This package provides an Worldle solver with SQL backend.
# Install this library via PyPI
pip install wordleaisql
# Then run the executable that comes with the library
wordleai-sql
# Alternatively, clone this repository and run without pip-install
python wordleai-sql.py$ wordleai-sql
Hi, this is Wordle AI (SQLite backend, approx).
12947 remaining candidates: ['cigar', 'rebut', 'sissy', 'humph', 'awake', 'blush', 'focal', 'evade', 'naval', 'serve', '...']
Type:
'[s]uggest <criterion>' to let AI suggest a word (<criterion> is optional)
'[u]pdate <word> <result>' to provide new information
'[e]xit' to finish the session
where
<criterion> is either 'max_n', 'mean_n', or 'mean_entropy'
<result> is a string of 0 (no match), 1 (partial match), and 2 (exact match)
> s
[INFO] Start AI evaluation (2022-03-09 00:37:13)
[INFO] End AI evaluation (2022-03-09 00:37:18, elapsed: 0:00:04.153101)
* Top 20 candidates ordered by mean_entropy
--------------------------------------------------------------------
input_word max_n mean_n mean_entropy is_candidate
--------------------------------------------------------------------
reais 30 12.0 3.094 1
laers 33 13.5 3.218 1
aeons 35 14.2 3.312 1
races 32 14.4 3.323 1
leads 34 15.1 3.349 1
strae 33 14.8 3.376 1
lines 43 16.4 3.386 1
soral 35 15.6 3.427 1
cries 48 17.4 3.429 1
scrae 34 16.2 3.471 1
rules 42 17.2 3.478 1
oared 41 17.9 3.511 1
losen 52 17.9 3.515 1
sedan 40 17.6 3.516 1
sured 52 19.1 3.546 1
artis 45 19.4 3.547 1
least 42 18.7 3.549 1
stire 46 18.5 3.552 1
stria 49 19.3 3.556 1
nails 55 18.7 3.557 1
--------------------------------------------------------------------
12947 remaining candidates: ['cigar', 'rebut', 'sissy', 'humph', 'awake', 'blush', 'focal', 'evade', 'naval', 'serve', '...']
Type:
'[s]uggest <criterion>' to let AI suggest a word (<criterion> is optional)
'[u]pdate <word> <result>' to provide new information
'[e]xit' to finish the session
where
<criterion> is either 'max_n', 'mean_n', or 'mean_entropy'
<result> is a string of 0 (no match), 1 (partial match), and 2 (exact match)
> u races 00000
896 remaining candidates: ['humph', 'outdo', 'digit', 'pound', 'booby', 'loopy', 'lying', 'moult', 'guild', 'thumb', '...']
Type:
'[s]uggest <criterion>' to let AI suggest a word (<criterion> is optional)
'[u]pdate <word> <result>' to provide new information
'[e]xit' to finish the session
where
<criterion> is either 'max_n', 'mean_n', or 'mean_entropy'
<result> is a string of 0 (no match), 1 (partial match), and 2 (exact match)
> s
[INFO] Start AI evaluation (2022-03-09 00:37:35)
[INFO] End AI evaluation (2022-03-09 00:37:39, elapsed: 0:00:03.439437)
* Top 20 candidates ordered by mean_entropy
--------------------------------------------------------------------
input_word max_n mean_n mean_entropy is_candidate
--------------------------------------------------------------------
monty 41 16.8 3.454 1
gipon 66 20.5 3.546 1
lofty 53 20.0 3.686 1
bilgy 70 24.2 3.746 1
bundt 69 23.2 3.779 1
limbo 69 23.6 3.780 1
bundy 63 23.5 3.782 1
found 56 23.7 3.816 1
youth 50 22.6 3.827 1
joint 65 23.9 3.895 1
downy 61 25.5 3.902 1
milko 78 27.5 3.924 1
fungo 86 29.6 3.926 1
lumbi 77 29.1 3.976 1
tupik 68 28.0 3.981 1
goopy 76 28.3 4.012 1
jolty 59 24.3 4.015 1
muhly 65 28.1 4.034 1
nouny 59 25.0 4.041 1
touzy 49 25.0 4.066 1
--------------------------------------------------------------------
896 remaining candidates: ['humph', 'outdo', 'digit', 'pound', 'booby', 'loopy', 'lying', 'moult', 'guild', 'thumb', '...']
Type:
'[s]uggest <criterion>' to let AI suggest a word (<criterion> is optional)
'[u]pdate <word> <result>' to provide new information
'[e]xit' to finish the session
where
<criterion> is either 'max_n', 'mean_n', or 'mean_entropy'
<result> is a string of 0 (no match), 1 (partial match), and 2 (exact match)
> u monty 22220
'month' should be the answer!
Thank you!Input words are evaludated by the three criteria as follows:
- "max_n": Maximum number of the candidate words that would remain.
- "mean_n": Average number of the candidate words that would remain.
- "mean_entropy": Average of the log2 of number of the candidate words that would remain.
Note that if there are n candidate words with the equal probability, then probability of each word i is p_i = 1/n.
Then, the entropy is given by -sum(p_i log2(p_i)) = - n * (1/n) log2(1/n) = log2(n).
Hence, the average of log2(n) can be seen as the average entropy.
"mean_entropy" is often used in practice and thus set as the default choice of the program. "max_n" can be seen as a pessimistic criterion since it reacts to the worst case. "mean_n" can seem an intutive criterion but does not work as well as "mean_entropy" perhaps due to the skewed distribution.
See also the simulation results for a comparison of the criteria (notebook at simulation/simulation-summary.ipynb or view on nbviewer).
- By default,
wordleai-sqlcommand starts an interactive solver session. wordleai-sql --playstarts a self-play game.wordleai-sql --challengestarts a competition against an AI.- In the play and challenge mode, the answer word is chosen in accordance with the answer weight by default. One can set
--no_answer_weightoption to make all words potentially become an answer word.
- The default word list is at wordleaisql/vocab/wordle-vocab.txt. The list perhaps is compatible with the original at New York Times version.
- One may use a custom word list by specifying
--vocabfile=<file path>option.- A file should contain words of the same length, separated by the line break ("\n").
- Each line may contain a nonnegative numeric value separated by a space, which is used as the relative probability that this word is chosen as the answer (in play and challenge mode). If not supecified, the word is given the weight one.
- A file can be gzip compressed, where the filename must end with ".gz".
- Although not tested thoroughly, the program would work with words containing multibyte characters (with utf8 encoding) or digits.
- By default, the file name without extension is used as the
vocabname. One may change this by--vocabname. - See
vocab-examples/folder for some examples.
# Example
wordleai-sql --vocabname myvocab --vocabfile my-vocab.txtwordleai-sql -b approx- With
-b approxoption, we employ approximate evaluation of words by sampling input and/or answer words. - The database setup completes quikckly since this does not require precompuation of the judge results.
- Evaluation also completes quickly since small numbers of input and/or answer words are involved in the calculation.
- Although approximate, the engine tends to provide close-to-optimal suggestions thanks to the law of large numbers.
wordleai-sql -b sqlite- This engine evaluates all words using the all answer candidates.
- To enhance the calculation the engine precomputes all judge results for all word pairs on the setup.
- The file size becomes about 8.4GB.
- The process may take about an hour, depending on the CPU speed.
- The time for the setup will be significantly reduced if c++ compiler command (e.g
g++orclang++) is available.
# --vocabname is used as the dataset name
wordleai-sql -bbq --bq_credential "gcp_credentials.json" --vocabname "wordle_dataset"- With
-bbqoption, we employ google bigquery as the backend SQL engine. - We need to supply a credential json file of the GCP service account with the following permissions:
bigquery.datasets.create bigquery.datasets.get bigquery.jobs.create bigquery.jobs.get bigquery.routines.create bigquery.routines.delete bigquery.routines.get bigquery.routines.update bigquery.tables.create bigquery.tables.delete bigquery.tables.get bigquery.tables.getData bigquery.tables.list bigquery.tables.update bigquery.tables.updateData
See wordleai-sql -h for other options, which should mostly be self-explanatory.
- A browser application built on streamlit is at streamlit/app.py. This can be run with the following command:
# install dependencies if not pip install pandas streamlit streamlit run ./streamlit/app.py - The app is also deployed on the streamlit cloud.