Portfolio entry to the UAI 2014 Probabilistic Inference Competition
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csv
.gitignore
Makefile
README.md
computescore.py
mplp2
narycsp
proteuspredictors.h
proteustrain.py
tb2incop
toulbar2
uai-proteus.cc

README.md

UAI-Proteus

This repository constitutes the Proteus submission to the UAI 2014 Inference Competition (MPE category) which achieved first place under both the 20 and 60 minute timouts, and second place for the 20 second timeout.

The portfolio aims to benefit from complimentary performances among solvers on various types of instances. By intelligently choosing the solver on a per-instance basis, significant performance gains can be achieved overall. This portfolio is composed of 5 component solvers:

  • Toulbar2
    Soft arc consistency revisited
    Martin C Cooper, Simon de Givry, Marti Sánchez, Thomas Schiex, Matthias Zytnicki, T Werner
    Artificial Intelligence 174 (7), 449-478

  • Toulbar2 preceeded by incop
    Bertrand Neveu, Gilles Trombettoni, Fred Glover
    ID Walk: A Candidate List Strategy with a Simple Diversification Device
    Principles and Practice of Constraint Programming -- CP 2004

  • mplp2
    David Sontag, Do Kook Choe, Yitao Li
    Efficiently Searching for Frustrated Cycles in MAP Inference
    Uncertainty in Artificial Intelligence -- UAI 2012

  • Two encodings, direct and tuple, to ILP and solved using CPLEX.
    The encodings and interface where implemented by George Katsileros, INRA.
    IBM ILOG CPLEX http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/

The interface to CPLEX is implemented directly in uai-proteus.cc, but other solvers are launched as external processes.

UAI Training Instances

Performance data of each solver was collected on a collection of 2556 instances which have been encoded in UAI format from fields such as MRF, CVPR, CFN, and Max CSP by INRA: [http://genoweb.toulouse.inra.fr/~degivry/evalgm/] We thank the GenoToul Bioinformatics Platform of INRA-Toulouse for providing computing resources and support to collect this data.

CSV data

  • feattimes.csv lists the time needed to compute features of the instances.
  • features.csv is the feature matrix
  • objectives.csv is a matrix containing the best objective obtained by each solver within the time limit.
  • success.csv is a matrix with a 0/1 for each solver indicating if that solver produced a valid solution on an instance.
  • times.csv is a matrix of the time used by each solver on each instance
  • scores.csv is computed from objectives and times.csv by computescore.py

Requirements

To compile the Proteus portfolio

  • C++x11 compiler
  • Boost
  • IBM ILOG CPLEX 12.5.* or 12.6

To retrain the prediction models (optional)

  • sklearn & numpy
  • Optionally pydot if you would like to plot the decision trees.