VIE: Variational Inference for Extremals

Code for Variational Disentanglement for Rare Event Modeling (https://arxiv.org/abs/2009.08541)

Model

We propose a variational disentanglement approach to semi-parametrically learn from rare events in heavily imbalanced classification problems.

Estimation of the tail Left: Distribution of a two-dimensional latent space z where the long tail associates with higher risk. Right: Tail estimations with different schemes for the long-tailed data in one-dimensional space. EVT provides more accurate characterization comparing to other mechanisms.

Prerequisites

The algorithm is built with:

• Python (version 3.7 or higher)
• Numpy (version 1.16 or higher)
• PyTorch (version 1.3.1)

Clone the repository, e.g.:

git clone https://github.com/ZidiXiu/VIE.git

Running the Binary Simulation Dataset

Here we present a toy synthetic dataset which enjoys a long-tailed behaviour in the latent space.

python train train_VIE_simulationDL.py --batch-size 200 --epochs 500 

Running the SLEEP Dataset

SLEEP A subset of the Sleep Heart Health Study (SHHS), a multi-center cohort study implemented by the National Heart Lung & Blood Institute to determine the cardiovascular and other consequences of sleep-disordered breathing. The dataset includes 5026 patients and 206 covariates.

python train train_VIE_SLEEP.py

Acknowledgments

When building the VIE framework, we refenreced the following sources:

• Fenchel Min-max
• IAF
• Data Preprocessing