Unscrabbler

Fun project, trying to find the best implementation for creating a scrabble playing bot!

In its current state I think Unscrabbler will tell you the highest scoring go possible - whether this is strategically the best move is another challenge!

To play

• Open up play.ipynb.
• Coords are in form (row, col).
• Blanks in your hand are represented by ?, blanks on the board are denoted by lowercase all other letters should be UPPERCASE, and an empty square is filled with an empty string i.e. ''.

Some programming lessons I learnt

1. Planning stuff on paper is good.
2. Pandas is great, but iloc is really slow compared to slicing a numpy array. If you have homogeneous data with RangeIndexs the trade off might not be worth it.
3. Tree data structures are rad! You can write something faster than 'word' in all_words.
4. Using functools.lru_cache on a generator is not smart.
5. Annoying indexing problems can often be circumvented by using iteration and slicing instead.

Some lessons on implementation

My first angle of attack on this project was to generate regex's for each row programmatically. Generating the regex's was slow and running them against the dictionary was too.

I decided to try the tree structure seen in word_search.py, not expecting it to be better than regex... it was.

I then came up with a super smart algorithm that split lines up into chunks that you could play in, then ran this through my tree. This worked pretty well.

However this wouldn't spot 'secondary words' e.g. the word ON when playing NEW:

BAMBOO
     NEW

Where ON is a word that's also formed when you play NEW.

At this stage I realised that for a go to be valid, it just needs at least 1 'secondary word' and that word needs to be in the dictionary.