torchsvm 2.0.0

torchsvm, a PyTorch-based library that trains kernel SVMs and other large-margin classifiers

torchsvm

This is a PyTorch-based package to solve kernel SVM with GPU.

Table of contents

• Introduction
• Installation
• Quick start
• Usage
• Getting help

Introduction

torchsvm, a PyTorch-based library that trains kernel SVMs and other large-margin classifiers with exact leave-one-out cross-validation (LOOCV) error computation. Conventional SVM solvers often face scalability and efficiency challenges, especially on large datasets or when multiple cross-validation runs are required. torchsvm computes LOOCV at the same cost as training a single SVM while boosting speed and scalability via CUDA-accelerated matrix operations. Benchmark experiments indicate that TorchKSVM outperforms existing kernel SVM solvers in efficiency and speed.

Installation

You can use pip to install this package.

pip install torchsvm

Quick start

The usages are similar with scikit-learn:

model = cvksvm(Kmat=Kmat, y=y_train, nlam=nlam, ulam=ulam, foldid=foldid, nfolds=nfolds, eps=1e-5, maxit=1000, gamma=1e-8, is_exact=0, device='cuda')
model.fit()

Usage

Generate simulation data

torchsvm provides a simulation data generation function to test functions in the library:

# Sample data
nn = 10000 # Number of samples
nm = 5    # Number of clusters per class
pp = 10   # Number of features
p1 = p2 = pp // 2    # Number of positive/negative centers
mu = 2.0  # Mean shift
ro = 3  # Standard deviation for normal distribution
sdn = 42  # Seed for reproducibility

nlam = 50
torch.manual_seed(sdn)
ulam = torch.logspace(3, -3, steps=nlam)

X_train, y_train, means_train = data_gen(nn, nm, pp, p1, p2, mu, ro, sdn)
X_test, y_test, means_test = data_gen(nn // 10, nm, pp, p1, p2, mu, ro, sdn)
X_train = standardize(X_train)
X_test = standardize(X_test)

sig = sigest(X_train)
Kmat = rbf_kernel(X_train, sig)

Basic operation

torchsvm mainly provides cvksvm to tune kernel SVM fast with GPU acceleration and compute exact leave-one-out cross-validation (LOOCV) errors if needed.

model = cvksvm(Kmat=Kmat, y=y_train, nlam=nlam, ulam=ulam, foldid=foldid, nfolds=nfolds, eps=1e-5, maxit=1000, gamma=1e-8, is_exact=0, device='cuda')
model.fit()

It also provides applications for other large-margin classifiers:

1. Kernel logistic regression

    model = cvklogit(Kmat=Kmat, y=y_train, nlam=nlam, ulam=ulam, foldid=foldid, nfolds=nfolds, eps=1e-5, maxit=1000, gamma=1e-8, is_exact=0, device='cuda')
    model.fit()
   

2. Kernel SVM with Huber loss

   model = cvkhuber(Kmat=Kmat, y=y_train, nlam=nlam, ulam=ulam, foldid=foldid, nfolds=nfolds, eps=1e-5, maxit=1000, gamma=1e-8, is_exact=0, device='cuda')
   model.fit()
   

3. Kernel squared SVM

    model = cvksqsvm(Kmat=Kmat, y=y_train, nlam=nlam, ulam=ulam, foldid=foldid, nfolds=nfolds, eps=1e-5, maxit=1000, gamma=1e-8, is_exact=0, device='cuda')
    model.fit()
   

Getting help

Any questions or suggestions please contact: [e-mail]