Tsetlin Machine for solving contextual bandit problems.

This repository contains the code for the experiment presented in the paper

Raihan Seraj, Jivitesh Sharma, Ole-Christoffer Granmo, " [Tsetlin Machine for Solving Contextual Bandit Problems] ", 2022

Installation

To install the dependencies of the code in a virtual environment run the setup script.

bash setup.sh

Usage

Activate the virtual environment installed using the following command:

source python-vms/cb_tm/bin/activate

To run the experiment using default configuration for Iris dataset use the following command

python src/main.py --dataset_name Iris --default_configuration True

The program accepts the following command line arguments:

Option	Description
--dataset_name	The name of the dataset. Possible choices are Iris, Breast_Cancer, Noisy_XOR, Simulated Article, Movielens, Adult, Statlog_Shuttle, MNist, Covertype
--default_configuration	Whether to use the default configuration, the value should be True or False
--max_bits	The maximum number of bits per feature to be used during binarization
--num_clauses	The number of clauses to be used for the Tsetlin Machine learner.
--T	The T parameter for the Tsetlin Machine learner.
--s	The s parameter for the Tsetlin Machine learner.
num_state_bits	The number of state bits to be used for the Tsetlin Machine learner.
--interpretability	Whether to perform interpretability for the Tsetlin Machine learner. If the value is set to True, boolean expressions for the decisions are saved in a .txtfile.
--drop_clause_p	The probability of dropping a clause at random for the Tsetlin Machine learner.
--num_runs	The number of independent runs of the experiment to be performed.
--num_rounds	The number of rounds or simulation steps for each run.
--exploration_probs	The probability that a random action will be chosen for epsilon greedy algorithms.
--use_cuda	Whether to use cuda for the TM learner.
--results_dir	The directory name where the results should be stored. The default value is Results/

Abalation for Binarization

To perform an abalation study for different levels of binarization use the following code

python src/abalation_binarization.py

The script abalation_binarization.py can be edited to run the abalation study for different datasets and different binarization values.