rgf-python 3.12.0

Features

Scikit-learn interface and possibility of usage for multiclass classification problem.

rgf_python contains both original RGF from the paper [1] and FastRGF implementations.

Note that FastRGF is developed to be used with large (and sparse) datasets, so on small datasets it often shows poorer performance compared to vanilla RGF.

Original RGF implementations are available only for regression and binary classification, but rgf_python is also available for multiclass classification by “One-vs-Rest” method.

Examples

from sklearn import datasets
from sklearn.utils.validation import check_random_state
from sklearn.model_selection import StratifiedKFold, cross_val_score
from rgf.sklearn import RGFClassifier

iris = datasets.load_iris()
rng = check_random_state(0)
perm = rng.permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]

rgf = RGFClassifier(max_leaf=400,
                    algorithm="RGF_Sib",
                    test_interval=100,
                    verbose=True)

n_folds = 3

rgf_scores = cross_val_score(rgf,
                             iris.data,
                             iris.target,
                             cv=StratifiedKFold(n_folds))

rgf_score = sum(rgf_scores)/n_folds
print('RGF Classifier score: {0:.5f}'.format(rgf_score))

More examples of using RGF estimators could be found here.

Examples of using FastRGF estimators could be found here.

Software Requirements

• Python (>= 3.7)

• joblib

• scikit-learn (>= 0.18)

Installation

From PyPI using pip:

pip install rgf_python

or from GitHub:

git clone https://github.com/RGF-team/rgf.git
cd rgf/python-package
python setup.py install

MacOS users, rgf_python after the 3.10.0 version is built with g++-11 and cannot be launched on systems with g++-10 and earlier. You should update your g++ compiler if you don’t want to build from sources or install rgf_python 3.10.0 from PyPI which is the last version built with g++-10.

MacOS users, rgf_python after the 3.8.0 version is built with g++-10 and cannot be launched on systems with g++-9 and earlier. You should update your g++ compiler if you don’t want to build from sources or install rgf_python 3.8.0 from PyPI which is the last version built with g++-9.

MacOS users, rgf_python after the 3.5.0 version is built with g++-9 and cannot be launched on systems with g++-8 and earlier. You should update your g++ compiler if you don’t want to build from sources or install rgf_python 3.5.0 from PyPI which is the last version built with g++-8.

MacOS users, rgf_python after the 3.1.0 version is built with g++-8 and cannot be launched on systems with g++-7 and earlier. You should update your g++ compiler if you don’t want to build from sources or install rgf_python 3.1.0 from PyPI which is the last version built with g++-7.

If you have any problems while installing by methods listed above, you should build RGF and FastRGF executable files from binaries on your own and place compiled executable files into directory which is included in environmental variable ‘PATH’ or into directory with installed package. Alternatively, you may specify actual locations of executable files and directory for placing temp files by corresponding flags in configuration file .rgfrc, which you should create into your home directory. The default values are the following: rgf_location=$HOME/rgf ($HOME/rgf.exe for Windows), fastrgf_location=$HOME, temp_location=tempfile.gettempdir() (here is more details about tempfile.gettempdir()). Please take a look at the example of the .rgfrc file:

rgf_location=C:/Program Files/RGF/bin/rgf.exe
fastrgf_location=C:/Program Files/FastRGF/bin
temp_location=C:/Program Files/RGF/temp

Note that while rgf_location should point to a concrete RGF executable file, fastrgf_location should point to a folder in which forest_train.exe and forest_predict.exe FastRGF executable files are located.

Also, you may directly specify installation without automatic compilation:

pip install rgf_python --install-option=--nocompilation

or

git clone https://github.com/RGF-team/rgf.git
cd rgf/python-package
python setup.py install --nocompilation

sudo (or administrator privileges in Windows) may be needed to perform installation commands.

Detailed guides how you can build executable files of RGF and FastRGF from source files could be found in their folders here and here respectively.

# Run docker image
docker run -it rgfteam/rgf /bin/bash
# Run RGF example
python ./rgf/python-package/examples/RGF/comparison_RGF_and_RF_regressors_on_boston_dataset.py
# Run FastRGF example
python ./rgf/python-package/examples/FastRGF/FastRGF_classifier_on_iris_dataset.py

Tuning Hyperparameters

Using at Kaggle Kernels

Kaggle Kernels support rgf_python. Please see this page.

Troubleshooting

If you meet any error, please try to run test_rgf_python.py to confirm successful package installation.

Then feel free to open new issue.

License

rgf_python is distributed under the MIT license. Please read file LICENSE for more information.

Many thanks to Rie Johnson and Tong Zhang (the authors of RGF).

Other

Shamelessly, some part of the implementation is based on the following code. Thanks!

References

[1] Rie Johnson and Tong Zhang. Learning Nonlinear Functions Using Regularized Greedy Forest. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(5):942-954, May 2014