Paper

Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descent

Setup

1. Install necessary packages

Create a new conda environment named dgt_env with python==3.6.8, pytorch==1.7.0 and install all dependencies inside:

$ conda env create -f dgt_env.yml
$ conda activate dgt_env

2. Change working directory to src:

$ cd src

3. Run the algorithm

To reproduce some of our results, please run bash run.sh.

• The script by default runs our algorithm with height 6 on ailerons. Commands for abalone, satimage, and pendigits are commented out.
• To change height of the tree learnt, change the argument corresponding to --height flag.
• The --proc_per_gpu option denotes how many processes to run per GPU. It defaults to 4 which is ideal for a typical GPU but on a GPU with small memory, reducing it from 4 might be required.
• The --num_gpu option denotes how many GPUs to parallelize over (and assumes device ordinal of GPUs start with 0). It defaults to 1.

Note: For abalone dataset we report the final performance across 5 different shuffles.

4. Check Results

Final scores, i.e. mean test RMSE/Accuracy and standard deviation, can be found in the file ./out/exp@{dataset}_{height}@{start_time}/meanstd-exps/meanstd-run-summary.csv under the columns test_acc_mean and test_acc_std.

Other Contributors

Naman Jain