LDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "1c2ee43c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "f2720af7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import base64\n",
    "import re\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option('display.width', None)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_colwidth', 0)\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "0a238f7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "df = pd.read_csv(r'./mesra.txt', names=['mesra'], header=None)\n",
    "df['beyt index'] = df.index // 2\n",
    "df = df.groupby(['beyt index']).agg({'mesra': ' '.join})\n",
    "df = df.rename(columns={'mesra':'beyt'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "1d204e02",
   "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>beyt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beyt index</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>الا یا ایها الساقی ادر کاسا و ناولها که عشق آسان نمود اول ولی افتاد مشکل‌ها</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>به بوی نافه‌ای کاخر صبا زان طره بگشاید ز تاب جعد مشکینش چه خون افتاد در دل‌ها</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>مرا در منزل جانان چه امن عیش چون هر دم جرس فریاد می‌دارد که بربندید محمل‌ها</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>به می سجاده رنگین کن گرت پیر مغان گوید که سالک بی‌خبر نبود ز راه و رسم منزل‌ها</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>شب تاریک و بیم موج و گردابی چنین هایل کجا دانند حال ما سبکباران ساحل‌ها</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164849</th>\n",
       "      <td>از من بگو بدان مه خرگاهی آه و فغان من به قلک برشد</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164850</th>\n",
       "      <td>سنگین دلت نیافته آگاهی با آهنین دل تو چه داند کرد؟</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164851</th>\n",
       "      <td>آه شب و فغان سحرگاهی ای همنشین بیهوده گو تا چند</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164852</th>\n",
       "      <td>جان مرا به خیره همی کاهی؟ راحت ز جان خسته چه می جویی؟</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164853</th>\n",
       "      <td>طاقت ز مرغ بسته چه میخواهی؟</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>164854 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                      beyt\n",
       "beyt index                                                                                \n",
       "0           الا یا ایها الساقی ادر کاسا و ناولها که عشق آسان نمود اول ولی افتاد مشکل‌ها   \n",
       "1           به بوی نافه‌ای کاخر صبا زان طره بگشاید ز تاب جعد مشکینش چه خون افتاد در دل‌ها \n",
       "2           مرا در منزل جانان چه امن عیش چون هر دم جرس فریاد می‌دارد که بربندید محمل‌ها   \n",
       "3           به می سجاده رنگین کن گرت پیر مغان گوید که سالک بی‌خبر نبود ز راه و رسم منزل‌ها\n",
       "4           شب تاریک و بیم موج و گردابی چنین هایل کجا دانند حال ما سبکباران ساحل‌ها       \n",
       "...                                                                                    ...\n",
       "164849      از من بگو بدان مه خرگاهی آه و فغان من به قلک برشد                             \n",
       "164850      سنگین دلت نیافته آگاهی با آهنین دل تو چه داند کرد؟                            \n",
       "164851      آه شب و فغان سحرگاهی ای همنشین بیهوده گو تا چند                               \n",
       "164852      جان مرا به خیره همی کاهی؟ راحت ز جان خسته چه می جویی؟                         \n",
       "164853      طاقت ز مرغ بسته چه میخواهی؟                                                   \n",
       "\n",
       "[164854 rows x 1 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "66df3b8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gensim and LDA\n",
    "import gensim\n",
    "import gensim.corpora as corpora\n",
    "from gensim.models import CoherenceModel\n",
    "from gensim.parsing.preprocessing import STOPWORDS\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim as gensimvis  # don't skip this\n",
    "\n",
    "# NLP stuff\n",
    "import string\n",
    "from hazm import *\n",
    "import codecs\n",
    "\n",
    "# Plotting tools\n",
    "# from bokeh.plotting import figure, output_file, show\n",
    "# from bokeh.models import Label\n",
    "# from bokeh.io import output_notebook\n",
    "import matplotlib.colors as mcolors\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Miscellaneous\n",
    "# from sklearn.manifold import TSNE\n",
    "from pprint import pprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "6392dd63",
   "metadata": {},
   "outputs": [],
   "source": [
    "normalizer = Normalizer()\n",
    "stopwords = [normalizer.normalize(x.strip()) for x in codecs.open('stopwords.dat','r','utf-8').readlines()]\n",
    "lemmatizer = Lemmatizer()\n",
    "\n",
    "def preprocess(text):\n",
    "    tokens = word_tokenize(text)\n",
    "    tokens = [lemmatizer.lemmatize(t) for t in tokens if t not in stopwords]\n",
    "    return tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "44179f4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_words = df['beyt'].apply(preprocess).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "6a52128e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the bigram and trigram models\n",
    "bigram = gensim.models.Phrases(data_words, min_count=5, threshold=30) # higher threshold fewer phrases.\n",
    "\n",
    "# Faster way to get a sentence clubbed as a trigram/bigram\n",
    "bigram_mod = gensim.models.phrases.Phraser(bigram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "00741227",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_bigrams(texts):\n",
    "    return [bigram_mod[doc] for doc in texts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "0a19cb88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['الا', 'یا', 'ای', 'الساق', 'ادر', 'کاسا', 'و', 'ناولها', 'که', 'عشق', 'آسان', 'نمود', 'اول', 'ولی', 'افتاد#افت', 'مشکل']\n",
      "['به', 'بو', 'نافه', 'کاخر', 'صبا', 'زان', 'طره', 'گشود#گشا', 'ز', 'تاب', 'جعد', 'مشکین', 'چه', 'خون', 'افتاد#افت', 'در', 'دل']\n",
      "['مرا', 'در', 'منزل', 'جانان', 'چه', 'امن', 'عیش', 'چون', 'هر', 'دم', 'جرس', 'فریاد', 'داشت#دار', 'که', 'بربندید', 'محمل']\n",
      "['به', 'می', 'سجاده', 'رنگین', 'کن', 'گر', 'پیر_مغان', 'گفت#گو', 'که', 'سالک', 'بی\\u200cخبر', 'بود#باش', 'ز', 'راه', 'و', 'رسم', 'منزل']\n",
      "['شب_تاریک', 'و', 'بیم', 'موج', 'و', 'گرداب', 'چنین', 'هایل', 'کجا', 'دانست#دان', 'حال', 'ما', 'سبکباران', 'ساحل']\n",
      "['همه', 'کاشت#کار', 'ز', 'خود', 'کام', 'به', 'بدنام', 'کشید#کش', 'آخر', 'نهان', 'کی', 'ماند#مان', 'آن', 'راز', 'کز', 'او', 'ساخت#ساز', 'محفل']\n",
      "['حضور', 'گر', 'همی\\u200cخواهی', 'از', 'او', 'غایب', 'مشو', 'حافظ', 'متی', 'ما', 'تلق', 'من', 'تهوی', 'دع', 'الدنیا', 'و', 'اهملها']\n",
      "['صلاح', 'کار', 'کجا', 'و', 'من', 'خراب', 'کجا', 'دید#بین', 'تفاوت', 'ره', 'کز', 'کجاست', 'تا', 'به', 'کجا']\n",
      "['دل', 'ز', 'صومعه', 'بگرفت', 'و', 'خرقه_سالوس', 'کجاست', 'دیر_مغان', 'و', 'شراب_ناب', 'کجا']\n",
      "['چه', 'نسبت', '#است', 'به', 'رند', 'صلاح', 'و', 'تقوا', 'را', 'سماع', 'وعظ', 'کجا', 'نغمه', 'رباب', 'کجا']\n",
      "['ز', 'روی', 'دوست', 'دل', 'دشمن', 'چه', 'دریابد', 'چراغ', 'مرده', 'کجا', 'شمع', 'آفتاب', 'کجا']\n",
      "['چو', 'کحل', 'بینش', 'ما', 'خاک', 'آستان', 'شماست', 'کجا', 'رفت#رو', 'فرمود#فرما', 'از', 'این', 'جناب', 'کجا']\n",
      "['مبین', 'به', 'سیب_زنخدان', 'که', 'چاه', 'در', 'راه', '#است', 'کجا', 'همی\\u200cروی', 'ای', 'دل', 'بدین', 'شتاب', 'کجا']\n",
      "['بشد', 'که', 'یاد', 'خوش', 'باد', 'روزگار', 'وصال', 'خود', 'آن', 'کرشمه', 'کجا', 'رفت#رو', 'و', 'آن', 'عتاب', 'کجا']\n",
      "['قرار', 'و', 'خواب', 'ز', 'حافظ', 'طمع_مدار', 'ای', 'دوست', 'قرار', 'چیست', 'صبور', 'کدام', 'و', 'خواب', 'کجا']\n",
      "['اگر', 'آن', 'ترک', 'شیراز', 'به', 'دست', 'آرد', 'دل', 'ما', 'را', 'به', 'خال_هندو', 'بخشید#بخش', 'سمرقند', 'و', 'بخارا', 'را']\n",
      "['داد#ده', 'ساقی', 'می', 'باقی', 'که', 'در', 'جنت', 'یافت#یاب', 'کنار', 'آب', 'رکن', 'آباد', 'و', 'گلگشت', 'مصلا', 'را']\n",
      "['فغان', 'کاین', 'لولیان', 'شوخ', 'شیرین', 'کار', 'شهرآشوب', 'چنان', 'برد#بر', 'صبر', 'از', 'دل', 'که', 'ترک', 'خوان', 'یغما', 'را']\n",
      "['ز', 'عشق', 'ناتمام', 'ما', 'جمال', 'یار', 'مستغنی', '#است', 'به', 'آب', 'و', 'رنگ', 'و', 'خال', 'و', 'خط', 'چه', 'حاجت', 'روی', 'زیبا', 'را']\n",
      "['من', 'از', 'آن', 'حسن', 'روزافزون', 'که', 'یوسف', 'داشت', 'دانست#دان', 'که', 'عشق', 'از', 'پرده', 'عصمت', 'برون_آرد', 'زلیخا', 'را']\n",
      "doc len:  164854\n"
     ]
    }
   ],
   "source": [
    "data_preprocessed = make_bigrams(data_words)\n",
    "print(*data_preprocessed[:20], sep='\\n')\n",
    "print('doc len: ', len(data_preprocessed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "e5ba98ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(56, 1), (59, 2), (77, 1), (108, 1), (109, 1), (110, 1), (111, 1), (112, 1), (113, 1), (114, 1), (115, 1)]\n"
     ]
    }
   ],
   "source": [
    "# Create dictionary and corpus needed for topic modeling\n",
    "\n",
    "# dictionary is a mapping of word_id --> word, as the variable name implies\n",
    "id2word = corpora.Dictionary(data_preprocessed)\n",
    "id2word.filter_extremes(no_below=50, no_above=0.3, keep_n=4000)\n",
    "\n",
    "# the preprocessed and lemmatized documents themselves\n",
    "texts = data_preprocessed\n",
    "\n",
    "# corpus is a mapping of word_id --> word frequency\n",
    "corpus = [id2word.doc2bow(text) for text in texts]\n",
    "print(corpus[11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "bf14dc19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build LDA model\n",
    "from gensim.models.ldamulticore import LdaMulticore\n",
    "\n",
    "\n",
    "def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\n",
    "    coherence_values = []\n",
    "    perplexity_values = []\n",
    "    model_list = []\n",
    "    for num_topics in range(start, limit, step):\n",
    "        print(\"Going to evaluate topic size:\",num_topics)\n",
    "        model =  LdaMulticore(corpus=corpus,\n",
    "                         id2word=id2word,\n",
    "                         num_topics=num_topics,\n",
    "                         random_state=100,\n",
    "                         chunksize=100,\n",
    "                         passes=4,\n",
    "                         per_word_topics=False)\n",
    "        \n",
    "        model_list.append(model)\n",
    "        coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v')\n",
    "        coherence_values.append(coherencemodel.get_coherence())\n",
    "        perplexity_values.append(model.log_perplexity(corpus))\n",
    "    return model_list, coherence_values, perplexity_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "9b54e436",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "logging.basicConfig(filename='gensim.log',\n",
    "                    format=\"%(asctime)s:%(levelname)s:%(message)s\",\n",
    "                    level=logging.INFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "0936d7d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Going to evaluate topic size: 1\n",
      "Going to evaluate topic size: 101\n",
      "Going to evaluate topic size: 201\n",
      "Going to evaluate topic size: 301\n",
      "Going to evaluate topic size: 401\n",
      "Going to evaluate topic size: 501\n",
      "Going to evaluate topic size: 601\n"
     ]
    }
   ],
   "source": [
    "limit=602; start=1; step=100;\n",
    "\n",
    "model_list, coherence_values, perplexity_values = compute_coherence_values(dictionary=id2word, \n",
    "                                                        corpus=corpus, \n",
    "                                                        texts=data_preprocessed, \n",
    "                                                        start=start, \n",
    "                                                        limit=limit, \n",
    "                                                        step=step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "f62e9519",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph coherence values vs number of topics\n",
    "x = range(start, limit, step)\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "plt.plot(x, coherence_values)\n",
    "plt.xlabel(\"Num Topics\", fontsize=18)\n",
    "plt.ylabel(\"Coherence score\", fontsize=18)\n",
    "plt.grid()\n",
    "fig.savefig('./figures/coherence_vs_numtopics.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "d411d18d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,6))\n",
    "plt.plot(x, perplexity_values)\n",
    "plt.xlabel(\"Num Topics\", fontsize=18)\n",
    "plt.ylabel(\"perplexity score\", fontsize=18)\n",
    "plt.grid()\n",
    "fig.savefig('./figures/perplexity_vs_numtopics.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "d380c583",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models.ldamulticore import LdaMulticore\n",
    "\n",
    "lda_model =  LdaMulticore(corpus=corpus,\n",
    "                         id2word=id2word,\n",
    "                         num_topics=200,\n",
    "                         random_state=100,\n",
    "                         chunksize=100,\n",
    "                         passes=50,\n",
    "                         per_word_topics=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "f755c1bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "lda_model.save('./results/lda_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "5ef64a38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "([(0.15039875, 'آخر'), (0.12573957, 'آه'), (0.09381816, 'از'), (0.092182145, 'دوش'), (0.08265742, 'بزم'), (0.079815835, 'شنید#شنو'), (0.060995083, 'دود'), (0.04741576, 'به'), (0.036797002, 'کرد#کن'), (0.031070696, 'بر'), (0.02821198, 'این'), (0.027944071, 'دل'), (0.026218109, 'ندامت'), (0.023023915, 'که'), (0.022359904, 'در'), (0.02005052, 'گذر'), (0.014240211, 'چه'), (0.011786445, '#است'), (0.011564763, 'و'), (0.0064958995, 'آن')], -2.832843962234711)\n",
      "--------------------------------------------------\n",
      "([(0.2348085, 'گرد'), (0.15858738, 'تن'), (0.080776446, 'گره'), (0.05992883, 'نوشت#نویس'), (0.05677861, 'در'), (0.053538747, 'به'), (0.04767173, 'بر'), (0.041917756, 'دل'), (0.035566457, 'من'), (0.032328334, 'تخم'), (0.030133624, 'سلسله'), (0.027761316, 'که'), (0.022168713, 'ز'), (0.018176079, 'تو'), (0.01694419, 'این'), (0.014794138, 'چه'), (0.013365213, 'کرد#کن'), (0.010782595, 'از'), (0.009807774, 'آن'), (0.009229268, '#است')], -2.840555123003074)\n",
      "--------------------------------------------------\n",
      "([(0.19494748, 'جا'), (0.11911954, 'کم'), (0.0943472, 'اما'), (0.08771165, 'دماغ'), (0.062761754, 'زند'), (0.051217582, 'از'), (0.03836526, 'به'), (0.035689387, 'که'), (0.034260288, 'ز'), (0.032778095, 'دل'), (0.029784286, 'این'), (0.025939623, 'آن'), (0.025244137, 'چه'), (0.025083106, 'بر'), (0.024591874, '#است'), (0.01951394, 'کرد#کن'), (0.015255374, 'بیم'), (0.014855669, 'در'), (0.014829036, 'ببرد'), (0.011202766, 'و')], -2.978665919666592)\n",
      "--------------------------------------------------\n",
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      "([(0.00030940596, 'حیوان'), (0.00030940596, 'نمی\\u200cباید'), (0.00030940596, 'مپسند'), (0.00030940596, 'رهبر'), (0.00030940596, 'نقش_نگین'), (0.00030940596, 'گزید'), (0.00030940596, 'قابل'), (0.00030940596, 'تست'), (0.00030940596, 'بباید'), (0.00030940596, 'حیرتم'), (0.00030940596, 'تنگنا'), (0.00030940596, 'کرانه'), (0.00030940596, 'ولیک'), (0.00030940596, 'طفل'), (0.00030940596, 'سفید'), (0.00030940596, 'پیشت'), (0.00030940596, 'سرو_خرامان'), (0.00030940596, 'مرجان'), (0.00030940596, 'دلیل'), (0.00030940596, 'چشمه_خورشید')], -18.47187532275684)\n",
      "--------------------------------------------------\n",
      "([(0.00030940596, 'حیوان'), (0.00030940596, 'نمی\\u200cباید'), (0.00030940596, 'مپسند'), (0.00030940596, 'رهبر'), (0.00030940596, 'نقش_نگین'), (0.00030940596, 'گزید'), (0.00030940596, 'قابل'), (0.00030940596, 'تست'), (0.00030940596, 'بباید'), (0.00030940596, 'حیرتم'), (0.00030940596, 'تنگنا'), (0.00030940596, 'کرانه'), (0.00030940596, 'ولیک'), (0.00030940596, 'طفل'), (0.00030940596, 'سفید'), (0.00030940596, 'پیشت'), (0.00030940596, 'سرو_خرامان'), (0.00030940596, 'مرجان'), (0.00030940596, 'دلیل'), (0.00030940596, 'چشمه_خورشید')], -18.47187532275684)\n",
      "--------------------------------------------------\n",
      "([(0.00030940596, 'حیوان'), (0.00030940596, 'نمی\\u200cباید'), (0.00030940596, 'مپسند'), (0.00030940596, 'رهبر'), (0.00030940596, 'نقش_نگین'), (0.00030940596, 'گزید'), (0.00030940596, 'قابل'), (0.00030940596, 'تست'), (0.00030940596, 'بباید'), (0.00030940596, 'حیرتم'), (0.00030940596, 'تنگنا'), (0.00030940596, 'کرانه'), (0.00030940596, 'ولیک'), (0.00030940596, 'طفل'), (0.00030940596, 'سفید'), (0.00030940596, 'پیشت'), (0.00030940596, 'سرو_خرامان'), (0.00030940596, 'مرجان'), (0.00030940596, 'دلیل'), (0.00030940596, 'چشمه_خورشید')], -18.47187532275684)\n",
      "--------------------------------------------------\n",
      "([(0.00030940596, 'حیوان'), (0.00030940596, 'نمی\\u200cباید'), (0.00030940596, 'مپسند'), (0.00030940596, 'رهبر'), (0.00030940596, 'نقش_نگین'), (0.00030940596, 'گزید'), (0.00030940596, 'قابل'), (0.00030940596, 'تست'), (0.00030940596, 'بباید'), (0.00030940596, 'حیرتم'), (0.00030940596, 'تنگنا'), (0.00030940596, 'کرانه'), (0.00030940596, 'ولیک'), (0.00030940596, 'طفل'), (0.00030940596, 'سفید'), (0.00030940596, 'پیشت'), (0.00030940596, 'سرو_خرامان'), (0.00030940596, 'مرجان'), (0.00030940596, 'دلیل'), (0.00030940596, 'چشمه_خورشید')], -18.47187532275684)\n",
      "--------------------------------------------------\n",
      "([(0.00030940596, 'حیوان'), (0.00030940596, 'نمی\\u200cباید'), (0.00030940596, 'مپسند'), (0.00030940596, 'رهبر'), (0.00030940596, 'نقش_نگین'), (0.00030940596, 'گزید'), (0.00030940596, 'قابل'), (0.00030940596, 'تست'), (0.00030940596, 'بباید'), (0.00030940596, 'حیرتم'), (0.00030940596, 'تنگنا'), (0.00030940596, 'کرانه'), (0.00030940596, 'ولیک'), (0.00030940596, 'طفل'), (0.00030940596, 'سفید'), (0.00030940596, 'پیشت'), (0.00030940596, 'سرو_خرامان'), (0.00030940596, 'مرجان'), (0.00030940596, 'دلیل'), (0.00030940596, 'چشمه_خورشید')], -18.47187532275684)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "([(0.00030940596, 'حیوان'), (0.00030940596, 'نمی\\u200cباید'), (0.00030940596, 'مپسند'), (0.00030940596, 'رهبر'), (0.00030940596, 'نقش_نگین'), (0.00030940596, 'گزید'), (0.00030940596, 'قابل'), (0.00030940596, 'تست'), (0.00030940596, 'بباید'), (0.00030940596, 'حیرتم'), (0.00030940596, 'تنگنا'), (0.00030940596, 'کرانه'), (0.00030940596, 'ولیک'), (0.00030940596, 'طفل'), (0.00030940596, 'سفید'), (0.00030940596, 'پیشت'), (0.00030940596, 'سرو_خرامان'), (0.00030940596, 'مرجان'), (0.00030940596, 'دلیل'), (0.00030940596, 'چشمه_خورشید')], -18.47187532275684)\n",
      "--------------------------------------------------\n",
      "([(0.00030940596, 'حیوان'), (0.00030940596, 'نمی\\u200cباید'), (0.00030940596, 'مپسند'), (0.00030940596, 'رهبر'), (0.00030940596, 'نقش_نگین'), (0.00030940596, 'گزید'), (0.00030940596, 'قابل'), (0.00030940596, 'تست'), (0.00030940596, 'بباید'), (0.00030940596, 'حیرتم'), (0.00030940596, 'تنگنا'), (0.00030940596, 'کرانه'), (0.00030940596, 'ولیک'), (0.00030940596, 'طفل'), (0.00030940596, 'سفید'), (0.00030940596, 'پیشت'), (0.00030940596, 'سرو_خرامان'), (0.00030940596, 'مرجان'), (0.00030940596, 'دلیل'), (0.00030940596, 'چشمه_خورشید')], -18.47187532275684)\n",
      "--------------------------------------------------\n",
      "([(0.00030940596, 'حیوان'), (0.00030940596, 'نمی\\u200cباید'), (0.00030940596, 'مپسند'), (0.00030940596, 'رهبر'), (0.00030940596, 'نقش_نگین'), (0.00030940596, 'گزید'), (0.00030940596, 'قابل'), (0.00030940596, 'تست'), (0.00030940596, 'بباید'), (0.00030940596, 'حیرتم'), (0.00030940596, 'تنگنا'), (0.00030940596, 'کرانه'), (0.00030940596, 'ولیک'), (0.00030940596, 'طفل'), (0.00030940596, 'سفید'), (0.00030940596, 'پیشت'), (0.00030940596, 'سرو_خرامان'), (0.00030940596, 'مرجان'), (0.00030940596, 'دلیل'), (0.00030940596, 'چشمه_خورشید')], -18.47187532275684)\n",
      "--------------------------------------------------\n",
      "([(0.00030940596, 'حیوان'), (0.00030940596, 'نمی\\u200cباید'), (0.00030940596, 'مپسند'), (0.00030940596, 'رهبر'), (0.00030940596, 'نقش_نگین'), (0.00030940596, 'گزید'), (0.00030940596, 'قابل'), (0.00030940596, 'تست'), (0.00030940596, 'بباید'), (0.00030940596, 'حیرتم'), (0.00030940596, 'تنگنا'), (0.00030940596, 'کرانه'), (0.00030940596, 'ولیک'), (0.00030940596, 'طفل'), (0.00030940596, 'سفید'), (0.00030940596, 'پیشت'), (0.00030940596, 'سرو_خرامان'), (0.00030940596, 'مرجان'), (0.00030940596, 'دلیل'), (0.00030940596, 'چشمه_خورشید')], -18.47187532275684)\n",
      "--------------------------------------------------\n",
      "([(0.00030940596, 'حیوان'), (0.00030940596, 'نمی\\u200cباید'), (0.00030940596, 'مپسند'), (0.00030940596, 'رهبر'), (0.00030940596, 'نقش_نگین'), (0.00030940596, 'گزید'), (0.00030940596, 'قابل'), (0.00030940596, 'تست'), (0.00030940596, 'بباید'), (0.00030940596, 'حیرتم'), (0.00030940596, 'تنگنا'), (0.00030940596, 'کرانه'), (0.00030940596, 'ولیک'), (0.00030940596, 'طفل'), (0.00030940596, 'سفید'), (0.00030940596, 'پیشت'), (0.00030940596, 'سرو_خرامان'), (0.00030940596, 'مرجان'), (0.00030940596, 'دلیل'), (0.00030940596, 'چشمه_خورشید')], -18.47187532275684)\n",
      "--------------------------------------------------\n",
      "([(0.00030940596, 'حیوان'), (0.00030940596, 'نمی\\u200cباید'), (0.00030940596, 'مپسند'), (0.00030940596, 'رهبر'), (0.00030940596, 'نقش_نگین'), (0.00030940596, 'گزید'), (0.00030940596, 'قابل'), (0.00030940596, 'تست'), (0.00030940596, 'بباید'), (0.00030940596, 'حیرتم'), (0.00030940596, 'تنگنا'), (0.00030940596, 'کرانه'), (0.00030940596, 'ولیک'), (0.00030940596, 'طفل'), (0.00030940596, 'سفید'), (0.00030940596, 'پیشت'), (0.00030940596, 'سرو_خرامان'), (0.00030940596, 'مرجان'), (0.00030940596, 'دلیل'), (0.00030940596, 'چشمه_خورشید')], -18.47187532275684)\n",
      "--------------------------------------------------\n",
      "([(0.00030940596, 'حیوان'), (0.00030940596, 'نمی\\u200cباید'), (0.00030940596, 'مپسند'), (0.00030940596, 'رهبر'), (0.00030940596, 'نقش_نگین'), (0.00030940596, 'گزید'), (0.00030940596, 'قابل'), (0.00030940596, 'تست'), (0.00030940596, 'بباید'), (0.00030940596, 'حیرتم'), (0.00030940596, 'تنگنا'), (0.00030940596, 'کرانه'), (0.00030940596, 'ولیک'), (0.00030940596, 'طفل'), (0.00030940596, 'سفید'), (0.00030940596, 'پیشت'), (0.00030940596, 'سرو_خرامان'), (0.00030940596, 'مرجان'), (0.00030940596, 'دلیل'), (0.00030940596, 'چشمه_خورشید')], -18.47187532275684)\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for i in range(lda_model.num_topics):\n",
    "    print(lda_model.top_topics(corpus=corpus)[i])\n",
    "    print('-'*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "41c4794b",
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus_infered = lda_model[corpus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "98e98791",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perplexity:  -27.21\n",
      "Coherence Score:  0.31\n"
     ]
    }
   ],
   "source": [
    "from gensim.models import CoherenceModel\n",
    "\n",
    "# Compute Perplexity (lower is better)\n",
    "print('Perplexity: ', round(lda_model.log_perplexity(corpus), 2))\n",
    "\n",
    "# Compute Coherence Score (higher is better)\n",
    "coherence_model_lda = CoherenceModel(model=lda_model, texts=data_preprocessed, dictionary=id2word, coherence='c_v')\n",
    "coherence_lda = coherence_model_lda.get_coherence()\n",
    "print('Coherence Score: ', round(coherence_lda, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "d3d5a83a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_query_LDA_likelihood(top_stats, query):\n",
    "    topic_probs = np.full((1,lda_model.num_topics), 0.0001)\n",
    "    for topics, probs in top_stats:\n",
    "        topic_probs[0, topics] = probs\n",
    "    return np.exp(np.sum(np.log(topic_probs @ lda_model.expElogbeta[:,query])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "1e16f267",
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus_infered.save(\"./results/lda_inferred_corp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "e6beedf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "id2word.save('./results/lda_dict')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "bd9b0219",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "def search(q):\n",
    "    text = word_tokenize(q)\n",
    "    text = [normalizer.normalize(w) for w in text]\n",
    "    text = [lemmatizer.lemmatize(w) for w in text]\n",
    "    doc = id2word.doc2idx(text)\n",
    "    doc = list(filter(lambda a: a != -1, doc))\n",
    "    res = list(map(lambda x: calc_query_LDA_likelihood(x, doc), tqdm(corpus_infered)))\n",
    "    res = np.array(res)\n",
    "    return np.argsort(res)[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "218ccf74",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 164854/164854 [01:20<00:00, 2046.31it/s]\n"
     ]
    }
   ],
   "source": [
    "pq_d = search('شهریار زیر آفتاب خورشید ماهی خورد')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "624e91cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "beyt index\n",
       "97064     ماه من روشن شده از آفتاب بر سپهر جان چنین مه هست نیست                              \n",
       "27412     سفری فتاد جان را به ولایت معانی که سپهر و ماه گوید که چنین سفر ندارم               \n",
       "152825    دوسه‌گام آخر ازین‌گذر توگران قدم زن و پا سبک اگرت به منظر بی‌نشان دم همتی بکشد عنان\n",
       "68128     اوحدی امسال اگر آهنگ رفتن می‌کند گو: سفر می‌کن، که من حیران آن ماهم دگر            \n",
       "21850     هله برجه هله برجه که ز خورشید سفر به قدم از خانه به در نه همگان را به سفر بر       \n",
       "131763    اگر به قدر عدس دیده‌اند دنیا را به احتیاط قدم زن‌که عافیت‌طلبان                    \n",
       "43088     اگر چه سیل بنالد ز راه ناهموار قدم قدم بودش در سفر تماشایی                         \n",
       "123339    زعجز شب پرهٔ آفتاب را چه ملامت ترا چه غم که بمیرد هزار همچو من از غم               \n",
       "49553     در پرتو آفتاب رویت خورشید سپهر ذره کردار                                           \n",
       "34710     ای آفتاب روی تو کرده هزیمت ماه را و آن ماه در راه آمده از هزم تو پا کوفته          \n",
       "161593    اگر بر هم نهی چشمی و طومار نظر پیچی اگر فقر از تو می‌نالد و گر جاه از تو می‌بالد   \n",
       "23268     غم تو بر سفرم زیر زیر می‌خندد که واقفست از این عشق زینهارآمیز                      \n",
       "80762     ریاض بهشتی بدین روح بخشی چراغ سپهری بدین تابناکی                                   \n",
       "75631     ماه فرو رفت و آفتاب برآمد شاهد سرمست من ز خواب برآمد                               \n",
       "130658    به نیم ملک بچربید نیم درویشی چه آسمان و چه انجم چه آفتاب چه ماه                    \n",
       "56255     میگیر درم قفا همی خور با رندی و عیبها عیون شو                                      \n",
       "17136     آن باده همانست اگر شیشه بدل شد بنگر که چه خوش بر سر خمار برآمد                     \n",
       "17245     آن باده همانست اگر شیشه بدل شد بنگر که چه خوش بر سر خمار برآمد                     \n",
       "61802     طاق پذیر است عشق جفت نخواهد حریف بر نمط عشق اگر پای نهی طاق نه                     \n",
       "100510    بر سر دار اگر نهی قدمی نعمت الله بود تو را سردار                                   \n",
       "Name: beyt, dtype: object"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.width', None)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_colwidth', 0)\n",
    "\n",
    "df['beyt'][pq_d[:20]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "94191a3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_precision_at_k_score(ground_truth, preds, k):\n",
    "    ground_truth = set(ground_truth)\n",
    "    preds = set(preds[:k])\n",
    "    intersection = ground_truth.intersection(preds)\n",
    "    return len(intersection) / min(k, len(ground_truth))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "728dc747",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "10c6cdca",
   "metadata": {},
   "outputs": [],
   "source": [
    "ranks1 = pickle.load(open('./small_eval/eval_ranks1.pkl', 'rb'))\n",
    "ranks2 = pickle.load(open('./small_eval/eval_ranks2.pkl', 'rb'))\n",
    "queries1 = pickle.load(open('./small_eval/queries1.pkl', 'rb'))\n",
    "queries2 = pickle.load(open('./small_eval/queries2.pkl', 'rb'))\n",
    "ranks = ranks1 + ranks2\n",
    "queries = queries1 + queries2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "1f3a3c63",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision_at_k_score for k=1 is 0.36372150786074675\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for i, q in enumerate(queries):\n",
    "    prec+= get_precision_at_k_score(ranks[i], search(q), 100)\n",
    "print(f\"precision_at_k_score for k={k} is {prec/len(queries)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a59b74b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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