MOA (Massive Online Analysis)

Language: Java

ML/AI / Streaming Machine Learning

MOA was developed to support research and development in online learning and streaming analytics. It is widely used in academic research and industry to handle real-time data streams where models must be updated incrementally, unlike traditional batch learning.

MOA is an open-source Java framework for data stream mining. It provides tools for classification, regression, clustering, and concept drift detection on high-speed streaming data.

Installation

maven:

<dependency>
  <groupId>org.github.moa-dev</groupId>
  <artifactId>moa</artifactId>
  <version>2018.05</version>
</dependency>

gradle: implementation 'org.github.moa-dev:moa:2018.05'

Usage

MOA allows processing continuous data streams using incremental learning algorithms. It supports various stream generators, classifiers, ensemble methods, and evaluation metrics. MOA can be integrated with Weka and other Java ML libraries.

Creating a stream and reading instances

import moa.streams.generators.RandomTreeGenerator;
import com.yahoo.labs.samoa.instances.Instance;

RandomTreeGenerator stream = new RandomTreeGenerator();
stream.prepareForUse();
Instance instance = stream.nextInstance().getData();

Generates a random data stream and retrieves the next instance for processing.

Training an incremental classifier

import moa.classifiers.trees.HoeffdingTree;
HoeffdingTree learner = new HoeffdingTree();
learner.setModelContext(stream.getHeader());
learner.prepareForUse();
learner.trainOnInstance(instance);

Creates a Hoeffding Tree classifier and trains it incrementally on each instance from the stream.

Evaluating a stream classifier

import moa.evaluation.WindowClassificationPerformanceEvaluator;
WindowClassificationPerformanceEvaluator evaluator = new WindowClassificationPerformanceEvaluator();
evaluator.setWindowSize(1000);
evaluator.addResult(learner, instance);

Evaluates classifier performance using a sliding window over the stream.

Using ensemble methods

import moa.classifiers.meta.OzaBag;
OzaBag ensemble = new OzaBag();
ensemble.setBaseLearner(new HoeffdingTree());

Creates an online bagging ensemble for better accuracy on streaming data.

Concept drift detection

import moa.classifiers.core.driftdetection.DDM;
DDM driftDetector = new DDM();

Detects concept drift in streaming data using Drift Detection Method (DDM).

Integrating with Weka

// MOA streams and classifiers can be converted to Weka instances for batch processing or evaluation

Facilitates hybrid workflows combining streaming and batch machine learning.

Error Handling

NullPointerException: Ensure the stream and model context are properly initialized before training.

IllegalArgumentException: Thrown if instance attributes or types do not match classifier expectations.

OutOfMemoryError: Use windowed evaluation or limit ensemble sizes to reduce memory footprint.

Best Practices

Use incremental learning algorithms designed for streaming data.

Monitor concept drift to adapt models to changing distributions.

Use ensemble methods for more robust predictions on streams.

Evaluate models with prequential or sliding window evaluation.

Keep memory usage low to handle high-speed data streams efficiently.

Official Docs Github