BlitzML is a fast, easy-to-use solver for training some popular linear machine learning models. Currently BlitzML trains:
- Sparse models with L1 regularization (Lasso-type problems).
You can use BlitzML as a Python package or C library. BlitzML uses minimal memory copying, making it convenient to incorporate as a subproblem solver in more ellaborate algorithms.
BlitzML trains sparse models by solving the problem
Here are the available loss functions:
Problem | Loss function |
---|---|
Lasso | ![]() |
Sparse logistic regression | ![]() |
Sparse Huber | ![]() |
Sparse smoothed hinge | ![]() |
Sparse squared hinge | ![]() |
To install, clone the repository and run pip install .
inside the project directory.
Sparse logistic regression example:
import blitzml
problem = blitzml.SparseLogisticRegressionProblem(A, b)
solution = problem.solve(l1_penalty=1.0)
A
can be a dense NumPy array or sparse SciPy matrix.
Linear SVM example:
problem = blitzml.LinearSVMProblem(A, b)
solution = problem.solve(l2_penalty=1.0)
For detailed documentation, see https://tbjohns.github.io/BlitzML/.