# CapyMOA

> # [CapyMOA](https://capymoa.org)
> 
> ![Banner Image](https://github.com/adaptive-machine-learning/CapyMOA/raw/main/docs/images/CapyMOA.jpeg)
> 
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> 
> 
> **CapyMOA does efficient machine learning for data streams in Python**. A data stream is
> a sequence of items that arrive one by one and are too large or urgent to process
> offline. CapyMOA is a toolbox of methods and evaluators for: classification, regression,
> clustering, anomaly detection, semi-supervised learning, online continual learning, and
> drift detection for data streams.
> 
> To install:
> 
> ```
> pip install capymoa
> ```
> 
> The deep-learning parts of CapyMOA (`capymoa.ocl`, `capymoa.core.torch.ann`, the `Batch*`
> learners) need PyTorch, which is an optional extra:
> 
> ```
> pip install capymoa[torch]
> ```
> 
> Refer to the [Setup](https://capymoa.org/setup) guide for other options,
> including CPU-only PyTorch and dev dependencies.
> 
> ```python
> from capymoa.datasets import Electricity
> from capymoa.classifier import HoeffdingTree
> from capymoa.evaluation import prequential_evaluation
> 
> # 1. Load a streaming dataset
> stream = Electricity()
> 
> # 2. Create a machine learning model
> model = HoeffdingTree(stream.get_schema())
> 
> # 3. Run with test-then-train evaluation
> results = prequential_evaluation(stream, model)
> 
> # 4. Success!
> print(f"Accuracy: {results.accuracy():.2f}%")
> ```
> 
> Next, we recommend the [Tutorials](https://capymoa.org/tutorials).
> 
> > **⚠️ 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](https://github.com/adaptive-machine-learning/CapyMOA/issues)
> > or talk to us on [Discord](https://discord.gg/spd2gQJGAb).
> 
> ---
> 
> ![Benchmark Image](https://github.com/adaptive-machine-learning/CapyMOA/raw/main/docs/images/arf100_cpu_time.png)
> Benchmark comparing CapyMOA against other data stream libraries. The benchmark
> was performed using an ensemble of 100 ARF learners trained on
> the `capymoa.datasets.RTG_2abrupt` dataset containing 100,000 samples and 30
> features.  You can find the code to reproduce this benchmark in
> [`benchmarks/README.md`](benchmarks/README.md), with the runnable script at
> [`benchmarks/benchmarking.py`](benchmarks/benchmarking.py).
> *CapyMOA has the speed of MOA with the flexibility of Python and the richness of
> Python's data science ecosystem.*
> 
> ## Cite Us 
> 
> If you use CapyMOA in your research, please cite us using the following BibTeX item.
> ``` 
> @misc{gomes2025,
>   title={{CapyMOA}: Efficient Machine Learning for Data Streams and Online Continual Learning in Python},
>   author={Heitor Murilo Gomes and Anton Lee and Nuwan Gunasekara and Yibin Sun and Guilherme Weigert Cassales and Justin Jia Liu and Marco Heyden and Vitor Cerqueira and Maroua Bahri and Yun Sing Koh and Bernhard Pfahringer and Albert Bifet},
>   year={2025},
>   eprint={2502.07432},
>   archivePrefix={arXiv},
>   primaryClass={cs.LG},
>   url={https://arxiv.org/abs/2502.07432}
> }
> ```


2026 CapyMOA Developers

## Pages in this subsection

- [Streaming classification](index.md): Learn how to classify data streams incrementally, one instance at a time.
- [Getting started with classification](getting_started.md): This notebook shows some basic usage of CapyMOA for streaming classification.
- [Evaluating classifiers in CapyMOA](evaluation.md): This notebook further explores **high-level evaluation functions**, **data abstraction** and **class...
- [Creating a new classifier in CapyMOA](new_learner.md): In this tutorial we show how simple it is to create a new learner in CapyMOA using Python.
- [Parallel ensembles](parallel_ensembles.md): This notebook is aimed at showing how to use the parallel and mini-batch variants of the ensembles a...

## Optional

- [Top-level llms.txt](../../llms.txt): Complete documentation index.