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.

• How to install CapyMOA.
• How to add documentation.
• How to add tests.
• How to add new algorithms or methods.
• How to format a commit message for CapyMOA.