EDUX - Java Machine Learning Library

EDUX is a user-friendly library for solving problems with a machine learning approach.

Features

EDUX supports a variety of machine learning algorithms including:

• Multilayer Perceptron (Neural Network): Suitable for regression and classification problems, MLPs can approximate non-linear functions.
• K Nearest Neighbors: A simple, instance-based learning algorithm used for classification and regression.
• Decision Tree: Offers visual and explicitly laid out decision making based on input features.
• Support Vector Machine: Effective for binary classification, and can be adapted for multi-class problems.
• RandomForest: An ensemble method providing high accuracy through building multiple decision trees.

Augmentations

Edux supports a variety of image augmentations, which can be used to increase the performance of your model.

Few examples:

Color Equalization

Monochrome + Noise

Code Example

Single Image

    AugmentationSequence augmentationSequence=
        new AugmentationBuilder()
        .addAugmentation(new ResizeAugmentation(250,250))
        .addAugmentation(new ColorEqualizationAugmentation())
        .build();

        BufferedImage augmentedImage=augmentationSequence.applyTo(image);

Run for all images in a directory

    AugmentationSequence augmentationSequence=
        new AugmentationBuilder()
        .addAugmentation(new ResizeAugmentation(250,250))
        .addAugmentation(new ColorEqualizationAugmentation())
        .addAugmentation(new BlurAugmentation(25))
        .addAugmentation(new RandomDeleteAugmentation(10,20,20))
        .build()
        .run(trainImagesDir,numberOfWorkers,outputDir);

Battle Royale - Which algorithm is the best?

We run all algorithms on the same dataset and compare the results. Benchmark

Goal

The main goal of this project is to create a user-friendly library for solving problems using a machine learning approach. The library is designed to be easy to use, enabling the solution of problems with just a few lines of code.

Features

The library currently supports:

• Multilayer Perceptron (Neural Network)
• K Nearest Neighbors
• Decision Tree
• Support Vector Machine
• RandomForest

Get started

Include the library as a dependency in your Java project file.

Gradle

 implementation 'io.github.samyssmile:edux:1.0.7'

Maven

  <dependency>
     <groupId>io.github.samyssmile</groupId>
     <artifactId>edux</artifactId>
     <version>1.0.7</version>
 </dependency>

Hardware Acceleration (preview feature)

EDUX supports Nvidia GPU acceleration.

Requirements

• Nvidia GPU with CUDA support
• CUDA Toolkit 11.8

Getting started tutorial

This section guides you through using EDUX to process your dataset, configure a multilayer perceptron (Multilayer Neural Network), perform training and evaluation.

A multi-layer perceptron (MLP) is a feedforward artificial neural network that generates a set of outputs from a set of input features. An MLP is characterized by several layers of input nodes connected as a directed graph between the input and output layers.

Step 1: Data Processing

Firstly, we will load and prepare the IRIS dataset:

sepal.length	sepal.width	petal.length	petal.width	variety
5.1	3.5	1.4	0.2	Setosa

var featureColumnIndices=new int[]{0,1,2,3}; // Specify your feature columns
        var targetColumnIndex=4; // Specify your target column

        var dataProcessor=new DataProcessor(new CSVIDataReader());
        var dataset=dataProcessor.loadDataSetFromCSV(
        "path/to/your/data.csv", // Replace with your CSV file path
        ',',                     // CSV delimiter
        true,                    // Whether to skip the header
        featureColumnIndices,
        targetColumnIndex
        );
        dataset.shuffle();
        dataset.normalize();
        dataProcessor.split(0.8); // Replace with your train-test split ratio

Step 2: Preparing Training and Test Sets:

Extract the features and labels for both training and test sets:

    var trainFeatures=dataProcessor.getTrainFeatures(featureColumnIndices);
        var trainLabels=dataProcessor.getTrainLabels(targetColumnIndex);
        var testFeatures=dataProcessor.getTestFeatures(featureColumnIndices);
        var testLabels=dataProcessor.getTestLabels(targetColumnIndex);

Step 3: Network Configuration

var networkConfiguration=new NetworkConfiguration(
        trainFeatures[0].length,     // Number of input neurons
        List.of(128,256,512),      // Number of neurons in each hidden layer
        3,                           // Number of output neurons
        0.01,                        // Learning rate
        300,                         // Number of epochs
        ActivationFunction.LEAKY_RELU, // Activation function for hidden layers
        ActivationFunction.SOFTMAX,    // Activation function for output layer
        LossFunction.CATEGORICAL_CROSS_ENTROPY, // Loss function
        Initialization.XAVIER,        // Weight initialization for hidden layers
        Initialization.XAVIER         // Weight initialization for output layer
        );

Step 4: Training and Evaluation

MultilayerPerceptron multilayerPerceptron=new MultilayerPerceptron(
        networkConfiguration,
        testFeatures,
        testLabels
        );
        multilayerPerceptron.train(trainFeatures,trainLabels);
        multilayerPerceptron.evaluate(testFeatures,testLabels);

Results

...
MultilayerPerceptron - Best accuracy after restoring best MLP model: 98,56%

Working examples

You can find more fully working examples for all algorithms in the examples folder.

For examples we use the

• IRIS dataset.
• SEABORNE PENGUINS dataset.

Contributions

Contributions are warmly welcomed! If you find a bug, please create an issue with a detailed description of the problem. If you wish to suggest an improvement or fix a bug, please make a pull request. Also checkout the Rules and Guidelines page for more information.