https://github.com/emilemathieu/pvae

code for "Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders".
https://github.com/emilemathieu/pvae

hierarchical-data hyperbolic-geometry poincare-embeddings pytorch vae

Last synced: about 5 hours ago
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code for "Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders".

• Host: GitHub
• URL: https://github.com/emilemathieu/pvae
• Owner: emilemathieu
• License: mit
• Created: 2019-04-23T13:03:27.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-10-03T22:35:50.000Z (12 months ago)
• Last Synced: 2024-09-08T14:29:11.962Z (17 days ago)
• Topics: hierarchical-data, hyperbolic-geometry, poincare-embeddings, pytorch, vae
• Language: Python
• Homepage: https://arxiv.org/abs/1901.06033
• Size: 507 KB
• Stars: 123
• Watchers: 5
• Forks: 43
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# [Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders](https://arxiv.org/abs/1901.06033)

![demonstrative figure](images/tree.png)

Code for reproducing the experiments in the paper:

```

@inproceedings{mathieu2019poincare,

  title={Continuous Hierarchical Representations with Poincar\'e Variational Auto-Encoders},

  author={Mathieu, Emile and Le Lan, Charline and Maddison, Chris J. and Tomioka, Ryota and Whye Teh, Yee},

  booktitle={Advances in Neural Information Processing Systems},

  year={2019}

}

```

## Prerequisites

`pip install -r -U requirements.txt` or `python3 setup.py install --user`

## Models

### VAE (`--manifold Euclidean`):

- Prior distribution (`--prior`): `Normal` (`WrappedNormal` is theoretically equivalent)

- Posterior distribution (`--posterior`): `Normal`  (`WrappedNormal` is theoretically equivalent)

- Decoder architecture (`--dec`): `Linear` (MLP) (`Wrapped` is theoretically equivalent)

- Encoder architecture (`--enc`): `Linear` (MLP) (`Wrapped` is theoretically equivalent)

    

### PVAE (`--manifold PoincareBall`):

- Curvature (`--c`): 1.0

- Prior distribution (`--prior`): `WrappedNormal` or `RiemannianNormal`

- Posterior distribution (`--posterior`): `WrappedNormal` or `RiemannianNormal`

- Decoder architecture (`--dec`):

    - `Linear` (MLP)

    - `Wrapped` (logarithm map followed by MLP),

    - `Geo` (first layer is based on geodesic distance to hyperplanes, followed by MLP)

    - `Mob` (based on Hyperbolic feed-forward layers from Ganea et al (2018))

- Encoder architecture (`--enc`): `Wrapped` or `Mob`

## Run experiments

### Synthetic dataset

```

python3 pvae/main.py --model tree --manifold PoincareBall --latent-dim 2 --hidden-dim 200 --prior-std 1.7 --c 1.2 --data-size 50 --data-params 6 2 1 1 5 5 --dec Wrapped --enc Wrapped  --prior RiemannianNormal --posterior RiemannianNormal --epochs 1000 --save-freq 1000 --lr 1e-3 --batch-size 64 --iwae-samples 5000

```

### MNIST dataset

```

python3 pvae/main.py --model mnist --manifold Euclidean             --latent-dim 2 --hidden-dim 600 --prior Normal        --posterior Normal        --dec Wrapped --enc Wrapped --lr 5e-4 --epochs 80 --save-freq 80 --batch-size 128 --iwae-samples 5000

python3 pvae/main.py --model mnist --manifold PoincareBall --c 0.7  --latent-dim 2 --hidden-dim 600 --prior WrappedNormal --posterior WrappedNormal --dec Geo     --enc Wrapped --lr 5e-4 --epochs 80 --save-freq 80 --batch-size 128 --iwae-samples 5000

```

### Custom dataset via csv file (placed in `/data`, no header, integer labels on last column)

```

python3 pvae/main.py --model csv --data-param CSV_NAME --data-size NB_FEATURES

```