fanok 0.0.4

Knockoffs in Python

FANOK: Knockoffs in linear time

FANOK is a Python implementation of the Gaussian knockoffs framework developed by Barber-Candès [1] [2]. It provides fast algorithms based on coordinate ascent to generate Gaussian knockoffs in high dimensions.

Installation

Requirements

This package requires NumPy, Scipy, Scikit-Learn. Optionally, CVXPY is required in order to solve the SDP with external solvers, or to run the tests. Use pip install requirements.txt to install them.

Installation

pip install fanok

Documentation

Here

Usage

Here is a minimal usage example:

from sklearn.datasets import make_regression
from fanok import GaussianKnockoffs, KnockoffSelector
from fanok.statistics import EstimatorStatistics

X, y, coef = make_regression(n_samples=100, n_features=150, n_informative=20, coef=True)

knockoffs = GaussianKnockoffs()
statistics = EstimatorStatistics()
selector = KnockoffSelector(knockoffs, statistics, alpha=0.2, offset=1)
selector.fit(X, y)

fdp, power = selector.score(X, y, coef)
print(f"FDP: {fdp}, Power: {power}")

See the folder examples/ for more illustrations, and in particular with fixed and low-rank knockoffs. We also provide a notebook with an example on text data.

Paper

You may find additional information in our paper.

References

[1] Barber, R. F. and Candès, E. J. (2015). Controlling the false discovery rate via knockoffs. Ann. Statist., 43(5):2055–2085.

[2] Candès, Emmanuel & Fan, Yingying & Janson, Lucas & Lv, Jinchi. (2016). Panning for Gold: Model-free Knockoffs for High-dimensional Controlled Variable Selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 80. 10.1111/rssb.12265.