Random Fourier Features

This repository provides the Python module rfflearn which is a Python library of random Fourier features (hereinafter abbreviated as RFF) [1, 2] for kernel methods, like support vector machine [3, 4] and Gaussian process model [5]. Features of this module are:

• User-friendly interfaces: Interfaces of the rfflearn module are quite close to
  the scikit-learn library,
• Example code first: This repository provides plenty of example code to
  demonstrate that RFF is useful for actual machine learning tasks,
• GPU support: Some classes in the rfflearn module provides both GPU training and inference
  for faster computation,
• Wrapper to the other library: Interface to Optuna and
  SHAP are provided for easier hyperparameter tuning and
  feature importance analysis.

Now, this module supports the following methods:

RFF can be applicable to many other machine learning algorithms than the above. The author will provide implementations of the other algorithms soon.

NOTE: The author confirmed that the rfflearn module works on PyTorch 2.0! 🎉

Installation

Please install from PyPI.

pip install rfflearn

If you need GPU support, Optuna support or SHAP support, you need to install the following packages.

# For GPU support.
pip install torch

# For Optuna support.
pip install optuna

# For SHAP support.
pip install matplotlib shap

The author recommends using venv or Docker to avoid polluting your environment.

Minimal example

Interfaces provided by the module rfflearn are quite close to scikit-learn. For example, the following Python code is a sample usage of RFFSVC (support vector machine with random Fourier features) class.

>>> import numpy as np
>>> import rfflearn.cpu as rfflearn                     # Import module
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])  # Define input data
>>> y = np.array([1, 1, 2, 2])                          # Defile label data
>>> svc = rfflearn.RFFSVC().fit(X, y)                   # Training (on CPU)
>>> svc.score(X, y)                                     # Inference (on CPU)
1.0
>>> svc.predict(np.array([[-0.8, -1]]))
array([1])

This module supports training/inference on GPU. For example, the following Python code is a sample usage of RFFGPC (Gaussian process classifier with random Fourier features) on GPU. The following code requires PyTorch (>= 1.7.0).

>>> import numpy as np
>>> import rfflearn.gpu as rfflearn                     # Import module
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])  # Define input data
>>> y = np.array([1, 1, 2, 2])                          # Defile label data
>>> gpc = rfflearn.RFFGPC().fit(X, y)                   # Training on GPU
>>> gpc.score(X, y)                                     # Inference on GPU
1.0
>>> gpc.predict(np.array([[-0.8, -1]]))
array([1])

See the examples directory for more detailed examples.

Example1: MNIST using random Fourier features

The author tried SVC (support vector classifier) and GPC (Gaussian process classifier) with RFF to the MNIST dataset which is one of the famous benchmark datasets on the image classification task, and the author has got better performance and much faster inference speed than kernel SVM. The following table gives a brief comparison of kernel SVM, SVM with RFF, and GPC with RFF. See the example of RFF SVC module and RFF GP module for more details.

Example2: Visualization of feature importance

This module also has interfaces to some feature importance methods, like SHAP [6] and permutation importance [7]. The author tried SHAP and permutation importance to RFFGPR trained on the California housing dataset, and the followings are the visualization results obtained by rfflearn.shap_feature_importance and rfflearn.permutation_feature_importance.

Quick Tutorial

At first, please clone the random-fourier-features repository from GitHub:

git clone https://github.com/tiskw/random-fourier-features.git
cd random-fourier-features

If you are using the docker image, enter into the docker container by the following command (not necessary to run thw following if you don't use docker):

docker run --rm -it --gpus all -v `pwd`:/work -w /work -u `id -u`:`id -g` tiskw/pytorch:latest bash

Then, launch python3 and try the following minimal code that runs support vector classification with random Fourier features on an artificial tiny dataset.

>>> import numpy as np                                  # Import Numpy
>>> import rfflearn.cpu as rfflearn                     # Import our module
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])  # Define input data
>>> y = np.array([1, 1, 2, 2])                          # Defile label data
>>> svc = rfflearn.RFFSVC().fit(X, y)                   # Training
>>> svc.score(X, y)                                     # Inference (on CPU)
1.0
>>> svc.predict(np.array([[-0.8, -1]]))
array([1])

Next Step

Now you succeeded in installing the rfflearn module. The author's recommendation for the next step is to see the examples directory and try a code you are interested in.

Notes

• The name of this module is changed from pyrff to rfflearn on Oct 2020, because the package
  name pyrff already exists in PyPI.
• If the number of training data is huge, an error message like RuntimeError: The task could not be sent to the workers as it is too large for 'send_bytes' will be raised from the joblib library.
  The reason for this error is that the sklearn.svm.LinearSVC uses joblib as a multiprocessing
  backend, but joblib cannot deal huge size of the array which cannot be managed with 32-bit
  address space. In this case, please try n_jobs = 1 option for the RFFSVC or ORFSVC function.
  Default settings are n_jobs = -1 which means automatically detecting available CPUs and using
  them. (This bug information was reported by Mr. Katsuya Terahata @ Toyota Research Institute
  Advanced Development. Thank you so much for the reporting!)
• Application of RFF to the Gaussian process model is not straightforward.
  See this document for mathematical details.

License

This software is distributed under the MIT Licence. See LICENSE for more information.

Reference

[1] A. Rahimi and B. Recht, "Random Features for Large-Scale Kernel Machines", NIPS, 2007. PDF

[2] F. X. Yu, A. T. Suresh, K. Choromanski, D. Holtmann-Rice and S. Kumar, "Orthogonal Random Features", NIPS, 2016. PDF

[3] V. Vapnik and A. Lerner, "Pattern recognition using generalized portrait method", Automation and Remote Control, vol. 24, 1963.

[4] B. Boser, I. Guyon and V. Vapnik, "A training algorithm for optimal margin classifiers", COLT, pp. 144-152, 1992 URL

[5] C. Rasmussen and C. Williams, "Gaussian Processes for Machine Learning", MIT Press, 2006.

[6] S. M. Lundberg and S. Lee, "A Unified Approach to Interpreting Model Predictions", NIPS, 2017. PDF

[7] L. Breiman, "Random Forests", Machine Learning, vol. 45, pp. 5-32, Springer, 2001. Springer website.

Author

Tetsuya Ishikawa (EMail, Website)