rgf-python 3.7.0

Scikit-learn Wrapper for Regularized Greedy Forest

|License|

rgf_python

The wrapper of machine learning algorithm Regularized Greedy Forest (RGF) [1] for Python.

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 (2.7 or >= 3.5)

• joblib

• six

• 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 3.5.0 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.

Docker image

We provide docker image with installed rgf_python.

# 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

RGF

You can tune hyperparameters as follows.

• max_leaf: Appropriate values are data-dependent and usually varied from 1000 to 10000.

• test_interval: For efficiency, it must be either multiple or divisor of 100 (default value of the optimization interval).

• algorithm: You can select “RGF”, “RGF Opt” or “RGF Sib”.

• loss: You can select “LS”, “Log”, “Expo” or “Abs”.

• reg_depth: Must be no smaller than 1. Meant for being used with algorithm = “RGF Opt” or “RGF Sib”.

• l2: Either 1, 0.1, or 0.01 often produces good results though with exponential loss (loss = “Expo”) and logistic loss (loss = “Log”), some data requires smaller values such as 1e-10 or 1e-20.

• sl2: Default value is equal to l2. On some data, l2/100 works well.

• normalize: If turned on, training targets are normalized so that the average becomes zero.

• min_samples_leaf: Smaller values may slow down training. Too large values may degrade model accuracy.

• n_iter: Number of iterations of coordinate descent to optimize weights.

• n_tree_search: Number of trees to be searched for the nodes to split. The most recently grown trees are searched first.

• opt_interval: Weight optimization interval in terms of the number of leaf nodes.

• learning_rate: Step size of Newton updates used in coordinate descent to optimize weights.

Detailed instruction of tuning hyperparameters is here.

FastRGF

• n_estimators: Typical range is [100, 10000], and a typical value is 1000.

• max_depth: Controls the tree depth.

• max_leaf: Controls the tree size.

• tree_gain_ratio: Controls when to start a new tree.

• min_samples_leaf: Controls the tree growth process.

• loss: You can select “LS”, “MODLS” or “LOGISTIC”.

• l1: Typical range is [0, 1000], and a large value induces sparsity.

• l2: Use a relatively large value such as 1000 or 10000. The larger value is, the larger n_estimators you need to use: the resulting accuracy is often better with a longer training time.

• opt_algorithm: You can select “rgf” or “epsilon-greedy”.

• learning_rate: Step size of epsilon-greedy boosting. Meant for being used with opt_algorithm = “epsilon-greedy”.

• max_bin: Typical range for dense data is [10, 65000] and for sparse data is [10, 250].

• min_child_weight: Controls the process of discretization (creating bins).

• data_l2: Controls the degree of L2 regularization for discretization (creating bins).

• sparse_max_features: Typical range is [1000, 10000000]. Meant for being used with sparse data.

• sparse_min_occurences: Controls which feature will be selected. Meant for being used with sparse data.

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.

Known Issues

• FastRGF crashes if training dataset is too small (#data < 28). (rgf#92)

• FastRGFClassifier and FastRGFRegressor do not provide any built-in method to calculate feature importances. (rgf#109)

FAQ

• Q: Temporary files use too much space on my hard drive (Kaggle Kernels disc space is exhausted while fitting rgf_python model).

  A: Please see rgf#75.

• Q: GridSearchCV/RandomizedSearchCV/RFECV or other scikit-learn tool with n_jobs parameter hangs/freezes/crashes when runs with rgf_python estimator.

  A: This is a known general problem of multiprocessing in Python. You should set n_jobs=1 parameter of either estimator or scikit-learn tool.

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