Deep direct likelihood knockoffs
Project description
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:
- Fit a joint distribution to model features
- Fit a knockoff generator
- 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):
Deep Direct Likelihood Knockoffs
M. Sudarshan, W. Tansey, R. Ranganath
arXiv preprint arXiv:2007.15835
Bibtex entry:
@misc{sudarshan2020deep,
title={Deep Direct Likelihood Knockoffs},
author={Mukund Sudarshan and Wesley Tansey and Rajesh Ranganath},
year={2020},
eprint={2007.15835},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
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