# CapyMOA

> # [CapyMOA](https://capymoa.org)
> 
> ![Banner Image](https://github.com/adaptive-machine-learning/CapyMOA/raw/main/docs/images/CapyMOA.jpeg)
> 
> [![PyPi Version](https://img.shields.io/pypi/v/capymoa)](https://pypi.org/project/capymoa/)
> [![Docker Image Version (tag)](https://img.shields.io/docker/v/tachyonic/jupyter-capymoa/latest?logo=docker&label=Docker&color=blue)](https://hub.docker.com/r/tachyonic/jupyter-capymoa)
> [![Join the Discord](https://img.shields.io/discord/1235780483845984367?label=Discord)](https://discord.gg/spd2gQJGAb)
> [![Documentation](https://img.shields.io/badge/docs-latest-blue)](https://capymoa.org)
> [![GitHub](https://img.shields.io/github/stars/adaptive-machine-learning/CapyMOA?style=social)](https://github.com/adaptive-machine-learning/CapyMOA)
> [![Coverage Status](https://coveralls.io/repos/github/adaptive-machine-learning/CapyMOA/badge.svg)](https://coveralls.io/github/adaptive-machine-learning/CapyMOA)
> 
> 
> **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

- [CapyMOA](index.md): CapyMOALink to PyPIimageLink to DiscordLink to GitHubDocker Image Version (tag)
- [Setup](setup/index.md): This document describes how to install CapyMOA and its dependencies. CapyMOA is
- [Docker Setup](setup/docker.md): CapyMOA provides a Docker image application containing a ready-to-go Jupyter
- [Developer Setup](setup/developer.md): If you want to make changes to CapyMOA, you should follow these steps to set up
- [User Guide](tutorials.md): * Streaming classification
- [Streaming classification](notebooks/classifier/index.md): Learn how to classify data streams incrementally, one instance at a time.
- [Getting started with classification](notebooks/classifier/getting_started.md): This notebook shows some basic usage of CapyMOA for streaming classification.
- [Evaluating classifiers in CapyMOA](notebooks/classifier/evaluation.md): This notebook further explores **high-level evaluation functions**, **data abstraction** and **class...
- [Creating a new classifier in CapyMOA](notebooks/classifier/new_learner.md): In this tutorial we show how simple it is to create a new learner in CapyMOA using Python.
- [Parallel ensembles](notebooks/classifier/parallel_ensembles.md): This notebook is aimed at showing how to use the parallel and mini-batch variants of the ensembles a...
- [Streaming drift detection](notebooks/drift/index.md): Learn how to detect concept and data drift in data streams as they evolve over time.
- [Drift Detection in CapyMOA](notebooks/drift/drift_detection.md): In this tutorial, we show how to conduct drift detection using CapyMOA.
- [Simulating concept drifts with the DriftStream API](notebooks/drift/drift_streams.md): This tutorial demonstrates how to use the DriftStream API in CapyMOA:
- [Optimizing Drift Detectors via Leave-One-Dataset-Out Cross-Validation](notebooks/drift/optimizing_detectors.md): Drift detectors have hyperparameters that control sensitivity. Defaults work
- [Data Drift Detection in CapyMOA](notebooks/drift/data_drift_detection.md): This tutorial shows how to detect **data drift** (changes in the input data
- [Streaming regression](notebooks/regressor/index.md): Learn how to predict continuous targets from data streams incrementally.
- [Getting started with regression](notebooks/regressor/getting_started.md): This notebook shows some basic usage of CapyMOA for streaming regression.
- [Evaluating regressors in CapyMOA](notebooks/regressor/evaluation.md): This notebook further explores **high-level evaluation functions** as applied to **regressors**.
- [Streaming clustering](notebooks/clusterer/index.md): Learn how to group data streams into clusters that evolve as new instances arrive.
- [Clustering tutorial](notebooks/clusterer/clustering.md): This tutorial demonstrates the experimental clustering API for CapyMOA.
- [Streaming anomaly detection](notebooks/anomaly/index.md): Learn how to identify anomalous instances in data streams as they arrive.
- [Anomaly Detection](notebooks/anomaly/anomaly_detection.md): This notebook shows some basic usage of CapyMOA for anomaly detection tasks.
- [Online continual learning](notebooks/ocl/index.md): Learn how to train models continually on streams without forgetting past tasks.
- [Online continual learning](notebooks/ocl/ocl.md): In machine learning, continual learning is a problem setting where a model is trained on a sequence ...
- [Online Continual Learning and Event Handlers](notebooks/ocl/ocl_event_system.md): The OCL model is experimenting with an event-based system to facilitate communication
- [Streaming semi-supervised learning](notebooks/ssl/index.md): Learn how to learn from data streams where only some instances are labelled.
- [Semi-supervised Learning](notebooks/ssl/ssl_example.md): * Preparing and executing partially and delayed labeling experiments.
- [Streaming automated machine learning](notebooks/automl/index.md): Learn how to automatically select and tune models for data streams.
- [Automated machine learning](notebooks/automl/automl.md): This notebook contains some basics for autoML using CapyMOA:
- [Streaming uncertainty estimation](notebooks/uncertainty/index.md): Learn how to quantify predictive uncertainty for data streams.
- [Prediction Intervals for Data Streams](notebooks/uncertainty/prediction_interval.md): * This notebook covers how to utilise prediction intervals for regression tasks in CapyMOA.
- [Streaming feature importance](notebooks/feature/index.md): Learn how to estimate feature importance for data streams as they evolve.
- [Feature Importance Tutorial](notebooks/feature/feature_importance.md): This notebook is a guided introduction to feature importance in streaming classification with CapyMO...
- [Common functionality](notebooks/common/index.md): Learn how to use CapyMOA’s cross-domain building blocks: pipelines, third-party
- [Using sklearn with CapyMOA](notebooks/common/sklearn_models.md): In this tutorial we demonstrate how someone can directly use scikit-learn learners in CapyMOA.
- [Using PyTorch with CapyMOA](notebooks/common/pytorch_integration.md): * This notebook demonstrates how use PyTorch with CapyMOA.
- [Exploring advanced features](notebooks/common/advanced_API.md): This notebook is targeted at advanced users that want to access MOA objects directly using CapyMOA’s...
- [Pipelines and transformers](notebooks/common/pipelines.md): This notebook showcases the current version of data processing pipelines in CapyMOA.
- [Save and Load a Model](notebooks/common/save_and_load_model.md): In this tutorial, we illustrate the process of saving and loading a model using CapyMOA.
- [API](api/index.md): Welcome to the CapyMOA API reference. This documentation is automatically
- [`classifier`](api/modules/capymoa.classifier.md): Classification.
- [`CSMOTE`](api/modules/capymoa.classifier.CSMOTE.md): Bases: `MOAClassifier`
- [`EFDT`](api/modules/capymoa.classifier.EFDT.md): Bases: `MOAClassifier`
- [`KNN`](api/modules/capymoa.classifier.KNN.md): Bases: `MOAClassifier`
- [`LAST`](api/modules/capymoa.classifier.LAST.md): Bases: `MOAClassifier`
- [`PLASTIC`](api/modules/capymoa.classifier.PLASTIC.md): Bases: `MOAClassifier`
- [`AdaptiveRandomForestClassifier`](api/modules/capymoa.classifier.AdaptiveRandomForestClassifier.md): Bases: `MOAClassifier`
- [`DynamicEnsembleMemberSelection`](api/modules/capymoa.classifier.DynamicEnsembleMemberSelection.md): Bases: `MOAClassifier`
- [`DynamicWeightedMajority`](api/modules/capymoa.classifier.DynamicWeightedMajority.md): Bases: `MOAClassifier`
- [`Finetune`](api/modules/capymoa.classifier.Finetune.md): Bases: `BatchClassifier`
- [`HoeffdingAdaptiveTree`](api/modules/capymoa.classifier.HoeffdingAdaptiveTree.md): Bases: `HoeffdingTree`
- [`HoeffdingTree`](api/modules/capymoa.classifier.HoeffdingTree.md): Bases: `MOAClassifier`
- [`LeveragingBagging`](api/modules/capymoa.classifier.LeveragingBagging.md): Bases: `MOAClassifier`
- [`MajorityClass`](api/modules/capymoa.classifier.MajorityClass.md): Bases: `MOAClassifier`
- [`NaiveBayes`](api/modules/capymoa.classifier.NaiveBayes.md): Bases: `MOAClassifier`
- [`NoChange`](api/modules/capymoa.classifier.NoChange.md): Bases: `MOAClassifier`
- [`OnlineAdwinBagging`](api/modules/capymoa.classifier.OnlineAdwinBagging.md): Bases: `MOAClassifier`
- [`OnlineBagging`](api/modules/capymoa.classifier.OnlineBagging.md): Bases: `MOAClassifier`
- [`OnlineSmoothBoost`](api/modules/capymoa.classifier.OnlineSmoothBoost.md): Bases: `MOAClassifier`
- [`OzaBoost`](api/modules/capymoa.classifier.OzaBoost.md): Bases: `MOAClassifier`
- [`PassiveAggressiveClassifier`](api/modules/capymoa.classifier.PassiveAggressiveClassifier.md): Bases: `SKClassifier`
- [`SAMkNN`](api/modules/capymoa.classifier.SAMkNN.md): Bases: `MOAClassifier`
- [`SGDClassifier`](api/modules/capymoa.classifier.SGDClassifier.md): Bases: `SKClassifier`
- [`ShrubsClassifier`](api/modules/capymoa.classifier.ShrubsClassifier.md): Bases: `_ShrubEnsembles`, `Classifier`
- [`StochasticGradientTree`](api/modules/capymoa.classifier.StochasticGradientTree.md): Bases: `MOAClassifier`
- [`StreamingGradientBoostedTrees`](api/modules/capymoa.classifier.StreamingGradientBoostedTrees.md): Bases: `MOAClassifier`
- [`StreamingRandomPatches`](api/modules/capymoa.classifier.StreamingRandomPatches.md): Bases: `MOAClassifier`
- [`WeightedkNN`](api/modules/capymoa.classifier.WeightedkNN.md): Bases: `MOAClassifier`
- [`drift`](api/modules/capymoa.drift.md): Drift detection.
- [`base_detector`](api/modules/capymoa.drift.base_detector.md): | `BaseDriftDetector`   | Drift Detector                                              |
- [`BaseDriftDetector`](api/modules/capymoa.drift.base_detector.BaseDriftDetector.md): Bases: `LearnerParamsMixin`, `ABC`
- [`MOADriftDetector`](api/modules/capymoa.drift.base_detector.MOADriftDetector.md): Bases: `BaseDriftDetector`
- [`detectors`](api/modules/capymoa.drift.detectors.md): Concept and data drift detectors.
- [`ABCD`](api/modules/capymoa.drift.detectors.ABCD.md): Bases: `BaseDriftDetector`
- [`ADWIN`](api/modules/capymoa.drift.detectors.ADWIN.md): Bases: `MOADriftDetector`
- [`BNDM`](api/modules/capymoa.drift.detectors.BNDM.md): Bases: `BaseDataDriftDetector`
- [`CUSUM`](api/modules/capymoa.drift.detectors.CUSUM.md): Bases: `MOADriftDetector`
- [`D3`](api/modules/capymoa.drift.detectors.D3.md): Bases: `BaseDataDriftDetector`
- [`DDM`](api/modules/capymoa.drift.detectors.DDM.md): Bases: `MOADriftDetector`
- [`EDDM`](api/modules/capymoa.drift.detectors.EDDM.md): Bases: `MOADriftDetector`
- [`MMD`](api/modules/capymoa.drift.detectors.MMD.md): Bases: `BaseDataDriftDetector`
- [`OPTWIN`](api/modules/capymoa.drift.detectors.OPTWIN.md): Bases: `BaseDriftDetector`
- [`PSI`](api/modules/capymoa.drift.detectors.PSI.md): Bases: `BaseDataDriftDetector`
- [`RDDM`](api/modules/capymoa.drift.detectors.RDDM.md): Bases: `MOADriftDetector`
- [`SEED`](api/modules/capymoa.drift.detectors.SEED.md): Bases: `MOADriftDetector`
- [`STEPD`](api/modules/capymoa.drift.detectors.STEPD.md): Bases: `MOADriftDetector`
- [`STUDD`](api/modules/capymoa.drift.detectors.STUDD.md): Bases: `BaseDriftDetector`
- [`AndersonDarling`](api/modules/capymoa.drift.detectors.AndersonDarling.md): Bases: `BaseDataDriftDetector`
- [`BaseDataDriftDetector`](api/modules/capymoa.drift.detectors.BaseDataDriftDetector.md): Bases: `BaseDriftDetector`
- [`ChiSquare`](api/modules/capymoa.drift.detectors.ChiSquare.md): Bases: `BaseDataDriftDetector`
- [`CramerVonMises`](api/modules/capymoa.drift.detectors.CramerVonMises.md): Bases: `BaseDataDriftDetector`
- [`DataDriftResult`](api/modules/capymoa.drift.detectors.DataDriftResult.md): Bases: `object`
- [`EWMAChart`](api/modules/capymoa.drift.detectors.EWMAChart.md): Bases: `MOADriftDetector`
- [`EnergyDistance`](api/modules/capymoa.drift.detectors.EnergyDistance.md): Bases: `BaseDataDriftDetector`
- [`GeometricMovingAverage`](api/modules/capymoa.drift.detectors.GeometricMovingAverage.md): Bases: `MOADriftDetector`
- [`HDDMAverage`](api/modules/capymoa.drift.detectors.HDDMAverage.md): Bases: `MOADriftDetector`
- [`HDDMWeighted`](api/modules/capymoa.drift.detectors.HDDMWeighted.md): Bases: `MOADriftDetector`
- [`Hellinger`](api/modules/capymoa.drift.detectors.Hellinger.md): Bases: `BaseDataDriftDetector`
- [`JensenShannon`](api/modules/capymoa.drift.detectors.JensenShannon.md): Bases: `BaseDataDriftDetector`
- [`KLDivergence`](api/modules/capymoa.drift.detectors.KLDivergence.md): Bases: `BaseDataDriftDetector`
- [`KolmogorovSmirnov`](api/modules/capymoa.drift.detectors.KolmogorovSmirnov.md): Bases: `BaseDataDriftDetector`
- [`PageHinkley`](api/modules/capymoa.drift.detectors.PageHinkley.md): Bases: `MOADriftDetector`
- [`Wasserstein`](api/modules/capymoa.drift.detectors.Wasserstein.md): Bases: `BaseDataDriftDetector`
- [`eval_detector`](api/modules/capymoa.drift.eval_detector.md): | `DriftDetectionMetrics`   | Metrics for evaluating drift detection performance.             |
- [`DriftDetectionMetrics`](api/modules/capymoa.drift.eval_detector.DriftDetectionMetrics.md): Bases: `object`
- [`EvaluateDriftDetector`](api/modules/capymoa.drift.eval_detector.EvaluateDriftDetector.md): Bases: `object`
- [`regressor`](api/modules/capymoa.regressor.md): Regression.
- [`ARFFIMTDD`](api/modules/capymoa.regressor.ARFFIMTDD.md): Bases: `MOARegressor`
- [`FIMTDD`](api/modules/capymoa.regressor.FIMTDD.md): Bases: `MOARegressor`
- [`ORTO`](api/modules/capymoa.regressor.ORTO.md): Bases: `MOARegressor`
- [`SOKNL`](api/modules/capymoa.regressor.SOKNL.md): Bases: `MOARegressor`
- [`SOKNLBT`](api/modules/capymoa.regressor.SOKNLBT.md): Bases: `MOARegressor`
- [`AdaptiveRandomForestRegressor`](api/modules/capymoa.regressor.AdaptiveRandomForestRegressor.md): Bases: `MOARegressor`
- [`FadingTargetMean`](api/modules/capymoa.regressor.FadingTargetMean.md): Bases: `MOARegressor`
- [`KNNRegressor`](api/modules/capymoa.regressor.KNNRegressor.md): Bases: `MOARegressor`
- [`NoChange`](api/modules/capymoa.regressor.NoChange.md): Bases: `Regressor`
- [`PassiveAggressiveRegressor`](api/modules/capymoa.regressor.PassiveAggressiveRegressor.md): Bases: `SKRegressor`
- [`SGDRegressor`](api/modules/capymoa.regressor.SGDRegressor.md): Bases: `SKRegressor`
- [`ShrubsRegressor`](api/modules/capymoa.regressor.ShrubsRegressor.md): Bases: `_ShrubEnsembles`, `Regressor`
- [`StochasticGradientTree`](api/modules/capymoa.regressor.StochasticGradientTree.md): Bases: `MOARegressor`
- [`StreamingGradientBoostedRegression`](api/modules/capymoa.regressor.StreamingGradientBoostedRegression.md): Bases: `MOARegressor`
- [`TargetMean`](api/modules/capymoa.regressor.TargetMean.md): Bases: `MOARegressor`
- [`cluster`](api/modules/capymoa.cluster.md): Clustering.
- [`ClusTree`](api/modules/capymoa.cluster.ClusTree.md): Bases: `MOAClusterer`
- [`Clustream`](api/modules/capymoa.cluster.Clustream.md): Bases: `MOAClusterer`
- [`Clustream_with_kmeans`](api/modules/capymoa.cluster.Clustream_with_kmeans.md): Bases: `MOAClusterer`
- [`Denstream_with_dbscan`](api/modules/capymoa.cluster.Denstream_with_dbscan.md): Bases: `MOAClusterer`
- [`anomaly`](api/modules/capymoa.anomaly.md): Anomaly detection.
- [`datasets`](api/modules/capymoa.anomaly.datasets.md): Collection of built in anomaly detection datasets.
- [`TinyBlobs`](api/modules/capymoa.anomaly.datasets.TinyBlobs.md): Bases: `NumpyStream`
- [`AdaptiveIsolationForest`](api/modules/capymoa.anomaly.AdaptiveIsolationForest.md): Bases: `AnomalyDetector`
- [`Autoencoder`](api/modules/capymoa.anomaly.Autoencoder.md): Bases: `AnomalyDetector`
- [`HalfSpaceTrees`](api/modules/capymoa.anomaly.HalfSpaceTrees.md): Bases: `MOAAnomalyDetector`
- [`IForestASD`](api/modules/capymoa.anomaly.IForestASD.md): Bases: `AnomalyDetector`
- [`Loda`](api/modules/capymoa.anomaly.Loda.md): Bases: `AnomalyDetector`
- [`OnlineIsolationForest`](api/modules/capymoa.anomaly.OnlineIsolationForest.md): Bases: `AnomalyDetector`
- [`RSHash`](api/modules/capymoa.anomaly.RSHash.md): Bases: `AnomalyDetector`
- [`RobustRandomCutForest`](api/modules/capymoa.anomaly.RobustRandomCutForest.md): Bases: `AnomalyDetector`
- [`StreamRHF`](api/modules/capymoa.anomaly.StreamRHF.md): Bases: `AnomalyDetector`
- [`StreamingIsolationForest`](api/modules/capymoa.anomaly.StreamingIsolationForest.md): Bases: `AnomalyDetector`
- [`ocl`](api/modules/capymoa.ocl.md): Online Continual Learning (OCL) module.
- [`datasets`](api/modules/capymoa.ocl.datasets.md): Use built-in datasets for online continual learning.
- [`DomainCIFAR100`](api/modules/capymoa.ocl.datasets.DomainCIFAR100.md): Bases: `_TorchVisionDownload`, `_BuiltInCIScenario`
- [`DomainCIFAR100ViT`](api/modules/capymoa.ocl.datasets.DomainCIFAR100ViT.md): Bases: `SplitCIFAR100ViT`
- [`RotatedFashionMNIST`](api/modules/capymoa.ocl.datasets.RotatedFashionMNIST.md): Bases: `_TorchVisionDownload`, `_BuiltInRotatedDomainScenario`
- [`RotatedMNIST`](api/modules/capymoa.ocl.datasets.RotatedMNIST.md): Bases: `_TorchVisionDownload`, `_BuiltInRotatedDomainScenario`
- [`RotatedTinyMNIST`](api/modules/capymoa.ocl.datasets.RotatedTinyMNIST.md): Bases: `_BuiltInRotatedDomainScenario`
- [`SplitCIFAR10`](api/modules/capymoa.ocl.datasets.SplitCIFAR10.md): Bases: `_TorchVisionDownload`, `_BuiltInCIScenario`
- [`SplitCIFAR10ViT`](api/modules/capymoa.ocl.datasets.SplitCIFAR10ViT.md): Bases: `SplitCIFAR100ViT`
- [`SplitCIFAR100`](api/modules/capymoa.ocl.datasets.SplitCIFAR100.md): Bases: `_TorchVisionDownload`, `_BuiltInCIScenario`
- [`SplitCIFAR100ViT`](api/modules/capymoa.ocl.datasets.SplitCIFAR100ViT.md): Bases: `_BuiltInCIScenario`
- [`SplitFashionMNIST`](api/modules/capymoa.ocl.datasets.SplitFashionMNIST.md): Bases: `_TorchVisionDownload`, `_BuiltInCIScenario`
- [`SplitMNIST`](api/modules/capymoa.ocl.datasets.SplitMNIST.md): Bases: `_TorchVisionDownload`, `_BuiltInCIScenario`
- [`TinySplitMNIST`](api/modules/capymoa.ocl.datasets.TinySplitMNIST.md): Bases: `_BuiltInCIScenario`
- [`evaluation`](api/modules/capymoa.ocl.evaluation.md): Evaluate online continual learning in classification tasks.
- [`events`](api/modules/capymoa.ocl.evaluation.events.md): Event definitions for OCL evaluation loops.
- [`EvalBatchPredict`](api/modules/capymoa.ocl.evaluation.events.EvalBatchPredict.md): Bases: `TestTaskBegin`
- [`TestBegin`](api/modules/capymoa.ocl.evaluation.events.TestBegin.md): Bases: `Event`
- [`TestEnd`](api/modules/capymoa.ocl.evaluation.events.TestEnd.md): Bases: `Event`
- [`TestTaskBegin`](api/modules/capymoa.ocl.evaluation.events.TestTaskBegin.md): Bases: `Event`
- [`TestTaskEnd`](api/modules/capymoa.ocl.evaluation.events.TestTaskEnd.md): Bases: `TestTaskBegin`
- [`TrainBatchPredict`](api/modules/capymoa.ocl.evaluation.events.TrainBatchPredict.md): Bases: `TrainTaskBegin`
- [`TrainBegin`](api/modules/capymoa.ocl.evaluation.events.TrainBegin.md): Bases: `Event`
- [`TrainEnd`](api/modules/capymoa.ocl.evaluation.events.TrainEnd.md): Bases: `Event`
- [`TrainTaskBegin`](api/modules/capymoa.ocl.evaluation.events.TrainTaskBegin.md): Bases: `Event`
- [`TrainTaskEnd`](api/modules/capymoa.ocl.evaluation.events.TrainTaskEnd.md): Bases: `TrainTaskBegin`
- [`OCLMetrics`](api/modules/capymoa.ocl.evaluation.OCLMetrics.md): Bases: `object`
- [`events`](api/modules/capymoa.ocl.events.md): | `Dispatcher`   | Publish events to subscribed callbacks.                                          ...
- [`Dispatcher`](api/modules/capymoa.ocl.events.Dispatcher.md): Bases: `object`
- [`Event`](api/modules/capymoa.ocl.events.Event.md): Bases: `object`
- [`Handler`](api/modules/capymoa.ocl.events.Handler.md): Bases: `ABC`
- [`strategy`](api/modules/capymoa.ocl.strategy.md): Online Continual Learning (OCL) strategies.
- [`l2p`](api/modules/capymoa.ocl.strategy.l2p.md): Learning to Prompt
- [`L2P`](api/modules/capymoa.ocl.strategy.l2p.L2P.md): Bases: `BatchClassifier`, `Handler`
- [`L2PViT`](api/modules/capymoa.ocl.strategy.l2p.L2PViT.md): Bases: `ABC`
- [`EWC`](api/modules/capymoa.ocl.strategy.EWC.md): Bases: `BatchClassifier`, `Module`, `Handler`
- [`LWF`](api/modules/capymoa.ocl.strategy.LWF.md): Bases: `BatchClassifier`, `Module`, `Handler`
- [`MAS`](api/modules/capymoa.ocl.strategy.MAS.md): Bases: `BatchClassifier`, `Module`, `Handler`
- [`NCM`](api/modules/capymoa.ocl.strategy.NCM.md): Bases: `BatchClassifier`
- [`RAR`](api/modules/capymoa.ocl.strategy.RAR.md): Bases: `BatchClassifier`, `Handler`
- [`SI`](api/modules/capymoa.ocl.strategy.SI.md): Bases: `BatchClassifier`, `Module`, `Handler`
- [`SLDA`](api/modules/capymoa.ocl.strategy.SLDA.md): Bases: `BatchClassifier`
- [`ExperienceReplay`](api/modules/capymoa.ocl.strategy.ExperienceReplay.md): Bases: `BatchClassifier`, `Handler`
- [`GDumb`](api/modules/capymoa.ocl.strategy.GDumb.md): Bases: `BatchClassifier`, `Handler`
- [`RWalk`](api/modules/capymoa.ocl.strategy.RWalk.md): Bases: `BatchClassifier`, `Module`, `Handler`
- [`util`](api/modules/capymoa.ocl.util.md): | `data`             | Utilities for continual learning when using PyTorch datasets.   |
- [`data`](api/modules/capymoa.ocl.util.data.md): Utilities for continual learning when using PyTorch datasets.
- [`functional`](api/modules/capymoa.ocl.util.functional.md): A collection of functional utilities for OCL.
- [`ssl`](api/modules/capymoa.ssl.md): Semi-supervised learning.
- [`OSNN`](api/modules/capymoa.ssl.OSNN.md): Bases: `ClassifierSSL`
- [`SLEADE`](api/modules/capymoa.ssl.SLEADE.md): Bases: `MOAClassifierSSL`
- [`automl`](api/modules/capymoa.automl.md): Automatic machine learning.
- [`AutoClass`](api/modules/capymoa.automl.AutoClass.md): Bases: `MOAClassifier`
- [`BanditClassifier`](api/modules/capymoa.automl.BanditClassifier.md): Bases: `Classifier`
- [`EpsilonGreedy`](api/modules/capymoa.automl.EpsilonGreedy.md): Bases: `object`
- [`SuccessiveHalvingClassifier`](api/modules/capymoa.automl.SuccessiveHalvingClassifier.md): Bases: `Classifier`
- [`uncertainty`](api/modules/capymoa.uncertainty.md): Uncertainty.
- [`MVE`](api/modules/capymoa.uncertainty.MVE.md): Bases: `MOAPredictionIntervalLearner`
- [`AdaPI`](api/modules/capymoa.uncertainty.AdaPI.md): Bases: `MOAPredictionIntervalLearner`
- [`feature`](api/modules/capymoa.feature.md): Feature selection.
- [`FeatureImportanceClassifier`](api/modules/capymoa.feature.FeatureImportanceClassifier.md): Bases: `Classifier`
- [`MOAFeatureImportanceClassifier`](api/modules/capymoa.feature.MOAFeatureImportanceClassifier.md): Bases: `FeatureImportanceClassifier`, `MOAClassifier`
- [`base`](api/modules/capymoa.base.md): Base classes for CapyMOA learners.
- [`AnomalyDetector`](api/modules/capymoa.base.AnomalyDetector.md): Bases: `LearnerParamsMixin`, `ABC`
- [`Batch`](api/modules/capymoa.base.Batch.md): Bases: `ABC`
- [`BatchClassifier`](api/modules/capymoa.base.BatchClassifier.md): Bases: `Classifier`, `Batch`, `ABC`
- [`BatchRegressor`](api/modules/capymoa.base.BatchRegressor.md): Bases: `Regressor`, `Batch`, `ABC`
- [`Classifier`](api/modules/capymoa.base.Classifier.md): Bases: `LearnerParamsMixin`, `ABC`
- [`ClassifierSSL`](api/modules/capymoa.base.ClassifierSSL.md): Bases: `Classifier`
- [`Clusterer`](api/modules/capymoa.base.Clusterer.md): Bases: `LearnerParamsMixin`, `ABC`
- [`ClusteringResult`](api/modules/capymoa.base.ClusteringResult.md): Bases: `object`
- [`LearnerParamsMixin`](api/modules/capymoa.base.LearnerParamsMixin.md): Bases: `object`
- [`LearnerSpec`](api/modules/capymoa.base.LearnerSpec.md): Bases: `TypedDict`
- [`MOAAnomalyDetector`](api/modules/capymoa.base.MOAAnomalyDetector.md): Bases: `AnomalyDetector`
- [`MOAClassifier`](api/modules/capymoa.base.MOAClassifier.md): Bases: `Classifier`
- [`MOAClassifierSSL`](api/modules/capymoa.base.MOAClassifierSSL.md): Bases: `MOAClassifier`, `ClassifierSSL`
- [`MOAClusterer`](api/modules/capymoa.base.MOAClusterer.md): Bases: `Clusterer`
- [`MOAPredictionIntervalLearner`](api/modules/capymoa.base.MOAPredictionIntervalLearner.md): Bases: `MOARegressor`, `PredictionIntervalLearner`
- [`MOARegressor`](api/modules/capymoa.base.MOARegressor.md): Bases: `Regressor`
- [`PredictionIntervalLearner`](api/modules/capymoa.base.PredictionIntervalLearner.md): Bases: `Regressor`
- [`Regressor`](api/modules/capymoa.base.Regressor.md): Bases: `LearnerParamsMixin`, `ABC`
- [`SKClassifier`](api/modules/capymoa.base.SKClassifier.md): Bases: `Classifier`
- [`SKRegressor`](api/modules/capymoa.base.SKRegressor.md): Bases: `Regressor`
- [`core`](api/modules/capymoa.core.md): Shared utilities and core types used across CapyMOA.
- [`io`](api/modules/capymoa.core.io.md): | `load_model`       | Load a model from a jpype pickle file.   |
- [`moa`](api/modules/capymoa.core.moa.md): MOA (Massive Online Analysis) interoperability common module.
- [`splitcriteria`](api/modules/capymoa.core.moa.splitcriteria.md): Module containing split criteria for decision trees.
- [`GiniSplitCriterion`](api/modules/capymoa.core.moa.splitcriteria.GiniSplitCriterion.md): Bases: `SplitCriterion`
- [`InfoGainSplitCriterion`](api/modules/capymoa.core.moa.splitcriteria.InfoGainSplitCriterion.md): Bases: `SplitCriterion`
- [`SplitCriterion`](api/modules/capymoa.core.moa.splitcriteria.SplitCriterion.md): Bases: `object`
- [`VarianceReductionSplitCriterion`](api/modules/capymoa.core.moa.splitcriteria.VarianceReductionSplitCriterion.md): Bases: `SplitCriterion`
- [`torch`](api/modules/capymoa.core.torch.md): PyTorch utilities for CapyMOA.
- [`ann`](api/modules/capymoa.core.torch.ann.md): Artificial Neural Networks for CapyMOA.
- [`LeNet5`](api/modules/capymoa.core.torch.ann.LeNet5.md): Bases: `Module`
- [`Perceptron`](api/modules/capymoa.core.torch.ann.Perceptron.md): Bases: `Module`
- [`Instance`](api/modules/capymoa.core.Instance.md): Bases: `object`
- [`Label`](api/modules/capymoa.core.Label.md): alias of `str`
- [`LabelIndex`](api/modules/capymoa.core.LabelIndex.md): alias of `int`
- [`LabeledInstance`](api/modules/capymoa.core.LabeledInstance.md): Bases: `Instance`
- [`RegressionInstance`](api/modules/capymoa.core.RegressionInstance.md): Bases: `Instance`
- [`TargetValue`](api/modules/capymoa.core.TargetValue.md): alias of `float64`
- [`datasets`](api/modules/capymoa.datasets.md): CapyMOA comes with some datasets ‘out of the box’. Simply import the dataset
- [`KDD99`](api/modules/capymoa.datasets.KDD99.md): Bases: `_DownloadableARFF`
- [`Airlines`](api/modules/capymoa.datasets.Airlines.md): Bases: `_DownloadableARFF`
- [`Bike`](api/modules/capymoa.datasets.Bike.md): Bases: `_DownloadableARFF`
- [`CovtFD`](api/modules/capymoa.datasets.CovtFD.md): Bases: `_DownloadableARFF`
- [`Covtype`](api/modules/capymoa.datasets.Covtype.md): Bases: `_DownloadableARFF`
- [`CovtypeNorm`](api/modules/capymoa.datasets.CovtypeNorm.md): Bases: `_DownloadableARFF`
- [`CovtypeTiny`](api/modules/capymoa.datasets.CovtypeTiny.md): Bases: `_DownloadableARFF`
- [`Electricity`](api/modules/capymoa.datasets.Electricity.md): Bases: `_DownloadableARFF`
- [`ElectricityTiny`](api/modules/capymoa.datasets.ElectricityTiny.md): Bases: `_DownloadableARFF`
- [`Fried`](api/modules/capymoa.datasets.Fried.md): Bases: `_DownloadableARFF`
- [`FriedTiny`](api/modules/capymoa.datasets.FriedTiny.md): Bases: `_DownloadableARFF`
- [`Hyper100k`](api/modules/capymoa.datasets.Hyper100k.md): Bases: `_DownloadableARFF`
- [`Nomao`](api/modules/capymoa.datasets.Nomao.md): Bases: `_DownloadableARFF`
- [`PokerHand`](api/modules/capymoa.datasets.PokerHand.md): Bases: `_DownloadableARFF`
- [`RBFm_100k`](api/modules/capymoa.datasets.RBFm_100k.md): Bases: `_DownloadableARFF`
- [`RTG_2abrupt`](api/modules/capymoa.datasets.RTG_2abrupt.md): Bases: `_DownloadableARFF`
- [`Sensor`](api/modules/capymoa.datasets.Sensor.md): Bases: `_DownloadableARFF`
- [`Spambase`](api/modules/capymoa.datasets.Spambase.md): Bases: `_DownloadableARFF`
- [`evaluation`](api/modules/capymoa.evaluation.md): Evaluation procedures and evaluators for CapyMOA learners.
- [`results`](api/modules/capymoa.evaluation.results.md): | `PrequentialResults`   |    |
- [`PrequentialResults`](api/modules/capymoa.evaluation.results.PrequentialResults.md): Bases: `object`
- [`AnomalyDetectionEvaluator`](api/modules/capymoa.evaluation.AnomalyDetectionEvaluator.md): Bases: `object`
- [`ClassificationEvaluator`](api/modules/capymoa.evaluation.ClassificationEvaluator.md): Bases: `object`
- [`ClassificationWindowedEvaluator`](api/modules/capymoa.evaluation.ClassificationWindowedEvaluator.md): Bases: `ClassificationEvaluator`
- [`ClusteringEvaluator`](api/modules/capymoa.evaluation.ClusteringEvaluator.md): Bases: `object`
- [`PredictionIntervalEvaluator`](api/modules/capymoa.evaluation.PredictionIntervalEvaluator.md): Bases: `RegressionEvaluator`
- [`PredictionIntervalWindowedEvaluator`](api/modules/capymoa.evaluation.PredictionIntervalWindowedEvaluator.md): Bases: `PredictionIntervalEvaluator`
- [`RegressionEvaluator`](api/modules/capymoa.evaluation.RegressionEvaluator.md): Bases: `object`
- [`RegressionWindowedEvaluator`](api/modules/capymoa.evaluation.RegressionWindowedEvaluator.md): Bases: `RegressionEvaluator`
- [`stream`](api/modules/capymoa.stream.md): Data stream representations and related utilities.
- [`drift`](api/modules/capymoa.stream.drift.md): Simulate concept drift in datastreams.
- [`AbruptDrift`](api/modules/capymoa.stream.drift.AbruptDrift.md): Bases: `Drift`
- [`Concept`](api/modules/capymoa.stream.drift.Concept.md): Bases: `object`
- [`Drift`](api/modules/capymoa.stream.drift.Drift.md): Bases: `object`
- [`DriftStream`](api/modules/capymoa.stream.drift.DriftStream.md): Bases: `Stream`
- [`GradualDrift`](api/modules/capymoa.stream.drift.GradualDrift.md): Bases: `Drift`
- [`IndexedCycle`](api/modules/capymoa.stream.drift.IndexedCycle.md): Bases: `object`
- [`RecurrentConceptDriftStream`](api/modules/capymoa.stream.drift.RecurrentConceptDriftStream.md): Bases: `DriftStream`
- [`generator`](api/modules/capymoa.stream.generator.md): Generate artificial data streams.
- [`AgrawalGenerator`](api/modules/capymoa.stream.generator.AgrawalGenerator.md): Bases: `MOAStream`
- [`HyperPlaneClassification`](api/modules/capymoa.stream.generator.HyperPlaneClassification.md): Bases: `MOAStream`
- [`HyperPlaneRegression`](api/modules/capymoa.stream.generator.HyperPlaneRegression.md): Bases: `MOAStream`
- [`LEDGenerator`](api/modules/capymoa.stream.generator.LEDGenerator.md): Bases: `MOAStream`
- [`LEDGeneratorDrift`](api/modules/capymoa.stream.generator.LEDGeneratorDrift.md): Bases: `MOAStream`
- [`MixedGenerator`](api/modules/capymoa.stream.generator.MixedGenerator.md): Bases: `MOAStream`
- [`RandomRBFGenerator`](api/modules/capymoa.stream.generator.RandomRBFGenerator.md): Bases: `MOAStream`
- [`RandomRBFGeneratorDrift`](api/modules/capymoa.stream.generator.RandomRBFGeneratorDrift.md): Bases: `MOAStream`
- [`RandomTreeGenerator`](api/modules/capymoa.stream.generator.RandomTreeGenerator.md): Bases: `MOAStream`
- [`SEA`](api/modules/capymoa.stream.generator.SEA.md): Bases: `MOAStream`
- [`STAGGERGenerator`](api/modules/capymoa.stream.generator.STAGGERGenerator.md): Bases: `MOAStream`
- [`SineGenerator`](api/modules/capymoa.stream.generator.SineGenerator.md): Bases: `MOAStream`
- [`WaveformGenerator`](api/modules/capymoa.stream.generator.WaveformGenerator.md): Bases: `MOAStream`
- [`WaveformGeneratorDrift`](api/modules/capymoa.stream.generator.WaveformGeneratorDrift.md): Bases: `MOAStream`
- [`preprocessing`](api/modules/capymoa.stream.preprocessing.md): Data stream preprocessing and pipelines.
- [`BasePipeline`](api/modules/capymoa.stream.preprocessing.BasePipeline.md): Bases: `PipelineElement`
- [`ClassifierPipeline`](api/modules/capymoa.stream.preprocessing.ClassifierPipeline.md): Bases: `BasePipeline`, `Classifier`
- [`ClassifierPipelineElement`](api/modules/capymoa.stream.preprocessing.ClassifierPipelineElement.md): Bases: `PipelineElement`
- [`DriftDetectorPipelineElement`](api/modules/capymoa.stream.preprocessing.DriftDetectorPipelineElement.md): Bases: `PipelineElement`
- [`MOATransformer`](api/modules/capymoa.stream.preprocessing.MOATransformer.md): Bases: `Transformer`
- [`PipelineElement`](api/modules/capymoa.stream.preprocessing.PipelineElement.md): Bases: `Protocol`
- [`RandomSearchClassifierPE`](api/modules/capymoa.stream.preprocessing.RandomSearchClassifierPE.md): Bases: `ClassifierPipelineElement`, `Classifier`
- [`RegressorPipeline`](api/modules/capymoa.stream.preprocessing.RegressorPipeline.md): Bases: `BasePipeline`, `Regressor`
- [`RegressorPipelineElement`](api/modules/capymoa.stream.preprocessing.RegressorPipelineElement.md): Bases: `PipelineElement`
- [`Transformer`](api/modules/capymoa.stream.preprocessing.Transformer.md): Bases: `ABC`
- [`TransformerPipelineElement`](api/modules/capymoa.stream.preprocessing.TransformerPipelineElement.md): Bases: `PipelineElement`
- [`ARFFStream`](api/modules/capymoa.stream.ARFFStream.md): Bases: `MOAStream`[`_AnyInstance`]
- [`CSVStream`](api/modules/capymoa.stream.CSVStream.md): Bases: `Stream`[`_AnyInstance`]
- [`MOAStream`](api/modules/capymoa.stream.MOAStream.md): Bases: `Stream`[`_AnyInstance`]
- [`NumpyStream`](api/modules/capymoa.stream.NumpyStream.md): Bases: `Stream`[`_AnyInstance`]
- [`Schema`](api/modules/capymoa.stream.Schema.md): Bases: `object`
- [`Stream`](api/modules/capymoa.stream.Stream.md): Bases: `ABC`, `Iterator`[`_AnyInstance`], [`Generic`](https://docs.python.org/3/library/typing.html#...
- [`TorchStream`](api/modules/capymoa.stream.TorchStream.md): Bases: `Stream`
- [`env`](api/modules/capymoa.env.md): Set and get capymoa environment variables.
- [About Us](about.md): Our approach combines research insights with practical development efforts,
- [Contributing](contributing/index.md): This part of the documentation is for developers and contributors.
- [Architecture](contributing/architecture.md): CapyMOA is organised into research domains.
- [Adding Tests](contributing/tests.md): Ensure you have installed the development dependencies by following the
- [Documentation](contributing/docs.md): To build the documentation, run the following command in the project root:
- [Git Tutorial](contributing/git.md): CapyMOA uses git for version control and GitHub for hosting the repository. We follow a
- [MOA](contributing/moa/index.md): CapyMOA calls into the Java library Massive Online Analysis (MOA) using JPype.
- [Implement a method in Java and use it in Python](contributing/moa/moa_integration.md): This guide walks through getting a Java class onto the classpath so you can call it from Python, usi...
- [Update MOA](contributing/moa/update_moa.md): This document describes how to change the version of MOA that the CapyMOA
- [Code Review](contributing/code_review.md): This document describes the code review process for CapyMOA. It is intended for reviewers only.
- [Performance Profiling](contributing/profiling.md): Code profiling is the process of measuring the amount of time a program spends on each function or l...
- [FAQ](contributing/faq.md): Add the Jupytext `.py` notebook to the appropriate directory under
- [Page Not Found](404.md): This page does not exist. This can happen when a version’s documentation is no