https://github.com/astrazeneca/dpp_imp
Improved clinical data imputation via classical and quantum determinantal point processes
https://github.com/astrazeneca/dpp_imp
Last synced: about 2 months ago
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Improved clinical data imputation via classical and quantum determinantal point processes
- Host: GitHub
- URL: https://github.com/astrazeneca/dpp_imp
- Owner: AstraZeneca
- Created: 2023-11-13T11:31:10.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-04-03T21:20:50.000Z (about 2 years ago)
- Last Synced: 2025-01-18T23:00:04.074Z (over 1 year ago)
- Language: Python
- Size: 15.6 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Data imputation using Determinantal Point Process (DPP) - based methods
Please contact [Philip Teare](mailto:philip.teare@astrazeneca.com) with any questions about this repo.
This work presents an implementation of the models presented in the "[Improved clinical data imputation via classical and quantum determinantal point processes](https://arxiv.org/abs/2303.17893)" paper
## Prerequisites
Python 3.9
## Usage
```python
from models.imputers import DPPMissForest
ddpp_mf = DPPMissForest(batch_size=100, max_iter=5, n_estimators=10)
X_imputed = ddpp_mf.fit_transform(X_missing)
```
## License
[MIT](https://choosealicense.com/licenses/mit/)