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‎.gitignore

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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data/
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pip-wheel-metadata/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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.idea/
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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.python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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#plots
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/plots
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/.idea

‎README.md

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# Improving Screening Processes via Calibrated Subset Selection
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This repo contains the code for the empirical evaluation in the paper
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[Improving Screening Processes via Calibrated Subset Selection](https://arxiv.org/abs/2202.01147),
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which includes an implementation of the Calibrated Subset Selection algorithm proposed in the paper.
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### Create Environment
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Make sure [conda](https://docs.conda.io/en/latest/) is installed. Run
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```angular2html
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conda env create -f environment.yml
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source activate alg_screen
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```
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### Download and Prepare Data
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Set prepare_data = True and submit = False in params_exp_noise.py and params_exp_diversity_noise.py
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Run
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```angular2html
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python ./scripts/run_exp_noise.py
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python ./scripts/run_exp_diversity_noise.py
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```
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### Run Experiments
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Set prepare_data = False and submit = True in params_exp_noise.py and params_exp_diversity_noise.py
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On a cluster with [Slurm](https://slurm.schedmd.com/documentation.html) workload manager, run
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```angular2html
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python ./scripts/run_exp_noise.py
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python ./scripts/run_exp_cal_size.py
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python ./scripts/run_exp_diversity_noise.py
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```
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### Plot Figures
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Run
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```angular2html
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python ./scripts/plot_exp_normal.py
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python ./scripts/plot_exp_diversity.py
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```
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### Bibtex
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```angular2html
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@article{wang2022improving,
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title={Improving Screening Processes via Calibrated Subset Selection},
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author={Wang, Lequn and Joachims, Thorsten and Rodriguez, Manuel Gomez},
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journal={arXiv preprint arXiv:2202.01147},
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year={2022}
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}
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```

‎environment.yml

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name: alg_screen
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channels:
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- defaults
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dependencies:
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- _libgcc_mutex=0.1=main
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- _openmp_mutex=4.5=1_gnu
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- blas=1.0=mkl
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- bottleneck=1.3.2=py39hdd57654_1
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- brotli=1.0.9=he6710b0_2
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- ca-certificates=2021.10.26=h06a4308_2
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- certifi=2021.10.8=py39h06a4308_0
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- cycler=0.11.0=pyhd3eb1b0_0
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- dbus=1.13.18=hb2f20db_0
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- expat=2.4.1=h2531618_2
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- fontconfig=2.13.1=h6c09931_0
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- fonttools=4.25.0=pyhd3eb1b0_0
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- freetype=2.11.0=h70c0345_0
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- giflib=5.2.1=h7b6447c_0
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- glib=2.69.1=h5202010_0
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- gst-plugins-base=1.14.0=h8213a91_2
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- gstreamer=1.14.0=h28cd5cc_2
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- icu=58.2=he6710b0_3
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- intel-openmp=2021.4.0=h06a4308_3561
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- joblib=1.1.0=pyhd3eb1b0_0
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- jpeg=9d=h7f8727e_0
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- kiwisolver=1.3.1=py39h2531618_0
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- lcms2=2.12=h3be6417_0
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- ld_impl_linux-64=2.35.1=h7274673_9
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- libffi=3.3=he6710b0_2
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- libgcc-ng=9.3.0=h5101ec6_17
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- libgfortran-ng=7.5.0=ha8ba4b0_17
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- libgfortran4=7.5.0=ha8ba4b0_17
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- libgomp=9.3.0=h5101ec6_17
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- libpng=1.6.37=hbc83047_0
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- libstdcxx-ng=9.3.0=hd4cf53a_17
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- libtiff=4.2.0=h85742a9_0
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- libuuid=1.0.3=h7f8727e_2
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- libwebp=1.2.0=h89dd481_0
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- libwebp-base=1.2.0=h27cfd23_0
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- libxcb=1.14=h7b6447c_0
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- libxml2=2.9.12=h03d6c58_0
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- lz4-c=1.9.3=h295c915_1
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- matplotlib=3.5.0=py39h06a4308_0
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- matplotlib-base=3.5.0=py39h3ed280b_0
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- mkl=2021.4.0=h06a4308_640
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- mkl-service=2.4.0=py39h7f8727e_0
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- mkl_fft=1.3.1=py39hd3c417c_0
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- mkl_random=1.2.2=py39h51133e4_0
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- munkres=1.1.4=py_0
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- ncurses=6.3=h7f8727e_2
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- numexpr=2.8.1=py39h6abb31d_0
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- numpy=1.21.2=py39h20f2e39_0
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- numpy-base=1.21.2=py39h79a1101_0
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- olefile=0.46=pyhd3eb1b0_0
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- openssl=1.1.1l=h7f8727e_0
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- packaging=21.3=pyhd3eb1b0_0
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- pcre=8.45=h295c915_0
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- pillow=8.4.0=py39h5aabda8_0
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- pip=21.2.4=py39h06a4308_0
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- pyparsing=3.0.4=pyhd3eb1b0_0
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- pyqt=5.9.2=py39h2531618_6
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- python=3.9.7=h12debd9_1
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- python-dateutil=2.8.2=pyhd3eb1b0_0
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- qt=5.9.7=h5867ecd_1
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- readline=8.1=h27cfd23_0
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- scikit-learn=1.0.1=py39h51133e4_0
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- scipy=1.7.1=py39h292c36d_2
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- setuptools=58.0.4=py39h06a4308_0
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- sip=4.19.13=py39h2531618_0
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- six=1.16.0=pyhd3eb1b0_0
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- sqlite=3.37.0=hc218d9a_0
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- threadpoolctl=2.2.0=pyh0d69192_0
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- tk=8.6.11=h1ccaba5_0
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- tornado=6.1=py39h27cfd23_0
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- tzdata=2021e=hda174b7_0
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- wheel=0.37.0=pyhd3eb1b0_1
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- xz=5.2.5=h7b6447c_0
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- zlib=1.2.11=h7f8727e_4
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- zstd=1.4.9=haebb681_0
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- pip:
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- charset-normalizer==2.0.9
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- folktables==0.0.11
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- idna==3.3
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- pandas==1.3.5
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- pytz==2021.3
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- requests==2.26.0
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- sklearn==0.0
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- urllib3==1.26.7

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