News-Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "l9k4nvPt6ttU"
   },
   "source": [
    "<div dir=\"auto\" align=\"center\">\n",
    "    <h3>\n",
    "        بسم الله الرحمن الرحیم\n",
    "    </h3>\n",
    "    <br>\n",
    "    <h1>\n",
    "        <strong>\n",
    "            بازیابی پیشرفته اطلاعات\n",
    "        </strong>\n",
    "    </h1>\n",
    "    <h2>\n",
    "        <strong>\n",
    "            پروژه نهایی (خوشه‌بندی اخبار)\n",
    "        </strong>\n",
    "    </h2>\n",
    "    <br>\n",
    "    <h3>\n",
    "        محمد هجری - ٩٨١٠٦١٥٦\n",
    "        <br><br>\n",
    "        ارشان دلیلی - ٩٨١٠٥٧٥١\n",
    "        <br><br>\n",
    "        سروش جهان‌زاد - ٩٨١٠٠٣٨٩\n",
    "    </h3>\n",
    "    <br>\n",
    "</div>\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "Uhyo5mtc6tta"
   },
   "source": [
    "<div>\n",
    "    <h3 style='direction:rtl;text-align:justify;'>\n",
    "        نصب و دسترسی به کتابخانه‌های مورد نیاز\n",
    "    </h3>\n",
    "</div>\n",
    "\n",
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        با اجرای دو قطعه کد زیر، کتابخانه‌هایی که از آن‌ها در این تمرین استفاده شده است، نصب و قابل استفاده می‌شوند.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5DJqkFOY6tta",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "! pip install bs4\n",
    "! pip install tqdm\n",
    "! pip install pandas\n",
    "! pip install requests\n",
    "! pip install hazm\n",
    "! pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "bs5P-S4T6ttb",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import csv\n",
    "import hazm\n",
    "import pickle\n",
    "import zipfile\n",
    "import requests\n",
    "import fasttext\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "from tqdm import tqdm\n",
    "from bs4 import BeautifulSoup\n",
    "from string import punctuation\n",
    "from IPython.display import display\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics.cluster import rand_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "HlgX1wDm6ttb"
   },
   "source": [
    "<div>\n",
    "    <h3 style='direction:rtl;text-align:justify;'>\n",
    "        ١. دریافت داده‌ها\n",
    "    </h3>\n",
    "</div>\n",
    "\n",
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        در این تمرین، بیش از ٦٨ هزار خبر از\n",
    "        <a href=\"https://www.hamshahrionline.ir/\"> وب‌سایت همشهری‌آنلاین </a>\n",
    "        گردآوری شده که در ١٠ دسته‌ی سیاسی، جهانی، اقتصادی، اجتماعی، شهری، ورزشی، علمی، فرهنگی، فناوری اطلاعات و مهارت‌های زندگی طبقه‌بندی شده‌اند.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "CATEGORIES = {\n",
    "    'Politics': 'سیاسی',\n",
    "    'World': 'جهانی',\n",
    "    'Economy': 'اقتصادی',\n",
    "    'Society': 'اجتماعی',\n",
    "    'City': 'شهری',\n",
    "    'Sport': 'ورزشی',\n",
    "    'Science': 'علمی',\n",
    "    'Culture': 'فرهنگی',\n",
    "    'IT': 'فناوری اطلاعات',\n",
    "    'LifeSkills': 'مهارت‌های زندگی',\n",
    "}\n",
    "\n",
    "CATEGORIES_CLASSES = {\n",
    "    'Politics': 0,\n",
    "    'World': 1,\n",
    "    'Economy': 2,\n",
    "    'Society': 3,\n",
    "    'City': 4,\n",
    "    'Sport': 5,\n",
    "    'Science': 6,\n",
    "    'Culture': 7,\n",
    "    'IT': 8,\n",
    "    'LifeSkills': 9,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        برای دریافت داده‌ها یک ماژول Scraper ساخته‌ایم که اخبار مربوط به ١٠ دسته‌ی مذکور را در بازه‌ی زمانی تعیین شده، کراول کرده و در فایل dataset.zip ذخیره و فشرده سازی می‌کند. کد مربوط به این ماژول را در زیر مشاهده می‌کنید.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class Scraper:\n",
    "\n",
    "    def __init__(self, current_year, current_month):\n",
    "        self.current_year = current_year\n",
    "        self.current_month = current_month\n",
    "\n",
    "    def get_URL_content(self, URL):\n",
    "        while True:\n",
    "            try:\n",
    "                return requests.get(URL, timeout=5).content\n",
    "            except:\n",
    "                pass\n",
    "\n",
    "    def generate_page_URL(self, page_index, category, year, month):\n",
    "        tp = {'Politics': 6, 'World': 11, 'Economy': 10, 'Society': 5, 'City': 7,\n",
    "              'Sport': 9, 'Science': 20, 'Culture': 26, 'IT': 718, 'LifeSkills': 21}[category]\n",
    "        return f'https://www.hamshahrionline.ir/archive?pi={page_index}&tp={tp}&ty=1&ms=0&mn={month}&yr={year}'\n",
    "\n",
    "    def get_page_URLs_by_time(self, category, year, month):\n",
    "        URLs = []\n",
    "        page_index = 1\n",
    "        while True:\n",
    "            URL = self.generate_page_URL(page_index, category, year, month)\n",
    "            content = self.get_URL_content(URL)\n",
    "            if re.findall('pagination', str(content)):\n",
    "                URLs.append(URL)\n",
    "                page_index += 1\n",
    "            else:\n",
    "                break\n",
    "        return URLs\n",
    "\n",
    "    def get_page_URLs_since(self, category, year, month):\n",
    "        URLs = []\n",
    "        with tqdm() as pbar:\n",
    "            while True:\n",
    "                if month > 12:\n",
    "                    month = 1\n",
    "                    year += 1\n",
    "                pbar.set_description(f'[{category}] [Extracting page URLs] [Date: {year}/{month}]')\n",
    "                URLs_by_time = self.get_page_URLs_by_time(category, year, month)\n",
    "                if URLs_by_time:\n",
    "                    for URL in URLs_by_time:\n",
    "                        URLs.append(URL)\n",
    "                    month += 1\n",
    "                elif self.current_year > year or (self.current_year == year and self.current_month > month):\n",
    "                    month += 1\n",
    "                else:\n",
    "                    break\n",
    "        return URLs\n",
    "\n",
    "    def get_news_URLs_since(self, category, year, month):\n",
    "        news_URLs = []\n",
    "        page_URLs = self.get_page_URLs_since(category, year, month)\n",
    "        with tqdm(page_URLs) as pbar:\n",
    "            for page_URL in pbar:\n",
    "                content = self.get_URL_content(page_URL)\n",
    "                soup = BeautifulSoup(content, 'html5lib')\n",
    "                for item in soup.findAll('li', attrs={'class': 'news'}):\n",
    "                    URL = item.find('div', attrs={'class': 'desc'}).find('h3').find('a')['href']\n",
    "                    URL = 'https://www.hamshahrionline.ir' + URL\n",
    "                    news_URLs.append(URL)\n",
    "                pbar.set_description(f'[{category}] [Extracting news URLs] [{len(news_URLs)} news until now]')\n",
    "        return news_URLs\n",
    "\n",
    "    def parse_news(self, URL, category):\n",
    "        try:\n",
    "            content = self.get_URL_content(URL)\n",
    "            soup = BeautifulSoup(content, 'html.parser')\n",
    "            date = soup.find('div', {'class': 'col-6 col-sm-4 col-xl-4 item-date'}).span.text.strip()\n",
    "            title = soup.find('div', {'class': 'item-title'}).h1.text.strip()\n",
    "            intro = soup.find('p', {'class': 'introtext', 'itemprop': 'description'}).text.strip()\n",
    "            body = soup.find('div', {'class': 'item-text', 'itemprop': 'articleBody'}).text.strip()\n",
    "            return {\n",
    "                'date': date,\n",
    "                'title': title,\n",
    "                'intro': intro,\n",
    "                'body': body,\n",
    "                'category': category,\n",
    "            }\n",
    "        except:\n",
    "            return None\n",
    "\n",
    "    def scrape(self, from_year, from_month):\n",
    "        categories = ['Politics', 'World', 'Economy', 'Society', 'City',\n",
    "                      'Sport', 'Science', 'Culture', 'IT', 'LifeSkills']\n",
    "        news = []\n",
    "        for category in categories:\n",
    "            URLs = self.get_news_URLs_since(category, from_year, from_month)\n",
    "            with tqdm(URLs) as pbar:\n",
    "                pbar.set_description(f'[{category}] [Scraping news]')\n",
    "                for URL in pbar:\n",
    "                    news.append(self.parse_news(URL, category))\n",
    "        news = list(filter(None, news))\n",
    "        pd.DataFrame(news).to_csv(f'dataset.csv', encoding='utf-8')\n",
    "        with zipfile.ZipFile('dataset.zip', 'w', zipfile.ZIP_DEFLATED) as zip_file:\n",
    "            zip_file.write('dataset.csv')\n",
    "        os.remove('dataset.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        با اجرای قطعه کد زیر، یک instance از ماژول Scraper ایجاد شده و شروع به دریافت و ذخیره‌سازی داده‌ها می‌کند. خبرهای دریافت شده همگی مربوط به قرن جدید، از سال ١٤٠٠ به بعد هستند.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "scraper = Scraper(current_year=1401, current_month=3)\n",
    "scraper.scrape(from_year=1400, from_month=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        بعد از ذخیره شدن داده‌ها در فایل فشرده dataset.zip، آن‌ها را از این فایل استخراج کرده و وارد برنامه می‌کنیم. با اجرای قطعه کد زیر، تعداد خبرهای هر دسته و تعداد کل خبرها را می‌توان مشاهده کرد.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def read_dataset_from_file():\n",
    "    dataset = []\n",
    "    with zipfile.ZipFile('dataset.zip', 'r') as zip_file:\n",
    "        zip_file.extractall()\n",
    "    with open('dataset.csv', encoding='utf-8') as file:\n",
    "        csv_reader = csv.reader(file)\n",
    "        header = next(csv_reader)\n",
    "        for row in csv_reader:\n",
    "            data = dict(zip(header[1:], row[1:]))\n",
    "            dataset.append(data)\n",
    "    os.remove('dataset.csv')\n",
    "    return dataset\n",
    "\n",
    "\n",
    "dataset = pd.DataFrame(read_dataset_from_file())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "    با اجرای قطعه کد زیر، تعداد خبرهای هر دسته و تعداد کل خبرها را می‌توان مشاهده کرد.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>دسته</th>\n",
       "      <th>تعداد</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>کل خبرها</td>\n",
       "      <td>68362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>سیاسی</td>\n",
       "      <td>15798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>جهانی</td>\n",
       "      <td>2895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>اقتصادی</td>\n",
       "      <td>8900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>اجتماعی</td>\n",
       "      <td>13585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>شهری</td>\n",
       "      <td>3853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ورزشی</td>\n",
       "      <td>8348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>علمی</td>\n",
       "      <td>3190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>فرهنگی</td>\n",
       "      <td>6512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>فناوری اطلاعات</td>\n",
       "      <td>437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>مهارت‌های زندگی</td>\n",
       "      <td>4844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               دسته  تعداد\n",
       "0          کل خبرها  68362\n",
       "1             سیاسی  15798\n",
       "2             جهانی   2895\n",
       "3           اقتصادی   8900\n",
       "4           اجتماعی  13585\n",
       "5              شهری   3853\n",
       "6             ورزشی   8348\n",
       "7              علمی   3190\n",
       "8            فرهنگی   6512\n",
       "9    فناوری اطلاعات    437\n",
       "10  مهارت‌های زندگی   4844"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def display_dataset_info():\n",
    "    global CATEGORIES, dataset\n",
    "\n",
    "    length_dict = {key: 0 for key in CATEGORIES.keys()}\n",
    "    for _, data in dataset.iterrows():\n",
    "        length_dict[data['category']] += 1\n",
    "\n",
    "    df_dict = {\n",
    "        'دسته': CATEGORIES.values(),\n",
    "        'تعداد': length_dict.values(),\n",
    "    }\n",
    "\n",
    "    df = pd.DataFrame(df_dict)\n",
    "    df.index += 1\n",
    "    df.loc[0] = ['کل خبرها', len(dataset)]\n",
    "    df = df.sort_index()\n",
    "    display(df)\n",
    "\n",
    "\n",
    "display_dataset_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<div>\n",
    "    <h3 style='direction:rtl;text-align:justify;'>\n",
    "        ٢. پیش پردازش اولیه‌ی متن\n",
    "    </h3>\n",
    "</div>\n",
    "\n",
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        ابزار مورد استفاده برای پیش‌پردازش متن ورودی به صورت ماژولار طراحی شده است؛ به طوری که با صدا زدن تابع preprocess از آن، متن داده شده با عبور از یک خط لوله به صورت مرحله به مرحله تغییر می‌کند تا به یک ساختار استاندارد برسد. این مراحل عبارتند از:\n",
    "    </p>\n",
    "</div>\n",
    "\n",
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <li style='direction:rtl;text-align:justify;'>\n",
    "        نرمال سازی داده‌ها (normalize)\n",
    "    </li>\n",
    "    <li style='direction:rtl;text-align:justify;'>\n",
    "        حذف لینک‌ها (remove_links)\n",
    "    </li>\n",
    "    <li style='direction:rtl;text-align:justify;'>\n",
    "        حذف نشانه‌های نگارشی (remove_punctuations)\n",
    "    </li>\n",
    "    <li style='direction:rtl;text-align:justify;'>\n",
    "        واحد سازی داده‌ها (word_tokenize)\n",
    "    </li>\n",
    "    <li style='direction:rtl;text-align:justify;'>\n",
    "        حذف کلمات نامعتبر (remove_invalid_words)\n",
    "    </li>\n",
    "    <li style='direction:rtl;text-align:justify;'>\n",
    "        حذف ایست‌واژه‌ها (remove_stopwords)\n",
    "    </li>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class Preprocessor:\n",
    "\n",
    "    def __init__(self, stopwords_path):\n",
    "        self.stopwords = []\n",
    "        with open(stopwords_path, encoding='utf-8') as file:\n",
    "            self.stopwords = file.read().split()\n",
    "\n",
    "    def preprocess(self, text):\n",
    "        text = self.normalize(text)\n",
    "        text = self.remove_links(text)\n",
    "        text = self.remove_punctuations(text)\n",
    "        words = self.word_tokenize(text)\n",
    "        words = self.remove_invalid_words(words)\n",
    "        words = self.remove_stopwords(words)\n",
    "        return words\n",
    "\n",
    "    def normalize(self, text):\n",
    "        return hazm.Normalizer().normalize(text)\n",
    "\n",
    "    def remove_links(self, text):\n",
    "        patterns = ['\\S*http\\S*', '\\S*www\\S*', '\\S+\\.ir\\S*', '\\S+\\.com\\S*', '\\S+\\.org\\S*', '\\S*@\\S*']\n",
    "        for pattern in patterns:\n",
    "            text = re.sub(pattern, ' ', text)\n",
    "        return text\n",
    "\n",
    "    def remove_punctuations(self, text):\n",
    "        return re.sub(f'[{punctuation}؟،٪×÷»«]+', '', text)\n",
    "\n",
    "    def word_tokenize(self, text):\n",
    "        return hazm.word_tokenize(text)\n",
    "\n",
    "    def remove_invalid_words(self, words):\n",
    "        return [word for word in words if len(word) > 3 or re.match('^[\\u0600-\\u06FF]{2,3}$', word)]\n",
    "\n",
    "    def remove_stopwords(self, words):\n",
    "        return [word for word in words if word not in self.stopwords]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def save_preprocessed_texts(texts, path=\"Preprocessed_texts.pickle\"):\n",
    "    with open(path, \"wb\") as file:\n",
    "        pickle.dump(texts, file)\n",
    "\n",
    "\n",
    "def load_preprocessed_texts(path=\"Preprocessed_texts.pickle\"):\n",
    "    with open(path, \"rb\") as file:\n",
    "        return pickle.load(file)\n",
    "\n",
    "\n",
    "def data_to_text(data):\n",
    "    return ' '.join([data['title'], data['intro'], data['body']]).lower()\n",
    "\n",
    "\n",
    "def get_preprocessed_texts(dataset, preprocessor, mode, save=False):\n",
    "    preprocessed_texts = []\n",
    "    if mode == 'process':\n",
    "        texts = [data_to_text(data) for _, data in dataset.iterrows()]\n",
    "        preprocessed_texts = [preprocessor.preprocess(text) for text in tqdm(texts)]\n",
    "    if mode == 'load':\n",
    "        preprocessed_texts = load_preprocessed_texts()\n",
    "    if save:\n",
    "        save_preprocessed_texts(preprocessed_texts)\n",
    "    return preprocessed_texts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        با اجرای قطعه کد زیر، یک instance از ماژول Preprocessor ایجاد کرده و شروع به پیش پردازش داده‌ها می‌کنیم، یا داده‌های پیش‌پردازش‌شده‌ی ذخیره‌شده را از فایل مربوطه بازیابی می‌کنیم.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "preprocessor = Preprocessor(stopwords_path='stopwords.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "preprocessed_texts = get_preprocessed_texts(dataset, preprocessor, mode='load', save=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        با توجه به این که حجم مجموعه داده‌ها باعث ایجاد محدودیت حافظه و زمان برای مدل tf-idf می‌شود، ناچار هستیم کسری از مجموعه‌ی داده‌ها را به عنوان ورودی برای یادگیری به آن بدهیم. با اجرای قطعه کد زیر، یک مجموعه داده‌ی کوچک‌تر از روی مجموعه داده‌ی اصلی ایجاد می‌کنیم.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4000/4000 [00:22<00:00, 174.27it/s]\n"
     ]
    }
   ],
   "source": [
    "def get_mini_dataset(len_each_category=400):\n",
    "    global CATEGORIES, dataset\n",
    "\n",
    "    mini_dataset = []\n",
    "    for category in CATEGORIES.keys():\n",
    "        dataset_by_category = dataset.loc[dataset['category'] == category]\n",
    "        length = min(len_each_category, dataset_by_category.shape[0])\n",
    "        mini_dataset.append(dataset_by_category.sample(length, random_state=1))\n",
    "\n",
    "    mini_dataset = pd.concat(mini_dataset).reset_index(drop=True)\n",
    "    texts = [data_to_text(data) for _, data in mini_dataset.iterrows()]\n",
    "    mini_preprocessed_texts = [preprocessor.preprocess(text) for text in tqdm(texts)]\n",
    "    return mini_dataset, mini_preprocessed_texts\n",
    "\n",
    "\n",
    "mini_dataset, mini_preprocessed_texts = get_mini_dataset()\n",
    "mini_dataset['category_code'] = mini_dataset['category'].apply(lambda x: CATEGORIES_CLASSES[x])\n",
    "labels = mini_dataset['category_code'].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<div>\n",
    "    <h3 style='direction:rtl;text-align:justify;'>\n",
    "        ٣. خوشه‌بندی اخبار\n",
    "    </h3>\n",
    "</div>\n",
    "\n",
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        در این مرحله، داده‌ها با استفاده از الگوریتم Kmeans در ده خوشه جای داده و در ادامه، نتیجه اجرای ده کوئری را بر روی هر یک مشاهده می‌کنیم. در نهایت، ارزیابی‌های مورد بحث در داک تمرین را انجام می‌دهیم.\n",
    "    </p>\n",
    "</div>\n",
    "\n",
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        کوئری‌های مورد استفاده در این بخش در زیر آمده‌اند.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "QUERIES = [\n",
    "    \"نتیجه توافق برجام\",\n",
    "    \"حمله ارتش روسیه به اوکراین\",\n",
    "    \"افزایش نرخ تورم در کشور\",\n",
    "    \"آمار فوتی‌های کرونا\",\n",
    "    \"اقدامات شهرداری تهران\",\n",
    "    \"صعود ایران به جام جهانی\",\n",
    "    \"بیماری آبله میمونی\",\n",
    "    \"جشنواره فیلم فجر\",\n",
    "    \"رونمایی از گوشی جدید اپل\",\n",
    "    \"روش پخت غذا\",\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<div>\n",
    "    <h4 style='direction:rtl;text-align:justify;'>\n",
    "        استفاده از مدل FastText برای محاسبه‌ی بردارهای تعبیه\n",
    "    </h4>\n",
    "</div>\n",
    "\n",
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        در این بخش، از کتاب‌خانه‌ی fasttext استفاده می‌کنیم و با استفاده از روش skipgram و یک فایل شامل داده‌های موجود اخبار، مدل را به صورت unsupervised به یادگیری وامی‌داریم. پارامترهای استفاده شده‌ی دیگر هم در کد مشخصند؛ به طور مثال، کمینه و بیشینه n را برابر ۲ و ۵ در نظر گرفته‌ایم. پس از انجام یادگیری، میانگین امبدینگ‌ها را در یک آرایه‌ی نامپای ذخیره می‌کنیم. تمام این کارها با صدا زدن متد prepare با حالت train رخ می‌دهند.\n",
    "    </p>\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        در نهایت، با استفاده از تابع get_embedding_vector بر روی کوئری‌ها می‌توان بردار تعیبه آن‌ها را به دست آورد تا در الگویتم Kmeans استفاده شوند.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class FastText:\n",
    "\n",
    "    def __init__(self, preprocessor=None, method='skipgram'):\n",
    "        self.method = method\n",
    "        self.mean_embed = []\n",
    "        self.model = None\n",
    "        self.preprocessor = preprocessor\n",
    "\n",
    "    def train(self, texts):\n",
    "        with open('FastText_train.txt', 'w', encoding='utf-8') as file:\n",
    "            file.write('\\n'.join(list(map(lambda doc: ' '.join(doc), texts))))\n",
    "        self.model = fasttext.train_unsupervised('FastText_train.txt', self.method, minn=2, maxn=5, wordNgrams=10)\n",
    "        os.remove('FastText_train.txt')\n",
    "        self.mean_embed = list(\n",
    "            map(lambda doc: np.mean(list(map(lambda word: self.model.get_word_vector(word), doc)), axis=0), texts))\n",
    "        self.mean_embed = np.array(self.mean_embed)\n",
    "        return self.mean_embed\n",
    "\n",
    "    def get_embedding_vector(self, query):\n",
    "        if self.preprocessor:\n",
    "            query = self.preprocessor.preprocess(query)\n",
    "        if type(query) == str:\n",
    "            query = query.split()\n",
    "        query_embed = np.mean(list(map(lambda word: self.model.get_word_vector(word), query)), axis=0)\n",
    "        return query_embed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Read 0M words\n",
      "Number of words:  16627\n",
      "Number of labels: 0\n",
      "Progress: 100.0% words/sec/thread:   26640 lr:  0.000000 avg.loss:  1.991242 ETA:   0h 0m 0s 37.5% words/sec/thread:   30975 lr:  0.031239 avg.loss:  2.045619 ETA:   0h 0m 8s 38.9% words/sec/thread:   30887 lr:  0.030555 avg.loss:  2.045082 ETA:   0h 0m 8s 55.2% words/sec/thread:   29252 lr:  0.022375 avg.loss:  1.982671 ETA:   0h 0m 6s 58.1% words/sec/thread:   28936 lr:  0.020947 avg.loss:  1.978537 ETA:   0h 0m 6s 72.3% words/sec/thread:   27915 lr:  0.013841 avg.loss:  1.969228 ETA:   0h 0m 4s 83.6% words/sec/thread:   27181 lr:  0.008210 avg.loss:  1.974618 ETA:   0h 0m 2s\n"
     ]
    }
   ],
   "source": [
    "FastText_model = FastText()\n",
    "vectors = FastText_model.train(mini_preprocessed_texts).astype('float64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        در این قسمت، ده خوشه تعریف کرده و با پاس دادن بردارهای تعبیه‌ی داده‌ها، الگوریتم Kmeans را بر روی آن‌ها اجرا می‌کنیم.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "kmeans = KMeans(10, max_iter=1000)\n",
    "cluster_labels = kmeans.fit_predict(vectors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<div>\n",
    "    <h4 style='direction:rtl;text-align:justify;'>\n",
    "        کاهش بعد بردارهای تعبیه برای نمایش سه بعدی\n",
    "    </h4>\n",
    "</div>\n",
    "\n",
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        در قطعه کد زیر، چند تابع تعریف شده است که با استفاده از آن‌ها می‌توان از دو روش TSNE و PCA برای کاهش بعد بردارهای تعبیه و نمایش آن‌ها در فضا استفاده کرد.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def TSNE_df_matrix(n_components, vectors, cluster_labels):\n",
    "    names = ['x', 'y', 'z']\n",
    "    matrix = TSNE(n_components=n_components).fit_transform(vectors)\n",
    "    df_matrix = pd.DataFrame(matrix)\n",
    "    df_matrix.rename({i: names[i] for i in range(n_components)}, axis=1, inplace=True)\n",
    "    df_matrix['labels'] = cluster_labels\n",
    "    return df_matrix\n",
    "\n",
    "\n",
    "def PCA_df_matrix(n_components, vectors, cluster_labels):\n",
    "    names = ['x', 'y', 'z']\n",
    "    matrix = PCA(n_components=n_components).fit_transform(vectors)\n",
    "    df_matrix = pd.DataFrame(matrix)\n",
    "    df_matrix.rename({i: names[i] for i in range(n_components)}, axis=1, inplace=True)\n",
    "    df_matrix['labels'] = cluster_labels\n",
    "    return df_matrix\n",
    "\n",
    "\n",
    "def plot_3d(df, name='labels'):\n",
    "    fig = px.scatter_3d(df, x='x', y='y', z='z', color=name, opacity=0.5)\n",
    "    fig.update_traces(marker=dict(size=3))\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<div>\n",
    "    <h5 style='direction:rtl;text-align:justify;'>\n",
    "        کاهش بعد بردارهای تعبیه به روش TSNE\n",
    "    </h5>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/manifold/_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/manifold/_t_sne.py:790: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "labels=1<br>x=%{x}<br>y=%{y}<br>z=%{z}<extra></extra>",
         "legendgroup": "1",
         "marker": {
          "color": "#636efa",
          "opacity": 0.5,
          "size": 3,
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "1",
         "scene": "scene",
         "showlegend": true,
         "type": "scatter3d",
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   "source": [
    "tsne_3d_df = TSNE_df_matrix(3, vectors, cluster_labels)\n",
    "tsne_3d_df['labels'] = tsne_3d_df['labels'].apply(lambda x: str(x))\n",
    "plot_3d(tsne_3d_df)"
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    "<div>\n",
    "    <h5 style='direction:rtl;text-align:justify;'>\n",
    "        کاهش بعد بردارهای تعبیه به روش PCA\n",
    "    </h5>\n",
    "</div>"
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   "source": [
    "pca_3d_df = PCA_df_matrix(3, vectors, cluster_labels)\n",
    "pca_3d_df['labels'] = pca_3d_df['labels'].apply(lambda x: str(x))\n",
    "plot_3d(pca_3d_df)"
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    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        در این قسمت، خوشه‌ی هر یک از کوئری‌های مشخص شده را مشخص می‌کنیم.\n",
    "    </p>\n",
    "</div>"
   ]
  },
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   "cell_type": "code",
   "execution_count": 19,
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     "name": "#%%\n"
    }
   },
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>کوئری</th>\n",
       "      <th>خوشه</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>نتیجه توافق برجام</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>حمله ارتش روسیه به اوکراین</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>افزایش نرخ تورم در کشور</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>آمار فوتی‌های کرونا</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>6</td>\n",
       "    </tr>\n",
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       "      <td>صعود ایران به جام جهانی</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>بیماری آبله میمونی</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>جشنواره فیلم فجر</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>رونمایی از گوشی جدید اپل</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>روش پخت غذا</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
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       "                        کوئری  خوشه\n",
       "0           نتیجه توافق برجام     1\n",
       "1  حمله ارتش روسیه به اوکراین     1\n",
       "2     افزایش نرخ تورم در کشور     0\n",
       "3         آمار فوتی‌های کرونا     5\n",
       "4       اقدامات شهرداری تهران     6\n",
       "5     صعود ایران به جام جهانی     7\n",
       "6          بیماری آبله میمونی     5\n",
       "7            جشنواره فیلم فجر     2\n",
       "8    رونمایی از گوشی جدید اپل     9\n",
       "9                 روش پخت غذا     3"
      ]
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   ],
   "source": [
    "queries_cluster_labels = []\n",
    "\n",
    "for query in QUERIES:\n",
    "    preprocessed_query = ' '.join(preprocessor.preprocess(query))\n",
    "    query_embed = FastText_model.get_embedding_vector(preprocessed_query)\n",
    "    cluster_label = kmeans.predict([query_embed.astype('float64')])[0]\n",
    "    queries_cluster_labels.append({\n",
    "        'کوئری': query,\n",
    "        'خوشه': cluster_label,\n",
    "    })\n",
    "\n",
    "display(pd.DataFrame(queries_cluster_labels))"
   ]
  },
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   "cell_type": "markdown",
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     "name": "#%% md\n"
    }
   },
   "source": [
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        با مشاهده‌ی نتیجه‌ی اجرای الگوریتم Kmeans بر روی کوئری‌های مذکور، متوجه می‌شویم که در خوشه‌های مختلفی قرار گرفته‌اند؛ این موضوع با توجه به این که کوئری‌ها، هر یک مربوط به یک دسته‌ی خاص از اخبار هستند، قابل پیش‌بینی است.\n",
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    "</div>"
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    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        در نهایت، با اجرای قطعه کد زیر، معیار ارزیابی RSS و نیز rand score را بر روی خوشه‌بندی انجام شده محاسبه می‌کنیم.\n",
    "    </p>\n",
    "</div>"
   ]
  },
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   "outputs": [
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     "name": "stdout",
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     "text": [
      "Residual Sum of Squares (RSS): 2047.404\n",
      "Rand Score: 0.887\n"
     ]
    }
   ],
   "source": [
    "print(f\"Residual Sum of Squares (RSS): {round(kmeans.inertia_, 3)}\")\n",
    "print(f\"Rand Score: {round(rand_score(labels, cluster_labels), 3)}\")"
   ]
  },
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   "metadata": {
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    "id": "t76h-25dj39y"
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    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        از این که وقت ارزشمند خود را برای بررسی این نوتبوک صرف کرده‌اید، صمیمانه سپاسگزاریم. (:\n",
    "    </p>\n",
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