budowa_krok_2_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ce247929",
   "metadata": {},
   "source": [
    "# Fakenews detection\n",
    "Karolina Mączka, Tymoteusz Urban"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "93248206",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\tymek\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\tymek\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\tymek\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package omw-1.4 to\n",
      "[nltk_data]     C:\\Users\\tymek\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package omw-1.4 is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
    "import nltk\n",
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "nltk.download('stopwords')\n",
    "nltk.download('punkt')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('omw-1.4')\n",
    "import re\n",
    "from wordcloud import WordCloud\n",
    "from langdetect import detect\n",
    "from langdetect import DetectorFactory\n",
    "DetectorFactory.seed = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f665a560",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('PreProcessedData.csv', header=0, sep=',', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "53b2da86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>Ground Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Ann Coulter Make Believes She Has ‘Gay Friend...</td>\n",
       "      <td>It s hard to believe, but Donald Trump does ha...</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Rating: Moody‘s verbessert Ausblick für Russla...</td>\n",
       "      <td>bankensektor Der russische Staat werde die Ban...</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>CAN WE ADD DIRTY MONEY ‘LAUNDERING’ To The Oba...</td>\n",
       "      <td>A member of the House Intelligence Committee i...</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Republicans on Obamacare repeal: 'We're going ...</td>\n",
       "      <td>WASHINGTON (Reuters) - House of Representative...</td>\n",
       "      <td>true</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Trump, on possible DACA deal, says border wall...</td>\n",
       "      <td>WASHINGTON (Reuters) - U.S. President Donald T...</td>\n",
       "      <td>true</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                              title  \\\n",
       "0           0   Ann Coulter Make Believes She Has ‘Gay Friend...   \n",
       "1           1  Rating: Moody‘s verbessert Ausblick für Russla...   \n",
       "2           2  CAN WE ADD DIRTY MONEY ‘LAUNDERING’ To The Oba...   \n",
       "3           3  Republicans on Obamacare repeal: 'We're going ...   \n",
       "4           4  Trump, on possible DACA deal, says border wall...   \n",
       "\n",
       "                                                text Ground Label  \n",
       "0  It s hard to believe, but Donald Trump does ha...         fake  \n",
       "1  bankensektor Der russische Staat werde die Ban...         fake  \n",
       "2  A member of the House Intelligence Committee i...         fake  \n",
       "3  WASHINGTON (Reuters) - House of Representative...         true  \n",
       "4  WASHINGTON (Reuters) - U.S. President Donald T...         true  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2784ed96",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ann Coulter Make Believes She Has ‘Gay Friend...</td>\n",
       "      <td>It s hard to believe, but Donald Trump does ha...</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rating: Moody‘s verbessert Ausblick für Russla...</td>\n",
       "      <td>bankensektor Der russische Staat werde die Ban...</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAN WE ADD DIRTY MONEY ‘LAUNDERING’ To The Oba...</td>\n",
       "      <td>A member of the House Intelligence Committee i...</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Republicans on Obamacare repeal: 'We're going ...</td>\n",
       "      <td>WASHINGTON (Reuters) - House of Representative...</td>\n",
       "      <td>true</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Trump, on possible DACA deal, says border wall...</td>\n",
       "      <td>WASHINGTON (Reuters) - U.S. President Donald T...</td>\n",
       "      <td>true</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  \\\n",
       "0   Ann Coulter Make Believes She Has ‘Gay Friend...   \n",
       "1  Rating: Moody‘s verbessert Ausblick für Russla...   \n",
       "2  CAN WE ADD DIRTY MONEY ‘LAUNDERING’ To The Oba...   \n",
       "3  Republicans on Obamacare repeal: 'We're going ...   \n",
       "4  Trump, on possible DACA deal, says border wall...   \n",
       "\n",
       "                                                text label  \n",
       "0  It s hard to believe, but Donald Trump does ha...  fake  \n",
       "1  bankensektor Der russische Staat werde die Ban...  fake  \n",
       "2  A member of the House Intelligence Committee i...  fake  \n",
       "3  WASHINGTON (Reuters) - House of Representative...  true  \n",
       "4  WASHINGTON (Reuters) - U.S. President Donald T...  true  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove index column and rename Gound Label for easier use\n",
    "df = df[['title', 'text', 'Ground Label']]\n",
    "df.rename(columns={'Ground Label': 'label'}, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93af8965",
   "metadata": {},
   "source": [
    "## Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38a2891c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting data into training and testing sets\n",
    "X_rest, X_test, y_rest, y_test = train_test_split(df[['title', 'text']], df['label'], test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "15a8c42e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fake    29459\n",
       "true    18872\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_rest.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "73ceaa3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_validate, y_train, y_validate = train_test_split(X_rest, y_rest, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c2d07c00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fake    20645\n",
      "true    13186\n",
      "Name: label, dtype: int64\n",
      "fake    8814\n",
      "true    5686\n",
      "Name: label, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(y_train.value_counts())\n",
    "print(y_validate.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c60f19fc",
   "metadata": {},
   "source": [
    "The ratio of false and true is the same everywhere, so we can assume that this is a good separation of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "604901e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.concat([X_train, y_train], axis=1).reset_index(drop=True)\n",
    "train_df.to_csv('training_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "88da294f",
   "metadata": {},
   "outputs": [],
   "source": [
    "testing_df = pd.concat([X_test, y_test], axis=1)\n",
    "testing_df.to_csv('testing_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "21794ee3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "validate_df = pd.concat([X_validate, y_validate], axis=1)\n",
    "validate_df.to_csv('fakenws_validation.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "176fc409",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df2 = pd.concat([X_train, y_train], axis=1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f56bfd9f",
   "metadata": {},
   "source": [
    "## Getting to know the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "17ffa716",
   "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>title</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>In the Western World Respect for Law No Longer...</td>\n",
       "      <td>In the Western World Respect for Law No Longer...</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WATCH: President Obama Unleashes On Trump, Sa...</td>\n",
       "      <td>During an interview on CBS s Face the Nation S...</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>THE GUY WHO Punched “Moldylocks” Speaks Out Ab...</td>\n",
       "      <td>Nathan Domingo is the founder of  Identity Eur...</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SAY WHAT? AMAZON TELLS CUSTOMER THEY WERE FORC...</td>\n",
       "      <td>When it comes to limiting our free speech, not...</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANTI-GUN Zealot KATIE COURIC Hit With $12 MILL...</td>\n",
       "      <td>Little Miss Sunshine  has fooled a lot of peo...</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  \\\n",
       "0  In the Western World Respect for Law No Longer...   \n",
       "1   WATCH: President Obama Unleashes On Trump, Sa...   \n",
       "2  THE GUY WHO Punched “Moldylocks” Speaks Out Ab...   \n",
       "3  SAY WHAT? AMAZON TELLS CUSTOMER THEY WERE FORC...   \n",
       "4  ANTI-GUN Zealot KATIE COURIC Hit With $12 MILL...   \n",
       "\n",
       "                                                text label  \n",
       "0  In the Western World Respect for Law No Longer...  fake  \n",
       "1  During an interview on CBS s Face the Nation S...  fake  \n",
       "2  Nathan Domingo is the founder of  Identity Eur...  fake  \n",
       "3  When it comes to limiting our free speech, not...  fake  \n",
       "4   Little Miss Sunshine  has fooled a lot of peo...  fake  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "55d2e697",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 33831 entries, 0 to 33830\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   title   33497 non-null  object\n",
      " 1   text    33800 non-null  object\n",
      " 2   label   33831 non-null  object\n",
      "dtypes: object(3)\n",
      "memory usage: 793.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb2442e2",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fd93a04",
   "metadata": {},
   "source": [
    "### Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "16eddf9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting fake and true into numeric values\n",
    "df2.loc[df2[\"label\"] == \"fake\",\"label\"] = 0\n",
    "df2.loc[df2[\"label\"] == \"true\",\"label\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e98600df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Before pipeline function\n",
    "def encodeLabels(df):\n",
    "    df.loc[df[\"label\"] == \"fake\",\"label\"] = 0\n",
    "    df.loc[df[\"label\"] == \"true\",\"label\"] = 1\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ad72669",
   "metadata": {},
   "source": [
    "### NaNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f998743c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 33831 entries, 0 to 33830\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   title   33831 non-null  object\n",
      " 1   text    33831 non-null  object\n",
      " 2   label   33831 non-null  int64 \n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 793.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# In order to perform operations on data we have to convert Nans into empty strings\n",
    "df2.fillna('', inplace=True)\n",
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0e163557",
   "metadata": {},
   "outputs": [],
   "source": [
    "# transformer function\n",
    "def handleNa(df):\n",
    "    df.fillna('', inplace=True)\n",
    "    print('NaNs replaced')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76f82217",
   "metadata": {},
   "source": [
    "### Combine columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90e5df98",
   "metadata": {},
   "source": [
    "Because of the fact that we will be analysing only words (or sequences of them) we are combining title and text columns - there will be no difference for our model which column those words come from"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4b426020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title    THE GUY WHO Punched “Moldylocks” Speaks Out Ab...\n",
       "text     Nathan Domingo is the founder of  Identity Eur...\n",
       "label                                                    0\n",
       "Name: 2, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "149765bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['text'] = df2[['title', 'text']].apply(\" \".join, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b3b235e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'THE GUY WHO Punched “Moldylocks” Speaks Out About Violent Antifa Female With A Bottle [VIDEO] Nathan Domingo is the founder of  Identity Europa  and is the guy who was seen by millions of viewers punching a female Antifa thug the media tried to portray as a helpless woman that just happened to be in the area when fighting broke out at a free speech rally in Berkeley, CA on April 16th.The last time these two group clashed, the Trump supporters were seriously outnumbered. This time was a different story. Trump supporters came prepared for the unprovoked attacks of these hate groups and took them to task. With all the violence that happened yesterday, leftist media outlets seemed to be obsessed with reporting on the video that was taken of an alleged Trump supporter who punched an antifa female who was part of the violent group  Oak Roots Collective  that traveled to Berkeley to confront Trump supporters and shut down their right to free speech.Here s the video. At the 18 second mark you can see the woman cock her arm back as though she is about to swing her arm at someone. When she brings her arm forward, that is where she gets hit. We are NOT condoning the hitting of a woman by a man, but if she had a bottle in her hand and was about to hit someone that changes everything:CBS San Fransisco Bay Area news interviewed anarchist Louise Rosealma, who along with her boyfriend is a member of the Oak Roots Collective. Here s a sample of the violence they promote on their Facebook page. This is how these people (that should be labeled as domestic terrorists) see themselves. These anarchists live in a world where it s okay to act with violence against law enforcement and innocent people who don t agree with their political views:Here s citizen journalist Tim Pool talking to the Antifa female who was hit. When he asks her if she thinks it s okay that Antifa rioters threw M-80 s at people on the Trump side, her response was,  Well, the revolution isn t f*cking easy!  (This video was taken after she was hit) Apparently she was correct about one thing For anyone wondering what an  identiatrian  is, here is a screen shot from the Identity Evropa website:'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc[2]['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c5188aff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2.drop(columns='title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "166cc472",
   "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>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>In the Western World Respect for Law No Longer...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WATCH: President Obama Unleashes On Trump, Sa...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>THE GUY WHO Punched “Moldylocks” Speaks Out Ab...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SAY WHAT? AMAZON TELLS CUSTOMER THEY WERE FORC...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANTI-GUN Zealot KATIE COURIC Hit With $12 MILL...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  label\n",
       "0  In the Western World Respect for Law No Longer...      0\n",
       "1   WATCH: President Obama Unleashes On Trump, Sa...      0\n",
       "2  THE GUY WHO Punched “Moldylocks” Speaks Out Ab...      0\n",
       "3  SAY WHAT? AMAZON TELLS CUSTOMER THEY WERE FORC...      0\n",
       "4  ANTI-GUN Zealot KATIE COURIC Hit With $12 MILL...      0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dd65cfee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# transformer function\n",
    "def combineColumns(df):\n",
    "    df['text'] = df[['title', 'text']].apply(\" \".join, axis=1)\n",
    "    df = df.drop(columns='title')\n",
    "    print('Columns combined')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "987616fe",
   "metadata": {},
   "source": [
    "### Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ec0eeb34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3.3423e+04, 3.3800e+02, 4.4000e+01, 1.4000e+01, 2.0000e+00,\n",
       "        1.0000e+00, 5.0000e+00, 1.0000e+00, 2.0000e+00, 1.0000e+00]),\n",
       " array([5.000000e+00, 1.434840e+04, 2.869180e+04, 4.303520e+04,\n",
       "        5.737860e+04, 7.172200e+04, 8.606540e+04, 1.004088e+05,\n",
       "        1.147522e+05, 1.290956e+05, 1.434390e+05]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df2['textlength'] = df2['text'].apply(len)\n",
    "plt.hist(df2['textlength'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d14dca82",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "text          32\n",
      "label         32\n",
      "textlength    32\n",
      "dtype: int64\n"
     ]
    },
    {
     "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>text</th>\n",
       "      <th>label</th>\n",
       "      <th>textlength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>Re: Why Are So Many People Choosing To Leave T...</td>\n",
       "      <td>0</td>\n",
       "      <td>115924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1612</th>\n",
       "      <td>Re: Why Is Obama Threatening Russia With World...</td>\n",
       "      <td>0</td>\n",
       "      <td>97429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4245</th>\n",
       "      <td>Benny Morris’s Untenable Denial of the Ethnic ...</td>\n",
       "      <td>0</td>\n",
       "      <td>92823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4709</th>\n",
       "      <td>President-Elect Donald Trump's First Televised...</td>\n",
       "      <td>0</td>\n",
       "      <td>41697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4762</th>\n",
       "      <td>Re: The Velocity Of Money In The U.S. Falls To...</td>\n",
       "      <td>0</td>\n",
       "      <td>43623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text  label  textlength\n",
       "720   Re: Why Are So Many People Choosing To Leave T...      0      115924\n",
       "1612  Re: Why Is Obama Threatening Russia With World...      0       97429\n",
       "4245  Benny Morris’s Untenable Denial of the Ethnic ...      0       92823\n",
       "4709  President-Elect Donald Trump's First Televised...      0       41697\n",
       "4762  Re: The Velocity Of Money In The U.S. Falls To...      0       43623"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df2[(df2['textlength'] > 40000)].count())\n",
    "df2[(df2['textlength'] > 40000)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c3acd6d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def shortenLongText(text):\n",
    "    return text[:40000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bfc6a5ae",
   "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>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>In the Western World Respect for Law No Longer...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WATCH: President Obama Unleashes On Trump, Sa...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>THE GUY WHO Punched “Moldylocks” Speaks Out Ab...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SAY WHAT? AMAZON TELLS CUSTOMER THEY WERE FORC...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANTI-GUN Zealot KATIE COURIC Hit With $12 MILL...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33826</th>\n",
       "      <td>STATE’S ATTORNEY LIED: BALTIMORE POLICE HAD PR...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33827</th>\n",
       "      <td>Russia denies its planes killed civilians in S...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33828</th>\n",
       "      <td>Hungary’s “Wall” Versus the U.S. “Wall” Op-Ed ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33829</th>\n",
       "      <td>Pastor Caught Trump’s Spiritual Advisor Steal...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33830</th>\n",
       "      <td>BOOM! SENATOR LINDSEY GRAHAM: I Have Reason to...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33831 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  label\n",
       "0      In the Western World Respect for Law No Longer...      0\n",
       "1       WATCH: President Obama Unleashes On Trump, Sa...      0\n",
       "2      THE GUY WHO Punched “Moldylocks” Speaks Out Ab...      0\n",
       "3      SAY WHAT? AMAZON TELLS CUSTOMER THEY WERE FORC...      0\n",
       "4      ANTI-GUN Zealot KATIE COURIC Hit With $12 MILL...      0\n",
       "...                                                  ...    ...\n",
       "33826  STATE’S ATTORNEY LIED: BALTIMORE POLICE HAD PR...      0\n",
       "33827  Russia denies its planes killed civilians in S...      1\n",
       "33828  Hungary’s “Wall” Versus the U.S. “Wall” Op-Ed ...      0\n",
       "33829   Pastor Caught Trump’s Spiritual Advisor Steal...      0\n",
       "33830  BOOM! SENATOR LINDSEY GRAHAM: I Have Reason to...      0\n",
       "\n",
       "[33831 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shortening really long texts (above 40000 words)\n",
    "df2['text'] = df2['text'].apply(shortenLongText)\n",
    "df2 = df2.drop(columns='textlength')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c4c543fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transformer function\n",
    "def shortenOutliers(df):\n",
    "    df['text'] = df['text'].apply(shortenLongText)\n",
    "    print('Outliers removed')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4b4bf9d",
   "metadata": {},
   "source": [
    "### Removing stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "995b9ffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stopwords removal\n",
    "stop = stopwords.words('english')\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "def remove_stopwords(s):\n",
    "    s = [lemmatizer.lemmatize(word.lower()) for word in s if not word.lower() in stop]\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9f14d113",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Punctuation and number removal\n",
    "def remove_punctuations(s):\n",
    "    s = re.sub(r'[^a-zA-Z\\s]', '', s)\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a9c79642",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['text'] = df2['text'].apply(remove_punctuations)\n",
    "df2['text'] = df2['text'].apply(word_tokenize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f85e04c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['text'] = df2['text'].apply(remove_stopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48034e92",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def list2string(text):\n",
    "    text = \" \".join(wrd for wrd in text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39d4205f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e3bffe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3[\"text\"] = df3[\"text\"].apply(list2string)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f53e0f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transformer function\n",
    "def removeStopwords(df):\n",
    "    df['text'] = df['text'].apply(remove_punctuations)\n",
    "    print('Punctuation removed')\n",
    "    df['text'] = df['text'].apply(word_tokenize)\n",
    "    print('Words tokenized')\n",
    "    df['text'] = df['text'].apply(remove_stopwords)\n",
    "    print('Stopwords removed')\n",
    "    df['text'] = df['text'].apply(list2string)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30eff8bb",
   "metadata": {},
   "source": [
    "### Extracting english"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94e0281f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def deleteUrl(text):\n",
    "    return re.sub(r'(?i)\\b((?:https?://|www\\d{0,3}[.]|[a-z0-9.\\-]+[.][a-z]{2,4}/)(?:[^\\s()<>]+|\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\))+(?:\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\)|[^\\s`!()\\[\\]{};:\\'\".,<>?«»“”‘’]))', '', text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2689436f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deleting URLs\n",
    "df3['text'] = df3['text'].apply(deleteUrl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3d452f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deleting very short texts\n",
    "df3 = df3[df3['text'].apply(len) > 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65dff531",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7047614e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = df3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7dd02bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "DetectorFactory.seed = 0\n",
    "df4['lang'] = df4['text'].apply(detect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3ce3a36",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_not_ang = df4[df4['lang']!='en']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d9b8a46",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_not_ang = df_not_ang[['text', 'label']].reset_index(drop=True)\n",
    "df_not_ang[['label']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "07db5ba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# robimy model który będzie wykrywał po angielsku\n",
    "df4 = df4[df4['lang']=='en']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2119bc0c",
   "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>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>western world respect law longer exists wester...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>watch president obama unleashes trump say terr...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>guy punched moldylocks speaks violent antifa f...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>say amazon tell customer forced federal govern...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>trump crisis lewd remark woman come light new ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29375</th>\n",
       "      <td>trump give brutal warning lawless sanctuary ci...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29376</th>\n",
       "      <td>state attorney lied baltimore police probable ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29377</th>\n",
       "      <td>hungary wall versus u wall oped catherine j fr...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29378</th>\n",
       "      <td>pastor caught trump spiritual advisor stealing...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29379</th>\n",
       "      <td>boom senator lindsey graham reason believe cli...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29380 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  label\n",
       "0      western world respect law longer exists wester...      0\n",
       "1      watch president obama unleashes trump say terr...      0\n",
       "2      guy punched moldylocks speaks violent antifa f...      0\n",
       "3      say amazon tell customer forced federal govern...      0\n",
       "4      trump crisis lewd remark woman come light new ...      1\n",
       "...                                                  ...    ...\n",
       "29375  trump give brutal warning lawless sanctuary ci...      0\n",
       "29376  state attorney lied baltimore police probable ...      0\n",
       "29377  hungary wall versus u wall oped catherine j fr...      0\n",
       "29378  pastor caught trump spiritual advisor stealing...      0\n",
       "29379  boom senator lindsey graham reason believe cli...      0\n",
       "\n",
       "[29380 rows x 2 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df4[['text', 'label']].reset_index(drop=True)\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5c7713a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#transformer function\n",
    "def dropLanguages(df):\n",
    "    DetectorFactory.seed = 0\n",
    "    df['text'] = df['text'].apply(deleteUrl)\n",
    "    df = df[df['text'].apply(len) > 10]\n",
    "    print('Text cleaned')\n",
    "    df['lang'] = df['text'].apply(detect)\n",
    "    df = df[df['lang']=='en']\n",
    "    df = df.drop(columns=['shorttext', 'lang'])\n",
    "    print('Languages dropped')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50a7ad58",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3fbdb0e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# żeby zapisać dane\n",
    "df4.to_csv('train_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c587f2f",
   "metadata": {},
   "source": [
    "### Word Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "60e13b91",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fake = df4[df4[\"label\"] == 0]\n",
    "df_true = df4[df4[\"label\"] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "720a69ee",
   "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": [
    "# Fałszywe\n",
    "consolidated_f = ' '.join(\n",
    "    word for word in df_fake['text'].astype(str))\n",
    "wordcloud = WordCloud().generate(consolidated_f)\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "b172e4c0",
   "metadata": {
    "scrolled": true
   },
   "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": [
    "# Prawdziwe\n",
    "consolidated_t = ' '.join(\n",
    "    word for word in df_true['text'].astype(str))\n",
    "wordcloud = WordCloud().generate(consolidated_t)\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "55297db4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df4 = pd.read_csv('train_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7f08f86e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bag of words - liczba wystąpień każdego słowa w danym tekście\n",
    "count_vect = CountVectorizer()\n",
    "X_train_counts = count_vect.fit_transform(df4['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7aaac33d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Zamiana częstości w częstotliwość wystąpień\n",
    "tfidf_transformer = TfidfTransformer()\n",
    "X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9841ed3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29380, 180166)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_tfidf.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4696ddf8",
   "metadata": {},
   "source": [
    "## Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "00e2560f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "na_transformer = FunctionTransformer(handleNa)\n",
    "column_transformer = FunctionTransformer(combineColumns)\n",
    "outliers_transformer = FunctionTransformer(shortenOutliers)\n",
    "stopwords_transformer = FunctionTransformer(removeStopwords)\n",
    "language_transformer = FunctionTransformer(dropLanguages)\n",
    "\n",
    "custom_pipe = Pipeline([\n",
    "    (\"na\", na_transformer),\n",
    "    ('columns', column_transformer),\n",
    "    ('outliers', outliers_transformer),\n",
    "    (\"stopwords\", stopwords_transformer),\n",
    "    (\"language\", language_transformer),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6e904402",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('training_data.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b4280492",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>In the Western World Respect for Law No Longer...</td>\n",
       "      <td>In the Western World Respect for Law No Longer...</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WATCH: President Obama Unleashes On Trump, Sa...</td>\n",
       "      <td>During an interview on CBS s Face the Nation S...</td>\n",
       "      <td>fake</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>THE GUY WHO Punched “Moldylocks” Speaks Out Ab...</td>\n",
       "      <td>Nathan Domingo is the founder of  Identity Eur...</td>\n",
       "      <td>fake</td>\n",
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       "      <th>3</th>\n",
       "      <td>SAY WHAT? AMAZON TELLS CUSTOMER THEY WERE FORC...</td>\n",
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       "      <td>fake</td>\n",
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       "      <th>4</th>\n",
       "      <td>ANTI-GUN Zealot KATIE COURIC Hit With $12 MILL...</td>\n",
       "      <td>Little Miss Sunshine  has fooled a lot of peo...</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>33826</th>\n",
       "      <td>STATE’S ATTORNEY LIED: BALTIMORE POLICE HAD PR...</td>\n",
       "      <td>Marilyn Mosby held a press conference today in...</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33827</th>\n",
       "      <td>Russia denies its planes killed civilians in S...</td>\n",
       "      <td>MOSCOW (Reuters) - Russia s Defence Ministry d...</td>\n",
       "      <td>true</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33828</th>\n",
       "      <td>Hungary’s “Wall” Versus the U.S. “Wall”</td>\n",
       "      <td>Op-Ed by Catherine J. Frompovich\\r\\nCurrently ...</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33829</th>\n",
       "      <td>Pastor Caught Trump’s Spiritual Advisor Steal...</td>\n",
       "      <td>It seems that Donald Trump s spiritual advisor...</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33830</th>\n",
       "      <td>BOOM! SENATOR LINDSEY GRAHAM: I Have Reason to...</td>\n",
       "      <td>Senator Lindsey Graham:  I have reason to beli...</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33831 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   title  \\\n",
       "0      In the Western World Respect for Law No Longer...   \n",
       "1       WATCH: President Obama Unleashes On Trump, Sa...   \n",
       "2      THE GUY WHO Punched “Moldylocks” Speaks Out Ab...   \n",
       "3      SAY WHAT? AMAZON TELLS CUSTOMER THEY WERE FORC...   \n",
       "4      ANTI-GUN Zealot KATIE COURIC Hit With $12 MILL...   \n",
       "...                                                  ...   \n",
       "33826  STATE’S ATTORNEY LIED: BALTIMORE POLICE HAD PR...   \n",
       "33827  Russia denies its planes killed civilians in S...   \n",
       "33828            Hungary’s “Wall” Versus the U.S. “Wall”   \n",
       "33829   Pastor Caught Trump’s Spiritual Advisor Steal...   \n",
       "33830  BOOM! SENATOR LINDSEY GRAHAM: I Have Reason to...   \n",
       "\n",
       "                                                    text label  \n",
       "0      In the Western World Respect for Law No Longer...  fake  \n",
       "1      During an interview on CBS s Face the Nation S...  fake  \n",
       "2      Nathan Domingo is the founder of  Identity Eur...  fake  \n",
       "3      When it comes to limiting our free speech, not...  fake  \n",
       "4       Little Miss Sunshine  has fooled a lot of peo...  fake  \n",
       "...                                                  ...   ...  \n",
       "33826  Marilyn Mosby held a press conference today in...  fake  \n",
       "33827  MOSCOW (Reuters) - Russia s Defence Ministry d...  true  \n",
       "33828  Op-Ed by Catherine J. Frompovich\\r\\nCurrently ...  fake  \n",
       "33829  It seems that Donald Trump s spiritual advisor...  fake  \n",
       "33830  Senator Lindsey Graham:  I have reason to beli...  fake  \n",
       "\n",
       "[33831 rows x 3 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "595f6cac",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = encodeLabels(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8561f4b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NaNs replaced\n",
      "Columns combined\n",
      "Outliers removed\n",
      "Punctuation removed\n",
      "Words tokenized\n",
      "Stopwords removed\n",
      "Text cleaned\n",
      "Languages dropped\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\karim\\AppData\\Local\\Temp/ipykernel_16104/446988254.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['lang'] = df['shorttext'].apply(detect)\n"
     ]
    }
   ],
   "source": [
    "train_transformed = custom_pipe.transform(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "27d631f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>western world respect law longer exists wester...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>watch president obama unleashes trump say terr...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>guy punched moldylocks speaks violent antifa f...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>say amazon tell customer forced federal govern...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>trump crisis lewd remark woman come light new ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33825</th>\n",
       "      <td>trump give brutal warning lawless sanctuary ci...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33826</th>\n",
       "      <td>state attorney lied baltimore police probable ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33828</th>\n",
       "      <td>hungary wall versus u wall oped catherine j fr...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33829</th>\n",
       "      <td>pastor caught trump spiritual advisor stealing...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33830</th>\n",
       "      <td>boom senator lindsey graham reason believe cli...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29380 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  label\n",
       "0      western world respect law longer exists wester...      0\n",
       "1      watch president obama unleashes trump say terr...      0\n",
       "2      guy punched moldylocks speaks violent antifa f...      0\n",
       "3      say amazon tell customer forced federal govern...      0\n",
       "5      trump crisis lewd remark woman come light new ...      1\n",
       "...                                                  ...    ...\n",
       "33825  trump give brutal warning lawless sanctuary ci...      0\n",
       "33826  state attorney lied baltimore police probable ...      0\n",
       "33828  hungary wall versus u wall oped catherine j fr...      0\n",
       "33829  pastor caught trump spiritual advisor stealing...      0\n",
       "33830  boom senator lindsey graham reason believe cli...      0\n",
       "\n",
       "[29380 rows x 2 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "622a5d0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_transformed_x = train_transformed['text']\n",
    "train_transformed_y = train_transformed['label']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caaf2520",
   "metadata": {},
   "source": [
    "### Transforming testing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b135ca89",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('testing_data.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1ad4d1d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = encodeLabels(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "676c17a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NaNs replaced\n",
      "Columns combined\n",
      "Outliers removed\n",
      "Punctuation removed\n",
      "Words tokenized\n",
      "Stopwords removed\n",
      "Text cleaned\n",
      "Languages dropped\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\karim\\AppData\\Local\\Temp/ipykernel_16104/446988254.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['lang'] = df['shorttext'].apply(detect)\n"
     ]
    }
   ],
   "source": [
    "test_transformed = custom_pipe.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "db71c7cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_transformed_x = test_transformed['text']\n",
    "test_transformed_y = test_transformed['label']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5287f67c",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af0d481a",
   "metadata": {},
   "source": [
    "### Baseline model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "639a32c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.linear_model import SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b9383ef4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pipeline with ready-made transformers and model\n",
    "model_pipe = Pipeline([\n",
    "    ('vectorizer', CountVectorizer()),\n",
    "    ('tfidf', TfidfTransformer()),\n",
    "    ('clf', MultinomialNB()),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "882a6dd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('vectorizer', CountVectorizer()),\n",
       "                ('tfidf', TfidfTransformer()), ('clf', MultinomialNB())])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_pipe.fit(train_transformed_x, train_transformed_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d41a9ec5",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = model_pipe.predict(test_transformed_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "38633223",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_probability = model_pipe.predict_proba(test_transformed_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b4cf8e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean(predicted == test_transformed_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d352c91c",
   "metadata": {},
   "source": [
    "### Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "99e54115",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "lr_pipe = Pipeline([\n",
    "    ('vectorizer', CountVectorizer(max_features=5000)),\n",
    "    ('classifier', LogisticRegression(random_state=42))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "c62c7607",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\karim\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('vectorizer', CountVectorizer(max_features=5000)),\n",
       "                ('classifier', LogisticRegression(random_state=42))])"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_pipe.fit(train_transformed_x, train_transformed_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "cc7f4ac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_predicted = lr_pipe.predict(test_transformed_x)\n",
    "lr_predicted_probability = lr_pipe.predict_proba(test_transformed_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05121f30",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "528eaca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_pipe = Pipeline([\n",
    "    ('vectorizer', CountVectorizer()),\n",
    "    ('tfidf', TfidfTransformer()),\n",
    "    ('clf', SGDClassifier(loss='hinge', penalty='l2',\n",
    "                          alpha=1e-3, random_state=42,\n",
    "                          max_iter=5, tol=None)),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "2fb074b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('vectorizer', CountVectorizer()),\n",
       "                ('tfidf', TfidfTransformer()),\n",
       "                ('clf',\n",
       "                 SGDClassifier(alpha=0.001, max_iter=5, random_state=42,\n",
       "                               tol=None))])"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_pipe.fit(train_transformed_x, train_transformed_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "15d0a64b",
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_predicted = svm_pipe.predict(test_transformed_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf8c190d",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean(svm_predicted == test_transformed_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cee78bb",
   "metadata": {},
   "source": [
    "A lot better than Naive-Bayes classifier. Lets check the score if we also take bigrams into account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "77040350",
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_pipe_2 = Pipeline([\n",
    "    ('vectorizer', CountVectorizer(ngram_range=(1,2))),\n",
    "    ('tfidf', TfidfTransformer()),\n",
    "    ('clf', SGDClassifier(loss='hinge', penalty='l2',\n",
    "                          alpha=1e-3, random_state=42,\n",
    "                          max_iter=5, tol=None)),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "a5c7f516",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('vectorizer', CountVectorizer(ngram_range=(1, 2))),\n",
       "                ('tfidf', TfidfTransformer()),\n",
       "                ('clf',\n",
       "                 SGDClassifier(alpha=0.001, max_iter=5, random_state=42,\n",
       "                               tol=None))])"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_pipe_2.fit(train_transformed_x, train_transformed_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "4eec98ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "svm2_predicted = svm_pipe_2.predict(test_transformed_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "e9efa38b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8779358437935844"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(svm2_predicted == test_transformed_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "318fc385",
   "metadata": {},
   "source": [
    "It's actually worse, so we will be taking only single words into account. Let's play with the classifier parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "9dc68a59",
   "metadata": {},
   "outputs": [],
   "source": [
    "huber_pipe = Pipeline([\n",
    "    ('vectorizer', CountVectorizer()),\n",
    "    ('tfidf', TfidfTransformer()),  \n",
    "    ('clf', SGDClassifier(loss='modified_huber', penalty='l2',\n",
    "                          alpha=1e-3, random_state=42,\n",
    "                          max_iter=10, tol=None)),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "61dae701",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('vectorizer', CountVectorizer()),\n",
       "                ('tfidf', TfidfTransformer()),\n",
       "                ('clf',\n",
       "                 SGDClassifier(alpha=0.001, loss='modified_huber', max_iter=10,\n",
       "                               random_state=42, tol=None))])"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "huber_pipe.fit(train_transformed_x, train_transformed_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "50bce8a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "huber_predicted = huber_pipe.predict(test_transformed_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "9e9c13a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "huber_predicted_prob = huber_pipe.predict_proba(test_transformed_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "8a0f19a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9424267782426778"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(huber_predicted == test_transformed_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f213814d",
   "metadata": {},
   "source": [
    "dictionary of models and probabilities of false and true labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "6a245b41",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dict = {'Logistic regression': [lr_predicted, lr_predicted_probability]}\n",
    "model_dict['Multinomial Naive Bayes'] = [predicted, predicted_probability]\n",
    "model_dict['SVM'] = [svm_predicted, np.empty(shape=(0,))]\n",
    "model_dict['SVM with bigrams'] = [svm2_predicted, np.empty(shape=(0,))]\n",
    "model_dict['SVM modified huber'] = [huber_predicted, huber_predicted_prob]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db6d7286",
   "metadata": {},
   "source": [
    "# Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1de0e683",
   "metadata": {},
   "outputs": [],
   "source": [
    "# co znaczą te miary: \n",
    "# precision the ability of the classifier not to label as positive a sample that is negative.\n",
    "# tp / (tp + fp)\n",
    "# The recall is intuitively the ability of the classifier to find all the positive samples. \n",
    "# tp / (tp + fn)\n",
    "# F1 = 2 * (precision * recall) / (precision + recall)\n",
    "# log_loss - how far is the predicted value from actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "1e78eddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# precision\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, log_loss, brier_score_loss\n",
    "def accuracy(name, y_true,y_pred, y_probabilities = np.empty(shape=(0,))):\n",
    "    precision = precision_score(y_true, y_pred)\n",
    "    recall = recall_score(y_true, y_pred, pos_label=0)\n",
    "    F1_score = f1_score(y_true, y_pred, pos_label=0)\n",
    "    auc_score = roc_auc_score(y_true, y_pred)\n",
    "    if len(y_probabilities.tolist()) == 0:\n",
    "        Log_loss=None\n",
    "    else:\n",
    "        Log_loss = log_loss(y_true, y_probabilities)\n",
    "    my_dict = {'Classifier': name, 'precision': [precision], 'recall': [recall], 'f1 score': [F1_score], 'auc score': [auc_score], 'log loss': [Log_loss] }\n",
    "    df = pd.DataFrame.from_dict(my_dict) \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "dcc5c629",
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy_dataframe(model_dict):\n",
    "    accuracy_df = pd.DataFrame()\n",
    "    for name, val in model_dict.items():\n",
    "        accuracy_df = accuracy_df.append(pd.DataFrame(accuracy(name ,test_transformed_y, val[0], val[1])).set_index('Classifier'))\n",
    "    accuracy_df.round(decimals=3)\n",
    "    return accuracy_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "934cc548",
   "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1 score</th>\n",
       "      <th>auc score</th>\n",
       "      <th>log loss</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Classifier</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>Logistic regression</th>\n",
       "      <td>0.939015</td>\n",
       "      <td>0.963110</td>\n",
       "      <td>0.959312</td>\n",
       "      <td>0.944996</td>\n",
       "      <td>0.267585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multinomial Naive Bayes</th>\n",
       "      <td>0.928728</td>\n",
       "      <td>0.967788</td>\n",
       "      <td>0.895736</td>\n",
       "      <td>0.826355</td>\n",
       "      <td>0.360352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.945933</td>\n",
       "      <td>0.970038</td>\n",
       "      <td>0.942395</td>\n",
       "      <td>0.912709</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM with bigrams</th>\n",
       "      <td>0.952497</td>\n",
       "      <td>0.975346</td>\n",
       "      <td>0.931632</td>\n",
       "      <td>0.890991</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM modified huber</th>\n",
       "      <td>0.950429</td>\n",
       "      <td>0.971387</td>\n",
       "      <td>0.954385</td>\n",
       "      <td>0.933279</td>\n",
       "      <td>0.234706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         precision    recall  f1 score  auc score  log loss\n",
       "Classifier                                                                 \n",
       "Logistic regression       0.939015  0.963110  0.959312   0.944996  0.267585\n",
       "Multinomial Naive Bayes   0.928728  0.967788  0.895736   0.826355  0.360352\n",
       "SVM                       0.945933  0.970038  0.942395   0.912709       NaN\n",
       "SVM with bigrams          0.952497  0.975346  0.931632   0.890991       NaN\n",
       "SVM modified huber        0.950429  0.971387  0.954385   0.933279  0.234706"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_dataframe(model_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "6ca00c26",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "def plot_histogram_pred(model_dict):\n",
    "    fig = plt.figure(figsize=(10, 8))\n",
    "    gs = GridSpec(2, 2, figure=fig)\n",
    "    grid_positions = [(0, 0), (0,1), (1, 0)]\n",
    "    i=0\n",
    "    for name, val in model_dict.items():\n",
    "        if len(val[1].tolist()) == 0:\n",
    "            continue\n",
    "        else:\n",
    "            row, col = grid_positions[i]\n",
    "            ax = fig.add_subplot(gs[row, col])\n",
    "            i+=1\n",
    "            data = val[1][:, 0]\n",
    "            sns.histplot(data, ax=ax, bins=10, kde=False, color=\"teal\")\n",
    "            sns.set_style(\"whitegrid\")\n",
    "            sns.set_context(\"notebook\", font_scale=1.2)\n",
    "            plt.title(name, fontsize=16)\n",
    "            plt.xlabel('Predicted value', fontsize=14)\n",
    "            plt.ylabel('Count', fontsize=14)\n",
    "            plt.xticks(fontsize=12)\n",
    "            plt.yticks(fontsize=12)\n",
    "            plt.ylim(0, 12000)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "ce3d75b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram_pred(model_dict)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}