SuperML Java - Comprehensive Framework Architecture
🏛️ Executive Summary
SuperML Java is a comprehensive machine learning framework inspired by scikit-learn, consisting of 21 specialized modules implementing 12+ algorithms with neural network support. The framework follows a modular, extensible architecture that supports everything from basic ML tasks to production deployment and Kaggle competitions.
📊 Framework Statistics
- 21 Modules: Modular architecture with specialized components
- 12+ Algorithms: Linear models, tree-based, clustering, neural networks
- 200+ Classes: Comprehensive implementation coverage
- Production Ready: Inference engine, model persistence, monitoring
- Neural Networks: MLP, CNN, RNN with specialized preprocessing
- Kaggle Integration: Competition workflows and automation
🏗️ High-Level System Architecture
┌─────────────────────────────────────────────────────────────────────────────────┐
│ SuperML Java Framework 2.0.0 │
│ (21 Modules • Neural Networks • Production) │
├─────────────────────────────────────────────────────────────────────────────────┤
│ 🎯 APPLICATION LAYER │
│ ├── Examples & Demos (11 comprehensive examples) │
│ ├── AutoML Workflows (AutoTrainer with neural network support) │
│ ├── Kaggle Integration (Competition automation & ensembles) │
│ └── Visualization (XChart GUI + ASCII terminal) │
├─────────────────────────────────────────────────────────────────────────────────┤
│ 🧠 ALGORITHM LAYER (12+ Implementations) │
│ ├── Linear Models (6) ├── Tree Models (5) ├── Neural (3) │
│ │ ├── LogisticRegression │ ├── DecisionTree │ ├── MLP │
│ │ ├── LinearRegression │ ├── RandomForest │ ├── CNN │
│ │ ├── Ridge │ ├── GradientBoosting │ └── RNN/LSTM/GRU │
│ │ ├── Lasso │ ├── ExtraTrees │ │
│ │ ├── SGDClassifier │ └── AdaBoost ├── Clustering (1) │
│ │ └── SGDRegressor │ │ └── KMeans │
├─────────────────────────────────────────────────────────────────────────────────┤
│ 🔧 FRAMEWORK INFRASTRUCTURE │
│ ├── Core Foundation ├── Data Processing ├── Production │
│ │ ├── Estimator Hierarchy │ ├── Preprocessing │ ├── Inference │
│ │ ├── Pipeline System │ ├── Feature Eng. │ ├── Persistence │
│ │ ├── Parameter Management │ ├── Datasets │ ├── Monitoring │
│ │ └── Validation │ └── Transformations │ └── Drift │
│ │ │ │ │
│ ├── Model Selection ├── Evaluation ├── Integration │
│ │ ├── GridSearchCV │ ├── Metrics │ ├── ONNX Export │
│ │ ├── RandomizedSearch │ ├── Cross-Validation │ ├── PMML Export │
│ │ ├── HyperOpt │ └── Visualization │ └── REST APIs │
│ │ └── AutoTrainer │ │ │
└─────────────────────────────────────────────────────────────────────────────────┘
📦 21-Module Detailed Architecture
🏛️ Core Foundation (2 modules)
superml-core
Purpose: Foundation interfaces and base classes for all ML components
org.superml.core/
├── Estimator.java // Base interface for all ML components
├── SupervisedLearner.java // Interface for supervised algorithms
├── UnsupervisedLearner.java // Interface for unsupervised algorithms
├── Classifier.java // Specialized classification interface
├── Regressor.java // Specialized regression interface
├── BaseEstimator.java // Abstract base with common functionality
└── package-info.java // Module documentation
Key Design Patterns:
- Estimator Pattern: All components implement
fit(),predict(),score() - Parameter Management: Consistent
getParams(),setParams()methods - State Management:
fittedflag and validation patterns
superml-utils
Purpose: Shared mathematical utilities and helper functions
org.superml.utils/
├── MathUtils.java // Statistical and mathematical functions
├── ArrayUtils.java // Array manipulation utilities
├── ValidationUtils.java // Input validation helpers
└── Constants.java // Framework-wide constants
🤖 Algorithm Implementation (4 modules)
superml-linear-models
Purpose: Linear and logistic regression variants
org.superml.linear_model/
├── LinearRegression.java // Ordinary Least Squares
├── LogisticRegression.java // Binary + multiclass classification
├── Ridge.java // L2 regularized regression
├── Lasso.java // L1 regularized regression
├── SGDClassifier.java // Stochastic Gradient Descent classifier
├── SGDRegressor.java // Stochastic Gradient Descent regressor
├── SoftmaxRegression.java // Multinomial logistic regression
└── OneVsRestClassifier.java // Meta-classifier for multiclass
Algorithm Architecture:
public class LogisticRegression extends BaseEstimator implements Classifier {
// Hyperparameters
private double learningRate = 0.01;
private int maxIter = 1000;
private double tolerance = 1e-6;
private double C = 1.0; // Inverse regularization
// Model state
private double[] weights;
private double[] classes;
private boolean fitted = false;
// Multiclass support
private OneVsRestClassifier multiclassClassifier;
private SoftmaxRegression softmaxClassifier;
}
superml-tree-models
Purpose: Tree-based algorithms and ensembles
org.superml.tree/
├── DecisionTree.java // CART algorithm (classification + regression)
├── RandomForest.java // Bootstrap aggregating ensemble
├── GradientBoosting.java // Sequential boosting ensemble
├── ExtraTrees.java // Extremely randomized trees
└── AdaBoost.java // Adaptive boosting
Tree Architecture Pattern:
public class DecisionTree extends BaseEstimator implements Classifier, Regressor {
// Tree parameters
private String criterion = "gini"; // "gini", "entropy", "mse"
private int maxDepth = Integer.MAX_VALUE;
private int minSamplesSplit = 2;
private int minSamplesLeaf = 1;
// Tree structure
private TreeNode root;
private double[] classes;
private boolean isClassification;
// Internal tree node representation
private static class TreeNode {
int featureIndex;
double threshold;
double prediction;
TreeNode left, right;
boolean isLeaf;
}
}
superml-neural
Purpose: Neural network implementations with specialized architectures
org.superml.neural/
├── MLPClassifier.java // Multi-layer perceptron
├── CNNClassifier.java // Convolutional neural network
├── RNNClassifier.java // Recurrent neural network (LSTM/GRU)
├── BaseNeuralNetwork.java // Shared neural network functionality
└── activations/
├── ActivationFunction.java
├── ReLU.java
├── Sigmoid.java
└── Tanh.java
Neural Network Architecture:
public class MLPClassifier extends BaseEstimator implements Classifier {
// Network architecture
private int[] hiddenLayerSizes = {100};
private String activation = "relu";
private String solver = "adam";
private double alpha = 0.0001; // L2 regularization
// Network components
private List<RealMatrix> weights;
private List<double[]> biases;
private List<Double> lossHistory;
// Training configuration
private int maxIter = 200;
private double learningRate = 0.001;
private int batchSize = 32;
private boolean earlyStopping = false;
}
superml-clustering
Purpose: Unsupervised clustering algorithms
org.superml.clustering/
├── KMeans.java // k-means++ clustering
├── DBSCAN.java // Density-based clustering
├── AgglomerativeClustering.java // Hierarchical clustering
└── ClusterEvaluator.java // Cluster quality metrics
🔄 Data Processing (3 modules)
superml-preprocessing
Purpose: Feature transformation and data preprocessing
org.superml.preprocessing/
├── StandardScaler.java // Z-score normalization
├── MinMaxScaler.java // Min-max scaling
├── RobustScaler.java // Median-based scaling
├── LabelEncoder.java // Categorical encoding
├── OneHotEncoder.java // One-hot encoding
├── PolynomialFeatures.java // Polynomial feature generation
├── NeuralNetworkPreprocessor.java // Neural network preprocessing
└── FeatureSelector.java // Feature selection utilities
Neural Network Preprocessing:
public class NeuralNetworkPreprocessor implements UnsupervisedLearner {
public enum NetworkType { MLP, CNN, RNN }
private NetworkType networkType;
private StandardScaler standardScaler;
private double outlierThreshold = 3.0;
private double temporalSmoothing = 0.1;
// Network-specific configurations
public NeuralNetworkPreprocessor configureMLP() {
// Standardization + outlier clipping for MLPs
return this;
}
public NeuralNetworkPreprocessor configureCNN() {
// Pixel normalization + data augmentation for CNNs
return this;
}
public NeuralNetworkPreprocessor configureRNN() {
// Temporal smoothing + sequence normalization for RNNs
return this;
}
}
superml-datasets
Purpose: Built-in datasets and synthetic data generation
org.superml.datasets/
├── DatasetLoader.java // CSV and data loading utilities
├── SyntheticDataGenerator.java // Generate classification/regression data
├── IrisDataset.java // Classic iris dataset
├── BostonHousing.java // Housing price regression
└── DigitsDataset.java // Handwritten digits (8x8)
superml-model-selection
Purpose: Model selection and hyperparameter tuning
org.superml.model_selection/
├── GridSearchCV.java // Exhaustive parameter search
├── RandomizedSearchCV.java // Random parameter search
├── CrossValidator.java // K-fold cross-validation
├── StratifiedKFold.java // Stratified sampling
├── TrainTestSplit.java // Data splitting utilities
└── NeuralNetworkGridSearchCV.java // Neural network hyperparameter tuning
Neural Network Grid Search:
public class NeuralNetworkGridSearchCV {
public static class StandardGrids {
public static Map<String, Object[]> mlpGrid() {
return Map.of(
"hidden_layer_sizes", new int[][]{ {50}, {100}, {100, 50}, {200, 100, 50} },
"activation", new String[]{"relu", "tanh"},
"learning_rate", new double[]{0.001, 0.01, 0.1},
"alpha", new double[]{0.0001, 0.001, 0.01}
);
}
public static Map<String, Object[]> cnnGrid() {
return Map.of(
"filters", new int[]{16, 32, 64},
"kernel_size", new int[]{3, 5},
"learning_rate", new double[]{0.001, 0.01}
);
}
}
}
🔧 Workflow Management (2 modules)
superml-pipeline
Purpose: ML pipelines and workflow automation
org.superml.pipeline/
├── Pipeline.java // Sklearn-style pipeline
├── PipelineStep.java // Individual pipeline component
├── NeuralNetworkPipelineFactory.java // Neural network pipeline factory
└── utils/
├── PipelineValidator.java // Pipeline validation
└── PipelineSerializer.java // Pipeline persistence
Pipeline Architecture:
public class Pipeline extends BaseEstimator implements SupervisedLearner {
private List<PipelineStep> steps;
private Map<String, Estimator> namedSteps;
public Pipeline addStep(String name, Estimator estimator) {
steps.add(new PipelineStep(name, estimator));
namedSteps.put(name, estimator);
return this;
}
@Override
public Pipeline fit(double[][] X, double[] y) {
double[][] currentX = X;
// Fit all transformers sequentially
for (int i = 0; i < steps.size() - 1; i++) {
Estimator estimator = steps.get(i).estimator;
if (estimator instanceof UnsupervisedLearner) {
UnsupervisedLearner transformer = (UnsupervisedLearner) estimator;
transformer.fit(currentX);
currentX = transformer.transform(currentX);
}
}
// Fit final estimator
Estimator finalEstimator = getFinalEstimator();
if (finalEstimator instanceof SupervisedLearner) {
((SupervisedLearner) finalEstimator).fit(currentX, y);
}
fitted = true;
return this;
}
}
Neural Network Pipeline Factory:
public class NeuralNetworkPipelineFactory {
public static Pipeline createMLPPipeline(int[] hiddenLayers, String activation,
double learningRate, int epochs) {
return new Pipeline()
.addStep("preprocessor", new NeuralNetworkPreprocessor(NetworkType.MLP).configureMLP())
.addStep("mlp", new MLPClassifier()
.setHiddenLayerSizes(hiddenLayers)
.setActivation(activation)
.setLearningRate(learningRate)
.setMaxIter(epochs));
}
public static Pipeline createCNNPipeline(int height, int width, int channels,
double learningRate, int epochs) {
return new Pipeline()
.addStep("preprocessor", new NeuralNetworkPreprocessor(NetworkType.CNN).configureCNN())
.addStep("cnn", new CNNClassifier()
.setInputShape(height, width, channels)
.setLearningRate(learningRate)
.setMaxIter(epochs));
}
}
superml-autotrainer
Purpose: Automated machine learning and model selection
org.superml.autotrainer/
├── AutoTrainer.java // Main AutoML orchestrator
├── ModelSelector.java // Algorithm selection logic
├── HyperparameterOptimizer.java // Automated hyperparameter tuning
├── NeuralNetworkAutoTrainer.java // Neural network AutoML
└── strategies/
├── GridSearchStrategy.java
├── RandomSearchStrategy.java
└── BayesianOptimization.java
Neural Network AutoTrainer:
public class NeuralNetworkAutoTrainer {
public static class AutoTrainerConfig {
public final String taskType; // "binary_classification", "multiclass", "regression"
public final String dataType; // "tabular", "image", "sequence"
public final String optimizationMetric; // "accuracy", "f1", "auc", "rmse"
public final int maxTrainingTime; // seconds
public final int maxModels; // maximum models to try
public final boolean enableEnsemble;
}
public static AutoTrainerResult autoTrain(double[][] X, double[] y, AutoTrainerConfig config) {
List<ModelCandidate> candidates = new ArrayList<>();
// Try different architectures based on data type
candidates.addAll(tryMLPArchitectures(X, y, config));
if ("image".equals(config.dataType)) {
candidates.addAll(tryCNNArchitectures(X, y, config));
}
if ("sequence".equals(config.dataType)) {
candidates.addAll(tryRNNArchitectures(X, y, config));
}
// Select best model and retrain on full dataset
ModelCandidate best = selectBestModel(candidates, config.optimizationMetric);
Estimator finalModel = retrainBestModel(best, X, y);
return new AutoTrainerResult(finalModel, best.metrics, candidates,
best.architecture, System.currentTimeMillis());
}
}
📊 Evaluation & Monitoring (3 modules)
superml-metrics
Purpose: Comprehensive evaluation metrics for all algorithm types
org.superml.metrics/
├── Metrics.java // Base metrics class
├── ClassificationMetrics.java // Precision, recall, F1, AUC
├── RegressionMetrics.java // MSE, MAE, R²
├── ClusteringMetrics.java // Silhouette, adjusted rand index
├── NeuralNetworkMetrics.java // Neural network specific metrics
└── utils/
├── ConfusionMatrix.java // Confusion matrix utilities
└── ROCCurve.java // ROC curve analysis
Neural Network Metrics:
public class NeuralNetworkMetrics {
public static double binaryCrossEntropy(double[] yTrue, double[] yPred) {
double loss = 0.0;
for (int i = 0; i < yTrue.length; i++) {
double p = Math.max(1e-15, Math.min(1 - 1e-15, yPred[i]));
loss += yTrue[i] * Math.log(p) + (1 - yTrue[i]) * Math.log(1 - p);
}
return -loss / yTrue.length;
}
public static double categoricalCrossEntropy(double[] yTrue, double[] yPred) {
double loss = 0.0;
for (int i = 0; i < yTrue.length; i++) {
double p = Math.max(1e-15, yPred[i]);
loss += yTrue[i] * Math.log(p);
}
return -loss / yTrue.length;
}
public static Map<String, Double> comprehensiveMetrics(double[] yTrue, double[] yPred, String taskType) {
Map<String, Double> metrics = new HashMap<>();
switch (taskType) {
case "binary_classification":
metrics.put("accuracy", Metrics.accuracy(yTrue, yPred));
metrics.put("precision", Metrics.precision(yTrue, yPred));
metrics.put("recall", Metrics.recall(yTrue, yPred));
metrics.put("f1_score", Metrics.f1Score(yTrue, yPred));
metrics.put("binary_crossentropy", binaryCrossEntropy(yTrue, yPred));
break;
case "multiclass":
metrics.put("accuracy", Metrics.accuracy(yTrue, yPred));
metrics.put("categorical_crossentropy", categoricalCrossEntropy(yTrue, yPred));
break;
case "regression":
metrics.put("mse", meanSquaredError(yTrue, yPred));
metrics.put("mae", meanAbsoluteError(yTrue, yPred));
metrics.put("r2", Metrics.r2Score(yTrue, yPred));
break;
}
return metrics;
}
}
superml-visualization
Purpose: Dual-mode visualization (GUI + Terminal)
org.superml.visualization/
├── PlotManager.java // Main plotting interface
├── gui/
│ ├── XChartPlotter.java // Professional GUI plots
│ ├── ScatterPlot.java // Scatter plot implementation
│ └── LinePlot.java // Line plot implementation
├── ascii/
│ ├── ASCIIPlotter.java // Terminal-based plots
│ ├── ASCIIHistogram.java // ASCII histogram
│ └── ASCIIScatter.java // ASCII scatter plot
└── NeuralNetworkVisualizer.java // Neural network specific plots
superml-drift
Purpose: Model drift detection and monitoring
org.superml.drift/
├── DriftDetector.java // Main drift detection interface
├── DataDriftDetector.java // Statistical data drift detection
├── ModelDriftDetector.java // Model performance drift
├── ConceptDriftDetector.java // Concept drift detection
└── monitoring/
├── DriftMonitor.java // Continuous monitoring
└── DriftAlert.java // Alert system
🚀 Production & Infrastructure (4 modules)
superml-inference
Purpose: High-performance model serving and inference
org.superml.inference/
├── InferenceEngine.java // Main inference orchestrator
├── BatchInferenceProcessor.java // Batch processing
├── StreamingInference.java // Real-time inference
├── ModelCache.java // Model caching system
├── NeuralNetworkInferenceEngine.java // Neural network inference
└── serving/
├── ModelServer.java // HTTP model server
└── InferenceAPI.java // REST API endpoints
Neural Network Inference Engine:
public class NeuralNetworkInferenceEngine {
public static class InferenceConfig {
public final boolean enablePreprocessing;
public final int batchSize;
public final int timeoutMs;
public final int parallelThreads;
public InferenceConfig(boolean enablePreprocessing, int batchSize, int timeoutMs) {
this.enablePreprocessing = enablePreprocessing;
this.batchSize = batchSize;
this.timeoutMs = timeoutMs;
this.parallelThreads = Runtime.getRuntime().availableProcessors();
}
}
public static InferenceResult batchInference(Estimator model, double[][] X, InferenceConfig config) {
long startTime = System.currentTimeMillis();
// Preprocess if enabled
double[][] processedX = X;
if (config.enablePreprocessing && model instanceof MLPClassifier) {
NeuralNetworkPreprocessor preprocessor = new NeuralNetworkPreprocessor(NetworkType.MLP).configureMLP();
preprocessor.fit(X);
processedX = preprocessor.transform(X);
}
// Perform inference
double[] predictions;
if (model instanceof Classifier) {
predictions = ((Classifier) model).predict(processedX);
} else if (model instanceof Regressor) {
predictions = ((Regressor) model).predict(processedX);
} else {
throw new IllegalArgumentException("Unsupported model type for inference");
}
long inferenceTime = System.currentTimeMillis() - startTime;
return new InferenceResult(predictions, null, inferenceTime, processedX.length);
}
}
superml-persistence
Purpose: Model serialization and persistence
org.superml.persistence/
├── ModelPersistence.java // Main persistence interface
├── ModelSerializer.java // Binary serialization
├── ModelMetadata.java // Model metadata management
└── formats/
├── JavaSerialization.java // Native Java serialization
├── JSONSerialization.java // JSON format
└── CustomFormat.java // Custom binary format
superml-onnx
Purpose: ONNX model export for cross-platform deployment
org.superml.onnx/
├── ONNXExporter.java // Main ONNX export interface
├── ModelConverter.java // Algorithm to ONNX conversion
└── exporters/
├── LinearModelExporter.java
├── TreeModelExporter.java
└── NeuralNetworkExporter.java
superml-pmml
Purpose: PMML model exchange format
org.superml.pmml/
├── PMMLExporter.java // PMML export functionality
├── PMMLImporter.java // PMML import functionality
└── converters/
├── LogisticRegressionPMML.java
├── DecisionTreePMML.java
└── RandomForestPMML.java
🌐 External Integration (2 modules)
superml-kaggle
Purpose: Kaggle competition automation and workflows
org.superml.kaggle/
├── KaggleClient.java // Kaggle API client
├── DatasetDownloader.java // Competition data download
├── SubmissionManager.java // Submission automation
├── NeuralNetworkKaggleHelper.java // Neural network competition utilities
└── workflows/
├── CompetitionWorkflow.java // End-to-end competition pipeline
├── EnsembleCreator.java // Model ensemble creation
└── FeatureEngineering.java // Automated feature engineering
Neural Network Kaggle Helper:
public class NeuralNetworkKaggleHelper {
public static class CompetitionResult {
public final List<Estimator> models;
public final Map<String, Double> scores;
public final String bestArchitecture;
public final long trainingTime;
public List<Estimator> getModels() { return models; }
}
public static CompetitionResult trainCompetitionModels(double[][] X, double[] y, String competitionType) {
List<Estimator> models = new ArrayList<>();
Map<String, Double> scores = new HashMap<>();
// Train multiple neural network architectures
models.add(trainMLPModel(X, y, competitionType));
models.add(trainCNNModel(X, y, competitionType));
models.add(trainRNNModel(X, y, competitionType));
// Evaluate each model
for (Estimator model : models) {
double score = evaluateModel(model, X, y, competitionType);
scores.put(model.getClass().getSimpleName(), score);
}
String bestArchitecture = selectBestArchitecture(scores);
return new CompetitionResult(models, scores, bestArchitecture, System.currentTimeMillis());
}
}
📚 Distribution & Examples (3 modules)
superml-examples
Purpose: Comprehensive usage examples and tutorials
org.superml.examples/
├── BasicClassification.java // Simple classification example
├── MulticlassExample.java // Multiclass classification
├── RegressionExample.java // Regression walkthrough
├── PipelineExample.java // Advanced pipeline usage
├── ModelSelectionExample.java // Hyperparameter tuning
├── KaggleIntegration.java // Competition workflow
├── InferenceExample.java // Production inference
├── NeuralNetworkExample.java // Neural network usage
├── AutoTrainerExample.java // AutoML demonstration
├── VisualizationExample.java // Plotting and visualization
└── ProductionExample.java // End-to-end production pipeline
superml-bundle-all
Purpose: Complete framework distribution
<dependency>
<groupId>org.superml</groupId>
<artifactId>superml-bundle-all</artifactId>
<version>2.1.0</version>
</dependency>
superml-java-parent
Purpose: Maven build coordination and dependency management
🔧 Core Design Patterns
1. Estimator Pattern
All ML components follow the consistent estimator pattern:
public interface Estimator {
Map<String, Object> getParams();
Estimator setParams(Map<String, Object> params);
boolean isFitted();
void validateParameters();
}
public interface SupervisedLearner extends Estimator {
SupervisedLearner fit(double[][] X, double[] y);
double[] predict(double[][] X);
double score(double[][] X, double[] y);
}
2. Pipeline Pattern
Components compose seamlessly in processing pipelines:
var pipeline = new Pipeline()
.addStep("scaler", new StandardScaler())
.addStep("selector", new FeatureSelector())
.addStep("classifier", new LogisticRegression());
pipeline.fit(X_train, y_train);
double[] predictions = pipeline.predict(X_test);
3. Factory Pattern
Specialized factories for complex object creation:
// Neural network pipelines
Pipeline mlpPipeline = NeuralNetworkPipelineFactory.createMLPPipeline(
new int[]{128, 64}, "relu", 0.001, 100);
// Automated model selection
AutoTrainerResult result = NeuralNetworkAutoTrainer.autoTrain(X, y, config);
4. Strategy Pattern
Pluggable algorithms and optimization strategies:
// Different optimization strategies
public enum OptimizationStrategy {
GRID_SEARCH, RANDOM_SEARCH, BAYESIAN_OPTIMIZATION
}
// Different neural network cell types
public enum CellType {
SIMPLE_RNN, LSTM, GRU
}
🧠 Neural Network Architecture
SuperML Java provides comprehensive neural network support with three main architectures:
MLP (Multi-Layer Perceptron)
MLPClassifier mlp = new MLPClassifier()
.setHiddenLayerSizes(new int[]{128, 64, 32})
.setActivation("relu")
.setSolver("adam")
.setLearningRate(0.001)
.setMaxIter(200)
.setEarlyStopping(true);
CNN (Convolutional Neural Network)
CNNClassifier cnn = new CNNClassifier()
.setInputShape(28, 28, 1)
.addConvLayer(32, 3, 3, "relu")
.addPoolingLayer(2, 2)
.addConvLayer(64, 3, 3, "relu")
.addDenseLayer(128, "relu")
.setLearningRate(0.001);
RNN (Recurrent Neural Network)
RNNClassifier rnn = new RNNClassifier()
.setSequenceLength(30)
.setHiddenSize(128)
.setNumLayers(2)
.setCellType("LSTM")
.setBidirectional(true)
.setUseAttention(true)
.setLearningRate(0.001);
🚀 Production Deployment Architecture
Inference Pipeline
// Configure inference
InferenceConfig config = new InferenceConfig(true, 1000, 5000);
// High-performance batch inference
InferenceResult result = NeuralNetworkInferenceEngine.batchInference(model, data, config);
// Real-time streaming inference
StreamingInference stream = NeuralNetworkInferenceEngine.createStreamingInference(model, config);
Model Persistence
// Save model with metadata
ModelPersistence.save(model, "model.superml", metadata);
// Load model for inference
Estimator loadedModel = ModelPersistence.load("model.superml");
Monitoring and Drift Detection
// Monitor model performance
DriftDetector detector = new ModelDriftDetector(originalModel);
boolean hasDrift = detector.detectDrift(newData, newPredictions);
// Data drift monitoring
DataDriftDetector dataDetector = new DataDriftDetector(referenceData);
boolean dataHasDrift = dataDetector.hasDataDrift(currentData);
📊 Framework Metrics
| Component | Count | Description |
|---|---|---|
| Modules | 21 | Specialized components |
| Algorithms | 12+ | ML algorithm implementations |
| Classes | 200+ | Total framework classes |
| Examples | 11 | Comprehensive usage examples |
| Interfaces | 15+ | Core abstraction interfaces |
| Neural Networks | 3 | MLP, CNN, RNN architectures |
| Exporters | 2 | ONNX, PMML format support |
| Dependencies | Minimal | Only essential external libs |
🎯 Key Benefits
🔧 Developer Experience
- Scikit-learn API: Familiar interface for Python developers
- Type Safety: Full Java type checking and IDE support
- Rich Examples: 11 comprehensive examples covering all use cases
- Consistent Patterns: Same patterns across all components
🚀 Production Ready
- High Performance: Optimized algorithms with proper complexity
- Monitoring: Built-in drift detection and performance tracking
- Serialization: Multiple persistence formats
- Deployment: ONNX/PMML export for cross-platform deployment
🧠 Neural Networks
- Multiple Architectures: MLP, CNN, RNN with LSTM/GRU support
- Specialized Preprocessing: Network-specific data preparation
- AutoML Integration: Automated architecture selection
- Competition Ready: Kaggle workflow integration
📦 Modularity
- Selective Dependencies: Include only needed components
- Plugin Architecture: Easy to extend with new algorithms
- Clean Interfaces: Well-defined component boundaries
- Composability: Components work seamlessly together
This comprehensive architecture enables SuperML Java to serve as both a learning framework for ML concepts and a production-ready platform for deploying machine learning solutions at scale.