Setup

It is recommended to develop using a pipenv.

A development environment can be set up automatically:

$ pipenv install --dev --pre

You can work directly inside the development environment:

$ pipenv shell

Use

Save BotTorch_Data-Training.csv to your working directory.

To generate a model file:

$ bottorch tune --samples BotTorch_Data-Training.csv

This will generate a model.pth file.

To make a prediction after a model has been created (model.pth is used by default):

$ bottorch predict Icewave Chomp --samples BotTorch_Data-Training.csv

To make a ranking of all competitors, assuming competitors are saved in competitors.csv with one competitor name per line:

$ bottorch rank --samples BotTorch_Data-Training.csv --competitors competitors.csv

Assuming the competitors file is in order of first seed to last seed, you can simulate a single elimination bracket:

$ bottorch bracket --samples BotTorch_Data-Training.csv --competitors competitors.csv

Limited "advanced" tuning parameters are available. --l1 and --l2 specify the sizes of the first two layers of the neural network. Their "ideal" size can be determined with hypertuning. Hypertuning also uses a configurable --step_size which determines the step size used for parameter optimization, larger step sizes are faster. There is also --epochs which can be used for number of tuning epochs run at each step. More epochs takes more time. For example:

$ bottorch hypertune --step_size 1000 --epochs 5 --samples BotTorch_Data-Training.csv

Will print "best" l1 and l2 parameters, for example Done! Best accuracy 58.7%, L1: 2032, L2: 1032. To generate a model file (model.pth) using the parameters:

$ bottorch tune --l1 2032 --l2 1032 --epochs 1000 --samples BotTorch_Data-Training.csv