☕ @learn - Machine Learning Function in Java
@learn - Machine Learning Function in Java
The @learn operator provides machine learning capabilities for Java applications, integrating with Spring Boot's ML frameworks, TensorFlow, Weka, and enterprise machine learning pipelines.
Basic Syntax
// TuskLang configuration
predicted_value: @learn("price_prediction", 1000.0)
recommendation: @learn("user_recommendation", "default_product")
classification: @learn("spam_detection", "ham")// Java Spring Boot integration
@Configuration
public class MachineLearningConfig {
@Bean
public MachineLearningService machineLearningService() {
return MachineLearningService.builder()
.framework("tensorflow")
.modelPath("/models")
.trainingDataPath("/data")
.build();
}
}Basic Learning
// Java machine learning service
@Component
public class MachineLearningService {
@Autowired
private MeterRegistry meterRegistry;
private final Map<String, MLModel> modelCache = new ConcurrentHashMap<>();
public PredictionResult learn(String modelName, Object input, Object defaultValue) {
MLModel model = getOrLoadModel(modelName);
try {
PredictionResult result = model.predict(input);
// Record prediction metrics
Timer.builder("ml.prediction.time")
.tag("model", modelName)
.register(meterRegistry)
.record(result.getPredictionTime(), TimeUnit.MILLISECONDS);
Counter.builder("ml.prediction.count")
.tag("model", modelName)
.register(meterRegistry)
.increment();
return result;
} catch (Exception e) {
log.warn("Prediction failed for model: {}, using default value: {}", modelName, defaultValue);
return PredictionResult.builder()
.prediction(defaultValue)
.confidence(0.0)
.isDefault(true)
.build();
}
}
public TrainingResult trainModel(String modelName, List<TrainingData> trainingData) {
MLModel model = createModel(modelName);
TrainingResult result = model.train(trainingData);
// Record training metrics
Timer.builder("ml.training.time")
.tag("model", modelName)
.register(meterRegistry)
.record(result.getTrainingTime(), TimeUnit.MILLISECONDS);
Gauge.builder("ml.model.accuracy")
.tag("model", modelName)
.register(meterRegistry, () -> result.getAccuracy());
return result;
}
private MLModel getOrLoadModel(String modelName) {
return modelCache.computeIfAbsent(modelName, this::loadModel);
}
private MLModel loadModel(String modelName) {
// Load model from file system or model registry
return ModelLoader.load(modelName);
}
private MLModel createModel(String modelName) {
// Create new model instance
return ModelFactory.create(modelName);
}
}// TuskLang machine learning
ml_config: {
# Basic learning
price_prediction: @learn("price_prediction", 1000.0)
user_recommendation: @learn("user_recommendation", "default_product")
spam_detection: @learn("spam_detection", "ham")
# Learning with confidence
prediction_with_confidence: @learn.confidence("fraud_detection", false, 0.8)
# Batch learning
batch_predictions: @learn.batch("sentiment_analysis", ["positive", "negative", "neutral"])
}Supervised Learning
// Java supervised learning
@Component
public class SupervisedLearningService {
@Autowired
private MachineLearningService mlService;
public ClassificationResult classify(String modelName, Object input, String defaultClass) {
return mlService.learn(modelName, input, defaultClass);
}
public RegressionResult predict(String modelName, Object input, double defaultValue) {
return mlService.learn(modelName, input, defaultValue);
}
public TrainingResult trainClassificationModel(String modelName, List<ClassificationData> trainingData) {
return mlService.trainModel(modelName, convertToTrainingData(trainingData));
}
public TrainingResult trainRegressionModel(String modelName, List<RegressionData> trainingData) {
return mlService.trainModel(modelName, convertToTrainingData(trainingData));
}
public ModelEvaluationResult evaluateModel(String modelName, List<TestData> testData) {
MLModel model = mlService.getModel(modelName);
return model.evaluate(testData);
}
}// TuskLang supervised learning
supervised_learning: {
# Classification
email_classification: @learn.classify("email_classifier", "spam", "ham")
sentiment_analysis: @learn.classify("sentiment_analyzer", "positive", "negative")
image_classification: @learn.classify("image_classifier", "cat", "dog")
# Regression
price_prediction: @learn.predict("price_predictor", 1000.0)
stock_prediction: @learn.predict("stock_predictor", 50.0)
temperature_prediction: @learn.predict("weather_predictor", 20.0)
# Model training
train_email_classifier: @learn.train("email_classifier", {
algorithm: "random_forest"
training_data: @load_training_data("emails")
test_split: 0.2
})
# Model evaluation
model_evaluation: @learn.evaluate("email_classifier", {
test_data: @load_test_data("emails")
metrics: ["accuracy", "precision", "recall", "f1"]
})
}Unsupervised Learning
// Java unsupervised learning
@Component
public class UnsupervisedLearningService {
@Autowired
private MachineLearningService mlService;
public ClusteringResult cluster(String modelName, List<Object> data, int numClusters) {
return mlService.cluster(modelName, data, numClusters);
}
public DimensionalityReductionResult reduceDimensions(String modelName, List<Object> data, int dimensions) {
return mlService.reduceDimensions(modelName, data, dimensions);
}
public AnomalyDetectionResult detectAnomalies(String modelName, Object input, boolean defaultResult) {
return mlService.detectAnomalies(modelName, input, defaultResult);
}
public AssociationRuleResult findAssociations(String modelName, List<Object> transactions, double minSupport) {
return mlService.findAssociations(modelName, transactions, minSupport);
}
}// TuskLang unsupervised learning
unsupervised_learning: {
# Clustering
customer_segments: @learn.cluster("customer_clustering", 5)
document_clusters: @learn.cluster("document_clustering", 10)
image_clusters: @learn.cluster("image_clustering", 8)
# Dimensionality reduction
feature_reduction: @learn.reduce_dimensions("pca", 10)
topic_modeling: @learn.reduce_dimensions("lda", 20)
# Anomaly detection
fraud_detection: @learn.detect_anomalies("fraud_detector", false)
network_anomalies: @learn.detect_anomalies("network_detector", false)
# Association rules
product_associations: @learn.find_associations("market_basket", 0.01)
web_page_associations: @learn.find_associations("web_usage", 0.005)
}Deep Learning
// Java deep learning
@Component
public class DeepLearningService {
@Autowired
private MachineLearningService mlService;
public NeuralNetworkResult predictWithNeuralNetwork(String modelName, Object input, Object defaultValue) {
return mlService.predictWithNeuralNetwork(modelName, input, defaultValue);
}
public TrainingResult trainNeuralNetwork(String modelName, NeuralNetworkConfig config, List<TrainingData> data) {
return mlService.trainNeuralNetwork(modelName, config, data);
}
public TransferLearningResult transferLearn(String sourceModel, String targetModel, List<TrainingData> data) {
return mlService.transferLearn(sourceModel, targetModel, data);
}
public ReinforcementLearningResult reinforcementLearn(String modelName, Environment environment, int episodes) {
return mlService.reinforcementLearn(modelName, environment, episodes);
}
}// TuskLang deep learning
deep_learning: {
# Neural networks
image_recognition: @learn.neural_network("image_classifier", "unknown")
text_generation: @learn.neural_network("text_generator", "default_text")
speech_recognition: @learn.neural_network("speech_recognizer", "unknown")
# Neural network training
train_image_classifier: @learn.train_neural_network("image_classifier", {
architecture: "cnn"
layers: [32, 64, 128, 256]
activation: "relu"
optimizer: "adam"
learning_rate: 0.001
epochs: 100
})
# Transfer learning
transfer_learning: @learn.transfer("pretrained_resnet", "custom_classifier", {
freeze_layers: 10
fine_tune_layers: 5
learning_rate: 0.0001
})
# Reinforcement learning
reinforcement_learning: @learn.reinforcement("q_learning_agent", {
environment: "cartpole"
episodes: 1000
learning_rate: 0.1
discount_factor: 0.95
})
}Feature Engineering
// Java feature engineering
@Component
public class FeatureEngineeringService {
@Autowired
private MachineLearningService mlService;
public FeatureSet extractFeatures(String modelName, Object input) {
return mlService.extractFeatures(modelName, input);
}
public FeatureSet selectFeatures(String modelName, List<String> features, int numFeatures) {
return mlService.selectFeatures(modelName, features, numFeatures);
}
public FeatureSet normalizeFeatures(String modelName, FeatureSet features) {
return mlService.normalizeFeatures(modelName, features);
}
public FeatureSet encodeCategoricalFeatures(String modelName, FeatureSet features) {
return mlService.encodeCategoricalFeatures(modelName, features);
}
public FeatureImportanceResult getFeatureImportance(String modelName) {
return mlService.getFeatureImportance(modelName);
}
}// TuskLang feature engineering
feature_engineering: {
# Feature extraction
text_features: @learn.extract_features("text_extractor")
image_features: @learn.extract_features("image_extractor")
audio_features: @learn.extract_features("audio_extractor")
# Feature selection
selected_features: @learn.select_features("feature_selector", 10)
important_features: @learn.select_features("importance_selector", 5)
# Feature normalization
normalized_features: @learn.normalize_features("standard_scaler")
scaled_features: @learn.normalize_features("min_max_scaler")
# Feature encoding
encoded_features: @learn.encode_categorical("one_hot_encoder")
label_encoded: @learn.encode_categorical("label_encoder")
# Feature importance
feature_importance: @learn.feature_importance("random_forest")
}Model Management
// Java model management
@Component
public class ModelManagementService {
@Autowired
private MachineLearningService mlService;
public ModelVersion saveModel(String modelName, MLModel model) {
return mlService.saveModel(modelName, model);
}
public MLModel loadModel(String modelName, String version) {
return mlService.loadModel(modelName, version);
}
public ModelComparisonResult compareModels(String modelName, List<String> versions) {
return mlService.compareModels(modelName, versions);
}
public ModelDeploymentResult deployModel(String modelName, String version, DeploymentConfig config) {
return mlService.deployModel(modelName, version, config);
}
public ModelMonitoringResult monitorModel(String modelName) {
return mlService.monitorModel(modelName);
}
}// TuskLang model management
model_management: {
# Model versioning
save_model: @learn.save_model("email_classifier", "v1.0")
load_model: @learn.load_model("email_classifier", "v1.0")
# Model comparison
model_comparison: @learn.compare_models("email_classifier", ["v1.0", "v1.1", "v1.2"])
# Model deployment
deploy_model: @learn.deploy_model("email_classifier", "v1.2", {
environment: "production"
replicas: 3
resources: { cpu: "2", memory: "4Gi" }
})
# Model monitoring
model_monitoring: @learn.monitor_model("email_classifier", {
metrics: ["accuracy", "latency", "throughput"]
alerts: { accuracy_threshold: 0.9, latency_threshold: 100 }
})
}A/B Testing
// Java A/B testing
@Component
public class ABTestingService {
@Autowired
private MachineLearningService mlService;
public ABTestResult runABTest(String testName, String modelA, String modelB,
List<TestData> testData, double trafficSplit) {
return mlService.runABTest(testName, modelA, modelB, testData, trafficSplit);
}
public ABTestResult getABTestResult(String testName) {
return mlService.getABTestResult(testName);
}
public boolean isTestSignificant(String testName, double confidenceLevel) {
ABTestResult result = getABTestResult(testName);
return result.isStatisticallySignificant(confidenceLevel);
}
public String getWinningModel(String testName) {
ABTestResult result = getABTestResult(testName);
return result.getWinningModel();
}
}// TuskLang A/B testing
ab_testing: {
# A/B test setup
ab_test: @learn.ab_test("recommendation_test", {
model_a: "collaborative_filtering"
model_b: "content_based"
traffic_split: 0.5
test_duration: "7d"
success_metric: "click_through_rate"
})
# A/B test results
test_results: @learn.ab_test_results("recommendation_test")
winning_model: @learn.ab_test_winner("recommendation_test")
statistical_significance: @learn.ab_test_significance("recommendation_test", 0.95)
}Model Explainability
// Java model explainability
@Component
public class ModelExplainabilityService {
@Autowired
private MachineLearningService mlService;
public ExplanationResult explainPrediction(String modelName, Object input) {
return mlService.explainPrediction(modelName, input);
}
public FeatureAttributionResult getFeatureAttribution(String modelName, Object input) {
return mlService.getFeatureAttribution(modelName, input);
}
public SHAPResult getSHAPValues(String modelName, Object input) {
return mlService.getSHAPValues(modelName, input);
}
public LIMEResult getLIMEExplanation(String modelName, Object input) {
return mlService.getLIMEExplanation(modelName, input);
}
}// TuskLang model explainability
model_explainability: {
# Prediction explanation
explanation: @learn.explain_prediction("fraud_detector", {
prediction: true
confidence: 0.95
features: ["transaction_amount", "location", "time"]
})
# Feature attribution
feature_attribution: @learn.feature_attribution("fraud_detector", {
transaction_amount: 0.6
location: 0.3
time: 0.1
})
# SHAP values
shap_values: @learn.shap_values("fraud_detector", {
base_value: 0.1
feature_values: { transaction_amount: 0.5, location: 0.2, time: 0.1 }
})
# LIME explanation
lime_explanation: @learn.lime_explanation("fraud_detector", {
local_prediction: true
feature_weights: { transaction_amount: 0.8, location: 0.1, time: 0.1 }
})
}Model Testing
// JUnit test for machine learning
@SpringBootTest
class MachineLearningServiceTest {
@Autowired
private MachineLearningService mlService;
@Test
void testBasicLearning() {
PredictionResult result = mlService.learn("test_model", "test_input", "default_value");
assertThat(result).isNotNull();
assertThat(result.getPrediction()).isNotNull();
assertThat(result.getConfidence()).isBetween(0.0, 1.0);
}
@Test
void testModelTraining() {
List<TrainingData> trainingData = createTestTrainingData();
TrainingResult result = mlService.trainModel("test_model", trainingData);
assertThat(result).isNotNull();
assertThat(result.getAccuracy()).isBetween(0.0, 1.0);
assertThat(result.getTrainingTime()).isPositive();
}
@Test
void testClassification() {
ClassificationResult result = mlService.classify("test_classifier", "test_input", "default_class");
assertThat(result).isNotNull();
assertThat(result.getPrediction()).isInstanceOf(String.class);
assertThat(result.getConfidence()).isBetween(0.0, 1.0);
}
private List<TrainingData> createTestTrainingData() {
// Create test training data
return Arrays.asList(
new TrainingData("input1", "output1"),
new TrainingData("input2", "output2"),
new TrainingData("input3", "output3")
);
}
}// TuskLang machine learning testing
test_machine_learning: {
# Test basic learning
test_prediction: @learn("test_model", "test_input", "default_value")
assert(@test_prediction != "default_value", "Should make prediction")
assert(@test_prediction.confidence > 0, "Should have confidence")
# Test classification
test_classification: @learn.classify("test_classifier", "test_input", "default_class")
assert(@test_classification in ["class1", "class2", "class3"], "Should classify correctly")
# Test regression
test_regression: @learn.predict("test_predictor", 100.0)
assert(@test_regression > 0, "Should predict positive value")
# Test model training
training_result: @learn.train("test_model", {
algorithm: "random_forest"
training_data: @create_test_data()
})
assert(@training_result.accuracy > 0.5, "Should have reasonable accuracy")
}Best Practices
1. Model Lifecycle Management
// Implement comprehensive model lifecycle management
@Component
public class ModelLifecycleService {
@Autowired
private MachineLearningService mlService;
public ModelLifecycleResult manageModelLifecycle(String modelName) {
// 1. Train model
TrainingResult trainingResult = trainModel(modelName);
// 2. Evaluate model
ModelEvaluationResult evaluationResult = evaluateModel(modelName);
// 3. Deploy model if performance is acceptable
if (evaluationResult.getAccuracy() > 0.9) {
ModelDeploymentResult deploymentResult = deployModel(modelName);
// 4. Monitor model performance
ModelMonitoringResult monitoringResult = monitorModel(modelName);
return ModelLifecycleResult.builder()
.training(trainingResult)
.evaluation(evaluationResult)
.deployment(deploymentResult)
.monitoring(monitoringResult)
.build();
}
return ModelLifecycleResult.builder()
.training(trainingResult)
.evaluation(evaluationResult)
.build();
}
}2. Continuous Learning
// Implement continuous learning pipeline
@Component
public class ContinuousLearningService {
@Autowired
private MachineLearningService mlService;
@Scheduled(fixedRate = 86400000) // Daily
public void performContinuousLearning() {
// Collect new training data
List<TrainingData> newData = collectNewTrainingData();
// Retrain models with new data
for (String modelName : getActiveModels()) {
TrainingResult result = mlService.trainModel(modelName, newData);
// Update model if performance improves
if (result.getAccuracy() > getCurrentModelAccuracy(modelName)) {
mlService.saveModel(modelName, result.getModel());
log.info("Updated model {} with improved accuracy: {}", modelName, result.getAccuracy());
}
}
}
}3. Model Monitoring and Alerting
// Implement comprehensive model monitoring
@Component
public class ModelMonitoringService {
@Autowired
private MachineLearningService mlService;
@Scheduled(fixedRate = 300000) // Every 5 minutes
public void monitorModels() {
for (String modelName : getActiveModels()) {
ModelMonitoringResult result = mlService.monitorModel(modelName);
// Check for performance degradation
if (result.getAccuracy() < 0.8) {
sendAlert("Model accuracy below threshold: " + modelName);
}
// Check for data drift
if (result.getDataDriftScore() > 0.1) {
sendAlert("Data drift detected for model: " + modelName);
}
// Check for prediction latency
if (result.getAverageLatency() > 100) {
sendAlert("High prediction latency for model: " + modelName);
}
}
}
}The @learn operator in Java provides comprehensive machine learning capabilities that enable applications to make intelligent predictions, classifications, and decisions based on data patterns and trained models in enterprise environments.