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https://github.com/ermongroup/bgm
Code for "Boosted Generative Models", AAAI 2018.
https://github.com/ermongroup/bgm
Last synced: 3 months ago
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Code for "Boosted Generative Models", AAAI 2018.
- Host: GitHub
- URL: https://github.com/ermongroup/bgm
- Owner: ermongroup
- License: mit
- Created: 2017-11-20T00:50:16.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2017-12-26T03:37:31.000Z (almost 7 years ago)
- Last Synced: 2024-07-02T08:18:18.136Z (4 months ago)
- Language: Python
- Homepage:
- Size: 77.1 KB
- Stars: 20
- Watchers: 6
- Forks: 10
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
- awesome-gradient-boosting-papers - [Code
README
Boosted Generative Models
============================================
This repository provides a reference implementation for boosted generative models as described in the paper:
> Boosted Generative Models
[Aditya Grover](https://aditya-grover.github.io) and [Stefano Ermon](https://cs.stanford.edu/~ermon/).
AAAI Conference on Artificial Intelligence (AAAI), 2018.
https://arxiv.org/pdf/1702.08484.pdf
### Requirements
The codebase is implemented in Python 3.6. To install the necessary requirements, run the following commands:
```
pip install -r requirements.txt
bash install.sh
```
### Datasets
The code takes an input dataset in csv file. Every row indicates one datapoint with comma-separated features. A sample train, validation, and test file for the `nltcs` dataset is included in the `data/` directory.
### Options
Learning and inference of boosted generative models is handled by the `main.py` script which provides the following command line arguments.
```
--seed INT Random seed for numpy, tensorflow
--datadir STR Directory containing dataset files
--dataset STR Name of dataset
--resultdir STR Directory for saving tf checkpoints
--run-addbgm BOOL Runs additive boosting if True
--addbgm-alpha FLOAT LIST Space-separated list of model weights for additive boosting
--run-genbgm BOOL Runs multiplicative generative boosting if True
--genbgm-alpha FLOAT LIST Space-separated list of model weights for multiplicative generative boosting
--genbgm-beta FLOAT LIST Space-separated list of reweighting exponents for multiplicative generative boosting
--run-discbgm BOOL Runs multiplicative discriminative boosting if True
--discbgm-alpha FLOAT LIST Space-separated list of model weights for multiplicative generative boosting
--discbgm-epochs INT Number of epochs of training for each discriminator
--discbgm-burn-in INT Number of discarded burn in samples for Markov chains
--run-classifier BOOL Uses generative model for classification if True
```
### Examples
The following commands learns boosted ensembles with two models and evaluates the ensemble for density estimation and classification.
Meta-algorithm: multiplicative generative boosting
```
python src/main.py --dataset nltcs --run-genbgm --genbgm-alpha 0.5 0.5 --genbgm-beta 0.25 0.125 --run-classifier
```
Meta-algorithm: multiplicative discriminative boosting
```
python src/main.py --dataset nltcs --run-discbgm --discbgm-alpha 1. 1. --run-classifier
```
Meta-algorithm: additive boosting
```
python src/main.py --dataset nltcs --run-addbgm --addbgm-alpha 0.5 0.25 --run-classifier
```
You can also run any combination of the meta-algorithms together as shown below.
```
python src/main.py --dataset nltcs --run-genbgm --genbgm-alpha 0.5 0.5 --genbgm-beta 0.25 0.125 --run-discbgm --discbgm-alpha 1. 1. --run-addbgm --addbgm-alpha 0.5 0.25 --run-classifier
```
### Citing
If you find boosted generative models useful in your research, please consider citing the following paper:
>@inproceedings{grover2018boosted,
title={Boosted Generative Models},
author={Grover, Aditya and Ermon, Stefano},
booktitle={AAAI Conference on Artificial Intelligence},
year={2018}}