Sessional 3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "466ddbd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import os\n",
    "from collections import Counter\n",
    "from progressbar import progressbar\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle as pkl\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "48dfb2f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH_TO_CORPUS = \"F:\\\\IR Lab\\\\classics\\\\\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "a6396729",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize(file_text, limit = 3):\n",
    "    stopwords = open(\"F:\\\\IR Lab\\\\stopwords.txt\",\"r\", encoding = \"utf-8\").read().split(\"\\n\")\n",
    "    text = re.sub(r'\\s+\\n', ' ', file_text)\n",
    "    pattern = re.compile(r\"^\\w*[-']?\\w*$\")\n",
    "    better_tokens = list(filter(pattern.match, text.split(\" \")))\n",
    "    token_list = [ele.lower() for ele in better_tokens if len(ele) > limit and ele.lower() not in stopwords]\n",
    "    return list(set(token_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "d4034344",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% |########################################################################|\r"
     ]
    }
   ],
   "source": [
    "files = os.listdir(PATH_TO_CORPUS)\n",
    "tokens = []\n",
    "index = {}\n",
    "vocab = []\n",
    "vocabplot = []\n",
    "\n",
    "bar = progressbar.ProgressBar(len(files)-1).start()\n",
    "\n",
    "for i, file in enumerate(files):\n",
    "    filetext = open(PATH_TO_CORPUS + file, \"r\", encoding = \"utf-8\").read()\n",
    "    token = tokenize(filetext)\n",
    "    tokens.append(token)\n",
    "    \n",
    "    for term in token:\n",
    "        if index.get(term) != None:\n",
    "            index[term].append(i)\n",
    "        else:\n",
    "            index[term] = [i]\n",
    "\n",
    "    vocab.extend(token)\n",
    "    vocab = list(set(vocab))\n",
    "    vocabplot.append(len(vocab))\n",
    "    bar.update(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "32ea4121",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Increase in Vocab Size with Document Number')"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x = range(0,12), y = vocabplot)\n",
    "plt.xlabel(\"Documents\")\n",
    "plt.ylabel(\"Vocab Size\")\n",
    "plt.title(\"Increase in Vocab Size with Document Number\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "df704c48",
   "metadata": {},
   "outputs": [],
   "source": [
    "def intersect(p1, p2):\n",
    "    \n",
    "    pt1 = 0\n",
    "    pt2 = 0\n",
    "    ans = []\n",
    "    \n",
    "    while pt1 < len(p1) and pt2 < len(p2):\n",
    "        if p1[pt1] == p2[pt2] :\n",
    "            ans.append(p2[pt2])\n",
    "            pt1 = pt1 + 1\n",
    "            pt2 = pt2 + 1\n",
    "        elif p1[pt1] < p2[pt2]:\n",
    "            pt1 = pt1 + 1\n",
    "        else:\n",
    "            pt2 = pt2 + 1\n",
    "    return ans\n",
    "            \n",
    "\n",
    "def IR_merge(query,index):\n",
    "    res = []\n",
    "    qterms = query.split(\"and\")\n",
    "    qterms = [i.strip() for i in qterms]\n",
    "    for ele in qterms:\n",
    "        if index.get(ele) != None:\n",
    "            res.append(index[ele])\n",
    "    res.sort(key=lambda x: len(x))\n",
    "    \n",
    "    temp = res[0]\n",
    "    bar = progressbar.ProgressBar(len(res)-1).start()\n",
    "    for i in range(1,len(res)):\n",
    "        p  = res[i]\n",
    "        temp = intersect(p,temp)\n",
    "        bar.update(i)\n",
    "    \n",
    "    print(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "e2ed14c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% |########################################################################|\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 5, 7, 8, 9]\n",
      "time :  6.994962692260742  ms \n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "IR_merge(\"rich and filthy\",index)\n",
    "end = time.time()\n",
    "print(\"time : \", (end - start)*1000, \" ms \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "a91e1a8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"index.pkl\", \"wb\") as f:\n",
    "    pkl.dump(index, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "0c5c130a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% |########################################################################|\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 5, 7, 8, 9]\n",
      "time :  1149.432897567749  ms \n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "with open(\"index.pkl\", \"rb\") as f:\n",
    "    ind = pkl.load(f)\n",
    "    IR_merge(\"rich and filthy\",index)\n",
    "    \n",
    "end = time.time()\n",
    "print(\"time : \", (end - start)*1000, \" ms \")"
   ]
  }
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