       "                                                          text language  \\\n",
       "author                                                                    \n",
       "-NO AUTHOR-  obamacare tax penalty ill take it millions say...  english   \n",
       "Corbett       at  am \\nim all for a recount because it coul...  english   \n",
       "No Author    email \\nwith protests erupting across the coun...  english   \n",
       "No Author    home  news  trump received by massive crowd in...  english   \n",
       "No Author    washington free beacon october   \\ndr ezekiel ...  english   \n",
       "\n",
       "                      site_url  \\\n",
       "author                           \n",
       "-NO AUTHOR-            wnd.com   \n",
       "Corbett      corbettreport.com   \n",
       "No Author        clickhole.com   \n",
       "No Author          departed.co   \n",
       "No Author           presstv.ir   \n",
       "\n",
       "                                                  main_img_url  type label  \\\n",
       "author                                                                       \n",
       "-NO AUTHOR-  http://mobile.wnd.com/files/2014/02/miriam-car...     0  Real   \n",
       "Corbett                                           No Image URL     3  Fake   \n",
       "No Author    http://images.onionstatic.com/clickhole/3459/3...     4  Fake   \n",
       "No Author    http://i1.wp.com/departed.co/wp-content/upload...     3  Fake   \n",
       "No Author    http://media.presstv.com/photo/20161027/4463b0...     7  Real   \n",
       "\n",
       "                                       title_without_stopwords  \\\n",
       "author                                                           \n",
       "-NO AUTHOR-               representative government thugocracy   \n",
       "Corbett      reddit bans pizzagate investigation corbett re...   \n",
       "No Author    life beautiful ceo firestone tires surprised s...   \n",
       "No Author    breaking illegal alien causes million damage d...   \n",
       "No Author    pakistan police detain dozens imran khans supp...   \n",
       "\n",
       "                                        text_without_stopwords  hasImage  \n",
       "author                                                                    \n",
       "-NO AUTHOR-  pope god promised land people israel statement...       0.0  \n",
       "Corbett      im recount could exactly james suggests puts w...       0.0  \n",
       "No Author    email protests erupting across country week em...       1.0  \n",
       "No Author    home news trump tsunami incoming trump florida...       1.0  \n",
       "No Author    washington free beacon october hillary clinton...       1.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.sample(frac=1)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "95820d42",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df1.type\n",
    "\n",
    "x = df1.loc[:,['site_url','text_without_stopwords']]\n",
    "x['source'] = x[\"site_url\"].astype(str) +\" \"+ x[\"text_without_stopwords\"] \n",
    "x = x.drop(['site_url','text_without_stopwords'],axis=1)\n",
    "x = x.source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7080874d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning:\n",
      "\n",
      "Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.30)\n",
    "\n",
    "tfidf_vect = TfidfVectorizer(stop_words = 'english')\n",
    "tfidf_train = tfidf_vect.fit_transform(x_train)\n",
    "tfidf_test = tfidf_vect.transform(x_test)\n",
    "tfidf_df = pd.DataFrame(tfidf_train.A, columns=tfidf_vect.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "62c0c4bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TfidfVectorizer(stop_words=&#x27;english&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(stop_words=&#x27;english&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "TfidfVectorizer(stop_words='english')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_vect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2c96adeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1431, 38849)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f9c55062",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.969\n"
     ]
    }
   ],
   "source": [
    "Adaboost = AdaBoostClassifier(DecisionTreeClassifier(max_depth=10),n_estimators=5,random_state=1)\n",
    "Adaboost.fit(tfidf_train, y_train)\n",
    "y_pred3 = Adaboost.predict(tfidf_test)\n",
    "ABscore = metrics.accuracy_score(y_test,y_pred3)\n",
    "print(\"accuracy: %0.3f\" %ABscore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1bbbd1ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:  0.873\n"
     ]
    }
   ],
   "source": [
    "RandomForest = RandomForestClassifier(n_estimators=100,random_state=0)\n",
    "RandomForest.fit(tfidf_train,y_train)\n",
    "y_pred1 = RandomForest.predict(tfidf_test)\n",
    "RFscore = metrics.accuracy_score(y_test,y_pred1)\n",
    "print(\"accuracy:  %0.3f\" %RFscore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "203a7d57",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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