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Bayesian Inferential Risk Evaluation On Multiple IR Systems

Animated Twitter Poster Containing Slides from Research Paper

This is repository for the 2020 SIGIR paper "Bayesian Inferential Risk Evaluation On Multiple IR Systems".

Watch the YouTube video explaining this work here.

Reproducible parts of the paper will be released in stages, as they are refactored out of system-specific bash/awk/R/python hacks from past papers and workarounds for lacking root access on shared compute resources.

Citation

If you use this code in a research setting, please consider citing the paper:

Rodger Benham, Ben Carterette, J. Shane Culpepper, and Alistair Moffat. 2020. Bayesian Inferential Risk Evaluation On Multiple IR Systems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’20), July 25--30, 2020, Virtual Event, China. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3397271.3401033

Current Progress

The part of our paper that is interesting to most researchers is the capacity to draw inferences between a champion, set of challengers, and artefact systems. This has been released as an MVP, and we leave risk-adjusted score explorations to later stages. More specifically, we provide the functionality to produce figures like Figure 4 in our SIGIR paper.

Project Structure

The project is structured similarly to the SIGIR 2019 codebase for the paper "Statistical Significance Testing in Information Retrieval: An Empirical Analysis of Type I, Type II and Type III Errors" from @julian-urbano et. al.

Run ./install.sh to check that you have all of the required R libraries installed, and if not, it will install the libraries listed in in install.R.

  • R/ contains the source code in R.
  • run_tests.sh executes the tests and the simulation, where the tests have examples on how to use the code.
  • make_graphs.sh builds Figure 4, which is most likely to be useful in your research.
  • data/ contains input data files, like the system AP scores for all artefact, challenger and champion systems.
  • output/ contains generated output files (none so far).

System-Specific Workarounds

This part of the readme is useful for those who do not have su access to install system libraries (for example, you are using the RMIT compute resources).

One issue was related to the absence of libssl headers being installed and not having root access to install it using the package manager. Since brms and rstanarm list shinystan as a dependency, and shinystan uses shiny to build an interactive web-app for posterior predictive checks, it wants these headers. Although shinystan is an incredibly useful tool for ensuring the Markov chains are converging, it was not useful on the shared compute resources as ports were not exposed on the server and I couldn't get the project to build without the libssl headers (YMMV, however, I gave up). Instead, I tore out every call rstanarm and brms made in source to shinystan to get the library to build on the servers. To validate the model using shinystan, I serialised the R object to disk after simulation, scp'd it to a local machine with the capacity to build and run shinystan and inspected there. I will not be maintaining a fork of that code as these were very specific issues related to having insufficient permissions to build the project.

Additionally, I had an issue on the RMIT servers which run Red Hat Linux, where Markov Chain Monte Carlo simulation was painfully slow and would take months to complete simulations that would only take my MacBook hours.

You need to disable fast-math in your compiler flags in ~/.R/Makevars and recompile brms and/or rstanarm from source and install using install.packages(folder_path, repos = NULL, type="source"):

CXX14 = g++ -std=c++1y -O3 -mtune=native -march=native -Wno-unused-variable -Wno-macro-redefined -fPIC -fno-fast-math -lcurl
CXXFLAGS = -O3 -mtune=native -march=native -Wno-unused-variable -Wno-macro-redefined -fno-fast-math