Skip to content

rajesh-lab/ddlk

master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
img
 
 
src
 
 
 
 
 
 
 
 

DDLK: Deep direct likelihood knockoffs

This package implements DDLK, a method for variable selection with explicit control of the false discovery rate. Install with:

pip install ddlk

Controlled variable selection with DDLK

Suppose you have a set of features and a response. DDLK identifies the features most predictive of the response at a pre-specified false discovery rate (FDR) threshold. For example, if you choose an FDR of 20%, DDLK can guarantee that no more than 20% of the selected features will be unimportant. To learn more about how it works, check out our paper.

Running DDLK

Variable selection with DDLK involves three stages:

  1. Fit a joint distribution to model features
  2. Fit a knockoff generator
  3. Sample knockoffs and apply knockoff filter to select variables at a pre-specified FDR

To see a complete working example, check our synthetic data example, used to generate the gif above. Below is an exceprt of how to run DDLK.

Fitting a joint distribution

This implementation of DDLK uses the fast and easy PyTorch Lightning framework to fit q_joint:

# initialize data
x, y = ...
# put your data in standard PyTorch format
trainloader = ...
# initialize joint distribution model with mean and std of data
((X_mu, ), (X_sigma, )) = utils.get_two_moments(trainloader)
hparams = argparse.Namespace(X_mu=X_mu, X_sigma=X_sigma)
q_joint = mdn.MDNJoint(hparams)
# create and fit a PyTorch Lightning trainer
trainer = pl.Trainer()
trainer.fit(q_joint, train_dataloader=trainloader)

Fitting a knockoff generator

# initialize and fit a DDLK knockoff generator
q_knockoff = ddlk.DDLK(hparams, q_joint=q_joint)
trainer = pl.Trainer()
trainer.fit(q_knockoff, train_dataloader=trainloader)

Variable selection

Using the knockoff generator, we sample knockoffs, and run a Holdout Randomization Test:

xTr_tilde = q_knockoff.sample(xTr)
knockoff_test = hrt.HRT_Knockoffs()
knockoff_test.fit(xTr, yTr, xTr_tilde)

Citing this code

If you use this code, please cite the following paper (available here):

Sudarshan, Mukund, Wesley Tansey, and Rajesh Ranganath. "Deep direct likelihood knockoffs." Advances in Neural Information Processing Systems 33 (2020).

Bibtex entry:

@article{sudarshan2020deep,
  title={Deep Direct Likelihood Knockoffs},
  author={Sudarshan, Mukund and Tansey, Wesley and Ranganath, Rajesh},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

About

Deep direct likelihood knockoffs

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages