class_clust.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "import seaborn as sns\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# if you have the raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2-a7f452626faa>:28: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_final = data_final.append(\n",
      "<ipython-input-2-a7f452626faa>:28: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_final = data_final.append(\n",
      "<ipython-input-2-a7f452626faa>:28: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_final = data_final.append(\n",
      "<ipython-input-2-a7f452626faa>:28: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_final = data_final.append(\n",
      "<ipython-input-2-a7f452626faa>:28: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_final = data_final.append(\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>article</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ينظم معهد الشارقة للفنون معرضاً فنياً تحت عنوا...</td>\n",
       "      <td>Culture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>تقيم الفنانة ليتا كابيلوت معرضاً في مطلع العام...</td>\n",
       "      <td>Culture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>تتواصل الليلة مسيرة التحدّي والمتعة والإثارة ض...</td>\n",
       "      <td>Culture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>عقدت ظهر أمس الأول في قصر الثقافة في الشارقة ا...</td>\n",
       "      <td>Culture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>اختارت صحيفة «التايمز» جورج أورويل ليحل في الم...</td>\n",
       "      <td>Culture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>رب اجعلني ممن تظلهم في ظل عرشك يوم لا ظل إلا ظ...</td>\n",
       "      <td>Religion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>د‮. ‬أحمد عمر هاشم‮ من أهم عوامل النصر الذي‮ ‬...</td>\n",
       "      <td>Religion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>تتردد في أركانه على مدار اليوم آيات القرآن الك...</td>\n",
       "      <td>Religion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>القاهرة «الخليج»: يُرجع العديد من الدراسات الح...</td>\n",
       "      <td>Religion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>لعل خطبة أسد بن الفرات فاتح صقلّية، وهو الفقيه...</td>\n",
       "      <td>Religion</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               article     label\n",
       "0    ينظم معهد الشارقة للفنون معرضاً فنياً تحت عنوا...   Culture\n",
       "1    تقيم الفنانة ليتا كابيلوت معرضاً في مطلع العام...   Culture\n",
       "2    تتواصل الليلة مسيرة التحدّي والمتعة والإثارة ض...   Culture\n",
       "3    عقدت ظهر أمس الأول في قصر الثقافة في الشارقة ا...   Culture\n",
       "4    اختارت صحيفة «التايمز» جورج أورويل ليحل في الم...   Culture\n",
       "..                                                 ...       ...\n",
       "995  رب اجعلني ممن تظلهم في ظل عرشك يوم لا ظل إلا ظ...  Religion\n",
       "996  د‮. ‬أحمد عمر هاشم‮ من أهم عوامل النصر الذي‮ ‬...  Religion\n",
       "997  تتردد في أركانه على مدار اليوم آيات القرآن الك...  Religion\n",
       "998  القاهرة «الخليج»: يُرجع العديد من الدراسات الح...  Religion\n",
       "999  لعل خطبة أسد بن الفرات فاتح صقلّية، وهو الفقيه...  Religion\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "def read_data(cat_name):\n",
    "    # Folder Path\n",
    "    path = \"D:\\Masters - 1st semster\\Data science applications\\Final-Project\\{}\".format(cat_name)\n",
    "    data, category = [],[]\n",
    "    # Change the directory\n",
    "    os.chdir(path)\n",
    "\n",
    "    def read_text_file(file_path):\n",
    "        with open(file_path, 'r', encoding=\"utf8\") as f:\n",
    "            data.append(f.read())\n",
    "            category.append(cat_name)\n",
    "\n",
    "    for file in os.listdir():\n",
    "        # Check whether file is in text format or not\n",
    "        if file.endswith(\".txt\"):\n",
    "            file_path = f\"{path}\\{file}\"\n",
    "\n",
    "            # call read text file function\n",
    "            read_text_file(file_path)\n",
    "    return data,category\n",
    "cats = [\"Culture\",\"Finance\",\"Medical\",\"Politics\",\"Religion\"]\n",
    "data_final = pd.DataFrame()\n",
    "for i in range(len(cats)):\n",
    "    data_df = pd.DataFrame()\n",
    "    data,cat = read_data(cats[i])\n",
    "    data_df[\"article\"] = data\n",
    "    data_df[\"label\"] = cat\n",
    "    data_final = data_final.append(\n",
    "        data_df[data_df.label == cats[i]].head(200), ignore_index=True)\n",
    "data_final\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"D:\\Masters - 1st semster\\Data science applications\\Final-Project\"\n",
    "os.chdir(path)\n",
    "data_final.to_csv(\n",
    "    \"final_data.csv\", encoding='utf-8')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# if you don't have the raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_final = pd.read_csv(\"final_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyarabic.araby as ar\n",
    "\n",
    "# import Stemmer\n",
    "import functools\n",
    "import operator\n",
    "\n",
    "import logging\n",
    "\n",
    "logging.basicConfig(level=logging.WARNING)\n",
    "logger = logging.getLogger(__name__)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 'ء',\n",
       " 'ءَ',\n",
       " 'آ',\n",
       " 'آب',\n",
       " 'آذار',\n",
       " 'آض',\n",
       " 'آل',\n",
       " 'آمينَ',\n",
       " 'آناء',\n",
       " 'آنفا',\n",
       " 'آه',\n",
       " 'آهاً',\n",
       " 'آهٍ',\n",
       " 'آهِ',\n",
       " 'أ',\n",
       " 'أبدا',\n",
       " 'أبريل',\n",
       " 'أبو',\n",
       " 'أبٌ',\n",
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       " 'أحد',\n",
       " 'أخبر',\n",
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       " 'أخو',\n",
       " 'أخٌ',\n",
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       " 'أسكن',\n",
       " 'أصبح',\n",
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       " 'أعطى',\n",
       " 'أعلم',\n",
       " 'أغسطس',\n",
       " 'أفريل',\n",
       " 'أفعل به',\n",
       " 'أفٍّ',\n",
       " 'أقبل',\n",
       " 'أكتوبر',\n",
       " 'أل',\n",
       " 'ألا',\n",
       " 'ألف',\n",
       " 'ألفى',\n",
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       " 'أمامك',\n",
       " 'أمامكَ',\n",
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       " 'أنتن',\n",
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       " 'أنشأ',\n",
       " 'أنه',\n",
       " 'أنًّ',\n",
       " 'أنّى',\n",
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       " 'أوت',\n",
       " 'أوشك',\n",
       " 'أول',\n",
       " 'أولئك',\n",
       " 'أولاء',\n",
       " 'أولالك',\n",
       " 'أوّهْ',\n",
       " 'أى',\n",
       " 'أي',\n",
       " 'أيا',\n",
       " 'أيار',\n",
       " 'أيضا',\n",
       " 'أيلول',\n",
       " 'أين',\n",
       " 'أيّ',\n",
       " 'أيّان',\n",
       " 'أُفٍّ',\n",
       " 'ؤ',\n",
       " 'إحدى',\n",
       " 'إذ',\n",
       " 'إذا',\n",
       " 'إذاً',\n",
       " 'إذما',\n",
       " 'إذن',\n",
       " 'إزاء',\n",
       " 'إلى',\n",
       " 'إلي',\n",
       " 'إليكم',\n",
       " 'إليكما',\n",
       " 'إليكنّ',\n",
       " 'إليكَ',\n",
       " 'إلَيْكَ',\n",
       " 'إلّا',\n",
       " 'إمّا',\n",
       " 'إن',\n",
       " 'إنَّ',\n",
       " 'إى',\n",
       " 'إياك',\n",
       " 'إياكم',\n",
       " 'إياكما',\n",
       " 'إياكن',\n",
       " 'إيانا',\n",
       " 'إياه',\n",
       " 'إياها',\n",
       " 'إياهم',\n",
       " 'إياهما',\n",
       " 'إياهن',\n",
       " 'إياي',\n",
       " 'إيهٍ',\n",
       " 'ئ',\n",
       " 'ا',\n",
       " 'ا?',\n",
       " 'ا?ى',\n",
       " 'االا',\n",
       " 'االتى',\n",
       " 'ابتدأ',\n",
       " 'ابين',\n",
       " 'اتخذ',\n",
       " 'اثر',\n",
       " 'اثنا',\n",
       " 'اثنان',\n",
       " 'اثني',\n",
       " 'اثنين',\n",
       " 'اجل',\n",
       " 'احد',\n",
       " 'اخرى',\n",
       " 'اخلولق',\n",
       " 'اذا',\n",
       " 'اربعة',\n",
       " 'اربعون',\n",
       " 'اربعين',\n",
       " 'ارتدّ',\n",
       " 'استحال',\n",
       " 'اصبح',\n",
       " 'اضحى',\n",
       " 'اطار',\n",
       " 'اعادة',\n",
       " 'اعلنت',\n",
       " 'اف',\n",
       " 'اكثر',\n",
       " 'اكد',\n",
       " 'الآن',\n",
       " 'الألاء',\n",
       " 'الألى',\n",
       " 'الا',\n",
       " 'الاخيرة',\n",
       " 'الان',\n",
       " 'الاول',\n",
       " 'الاولى',\n",
       " 'التى',\n",
       " 'التي',\n",
       " 'الثاني',\n",
       " 'الثانية',\n",
       " 'الحالي',\n",
       " 'الذاتي',\n",
       " 'الذى',\n",
       " 'الذي',\n",
       " 'الذين',\n",
       " 'السابق',\n",
       " 'الف',\n",
       " 'اللاتي',\n",
       " 'اللتان',\n",
       " 'اللتيا',\n",
       " 'اللتين',\n",
       " 'اللذان',\n",
       " 'اللذين',\n",
       " 'اللواتي',\n",
       " 'الماضي',\n",
       " 'المقبل',\n",
       " 'الوقت',\n",
       " 'الى',\n",
       " 'الي',\n",
       " 'اليه',\n",
       " 'اليها',\n",
       " 'اليوم',\n",
       " 'اما',\n",
       " 'امام',\n",
       " 'امس',\n",
       " 'امسى',\n",
       " 'ان',\n",
       " 'انبرى',\n",
       " 'انقلب',\n",
       " 'انه',\n",
       " 'انها',\n",
       " 'او',\n",
       " 'اول',\n",
       " 'اي',\n",
       " 'ايار',\n",
       " 'ايام',\n",
       " 'ايضا',\n",
       " 'ب',\n",
       " 'بؤسا',\n",
       " 'بإن',\n",
       " 'بئس',\n",
       " 'باء',\n",
       " 'بات',\n",
       " 'باسم',\n",
       " 'بان',\n",
       " 'بخٍ',\n",
       " 'بد',\n",
       " 'بدلا',\n",
       " 'برس',\n",
       " 'بسبب',\n",
       " 'بسّ',\n",
       " 'بشكل',\n",
       " 'بضع',\n",
       " 'بطآن',\n",
       " 'بعد',\n",
       " 'بعدا',\n",
       " 'بعض',\n",
       " 'بغتة',\n",
       " 'بل',\n",
       " 'بلى',\n",
       " 'بن',\n",
       " 'به',\n",
       " 'بها',\n",
       " 'بهذا',\n",
       " 'بيد',\n",
       " 'بين',\n",
       " 'بَسْ',\n",
       " 'بَلْهَ',\n",
       " 'ة',\n",
       " 'ت',\n",
       " 'تاء',\n",
       " 'تارة',\n",
       " 'تاسع',\n",
       " 'تانِ',\n",
       " 'تانِك',\n",
       " 'تبدّل',\n",
       " 'تجاه',\n",
       " 'تحت',\n",
       " 'تحوّل',\n",
       " 'تخذ',\n",
       " 'ترك',\n",
       " 'تسع',\n",
       " 'تسعة',\n",
       " 'تسعمئة',\n",
       " 'تسعمائة',\n",
       " 'تسعون',\n",
       " 'تسعين',\n",
       " 'تشرين',\n",
       " 'تعسا',\n",
       " 'تعلَّم',\n",
       " 'تفعلان',\n",
       " 'تفعلون',\n",
       " 'تفعلين',\n",
       " 'تكون',\n",
       " 'تلقاء',\n",
       " 'تلك',\n",
       " 'تم',\n",
       " 'تموز',\n",
       " 'تينك',\n",
       " 'تَيْنِ',\n",
       " 'تِه',\n",
       " 'تِي',\n",
       " 'ث',\n",
       " 'ثاء',\n",
       " 'ثالث',\n",
       " 'ثامن',\n",
       " 'ثان',\n",
       " 'ثاني',\n",
       " 'ثلاث',\n",
       " 'ثلاثاء',\n",
       " 'ثلاثة',\n",
       " 'ثلاثمئة',\n",
       " 'ثلاثمائة',\n",
       " 'ثلاثون',\n",
       " 'ثلاثين',\n",
       " 'ثم',\n",
       " 'ثمان',\n",
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       " 'ثماني',\n",
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       " 'ثمانين',\n",
       " 'ثمنمئة',\n",
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       " 'ثمّ',\n",
       " 'ثمّة',\n",
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       " 'حاء',\n",
       " 'حادي',\n",
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       " 'حاشا',\n",
       " 'حاليا',\n",
       " 'حاي',\n",
       " 'حبذا',\n",
       " 'حبيب',\n",
       " 'حتى',\n",
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       " 'حدَث',\n",
       " 'حرى',\n",
       " 'حزيران',\n",
       " 'حسب',\n",
       " 'حقا',\n",
       " 'حمدا',\n",
       " 'حمو',\n",
       " 'حمٌ',\n",
       " 'حوالى',\n",
       " 'حول',\n",
       " 'حيث',\n",
       " 'حيثما',\n",
       " 'حين',\n",
       " 'حيَّ',\n",
       " 'حَذارِ',\n",
       " 'خ',\n",
       " 'خاء',\n",
       " 'خاصة',\n",
       " 'خال',\n",
       " 'خامس',\n",
       " 'خبَّر',\n",
       " 'خلا',\n",
       " 'خلافا',\n",
       " 'خلال',\n",
       " 'خلف',\n",
       " 'خمس',\n",
       " 'خمسة',\n",
       " 'خمسمئة',\n",
       " 'خمسمائة',\n",
       " 'خمسون',\n",
       " 'خمسين',\n",
       " 'خميس',\n",
       " 'د',\n",
       " 'دال',\n",
       " 'درهم',\n",
       " 'درى',\n",
       " 'دواليك',\n",
       " 'دولار',\n",
       " 'دون',\n",
       " 'دونك',\n",
       " 'ديسمبر',\n",
       " 'دينار',\n",
       " 'ذ',\n",
       " 'ذا',\n",
       " 'ذات',\n",
       " 'ذاك',\n",
       " 'ذال',\n",
       " 'ذانك',\n",
       " 'ذانِ',\n",
       " 'ذلك',\n",
       " 'ذهب',\n",
       " 'ذو',\n",
       " 'ذيت',\n",
       " 'ذينك',\n",
       " 'ذَيْنِ',\n",
       " 'ذِه',\n",
       " 'ذِي',\n",
       " 'ر',\n",
       " 'رأى',\n",
       " 'راء',\n",
       " 'رابع',\n",
       " 'راح',\n",
       " 'رجع',\n",
       " 'رزق',\n",
       " 'رويدك',\n",
       " 'ريال',\n",
       " 'ريث',\n",
       " 'رُبَّ',\n",
       " 'ز',\n",
       " 'زاي',\n",
       " 'زعم',\n",
       " 'زود',\n",
       " 'زيارة',\n",
       " 'س',\n",
       " 'ساء',\n",
       " 'سابع',\n",
       " 'سادس',\n",
       " 'سبت',\n",
       " 'سبتمبر',\n",
       " 'سبحان',\n",
       " 'سبع',\n",
       " 'سبعة',\n",
       " 'سبعمئة',\n",
       " 'سبعمائة',\n",
       " 'سبعون',\n",
       " 'سبعين',\n",
       " 'ست',\n",
       " 'ستة',\n",
       " 'ستكون',\n",
       " 'ستمئة',\n",
       " 'ستمائة',\n",
       " 'ستون',\n",
       " 'ستين',\n",
       " 'سحقا',\n",
       " 'سرا',\n",
       " 'سرعان',\n",
       " 'سقى',\n",
       " 'سمعا',\n",
       " 'سنة',\n",
       " 'سنتيم',\n",
       " 'سنوات',\n",
       " 'سوف',\n",
       " 'سوى',\n",
       " 'سين',\n",
       " 'ش',\n",
       " 'شباط',\n",
       " 'شبه',\n",
       " 'شتانَ',\n",
       " 'شخصا',\n",
       " 'شرع',\n",
       " 'شمال',\n",
       " 'شيكل',\n",
       " 'شين',\n",
       " 'شَتَّانَ',\n",
       " 'ص',\n",
       " 'صاد',\n",
       " 'صار',\n",
       " 'صباح',\n",
       " 'صبر',\n",
       " 'صبرا',\n",
       " 'صدقا',\n",
       " 'صراحة',\n",
       " 'صفر',\n",
       " 'صهٍ',\n",
       " 'صهْ',\n",
       " 'ض',\n",
       " 'ضاد',\n",
       " 'ضحوة',\n",
       " 'ضد',\n",
       " 'ضمن',\n",
       " 'ط',\n",
       " 'طاء',\n",
       " 'طاق',\n",
       " 'طالما',\n",
       " 'طرا',\n",
       " 'طفق',\n",
       " 'طَق',\n",
       " 'ظ',\n",
       " 'ظاء',\n",
       " 'ظل',\n",
       " 'ظلّ',\n",
       " 'ظنَّ',\n",
       " 'ع',\n",
       " 'عاد',\n",
       " 'عاشر',\n",
       " 'عام',\n",
       " 'عاما',\n",
       " 'عامة',\n",
       " 'عجبا',\n",
       " 'عدا',\n",
       " 'عدة',\n",
       " 'عدد',\n",
       " 'عدم',\n",
       " 'عدَّ',\n",
       " 'عسى',\n",
       " 'عشر',\n",
       " 'عشرة',\n",
       " 'عشرون',\n",
       " 'عشرين',\n",
       " 'عل',\n",
       " 'علق',\n",
       " 'علم',\n",
       " 'على',\n",
       " 'علي',\n",
       " 'عليك',\n",
       " 'عليه',\n",
       " 'عليها',\n",
       " 'علًّ',\n",
       " 'عن',\n",
       " 'عند',\n",
       " 'عندما',\n",
       " 'عنه',\n",
       " 'عنها',\n",
       " 'عوض',\n",
       " 'عيانا',\n",
       " 'عين',\n",
       " 'عَدَسْ',\n",
       " 'غ',\n",
       " 'غادر',\n",
       " 'غالبا',\n",
       " 'غدا',\n",
       " 'غداة',\n",
       " 'غير',\n",
       " 'غين',\n",
       " 'ـ',\n",
       " 'ف',\n",
       " 'فإن',\n",
       " 'فاء',\n",
       " 'فان',\n",
       " 'فانه',\n",
       " 'فبراير',\n",
       " 'فرادى',\n",
       " 'فضلا',\n",
       " 'فقد',\n",
       " 'فقط',\n",
       " 'فكان',\n",
       " 'فلان',\n",
       " 'فلس',\n",
       " 'فهو',\n",
       " 'فو',\n",
       " 'فوق',\n",
       " 'فى',\n",
       " 'في',\n",
       " 'فيفري',\n",
       " 'فيه',\n",
       " 'فيها',\n",
       " 'ق',\n",
       " 'قاطبة',\n",
       " 'قاف',\n",
       " 'قال',\n",
       " 'قام',\n",
       " 'قبل',\n",
       " 'قد',\n",
       " 'قرش',\n",
       " 'قطّ',\n",
       " 'قلما',\n",
       " 'قوة',\n",
       " 'ك',\n",
       " 'كأن',\n",
       " 'كأنّ',\n",
       " 'كأيّ',\n",
       " 'كأيّن',\n",
       " 'كاد',\n",
       " 'كاف',\n",
       " 'كان',\n",
       " 'كانت',\n",
       " 'كانون',\n",
       " 'كثيرا',\n",
       " 'كذا',\n",
       " 'كذلك',\n",
       " 'كرب',\n",
       " 'كسا',\n",
       " 'كل',\n",
       " 'كلتا',\n",
       " 'كلم',\n",
       " 'كلَّا',\n",
       " 'كلّما',\n",
       " 'كم',\n",
       " 'كما',\n",
       " 'كن',\n",
       " 'كى',\n",
       " 'كيت',\n",
       " 'كيف',\n",
       " 'كيفما',\n",
       " 'كِخ',\n",
       " 'ل',\n",
       " 'لأن',\n",
       " 'لا',\n",
       " 'لا سيما',\n",
       " 'لات',\n",
       " 'لازال',\n",
       " 'لاسيما',\n",
       " 'لام',\n",
       " 'لايزال',\n",
       " 'لبيك',\n",
       " 'لدن',\n",
       " 'لدى',\n",
       " 'لدي',\n",
       " 'لذلك',\n",
       " 'لعل',\n",
       " 'لعلَّ',\n",
       " 'لعمر',\n",
       " 'لقاء',\n",
       " 'لكن',\n",
       " 'لكنه',\n",
       " 'لكنَّ',\n",
       " 'للامم',\n",
       " 'لم',\n",
       " 'لما',\n",
       " 'لمّا',\n",
       " 'لن',\n",
       " 'له',\n",
       " 'لها',\n",
       " 'لهذا',\n",
       " 'لهم',\n",
       " 'لو',\n",
       " 'لوكالة',\n",
       " 'لولا',\n",
       " 'لوما',\n",
       " 'ليت',\n",
       " 'ليرة',\n",
       " 'ليس',\n",
       " 'ليسب',\n",
       " 'م',\n",
       " 'مئة',\n",
       " 'مئتان',\n",
       " 'ما',\n",
       " 'ما أفعله',\n",
       " 'ما انفك',\n",
       " 'ما برح',\n",
       " 'مائة',\n",
       " 'ماانفك',\n",
       " 'مابرح',\n",
       " 'مادام',\n",
       " 'ماذا',\n",
       " 'مارس',\n",
       " 'مازال',\n",
       " 'مافتئ',\n",
       " 'ماي',\n",
       " 'مايزال',\n",
       " 'مايو',\n",
       " 'متى',\n",
       " 'مثل',\n",
       " 'مذ',\n",
       " 'مرّة',\n",
       " 'مساء',\n",
       " 'مع',\n",
       " 'معاذ',\n",
       " 'معه',\n",
       " 'معها',\n",
       " 'مقابل',\n",
       " 'مكانكم',\n",
       " 'مكانكما',\n",
       " 'مكانكنّ',\n",
       " 'مكانَك',\n",
       " 'مليار',\n",
       " 'مليم',\n",
       " 'مليون',\n",
       " 'مما',\n",
       " 'من',\n",
       " 'منذ',\n",
       " 'منه',\n",
       " 'منها',\n",
       " 'مه',\n",
       " 'مهما',\n",
       " 'ميم',\n",
       " 'ن',\n",
       " 'نا',\n",
       " 'نبَّا',\n",
       " 'نحن',\n",
       " 'نحو',\n",
       " 'نعم',\n",
       " 'نفس',\n",
       " 'نفسه',\n",
       " 'نهاية',\n",
       " 'نوفمبر',\n",
       " 'نون',\n",
       " 'نيسان',\n",
       " 'نيف',\n",
       " 'نَخْ',\n",
       " 'نَّ',\n",
       " 'ه',\n",
       " 'هؤلاء',\n",
       " 'ها',\n",
       " 'هاء',\n",
       " 'هاكَ',\n",
       " 'هبّ',\n",
       " 'هذا',\n",
       " 'هذه',\n",
       " 'هل',\n",
       " 'هللة',\n",
       " 'هلم',\n",
       " 'هلّا',\n",
       " 'هم',\n",
       " 'هما',\n",
       " 'همزة',\n",
       " 'هن',\n",
       " 'هنا',\n",
       " 'هناك',\n",
       " 'هنالك',\n",
       " 'هو',\n",
       " 'هي',\n",
       " 'هيا',\n",
       " 'هيهات',\n",
       " 'هيّا',\n",
       " 'هَؤلاء',\n",
       " 'هَاتانِ',\n",
       " 'هَاتَيْنِ',\n",
       " 'هَاتِه',\n",
       " 'هَاتِي',\n",
       " 'هَجْ',\n",
       " 'هَذا',\n",
       " 'هَذانِ',\n",
       " 'هَذَيْنِ',\n",
       " 'هَذِه',\n",
       " 'هَذِي',\n",
       " 'هَيْهات',\n",
       " 'و',\n",
       " 'و6',\n",
       " 'وأبو',\n",
       " 'وأن',\n",
       " 'وا',\n",
       " 'واحد',\n",
       " 'واضاف',\n",
       " 'واضافت',\n",
       " 'واكد',\n",
       " 'والتي',\n",
       " 'والذي',\n",
       " 'وان',\n",
       " 'واهاً',\n",
       " 'واو',\n",
       " 'واوضح',\n",
       " 'وبين',\n",
       " 'وثي',\n",
       " 'وجد',\n",
       " 'وراءَك',\n",
       " 'ورد',\n",
       " 'وعلى',\n",
       " 'وفي',\n",
       " 'وقال',\n",
       " 'وقالت',\n",
       " 'وقد',\n",
       " 'وقف',\n",
       " 'وكان',\n",
       " 'وكانت',\n",
       " 'ولا',\n",
       " 'ولايزال',\n",
       " 'ولكن',\n",
       " 'ولم',\n",
       " 'وله',\n",
       " 'وليس',\n",
       " 'ومع',\n",
       " 'ومن',\n",
       " 'وهب',\n",
       " 'وهذا',\n",
       " 'وهو',\n",
       " 'وهي',\n",
       " 'وَيْ',\n",
       " 'وُشْكَانَ',\n",
       " 'ى',\n",
       " 'ي',\n",
       " 'ياء',\n",
       " 'يفعلان',\n",
       " 'يفعلون',\n",
       " 'يكون',\n",
       " 'يلي',\n",
       " 'يمكن',\n",
       " 'يمين',\n",
       " 'ين',\n",
       " 'يناير',\n",
       " 'يوان',\n",
       " 'يورو',\n",
       " 'يوليو',\n",
       " 'يوم',\n",
       " 'يونيو',\n",
       " 'ّأيّان']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arabic_stop_words = []\n",
    "with open('Arabic_stop_words.txt', encoding='utf-8') as f:\n",
    "    for i in f.readlines():\n",
    "        arabic_stop_words.append(i)\n",
    "        arabic_stop_words[-1] = arabic_stop_words[-1][:-1]\n",
    "arabic_stop_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Unnamed: 0</th>\n",
       "      <th>article</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>ظم معهد الشارقة لفنون معرضا فنيا تحت عنوان باق...</td>\n",
       "      <td>Culture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>يم الفنانة ليتا كابيلوت معرضا في مطلع العام ال...</td>\n",
       "      <td>Culture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>اصل اليلة مسيرة التحدي والمتعة والاثارة ضمن ال...</td>\n",
       "      <td>Culture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>دت ظهر امس الاول في قصر الثقافة في الشارقة الج...</td>\n",
       "      <td>Culture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>تارت صحيفة التايمز جورج اورويل ليحل في المرتبة...</td>\n",
       "      <td>Culture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>995</td>\n",
       "      <td>اجعلني من تظلهم في ظل عرشك يوم لا ظل الا ظلك ...</td>\n",
       "      <td>Religion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>996</td>\n",
       "      <td>احمد عمر هاشم من اهم عوامل النصر الذي وعد اله...</td>\n",
       "      <td>Religion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>997</td>\n",
       "      <td>د في اركانه على مدار اليوم ايات القران الكريم ...</td>\n",
       "      <td>Religion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>998</td>\n",
       "      <td>قاهرة الخليج يرجع العديد من الدراسات الحديثة ا...</td>\n",
       "      <td>Religion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>999</td>\n",
       "      <td>ل خطبة اسد بن الفرات فاتح صقلية وهو الفقيه الم...</td>\n",
       "      <td>Religion</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0                                            article     label\n",
       "0             0  ظم معهد الشارقة لفنون معرضا فنيا تحت عنوان باق...   Culture\n",
       "1             1  يم الفنانة ليتا كابيلوت معرضا في مطلع العام ال...   Culture\n",
       "2             2  اصل اليلة مسيرة التحدي والمتعة والاثارة ضمن ال...   Culture\n",
       "3             3  دت ظهر امس الاول في قصر الثقافة في الشارقة الج...   Culture\n",
       "4             4  تارت صحيفة التايمز جورج اورويل ليحل في المرتبة...   Culture\n",
       "..          ...                                                ...       ...\n",
       "995         995   اجعلني من تظلهم في ظل عرشك يوم لا ظل الا ظلك ...  Religion\n",
       "996         996   احمد عمر هاشم من اهم عوامل النصر الذي وعد اله...  Religion\n",
       "997         997  د في اركانه على مدار اليوم ايات القران الكريم ...  Religion\n",
       "998         998  قاهرة الخليج يرجع العديد من الدراسات الحديثة ا...  Religion\n",
       "999         999  ل خطبة اسد بن الفرات فاتح صقلية وهو الفقيه الم...  Religion\n",
       "\n",
       "[1000 rows x 3 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "import string\n",
    "import emoji\n",
    "#============= Read CSV and apply data preperation =============#\n",
    "\n",
    "def data_cleaning(text):\n",
    "    text = re.sub(r'^https?:\\/\\/.*[\\r\\n]*', '', text, flags=re.MULTILINE)\n",
    "    text = re.sub(r'^http?:\\/\\/.*[\\r\\n]*', '', text, flags=re.MULTILINE)\n",
    "    text = re.sub(r\"http\\S+\", \"\", text)\n",
    "    text = re.sub(r\"https\\S+\", \"\", text)\n",
    "    text = re.sub(r'\\s+', ' ', text)\n",
    "    text = re.sub(\"(\\s\\d+)\", \"\", text)\n",
    "    text = re.sub(r\"$\\d+\\W+|\\b\\d+\\b|\\W+\\d+$\", \"\", text)\n",
    "    text = re.sub(\"\\d+\", \" \", text)\n",
    "    text = ar.strip_tashkeel(text)\n",
    "    text = ar.strip_tatweel(text)\n",
    "    text = text.replace(\"#\", \" \")\n",
    "    text = text.replace(\"@\", \" \")\n",
    "    text = text.replace(\"_\", \" \")\n",
    "    translator = str.maketrans('', '', string.punctuation)\n",
    "    text = text.translate(translator)\n",
    "    em = text\n",
    "    em_split_emoji = emoji.get_emoji_regexp().split(em)\n",
    "    em_split_whitespace = [substr.split() for substr in em_split_emoji]\n",
    "    em_split = functools.reduce(operator.concat, em_split_whitespace)\n",
    "    text = \" \".join(em_split)\n",
    "    text = re.sub(r'(.)\\1+', r'\\1', text)\n",
    "    # text_stem = \" \".join([st.stemWord(i) for i in text.split()])\n",
    "    # text = text +\" \"+ text_stem\n",
    "    text = text.replace(\"آ\", \"ا\")\n",
    "    text = text.replace(\"إ\", \"ا\")\n",
    "    text = text.replace(\"أ\", \"ا\")\n",
    "    text = text.replace(\"ؤ\", \"و\")\n",
    "    text = text.replace(\"ئ\", \"ي\")\n",
    "\n",
    "    return text\n",
    "\n",
    "def data_preprocessing(data_frame):\n",
    "    # clean-up: remove #tags, http links and special symbols\n",
    "    data_frame['article'] = data_frame['article'].apply(lambda x: x[2:-2])\n",
    "    data_frame['article'] = data_frame['article'].apply(\n",
    "        lambda x: re.sub(r'http\\S+', '', x))\n",
    "    data_frame['article'] = data_frame['article'].apply(\n",
    "        lambda x: re.sub(r'[@|#]\\S*', '', x))\n",
    "    data_frame['article'] = data_frame['article'].apply(\n",
    "        lambda x: re.sub(r'\"+', '', x))\n",
    "\n",
    "    # Remove arabic signs\n",
    "    data_frame['article'] = data_frame['article'].apply(lambda x: re.sub(\n",
    "        r'([@A-Za-z0-9_ـــــــــــــ]+)|[^\\w\\s]|#|http\\S+', '', x))\n",
    "\n",
    "    # Remove repeated letters like \"الللللللللللللللله\" to \"الله\"\n",
    "    data_frame['article'] = data_frame['article'].apply(lambda x: x[0:2] + ''.join(\n",
    "        [x[i] for i in range(2, len(x)) if x[i] != x[i-1] or x[i] != x[i-2]]))\n",
    "\n",
    "    # remove stop words\n",
    "    data_frame['article'] = data_frame['article'].apply(\n",
    "        lambda x: '' if x in arabic_stop_words else x)\n",
    "\n",
    "    from nltk.stem.isri import ISRIStemmer\n",
    "    data_frame['article'] = data_frame['article'].apply(lambda x: ISRIStemmer().stem(x))\n",
    "\n",
    "    return data_frame\n",
    "\n",
    "\n",
    "data_final['article'] = data_final['article'].apply(lambda x: data_cleaning(x))\n",
    "df = data_preprocessing(data_final)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "\n",
    "df = shuffle(df)\n",
    "X_train, X_test, y_train, y_test=train_test_split(df['article'], df['label'], test_size=0.3, random_state=42)\n",
    "X_test\n",
    "\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "y_train = label_encoder.fit_transform(y_train)\n",
    "y_test = label_encoder.fit_transform(y_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 4, 3, 0, 4, 1, 4, 4, 2, 0, 2, 0, 2, 2, 4, 3, 3, 1, 2, 0, 3,\n",
       "       0, 0, 3, 3, 3, 3, 2, 1, 0, 4, 3, 0, 2, 3, 2, 3, 4, 1, 0, 0, 2, 1,\n",
       "       1, 2, 0, 4, 4, 1, 4, 3, 1, 1, 2, 2, 1, 3, 0, 3, 1, 3, 0, 0, 3, 1,\n",
       "       3, 2, 1, 4, 0, 2, 0, 3, 1, 3, 1, 1, 0, 1, 4, 3, 3, 4, 4, 4, 0, 2,\n",
       "       4, 0, 3, 3, 3, 0, 3, 2, 4, 1, 3, 3, 2, 4, 0, 3, 0, 4, 3, 3, 2, 3,\n",
       "       4, 3, 4, 4, 0, 2, 2, 0, 0, 2, 1, 0, 4, 1, 2, 4, 2, 3, 2, 4, 3, 3,\n",
       "       3, 3, 4, 2, 1, 2, 4, 4, 0, 4, 0, 2, 4, 0, 1, 2, 0, 0, 2, 2, 1, 4,\n",
       "       0, 3, 3, 2, 4, 2, 2, 0, 1, 0, 1, 2, 0, 0, 1, 1, 3, 2, 3, 3, 4, 4,\n",
       "       2, 3, 2, 3, 1, 4, 4, 4, 3, 3, 2, 0, 1, 4, 2, 1, 1, 3, 0, 1, 4, 2,\n",
       "       0, 1, 2, 4, 3, 3, 3, 2, 0, 2, 3, 4, 0, 3, 1, 2, 1, 4, 1, 4, 3, 2,\n",
       "       4, 2, 4, 0, 4, 1, 2, 4, 4, 0, 3, 0, 4, 0, 0, 3, 3, 1, 2, 2, 1, 3,\n",
       "       4, 3, 4, 1, 3, 3, 4, 3, 3, 0, 0, 2, 2, 1, 2, 2, 0, 4, 4, 0, 3, 2,\n",
       "       4, 3, 3, 0, 2, 0, 4, 4, 3, 1, 0, 4, 0, 0, 3, 4, 4, 0, 2, 0, 2, 1,\n",
       "       4, 2, 2, 1, 3, 0, 1, 0, 1, 2, 3, 0, 3, 1, 2, 3, 0, 2, 2, 2, 2, 2,\n",
       "       0, 3, 3, 0, 0, 2, 0, 3, 1, 1, 3, 0, 3, 0, 2, 4, 1, 1, 0, 2, 3, 4,\n",
       "       2, 0, 0, 0, 3, 2, 2, 4, 1, 3, 3, 0, 1, 1, 0, 3, 2, 4, 0, 4, 1, 0,\n",
       "       0, 3, 2, 3, 0, 4, 1, 4, 4, 0, 3, 1, 1, 1, 3, 4, 2, 4, 3, 0, 1, 3,\n",
       "       1, 4, 1, 1, 1, 0, 3, 1, 3, 2, 4, 0, 0, 2, 4, 3, 0, 1, 0, 2, 3, 1,\n",
       "       1, 1, 1, 1, 0, 1, 2, 4, 0, 2, 4, 2, 4, 3, 1, 4, 3, 4, 4, 1, 3, 2,\n",
       "       3, 0, 4, 1, 0, 3, 3, 0, 0, 2, 0, 0, 2, 2, 3, 0, 1, 1, 1, 2, 4, 0,\n",
       "       2, 2, 4, 2, 2, 2, 1, 2, 0, 1, 0, 2, 0, 3, 1, 2, 0, 3, 1, 1, 1, 2,\n",
       "       4, 1, 3, 4, 4, 3, 1, 1, 3, 0, 2, 3, 4, 0, 1, 0, 1, 2, 1, 3, 3, 3,\n",
       "       2, 0, 1, 4, 2, 0, 3, 4, 4, 1, 1, 4, 0, 4, 1, 0, 2, 1, 4, 1, 0, 3,\n",
       "       0, 3, 4, 2, 3, 1, 1, 2, 3, 0, 3, 0, 2, 3, 1, 0, 4, 0, 2, 0, 0, 4,\n",
       "       2, 3, 1, 3, 2, 1, 3, 1, 3, 1, 2, 0, 0, 4, 0, 2, 2, 1, 0, 3, 3, 4,\n",
       "       1, 4, 2, 0, 3, 2, 1, 0, 2, 0, 3, 0, 4, 3, 4, 0, 1, 1, 3, 1, 3, 3,\n",
       "       1, 1, 4, 4, 3, 0, 1, 4, 1, 2, 4, 1, 4, 2, 2, 1, 1, 3, 4, 3, 4, 4,\n",
       "       4, 2, 1, 3, 1, 2, 4, 4, 2, 2, 0, 1, 1, 3, 0, 2, 1, 0, 2, 2, 4, 4,\n",
       "       1, 4, 2, 1, 4, 1, 1, 3, 4, 4, 3, 0, 4, 3, 0, 0, 4, 3, 3, 2, 0, 2,\n",
       "       2, 2, 3, 4, 2, 2, 2, 1, 0, 1, 4, 0, 2, 1, 4, 0, 1, 4, 3, 3, 2, 4,\n",
       "       3, 0, 4, 2, 4, 4, 4, 4, 1, 0, 4, 0, 1, 2, 4, 3, 3, 3, 3, 4, 3, 4,\n",
       "       4, 4, 2, 4, 1, 0, 1, 0, 3, 2, 3, 1, 0, 3, 1, 4, 1, 1])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Finance', 'Culture', 'Religion', 'Politics', 'Culture',\n",
       "       'Religion', 'Finance', 'Religion', 'Religion', 'Medical',\n",
       "       'Culture', 'Medical', 'Culture', 'Medical', 'Medical', 'Religion',\n",
       "       'Politics', 'Politics', 'Finance', 'Medical', 'Culture',\n",
       "       'Politics', 'Culture', 'Culture', 'Politics', 'Politics',\n",
       "       'Politics', 'Politics', 'Medical', 'Finance', 'Culture',\n",
       "       'Religion', 'Politics', 'Culture', 'Medical', 'Politics',\n",
       "       'Medical', 'Politics', 'Religion', 'Finance', 'Culture', 'Culture',\n",
       "       'Medical', 'Finance', 'Finance', 'Medical', 'Culture', 'Religion',\n",
       "       'Religion', 'Finance', 'Religion', 'Politics', 'Finance',\n",
       "       'Finance', 'Medical', 'Medical', 'Finance', 'Politics', 'Culture',\n",
       "       'Politics', 'Finance', 'Politics', 'Culture', 'Culture',\n",
       "       'Politics', 'Finance', 'Politics', 'Medical', 'Finance',\n",
       "       'Religion', 'Culture', 'Medical', 'Culture', 'Politics', 'Finance',\n",
       "       'Politics', 'Finance', 'Finance', 'Culture', 'Finance', 'Religion',\n",
       "       'Politics', 'Politics', 'Religion', 'Religion', 'Religion',\n",
       "       'Culture', 'Medical', 'Religion', 'Culture', 'Politics',\n",
       "       'Politics', 'Politics', 'Culture', 'Politics', 'Medical',\n",
       "       'Religion', 'Finance', 'Politics', 'Politics', 'Medical',\n",
       "       'Religion', 'Culture', 'Politics', 'Culture', 'Religion',\n",
       "       'Politics', 'Politics', 'Medical', 'Politics', 'Religion',\n",
       "       'Politics', 'Religion', 'Religion', 'Culture', 'Medical',\n",
       "       'Medical', 'Culture', 'Culture', 'Medical', 'Finance', 'Culture',\n",
       "       'Religion', 'Finance', 'Medical', 'Religion', 'Medical',\n",
       "       'Politics', 'Medical', 'Religion', 'Politics', 'Politics',\n",
       "       'Politics', 'Politics', 'Religion', 'Medical', 'Finance',\n",
       "       'Medical', 'Religion', 'Religion', 'Culture', 'Religion',\n",
       "       'Culture', 'Medical', 'Religion', 'Culture', 'Finance', 'Medical',\n",
       "       'Culture', 'Culture', 'Medical', 'Medical', 'Finance', 'Religion',\n",
       "       'Culture', 'Politics', 'Politics', 'Medical', 'Religion',\n",
       "       'Medical', 'Medical', 'Culture', 'Finance', 'Culture', 'Finance',\n",
       "       'Medical', 'Culture', 'Culture', 'Finance', 'Finance', 'Politics',\n",
       "       'Medical', 'Politics', 'Politics', 'Religion', 'Religion',\n",
       "       'Medical', 'Politics', 'Medical', 'Politics', 'Finance',\n",
       "       'Religion', 'Religion', 'Religion', 'Politics', 'Politics',\n",
       "       'Medical', 'Culture', 'Finance', 'Religion', 'Medical', 'Finance',\n",
       "       'Finance', 'Politics', 'Culture', 'Finance', 'Religion', 'Medical',\n",
       "       'Culture', 'Finance', 'Medical', 'Religion', 'Politics',\n",
       "       'Politics', 'Politics', 'Medical', 'Culture', 'Medical',\n",
       "       'Politics', 'Religion', 'Culture', 'Politics', 'Finance',\n",
       "       'Medical', 'Finance', 'Religion', 'Finance', 'Religion',\n",
       "       'Politics', 'Medical', 'Religion', 'Medical', 'Religion',\n",
       "       'Culture', 'Religion', 'Finance', 'Medical', 'Religion',\n",
       "       'Religion', 'Culture', 'Politics', 'Culture', 'Religion',\n",
       "       'Culture', 'Culture', 'Politics', 'Politics', 'Finance', 'Medical',\n",
       "       'Medical', 'Finance', 'Politics', 'Religion', 'Politics',\n",
       "       'Religion', 'Finance', 'Politics', 'Politics', 'Religion',\n",
       "       'Politics', 'Politics', 'Culture', 'Culture', 'Medical', 'Medical',\n",
       "       'Finance', 'Medical', 'Medical', 'Culture', 'Religion', 'Religion',\n",
       "       'Culture', 'Politics', 'Medical', 'Religion', 'Politics',\n",
       "       'Politics', 'Culture', 'Medical', 'Culture', 'Religion',\n",
       "       'Religion', 'Politics', 'Finance', 'Culture', 'Religion',\n",
       "       'Culture', 'Culture', 'Politics', 'Religion', 'Religion',\n",
       "       'Culture', 'Medical', 'Culture', 'Medical', 'Finance', 'Religion',\n",
       "       'Medical', 'Medical', 'Finance', 'Politics', 'Culture', 'Finance',\n",
       "       'Culture', 'Finance', 'Medical', 'Politics', 'Culture', 'Politics',\n",
       "       'Finance', 'Medical', 'Politics', 'Culture', 'Medical', 'Medical',\n",
       "       'Medical', 'Medical', 'Medical', 'Culture', 'Politics', 'Politics',\n",
       "       'Culture', 'Culture', 'Medical', 'Culture', 'Politics', 'Finance',\n",
       "       'Finance', 'Politics', 'Culture', 'Politics', 'Culture', 'Medical',\n",
       "       'Religion', 'Finance', 'Finance', 'Culture', 'Medical', 'Politics',\n",
       "       'Religion', 'Medical', 'Culture', 'Culture', 'Culture', 'Politics',\n",
       "       'Medical', 'Medical', 'Religion', 'Finance', 'Politics',\n",
       "       'Politics', 'Culture', 'Finance', 'Finance', 'Culture', 'Politics',\n",
       "       'Medical', 'Religion', 'Culture', 'Religion', 'Finance', 'Culture',\n",
       "       'Culture', 'Politics', 'Medical', 'Politics', 'Culture',\n",
       "       'Religion', 'Finance', 'Religion', 'Religion', 'Culture',\n",
       "       'Politics', 'Finance', 'Finance', 'Finance', 'Politics',\n",
       "       'Religion', 'Medical', 'Religion', 'Politics', 'Culture',\n",
       "       'Finance', 'Politics', 'Finance', 'Religion', 'Finance', 'Finance',\n",
       "       'Finance', 'Culture', 'Politics', 'Finance', 'Politics', 'Medical',\n",
       "       'Religion', 'Culture', 'Culture', 'Medical', 'Religion',\n",
       "       'Politics', 'Culture', 'Finance', 'Culture', 'Medical', 'Politics',\n",
       "       'Finance', 'Finance', 'Finance', 'Finance', 'Finance', 'Culture',\n",
       "       'Finance', 'Medical', 'Religion', 'Culture', 'Medical', 'Religion',\n",
       "       'Medical', 'Religion', 'Politics', 'Finance', 'Religion',\n",
       "       'Politics', 'Religion', 'Religion', 'Finance', 'Politics',\n",
       "       'Medical', 'Politics', 'Culture', 'Religion', 'Finance', 'Culture',\n",
       "       'Politics', 'Politics', 'Culture', 'Culture', 'Medical', 'Culture',\n",
       "       'Culture', 'Medical', 'Medical', 'Politics', 'Culture', 'Finance',\n",
       "       'Finance', 'Finance', 'Medical', 'Religion', 'Culture', 'Medical',\n",
       "       'Medical', 'Religion', 'Medical', 'Medical', 'Medical', 'Finance',\n",
       "       'Medical', 'Culture', 'Finance', 'Culture', 'Medical', 'Culture',\n",
       "       'Politics', 'Finance', 'Medical', 'Culture', 'Politics', 'Finance',\n",
       "       'Finance', 'Finance', 'Medical', 'Religion', 'Finance', 'Politics',\n",
       "       'Religion', 'Religion', 'Politics', 'Finance', 'Finance',\n",
       "       'Politics', 'Culture', 'Medical', 'Politics', 'Religion',\n",
       "       'Culture', 'Finance', 'Culture', 'Finance', 'Medical', 'Finance',\n",
       "       'Politics', 'Politics', 'Politics', 'Medical', 'Culture',\n",
       "       'Finance', 'Religion', 'Medical', 'Culture', 'Politics',\n",
       "       'Religion', 'Religion', 'Finance', 'Finance', 'Religion',\n",
       "       'Culture', 'Religion', 'Finance', 'Culture', 'Medical', 'Finance',\n",
       "       'Religion', 'Finance', 'Culture', 'Politics', 'Culture',\n",
       "       'Politics', 'Religion', 'Medical', 'Politics', 'Finance',\n",
       "       'Finance', 'Medical', 'Politics', 'Culture', 'Politics', 'Culture',\n",
       "       'Medical', 'Politics', 'Finance', 'Culture', 'Religion', 'Culture',\n",
       "       'Medical', 'Culture', 'Culture', 'Religion', 'Medical', 'Politics',\n",
       "       'Finance', 'Politics', 'Medical', 'Finance', 'Politics', 'Finance',\n",
       "       'Politics', 'Finance', 'Medical', 'Culture', 'Culture', 'Religion',\n",
       "       'Culture', 'Medical', 'Medical', 'Finance', 'Culture', 'Politics',\n",
       "       'Politics', 'Religion', 'Finance', 'Religion', 'Medical',\n",
       "       'Culture', 'Politics', 'Medical', 'Finance', 'Culture', 'Medical',\n",
       "       'Culture', 'Politics', 'Culture', 'Religion', 'Politics',\n",
       "       'Religion', 'Culture', 'Finance', 'Finance', 'Politics', 'Finance',\n",
       "       'Politics', 'Politics', 'Finance', 'Finance', 'Religion',\n",
       "       'Religion', 'Politics', 'Culture', 'Finance', 'Religion',\n",
       "       'Finance', 'Medical', 'Religion', 'Finance', 'Religion', 'Medical',\n",
       "       'Medical', 'Finance', 'Finance', 'Politics', 'Religion',\n",
       "       'Politics', 'Religion', 'Religion', 'Religion', 'Medical',\n",
       "       'Finance', 'Politics', 'Finance', 'Medical', 'Religion',\n",
       "       'Religion', 'Medical', 'Medical', 'Culture', 'Finance', 'Finance',\n",
       "       'Politics', 'Culture', 'Medical', 'Finance', 'Culture', 'Medical',\n",
       "       'Medical', 'Religion', 'Religion', 'Finance', 'Religion',\n",
       "       'Medical', 'Finance', 'Religion', 'Finance', 'Finance', 'Politics',\n",
       "       'Religion', 'Religion', 'Politics', 'Culture', 'Religion',\n",
       "       'Politics', 'Culture', 'Culture', 'Religion', 'Politics',\n",
       "       'Politics', 'Medical', 'Culture', 'Medical', 'Medical', 'Medical',\n",
       "       'Politics', 'Religion', 'Medical', 'Medical', 'Medical', 'Finance',\n",
       "       'Culture', 'Finance', 'Religion', 'Culture', 'Medical', 'Finance',\n",
       "       'Religion', 'Culture', 'Finance', 'Religion', 'Politics',\n",
       "       'Politics', 'Medical', 'Religion', 'Politics', 'Culture',\n",
       "       'Religion', 'Medical', 'Religion', 'Religion', 'Religion',\n",
       "       'Religion', 'Finance', 'Culture', 'Religion', 'Culture', 'Finance',\n",
       "       'Medical', 'Religion', 'Politics', 'Politics', 'Politics',\n",
       "       'Politics', 'Religion', 'Politics', 'Religion', 'Religion',\n",
       "       'Religion', 'Medical', 'Religion', 'Finance', 'Culture', 'Finance',\n",
       "       'Culture', 'Politics', 'Medical', 'Politics', 'Finance', 'Culture',\n",
       "       'Politics', 'Finance', 'Religion', 'Finance', 'Finance'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoder.inverse_transform(\n",
    "    y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "\n",
    "\n",
    "def bow(X_train, X_tes):\n",
    "    BOW = CountVectorizer()\n",
    "    BOW_train = BOW.fit_transform(X_train)\n",
    "    BOW_test = BOW.transform(X_tes)\n",
    "    return BOW_train,BOW_test\n",
    "\n",
    "def tfidf_ngram(n_gram, X_train, X_tes):\n",
    "    vectorizer = TfidfVectorizer(ngram_range=(n_gram, n_gram))\n",
    "    x_train_vec = vectorizer.fit_transform(X_train)\n",
    "    x_test_vec = vectorizer.transform(X_tes)\n",
    "    return x_train_vec, x_test_vec, vectorizer\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "BOW_train,BOW_test = bow(X_train, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trained1g_cv, X_test1g_cv,vector_1gram = tfidf_ngram(1, X_train, X_test)\n",
    "X_trained2g_cv, X_test2g_cv,_ = tfidf_ngram(2, X_train, X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data shape for classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(700, 47543)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_trained1g_cv.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import cross_validate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "champion_model is  SVC\n",
      "champion_embedding is TF_IDF 1_gram\n"
     ]
    }
   ],
   "source": [
    "text_embedding = {\n",
    "    'BOW': (BOW_train, BOW_test),\n",
    "    'TF_IDF 1_gram': (X_trained1g_cv, X_test1g_cv),\n",
    "    'TF_IDF 2_gram': (X_trained2g_cv, X_test2g_cv),\n",
    "}\n",
    "models = [SVC(),\n",
    "          GaussianNB(),\n",
    "          KNeighborsClassifier(),\n",
    "          DecisionTreeClassifier(max_depth=10)\n",
    "          ]\n",
    "highest_test_accuracy = 0\n",
    "champion_model_name = ''\n",
    "champion_model = ''\n",
    "champion_embedding = ''\n",
    "results_dict = {'Model Name': [], 'Embedding type': [],\n",
    "                'Training Accuracy': [], 'Testing Accuracy': []}\n",
    "\n",
    "for model in models:\n",
    "  for embedding_vector in text_embedding.keys():\n",
    "    if 'gram' in embedding_vector or 'BOW' in embedding_vector:\n",
    "        train = text_embedding[embedding_vector][0].toarray()\n",
    "        test = text_embedding[embedding_vector][1].toarray()\n",
    "    else:\n",
    "        train = text_embedding[embedding_vector][0]\n",
    "        test = text_embedding[embedding_vector][1]\n",
    "    model.fit(train, y_train)\n",
    "    # scores=cross_validate(model,train,y_train,cv=10,return_estimator=True)\n",
    "    # model=scores['estimator'][scores['test_score'].argmax()]\n",
    "\n",
    "    # print('Model Name',type(model).__name__)\n",
    "    results_dict['Model Name'].append(type(model).__name__)\n",
    "    # print('Using',embedding_vector)\n",
    "    results_dict['Embedding type'].append(embedding_vector)\n",
    "    train_acc = model.score(train, y_train)\n",
    "    # print(\"Training accuracy\",train_acc)\n",
    "    results_dict['Training Accuracy'].append(train_acc)\n",
    "    test_acc = model.score(test, y_test)\n",
    "    # print('Testing accuracy',test_acc)\n",
    "    results_dict['Testing Accuracy'].append(test_acc)\n",
    "    # print('*'*40)\n",
    "    if test_acc > highest_test_accuracy:\n",
    "      highest_test_accuracy = test_acc\n",
    "      champion_model_name = type(model).__name__\n",
    "      champion_model = model\n",
    "      champion_embedding = embedding_vector\n",
    "\n",
    "results_df = pd.DataFrame(results_dict)\n",
    "\n",
    "print('champion_model is ', champion_model_name)\n",
    "print('champion_embedding is', champion_embedding)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Model Name</th>\n",
       "      <th>Embedding type</th>\n",
       "      <th>Training Accuracy</th>\n",
       "      <th>Testing Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVC</td>\n",
       "      <td>BOW</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.756667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVC</td>\n",
       "      <td>TF_IDF 1_gram</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.923333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVC</td>\n",
       "      <td>TF_IDF 2_gram</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>BOW</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.843333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>TF_IDF 1_gram</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>TF_IDF 2_gram</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KNeighbors</td>\n",
       "      <td>BOW</td>\n",
       "      <td>0.678571</td>\n",
       "      <td>0.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KNeighbors</td>\n",
       "      <td>TF_IDF 1_gram</td>\n",
       "      <td>0.922857</td>\n",
       "      <td>0.903333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>KNeighbors</td>\n",
       "      <td>TF_IDF 2_gram</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>0.893333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>BOW</td>\n",
       "      <td>0.894286</td>\n",
       "      <td>0.726667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>TF_IDF 1_gram</td>\n",
       "      <td>0.895714</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>TF_IDF 2_gram</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.630000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Model Name Embedding type  Training Accuracy  Testing Accuracy\n",
       "0            SVC            BOW           0.900000          0.756667\n",
       "1            SVC  TF_IDF 1_gram           1.000000          0.923333\n",
       "2            SVC  TF_IDF 2_gram           1.000000          0.870000\n",
       "3     GaussianNB            BOW           1.000000          0.843333\n",
       "4     GaussianNB  TF_IDF 1_gram           1.000000          0.840000\n",
       "5     GaussianNB  TF_IDF 2_gram           1.000000          0.910000\n",
       "6     KNeighbors            BOW           0.678571          0.610000\n",
       "7     KNeighbors  TF_IDF 1_gram           0.922857          0.903333\n",
       "8     KNeighbors  TF_IDF 2_gram           0.910000          0.893333\n",
       "9   DecisionTree            BOW           0.894286          0.726667\n",
       "10  DecisionTree  TF_IDF 1_gram           0.895714          0.720000\n",
       "11  DecisionTree  TF_IDF 2_gram           0.630000          0.630000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df['Model Name'] = results_df['Model Name'].apply(\n",
    "    lambda x: x[:-10] if 'Classifier' in x else x)\n",
    "results_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, axes = plt.subplots(1, 3,  figsize=(20, 8))\n",
    "\n",
    "fig.suptitle(\n",
    "    \"Comparison between Models' Testing Scores for Each Embedding Type\")\n",
    "k = 0\n",
    "\n",
    "for i in range(0, 3):\n",
    "    embed_type = results_df['Embedding type'].unique()[k]\n",
    "    plt.ylim((0, 1))\n",
    "    axes[i].tick_params(axis='x', rotation=-45)\n",
    "    axes[i].set_title(f\"{embed_type}\")\n",
    "    sns.barplot(ax=axes[i], x=list(results_df['Model Name'].unique()), y=list(\n",
    "        results_df[results_df['Embedding type'] == embed_type]['Testing Accuracy']), palette=\"Blues\")\n",
    "    k += 1\n",
    "plt.ylim([0, 1])\n",
    "plt.subplots_adjust(left=0.1,\n",
    "                    bottom=0.1,\n",
    "                    right=0.9,\n",
    "                    top=0.9,\n",
    "                    wspace=0.3,\n",
    "                    hspace=0.4)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## predict summarized text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def class_champ_model_pred(xtest):\n",
    "    return champion_model.predict(xtest)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from yellowbrick.cluster import SilhouetteVisualizer\n",
    "import warnings\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = shuffle(df)\n",
    "X_train, y_actual = df['article'].to_numpy(), df['label'].to_numpy()\n",
    "y_actual\n",
    "y_actual = label_encoder.fit_transform(y_actual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def bow(X_train):\n",
    "    BOW = CountVectorizer()\n",
    "    BOW_train = BOW.fit_transform(X_train)\n",
    "    return BOW_train\n",
    "\n",
    "\n",
    "def tfidf_ngram(n_gram, X_train):\n",
    "    vectorizer = TfidfVectorizer(ngram_range=(n_gram, n_gram))\n",
    "    x_train_vec = vectorizer.fit_transform(X_train)\n",
    "    return x_train_vec,vectorizer\n",
    "\n",
    "\n",
    "BOW_train = bow(X_train)\n",
    "\n",
    "X_trained1g_cv,vect2 = tfidf_ngram(1, X_train)\n",
    "X_trained2g_cv,_ = tfidf_ngram(2, X_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_embedding = {\n",
    "    'BOW': BOW_train,\n",
    "    'TF_IDF 1_gram': X_trained1g_cv,\n",
    "    'TF_IDF 2_gram': X_trained2g_cv,\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## visualize actual clusters after feature extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "pca = TruncatedSVD(n_components=2,)\n",
    "embedding = text_embedding.copy()\n",
    "\n",
    "for key in embedding.keys():\n",
    "  embedding[key] = pca.fit_transform(embedding[key])\n",
    "  df = pd.DataFrame(\n",
    "      {'PCA1': embedding[key][:, 0], 'PCA2': embedding[key][:, 1], 'Target': y_actual})\n",
    "  fig = px.scatter(data_frame=df, x='PCA1', y='PCA2', color='Target')\n",
    "  fig.update_layout(title={'text': f'{key}', 'x': 0.5}, height=500, width=700)\n",
    "  fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_KMeans(max_k, data):\n",
    "    max_k += 1\n",
    "    kmeans_elb_results = dict()\n",
    "    kmeans_sil_results = dict()\n",
    "    for k in range(2, max_k):\n",
    "        kmeans = KMeans(n_clusters=k, init='k-means++', n_init=10,\n",
    "                        max_iter=300, random_state=42, algorithm='full')\n",
    "        kmeans.fit(data)\n",
    "        kmeans_elb_results.update({k: kmeans.inertia_})\n",
    "        kmeans_sil_results.update({k: silhouette_score(data, kmeans.labels_)})\n",
    "\n",
    "    return kmeans_elb_results, kmeans_sil_results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans_elb_vec = dict()\n",
    "kmeans_sil_vec = dict()\n",
    "for k, v in text_embedding.items():\n",
    "    kmeans_elb_results, kmeans_sil_results = run_KMeans(10, v)\n",
    "    kmeans_elb_vec[k] = kmeans_elb_results\n",
    "    kmeans_sil_vec[k] = kmeans_sil_results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
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    "fig = make_subplots(rows=1, cols=3,horizontal_spacing=0.1,vertical_spacing=0.1);\n",
    "i=j=1\n",
    "for elb_vec_key, elb_vec_value in kmeans_elb_vec.items():\n",
    "        fig.add_trace( \n",
    "            go.Scatter(\n",
    "                x=list(elb_vec_value.keys()),\n",
    "                y=list(elb_vec_value.values()),\n",
    "                mode='lines',\n",
    "                name=elb_vec_key,\n",
    "                      ),row=i, col=j)\n",
    "        j+=1\n",
    "        if j == 4:\n",
    "            j=1\n",
    "            i+=1\n",
    "fig.update_layout(height=500, width=1000,title={'text':'K Means for each vectorizer using Elbow Method','x':0.5})\n",
    "fig.show()"
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  },
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         "x": 0.5
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     "metadata": {},
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   ],
   "source": [
    "fig = make_subplots(rows=1, cols=3, horizontal_spacing=0.1,\n",
    "                    vertical_spacing=0.1)\n",
    "i = j = 1\n",
    "for sil_vec_key, sil_vec_value in kmeans_sil_vec.items():\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            x=list(sil_vec_value.keys()),\n",
    "            y=list(sil_vec_value.values()),\n",
    "            mode='lines',\n",
    "            name=sil_vec_key,\n",
    "        ), row=i, col=j)\n",
    "    j += 1\n",
    "    if j == 4:\n",
    "        j = 1\n",
    "        i += 1\n",
    "fig.update_layout(height=500, width=1000, title={\n",
    "                  'text': 'K Means for each vectorizer using Silhouette Method', 'x': 0.5})\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.218037</td>\n",
       "      <td>-0.162323</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.484483</td>\n",
       "      <td>0.440372</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.110546</td>\n",
       "      <td>-0.072403</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.141189</td>\n",
       "      <td>-0.127417</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.094887</td>\n",
       "      <td>-0.076370</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0.115846</td>\n",
       "      <td>-0.088635</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0.177909</td>\n",
       "      <td>-0.171701</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0.120260</td>\n",
       "      <td>-0.108224</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.085476</td>\n",
       "      <td>-0.081967</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.115250</td>\n",
       "      <td>-0.105630</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     principal component 1  principal component 2  target\n",
       "0                 0.218037              -0.162323       2\n",
       "1                 0.484483               0.440372       1\n",
       "2                 0.110546              -0.072403       2\n",
       "3                 0.141189              -0.127417       3\n",
       "4                 0.094887              -0.076370       3\n",
       "..                     ...                    ...     ...\n",
       "995               0.115846              -0.088635       0\n",
       "996               0.177909              -0.171701       0\n",
       "997               0.120260              -0.108224       3\n",
       "998               0.085476              -0.081967       3\n",
       "999               0.115250              -0.105630       3\n",
       "\n",
       "[1000 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def apply_PCA(data):\n",
    "    pca = TruncatedSVD(n_components=2)\n",
    "    principalComponents = pca.fit_transform(data)\n",
    "    principalDf = pd.DataFrame(data=principalComponents, columns=[\n",
    "                               'principal component 1', 'principal component 2'])\n",
    "    return principalDf\n",
    "\n",
    "\n",
    "pca_df = apply_PCA(text_embedding['TF_IDF 1_gram'])\n",
    "pca_df_final = pd.concat([pca_df, pd.Series(y_actual, name='target')], axis=1)\n",
    "pca_df_final\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.218037</td>\n",
       "      <td>-0.162323</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.484483</td>\n",
       "      <td>0.440372</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.110546</td>\n",
       "      <td>-0.072403</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.141189</td>\n",
       "      <td>-0.127417</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.094887</td>\n",
       "      <td>-0.076370</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0.115846</td>\n",
       "      <td>-0.088635</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0.177909</td>\n",
       "      <td>-0.171701</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0.120260</td>\n",
       "      <td>-0.108224</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.085476</td>\n",
       "      <td>-0.081967</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.115250</td>\n",
       "      <td>-0.105630</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     principal component 1  principal component 2  target\n",
       "0                 0.218037              -0.162323       4\n",
       "1                 0.484483               0.440372       1\n",
       "2                 0.110546              -0.072403       2\n",
       "3                 0.141189              -0.127417       4\n",
       "4                 0.094887              -0.076370       2\n",
       "..                     ...                    ...     ...\n",
       "995               0.115846              -0.088635       2\n",
       "996               0.177909              -0.171701       4\n",
       "997               0.120260              -0.108224       2\n",
       "998               0.085476              -0.081967       2\n",
       "999               0.115250              -0.105630       2\n",
       "\n",
       "[1000 rows x 3 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=5, init='k-means++', n_init=10,\n",
    "                max_iter=300, random_state=42, algorithm='full')\n",
    "kmeans.fit(pca_df)\n",
    "kmeans_labels = kmeans.predict(pca_df)\n",
    "kmeans_df_final = pd.concat(\n",
    "    [pca_df, pd.Series(kmeans_labels, name='target')], axis=1)\n",
    "centroids = kmeans.cluster_centers_\n",
    "kmeans_df_final\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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            "gridwidth": 2,
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            "ticks": ""
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           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
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           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
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           "zerolinecolor": "white",
           "zerolinewidth": 2
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         }
        },
        "title": {
         "text": "The Clusters with K-means",
         "x": 0.5
        },
        "width": 600,
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
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         "title": {
          "text": "principal component 1"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
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         "title": {
          "text": "principal component 2"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.subplots import make_subplots\n",
    "fig1 = px.scatter(data_frame=pca_df_final, x='principal component 1',\n",
    "                  y='principal component 2', color='target')\n",
    "fig1.update_layout({'title': {\n",
    "                   'text': 'The Clusters with Actual Labels', 'x': 0.5}}, height=600, width=600)\n",
    "fig1.show()\n",
    "fig2 = px.scatter(data_frame=kmeans_df_final, x='principal component 1',\n",
    "                  y='principal component 2', color='target')\n",
    "fig2.update_layout(annotations=[\n",
    "    dict(\n",
    "        font=dict(color='black', size=30),\n",
    "        showarrow=False,\n",
    "        x=centroids[0][0],\n",
    "        y=centroids[0][1],\n",
    "        text=\"X\"),\n",
    "    dict(\n",
    "        font=dict(color='black', size=30),\n",
    "        showarrow=False,\n",
    "        x=centroids[1][0],\n",
    "        y=centroids[1][1],\n",
    "        text=\"X\",\n",
    "    ),\n",
    "    dict(font=dict(color='black', size=30),\n",
    "         showarrow=False,\n",
    "         x=centroids[2][0],\n",
    "         y=centroids[2][1],\n",
    "         text=\"X\",\n",
    "         ),\n",
    "    dict(\n",
    "        font=dict(color='black', size=30),\n",
    "        showarrow=False,\n",
    "        x=centroids[3][0],\n",
    "        y=centroids[3][1],\n",
    "        text=\"X\",\n",
    "    ), dict(\n",
    "        font=dict(color='black', size=30),\n",
    "        showarrow=False,\n",
    "        x=centroids[4][0],\n",
    "        y=centroids[4][1],\n",
    "        text=\"X\",\n",
    "    )], height=600, width=600)\n",
    "fig2.update_layout(\n",
    "    {'title': {'text': 'The Clusters with K-means', 'x': 0.5}, }, height=600, width=600)\n",
    "fig2.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cluster Champion model predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clust_champ_model_pred(xtest):\n",
    "    return kmeans.predict(xtest)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save Champion Models for classifiaction and clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "champs = {\n",
    "    \"tf-idf-class\": vector_1gram,\n",
    "    \"tf-idf-clust\": vect2,\n",
    "    \"classification\": champion_model,\n",
    "    \"cluster\": kmeans\n",
    "}\n",
    "\n",
    "with open(\"champion_models.pickle\", \"wb\") as file:\n",
    "    pickle.dump(champs, file, pickle.HIGHEST_PROTOCOL)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['tf-idf-class', 'tf-idf-clust', 'classification', 'cluster'])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"champion_models.pickle\", \"rb\") as file:\n",
    "    models = pickle.load(file)\n",
    "\n",
    "models.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data shape for clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 59336)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_trained1g_cv.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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