---
language:
- en
license: apache-2.0
library_name: keras
tags:
- doe2vec
- exploratory-landscape-analysis
- autoencoders
datasets:
- BasStein/250000-randomfunctions-10d
metrics:
- mse
co2_eq_emissions:
  emissions: 0.0363
  source: "code carbon"
  training_type: "pre-training"
  geographical_location: "Leiden, The Netherlands"
  hardware_used: "1 Tesla T4"
---

## Model description

DoE2Vec model that can transform any design of experiments (function landscape) to a feature vector.  
For different input dimensions or sample size you require a different model.  
Each model name is build up like doe2vec-d{dimension\}-m{sample size}-ls{latent size}-{AE or VAE}-kl{Kl loss weight}

Example code of loading this huggingface model using the doe2vec package.

First install the package

```zsh
pip install doe2vec
```

Then import and load the model.

```python
from doe2vec import doe_model

obj = doe_model(
    10,
    8,
    latent_dim=24,
    kl_weight=0.001,
    model_type="VAE"
)
obj.load_from_huggingface()
#test the model
obj.plot_label_clusters_bbob()
```

## Intended uses & limitations

The model is intended to be used to generate feature representations for optimization function landscapes.
The representations can then be used for downstream tasks such as automatic optimization pipelines and meta-learning.


## Training procedure

The model is trained using a weighed KL loss and mean squared error reconstruction loss.
The model is trained using 250.000 randomly generated functions (see the dataset) over 100 epochs.

- **Hardware:** 1x Tesla T4 GPU
- **Optimizer:** Adam