wazeerzulfikar/deep-split-ensembles

Why have a Unified Uncertainty? Disentangling it using Deep Split Ensembles

Why have a Unified Uncertainty? Disentangling it using Deep Split Ensembles

Code for the paper - https://arxiv.org/abs/2009.12406

Toy Regression on 3D Problem

The red points are the observed noisy training samples and the black points are their projections on the respective axes. The grey regions show the predicted mean along with three standard deviations. The disentangled uncertainties have different grey regions on the two dimensions, and are able to contain the black points well. This illustrates how decomposed uncertainties can capture disentangled information about the individual noise in the input features

Setup

1. Setup Virtual Environment

pip install virtualenv
virtualenv venv
source venv/bin/activate

2. Install dependencies pip install -r requirements.txt

3. Run the code

Run

Train

python main.py train --datasets_dir datasets --dataset boston --model_dir boston_models --verbose 1

Check for dataset name mapping below

Evaluate

python main.py evaluate --datasets_dir datasets --dataset boston --model_dir boston_models

Experiments

Calibration - Clusterwise OOD

python main.py experiment --exp_name clusterwise_ood --plot_path plots --datasets_dir datasets --dataset boston --model_dir boston_models

Empirical rule test

python main.py experiment --exp_name empirical_rule_test --datasets_dir datasets --dataset boston --model_dir boston_models

Calibration - Defer Simulation

python main.py experiment --exp_name defer_simulation --plot_path plots --datasets_dir datasets --dataset boston --model_dir boston_models

Calibration - KL Divergence vs Mode

python main.py experiment --exp_name kl_mode --plot_path plots --datasets_dir datasets --dataset boston --model_dir boston_models

Toy regression

python main.py experiment --exp_name toy_regression --plot_path toy --model_dir toy_models --dataset toy

Show model parameters

python main.py experiment --exp_name show_summary --datasets_dir datasets --dataset boston

Further Notes

Mapping for regression datasets to --dataset flag

1. Boston Housing : boston
2. Concrete : cement
3. Energy Efficiency : energy_efficiency
4. Kin8nm : kin8nm
5. Naval Propulsion Plant : naval
6. Power Plant Output : power_plant
7. Protein Structure : protein
8. Red Wine Quality : wine
9. Yacht Hydrodynamics : yacht
10. Year prediction MSD : msd

Human experts

Set --mod_split flag in all commands to human, to access splits created by human experts. Only available for Power Plant Output and Red Wine Quality

ADReSS - Compare features extraction

1. Install and setup OpenSmile for Compare features extraction following COMPARE.md
2. Extract compare features

Citation

If you find this project useful for your research, please use the following BibTeX entry to cite our paper https://arxiv.org/abs/2009.12406.

@misc{sarawgi2020unified,
      title={Why have a Unified Predictive Uncertainty? Disentangling it using Deep Split Ensembles}, 
      author={Utkarsh Sarawgi and Wazeer Zulfikar and Rishab Khincha and Pattie Maes},
      year={2020},
      eprint={2009.12406},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}