Find and replace FlashText and regex comparison

flashtext_regex_timing_find_and_replace.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pip install flashtext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from flashtext.keyword import KeywordProcessor\n",
    "import random\n",
    "import string\n",
    "import re\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_word_of_length(str_length):\n",
    "    \"\"\"\n",
    "    generate a radom word of given length\n",
    "    \"\"\"\n",
    "    return ''.join(random.choice(string.ascii_lowercase) for _ in range(str_length))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'jfyzq'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_word_of_length(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# generate a list of 100K words\n",
    "all_words = [get_word_of_length(random.choice([3,4,5,6,7,8])) for i in range(100000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "5000\n",
      "6000\n",
      "7000\n",
      "8000\n",
      "9000\n",
      "10000\n",
      "11000\n",
      "12000\n",
      "13000\n",
      "14000\n",
      "15000\n",
      "16000\n",
      "17000\n",
      "18000\n",
      "19000\n",
      "20000\n",
      "21000\n",
      "22000\n",
      "23000\n",
      "24000\n",
      "25000\n",
      "26000\n",
      "27000\n",
      "28000\n",
      "29000\n",
      "30000\n",
      "31000\n",
      "32000\n",
      "33000\n",
      "34000\n",
      "35000\n",
      "36000\n",
      "37000\n",
      "38000\n",
      "39000\n",
      "40000\n",
      "41000\n",
      "42000\n",
      "43000\n",
      "44000\n",
      "45000\n",
      "46000\n",
      "47000\n",
      "48000\n",
      "49000\n"
     ]
    }
   ],
   "source": [
    "string_length_to_time_map = []\n",
    "\n",
    "for keywords_length in range(0, 50000, 1000):\n",
    "    print(keywords_length, flush=True)\n",
    "    # randomly generate words with different lengths from list [3,4,5,6,7,8]\n",
    "    # no of words generated = keywords_length\n",
    "    all_words_chosen = random.sample(all_words, 1000)\n",
    "    story = ' '.join(all_words_chosen)\n",
    "    \n",
    "    # get unique keywords from the list of words generated.\n",
    "    unique_keywords_sublist = list(set(random.sample(all_words, keywords_length)))\n",
    "    \n",
    "    # compile regex\n",
    "    # compiled_re = re.compile('|'.join([r'\\b' + keyword + r'\\b' for keyword in unique_keywords_sublist]))\n",
    "\n",
    "    # add keywords to flashtext\n",
    "    keyword_processor = KeywordProcessor()\n",
    "    for keyword in unique_keywords_sublist:\n",
    "        keyword_processor.add_keyword(keyword, 'keyword')\n",
    "    \n",
    "    # time the modules now\n",
    "    start = time.time()\n",
    "    _ = keyword_processor.replace_keywords(story)\n",
    "    mid = time.time()\n",
    "    for keyword in unique_keywords_sublist:\n",
    "        story = re.sub(r'\\b' + keyword + r'\\b', 'keyword', story)\n",
    "    end = time.time()\n",
    "    \n",
    "    # add results to dictionary\n",
    "    string_length_to_time_map.append((len(unique_keywords_sublist), mid - start, end - mid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# string length list\n",
    "counts = [v[0] for v in string_length_to_time_map]\n",
    "# time taken for flashtext\n",
    "time_flash_text = [v[1] for v in string_length_to_time_map]\n",
    "# time taken by regex\n",
    "time_regex = [v[2] for v in string_length_to_time_map]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1088817b8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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OKBpDKBpHi8uG9iE/fMHpMRR6Qk9rABwQQhwSQoQBPAZgQ9I+GwA8KCTeAlBDRLN1HPsz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\nVpIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c7286a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# time taken by only flashtext\n",
    "plt.plot(counts, time_flash_text)\n",
    "#plt.plot(counts, time_regex)\n",
    "plt.legend(['FlashText'], loc='upper left')\n",
    "plt.title('No of keywords vs time (sec)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1064d8358>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nNTD6CtiyHLoPgg79w65IJO4ouKVopKXC/DH+cvXMdLhqHDQ6I+yqROKSglti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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10886dbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# time taken by flashtext and regex compared\n",
    "plt.plot(counts, time_flash_text)\n",
    "plt.plot(counts, time_regex)\n",
    "plt.legend(['FlashText', 'Regex'], loc='upper left')\n",
    "plt.title('No of keywords vs time (sec)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0.0021049976348876953, 1.1920928955078125e-06),\n",
       " (999, 0.002577066421508789, 0.22324013710021973),\n",
       " (1998, 0.0027570724487304688, 0.3517110347747803),\n",
       " (2996, 0.003284931182861328, 0.5657999515533447),\n",
       " (3987, 0.002735137939453125, 0.7377369403839111),\n",
       " (4984, 0.002649068832397461, 0.9025490283966064),\n",
       " (5972, 0.003771066665649414, 1.1736340522766113),\n",
       " (6950, 0.0030558109283447266, 1.2782330513000488),\n",
       " (7951, 0.0028772354125976562, 1.4533758163452148),\n",
       " (8942, 0.0029239654541015625, 1.6046521663665771),\n",
       " (9902, 0.002691030502319336, 1.8706660270690918),\n",
       " (10919, 0.003328084945678711, 1.918699026107788),\n",
       " (11865, 0.0025870800018310547, 2.0748438835144043),\n",
       " (12854, 0.0027561187744140625, 2.2390809059143066),\n",
       " (13845, 0.0032541751861572266, 2.445417881011963),\n",
       " (14821, 0.0027971267700195312, 2.6253089904785156),\n",
       " (15753, 0.0027189254760742188, 2.7199621200561523),\n",
       " (16771, 0.002744913101196289, 2.920759916305542),\n",
       " (17717, 0.002886056900024414, 3.093148946762085),\n",
       " (18734, 0.003203868865966797, 3.227185010910034),\n",
       " (19660, 0.002888202667236328, 3.4561359882354736),\n",
       " (20639, 0.002869129180908203, 3.6165518760681152),\n",
       " (21600, 0.002788066864013672, 3.7714359760284424),\n",
       " (22589, 0.0032279491424560547, 3.940941095352173),\n",
       " (23556, 0.0030660629272460938, 4.09523606300354),\n",
       " (24536, 0.002816915512084961, 4.297428131103516),\n",
       " (25454, 0.002961874008178711, 4.432486057281494),\n",
       " (26426, 0.0028591156005859375, 4.60263991355896),\n",
       " (27435, 0.003358125686645508, 4.727710962295532),\n",
       " (28389, 0.002892017364501953, 4.881420135498047),\n",
       " (29326, 0.0029151439666748047, 5.134897947311401),\n",
       " (30268, 0.0035219192504882812, 5.279383182525635),\n",
       " (31216, 0.0031969547271728516, 5.4562530517578125),\n",
       " (32141, 0.0031409263610839844, 5.671065092086792),\n",
       " (33145, 0.0028619766235351562, 5.9297380447387695),\n",
       " (34082, 0.0029730796813964844, 5.980953931808472),\n",
       " (35090, 0.0031490325927734375, 6.228991985321045),\n",
       " (36031, 0.003178119659423828, 6.31782603263855),\n",
       " (36955, 0.0031049251556396484, 6.501192092895508),\n",
       " (37798, 0.003946065902709961, 6.715828895568848),\n",
       " (38837, 0.002928018569946289, 6.879624843597412),\n",
       " (39696, 0.003930091857910156, 7.400384902954102),\n",
       " (40632, 0.003880023956298828, 7.678287982940674),\n",
       " (41620, 0.004044055938720703, 8.32214093208313),\n",
       " (42594, 0.0032989978790283203, 8.313017845153809),\n",
       " (43512, 0.003055095672607422, 8.971843957901001),\n",
       " (44420, 0.004041194915771484, 8.863094806671143),\n",
       " (45451, 0.003228902816772461, 9.057163000106812),\n",
       " (46306, 0.004168033599853516, 9.129202842712402),\n",
       " (47286, 0.0029630661010742188, 8.960461854934692)]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of terms, FlashText time, RegexTime\n",
    "string_length_to_time_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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