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import numpy as np # for stats
import random # for randomly generating target and start words
import operator # for sorting letter frequency distribution
import time # for #dramaticeffect
import pandas as pd
import streamlit as st
english_alphabet = "abcdefghijklmnopqrstuvwxyz"
def get_letter_counts(word_list: list, letters: str = english_alphabet, sort: str = "descending", unique: bool = True):
"""
Given a passed str of letters and a list of words, produces a frequency distribution of all letters
Parameters:
------
`word_list`: list
list of words (str) from which word frequencies will be counted
`letters`: str
a string of letters to be counted. String must only be desired letters, with no spaces. Default is local variable containing all letters of the English alphabet
`sort`: str
if either "descending" or "ascending" are passed, returned list of tuples will be sorted accordingly, else returned dictionary will be unsorted
`unique`: bool
if True, only unique letters in a word are counted. That means that words with more unique letters are rated more highly than any words with duplicate letters
Returns:
------
`letters_counts_dict`: dict
dictionary of {letter : count} pairs for each letter in passed `letters` sequence
`sorted_counts_dicts`: list of tuples
list of tuples. Format is ("letter", frequency). Ordered according to `sort` values
"""
words_counts_dict = {}
if unique == False:
for word in word_list: # real dataset
word_dict = {}
for letter in word:
if letter in word_dict:
word_dict[letter] += 1
else:
word_dict[letter] = 1
words_counts_dict[word] = word_dict
else: # if unique == True
for word in word_list: # real dataset
word_dict = {}
word_letters = set(letter for letter in word)
for letter in word_letters:
if letter in word_dict:
word_dict[letter] += 1
else:
word_dict[letter] = 1
words_counts_dict[word] = word_dict
letters_counts_dict = {}
for letter in letters:
letters_counts_dict[letter] = 0
for word, count_dict in words_counts_dict.items():
# # print (word, count_dict)
for letter, count in count_dict.items():
letters_counts_dict[letter] += count
if sort == "ascending":
sorted_counts_dict = (sorted(letters_counts_dict.items(), key = operator.itemgetter(1), reverse = False))
return sorted_counts_dicts
if sort == "descending":
sorted_counts_dict = sorted(letters_counts_dict.items(), key = operator.itemgetter(1), reverse = True)
return sorted_counts_dict
else:
return letters_counts_dict
### Best first guesses for a given Wordle list
def best_guess_words(word_list: list, show_letters: bool = False):
"""
Given a passed list of English words of a consistent length, calculates the most statistically optimal first guess words, alongside a rating for each word.
Rating = sum(frequency of each unique letter in that word) / sum (all unique letter frequencies in word_list) * 100, rounded to 2 decimals.
------
Parameters:
------
`word_list`: list
list of words (str) of consistent length
`show_letters`: bool
if True, also # prints set of most optimal letters to guess
------
Returns:
------
`word_ratings`: list
list of tuples. Format is [(word, rating)], where rating is calculated according to above formula
`sorted_counts`: list of tuples
list of tuples. Format is ("letter", frequency). Sorted according to `sort` value; ["descending" or "ascending"] if passed
"""
english_alphabet = "abcdefghijklmnopqrstuvwxyz"
sorted_counts = get_letter_counts(english_alphabet, word_list, sort = "descending")
max_len_possible = len(word_list[0])
### Get words with the highest letter diversity
while max_len_possible:
best_letters = set()
best_words = []
for letter, freq in sorted_counts:
best_letters.add(letter)
if len(best_letters) == max_len_possible:
break
### Get all words that have one of each of the 5 top most frequent letters
for word in word_list:
word_set = set()
for letter in word:
word_set.add(letter)
if best_letters.issubset(word_set):
best_words.append(word)
if len(best_words) > 0:
break
else:
max_len_possible -= 1 # only try the top 4 letters, then 3, then 2, ...
if max_len_possible == 0:
break
all_letters_count = 0
for letter, freq in sorted_counts:
all_letters_count += freq
word_ratings = []
for word in best_words:
ratings_dict = {}
for letter in word:
for freq_letter, freq in sorted_counts:
if letter == freq_letter:
ratings_dict[letter] = freq
total_rating = 0
for letter, rating in ratings_dict.items():
total_rating += rating
word_ratings.append((word, round(total_rating / all_letters_count * 100, 2)))
word_ratings = sorted(word_ratings, key = operator.itemgetter(1), reverse = True)
if show_letters == True:
return word_ratings, sorted_counts
else:
return word_ratings
def count_vows_cons(word: str, y_vow = True):
"""
Given a passed word, calculate the number of non-unique vowels and consonants in the word (duplicates counted more than once).
------
Parameters:
------
`word`: str
a single passed word (str)
`y_vow`: bool
if True, "y" is considered a vowel. If False, "y" considered a consonant. Default is True
------
Returns:
------
`counts`: dict
dictionary, where format is {letter type : count}
"""
word = word.lower() # for consistency
if y_vow == True:
vows = "aeiouy"
cons = "bcdfghjklmnpqrstvwxz"
elif y_vow == False:
vows = "aeiou"
cons = "bcdfghjklmnpqrstvwxyz"
counts = {}
counts["vows"] = 0
counts["cons"] = 0
for letter in word:
if letter in vows:
counts["vows"] += 1
if letter in cons:
counts["cons"] += 1
return counts
def get_word_rating(words_to_rate: list, word_list: list, normalized: bool = True, ascending: bool = False):
"""
Given a word and a word list, calculates rating each word as a measure of its impact to the next possible guesses in Wordle, ordered according to `reverse` parameter.
------
Parameters:
------
`words_to_rate`: list
list of strings to be rated
`word_list`: list
list of all possible words (str) of consistent length, to which each word in `words_to_rate` will be compared
`normalized`: bool
if True, normalizes all ratings on a scale of 0-100, with 100 being the rating for the most optimal word, and 0 for the least optimal word
`ascending`: bool
if True, returns list ordered ascending. If False, returns list in descending order
------
Returns:
------
`word_ratings`: list
list of tuples. Format is [(word, rating)], where rating is calculated according to above formula
`sorted_counts`: list of tuples
list of tuples. Format is ("letter", frequency). Sorted according to `sort` value; ["descending" or "ascending"] if passed
"""
if ascending == True:
# sorted_counts = get_letter_counts(english_alphabet, word_list, sort = "ascending")
sorted_counts = get_letter_counts(word_list = word_list, letters = english_alphabet, sort = "ascending", unique = True)
else:
# sorted_counts = get_letter_counts(english_alphabet, word_list, sort = "descending")
sorted_counts = get_letter_counts(word_list = word_list, letters = english_alphabet, sort = "descending", unique = True)
all_letters_count = 0
for letter, freq in sorted_counts:
all_letters_count += freq
unnormalized_ratings = []
for word in words_to_rate:
word = word.lower()
ratings_dict = {}
for letter in word:
for freq_letter, freq in sorted_counts:
if letter == freq_letter:
ratings_dict[letter] = freq
total_rating = 0
for letter, rating in ratings_dict.items():
total_rating += rating
unnormalized_ratings.append((word, round(total_rating / all_letters_count * 100, 2)))
word_ratings = sorted(unnormalized_ratings, key = operator.itemgetter(1), reverse = True)
# # print (word_ratings)
if normalized == True:
if len(word_ratings) > 1:
new_tests = []
for tup in word_ratings:
try:
normd = round(((tup[1] - word_ratings[-1][1]) / (word_ratings[0][1] - word_ratings[-1][1])) * 100, 2)
new_tests.append((tup[0], normd))
except:
ZeroDivisionError
new_tests.append((tup[0], 0.0))
return new_tests
else:
return [(word_ratings[0][0], float(100))]
elif normalized == False:
return word_ratings
### Gets most common words of all words of the dataset
def get_word_distribution(word_list: list, sort: str = "descending"):
"""
Given a passed str of words and a list of words, produces a frequency distribution of all words
------
Parameters:
------
`word_list`: list
list of words (str) from which word frequencies will be counted
`sort`: str
if either "descending" or "ascending" are passed, returned list of tuples will be sorted accoringly, else returned dictionary will be unsorted
------
Returns:
------
`words_counts_dict`: dict
dictionary of {word : count} pairs for each word in passed `word_list`
`sorted_counts_dicts`: list of tuples
list of tuples. Format is ("word", frequency). Ordered according to `sort` values
"""
words_counts_dict = {}
for word in word_list:
if word in words_counts_dict:
words_counts_dict[word] += 1
else:
words_counts_dict[word] = 1
if sort == "ascending":
sorted_counts_dict = (sorted(words_counts_dict.items(), key = operator.itemgetter(1), reverse = False))
return sorted_counts_dict
if sort == "descending":
sorted_counts_dict = sorted(words_counts_dict.items(), key = operator.itemgetter(1), reverse = True)
return sorted_counts_dict
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## lines 305 - 835
# def wordle_wizard(word_list: list, max_guesses: int = None,
# guess: str = None, target: str = None,
# random_guess: bool = False, random_target: bool = False,
# verbose: bool = False, drama: float = None,
# return_stats: bool = False, record: bool = False):
# """
# Mimicking the popular web game, this function matches a current word to a target word automatically, in the most statistically optimal way possible.
# Parameters:
# ------
# `word_list`: list
# list of valid words to be considered
# `guess`: str
# a string -- must be the same length as `target_word`
# `target`: str
# a string -- must be the same length as `opening_word`
# `max_guesses`: int
# the maximum number of attempts allowed to solve the Wordle
# `random_guess`: bool
# if True, randomly chooses a starting word from all words within `word_list`. If False, passed starting word must be used instead
# `random_target`: bool
# if True, randomly chooses a target word from all words within `word_list`. If False, passed target word must be used instead
# `verbose`: bool
# if True, # prints progress and explanation of how function solves the puzzle. If False, # prints only the guessed word at each guess.
# `drama`: float or int
# if int provided, each guess' output is delayed by that number of seconds, else each output is shown as quickly as possible. For ~dRaMaTiC eFfEcT~
# `return_stats`: bool
# if True, # prints nothing and returns a dictionary of various statistics about the function's performance trying to solve the puzzle
# `record`: bool
# if True, creates a .txt file with the same information # printed according to the indicated verbosity
# Returns:
# ------
# `stats_dict`: dict
# dictionary containing various statistics about the function's performance trying to solve the puzzle
# """
# guess = guess.lower()
# target = target.lower()
# sugg_words = []
# for i in range(0, 20):
# ran_int = random.randint(0, len(word_list) - 1)
# word = word_list[ran_int]
# sugg_words.append(word)
# if guess not in word_list:
# # print ("Guess word not in passed word list.\nOnly words within the given word list are valid.")
# # print (f"Here are some examples of valid words from the passed word list.\n\t{sugg_words[:10]}")
# return None
# if target not in word_list:
# # print ("Target word not in passed word list.\nOnly words within the given word list are valid.")
# # print (f"Here are some examples of valid words from the passed word list.\n\t{sugg_words[-10:]}")
# return None
# if random_guess == True:
# randomint_guess = random.randint(0, len(word_list) - 1)
# guess = word_list[randomint_guess]
# if random_target == True:
# randomint_target = random.randint(0, len(word_list) - 1)
# target = word_list[randomint_target]
# stats_dict = {}
# stats_dict['first_guess'] = guess
# stats_dict['target_word'] = target
# stats_dict['first_guess_vowels'] = float(count_vows_cons(guess, y_vow = True)['vows'])
# stats_dict['first_guess_consonants'] = float(count_vows_cons(guess, y_vow = True)['cons'])
# stats_dict['target_vowels'] = float(count_vows_cons(target, y_vow = True)['vows'])
# stats_dict['target_consonants'] = float(count_vows_cons(target, y_vow = True)['cons'])
# # get rating of the first guess word and target word in the entire word_list
# for tup in get_word_rating(word_list, word_list, normalized = True):
# if tup[0] == guess:
# stats_dict['first_guess_rating'] = tup[1]
# if tup[0] == target:
# stats_dict['target_rating'] = tup[1]
# guess_entropies = []
# guess_entropies.append(stats_dict['first_guess_rating'])
# # luck_guess_1 = round(1 - ((1 / len(word_list)) * guess_entropies[0] / 100), 2) * 100
# english_alphabet = "abcdefghijklmnopqrstuvwxyz"
# # word_list_sorted_counts = get_letter_counts(english_alphabet, word_list, sort = "descending")
# word_list_sorted_counts = get_letter_counts(word_list = word_list, letters = english_alphabet, sort = "descending", unique = True)
# wordlen = len(guess)
# letter_positions = set(i for i in range(0, wordlen))
# guess_set = set()
# perfect_dict = {}
# wrong_pos_dict = {}
# wrong_pos_set = set()
# dont_guess_again = set()
# guessed_words = [] # running set of guessed words
# guess_num = 0 # baseline for variable
# dont_guess_words = set()
# incorrect_positions = []
# reduction_per_guess = []
# if max_guesses == None: # if no value is passed, default is len(guess)
# max_guesses = wordlen
# else: # else it is the value passed
# max_guesses = max_guesses
# perfect_letts_per_guess = []
# wrong_pos_per_guess = []
# wrong_letts_per_guess = []
# while guess: # while there is any guess -- there are conditions to break it at the bottom
# guess_num += 1
# guessed_words.append(guess)
# if drama:
# time.sleep(drama)
# # guess_num += 1 # each time the guess is processed
# if return_stats == False:
# if guess_num == 1:
# print("-----------------------------\n")
# if guess == target:
# stats_dict['target_guessed'] = True
# if return_stats == False:
# if guess_num == 1:
# # print(f"Congratulations! The Wordle has been solved in {guess_num} guess, that's amazingly lucky!")
# print(f"The starting word and target word are the same. Try entering two different words to see how the puzzle can be solved.")
# # print(f"The target word was {target}")
# perfect_letts_per_guess.append(5)
# wrong_pos_per_guess.append(0)
# wrong_letts_per_guess.append(0)
# break
# if return_stats == False:
# print(f"**Guess {guess_num}: '{guess}'**")
# guess_set = set()
# wrong_pos_set = set()
# #### Step 2 -- ALL PERFECT
# for i in letter_positions: # number of letters in each word (current word and target word)
# guess_set.add(guess[i])
# if guess[i] not in perfect_dict:
# perfect_dict[guess[i]] = set()
# if guess[i] not in wrong_pos_dict:
# wrong_pos_dict[guess[i]] = set()
# ### EVALUATE CURRENT GUESS
# if guess[i] == target[i]: # letter == correct and position == correct
# perfect_dict[guess[i]].add(i)
# if (guess[i] != target[i] and guess[i] in target): # letter == correct and position != correct
# wrong_pos_dict[guess[i]].add(i)
# wrong_pos_set.add(guess[i])
# if guess[i] not in target: # if letter is not relevant at all
# dont_guess_again.add(guess[i])
# #### Step 3 -- ALL PERFECT
# next_letters = set()
# for letter, positions in perfect_dict.items():
# if len(positions) > 0:
# next_letters.add(letter)
# for letter, positions in wrong_pos_dict.items():
# if len(positions) > 0:
# next_letters.add(letter)
# #### List of tuples of correct letter positions in new valid words. Eg: [('e', 2), ('a', 3)]
# perfect_letters = []
# for letter, positions in perfect_dict.items():
# for pos in positions:
# if len(positions) > 0:
# perfect_letters.append((letter, pos))
# #### all words that have correct letters in same spots
# words_matching_correct_all = []
# for word in word_list:
# word_set = set()
# for letter, pos in perfect_letters:
# if pos < len(word):
# if word[pos] == letter:
# words_matching_correct_all.append(word)
# #### excluding words with letters in known incorrect positions
# for letter, positions in wrong_pos_dict.items():
# for pos in positions:
# if len(positions) > 0:
# if (letter, pos) not in incorrect_positions:
# incorrect_positions.append((letter, pos))
# # sorting lists of tuples just to make them look nice in the # printout
# incorrect_positions = sorted(incorrect_positions, key = operator.itemgetter(1), reverse = False)
# perfect_letters = sorted(perfect_letters, key = operator.itemgetter(1), reverse = False)
# #### all words that have correct letters in incorrect spots -- so they can be excluded efficiently
# # print(incorrect_positions)
# for word in word_list:
# word_set = set()
# for letter, pos in incorrect_positions:
# if pos < len(word):
# if word[pos] == letter:
# dont_guess_words.add(word)
# for word in word_list:
# word_set = set()
# for letter, pos in incorrect_positions:
# if pos < len(word):
# if word[pos] == letter:
# dont_guess_words.add(word)
# for bad_letter in dont_guess_again:
# for word in word_list:
# if (bad_letter in word and word not in dont_guess_words):
# dont_guess_words.add(word)
# if return_stats == False:
# if verbose == True:
# print(f"Letters in correct positions:\n\t{perfect_letters}\n")
# print(f"Letters in incorrect positions:\n\t{incorrect_positions}\n")
# # print (f"Letters to guess again:\n\t{sorted(list(next_letters), reverse = False)}\n")
# print(f"Letters to not guess again:\n\t{sorted(list(dont_guess_again), reverse = False)}\n") # works
# # Returns True
# # print(A.issubset(B)) # "if everything in A is in B", returns Bool
# perfect_letts_per_guess.append(len(perfect_letters))
# wrong_pos_per_guess.append(len(incorrect_positions))
# wrong_letts_per_guess.append(len(dont_guess_again))
# potential_next_guesses = set()
# middle_set = set()
# if len(perfect_letters) == 0 and len(incorrect_positions) == 0: # if there are NEITHER perfect letters, NOR incorrect positions, ....
# for word in word_list:
# if word not in dont_guess_words:
# if word not in guessed_words:
# potential_next_guesses.add(word)
# # print(f"GUESS {guess_num} : TEST 1-1")
# if len(perfect_letters) == 0 and len(incorrect_positions) != 0: # if there are no perfect letters whatsoever, but there ARE incorrect positions ....
# for word in word_list:
# for incor_letter, incor_pos in incorrect_positions:
# if incor_pos < len(word):
# if word[incor_pos] != incor_letter:
# if word not in dont_guess_words: # just in case
# word_set = set()
# for letter in word:
# word_set.add(letter)
# if next_letters.issubset(word_set):
# if word not in guessed_words:
# if len(dont_guess_again) > 0:
# for bad_letter in dont_guess_again:
# if bad_letter not in word:
# # potential_next_guesses.append(word)
# potential_next_guesses.add(word)
# else:
# potential_next_guesses.add(word)
# # print(f"GUESS {guess_num} : TEST 2-1")
# else:
# for word in word_list:
# if word not in dont_guess_words: # just in case
# word_set = set()
# for letter in word:
# word_set.add(letter)
# if next_letters.issubset(word_set):
# if word not in guessed_words:
# # # print ("TEST 3-2")
# if len(dont_guess_again) > 0:
# for bad_letter in dont_guess_again:
# if bad_letter not in word:
# middle_set.add(word)
# else:
# middle_set.add(word)
# for word in middle_set:
# dummy_list = []
# for good_lett, good_pos in perfect_letters:
# if word[good_pos] == good_lett:
# dummy_list.append(1)
# if len(dummy_list) == len(perfect_letters):
# potential_next_guesses.add(word)
# for word in middle_set:
# dummy_list = []
# for bad_lett, bad_pos in incorrect_positions:
# if bad_pos < len(word):
# if word[bad_pos] == bad_lett:
# dummy_list.append(1)
# if len(dummy_list) > 0:
# potential_next_guesses.remove(word)
# # print(f"GUESS {guess_num} : TEST 3-1")
# if return_stats == False:
# if verbose == True:
# if len(potential_next_guesses) > 1:
# # print(f"At this point:")
# print(f"\t{len(word_list) - len(potential_next_guesses)}, {round((len(word_list) - len(potential_next_guesses)) / len(word_list) * 100, 2)}% of total words have been eliminated, and")
# print(f"\t{len(potential_next_guesses)}, {round(len(potential_next_guesses) / len(word_list) * 100, 2)}% of total words remain possible.\n")
# else:
# # print(f"At this point:")
# print(f"\t{len(word_list) - len(potential_next_guesses)}, {round((len(word_list) - len(potential_next_guesses)) / len(word_list) * 100, 2)}% of total words have been eliminated, and")
# print(f"\t{len(potential_next_guesses)}, {round(len(potential_next_guesses) / len(word_list) * 100, 2)}% of total words remain possible.\n")
# reduction_per_guess.append(len(potential_next_guesses))
# #### Guessing next word
# if len(potential_next_guesses) == 1:
# if return_stats == False:
# if verbose == True:
# print(f"All potential next guesses:\n\t{get_word_rating(words_to_rate = list(potential_next_guesses), word_list = word_list)}\n")
# print(f"Words guessed so far:\n\t{guessed_words}.\n")
# print(f"The only remaining possible word is:\n\t'{list(potential_next_guesses)[0]}'\n")
# guess = list(potential_next_guesses)[0]
# guess_entropies.append(get_word_rating([guess], word_list, normalized = False, ascending = False)[0][1])
# else:
# best_next_guesses = list(potential_next_guesses)
# # # print (best_next_guesses)
# word_ratings = get_word_rating(best_next_guesses, word_list, normalized = False, ascending = False) # "internal" ratings
# # Get max rating of all words
# max_rating = -np.inf
# for word, rating in word_ratings:
# if rating > max_rating:
# max_rating = rating
# # add best rated words (all equally best rating in next guess list) to set
# best_of_the_best_1 = []
# for word, rating in word_ratings:
# if rating == max_rating:
# best_of_the_best_1.append(word)
# # only using top ten most frequent prefixes suffixes to bias. After that it the impact is especially negligible
# test_starts = get_gram_freq(word_list = word_list, letters_length = 1, position = "start", search = None)[:10]
# test_ends = get_gram_freq(word_list = word_list, letters_length = 1, position = "end", search = None)[:10]
# # list of the best words that also have the most frequent starting and ending letters (suffixes and prefixes didn't have an impact)
# best_of_the_best_2 = []
# for start_gram, start_count in test_starts:
# for end_gram, end_count in test_ends:
# for word in best_of_the_best_1:
# if word[:1] == start_gram and word[-1:] == end_gram:
# best_of_the_best_2.append(word)
# if len(best_of_the_best_2) > 0:
# guess = best_of_the_best_2[0]
# else:
# guess = best_of_the_best_1[0] # they're all equally the best of the best possible guesses so just pick the first
# # guess_entropies.append(get_word_rating([guess], word_list, normalized = False, ascending = False)[0][1])
# if return_stats == False:
# if verbose == True:
# if len(word_ratings) <= 40:
# print(f"All potential next guesses:\n\t{word_ratings}\n")
# print(f"Words guessed so far:\n\t{guessed_words}.\n")
# else:
# print(f"The top 40 potential next guesses are:\n\t{word_ratings[:40]}\n")
# print(f"Words guessed so far:\n\t{guessed_words}.\n")
# guess_entropies.append(get_word_rating([guess], word_list, normalized = False, ascending = False)[0][1])
# #### Guess has now been made -- what to do next
# if guess_num == max_guesses: # if at max guesses allowed
# guessed_words.append(guess)
# stats_dict['target_guessed'] = False
# if return_stats == False:
# if verbose == True:
# print("-----------------------------\n")
# print(f"Unfortunately, the Wordle could not be solved in {max_guesses} guesses.\n")
# print(f"The target word was '{target}'.\n")
# print("-----------------------------\n")
# else:
# print(f"\nUnfortunately, Wordle Wizard couldn't solve the puzzle in {max_guesses} guesses. Could you?")
# print(f"The target word was '{target}'.\n")
# break
# else: # if not at max guesses yet allowed
# # stats_dict['target_guessed'] = False
# if return_stats == False:
# if verbose == True:
# print(f"Next guess:\n\t'{guess}'")
# print("\n-----------------------------\n")
# if guess == target:
# guess_num += 1
# guessed_words.append(guess)
# stats_dict['target_guessed'] = True
# if return_stats == False:
# print(f"**Guess {guess_num}: '{guess}'**\n")
# print(f"Wordle Wizard has solved the puzzle in {guess_num} guesses!")
# if max_guesses - guess_num == 1:
# print(f"There was only {max_guesses - guess_num} guess remaining.")
# else:
# print(f"There were still {max_guesses - guess_num} guesses remaining.")
# if return_stats == False:
# # stats_dict['target_guessed'] = True
# print(f"\nThe target word was **'{target}'**.")
# print("\n-----------------------------")
# break
# #### STATS STUFF
# mid_guesses_vows = 0
# mid_guesses_cons = 0
# avg_perf_letters = 0
# avg_wrong_pos_letters = 0
# avg_wrong_letters = 0
# for i, word in enumerate(guessed_words):
# mid_guesses_vows += count_vows_cons(word, y_vow = True)['vows']
# mid_guesses_cons += count_vows_cons(word, y_vow = True)['cons']
# for i in range(0, len(guessed_words) - 1):
# avg_perf_letters += perfect_letts_per_guess[i]
# avg_wrong_pos_letters += wrong_pos_per_guess[i]
# avg_wrong_letters += wrong_letts_per_guess[i]
# stats_dict['mid_guesses_avg_vows'] = float(round(mid_guesses_vows / len(guessed_words), 2))
# stats_dict['mid_guesses_avg_cons'] = float(round(mid_guesses_cons / len(guessed_words), 2))
# stats_dict['avg_perf_letters'] = float(round(np.mean(avg_perf_letters), 2))
# stats_dict['avg_wrong_pos_letters'] = float(round(np.mean(avg_wrong_pos_letters), 2))
# stats_dict['avg_wrong_letters'] = float(round(np.mean(avg_wrong_letters), 2))
# # average number of words remaining after each guess -- the higher this is, the luckier the person got (the lower, the more guesses it took)
# stats_dict['avg_remaining'] = float(round(np.mean(reduction_per_guess), 2))
# # avg rating of each guessed word relative to all other words possible at that moment -- this should consistently be 100 for the algorithm, but will be different for user
# if len(guess_entropies) > 1: # in case of guessing it correctly on the first try
# sum_entropies = 0
# for rating in guess_entropies:
# sum_entropies += rating
# average_rating = float(round(sum_entropies / len(guess_entropies), 2))
# stats_dict['avg_intermediate_guess_rating'] = average_rating
# else:
# stats_dict['avg_intermediate_guess_rating'] = float(100)
# expected_guesses = 3.85
# # guess_num = 3
# # average_rating = 95
# luck = round(1 - ((((guess_num / expected_guesses) * (stats_dict['avg_intermediate_guess_rating'] / 100)) / max_guesses) * 5), 2)
# stats_dict['luck'] = luck
# if record == True:
# if verbose == True:
# with open(f"solutions/{guessed_words[0]}_{target}_wizard_detailed.txt", "w") as fout:
# fout.write(line + "\n") # write each line of list of # printed text to .txt file
# else:
# with open(f"solutions/{guessed_words[0]}_{target}_wizard_summary.txt", "w") as fout:
# fout.write(line + "\n") # write
# # if guess_num <= len(guess):
# if guess_num <= 6:
# stats_dict['valid_success'] = True
# else:
# stats_dict['valid_success'] = False
# stats_dict['num_guesses'] = float(guess_num)
# # if return_stats == True:
# # return stats_dict
def wordle_wizard_cheat(guesses: list, word_list: list, max_guesses: int = None,
target: str = None,
random_guess: bool = False, random_target: bool = False,
verbose: bool = False, drama: float = None,
return_stats: bool = False, record: bool = False):
"""
Mimicking the popular web game, this function matches a current word to a target word automatically, in the most statistically optimal way possible.
Parameters:
------
`word_list`: list
list of valid words to be considered
`guess`: str
a string -- must be the same length as `target_word`
`target`: str
a string -- must be the same length as `opening_word`
`max_guesses`: int
the maximum number of attempts allowed to solve the Wordle
`random_guess`: bool
if True, randomly chooses a starting word from all words within `word_list`. If False, passed starting word must be used instead
`random_target`: bool
if True, randomly chooses a target word from all words within `word_list`. If False, passed target word must be used instead
`verbose`: bool
if True, # st.writes progress and explanation of how function solves the puzzle. If False, # st.writes only the guessed word at each guess.
`drama`: float or int
if int provided, each guess' output is delayed by that number of seconds, else each output is shown as quickly as possible. For ~dRaMaTiC eFfEcT~
`return_stats`: bool
if True, # st.writes nothing and returns a dictionary of various statistics about the function's performance trying to solve the puzzle
`record`: bool
if True, creates a .txt file with the same information # st.writeed according to the indicated verbosity
Returns:
------
`stats_dict`: dict
dictionary containing various statistics about the function's performance trying to solve the puzzle
"""
# guess = guess.lower()
target = target.lower()
if target not in word_list:
word_list.append(target)
sugg_words = []
for i in range(0, 20):
ran_int = random.randint(0, len(word_list) - 1)
word = word_list[ran_int]
sugg_words.append(word)
guess = guesses[0]
stats_dict = {}
stats_dict['first_guess'] = guess
stats_dict['target_word'] = target
stats_dict['first_guess_vowels'] = float(count_vows_cons(guess, y_vow = True)['vows'])
stats_dict['first_guess_consonants'] = float(count_vows_cons(guess, y_vow = True)['cons'])
stats_dict['target_vowels'] = float(count_vows_cons(target, y_vow = True)['vows'])
stats_dict['target_consonants'] = float(count_vows_cons(target, y_vow = True)['cons'])
# get rating of the first guess word and target word in the entire word_list
for tup in get_word_rating(word_list, word_list, normalized = True):
if tup[0] == guess:
stats_dict['first_guess_rating'] = tup[1]
if tup[0] == target:
stats_dict['target_rating'] = tup[1]
guess_entropies = []
guess_entropies.append(stats_dict['first_guess_rating'])
# luck_guess_1 = round(1 - ((1 / len(word_list)) * guess_entropies[0] / 100), 2) * 100
english_alphabet = "abcdefghijklmnopqrstuvwxyz"
# word_list_sorted_counts = get_letter_counts(english_alphabet, word_list, sort = "descending")
word_list_sorted_counts = get_letter_counts(word_list = word_list, letters = english_alphabet, sort = "descending", unique = True)
wordlen = len(guesses[0])
letter_positions = set(i for i in range(0, wordlen))
guess_set = set()
perfect_dict = {}
wrong_pos_dict = {}
wrong_pos_set = set()
dont_guess_again = set()
guessed_words = [] # running set of guessed words
guess_num = 0 # baseline for variable
dont_guess_words = set()
incorrect_positions = []
reduction_per_guess = []
if max_guesses == None: # if no value is passed, default is len(guess)
max_guesses = wordlen
else: # else it is the value passed
max_guesses = max_guesses
perfect_letts_per_guess = []
wrong_pos_per_guess = []
wrong_letts_per_guess = []
# while guess: # while there is any guess -- there are conditions to break it at the bottom
for guess_num, guess in enumerate(guesses):
guess_num += 1
guessed_words.append(guess)
if drama:
time.sleep(drama)
# guess_num += 1 # each time the guess is processed
if return_stats == False:
if guess_num == 1:
st.write("-----------------------------\n")
if guess == target:
stats_dict['target_guessed'] = True
if return_stats == False:
if guess_num == 1:
# st.write(f"Congratulations! The Wordle has been solved in {guess_num} guess, that's amazingly lucky!")
st.write(f"The starting word and target word are the same. Try entering two different words to see how the puzzle can be solved.")
# st.write(f"The target word was {target}")
perfect_letts_per_guess.append(5)
wrong_pos_per_guess.append(0)
wrong_letts_per_guess.append(0)
break
if return_stats == False:
st.write(f"**Guess {guess_num}: '{guess}'**")
guess_set = set()
wrong_pos_set = set()
#### Step 2 -- ALL PERFECT
for i in letter_positions: # number of letters in each word (current word and target word)
guess_set.add(guess[i])
if guess[i] not in perfect_dict:
perfect_dict[guess[i]] = set()
if guess[i] not in wrong_pos_dict:
wrong_pos_dict[guess[i]] = set()
### EVALUATE CURRENT GUESS
if guess[i] == target[i]: # letter == correct and position == correct
perfect_dict[guess[i]].add(i)
if (guess[i] != target[i] and guess[i] in target): # letter == correct and position != correct
wrong_pos_dict[guess[i]].add(i)
wrong_pos_set.add(guess[i])
if guess[i] not in target: # if letter is not relevant at all
dont_guess_again.add(guess[i])
#### Step 3 -- ALL PERFECT
next_letters = set()
for letter, positions in perfect_dict.items():
if len(positions) > 0:
next_letters.add(letter)
for letter, positions in wrong_pos_dict.items():
if len(positions) > 0:
next_letters.add(letter)
#### List of tuples of correct letter positions in new valid words. Eg: [('e', 2), ('a', 3)]
perfect_letters = []
for letter, positions in perfect_dict.items():
for pos in positions:
if len(positions) > 0:
perfect_letters.append((letter, pos))
#### all words that have correct letters in same spots
words_matching_correct_all = []
for word in word_list:
word_set = set()
for letter, pos in perfect_letters:
if pos < len(word):
if word[pos] == letter:
words_matching_correct_all.append(word)
#### excluding words with letters in known incorrect positions
for letter, positions in wrong_pos_dict.items():
for pos in positions:
if len(positions) > 0:
if (letter, pos) not in incorrect_positions:
incorrect_positions.append((letter, pos))
# sorting lists of tuples just to make them look nice in the # st.writeout
incorrect_positions = sorted(incorrect_positions, key = operator.itemgetter(1), reverse = False)
perfect_letters = sorted(perfect_letters, key = operator.itemgetter(1), reverse = False)
#### all words that have correct letters in incorrect spots -- so they can be excluded efficiently
# st.write(incorrect_positions)
for word in word_list:
word_set = set()
for letter, pos in incorrect_positions:
if pos < len(word):
if word[pos] == letter:
dont_guess_words.add(word)
for word in word_list:
word_set = set()
for letter, pos in incorrect_positions:
if pos < len(word):
if word[pos] == letter:
dont_guess_words.add(word)
for bad_letter in dont_guess_again:
for word in word_list:
if (bad_letter in word and word not in dont_guess_words):
dont_guess_words.add(word)
if return_stats == False:
if verbose == True:
st.write(f"Letters in correct positions:\n\t{perfect_letters}\n")
st.write(f"Letters in incorrect positions:\n\t{incorrect_positions}\n")
# st.write (f"Letters to guess again:\n\t{sorted(list(next_letters), reverse = False)}\n")
st.write(f"Letters to not guess again:\n\t{sorted(list(dont_guess_again), reverse = False)}\n") # works
# Returns True
# st.write(A.issubset(B)) # "if everything in A is in B", returns Bool
perfect_letts_per_guess.append(len(perfect_letters))
wrong_pos_per_guess.append(len(incorrect_positions))
wrong_letts_per_guess.append(len(dont_guess_again))
potential_next_guesses = set()
middle_set = set()
if len(perfect_letters) == 0 and len(incorrect_positions) == 0: # if there are NEITHER perfect letters, NOR incorrect positions, ....
for word in word_list:
if word not in dont_guess_words:
if word not in guessed_words:
potential_next_guesses.add(word)
# st.write(f"GUESS {guess_num} : TEST 1-1")
if len(perfect_letters) == 0 and len(incorrect_positions) != 0: # if there are no perfect letters whatsoever, but there ARE incorrect positions ....
for word in word_list:
for incor_letter, incor_pos in incorrect_positions:
if incor_pos < len(word):
if word[incor_pos] != incor_letter:
if word not in dont_guess_words: # just in case
word_set = set()
for letter in word:
word_set.add(letter)
if next_letters.issubset(word_set):
if word not in guessed_words:
if len(dont_guess_again) > 0:
for bad_letter in dont_guess_again:
if bad_letter not in word:
# potential_next_guesses.append(word)
potential_next_guesses.add(word)
else:
potential_next_guesses.add(word)
# st.write(f"GUESS {guess_num} : TEST 2-1")
else:
for word in word_list:
if word not in dont_guess_words: # just in case
word_set = set()
for letter in word:
word_set.add(letter)
if next_letters.issubset(word_set):
if word not in guessed_words:
# # st.write ("TEST 3-2")
if len(dont_guess_again) > 0:
for bad_letter in dont_guess_again:
if bad_letter not in word:
middle_set.add(word)
else:
middle_set.add(word)
for word in middle_set:
dummy_list = []
for good_lett, good_pos in perfect_letters:
if word[good_pos] == good_lett:
dummy_list.append(1)
if len(dummy_list) == len(perfect_letters):
potential_next_guesses.add(word)
for word in middle_set:
dummy_list = []
for bad_lett, bad_pos in incorrect_positions:
if bad_pos < len(word):
if word[bad_pos] == bad_lett:
dummy_list.append(1)
if len(dummy_list) > 0:
potential_next_guesses.remove(word)
# st.write(f"GUESS {guess_num} : TEST 3-1")
if return_stats == False:
if verbose == True:
if len(potential_next_guesses) > 1:
# st.write(f"At this point:")
st.write(f"\t{len(word_list) - len(potential_next_guesses)}, {round((len(word_list) - len(potential_next_guesses)) / len(word_list) * 100, 2)}% of total words have been eliminated, and")
st.write(f"\t{len(potential_next_guesses)}, {round(len(potential_next_guesses) / len(word_list) * 100, 2)}% of total words remain possible.\n")
else:
# st.write(f"At this point:")
st.write(f"\t{len(word_list) - len(potential_next_guesses)}, {round((len(word_list) - len(potential_next_guesses)) / len(word_list) * 100, 2)}% of total words have been eliminated, and")
st.write(f"\t{len(potential_next_guesses)}, {round(len(potential_next_guesses) / len(word_list) * 100, 2)}% of total words remain possible.\n")
reduction_per_guess.append(len(potential_next_guesses))
#### Guessing next word
if len(potential_next_guesses) == 1:
if return_stats == False:
if verbose == True:
st.write(f"All potential next guesses:\n\t{get_word_rating(words_to_rate = list(potential_next_guesses), word_list = word_list)}\n")
st.write(f"Words guessed so far:\n\t{guessed_words}.\n")
st.write(f"The only remaining possible word is:\n\t'{list(potential_next_guesses)[0]}'")
# guess = list(potential_next_guesses)[0]
if guess_num < len(guesses):
guess = guesses[guess_num]
guess_entropies.append(get_word_rating([guess], word_list, normalized = False, ascending = False)[0][1])
else:
best_next_guesses = list(potential_next_guesses)
# # st.write (best_next_guesses)
word_ratings = get_word_rating(best_next_guesses, word_list, normalized = False, ascending = False) # "internal" ratings
# Get max rating of all words
max_rating = -np.inf
for word, rating in word_ratings:
if rating > max_rating:
max_rating = rating
# add best rated words (all equally best rating in next guess list) to set
best_of_the_best_1 = []
for word, rating in word_ratings:
if rating == max_rating:
best_of_the_best_1.append(word)
# only using top ten most frequent prefixes suffixes to bias. After that it the impact is especially negligible
test_starts = get_gram_freq(word_list = word_list, letters_length = 1, position = "start", search = None)[:10]
test_ends = get_gram_freq(word_list = word_list, letters_length = 1, position = "end", search = None)[:10]
# list of the best words that also have the most frequent starting and ending letters (suffixes and prefixes didn't have an impact)
best_of_the_best_2 = []
for start_gram, start_count in test_starts:
for end_gram, end_count in test_ends:
for word in best_of_the_best_1:
if word[:1] == start_gram and word[-1:] == end_gram:
best_of_the_best_2.append(word)
# if len(best_of_the_best_2) > 0:
# guess = best_of_the_best_2[0]
# else:
# guess = best_of_the_best_1[0] # they're all equally the best of the best possible guesses so just pick the first
if guess_num < len(guesses):
guess = guesses[guess_num]
# guess_entropies.append(get_word_rating([guess], word_list, normalized = False, ascending = False)[0][1])
if return_stats == False:
if verbose == True:
if len(word_ratings) <= 40:
st.write(f"All potential next guesses:\n\t{word_ratings}\n")
st.write(f"Words guessed so far:\n\t{guessed_words}.\n")
else:
st.write(f"The top 40 potential next guesses are:\n\t{word_ratings[:40]}\n")
st.write(f"Words guessed so far:\n\t{guessed_words}.\n")
guess_entropies.append(get_word_rating([guess], word_list, normalized = False, ascending = False)[0][1])
#### Guess has now been made -- what to do next
if guess_num == max_guesses: # if at max guesses allowed
guessed_words.append(guess)
stats_dict['target_guessed'] = False
if return_stats == False:
if verbose == True:
st.write("-----------------------------\n")
st.write(f"\nUnfortunately, the puzzle was not solved in {max_guesses} guesses. Better luck next time!")
st.write(f"The target word was '{target}'.\n")
st.write("-----------------------------\n")
else:
st.write(f"\nUnfortunately, the puzzle was not solved in {max_guesses} guesses. Better luck next time!")
st.write(f"The target word was '{target}'.\n")
break
else: # if not at max guesses yet allowed
# stats_dict['target_guessed'] = False
if return_stats == False:
if verbose == True:
if len(potential_next_guesses) > 1:
st.write(f"Recommended next guess:\n\t'{word_ratings[0][0]}'")
# st.write(f"Next guess:\n\t'{guess}'")
st.write("\n-----------------------------\n")
if guess == target:
guess_num += 1
guessed_words.append(guess)
stats_dict['target_guessed'] = True
if return_stats == False:
st.write(f"**Guess {guess_num}: '{guess}'**\n")
st.write(f"You solved the puzzle in {guess_num} guesses!")
if max_guesses - guess_num == 1:
st.write(f"There was only {max_guesses - guess_num} guess remaining.")
else:
st.write(f"There were still {max_guesses - guess_num} guesses remaining.")
if return_stats == False:
# stats_dict['target_guessed'] = True
st.write(f"\nThe target word was **'{target}'**.")
st.write("\n-----------------------------")
break
#### STATS STUFF
mid_guesses_vows = 0
mid_guesses_cons = 0
avg_perf_letters = 0
avg_wrong_pos_letters = 0
avg_wrong_letters = 0
for i, word in enumerate(guessed_words):
mid_guesses_vows += count_vows_cons(word, y_vow = True)['vows']
mid_guesses_cons += count_vows_cons(word, y_vow = True)['cons']
for i in range(0, len(guessed_words) - 1):
avg_perf_letters += perfect_letts_per_guess[i]
avg_wrong_pos_letters += wrong_pos_per_guess[i]
avg_wrong_letters += wrong_letts_per_guess[i]
stats_dict['mid_guesses_avg_vows'] = float(round(mid_guesses_vows / len(guessed_words), 2))
stats_dict['mid_guesses_avg_cons'] = float(round(mid_guesses_cons / len(guessed_words), 2))
stats_dict['avg_perf_letters'] = float(round(np.mean(avg_perf_letters), 2))
stats_dict['avg_wrong_pos_letters'] = float(round(np.mean(avg_wrong_pos_letters), 2))
stats_dict['avg_wrong_letters'] = float(round(np.mean(avg_wrong_letters), 2))
# average number of words remaining after each guess -- the higher this is, the luckier the person got (the lower, the more guesses it took)
stats_dict['avg_remaining'] = float(round(np.mean(reduction_per_guess), 2))
# avg rating of each guessed word relative to all other words possible at that moment -- this should consistently be 100 for the algorithm, but will be different for user
if len(guess_entropies) > 1: # in case of guessing it correctly on the first try
sum_entropies = 0
for rating in guess_entropies:
sum_entropies += rating
average_rating = float(round(sum_entropies / len(guess_entropies), 2))
stats_dict['avg_intermediate_guess_rating'] = average_rating
else:
stats_dict['avg_intermediate_guess_rating'] = float(100)
expected_guesses = 3.85
# guess_num = 3
# average_rating = 95
luck = round(1 - ((((guess_num / expected_guesses) * (stats_dict['avg_intermediate_guess_rating'] / 100)) / max_guesses) * 5), 2)
stats_dict['luck'] = luck
if record == True:
if verbose == True:
with open(f"solutions/{guessed_words[0]}_{target}_wizard_detailed.txt", "w") as fout:
fout.write(line + "\n") # write each line of list of # st.writeed text to .txt file
else:
with open(f"solutions/{guessed_words[0]}_{target}_wizard_summary.txt", "w") as fout:
fout.write(line + "\n") # write
if guess_num <= 6:
stats_dict['valid_success'] = True
else:
stats_dict['valid_success'] = False
stats_dict['num_guesses'] = float(guess_num)
############################################################################################################################################################
############################################################################################################################################################
############################################################################################################################################################
############################################################################################################################################################
def get_gram_freq(word_list: list, letters_length: int = 2, position: bool = "start", search: any = None):
"""
Given a word list, a selected number of letter, a selected word position to start from ("start" or "end"),
and an optional gram to search within the list, this function will get a frequency distribution of all n-grams
from the passed word list and returned a frequency distribution in descending order.
Parameters:
------
`word_list`: list
list of words of the same
`letters_length`: int
number of letters in succession. Size/length of "gram". Must be between 1 and length of words in word list
`position`: bool
Whether to start the gram from the start of the word (like a prefix) or the end of the word (like a suffix)
`search`: str
If != None, string of characters to search for within the generated list. If string not found in list, function will # print an error message.
Returns:
------
`tup`: tuple
If search != None, will return a tuple with the passed search criteria, and its count
`sorted_gram_list`: list
List of tuples in the form of (gram, count) for each combination of the gram size in the pass word_list
"""
gram_freq_dist = {}
for word in word_list:
if position == "start":
gram = word[:letters_length] # first 2 letters
if position == "end":
gram = word[-(letters_length):] # first 2 letters
if gram not in gram_freq_dist:
gram_freq_dist[gram] = 1
else:
gram_freq_dist[gram] += 1
sorted_gram_dist = sorted(gram_freq_dist.items(), key = operator.itemgetter(1), reverse = True)
if search:
nos = []
for tup in sorted_gram_dist:
if tup[0] == search:
return tup
else:
nos.append("not here")
if len(nos) == len(sorted_gram_dist):
print ("Search criteria not found in list. Please enter a gram from within the list.")
else:
return sorted_gram_dist |