mlpack is an intuitive, fast, scalable C++ machine learning library, meant to be a machine learning analog to LAPACK. It aims to implement a wide array of machine learning methods and functions as a "swiss army knife" for machine learning researchers.
Download current stable version (2.0.1).
- Contents
-
Introduction
The mlpack website can be found at http://www.mlpack.org and contains numerous tutorials and extensive documentation. This README serves as a guide for what mlpack is, how to install it, how to run it, and where to find more documentation. The website should be consulted for further information:
- Citation details
If you use mlpack in your research or software, please cite mlpack using the citation below (given in BiBTeX format):
@article{mlpack2013,
title = {{mlpack}: A Scalable {C++} Machine Learning Library},
author = {Curtin, Ryan R. and Cline, James R. and Slagle, Neil P. and
March, William B. and Ram, P. and Mehta, Nishant A. and Gray,
Alexander G.},
journal = {Journal of Machine Learning Research},
volume = {14},
pages = {801--805},
year = {2013}
}
Citations are beneficial for the growth and improvement of mlpack.
- Dependencies
mlpack has the following dependencies:
Armadillo >= 4.100.0
Boost (program_options, math_c99, unit_test_framework, serialization)
CMake >= 2.8.5
All of those should be available in your distribution's package manager. If not, you will have to compile each of them by hand. See the documentation for each of those packages for more information.
If you are compiling Armadillo by hand, ensure that LAPACK and BLAS are enabled.
- Building mlpack from source
(see also Building mlpack From Source)
mlpack uses CMake as a build system and allows several flexible build configuration options. One can consult any of numerous CMake tutorials for further documentation, but this tutorial should be enough to get mlpack built and installed.
First, unpack the mlpack source and change into the unpacked directory. Here we use mlpack-x.y.z where x.y.z is the version.
$ tar -xzf mlpack-x.y.z.tar.gz
$ cd mlpack-x.y.z
Then, make a build directory. The directory can have any name, not just 'build', but 'build' is sufficient.
$ mkdir build
$ cd build
The next step is to run CMake to configure the project. Running CMake is the
equivalent to running ./configure with autotools. If you run CMake with no
options, it will configure the project to build with no debugging symbols and no
profiling information:
$ cmake ../
You can specify options to compile with debugging information and profiling information:
$ cmake -D DEBUG=ON -D PROFILE=ON ../
Options are specified with the -D flag. A list of options allowed:
DEBUG=(ON/OFF): compile with debugging symbols
PROFILE=(ON/OFF): compile with profiling symbols
ARMA_EXTRA_DEBUG=(ON/OFF): compile with extra Armadillo debugging symbols
BOOST_ROOT=(/path/to/boost/): path to root of boost installation
ARMADILLO_INCLUDE_DIR=(/path/to/armadillo/include/): path to Armadillo headers
ARMADILLO_LIBRARY=(/path/to/armadillo/libarmadillo.so): Armadillo library
Other tools can also be used to configure CMake, but those are not documented here.
Once CMake is configured, building the library is as simple as typing 'make'. This will build all library components as well as 'mlpack_test'.
$ make
You can specify individual components which you want to build, if you do not want to build everything in the library:
$ make mlpack_pca mlpack_knn mlpack_kfn
If the build fails and you cannot figure out why, register an account on Github and submit an issue; the mlpack developers will quickly help you figure it out:
Alternately, mlpack help can be found in IRC at #mlpack on irc.freenode.net.
If you wish to install mlpack to /usr/local/include/mlpack/ and /usr/local/lib/
and /usr/local/bin/, once it has built, make sure you have root privileges (or
write permissions to those three directories), and simply type
$ make install
You can now run the executables by name; you can link against mlpack with
-lmlpack
and the mlpack headers are found in
/usr/local/include/mlpack/.
If running the programs (i.e. $ mlpack_knn -h) gives an error of the form
error while loading shared libraries: libmlpack.so.2: cannot open shared object file: No such file or directory
then be sure that the runtime linker is searching the directory where
libmlpack.so was installed (probably /usr/local/lib/ unless you set it
manually). One way to do this, on Linux, is to ensure that the
LD_LIBRARY_PATH environment variable has the directory that contains
libmlpack.so. Using bash, this can be set easily:
export LD_LIBRARY_PATH=/usr/local/lib/
(or whatever directory libmlpack.so is installed in.)
- Running mlpack programs
After building mlpack, the executables will reside in build/bin/. You can call
them from there, or you can install the library and (depending on system
settings) they should be added to your PATH and you can call them directly. The
documentation below assumes the executables are in your PATH.
Consider the 'mlpack_knn' program, which finds the k nearest neighbors in a reference dataset of all the points in a query set. That is, we have a query and a reference dataset. For each point in the query dataset, we wish to know the k points in the reference dataset which are closest to the given query point.
Alternately, if the query and reference datasets are the same, the problem can be stated more simply: for each point in the dataset, we wish to know the k nearest points to that point.
Each mlpack program has extensive help documentation which details what the method does, what each of the parameters are, and how to use them:
$ mlpack_knn --help
Running mlpack_knn on one dataset (that is, the query and reference
datasets are the same) and finding the 5 nearest neighbors is very simple:
$ mlpack_knn -r dataset.csv -n neighbors_out.csv -d distances_out.csv -k 5 -v
The -v (--verbose) flag is optional; it gives informational output. It is not
unique to mlpack_knn but is available in all mlpack programs. Verbose
output also gives timing output at the end of the program, which can be very
useful.
- Further documentation
The documentation given here is only a fraction of the available documentation
for mlpack. If doxygen is installed, you can type make doc to build the
documentation locally. Alternately, up-to-date documentation is available for
older versions of mlpack:
- Bug reporting
(see also mlpack help)
If you find a bug in mlpack or have any problems, numerous routes are available for help.
Github is used for bug tracking, and can be found at https://github.com/mlpack/mlpack/. It is easy to register an account and file a bug there, and the mlpack development team will try to quickly resolve your issue.
In addition, mailing lists are available. The mlpack discussion list is available at
and the git commit list is available at
Lastly, the IRC channel #mlpack on Freenode can be used to get help.