Regularized Greedy Forest wrapper of the 'Regularized Greedy Forest' < https://github.com/RGF-team/rgf/tree/master/python-package> 'python' package, which also includes a Multi-core implementation (FastRGF) < https://github.com/RGF-team/rgf/tree/master/FastRGF>.
UPDATE 17-08-2018: Now RGF is part of the official RGF repository and active development is performed there. The mlampros/RGF repository is archived.
The RGF package is a wrapper of the Regularized Greedy Forest (RGF) python package, which also includes a Multi-core implementation (FastRGF). More details on the functionality of the RGF package can be found in the blog-post and in the package Documentation.
UPDATE 26-07-2018: A Singularity image file is available in case that someone intends to run RGF on Ubuntu Linux (locally or in a cloud instance) with all package requirements pre-installed. This allows the user to utilize the RGF package without having to spend time on the installation process.
All modules should be installed in the default python configuration (the configuration that the R-session displays as default), otherwise errors will occur during the RGF package installation (reticulate::py_discover_config() might be useful here).
First install / upgrade the dependencies,
sudo pip install --upgrade pip setuptoolssudo pip install -U numpysudo pip install --upgrade scipysudo pip install -U scikit-learn
Then, download the rgf-python package and install it using the following commands,
git clonecd rgf/python-packagesudo python setup.py install
FastRGF will be installed successfully only if gcc >= 5.0.
Upgrade python to version 3 using,
brew upgrade python
Then install the dependencies for RGF and FastRGF
sudo pip3 install --upgrade setuptoolssudo pip3 install -U numpysudo pip3 install --upgrade scipysudo pip3 install -U scikit-learn
The FastRGF module requires a gcc >= 8.0. To install gcc-8 (or the most recent gcc) with brew follow the next steps,
# before running the following commands make sure that the most recent Apple command line tools for Xcode are installedbrew updatebrew upgradebrew info gccbrew install gccbrew cleanup
After the newest gcc version is installed the user should navigate to /usr/local/bin and if a gcc file exists (symbolic link) then the user should delete it. Then the user should run the following command,
sudo ln -s /usr/local/bin/gcc-8 /usr/local/bin/gcc
The user should then verify that the gcc has been updated using,
gcc -vwhich gcc
After the new gcc is installed the user should continue with the installation of rgf_python,
git clonecd /rgf/RGF/buildexport CXX=/usr/local/bin/g++-8 && export CC=/usr/local/bin/gcc-8cmake /rgf/RGF /rgf/FastRGFmakesudo make installcd /rgf/python-packagesudo python3 setup.py install
After a successful rgf-python installation the user should open an R session and give the following reticulate command to change to the relevant (brew-python) directory (otherwise the RGF package won't work properly),
to validate that a user is in the python version where RGF or FastRGF are installed. Then,
to load the R package. It is possible that the following warning in the R session appears if FastRGF is not installed,
UserWarning: Cannot find FastRGF executable files. FastRGF estimators will be unavailable for usage.warnings.warn("Cannot find FastRGF executable files. FastRGF estimators will be unavailable for usage.")
NOTE : CURRENTLY THE PACKAGE ON WINDOWS CAN BE USED ONLY FROM THE COMMAND LINE (cmd)
First download of get-pip.py for windows
Update the Environment variables ( Control Panel >> System and Security >> System >> Advanced system settings >> Environment variables >> System variables >> Path >> Edit ) by adding ( for instance in case of python 3 ),
Install the Build Tools for Visual Studio
Open the Command prompt (console) and install the rgf_python dependencies,
pip3 install --upgrade setuptoolspip3 install -U numpypip3 install --upgrade scipypip3 install -U scikit-learn
Then download git for windows,
and run the downloaded .exe file. Then do,
FastRGF requires a gcc version > 5.0 . To find out the gcc version, open a command prompt (console) and type,
Installation / Upgrade of MinGW
Perform the following steps to upgrade the MinGW (so that simple RGF functions work – but not FastRGF)
Normally MinGW is installed in the C:\ directory. So, first delete the folder C:\MinGW (if it already exists), and then remove the environment variable from (Control Panel >> System and Security >> System >> Advanced system settings >> Environment variables >> System variables >> Path >> Edit) which usually is C:\MinGW\bin. Then download the most recent version of MinGW, and especially the mingw-get-setup.exe which is an automated GUI installer assistant. After the new version is installed successfully, update the environment variable by adding C:\MinGW\bin in (Control Panel >> System and Security >> System >> Advanced system settings >> Environment variables >> System variables >> Path >> Edit). Then open a new command prompt (console) and type,
to find out if the new version of MinGW is installed properly.
A word of caution, If Rtools is already installed then make sure that it does not point to an older version of gcc. Just observe the Path field of the environment variables (accessible as explained previously).
Perform the following steps only in case that a FastRGF installation is desired and gcc version is < 5.0
FastRGF works only with MinGW-w64 because only this version provides POSIX threads. It can be downloaded from the project's official SourceForge page. After a successful download and installation the user should also update the environment variables field in (Control Panel >> System and Security >> System >> Advanced system settings >> Environment variables >> System variables >> Path >> Edit) by adding the following path (assuming the software is installed in C:\Program Files (x86) folder),
C:\Program Files (x86)\mingw-w64\i686-7.2.0-posix-dwarf-rt_v5-rev1\mingw32\bin
Installation of cmake
First download cmake for Windows, win64-x64 Installer. Once the file is downloaded run the .exe file and during installation make sure to add CMake to the system PATH for all users.
Before the installation of rgf I might have to remove Rtools environment variables, such as C:\Rtools\bin (accessible as explained previously), otherwise errors might occur.
Installation of RGF, FastRGF and rgf_python [ assuming the installation takes place in the c:/ directory ]
Open a console with administrator privileges (right click on cmd and run as administrator), then
# download the most recent version of rgf-python from the GitHub repository#--------------------------------------------------------------------------git clone# then navigate to#-----------------cd /rgf/RGF/mkdir bincd c:/# then download the latest "rgf.exe" from and place the "rgf.exe" inside the previously created "bin" folder ( /rgf/RGF/bin )# installation of RGF#--------------------cd /rgf/RGF/buildmingw32-makecd c:/# installation of FastRGF#------------------------cd /rgf/FastRGF/mkdir buildcd build# BEFORE PROCEEDING WITH cmake MAKE SURE THAT THE "Rtools" folder IS NOT IN THE SAME DIRECTORY (IF THAT IS THE CASE THEN REMOVE IT TEMPORARILY, i.e. copy-paste the "Rtools" folder somewhere else)cmake .. -G "MinGW Makefiles"mingw32-makemingw32-make installcd c:/# IF APPLICABLE, PASTE THE "Rtools" FOLDER IN THE INITIAL LOCATION / DIRECTORY# installation of rgf-python#---------------------------cd rgf/python-packagepython setup.py install
Then open a command prompt (console) and type,
to launch Python and then type
to observe if rgf is installed properly. Then continue with the installation of the RGF package,
On windows the user can take advantage of the RGF package currently only from within the command prompt (console). First, find the full path of the installation location of R (possible if someone right-clicks in the R short-cut (probably on Desktop) and navigates to properties >> shortcut >> target). In case, for instance, that R is located in C:\Program Files\R\R-3.4.0\bin\x64\R, then, by opening a command prompt (console) and giving,
cd C:\Program Files\R\R-3.4.0\bin\x64\Rlibrary(RGF)
one can proceed with the usage of the RGF package.
To install the package from CRAN use,
and to download the latest version from GitHub use the install_github function of the devtools package,
devtools::install_github(repo = 'RGF-team/rgf', subdir = 'R-package')
The RGF R package was integrated in the home repository of the Regularized Greedy Forest (RGF) library (https://github.com/RGF-team).
We commented the example(s) and test(s) related to the dgCMatrix_2scipy_sparse function [ if (Sys.info()["sysname"] != 'Darwin') ], because the scipy-sparse library on CRAN is not upgraded and the older version includes a bug (TypeError : could not interpret data type). This leads to an error on Mac OS X ( reference : https://github.com/scipy/scipy/issues/5353 ).
We added links to the GitHub repository (master repository, issues).