Metadata-Version: 2.1
Name: doddle
Version: 1.0.0
Summary: doddle: Make Wordle A Doddle
Home-page: https://github.com/CatchemAl/Doddle
Author: Alex Cross
Author-email: AlexJCross90@gmail.com
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/CatchemAl/Doddle/issues
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numba
Requires-Dist: numpy
Requires-Dist: colorama
Provides-Extra: dev
Requires-Dist: black ; extra == 'dev'
Requires-Dist: flake8 ; extra == 'dev'
Requires-Dist: isort ; extra == 'dev'
Requires-Dist: mypy ; extra == 'dev'
Requires-Dist: pytest-cov ; extra == 'dev'
Requires-Dist: tox ; extra == 'dev'

<img src="https://i.imgur.com/eLnkxkp.png" width="420">


## Features
- **Run** the solver to see how the game is optimally played
- **Solve** a game in realtime using Doddle's solver
- Play a variation of the game where the solver attempts to **hide** the answer from you for as long as possible (inspired by [Absurdle](https://qntm.org/files/absurdle/absurdle.html))
- Play using words of length 4-9 (inclusive) by adding the optional `--size` argument (default is 5).
- Choose your solver using the `--solver=ENTROPY` `--solver=MINIMAX` argument (default is minimax)
- Run deep searches using the depth argument (default is 1)
- Doddle can solve multiple games of Wordle at the same time. This mode is inspired by popular spin-offs such as [Dordle](https://zaratustra.itch.io/dordle), [Quordle](https://www.quordle.com/#/) and [Octordle](https://octordle.com/). Playing multiple games with Doddle is easy: just add more solutions to the run `doddle run --answer=ULTRA,QUICK,SOLVE` and Doddle will solve them all at the same time.

## Install
![example workflow](https://github.com/CatchemAl/Doddle/actions/workflows/python-app.yml/badge.svg)

`pip install doddle`

## Commands
### Run

```ruby
doddle run --answer=SALTY
doddle run --answer=SPEED --guess=SOLVE
doddle run --answer=FABULOUS --guess=SOLUTION --solver=ENTROPY
doddle run --answer=DEEP --guess=MIND --solver=ENTROPY --depth=2
```

Run a simulation with an answer of your choosing to see how Doddle solves the problem. You can optionally provide your own starting `guess` to see how the game plays out. With every guess, Doddle acquires more information and prunes the list of possible solutions. The output (far right column) shows you how many possible solutions still exist at each step of the solve.

Doddle provides multiple ways to solve the game. You can choose between a **minimax** `solver` (the default), or an **entropy** reduction approach. An entropy based approach optimises for best average play; a minimax approach tries to avoid worst-case scenarios and never goes above 5 moves.

Doddle supports deep searches for all of its solvers 🧠🧠🧠. These are slower, more exhaustive searches for those that want to go deeper. By default, Doddle plays the move that will yield the 'best' outcome on its next turn. In the case of minimax, that means playing the move that will result in the fewest number of possible words to search in the worst-case scenario (it choose a word that **mini**mises the outcome that provides **max**imum uncertainty). In the case of `--solver=ENTROPY`, it plays the move that results in the greatest expected reduction in [Shannon entropy](https://en.wikipedia.org/wiki/Entropy_(information_theory)). With deep searches, it thinks `n` steps ahead. So for `--depth=2`, the minimax solver plays the word that minimses the number of possible solutions in the worst case scenario on the turn after next taking into consideration ***all*** sensible first moves. The performance is still decent - a few seconds per game - but it is noticably slower as the search space explodes exponentially with depth.

<img src="https://i.imgur.com/Al2ucap.png" width="400">

### Solve

```ruby
doddle solve
doddle solve --size=7
doddle solve --guess=RAISE
doddle solve --guess=MIND --solver=ENTROPY
doddle solve --guess=MIND --solver=MINIMAX --depth=2
```

Work smarter not harder. Use Doddle's solver to solve Wordle as fast as possible. If you're playing Wordle and need some... ahem... *divine inspiration*, fire up Doddle's solver. Doddle will give you the optimal word to use. Type the response back into Wordle to generate the next guess. Doddle represents answers as ternary numbers! Doddle uses `2` for exact matches, `1` for partial matches and `0` for unmatched letters (e.g. `10202`).

```
>>> doddle solve --guess=TORCH
Enter score for TORCH:
>>> 10000

The best guess is SNAIL
Enter score for SNAIL:
>>> 20101

The best guess is SALTY
Enter score for SALTY:
>>> 22222

Great success! ✨ 🔮 ✨
```

Half way through a game and just want to know the next best move? Doddle's got you covered. With `solve`, you're not forced to play the move Doddle suggests. Doddle accepts two syntaxes when entering a score: 1) just a score (as above e.g. `20101`); or 2) a `WORD=SCORE` pair (`SNAKE=20100`). If you're half way through and need the best move, fill in what you've done so far using the second input method and let Doddle take over from there.


```
The best guess is SNAIL
Enter score for SNAIL:
>>> SNAKE=20100

The best guess is SALTY
...
```

### Hide
```ruby
doddle hide --guess=SALTY
```
Hide is a spin on the conventional Wordle game. Here, Doddle uses its solver to hide the final answer for as long as possible. Doddle doesn't choose an answer before the game starts - instead it always presents you with the score that results in maximum ambiguity. You'll get there in the end, but the game might take a while. 😈

<img src="https://i.imgur.com/JrMj21n.png" width="350">

Similar to the original Wordle game, a keyboard is rendered to display what characters have been guessed so far.

## Algorithm
Doddle offers two choices of algorithms for solving Wordle: Minimax and Entropy.

### Minimax
By default, Doddle uses a [minimax](https://en.wikipedia.org/wiki/Minimax) algorithm to solve the game. The easiest way to explain the algorithm is through example. 

Suppose you are half way through a game and have narrowed the solution down to one of four possibilties: `SKILL`, `SPILL`, `SWILL`, `STILL`.

Clearly, if we work our way through these words sequentially, the worst case scenario will be a further four guesses. To make things precise, let's create a histogram of all the scores that Wordle could return for each guess. We will consider the case where we naïvely choose the word `SKILL`.

| Guess   | Score        | Partition Size | Possible Words               |
|---------|--------------|----------------|------------------------------|
| `SKILL` | 🟩🟨🟩🟩🟩 |             3 | { `SPILL`, `SWILL`, `STILL` } |
| `SKILL` | 🟩🟩🟩🟩🟩 |             1 | { `SKILL` }                   |

The histogram is a great way to see how any guess **partitions** the remaining words. In the case above, there are two partitions with the worst case scenario being three (because three is the size of the largest partition).

Minimax works by considering all possible words in the dictionary and choosing the word that minimises the size of its largest partition. So, in searching through all possible words, minimax would stumble upon a word like 💥 `KAPOW` 💥.

| Guess   | Score        | Partition Size | Possible Words      |
|---------|--------------|----------------|---------------------|
| `KAPOW` | ⬜⬜⬜⬜⬜ |             1 | { `STILL` }         |
| `KAPOW` | 🟨⬜⬜⬜⬜ |             1 | { `SKILL` }         |
| `KAPOW` | ⬜⬜🟨⬜⬜ |             1 | { `SPILL` }         |
| `KAPOW` | ⬜⬜⬜⬜🟨 |             1 | { `SWILL` }         |

In this case, each word is partitioned perfectly into its own bucket of length one and the game can be immediately solved on the next move. It's simple enough to compute this histogram for every possible word and the approach generalises all the way through the game.

### Entropy
As an alternative to minimax, it is possible to play the game using an entropy based approach. Here, the solver always chooses the word that, on average, lowers the Shannon entropy of the game. To see how this works, let's assume we have reduced the game down to 20 possible words and decide to play the (excellent) move `THURL`. We shall construct a histogram as before - they're very useful.


| Guess   | Score        | Partition Size | Probability | Possible Words                                 |
|---------|--------------|----------------|-------------|------------------------------------------------|
| `THURL` | ⬜⬜⬜⬜⬜ |             3 |        0.15 | { `SNAKE`, `SPACE`, `SPADE` }                   |
| `THURL` | ⬜⬜⬜⬜🟨 |             1 |        0.05 | { `SCALE` }                                     |
| `THURL` | ⬜⬜⬜🟩⬜ |             3 |        0.15 | { `SCARE`, `SNARE`, `SPARE` }                   |
| `THURL` | ⬜🟩⬜⬜⬜ |             5 |        0.25 | { `SHADE`, `SHAKE`, `SHAME`, `SHAPE`, `SHAVE` } |
| `THURL` | ⬜🟩⬜⬜🟨 |             1 |        0.05 | { `SHALE` }                                     |
| `THURL` | ⬜🟩⬜🟩⬜ |             2 |        0.10 | { `SHARE`, `SHARK` }                            |
| `THURL` | 🟨⬜⬜⬜⬜ |             3 |        0.15 | { `SKATE`, `STAGE`, `STAGE` }                   |
| `THURL` | 🟨⬜⬜⬜🟨 |             2 |        0.10 | { `SLATE`, `STALE` }                            |

Under minimax, we would simply look at the largest bucket and assign a score of 5 to the word `THURL`. However, with an entropy based approach, we take into consideration how much each guess cuts down the entire problem *on average*. To do that, we need to look at all possible outcomes in the histogram and calculate the expected value of the number of bits of entropy that each guess provides. 

The probability of any outcome is calculated simply as the **Partition Size** / **Total Number of Words**. The number of bits associated with any outcome is calculates as -log(probability, base=2) and, hence, the expected number of bits is simply the sum of the bits multiplied by their respective probabilities.

In the example above, the expected number of Shannon bits is 2.83 which tells us that the guess `THURL` roughly cuts the problem size in half 2.83 times. To be explicit, cutting the problem in half once would leave 10 words left to search on average. Cutting the problem in half twice would leave 5. And cutting the problem in half 2.83 times would leave 2.82 words on average which looks eminently sensible when we look at the partition sizes remaining.

The guess with the highest information content, as measured in Shannon bits, is picked. In this case, `THURL` is pretty optimal.


## Simultaneous Play
Documentation on playing mutliple games at once coming soon!

## Coming soon
- An `explain` feature to show what the solver is thinking
- A facade layer - for those who want to call a simple API in their code
- Best pair mode - Find the best pair of moves for those who like to take it easy
- Rate my guess - see how your guess compares against the optimal move
- Proper setup of CI/CD and containerisation to check deployment on UNIX based systems
- More algorithms for solving (DNNs, RL, MIPs, decision trees!)
- Post match analysis
- Documentation
- More views! For snazzy reporting. e.g.:

Wordle 242 3/6

⬜🟩⬜⬜⬜

⬜🟨⬜⬜⬜

🟩🟩🟩🟩🟩



