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    "info": {
        "author": "Lucas Rosen, Hossein Soleimani, Tony Wang",
        "author_email": "lrosen27@jhu.edu",
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            "License :: OSI Approved :: MIT License",
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        "description": "## Example\nSample code in `AL_Notebook.ipynb` notebook\n\n## Install\n- `pip install active_learning`\n- OR\n- `python setup.py sdist`\n- `python setup.py install`\n\n## Environment Setup\n - Make sure that\n   [conda](https://conda.io/docs/user-guide/install/index.html#regular-installation) is\n   installed.\n - Run the following command in the root directory to build the conda\n   environment \"trews\": `conda env create -f environment.yml`\n - Run `source activate trews` before executing the Jupyter\n   notebook.\n - \"trews\" has a package nb\\_conda which allows you to specify the\n   conda env you want as a Jupyter kernel. __You must have \"trews\"\n   activated for the Jupyter notebook server to manage conda\n   environments__\n\n## Objective\n - Provide justification for custom sepsis definition generated from\n   soliciting rich feedback from physicians.\n - Create an AL implementation that incorporates rich feedback from\n   physicians to improve the TREWS tool.\n - Long-term goal: Create a library of active learning tools for any\n   CDSS we design for new clinical problems.\n\n## Code Organization\n - General functions should be written in .py files under folder\n   'python_scripts'\n - Experiments should load these python files into iPython notebooks\n   for visualization/output neatness and reproducibility.\n   - Large experimental datasets should be stored locally and tracked\n     using plaintext files or logs.\n - Datasets used for testing implementation should be kept under\n   repository 'dev_data'",
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        "name": "active-learning",
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        "summary": "Active Learning With Rich feedabck",
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