Wordle-Vision.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='green'>WORDLE-VISION!</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from urllib.request import urlopen\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in and filter lexicon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/dwyl/english-words/master/words_alpha.txt'\n",
    "file = urlopen(url)\n",
    "all_words = set(file.read().decode('utf-8').split())\n",
    "\n",
    "### Or if you have a word file already on your computer, you can read it in as follows:\n",
    "# local_file = 'words_alpha.txt'\n",
    "# with open(local_file) as word_file:\n",
    "#     all_words = set(word_file.read().split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 15918 5-letter words in the lexicon.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['AMARA',\n",
       " 'AGATY',\n",
       " 'PSYCH',\n",
       " 'ALLIS',\n",
       " 'GRAIL',\n",
       " 'AXONS',\n",
       " 'GUTTY',\n",
       " 'RUPIA',\n",
       " 'GOMER',\n",
       " 'NIDOR']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keep only the 5-letter words\n",
    "wordle_words = [i.upper() for i in all_words if len(i) == 5]\n",
    "print(f\"There are {len(wordle_words)} 5-letter words in the lexicon.\")\n",
    "wordle_words[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get frequency distribution of letters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'A': 8392,\n",
       "         'M': 2494,\n",
       "         'R': 5143,\n",
       "         'G': 1971,\n",
       "         'T': 4189,\n",
       "         'Y': 2521,\n",
       "         'P': 2299,\n",
       "         'S': 6537,\n",
       "         'C': 2744,\n",
       "         'H': 2284,\n",
       "         'L': 4246,\n",
       "         'I': 5067,\n",
       "         'X': 361,\n",
       "         'O': 5219,\n",
       "         'N': 4043,\n",
       "         'U': 3361,\n",
       "         'E': 7800,\n",
       "         'D': 2811,\n",
       "         'B': 2089,\n",
       "         'K': 1743,\n",
       "         'W': 1171,\n",
       "         'V': 878,\n",
       "         'Z': 474,\n",
       "         'F': 1238,\n",
       "         'J': 376,\n",
       "         'Q': 139})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt = Counter()\n",
    "for i in wordle_words:\n",
    "    cnt.update(Counter(i))\n",
    "cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': 8392,\n",
       " 'E': 7800,\n",
       " 'S': 6537,\n",
       " 'O': 5219,\n",
       " 'R': 5143,\n",
       " 'I': 5067,\n",
       " 'L': 4246,\n",
       " 'T': 4189,\n",
       " 'N': 4043,\n",
       " 'U': 3361,\n",
       " 'D': 2811,\n",
       " 'C': 2744,\n",
       " 'Y': 2521,\n",
       " 'M': 2494,\n",
       " 'P': 2299,\n",
       " 'H': 2284,\n",
       " 'B': 2089,\n",
       " 'G': 1971,\n",
       " 'K': 1743,\n",
       " 'F': 1238,\n",
       " 'W': 1171,\n",
       " 'V': 878,\n",
       " 'Z': 474,\n",
       " 'J': 376,\n",
       " 'X': 361,\n",
       " 'Q': 139}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt_sorted = dict(cnt.most_common())\n",
    "cnt_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': 0.1054,\n",
       " 'E': 0.098,\n",
       " 'S': 0.0821,\n",
       " 'O': 0.0656,\n",
       " 'R': 0.0646,\n",
       " 'I': 0.0637,\n",
       " 'L': 0.0533,\n",
       " 'T': 0.0526,\n",
       " 'N': 0.0508,\n",
       " 'U': 0.0422,\n",
       " 'D': 0.0353,\n",
       " 'C': 0.0345,\n",
       " 'Y': 0.0317,\n",
       " 'M': 0.0313,\n",
       " 'P': 0.0289,\n",
       " 'H': 0.0287,\n",
       " 'B': 0.0262,\n",
       " 'G': 0.0248,\n",
       " 'K': 0.0219,\n",
       " 'F': 0.0156,\n",
       " 'W': 0.0147,\n",
       " 'V': 0.011,\n",
       " 'Z': 0.006,\n",
       " 'J': 0.0047,\n",
       " 'X': 0.0045,\n",
       " 'Q': 0.0017}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_sorted = {k: round(v / (5*len(wordle_words)), 4) for k, v in cnt_sorted.items()}\n",
    "freq_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_keys = list(freq_sorted.keys())\n",
    "sorted_vals = list(freq_sorted.values())\n",
    "\n",
    "fig = plt.figure(figsize=(10, 4))\n",
    "sns.set(style='whitegrid')\n",
    "sns.barplot(x=sorted_keys, y=sorted_vals, color='tab:green')\n",
    "plt.ylabel('Frequency of Appearance')\n",
    "fig.savefig('letter_freq.png', dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The top 7 letters account for more than half of letters in all 5-letter words\n",
    "\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "sns.set(style='whitegrid')\n",
    "sns.barplot(x=sorted_keys, y=np.cumsum(sorted_vals), color='tab:green')\n",
    "plt.yticks(np.arange(0, 1.1, 0.1))\n",
    "plt.axhline(y=0.5, ls='--', lw=4, c='gold', alpha=0.75)\n",
    "plt.ylabel('Cumulative Frequency of Appearance')\n",
    "fig.savefig('letter_freq_cumulative.png', dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find words that contain only the top 7 letters\n",
    "### These will be the best starting guesses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 231 words containing only the top 7 letters.\n"
     ]
    }
   ],
   "source": [
    "top_letters = ''.join(sorted_keys[0:7])\n",
    "top_letter_words = []\n",
    "\n",
    "for i in wordle_words:\n",
    "    is_best = all(x in top_letters for x in i)\n",
    "    if is_best:\n",
    "        top_letter_words.append(i)\n",
    "top_letter_words.sort()\n",
    "print(f\"There are {len(top_letter_words)} words containing only the top 7 letters.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We can further refine this by requiring that each letter be different"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 60 words containing only the top 7 letters where each letter is different.\n"
     ]
    }
   ],
   "source": [
    "best_guesses = []\n",
    "\n",
    "for i in top_letter_words:\n",
    "    num_uniq_letters = len(set(i))\n",
    "    if num_uniq_letters == 5:\n",
    "        best_guesses.append(i)\n",
    "print(f\"There are {len(best_guesses)} words containing only the top 7 letters where each letter is different.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AESIR',\n",
       " 'AISLE',\n",
       " 'ALISO',\n",
       " 'ALOES',\n",
       " 'ALOIS',\n",
       " 'ALOSE',\n",
       " 'ARIEL',\n",
       " 'ARIES',\n",
       " 'ARILS',\n",
       " 'ARISE',\n",
       " 'ARLES',\n",
       " 'AROSE',\n",
       " 'ARSLE',\n",
       " 'EARLS',\n",
       " 'ELIAS',\n",
       " 'IREOS',\n",
       " 'LAIRS',\n",
       " 'LARES',\n",
       " 'LASER',\n",
       " 'LEARS',\n",
       " 'LEORA',\n",
       " 'LIARS',\n",
       " 'LIERS',\n",
       " 'LIRAS',\n",
       " 'LORES',\n",
       " 'LORIS',\n",
       " 'LOSER',\n",
       " 'OILER',\n",
       " 'ORALE',\n",
       " 'ORALS',\n",
       " 'OREAS',\n",
       " 'ORIAS',\n",
       " 'ORIEL',\n",
       " 'ORLES',\n",
       " 'ORSEL',\n",
       " 'OSELA',\n",
       " 'OSIER',\n",
       " 'RAILS',\n",
       " 'RAISE',\n",
       " 'RALES',\n",
       " 'REALS',\n",
       " 'REOIL',\n",
       " 'RIALS',\n",
       " 'RIELS',\n",
       " 'RILES',\n",
       " 'ROILS',\n",
       " 'ROLES',\n",
       " 'ROSAL',\n",
       " 'ROSEL',\n",
       " 'SEORA',\n",
       " 'SERAI',\n",
       " 'SERAL',\n",
       " 'SERIO',\n",
       " 'SLARE',\n",
       " 'SLIER',\n",
       " 'SOLAR',\n",
       " 'SOLEA',\n",
       " 'SOLER',\n",
       " 'SORAL',\n",
       " 'SOREL']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_guesses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save list of words to text file\n",
    "outfile = open('best_guesses.txt', 'w')\n",
    "for i in best_guesses:\n",
    "    outfile.write(i + '\\n')\n",
    "outfile.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## You can manually filter out the less common words above to get even better guesses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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