capymoa

v0.7.0

Python wrapper for MOA to allow efficient use of existing algorithms with a more modern API. For more information about how to use this package see README

Latest version published 4 months ago

License: BSD-3-Clause

PyPI

GitHub

Package Health Score

72 / 100

security
No known security issues
popularity
Limited
maintenance
Healthy
community
Sustainable

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Popularity

Limited

GitHub Stars: 61
Forks: 20
Contributors: 20

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package capymoa, we found that it has been starred 61 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Aug 3, 2024
Direct Vulnerabilities: 0
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0
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0
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0
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Indirect Vulnerabilities: 0
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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

Healthy

Commit Frequency

Open Issues: 6
Open PR: 7
Last Release: 4 months ago
Last Commit: 2 months ago

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

We found that capymoa demonstrates a positive version release cadence with at least one new version released in the past 12 months.

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

Sustainable

Readme: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 20
Funding: No

With more than 10 contributors for the capymoa repository, this is possibly a sign for a growing and inviting community.

We found a way for you to contribute to the project! Looks like capymoa is missing a Code of Conduct.

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Package

Python Versions Compatibility: >=3.9

Age: 7 months
Latest Release: 4 months ago
Dependencies: 9 Direct
Versions: 10
Maintainers: 2
Wheels: OS Independent

Readme

CapyMOA

Banner Image

Machine learning library tailored for data streams. Featuring a Python API tightly integrated with MOA (Stream Learners), PyTorch (Neural Networks), and scikit-learn (Machine Learning). CapyMOA provides a fast python interface to leverage the state-of-the-art algorithms in the field of data streams.

To setup CapyMOA, simply install it via pip. If you have any issues with the installation (like not having Java installed) or if you want GPU support, please refer to the installation guide. Once installed take a look at the tutorials to get started.

# CapyMOA requires Java. This checks if you have it installed
java -version

# CapyMOA requires PyTorch. This installs the CPU version
pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu

# Install CapyMOA and its dependencies
pip install capymoa

# Check that the install worked
python -c "import capymoa; print(capymoa.__version__)"

⚠️ WARNING

CapyMOA is still in the early stages of development. The API is subject to change until version 1.0.0. If you encounter any issues, please report them in GitHub Issues or talk to us on Discord.

Benchmark Image Benchmark comparing CapyMOA against other data stream libraries. The benchmark was performed using an ensemble of 100 ARF learners trained on capymoa.datasets.RTG_2abrupt dataset containing 100,000 samples and 30 features. You can find the code to reproduce this benchmark in notebooks/benchmarking.py. CapyMOA has the speed of MOA with the flexibility of Python and the richness of Python's data science ecosystem.

capymoa dependencies

click jpype1 matplotlib numpy pandas pyarrow scikit-learn seaborn wget

FAQs

What is capymoa?

Python wrapper for MOA to allow efficient use of existing algorithms with a more modern API. Visit Snyk Advisor to see a full health score report for capymoa, including popularity, security, maintenance & community analysis.

Is capymoa popular?

We’re still processing downloads data for the python package capymoa. Visit the popularity section on Snyk Advisor to see the full popularity analysis.

Is capymoa well maintained?

We found that capymoa demonstrated a healthy version release cadence and project activity. It has a community of 20 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is capymoa safe to use?

The python package capymoa was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 11 November-2024, at 03:31 (UTC).

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