Package: knockpy
KnockpyA python implementation of the knockoffs framework for variable selection. Seehttps://amspector100.github.io/knockpy/for detailed documentation and tutorials.InstallationTo install knockpy, first install choldate using the following command:pip install git+https://github.com/jcrudy/choldate.gitThen, install knockpy using pip:pip install knockpy[fast]To use the (optional) kpytorch submodule, you will need to installpytorch.What if installation fails?knockpy relies on heavy-duty linear algebra routines which sometimes fail on non-Linux environments.To start, install a lightweight version of knockpy usingpip install knockpy. This should install correctly on all devices, and contains nearly all of the functionality of the prior installation. However, the algorithms for computing optimal distributions for Gaussian knockoffs, such asminimum reconstructability knockoffsandSDP knockoffs, may be an order of magnitude slower.[Optional] To speed up computation for minimum reconstructability knockoffs (the default knockoff type):(a) Run``pip install cython>=0.29.14``If the installation fails, likely due to the incorrect configuration of a C compiler, you have three options. First, theAnacondapackage manager includes a compiler, so the command``conda install cython``should work on all platforms. Second, on Windows, you can install precompiled binaries for cythonhere. Lastly, on all platforms, the documentationheredescribes how to properly configure a C compiler during installation.(b) Run``pip install git+https://github.com/jcrudy/choldate.git``[Optional] To speed up computation for (non-default) SDP knockoffs, you will need to installscikit-dsdp. This can be challenging on non-Linux environments. We hope to provide more explicit instructions for installation of this package in the future.QuickstartGiven a data-matrixXand a response vectory, knockpy makes it easy to use knockoffs to perform variable selection using a wide variety of machine learning algorithms (also known as "feature statistic") and types of knockoffs. One quick example is shown below, where we use the cross-validated lasso to assign variable importances to the features and knockoffs.import knockpy as kpy
    from knockpy.knockoff_filter import KnockoffFilter

    # Generate synthetic data from a Gaussian linear model
    data_gen_process = kpy.dgp.DGP()
    data_gen_process.sample_data(
        n=1500, # Number of datapoints
        p=500, # Dimensionality
        sparsity=0.1,
        x_dist='gaussian',
    )
    X = data_gen_process.X
    y = data_gen_process.y
    Sigma=data_gen_process.Sigma

    # Run model-X knockoffs
    kfilter = KnockoffFilter(
        fstat='lasso',
        ksampler='gaussian',
    )
    rejections = kfilter.forward(X=X, y=y, Sigma=Sigma)Most importantly,knockpyis built to be modular, so researchers and analysts can easily layer functionality on top of it.ReferenceIf you use knockpy in an academic publication, please consider citingSpector and Janson (2020). The bibtex entry is below:@article{AS-LJ:2020,
  title={Powerful Knockoffs via Minimizing Reconstructability},
  author={Spector, Asher and Janson, Lucas},
  journal={Annals of Statistics},
  year={2021+},
  note={To Appear}
}