Metadata-Version: 2.1
Name: fanok
Version: 0.0.2
Summary: Knockoffs in Python
Home-page: https://github.com/qrebjock/fanok
Author: Armin Askari, Quentin Rebjock, Laurent El Ghaoui, and Alexandre d’Aspremont
Author-email: aaskari@berkeley.edu, quentin.rebjock@gmail.com, elghaoui@berkeley.edu, aspremon@ens.fr
License: MIT
Description: # FANOK: Generating knockoffs in linear time
        
        FANOK is a Python implementation of the Gaussian knockoffs framework
        developed by Barber-Candès [[1]](#1) [[2]](#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 and CVXPY.
        Use `pip install requirements.txt` to install them.
        
        ### Installation
        
        `pip install fanok`
        
        ## Usage
        
        Here is a minimal usage example:
        ```python
        from sklearn.datasets import make_regression
        from fanok import GaussianKnockoffs, KnockoffSelector
        from fanok.statistics import EstimatorKnockoffStatistics
        
        X, y, coef = make_regression(n_samples=100, n_features=150, n_informative=20, coef=True)
        
        knockoffs = GaussianKnockoffs()
        statistics = EstimatorKnockoffStatistics()
        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.
        
        ## References
        
        <a id="1">[1]</a>
        Barber, R. F. and Candès, E. J. (2015).
        Controlling the false discovery rate via knockoffs.
        Ann. Statist., 43(5):2055–2085.
        
        <a id="2">[2]</a>
        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. 
        
Keywords: knockoffs,fdr,statistics
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5.0
Description-Content-Type: text/markdown
