This repository contains code to reproduce our paper, An Automatic Finite-Sample Robustness Metric: When Can Dropping a Little Data Make a Big Difference? by Tamara Broderick, Ryan Giordano, and Rachael Meager.
The writing directory is writing/output/
. There is data processing
code in both examples
and in writing/applications
.
The best guide to reproducing the paper and its analyses is found in
writing/output/makefile
. To run it, first
- Set the
GIT_REPO_LOC
variable at the top ofmakefile
to point to the full path of the location of the clonedAMIPPaper
repository - Run
make all
in theoutput
directory. - Follow the instructions to download the needed data.
- Continue to run
make all
and follow the instructions until the paper succesfully compiles.
To clear paper output, run make clean
.
To pre-process data for individual analyses, you can run any of the subsidiary targets:
make sim_data
make cash_data
make mc_data
make mc_data
Note that the most complicated analysis, the mixture model, requires more
effort to run than the R
analysis.
In order to simply compile the paper without running any of the individual
analyses, you can uncompress the file writing/output/applications_data.tgz
,
which contains the processed data we used for our original paper.
The contents of the arxiv should replace the contents of the
writing/output/applications_data/
directory, and satisfy
the makefile
target $(PP_DATA)
.
These can also be used to sanity check your own runs against ours.
If you have problems reproducing any aspect of the pipeline, please send Ryan an email.