readme: ---
  language:
  - en
  license: apache-2.0
  library_name: keras
  tags:
  - doe2vec
  - exploratory-landscape-analysis
  - autoencoders
  datasets:
  - BasStein/1595-randomfunctions-2d
  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(
      2,
      8,
      latent_dim=32,
      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
  
  