ROLEX

ROLEX (Robust Latent Space Exploration) is a lightweight package designed to perform uncertainty-constrained optimization and help you apply uncertainty quantification techniques to generative models.

This package provides a multi-level API, including:

• ready-to-train VAE models on tabular datasets
• layers available for use in your networks
• optimization routines in the learned latent space

Installation

conda create -n uncertainty python=3.10
conda activate uncertainty
pip install -e .

Running an experiment

cmd="python .\experiments\dummy\learn_vae.py \
    --seed=42 \
    --embedding_dim=2 \
    --compress_dims=[128,128] \
    --decompress_dims=[128,128] \
    --dropout=0.3 \
    --precision=64 \
    --root=./data/dummy_data.csv \
    --max_epochs=100 \
    --batch_size=2048 \
    --accelerator=gpu \
    --lr=0.001 \
    --weight_decay=0.00001 \
    --correlation_threshold=0.8 \
    --oversample_quantile=0.5 \
    --qq_threshold=0.96 \
    --beta_on_kld=0.1 \
    --log_graph"
echo $cmd
$cmd

Hyperparameters Explanation

Here's a breakdown of the hyperparameters used in the command:

• --seed: Seed for random number generation.
• --embedding_dim: Dimension of the learned embeddings.
• --compress_dims: List of dimensions for each compression layer.
• --decompress_dims: List of dimensions for each decompression layer.
• --dropout: Dropout probability for preventing overfitting.
• --precision: Precision of floating-point numbers (32 or 64).
• --root: Path to the dataset (CSV format).
• --max_epochs: Maximum number of training epochs.
• --batch_size: Number of samples in each training batch.
• --accelerator: Hardware accelerator for training (e.g., "cpu", "gpu").
• --lr: Learning rate for optimizer.
• --weight_decay: Weight decay regularization strength.
• --correlation_threshold: Threshold for correlation matrix filtering.
• --oversample_quantile: Quantile for oversampling rare events.
• --qq_threshold: Threshold for quantile-quantile loss.
• --beta_on_kld: Coefficient for the KLD loss term.
• --log_graph: Flag to log computation graph.

Implemented methods

Layers

To date, the following layers are implemented:

• Bayesian Neural Networks
• Bayesian Flipout Neural Networks
• DropConnect
• Lipschitz Constrained Neural Networks

Pre-Processing methods

To date, the following post-processing methods are implemented:

• Mode Specific Normalization

Uncertainty metrics

To date, the following uncertainty metrics are implemented:

• Negative marginal log-likelihood
• Entropy
• Effective Sampling Size
• Mutual Information

Optimization utilities

To date, the following optimization routines are implemented:

• Uncertainty-constrained gradient ascent (descent)
• Bayesian Optimization with uncertainty censoring
• Single-objective genetic algorithm optimization with uncertainty censoring