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FAQ

General

Why ffsvm? What is the problem with libSVM?

First, in many cases there is nothing wrong with libSVM. If extreme classification performance is not an issue, it is probably the more flexible choice.

However, when using libSVM in real-time applications (e.g., VR games), a number of problems become noticeable:

  • it does lots of small allocations per classification call
  • non-optimal cache locality
  • lots of cycles per vector processed

ffsvm tries to address that by:

  • being zero-allocation during classification
  • packing all data SIMD-friendly, and using SIMD intrinsics where it makes sense
  • safe and parallelization friendly API
  • being designed and measured, from day 1, for speed

With this in mind, libSVM still has nice, portable tools for training and grid search. The ultimate plan for ffsvm is not to replace these, but to use their output.

Usage

How can I train a model?

Although FFSVM is 100% Rust code without any native dependencies, creating a model for use in this library requires the libSVM tools for your current platform:

  • On Windows see the official builds
  • For MacOS use Homebrew and run brew install libsvm,
  • Linux users need to check with their distro

Then make sure you have labeled training data in a libSVM compatible file format:

> cat ./my.training-data
+1 0:0.708333 1:1 2:1 3:-0.320755 4:-0.105023 5:-1 6:1 7:-0.419847
-1 0:0.583333 1:-1 2:0.333333 3:-0.603774 4:1 5:-1 6:1 7:0.358779
+1 0:0.166667 1:1 2:-0.333333 3:-0.433962 4:-0.383562 5:-1 6:-1 7:0.0687023
-1 0:0.458333 1:1 2:1 3:-0.358491 4:-0.374429 5:-1 6:-1 7:-0.480916
  • If you want to use a DenseSVM you must make sure all attributes for each sample are present, and all attributes are numbered in sequential, increasing order starting with 0! For SparseSVMs these restrictions don't apply.
  • In any case, make sure your data is scaled. That means each attribute is in the range [0; 1], or [-1; 1] respectively. If you do not scale your data, you will get poor accuracy and lots of "obviously wrong" classification results. Whatever scaling you apply, don't forget you have to apply the same scaling when you then classify with ffsvm.

Next, run svm-train on your data:

svm-train ./my.training-data ./my.model

This will create the file my.model you can then include in the example above. For more advanced use cases and best classification accuracy, you should consider to run grid search before you train your model. LibSVM comes with a tool tools/grid.py that you can run:

> python3 grid.py ./my.training-data
[local] 5 -7 0.0 (best c=32.0, g=0.0078125, rate=0.0)
[local] -1 -7 0.0 (best c=0.5, g=0.0078125, rate=0.0)
[local] 5 -1 0.0 (best c=0.5, g=0.0078125, rate=0.0)
[local] -1 -1 0.0 (best c=0.5, g=0.0078125, rate=0.0)
...

The best parameters (in this case c=0.5, g=0.0078125) can then be used on svm-train. The optional paramter -b 1 allows the model to also predict probabilty estimates for its classification.

> svm-train -c 0.5 -g 0.0078125 -b 1 ./my.training-data ./my.model

For more information how to use libSVM to generate the best models, see the Practical Guide to SVM Classification and the libSVM FAQ.

How can I use a trained libSVM model?

Since version 0.6 we should be able to load practically all libSVM models. Two caveats:

  • For "regular speed" classification with any model use the provided SparseSVM.
  • For "high speed" classification you can use DenseSVM. However, then all attributes must start with index 0, have the same length and there must be no "holes".