<jupyter_start><jupyter_text>Exhaustive Search and the Theoretical MinimumDoddle is primarily a heuristic solver. However, it is possible to find a theoretically optimal tree via an exhaustive recursive search. To the best of my knowledge, [<NAME>](http://sonorouschocolate.com/notes/index.php?title=The_best_strategies_for_Wordle) was the first to prove that `SALET` is capable of generating the optimal decision tree with a total of 7,920 guesses to solve the 2,315 problems from the original Wordle dataset. Here we show how to derive such a tree using Doddle. We will save the results to PDF so we can marvel at its beauty.<jupyter_code>from doddle import Doddle

seed = "SALET"
doddle = Doddle()
benchmark = doddle.tree_search(seed)
benchmark
graph = benchmark.digraph()
graph.render(format="pdf", outfile=f"Doddle-{seed}.pdf")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.07424593967517401, 'fraction_numerical': 0.009280742459396751, 'mean_word_length': 5.959677419354839, 'pattern_counts': {'":': 0, '<': 6, '<?xml version=': 0, '>': 6, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '606284', 'license': 'MIT', 'n_tokens_mistral': 272, 'n_tokens_neox': 253, 'n_words': 103, 'path': 'tutorial/03 Exhaustive Search.ipynb', 'repo_name': 'CatchemAL/Doddle', 'size': '866'}