Neural Oblivious Decision Ensembles
A supplementary code for Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data paper.
What does it do?
It learns deep ensembles of oblivious differentiable decision trees on tabular data
What do i need to run it?
- A machine with some CPU (preferably 2+ free cores) and GPU(s)
- Running without GPU is possible but takes 8-10x as long even on high-end CPUs
- Our implementation is memory inefficient and may require a lot of GPU memory to converge
- Some popular Linux x64 distribution
- Tested on Ubuntu16.04, should work fine on any popular linux64 and even MacOS;
- Windows and x32 systems may require heavy wizardry to run;
- When in doubt, use Docker, preferably GPU-enabled (i.e. nvidia-docker)
How do I run it?
- Clone or download this repo.
cdyourself to it's root directory.
- Grab or build a working python enviromnent. Anaconda works fine.
- Install packages from
- It is critical that you use torch >= 1.1, not 1.0 or earlier
- You will also need jupyter or some other way to work with .ipynb files
- Run jupyter notebook and open a notebook in
- Before you run the first cell, change
%env CUDA_VISIBLE_DEVICES=#to an index that you plan to use.
- The notebook downloads data from dropbox. You will need 1-5Gb of disk space depending on dataset.
We showcase two typical learning scenarios for classification and regression. Please consult the original paper for training details.