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https://github.com/yuneg11/supnotmiwae-with-obsdropout
Official implementation of "Probabilistic Imputation for Time-series Classification with Missing Data (ICML 2023)"
https://github.com/yuneg11/supnotmiwae-with-obsdropout
Last synced: 9 days ago
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Official implementation of "Probabilistic Imputation for Time-series Classification with Missing Data (ICML 2023)"
- Host: GitHub
- URL: https://github.com/yuneg11/supnotmiwae-with-obsdropout
- Owner: yuneg11
- License: bsd-3-clause
- Created: 2023-08-22T13:18:26.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-09-04T01:54:36.000Z (about 1 year ago)
- Last Synced: 2023-09-04T21:59:14.536Z (about 1 year ago)
- Language: Python
- Size: 294 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Probabilistic Imputation for Time-series Classification with Missing Data
**Currently, we are refactoring the code. We will update README and the refactored code soon. This repository is not ready for use yet.**
This repository contains the implementation for _Probabilistic Imputation for Time-series Classification with Missing Data_ (ICML 2023).
SeungHyun Kim\*, Hyunsu Kim\*, [EungGu Yun](https://github.com/yuneg11)\*, Hwangrae Lee, Jaehun Lee, [Juho Lee](https://juho-lee.github.io)
[[`Paper`](https://arxiv.org/abs/2308.06738)][[`ICML`](https://icml.cc/virtual/2023/poster/23522)][[`BibTeX`](#citation)]
## Installation
```bash
pip install -r requirements.txt
```
## Datasets
Please see [Set_Functions_for_Time_Series](https://github.com/BorgwardtLab/Set_Functions_for_Time_Series) for the details and the preparation of the datasets for now.
We will add the details of the datasets soon.
## Usage
### Train model
```bash
python scripts/train.py \
-f, --config-file CONFIG_FILE \
[-o, --output-dir OUTPUT_DIR] \
[--dev] \
[additional options]
```
You can change the values of the parameters in the config file using `-- ` options.
For example, if you want to change the `early_stopping` to 10, you can use `--train.early_stopping 10`.
Similarly, if you want to change the `learning_rate` to 0.1, you can use `--train.learning_rate 0.1` or `-lr 0.1`, because `-lr` is registered as an alias of `--train.learning_rate` in `scripts/train.py`.
**NOTE**: The outputs are actually saved in `outs/_/-
#### See help
```bash
python scripts/train.py -f CONFIG_FILE --help
```
#### Example
- Basic case:
```bash
python scripts/train.py -f configs/physionet2012/SupNotMIWAE.yaml
```
- Use config file `configs/physionet2012/SupNotMIWAE.yaml` to train a SupNotMIWAE model.
- Save outputs under `outs/physionet2012/SupNotMIWAE/` (default output defined in `scripts/train.py`)
- Advanced case:
```bash
python scripts/train.py \
-f configs/physionet2012/SupNotMIWAE.yaml \
-o outs/physionet2012/SupNotMIWAE/test1 \
-lr 0.005 \
--model.n_units 64 \
--dev
```
- Use config file `configs/physionet2012/SupNotMIWAE.yaml` to train a SupNotMIWAE model.
- Save outputs under `outs/physionet2012/SupNotMIWAE/test1/`.
- Set learning rate `0.005`.
- Change the model arguments `n_units` to `64`.
- Mark this as a development run.
## Acknowledgement
Our code is based on [Set_Functions_for_Time_Series](https://github.com/BorgwardtLab/Set_Functions_for_Time_Series) and includes [medical_ts_datasets](https://github.com/ExpectationMax/medical_ts_datasets) with some modifications.
## License
See [LICENSE](LICENSE).
## Citation
```
@inproceedings{kim2023probabilistic,
title = {Probabilistic Imputation for Time-series Classification with Missing Data},
author = {Kim, SeungHyun and Kim, Hyunsu and Yun, EungGu and Lee, Hwangrae and Lee, Jaehun and Lee, Juho},
booktitle = {Proceedings of the 40th International Conference on Machine Learning (ICML 2023)},
year = {2023},
}
```