| datasets: | |
| - BasStein/1595-randomfunctions-2d | |
| language: | |
| - en | |
| library_name: tf-keras | |
| license: apache-2.0 | |
| metrics: | |
| - mse | |
| tags: | |
| - doe2vec | |
| - exploratory-landscape-analysis | |
| - autoencoders | |
| 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 | |