Package Health Score

54 / 100

security
Security review needed
popularity
Recognized
maintenance
Inactive
community
Active

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62

Popularity

Recognized

GitHub Stars: 1.64K
Forks: 289
Contributors: 9

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package gplearn, we found that it has been starred 1,636 times.

Security

Security review needed

Security and license risk for latest version

Release Date: May 3, 2022
Direct Vulnerabilities: 0
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0
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Indirect Vulnerabilities: 0
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0
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License Risk: 0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

BSD-3-Clause

Security Policy

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Maintenance

Inactive

Commit Frequency

No Recent Commits

Open Issues: 22
Open PR: 3
Last Release: 3 years ago
Last Commit: 1 year ago

Further analysis of the maintenance status of gplearn 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 gplearn 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.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Active

Readme: Yes
Contributing.md: Yes
Code of Conduct: Yes
Contributors: 9
Funding: No

This project has seen only 10 or less contributors.

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Package

Python Versions Compatibility: ==3.10.*, ==3.8.*, ==3.9.*

Age: 10 years
Latest Release: 3 years ago
Dependencies: 2 Direct / 5 Total
Versions: 6
Maintainers: 0
Wheels: OS Independent

Readme

Welcome to gplearn!

gplearn implements Genetic Programming in Python, with a scikit-learn inspired and compatible API.

While Genetic Programming (GP) can be used to perform a very wide variety of tasks, gplearn is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straight-forward to implement.

Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best describes a relationship. It begins by building a population of naive random formulas to represent a relationship between known independent variables and their dependent variable targets in order to predict new data. Each successive generation of programs is then evolved from the one that came before it by selecting the fittest individuals from the population to undergo genetic operations.

gplearn retains the familiar scikit-learn fit/predict API and works with the existing scikit-learn pipeline and grid search modules. The package attempts to squeeze a lot of functionality into a scikit-learn-style API. While there are a lot of parameters to tweak, reading the documentation should make the more relevant ones clear for your problem.

gplearn supports regression through the SymbolicRegressor, binary classification with the SymbolicClassifier, as well as transformation for automated feature engineering with the SymbolicTransformer, which is designed to support regression problems, but should also work for binary classification.

gplearn is built on scikit-learn and a fairly recent copy (1.0.2+) is required for installation. If you come across any issues in running or installing the package, please submit a bug report.

gplearn dependencies

joblib scikit-learn

FAQs

What is gplearn?

Genetic Programming in Python, with a scikit-learn inspired API. Visit Snyk Advisor to see a full health score report for gplearn, including popularity, security, maintenance & community analysis.

Is gplearn popular?

The python package gplearn receives a total of 2,167 weekly downloads. As such, gplearn popularity was classified as a recognized. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is gplearn well maintained?

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

Is gplearn safe to use?

While scanning the latest version of gplearn, 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 23 December-2024, at 11:34 (UTC).

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gplearn

Genetic Programming in Python, with a scikit-learn inspired API For more information about how to use this package see README

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Readme

Welcome to gplearn!

gplearn dependencies

FAQs

What is gplearn?

Is gplearn popular?

Is gplearn well maintained?

Is gplearn safe to use?

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Readme

Welcome to gplearn!

gplearn dependencies

FAQs

What is gplearn?

Is gplearn popular?

Is gplearn well maintained?

Is gplearn safe to use?

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Source Code

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