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For a detailed explanation of the methods used here for the cost-sensetive health dataset, please refer to: "Cost-Sensitive Diagnosis and Learning Leveraging Public Health Data"

For a detailed explanation of the opportunsic learning method, please refer to: "Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams"

Link to a preprocessed version of the diabetes dataset diabetes.pkl

File list:

  • nhanes.py: implementation of the data preprocessing logic as well as definition of a few example datasets such as diabetes, heart disease, hypertention, etc.
  • Demo_Dataset.ipynb: Jupyter notebook file to demonstrate the basic usage of each sample dataset.
  • Demo_OL_DQN.ipynb: Jupyter notebook file to demonstrate a simple implementation of the Opportunistic Learning method.
  • Other source files are used in the Demo_OL_DQN.ipynb.

How to use:

  1. Download raw data files and decompress them.
  2. Install Python 3 and the following packages: joblib, numpy, pandas, matplotlib, scipy, sklearn, jupyter, pytorch.
  3. Use Demo_Dataset.ipynb and Demo_OL_DQN.ipynb to see a few examples on how to use the predefined tasks.
  4. Alternatively, you can expand nhanes.py to define new tasks by following the implementation logic of the provided samples.

Citation Request

If you find this repository useful, please cite the following papers:

  • M. Kachuee, O. Goldstein, K. Kärkkäinen, S. Darabi, M. Sarrafzadeh, Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams, International Conference on Learning Representations (ICLR), 2019. Paper
  • M. Kachuee, K. Kärkkäinen, O. Goldstein, D. Zamanzadeh, M. Sarrafzadeh, Cost-Sensitive Diagnosis and Learning Leveraging Public Health Data, 2019.

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