Neural Oblivious Decision Ensembles

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Deep Learningnode
Overview

Neural Oblivious Decision Ensembles

A supplementary code for anonymous ICLR 2020 submission.

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?

  1. Clone or download this repo. cd yourself to it's root directory.
  2. Grab or build a working python enviromnent. Anaconda works fine.
  3. Install packages from requirements.txt
  • 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
  1. Run jupyter notebook and open a notebook in ./notebooks/
  • 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.

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