Cyanure: An Open-Source Toolbox for Empirical Risk Minimization for Python, C++, and soon more
This provides a practical tool for researchers and practitioners in machine learning to implement linear models with improved optimization, though it is incremental as it builds on existing stochastic variance-reduced methods.
The authors tackled the problem of efficiently solving empirical risk minimization for linear models by developing Cyanure, an open-source toolbox that provides state-of-the-art solvers with acceleration mechanisms, resulting in support for various loss and regularization functions across Python and C++.
Cyanure is an open-source C++ software package with a Python interface. The goal of Cyanure is to provide state-of-the-art solvers for learning linear models, based on stochastic variance-reduced stochastic optimization with acceleration mechanisms. Cyanure can handle a large variety of loss functions (logistic, square, squared hinge, multinomial logistic) and regularization functions (l_2, l_1, elastic-net, fused Lasso, multi-task group Lasso). It provides a simple Python API, which is very close to that of scikit-learn, which should be extended to other languages such as R or Matlab in a near future.