https://github.com/jolars/sortedl1

Python package for Sorted L-One Penalized Estimation (SLOPE)
https://github.com/jolars/sortedl1

machine-learning python regression slope statistics

Last synced: about 2 months ago
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Python package for Sorted L-One Penalized Estimation (SLOPE)

• Host: GitHub
• URL: https://github.com/jolars/sortedl1
• Owner: jolars
• License: gpl-3.0
• Created: 2023-11-29T14:25:45.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-08-05T21:58:19.000Z (5 months ago)
• Last Synced: 2024-09-15T08:25:42.083Z (4 months ago)
• Topics: machine-learning, python, regression, slope, statistics
• Language: C++
• Homepage: https://jolars.github.io/sortedl1/
• Size: 1.05 MB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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README

        
# sortedl1 

[![CI](https://github.com/jolars/sortedl1/actions/workflows/ci.yaml/badge.svg?branch=main)](https://github.com/jolars/sortedl1/actions/workflows/ci.yaml)

[![PyPI version](https://badge.fury.io/py/sortedl1.svg)](https://badge.fury.io/py/sortedl1)

[![codecov](https://codecov.io/gh/jolars/sortedl1/graph/badge.svg?token=tMVGB2LHcM)](https://codecov.io/gh/jolars/sortedl1)

**sortedl1** is a python package for Sorted L-One Penalized Estimation (SLOPE).

## Installing

The current release can be installed from PyPI by running

```python

pip install sortedl1

```

You can also install the latest development version via pip by calling

```python

pip install git+https://github.com/jolars/sortedl1

```

Alternatively, you can clone the repository and install the package locally by running

```python

pip install .

```

Installing from source requires a C++17 compatible compiler.

## Usage

Estimators in **sortedl1** are compatible with the scikit-learn interface.

```python

import numpy as np

from numpy.random import default_rng

from sortedl1 import Slope

# Generate some random data

n = 100

p = 3

seed = 31

rng = default_rng(seed)

x = rng.standard_normal((n, p))

beta = rng.standard_normal(p)

y = x @ beta + rng.standard_normal(n)

# Fit the model

model = Slope(alpha=0.1)

model.fit(x, y)

# Print the coefficients

print(model.coef_)

```

## Contributing

The backbone of the package is written in C++ and developed in a separate repository at . So if you have any issues with the package other than such that are specific to the python interface, please report them there. But feel free to request features here.

When writing commit messages, please use the [conventional commits format](https://www.conventionalcommits.org/en/v1.0.0/).

## Versioning

sortedl1 uses [semantic versioning](https://semver.org).