doe2vec 0.8.0

Feature extraction for exploratory landscape analysis

DoE2Vec

DoE2Vec is a self-supervised approach to learn exploratory landscape analysis features from design of experiments. The model can be used for downstream meta-learning tasks such as learninig which optimizer works best on a given optimization landscape. Or to classify optimization landscapes in function groups.

The approach uses randomly generated functions and can also be used to find a "cheap" reference function given a DOE. The model uses Sobol sequences as the default sampling method. A custom sampling method can also be used. Both the samples and the landscape should be scaled between 0 and 1.

Install package via pip

`pip install doe2vec`

Afterwards you can use the package via:

from doe2vec import doe_model

Load a model from the HuggingFace Hub

Available models can be viewed here: https://huggingface.co/BasStein A model name is build up like BasStein/doe2vec-d2-m8-ls16-VAE-kl0.001
Where d is the number of dimensions, 8 the number (2^8) of samples, 16 the latent size, VAE the model type (variational autoencoder) and 0.001 the KL loss weight.

Example code of loading a huggingface model

obj = doe_model(
            2,
            8,
            n= 50000,
            latent_dim=16,
            kl_weight=0.001,
            use_mlflow=False,
            model_type="VAE"
        )
obj.load_from_huggingface()
#test the model
obj.plot_label_clusters_bbob()

How to Setup your Environment for Development

• python3.8 -m venv env
• source ./env/bin/activate
• pip install -r requirements.txt

Generate the Data Set

To generate the artificial function dataset for a given dimensionality and sample size run the following code

from doe2vec inport doe_model

obj = doe_model(d, m, n=50000, latent_dim=latent_dim)
if not obj.load():
    obj.generateData()
    obj.compile()
    obj.fit(100)
    obj.save()

Where d is the number of dimensions, m the number of samples (2^m) per DOE, n the number of functions generated and latent_dim the size of the output encoding vector.

Once a data set and encoder has been trained it can be loaded with the load() function.