Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are:
- fully integrated hyper-parameter selection,
- extreme speed on both small and large data sets,
- Bindings for R, Python, MATLAB / Octave, Java, and Spark,
- full flexibility for experts, and
- inclusion of a variety of different learning scenarios:
- multi-class classification, ROC, and Neyman-Pearson learning,
- least-squares, quantile, and expectile regression.
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Installation instructions for the command line versions.
|Terminal version for Linux/OS X||liquidSVM.tar.gz|
|Terminal version for Windows (64bit)||avx2: liquidSVM.zip|
|Previous versions||v1.1 (June 2016), v1.0 (January 2016)|
On Linux and Mac on the terminal
liquidSVM can be used in the following way:
wget www.isa.uni-stuttgart.de/software/liquidSVM.tar.gz tar xzf liquidSVM.tar.gz cd liquidSVM make all scripts/mc-svm.sh banana-mc 1 2
An easy usage is:
install.packages("liquidSVM") library(liquidSVM) banana <- liquidData('banana-mc') model <- mcSVM( Y~. , banana$train, display=1, threads=2) result <- test(model, banana$test) errors(result)
To install use:
pip install --user liquidSVM
and then in Python you can use it e.g. like:
from liquidSVM import * banana = LiquidData('banana-mc') model = mcSVM(banana.train, display=1, threads=2) result, err = model.test(banana.test)
The MATLAB bindings are currently getting a better interface, and this is a preview version.
It does currently not work on Windows.
For installation download the Toolbox liquidSVM.mltbx and install it in MATLAB by double clicking it. To compile and add paths issue:
Then you can use it like:
banana = liquidData('banana-mc'); model = svm_mc(banana.train, 'DISPLAY', 1, 'THREADS', 2); [result, err] = model.test(banana.test);
Most of the code also works in
if you use liquidSVM-octave.zip.
LiquidData banana = new LiquidData("banana-mc"); SVM model = new MC(banana.train, new Config().display(1).threads(2)); ResultAndErrors result = model.test(banana.test);
If this is implemented in the file
Example.java this can be compiled and run using
# if you want to compile the JNI-native library: make lib # compile your Java-Code javac -classpath liquidSVM.jar Example.java # and run it java -Djava.library.path=. -cp .:liquidSVM.jar Example
make lib export LD_LIBRARY_PATH=.:$LD_LIBRARY_PATH $SPARK_HOME/bin/spark-submit \ --class de.uni_stuttgart.isa.liquidsvm.spark.App \ liquidSVM-spark.jar banana-mc
If you have configured
Spark to be used on a cluster with
hdfs dfs -put data/covtype-full.train.csv data/covtype-full.test.csv . make lib $SPARK_HOME/bin/spark-submit --files ../libliquidsvm.so \ --conf spark.executor.extraLibraryPath=. \ --conf spark.driver.extraLibraryPath=. \ --class de.uni_stuttgart.isa.liquidsvm.spark.App \ --num-executors 14 liquidSVM-spark.jar covtype-full
Both datasets were compiled from LIBSVM's version of the covertype dataset, which in turn was taken from the UCI repository and preprocessed as in [RC02a]. Copyright for this dataset is by Jock A. Blackard and Colorado State University.
If you use liquidSVM, please cite it as:
I. Steinwart and P. Thomann. liquidSVM: A fast and versatile SVM package. ArXiv e-prints 1702.06899, February 2017.