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https://github.com/ermongroup/bgm

Code for "Boosted Generative Models", AAAI 2018.
https://github.com/ermongroup/bgm

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Code for "Boosted Generative Models", AAAI 2018.

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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}}