Package Health Score

60 / 100

security
Security review needed
popularity
Small
maintenance
Inactive
community
Active

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Popularity

Small

GitHub Stars: 28.63K
Forks: 3.4K
Contributors: 450

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package mlopt, we found that it has been starred 28,632 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Jun 8, 2020
Direct Vulnerabilities: 0
C
0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
1
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

Apache-2.0

Security Policy

Yes

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Maintenance

Inactive

Commit Frequency

Open Issues: 829
Open PR: 58
Last Release: 5 years ago
Last Commit: 13 days ago

Further analysis of the maintenance status of mlopt based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for mlopt is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Active

Readme: Yes
Contributing.md: Yes
Code of Conduct: Yes
Contributors: 450
Funding: No

A good and healthy external contribution signal for mlopt project, which invites more than one hundred open source maintainers to collaborate on the repository.

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Package

Python Versions Compatibility: Unspecified

Age: 7 years
Latest Release: 5 years ago
Dependencies: N/A
Versions: 2
Maintainers: 0
Wheels: No

Not sure how to use mlopt?

To help you get started, we've collected the most common ways that mlopt is being used within popular public projects.

pklauke / mlopt / tests / test_particle_swarm_optimizer.py View on Github

def test_update_monotonic_best_scores_minimize():
    """Test if each particle of the particle swarm optimizer monotonically converges for minimization problems."""
    pso = ParticleSwarmOptimizer(func=opt_func, maximize=False, particles=20)

    params = {'x': (-1, 1), 'y': (-1, 1)}
    pso.init(params=params, random_state=1)

    scores = {p: [pso._score_all[p]] for p in range(20)}
    for i in range(100):
        pso.update(params)
        for particle in range(20):
            scores[particle] = scores[particle] + [pso._score_all[particle]]

    assert all(all(scores[particle][i+1] &lt;= scores[particle][i] for i in range(len(scores[particle])-1))
               for particle in range(20))

View more ways to use mlopt

Machine Learning Optimizer

mlopt is a package to learn how to solve numerical optimization problems from data. It relies on cvxpy for modeling and gurobi for solving the problem offline.

mlopt learns how to solve programs using pytorch (pytorch-lightning), xgboost or optimaltrees. The machine learning hyperparameter optimization is performed using optuna.

Online, mlopt only requires to predict the strategy and solve a linear system using scikit-umfpack.

Examples

To see mlopt in action, have a look at the notebooks in the examples/ folder.

Documentation

Coming soon at mlopt.org!

Citing

If you use mlopt for research, please cite the following papers:

The Voice of Optimization:

@article{mlopt,
  author = {{Bertsimas}, D. and {Stellato}, B.},
  title = {The Voice of Optimization},
  journal = {Machine Learning (to appear)},
  year = {2020},
  month = jun,
}

Online Mixed-Integer Optimization in Milliseconds

@article{stellato2019a,
  author = {{Bertsimas}, D. and {Stellato}, B.},
  title = {Online Mixed-Integer Optimization in Milliseconds},
  journal = {INFORMS Journal on Computing (major revision)},
  year = {2019},
  month = jul,
}

Projects using mlopt framework

Learning Mixed-Integer Convex Optimization Strategies for Robot Planning and Control

FAQs

What is mlopt?

The Machine Learning Optimizer. Visit Snyk Advisor to see a full health score report for mlopt, including popularity, security, maintenance & community analysis.

Is mlopt popular?

The python package mlopt receives a total of 31 weekly downloads. As such, mlopt popularity was classified as small. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is mlopt well maintained?

We found indications that mlopt is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is mlopt safe to use?

While scanning the latest version of mlopt, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 25 December-2024, at 13:34 (UTC).

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mlopt

The Machine Learning Optimizer For more information about how to use this package see README

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Package

Not sure how to use mlopt?

Machine Learning Optimizer

Examples

Documentation

Citing

Projects using mlopt framework

FAQs

What is mlopt?

Is mlopt popular?

Is mlopt well maintained?

Is mlopt safe to use?

Build a secure application checklist

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Scan your app for vulnerabilities

Fix identified vulnerabilities

Monitor for new issues

Package Health Score

Explore Similar Packages

Direct Usage Popularity

Security and license risk for latest version

Commit Frequency

Embed Package Health Score Badge

Keep your project healthy

Check your requirements.txt

Snyk Vulnerability Scanner

Not sure how to use mlopt?

Machine Learning Optimizer

Examples

Documentation

Citing

Projects using mlopt framework

FAQs

What is mlopt?

Is mlopt popular?

Is mlopt well maintained?

Is mlopt safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Source Code

Open Source

Container

Config

Fix identified vulnerabilities

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