add letter_guess symlink, add model loading into ai.py

eric_wordle/ai.py

@@ -3,9 +3,25 @@ import string
 
 import numpy as np
 
+from stable_baselines3 import PPO, DQN
+from letter_guess import LetterGuessingEnv
+
+def load_valid_words(file_path='wordle_words.txt'):
+    """
+    Load valid five-letter words from a specified text file.
+
+    Parameters:
+    - file_path (str): The path to the text file containing valid words.
+
+    Returns:
+    - list[str]: A list of valid words loaded from the file.
+    """
+    with open(file_path, 'r') as file:
+        valid_words = [line.strip() for line in file if len(line.strip()) == 5]
+    return valid_words
 
 class AI:
-    def __init__(self, vocab_file, num_letters=5, num_guesses=6):
+    def __init__(self, vocab_file, model_file, num_letters=5, num_guesses=6, use_q_model = False):
         self.vocab_file = vocab_file
         self.num_letters = num_letters
         self.num_guesses = 6
@@ -16,16 +32,32 @@ class AI:
         self.domains = None
         self.possible_letters = None
 
+        self.use_q_model = use_q_model
+        if use_q_model:
+            # we initialize the same q env as the model train ONLY to simplify storing/calculating the gym state, not used to control the game at all
+            self.q_env = LetterGuessingEnv(vocab_file)
+            # load model
+            self.q_model = PPO.load(model_file)
+            self.q_env_state = None
+
         self.reset()
         
     def solve_eval(self, results_callback):
         num_guesses = 0
         while [len(e) for e in self.domains] != [1 for _ in range(self.num_letters)]:
             num_guesses += 1
+            # sample a word, this would use the q_env_state if the q_model is used
             word = self.sample()
+            # get emulated results
             results = results_callback(word)
+            if self.use_q_model:
+                # step the q_env to match the guess we just made
+                for i in range(len(word)):
+                    char = word[i]
+                    action = ord(char) - ord('a')
+                    self.q_env_state, _, _, _, _ = self.q_env.step(action)
+
             self.arc_consistency(word, results)
-            # print(num_guesses, word, results)
         return num_guesses, word
     
     def solve(self):
@@ -58,8 +90,6 @@ class AI:
                     results.append(result)
                     break
 
-            print(results)
-
             self.arc_consistency(word, results)
 
         print(f'You did it! The word is {"".join([e[0] for e in self.domains])}')
@@ -87,6 +117,9 @@ class AI:
         self.domains = [list(string.ascii_lowercase) for _ in range(self.num_letters)]
         self.possible_letters = []
 
+        if self.use_q_model:
+            self.q_env_state, _ = self.q_env.reset()
+
     def sample(self):
         """
         Samples a best word given the current domains

eric_wordle/eval.py

@@ -1,6 +1,7 @@
 import argparse
 from ai import AI
 import numpy as np
+from tqdm import tqdm
 
 global solution
 
@@ -27,12 +28,13 @@ def main(args):
     if args.n is None:
         raise Exception('Need to specify n (i.e. n = 1 for wordle, n = 4 for quordle, n = 16 for sedecordle).')
 
-    ai = AI(args.vocab_file)
+    ai = AI(args.vocab_file, args.model_file, use_q_model=args.q_model)
 
     total_guesses = 0
+    wins = 0
     num_eval = args.num_eval
 
-    for i in range(num_eval):
+    for i in tqdm(range(num_eval)):
         idx = np.random.choice(range(len(ai.vocab)))
         solution = ai.vocab[idx]
         guesses, word = ai.solve_eval(results_callback=result_callback)
@@ -40,14 +42,17 @@ def main(args):
             total_guesses += 5
         else:
             total_guesses += guesses
+            wins += 1
         ai.reset()
 
-    print(total_guesses / num_eval)
+    print(f"q_model?: {args.q_model} \t average guesses per game: {total_guesses / num_eval} \t win rate: {wins / num_eval}")
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--n', dest='n', type=int, default=None)
     parser.add_argument('--vocab_file', dest='vocab_file', type=str, default='wordle_words.txt')
     parser.add_argument('--num_eval', dest="num_eval", type=int, default=1000)
+    parser.add_argument('--model_file', dest="model_file", type=str, default='wordle_ppo_model')
+    parser.add_argument('--q_model', dest="q_model", type=bool, default=False)
     args = parser.parse_args()
     main(args)

eric_wordle/letter_guess.py

@@ -0,0 +1 @@
+../letter_guess.py

eval.sh

@@ -1 +1,2 @@
-python eric_wordle/eval.py --n 1 --vocab_file wordle_words.txt  --num_eval 1000
+python eric_wordle/eval.py --n 1 --vocab_file wordle_words.txt  --num_eval 5000
+python eric_wordle/eval.py --n 1 --vocab_file wordle_words.txt  --num_eval 5000 --q_model True --model_file wordle_ppo_model