Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

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

On the consistency of supervised learning with missing values

Authors: Julie Josse (CMAP, Inria), Nicolas Prost (CMAP, Inria), Erwan Scornet (CMAP), Gaël Varoquaux (Inria).

Simple example notebook

Update: the directory Notebook contains a tutorial on key results of the paper.

Binder

Access the Notebok via Binder at the following URL

https://mybinder.org/v2/gh/nprost/supervised_missing/master?filepath=Notebook%2FA%20toy%20regression%20model%20with%20missing%20values.ipynb

Notes Without Binder, install the required Python libraries via

pip install -r requirements.txt

Code for the simulations in the paper

This repository contains the code for the paper:

Julie Josse, Nicolas Prost, Erwan Scornet, Gaël Varoquaux. On the consistency of supervised learning with missing values. 2019. 〈hal-02024202〉https://arxiv.org/abs/1902.06931

The directory analysis contains the code for figures 1 and 2 (section 5).

boxplots corresponds to figures 3 and 4 (section 6). There are three separate files: one containing the functions, one containing the script for computation, and two for the visualisation (one of each of the two boxplots).

consistency is used for figure 5 (section 6). There are three files as for the boxplot, but in addition, approximate Bayes rates are computed in bayesrates.R with oracle multiple imputation, as detailed in the paper.

The scripts require the following R packages:

rpart
party
ranger
xgboost
MASS
norm
doParallel
doSNOW
gridExtra
viridis

To run script_boxplots.R or script_consistency.R with, say, 20 jobs to parallelize the "for" loop and 10 threads per forest/boosting, do

Rscript boxplots/script_boxplots.R 20 10
Rscript consistency/script_consistency.R 20 10

To build the figures, just run the scripts,

Rscript boxplots/visualisation_boxplot1.R
Rscript boxplots/visualisation_boxplot2.R
python consistency/visualisation_consistency.py

All figure outputs go to the directory figures (created when nonexistent).

Nicolas Prost

July 10, 2019

About

Code for the paper "On the consistency of supervised learning with missing values"

Resources

Releases

No releases published

Packages

No packages published