Neural Ensemble Trees

This repository is improved from neural random forest. Thanks the original author a lot!

Relevant paper:

Neural Random Forests.

Based on it, we add LightGBM model in the framework and impove the performance a lot.

Requirements

This code is based on python3 and uses tensorflow 1.3.0.

First, let's make sure you have all packages needed:

pip3 install -r requirements.txt

Notice that the newest version (installed from github source code) of LightGBM is needed and can't installed by pip temporarily!

Quick Start

For a quick start, let's download the mpg dataset from the UCI Machine Learning Repository (30KB):

cd datasets/data/mpg_data
sh download.sh

To run different Neural Random Forest models on the mpg dataset, execute this (takes ~2min) from the repository root directory:

python3 main.py mpg <randomforest or lightgbm>

Other Datasets

To run the model on a new dataset, you must write a data loader function and add an option to data_loader.py. For inspiration, check out the data loaders in preprocessing/ which are for other datasets used in the paper .

The data loader functions all return a pair (X, Y), where X is an input matrix of size [# samples, # features], and Y is a vector of regression outputs with size [# samples].