Setup#

This document describes how to install CapyMOA and its dependencies. CapyMOA is tested against Python 3.10, 3.11, and 3.12. Newer versions of Python will likely work but have yet to be tested.

Once you have installed the Dependencies, you may install CapyMOA using pip (optionally in a Virtual Environment):

pip install capymoa

To verify your installation, run:

python -c "import capymoa; print(capymoa.__version__)"

Virtual Environment#

We recommend using a virtual environment to isolate CapyMOA and its dependencies from your other projects. This is especially important if you have other projects that require different versions of the same dependencies.

If you chose to use a virtual environment, you have some choices:

Python Virtual Environment PyVenv is a built-in tool for creating virtual environments in Python. You can create a new virtual environment with:
```
python3 -m venv .capymoa-venv
source .capymoa-venv/bin/activate
# On Windows, use `.capymoa-venv\Scripts\activate`
```
Conda Environment Miniconda is a good choice for managing Python environments. You can install Miniconda from here. Once you have Miniconda installed, you can create a new environment with:
```
conda create -n capymoa python=3.11
conda activate capymoa
```
When your environment is activated, you can install CapyMOA by following the instructions below.

Dependencies#

CapyMOA has some required dependencies that may require manual installation before CapyMOA can be used:

Java#

CapyMOA requires a Java runtime. You can check if Java is installed by running the following command in your terminal:

java -version

If Java is not installed, you can download OpenJDK (Open Java Development Kit) from this link, or alternatively the Oracle JDK from this link. You only need to install the Java Runtime (JRE). Linux and macOS users can also install OpenJDK using their distribution’s package manager:

Ubuntu

sudo apt-get install -y default-jre-headless

macOS

brew install openjdk

CapyMOA will attempt to find the Java automatically unless the JAVA_HOME environment variable is set. This allows you to have multiple Java versions or have Java installed outside of the system path.

PyTorch#

PyTorch is optional. pip install capymoa does not install it, so the core of CapyMOA – streams, classifiers, regressors, drift detectors and evaluation – installs without pulling a deep-learning stack.

The parts of CapyMOA that use deep learning do require it: capymoa.ocl, capymoa.ann, capymoa.stream.TorchStream, the Batch* learners, capymoa.anomaly.Autoencoder, capymoa.classifier.Finetune and capymoa.ssl.OSNN. Using one of those without PyTorch raises an OptionalDependencyError telling you what to install.

capymoa.drift.detectors.ABCD is a partial case: it works without PyTorch on its default model_id="pca" and on "kpca", and needs the extra only for the autoencoder model, model_id="ae".

Install CapyMOA with PyTorch using the torch extra:

pip install capymoa[torch]

Note

On Linux the default PyPI PyTorch wheel is CUDA-enabled and pulls the NVIDIA stack (several GB). If you do not need a GPU, install the CPU build first and then CapyMOA – pip will keep the version you already have:

pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
pip install capymoa[torch]

To match a specific GPU or CUDA version instead, follow the instructions here, and make sure PyTorch goes into the same virtual environment as CapyMOA.