Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs, ICML 2016
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README.md

Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs

This is a Matlab implementation of the Structured SVM (SSVM) solvers proposed in the ICML-2016 paper Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs.

This code is based on the BCFWstruct library and is organized in a similar way:

  • solvers contains the optimization methods
  • applications contains the application-dependent code, such as MAP decoding or the feature map computation. The source code includes the following application:
  • experiments contains scripts to reproduce experiments of the ICML 2016 paper.
  • data is initally empty and is used to store the data files required for the demos.

The code is released under Apache v2 License allowing to use the code in any way you want. All the datasets are available on the project webpage.

Citation

If you are using this software please cite the following paper in any resulting publication:

@InProceedings{osokin2016gapBCFW,
author = {Osokin, Anton and Alayrac, Jean-Baptiste and Lukasewitz, Isabella and Dokania, Puneet K. and Lacoste-Julien, Simon},
title = {Minding the Gaps for Block {F}rank-{W}olfe Optimization of Structured {SVM}s},
booktitle = {Proceedings of The 33rd International Conference on Machine Learning (ICML)},
year = {2016} }

Authors

* Both authors contributed equally.

Installation

  1. You need a working installation of MATLAB (with C++ compiler configured).

  2. Clone the git repo (git clone https://github.com/aosokin/gapBCFW.git) or download the zip archive.

  3. Obtain the data files required to run the demos. You can use our scripts to download the data: application/OCR/download_ocr.m, application/text_chunking/download_conll.m, application/binary_segmentation/download_horseSeg.m, applications/pose_estimation/download_LSP.m

  4. Compile the binaries using compile_BCFW.m

  5. Run application/OCR/demo_ocr.m, application/text_chunking/demo_conll.m, application/binary_segmentation/demo_horseSeg.m, applications/pose_estimation/demo_LSP.m

The code was tested on Ubuntu 12.04, gcc 4.6.3, Matlab-R2014b, but should run on other systems as well.

Usage

If you want to use our solvers for your own structured prediction problem you will need to implement several functions:

  • The feature map.
  • The maximization oracle.
  • The loss function.
  • (required for some regimes) The label hashing function.

You can find example implementations in the applications folder. For an overview of the exact usage and supported options, please check the Matlab documentation of the solvers: solvers/solver_BCFW_hybrid.m and solvers/solver_multiLambda_BCFW_hybrid.m.

Note that the interface of our code is very similar to the interfaces of BCFWstruct and the Matlab wrapper to SVM^struct. Users of these packages can easily use our package as well.

Reproducing our experiments

Folder experiments contains code to reproduce Figures 3, 5, 6, 8 of the ICML 2016 paper.

To just reproduce the plots, go to subfolder experiments/plots_icml2016, download our result files by running download_results.m, run plotting scripts

  • plots_icml2016_BCWH_ocrLarge.m
  • plots_icml2016_BCWH_connl.m
  • plots_icml2016_BCWH_horseSegSmall.m
  • plots_icml2016_BCWH_horseSegMedium.m
  • plots_icml2016_BCWH_horseSegLarge.m
  • plots_icml2016_BCWH_lspSmall.m
  • plots_icml2016_regPath_ocrSmall.m
  • plots_icml2016_regPath_ocrLarge.m
  • plots_icml2016_regPath_horseSegSmall.m
  • plots_icml2016_regPath_horseSegMedium.m

To rerun the experiment of section 5.1, use scripts

  • experiments/ocr_dataset/ocr_large_BCFW_hybrid_run_experiments.m
  • experiments/conll_dataset/conll_BCFW_hybrid_run_experiments.m
  • experiments/horseSeg_dataset/horse_small_BCFW_hybrid_run_experiments.m
  • experiments/horseSeg_dataset/horse_medium_BCFW_hybrid_run_experiments.m
  • experiments/horseSeg_dataset/horse_large_BCFW_hybrid_run_experiments.m
  • experiments/LSP_dataset/lsp_small_BCFW_hybrid_run_experiments.m

To rerun the experiment of section 5.2, use scripts

  • experiments/ocr_dataset/regularization_path/ocr_small_regPath_run_experiments.m
  • experiments/ocr_dataset/regularization_path/ocr_small_multiLambda_run_experiments.m
  • experiments/ocr_dataset/regularization_path/ocr_large_regPath_run_experiments.m
  • experiments/ocr_dataset/regularization_path/ocr_large_multiLambda_run_experiments.m
  • experiments/horseSeg_dataset/regularization_path/horse_small_regPath_run_experiments.m
  • experiments/horseSeg_dataset/regularization_path/horse_small_multiLambda_run_experiments.m
  • experiments/horseSeg_dataset/regularization_path/horse_medium_regPath_run_experiments.m
  • experiments/horseSeg_dataset/regularization_path/horse_medium_multiLambda_run_experiments.m

Note, that to run all experiments you will need significant computational resources. We recommend using a cluster to run the experiments in reasonable time.