How To Build a Fraud Detection Model in Java Using Deeplearning4J

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<dependencies>  
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    <dependency>  
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        <groupId>org.deeplearning4j</groupId>  
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        <artifactId>deeplearning4j-core</artifactId>  
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        <version>1.0.0-M2.1</version>  
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    </dependency>  
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    <dependency>  
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        <groupId>org.nd4j</groupId>  
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        <artifactId>nd4j-native-platform</artifactId>  
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        <version>1.0.0-M2.1</version>  
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    </dependency>  
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    <dependency>  
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        <groupId>org.mongodb</groupId>  
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        <artifactId>mongodb-driver-sync</artifactId>  
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        <version>5.2.0</version>  
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    </dependency>  
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    <dependency>  
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        <groupId>org.slf4j</groupId>  
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        <artifactId>slf4j-simple</artifactId>  
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        <version>2.0.16</version>  
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    </dependency>  
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</dependencies>

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package com.mongodb;
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import com.mongodb.client.MongoClient;
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import com.mongodb.client.MongoClients;
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import com.mongodb.client.MongoCollection;
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import com.mongodb.client.MongoDatabase;
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import org.bson.Document;
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import java.util.concurrent.TimeUnit;
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public class MongoDBConnector {
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    private static final String URI = "YOUR-CONNECTION-STRING";
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    private static final String DATABASE_NAME = "fraudDetection";
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    private static final String COLLECTION_NAME = "transactions";
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    private final MongoClient mongoClient;
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    public MongoDBConnector() {
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        MongoClientSettings settings = MongoClientSettings.builder()
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                .applyConnectionString(new ConnectionString(URI))
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                .applyToSocketSettings(builder ->
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                        builder.connectTimeout(30, TimeUnit.SECONDS)
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                                .readTimeout(30, TimeUnit.SECONDS))
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                .build();
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        mongoClient = MongoClients.create(settings);
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    }
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    public MongoCollection<Document> getCollection() {
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        return mongoClient.getDatabase(DATABASE_NAME).getCollection(COLLECTION_NAME);
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    }
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}

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package com.mongodb;  
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import org.bson.Document;  
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public class Transaction {  
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    private double amount;  
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    private boolean isFraudulent;  
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    public Transaction(double amount, boolean isFraudulent) {  
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        this.amount = amount;  
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        this.isFraudulent = isFraudulent;  
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    }  
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    public double getAmount() { return amount; }  
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    public boolean isFraudulent() { return isFraudulent; }  
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    public Document toDocument() {  
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        Document doc = new Document();  
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        doc.append("amount", amount);  
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        doc.append("isFraudulent", isFraudulent);  
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        return doc;  
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    }  
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}

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package com.mongodb;
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import com.mongodb.client.MongoCollection;
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import com.mongodb.client.model.BulkWriteOptions;
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import com.mongodb.client.model.WriteModel;
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import org.bson.Document;
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import java.util.ArrayList;
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import java.util.List;
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public class TransactionRepository {
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    private final MongoCollection<Document> collection;
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    public TransactionRepository(MongoDBConnector connector) {
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        this.collection = connector.getCollection();
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    }
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    public void bulkSaveTransactions(List<WriteModel<Document>> transactions) {
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        if (!transactions.isEmpty()) {
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            try {
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                BulkWriteOptions options = new BulkWriteOptions().ordered(true);
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                collection.bulkWrite(transactions, options); // Perform bulk write operation
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            } catch (Exception e) {
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                e.printStackTrace();
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            }
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        }
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    }
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    public List<Transaction> getAllTransactions() {
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        List<Transaction> transactions = new ArrayList<>();
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        for (Document doc : collection.find()) {
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            double amount = doc.getDouble("amount");
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            boolean isFraudulent = doc.getBoolean("isFraudulent");
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            transactions.add(new Transaction(amount, isFraudulent));
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        }
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        return transactions;
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    }
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}

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package com.mongodb;
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import com.mongodb.client.model.WriteModel;
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import com.mongodb.client.model.InsertOneModel;
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import org.bson.Document;
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import java.io.BufferedReader;
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import java.io.FileReader;
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import java.io.IOException;
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import java.util.ArrayList;
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import java.util.List;
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import java.util.stream.Collectors;
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public class DataPreprocessor {
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    TransactionRepository transactionRepository;
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    private static final int BATCH_SIZE = 500; // Define batch size for bulk writes
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    private static final int DOCUMENT_LIMIT = 250000;  // Maximum documents to insert
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    private int documentCount = 0;  // Counter for total inserted documents
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    public DataPreprocessor(TransactionRepository transactionRepository) {
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        this.transactionRepository = transactionRepository;
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    }
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    public void loadData(String filePath) throws IOException {
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        try (BufferedReader reader = new BufferedReader(new FileReader(filePath))) {
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            // Skip the header by reading the first line
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            reader.readLine();
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            List<String> batch = new ArrayList<>();
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            String line;
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            // Read the file line-by-line
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            while ((line = reader.readLine()) != null) {
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                if (documentCount >= DOCUMENT_LIMIT) {
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                    System.out.println("Reached the document limit of " + DOCUMENT_LIMIT + ". Stopping data load.");
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                    break;  // Stop processing when the limit is reached
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                }
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                batch.add(line);
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                documentCount++;
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                // When batch size is reached, process it
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                if (batch.size() == BATCH_SIZE) {
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                    processBatch(batch);
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                    batch.clear(); // Clear the batch for the next set of lines
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                }
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            }
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            // Process any remaining lines
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            if (!batch.isEmpty()) {
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                processBatch(batch);
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            }
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        }
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    }
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    private void processBatch(List<String> batch) {
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        List<WriteModel<Document>> bulkOperations = batch.stream()
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                .map(line -> {
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                    String[] fields = line.split(",");
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                    double amount = Double.parseDouble(fields[29]); // Adjust index as needed
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                    boolean isFraudulent = "1".equals(fields[30]);
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                    Transaction transaction = new Transaction(amount, isFraudulent);
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                    return new InsertOneModel<>(transaction.toDocument());
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                })
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                .collect(Collectors.toList());
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        transactionRepository.bulkSaveTransactions(bulkOperations);
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    }
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}

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package com.mongodb;  
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import org.deeplearning4j.nn.conf.MultiLayerConfiguration;  
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import org.deeplearning4j.nn.conf.NeuralNetConfiguration;  
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import org.deeplearning4j.nn.conf.layers.DenseLayer;  
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import org.deeplearning4j.nn.conf.layers.OutputLayer;  
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;  
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import org.deeplearning4j.nn.weights.WeightInit;  
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import org.deeplearning4j.optimize.listeners.ScoreIterationListener;  
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import org.nd4j.evaluation.classification.Evaluation;  
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import org.nd4j.linalg.activations.Activation;  
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import org.nd4j.linalg.api.ndarray.INDArray;  
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import org.nd4j.linalg.dataset.DataSet;  
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import org.nd4j.linalg.factory.Nd4j;  
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import org.nd4j.linalg.learning.config.Adam;  
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import org.nd4j.linalg.lossfunctions.LossFunctions;  
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import java.util.Collections;  
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import java.util.List;  
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public class FraudDetectionModel {  
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    private MultiLayerNetwork model;  
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    private static final int NUM_INPUT_FEATURES = 1; 
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    private static final int NUM_CLASSES = 2;  
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    public FraudDetectionModel() {  
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        initializeModel();  
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    }  
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    private void initializeModel() {  
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        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()  
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                .seed(123)  
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                .weightInit(WeightInit.XAVIER)  
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                .updater(new Adam(0.001))  
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                .list()  
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                .layer(0, new DenseLayer.Builder()  
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                        .nIn(NUM_INPUT_FEATURES)  
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                        .nOut(10)  
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                        .activation(Activation.RELU)  
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                        .build())  
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                .layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)  
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                        .nIn(10)  
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                        .nOut(NUM_CLASSES)  
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                        .activation(Activation.SOFTMAX)  
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                        .build())  
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                .build();  
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        model = new MultiLayerNetwork(conf);  
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        model.init();  
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        model.setListeners(new ScoreIterationListener(10));  
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    }  
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}

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private MultiLayerNetwork model;
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private static final int NUM_INPUT_FEATURES = 1;
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private static final int NUM_CLASSES = 2;

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private void initializeModel() {
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    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
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            .seed(123)
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            .weightInit(WeightInit.XAVIER)
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            .updater(new Adam(0.001))
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            .list()

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.layer(0, new DenseLayer.Builder()
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        .nIn(NUM_INPUT_FEATURES)
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        .nOut(10)
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        .activation(Activation.RELU)
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        .build())

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.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
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        .nIn(10)
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        .nOut(NUM_CLASSES)
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        .activation(Activation.SOFTMAX)
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        .build())

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model = new MultiLayerNetwork(conf);
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model.init();
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model.setListeners(new ScoreIterationListener(10));

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    private DataSet prepareTrainingData(List<Transaction> transactions) {  
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        int numTransactions = transactions.size();  
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        INDArray features = Nd4j.create(numTransactions, NUM_INPUT_FEATURES);  
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        INDArray labels = Nd4j.create(numTransactions, NUM_CLASSES);  
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        DataSet dataSet;  
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        for (int i = 0; i < numTransactions; i++) {  
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            Transaction transaction = transactions.get(i);  
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            // Use transaction amount as feature  
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            features.putScalar(new int[]{i, 0}, transaction.getAmount());  
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            // One-hot encoding for labels  
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            if (transaction.isFraudulent()) {  
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                labels.putScalar(new int[]{i, 1}, 1.0);  
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                labels.putScalar(new int[]{i, 0}, 0.0);  
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            } else {  
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                labels.putScalar(new int[]{i, 0}, 1.0);  
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                labels.putScalar(new int[]{i, 1}, 0.0);  
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            }  
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        }  
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        return dataSet = new DataSet(features, labels);  
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    }

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    public void trainModel(List<Transaction> transactions) {  
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        // Shuffle the data to ensure random distribution  
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        Collections.shuffle(transactions);  
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        // Prepare training data  
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        DataSet dataSet = prepareTrainingData(transactions);;  
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        // Train the model  
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        for (int epoch = 0; epoch < 100; epoch++) {  
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            model.fit(dataSet);  
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        }  
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        System.out.println("Model trained successfully.");  
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    }

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public void evaluateModel(List<Transaction> transactions) {  
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    // Split data into train and test sets  
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    int trainSize = (int)(transactions.size() * 0.8);  
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    List<Transaction> testSet = transactions.subList(trainSize, transactions.size());  
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    // Prepare test data  
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    DataSet testData = prepareTrainingData(testSet);  
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    // Perform evaluation  
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    Evaluation evaluation = new Evaluation(NUM_CLASSES);  
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    INDArray predicted = model.output(testData.getFeatures());  
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    evaluation.eval(testData.getLabels(), predicted);  
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    // Print evaluation statistics  
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    System.out.println(evaluation.stats());  
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}

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    public boolean predictFraud(Transaction transaction) {  
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        // Convert transaction to INDArray  
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        INDArray input = Nd4j.create(new double[][]{{transaction.getAmount()}});  
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        // Perform prediction  
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        INDArray output = model.output(input);  
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        // Interpret the output  
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        // Index 1 corresponds to fraud class (assuming one-hot encoding)
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        return output.getDouble(0, 1) > 0.5;  
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    }

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package com.mongodb;  
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import java.io.IOException;  
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import java.util.Collections;  
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import java.util.List;  
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import java.util.Random;  
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public class FraudDetectionApp { 
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    private static FraudDetectionModel fraudDetectionModel;  
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    public static void main(String[] args) {  
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        MongoDBConnector mongoDBConnector = new MongoDBConnector();  
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        TransactionRepository transactionRepository = new TransactionRepository(mongoDBConnector);  
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        DataPreprocessor preprocessor = new DataPreprocessor(transactionRepository);  
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        try {  
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            // Load and prepare training data, and add to MongoDB  
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            preprocessor.loadData("src/main/resources/creditcard.csv");  
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            List<Transaction> transactions = transactionRepository.getAllTransactions();  
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            // Shuffle and split data
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            Collections.shuffle(transactions);  
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            int trainSize = (int) (transactions.size() * 0.8);  
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            List<Transaction> trainSet = transactions.subList(0, trainSize);  
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            List<Transaction> testSet = transactions.subList(trainSize, transactions.size());  
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            System.out.println("Train size: " + trainSet.size() + ", Test size: " + testSet.size());  
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            // Create and train fraud detection model  
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            fraudDetectionModel = new FraudDetectionModel();  
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            fraudDetectionModel.trainModel(trainSet);  
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            // Evaluate model  
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            fraudDetectionModel.evaluateModel(testSet);  
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        } catch (IOException e) {  
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            e.printStackTrace();  
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        }  
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    }  
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}

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========================Evaluation Metrics========================
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 # of classes:    2
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 Accuracy:        0.8441
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 Precision:       0.0011
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 Recall:          0.1053
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 F1 Score:        0.0022
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Precision, recall & F1: reported for positive class (class 1 - "1") only

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double calculateAccuracy(int TP, int TN, int FP, int FN) {
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    int totalPredictions = TP + TN + FP + FN;
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    double accuracy = (double)(TP + TN) / totalPredictions;
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    return accuracy;
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}

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double calculatePrecision(int TP, int FP) {
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    double precision = (double) TP / (TP + FP);
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    return precision;
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}

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double calculateRecall(int TP, int FN) {
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    double recall = (double) TP / (TP + FN);
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    return recall;
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}

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double calculateF1Score(double precision, double recall) {
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    double f1Score = 2 * (precision * recall) / (precision + recall);
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    return f1Score;
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}

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=========================Confusion Matrix=========================
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    0    1
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-----------
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 9615 1759 | 0 = 0
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   17    2 | 1 = 1
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Confusion matrix format: Actual (rowClass) predicted as (columnClass) N times
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==================================================================

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========================Evaluation Metrics========================
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 # of classes:    2
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 Accuracy:        0.9986
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 Precision:       0.0000
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 Recall:          0.0000
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 F1 Score:        0.0000
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Precision, recall & F1: reported for positive class (class 1 - "1") only
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Warning: 1 class was never predicted by the model and was excluded from average precision
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Classes excluded from average precision: [1]
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=========================Confusion Matrix=========================
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     0     1
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-------------
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 11377     0 | 0 = 0
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    16     0 | 1 = 1
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Confusion matrix format: Actual (rowClass) predicted as (columnClass) N times
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==================================================================