semvec/pypi-packages huggingface dataset:
lasso-python 2.0.4
An open-source CAE and Machine Learning library.
Requires Python: >=3.11


|LASSO| Python Library
======================

|test-main| |test-dev|

.. |test-main| image:: https://github.com/open-lasso-python/lasso-python/actions/workflows/ci-cd.yml/badge.svg?branch=main
   :target: https://github.com/open-lasso-python/lasso-python/actions/workflows/test-runner.yml

.. |test-dev| image:: https://github.com/open-lasso-python/lasso-python/actions/workflows/ci-cd.yml/badge.svg?branch=develop
   :target: https://github.com/open-lasso-python/lasso-python/actions/workflows/test-runner.yml

This python library is designed for general purpose usage in the field of
Computer Aided Engineering (CAE).
It's name originates from the original initiator and donator of the project
`LASSO GmbH`_.
The library is now maintained by an open-source community.

Module Overview:
 - `lasso.dyna`_
 - `lasso.dimred`_
 - `lasso.femzip`_
 - `lasso.diffcrash`_

For further infos please read the Documentation:

    |DOCS| `Documentation`_

.. _LASSO GmbH: https://www.lasso.de/en
.. _Documentation: https://open-lasso-python.github.io/lasso-python/
.. _lasso.dyna: https://open-lasso-python.github.io/lasso-python/dyna/
.. _lasso.dimred: https://open-lasso-python.github.io/lasso-python/dimred/
.. _lasso.femzip: https://open-lasso-python.github.io/lasso-python/femzip/
.. _lasso.diffcrash: https://open-lasso-python.github.io/lasso-python/diffcrash/


Installation
------------

..  code-block:: bash

    python -m pip install lasso-python


Community
---------

Join our open-source community on: 

    |DISCORD| `Discord`_
 
.. _Discord:  https://discord.gg/jYUgTsEWtN

.. |LASSO| image:: ./docs/lasso-logo.png
    :target: https://open-lasso-python.github.io/lasso-python/build/html/index.html
.. |DOCS| image:: ./docs/icon-home.png 
    :target: https://open-lasso-python.github.io/lasso-python/build/html/index.html
.. |DISCORD| image:: ./docs/icon-discord.png
    :target: https://discord.gg/GeHu79b


Development
-----------

For development install `poetry`_ and `task`_:

..  code-block:: bash

    python -m pip install poetry
    sh -c "$(curl --location https://taskfile.dev/install.sh)" \
        -- -d -b ~/.local/bin

Then by simply running the command ``task`` you can find a variety of available
commands such as ``task setup`` to install all dependencies or ``task test`` to
run the test suite.
Happy Coding 🥳🎉

.. _poetry: https://python-poetry.org/
.. _task: https://taskfile.dev/
qpalm 1.2.6
Proximal Augmented Lagrangian method for Quadratic Programs
Requires Python: >=3.9
Keywords: optimization, augmented-lagrangian, proximal, qp, quadratic-programming, nonconvex, ladel

Proximal Augmented Lagrangian method for Quadratic Programs
===========================================================
| QPALM is a numerical optimization package that finds stationary points of (possibly **nonconvex**) quadratic programs, that is

.. math::

        \begin{aligned}
            & \underset{x}{\textbf{minimize}}
            & & \tfrac12 x^\top Q x + q^\top x\\
            & \textbf{subject to}
            & & b_\mathrm{min} \le Ax \le b_\mathrm{max} \\
        \end{aligned}

Documentation
-------------
| The documentation can be found at: `<https://kul-optec.github.io/QPALM/Doxygen>`_  
| Examples are included as well: `<https://kul-optec.github.io/QPALM/Doxygen/examples.html>`_

Installation
------------

Python
^^^^^^
| The QPALM Python interface is available from `PyPI <https://pypi.org/project/qpalm>`_, you can install it using:

.. code-block:: sh

    python3 -m pip install qpalm


Julia, Matlab, C/C++/Fortran
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
| Installation instructions for the Julia, Matlab, C, C++ and Fortran interfaces, as well as instructions for building QPALM from source, can be found on `GitHub <https://github.com/kul-optec/QPALM/>`_.

Supported platforms
-------------------
| QPALM is written in C, with interfaces for C++, Python, Julia, Matlab and Fortran. The code itself is portable across all major platforms. Binaries are available for Linux on x86-64, AArch64, ARMv7 and ARMv6, for macOS on x86-64 and ARM64, and for Windows on x86-64.

Benchmarks
----------
| Check out the papers below for detailed benchmark tests comparing QPALM with state-of-the-art solvers.

* `QPALM: A Newton-type Proximal Augmented Lagrangian Method for Quadratic Programs <https://arxiv.org/abs/1911.02934>`_.
* `QPALM: A Proximal Augmented Lagrangian Method for Nonconvex Quadratic Programs <https://arxiv.org/abs/2010.02653>`_.

Citing
------
| If you use QPALM in your research, please cite the following paper:

.. code-block:: bib

    @inproceedings{hermans2019qpalm,
        author      = {Hermans, B. and Themelis, A. and Patrinos, P.},
        booktitle   = {58th IEEE Conference on Decision and Control},
        title       = {{QPALM}: {A} {N}ewton-type {P}roximal {A}ugmented {L}agrangian {M}ethod for {Q}uadratic {P}rograms},
        year        = {2019},
        volume      = {},
        number      = {},
        pages       = {},
        doi         = {},
        issn        = {},
        month       = {Dec.},
    }
pygitpub 0.0.58
Pygitpub will help you work with github
Requires Python: >=3.12
Keywords: github, pygithub

# *pygitpub* project by Mark Veltzer

description: Pygitpub will help you work with github

project website: https://veltzer.github.io/pygitpub

author: Mark Veltzer

version: 0.0.58

![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)

## github

![License](https://img.shields.io/github/license/veltzer/pygitpub)

## build

![build](https://github.com/veltzer/pygitpub/workflows/build/badge.svg)

## pypi

![PyPI - Status](https://img.shields.io/pypi/status/pygitpub)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pygitpub)
![PyPI - License](https://img.shields.io/pypi/l/pygitpub)
![PyPI - Package Name](https://img.shields.io/pypi/v/pygitpub)
![PyPI - Format](https://img.shields.io/pypi/format/pygitpub)

## pypi download

![PyPI - Downloads](https://img.shields.io/pypi/dd/pygitpub)
![PyPI - Downloads](https://img.shields.io/pypi/dw/pygitpub)
![PyPI - Downloads](https://img.shields.io/pypi/dm/pygitpub)



## contact me
[mailto](mailto:mark.veltzer@gmail.com)
![gitter](https://img.shields.io/gitter/room/veltzer/mark.veltzer)
![discord](https://img.shields.io/discord/719336281624281119)
![discord](https://img.shields.io/discord/719336282194444302)

Mark Veltzer, Copyright © 2022, 2023, 2024, 2025
mlrisko 0.1.0
A toolkit to calibrate predictive algorithms to achieve risk control.
Requires Python: >=3.13
Keywords: risk-control, machine-learning, ai, statistics, multiple-testing

# Risk Control Project (mlrisko)

[![CI/CD Pipeline](https://github.com/thibaultcordier/risk-control/workflows/CI%2FCD%20Pipeline/badge.svg)](https://github.com/thibaultcordier/risk-control/actions)
[![Code Coverage](https://codecov.io/gh/thibaultcordier/risk-control/branch/main/graph/badge.svg)](https://codecov.io/gh/thibaultcordier/risk-control)
[![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/)
[![License: BSD-3-Clause](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)
[![PyPI version](https://badge.fury.io/py/mlrisko.svg)](https://badge.fury.io/py/mlrisko)
[![Documentation](https://img.shields.io/badge/docs-mkdocs%20material-blue.svg?colorB=319795)](https://thibaultcordier.github.io/risk-control/)
[![Code style: ruff](https://img.shields.io/badge/code%20style-ruff-black.svg)](https://github.com/astral-sh/ruff)
[![Linting: ruff](https://img.shields.io/badge/linting-ruff-red.svg)](https://github.com/astral-sh/ruff)
[![Type checking: mypy](https://img.shields.io/badge/type%20checking-mypy-blue.svg)](https://mypy-lang.org/)

**mlrisko** (MLRiskControl) is a comprehensive toolkit for implementing risk control mechanisms for predictive algorithms based on the paper "Learn then test: Calibrating predictive algorithms to achieve risk control" by Angelopoulos et al. (2025).

The primary goal is to ensure that machine learning algorithms perform reliably and maintain a controlled level of risk through advanced calibration techniques.

## Installation

To install the necessary dependencies, run:

```bash
uv sync
uv pip install -e .
```

For development purposes, you can install the development dependencies with:
```bash
uv sync --all-groups
```

## Running the Example

To run the example, execute the following command:

```bash
uv run python examples/plot_regression.py
uv run python examples/plot_classification.py
uv run python examples/plot_classification_bis.py
```

## Documentation

For detailed documentation, refer to the [docs](https://thibaultcordier.github.io/risk-control/).

Or you can build the documentation with:
```bash
uv run mkdocs serve
```

## License

This project is licensed under the BSD 3-Clause License. See the [LICENSE](LICENSE) file for details.

## References

Angelopoulos, A. N., Bates, S., Candès, E. J., Jordan, M. I., & Lei, L. (2025). Learn then test: Calibrating predictive algorithms to achieve risk control. The Annals of Applied Statistics, 19(2), 1641-1662.