https://github.com/usarawgi911/robustness-to-missing-features

https://github.com/usarawgi911/robustness-to-missing-features

Last synced: 22 days ago
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• Host: GitHub
• URL: https://github.com/usarawgi911/robustness-to-missing-features
• Owner: usarawgi911
• Created: 2020-07-21T21:45:47.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-11-21T22:55:36.000Z (almost 2 years ago)
• Last Synced: 2023-03-05T12:04:29.026Z (over 1 year ago)
• Language: Python
• Size: 2.77 MB
• Stars: 6
• Watchers: 2
• Forks: 2
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# Robustness to Missing Features using Hierarchical Clustering with Split Neural Networks

Code for the paper - [https://ojs.aaai.org/index.php/AAAI/article/view/17905](https://ojs.aaai.org/index.php/AAAI/article/view/17905)

The following readme has simple steps to reproduce the training and evaluation for any of the datasets mentioned.

## 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 --dataset_dir datasets --dataset life --model_dir life_models --verbose 1

```

### Evaluate

```

python main.py evaluate --dataset_dir datasets --dataset life --model_dir life_models --verbose 1

```

## Further Notes

### Mapping for datasets to `--dataset` flag

1. Life Expectancy (WHO) : life

2. Bands : bands

3. Kidney Disease : kidney_disease

4. Mammographics : mammographics

5. Horse Colic : horse

6. Pima Indians : pima

7. Hepatitis : hepatitis

8. Breast Cancer Winconsin : winconsin

## Citation

If you find this project useful for your research, please use the following BibTeX entry to cite our paper [https://ojs.aaai.org/index.php/AAAI/article/view/17905](https://ojs.aaai.org/index.php/AAAI/article/view/17905).

    @article{khincha2021missing,

            author={Khincha, Rishab and Sarawgi, Utkarsh and Zulfikar, Wazeer and Maes, Pattie}, 

            title={Robustness to Missing Features using Hierarchical Clustering with Split Neural Networks (Student Abstract)}, 

            volume={35}, 

            url={https://ojs.aaai.org/index.php/AAAI/article/view/17905}, 

            number={18}, 

            journal={Proceedings of the AAAI Conference on Artificial Intelligence}, 

            year={2021}, 

            month={May}, 

            pages={15817-15818}

    }