wordle-solver.py

import pandas as pd
import numpy as np
import string
from collections import defaultdict
import math
import os
import warnings
warnings.filterwarnings("ignore")
import argparse

def cls():
    os.system('cls' if os.name=='nt' else 'clear')

p = argparse.ArgumentParser()
p.add_argument('-v', action='store_true')
p.add_argument('-a', action='store_true')
args = p.parse_args()
verbose = args.v

print('Preparing vocabulary...')

# Alphabet
az = string.ascii_lowercase

# Word frequency table, from Google trillion words corpus
# https://www.kaggle.com/datasets/rtatman/english-word-frequency?resource=download
words = pd.read_csv('./data/unigram_freq.csv')
# Filter to 5 letter words
words = words[words['word'].str.len() == 5]
# Scale word count to 0...1
words['freq'] = words['count'] / words['count'].max()
# print(len(words), '5 letter words in Google trillion words corpus.')

# Wordle solutions, from Wordle source code
# https://www.nytimes.com/games/wordle/main.9622bc55.js
solutions = ["cigar","rebut","sissy","humph","awake","blush","focal","evade","naval","serve","heath","dwarf","model","karma","stink","grade","quiet","bench","abate","feign","major","death","fresh","crust","stool","colon","abase","marry","react","batty","pride","floss","helix","croak","staff","paper","unfed","whelp","trawl","outdo","adobe","crazy","sower","repay","digit","crate","cluck","spike","mimic","pound","maxim","linen","unmet","flesh","booby","forth","first","stand","belly","ivory","seedy","print","yearn","drain","bribe","stout","panel","crass","flume","offal","agree","error","swirl","argue","bleed","delta","flick","totem","wooer","front","shrub","parry","biome","lapel","start","greet","goner","golem","lusty","loopy","round","audit","lying","gamma","labor","islet","civic","forge","corny","moult","basic","salad","agate","spicy","spray","essay","fjord","spend","kebab","guild","aback","motor","alone","hatch","hyper","thumb","dowry","ought","belch","dutch","pilot","tweed","comet","jaunt","enema","steed","abyss","growl","fling","dozen","boozy","erode","world","gouge","click","briar","great","altar","pulpy","blurt","coast","duchy","groin","fixer","group","rogue","badly","smart","pithy","gaudy","chill","heron","vodka","finer","surer","radio","rouge","perch","retch","wrote","clock","tilde","store","prove","bring","solve","cheat","grime","exult","usher","epoch","triad","break","rhino","viral","conic","masse","sonic","vital","trace","using","peach","champ","baton","brake","pluck","craze","gripe","weary","picky","acute","ferry","aside","tapir","troll","unify","rebus","boost","truss","siege","tiger","banal","slump","crank","gorge","query","drink","favor","abbey","tangy","panic","solar","shire","proxy","point","robot","prick","wince","crimp","knoll","sugar","whack","mount","perky","could","wrung","light","those","moist","shard","pleat","aloft","skill","elder","frame","humor","pause","ulcer","ultra","robin","cynic","aroma","caulk","shake","dodge","swill","tacit","other","thorn","trove","bloke","vivid","spill","chant","choke","rupee","nasty","mourn","ahead","brine","cloth","hoard","sweet","month","lapse","watch","today","focus","smelt","tease","cater","movie","saute","allow","renew","their","slosh","purge","chest","depot","epoxy","nymph","found","shall","stove","lowly","snout","trope","fewer","shawl","natal","comma","foray","scare","stair","black","squad","royal","chunk","mince","shame","cheek","ample","flair","foyer","cargo","oxide","plant","olive","inert","askew","heist","shown","zesty","trash","larva","forgo","story","hairy","train","homer","badge","midst","canny","shine","gecko","farce","slung","tipsy","metal","yield","delve","being","scour","glass","gamer","scrap","money","hinge","album","vouch","asset","tiara","crept","bayou","atoll","manor","creak","showy","phase","froth","depth","gloom","flood","trait","girth","piety","goose","float","donor","atone","primo","apron","blown","cacao","loser","input","gloat","awful","brink","smite","beady","rusty","retro","droll","gawky","hutch","pinto","egret","lilac","sever","field","fluff","agape","voice","stead","berth","madam","night","bland","liver","wedge","roomy","wacky","flock","angry","trite","aphid","tryst","midge","power","elope","cinch","motto","stomp","upset","bluff","cramp","quart","coyly","youth","rhyme","buggy","alien","smear","unfit","patty","cling","glean","label","hunky","khaki","poker","gruel","twice","twang","shrug","treat","waste","merit","woven","needy","clown","irony","ruder","gauze","chief","onset","prize","fungi","charm","gully","inter","whoop","taunt","leery","class","theme","lofty","tibia","booze","alpha","thyme","doubt","parer","chute","stick","trice","alike","recap","saint","glory","grate","admit","brisk","soggy","usurp","scald","scorn","leave","twine","sting","bough","marsh","sloth","dandy","vigor","howdy","enjoy","valid","ionic","equal","floor","catch","spade","stein","exist","quirk","denim","grove","spiel","mummy","fault","foggy","flout","carry","sneak","libel","waltz","aptly","piney","inept","aloud","photo","dream","stale","unite","snarl","baker","there","glyph","pooch","hippy","spell","folly","louse","gulch","vault","godly","threw","fleet","grave","inane","shock","crave","spite","valve","skimp","claim","rainy","musty","pique","daddy","quasi","arise","aging","valet","opium","avert","stuck","recut","mulch","genre","plume","rifle","count","incur","total","wrest","mocha","deter","study","lover","safer","rivet","funny","smoke","mound","undue","sedan","pagan","swine","guile","gusty","equip","tough","canoe","chaos","covet","human","udder","lunch","blast","stray","manga","melee","lefty","quick","paste","given","octet","risen","groan","leaky","grind","carve","loose","sadly","spilt","apple","slack","honey","final","sheen","eerie","minty","slick","derby","wharf","spelt","coach","erupt","singe","price","spawn","fairy","jiffy","filmy","stack","chose","sleep","ardor","nanny","niece","woozy","handy","grace","ditto","stank","cream","usual","diode","valor","angle","ninja"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solutions = set(solutions)
print(len(solutions), 'solution words in Wordle source code.')

# Wordle allowable words, from Wordle source code
# https://www.nytimes.com/games/wordle/main.9622bc55.jsallowed_words = pd.read_csv('./data/wordle-allowed-words.txt', header=None)
allowed_words = 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allowed_words = set(allowed_words).union(solutions)
# print(len(allowed_words), 'allowed words in Wordle source code.')

# Filter word frequency table to include allowed words only
words = words[words['word'].isin(allowed_words)]
# print(len(words), 'allowed words in both Wordle source code and the Google corpus')

# Add the allowed words that weren't in word frequency table
missing_allowed_words = allowed_words.difference(set(words['word'].values))
words = words.append(pd.DataFrame(
    [list(missing_allowed_words),
     [0]*len(missing_allowed_words),
     [0]*len(missing_allowed_words)],
    index=['word', 'count', 'freq']).T)

# Create word frequency index
words['freq_idx'] = range(len(words))

words.index = words['word']
words.index.name = None
words = words.drop(['word'], axis = 1)
        
# Letter frequency
letter_freq = defaultdict(int)
for word, word_freq in words[['freq']].itertuples():
    for char in word:
#         Many ways to weight this:
#         letter_freq[char] += w['freq']
#         letter_freq[char] += 1
        letter_freq[char] += 1 + word_freq

sorted_letters = sorted(letter_freq.items(), key=lambda x: -x[1])
# print('Top 5 most frequent letters overall:')
# print(*sorted_letters[:5], sep='\n')

# Letter frequency based on position
letter_pos_freq = [defaultdict(float) for _ in range(5)]
for word, word_freq in words[['freq']].itertuples():
    for i, char in enumerate(word):
#         Many ways to weight this:
#         letter_freq[char] += w['freq']
#         letter_freq[char] += 1
        letter_pos_freq[i][char] += 1 + word_freq

sorted_pos_letters = [sorted(letter_pos_freq[i].items(), key=lambda x: -x[1]) for i in range(5)]
# print('\nMost frequent letter for positions 1 to 5:')
# print(*[s[0] for s in sorted_pos_letters], sep='\n')

# Find the best "starting words"
# Maximizing total letter_freq (most frequent letters overall)
# No points for a repeated letter
scores = []
for word in words.index:
    used_chars = set()
    score = 0
    for char in word:
        if char in used_chars:
            continue
        score += letter_freq[char]
        used_chars.add(char)
    scores.append(score)
words['no_position_score'] = scores

# Maximizing letter_pos_freq (most frequent letters, per position)
# No points for a repeated letter
scores = []
for word in words.index:
    used_chars = set()
    score = 0
    for i, char in enumerate(word):
        if char in used_chars:
            continue
        score += letter_pos_freq[i][char]
        used_chars.add(char)
    scores.append(score)
words['per_position_score'] = scores

# Maximizing letter_pos_freq (most frequent letters, per position)
# Repeated letters OK
scores = []
for word in words.index:
    score = 0
    for i, char in enumerate(word):
        score += letter_pos_freq[i][char]
    scores.append(score)
words['per_pos_score_repeats'] = scores

# Scale each score to 0...1 and take the mean for a combined score
words['combined_score'] = (
    (words['no_position_score'] / words['no_position_score'].max())
    + (words['per_position_score'] / words['per_position_score'].max())) / 2

# print('Best starting words (sorted by a scoring metric):')
# words.head()

# Update constraints based on a new clue
def use_clues(word, result):
    for i, r in enumerate(result):
        char = word[i]
        if r == 'green':
            a[i] = set([char])
        elif r == 'yellow':
            try:
                a[i].remove(char)
            except:
                continue
            b.add(char)
        elif r == 'grey':
            # Remove from all positions except green ones
            for pos in a:
                if len(pos) == 1:
                    continue
                try:
                    pos.remove(char)
                except:
                    continue

# Find all allowable words matching the constraints
def find_ok_words(a, b):
    a_met = (
        np.in1d(words_array[:,0], list(a[0]))
        & np.in1d(words_array[:,1], list(a[1]))
        & np.in1d(words_array[:,2], list(a[2]))
        & np.in1d(words_array[:,3], list(a[3]))
        & np.in1d(words_array[:,4], list(a[4]))
    )
    if len(b) > 0:
        b_met_each = []
        for char in b:
            b_met_each.append(np.array([char in word for word in words.index]))
        b_met_all = np.array([True] * len(words.index))
        for b_met in b_met_each:
            b_met_all = b_met_all & b_met
    else:
        b_met_all = np.array([True] * len(words.index))
    ok_words = words_array[a_met & b_met_all]
    return [''.join(w) for w in ok_words]

# Recommend guess words based on the constraints
def find_clues_solutions(a, b, guess_num, verbose=False):
    ok_words = find_ok_words(a, b)
    
    if not verbose=='quiet':
        print()
        print(len(ok_words), 'valid words.')
    
    # Get the most common word in ok_words
    ok_words = words[words['word'].isin(ok_words)]

    # Balance information gain with how common the word is
    # Increasingly favor common words
    freq_weight = min(0.3 * guess_num, 1)
    info_weight = 1 - freq_weight
    
    ok_words['weighted_idx'] = (ok_words['info_idx'] * info_weight
                                  + ok_words['freq_idx'] * freq_weight)
    ok_words = ok_words.sort_values(['weighted_idx'], ascending=True)
    
    if verbose == True:
        print(f'\nWeighted combination (w_info={info_weight:.1f}, w_freq={freq_weight:.1f}):')
        print(ok_words[['weighted_idx', 'info_idx', 'freq_idx']].head(5))
    
    if not verbose == 'quiet':
        print('\nRecommended word:')
        print(ok_words.index[0])
        print()
    return ok_words.index[0]
    
# Shorthands to make entering results easier
sh = ['grey', 'yellow', 'green']

# How we sort the "words" dataframe determines which word is considered the "best" guess
words = words.sort_values(['combined_score'], ascending=False)
words['info_idx'] = range(len(words))
words['word'] = words.index
words_array = np.array([np.array([c for c in w]) for w in words.index])

auto_mode = args.a
while True:
    # Reset allowed (yellow, green, or untried) letters
    # Initially, set of all letters in the alphabet at each position
    a = [set(az) for _ in range(5)]
    # Reset necessary (green) letters
    # Initially, empty set
    b = set()
    
    quit = False
    cls()
    
    print('\n=============\n')
    print('\nWORDLE SOLVER')
    word = find_clues_solutions(a, b, 0, verbose=verbose)
    print()
    for i in range(1, 7):
        if not auto_mode:
            word = input(f'Guess {i} (or type "exit", "restart"): ').lower()
        else:
            print(f"Enter the word ({word}) in Wordle now.")
        if word == 'restart':
            break
        elif word == 'exit':
            quit = True
            break
        elif not word in words.index:
            print('Word not allowed. Restarting.')
            break
        result = input('Result? (0=grey, 1=yellow, 2=green): ')
        try:
            result = [sh[int(s)] for s in result]
        except:
            input('You need to enter 5 digits. Press any key to restart.')
            break
        use_clues(word, result)
        cls()
        try:
            word = find_clues_solutions(a, b, i, verbose=verbose)
        except:
            input('Impossible! Press any key to restart.')
            break
        print('')
    
    if quit:
        break