Pipeline Framework

/**
 * Linear workflow for combining estimators and transformers
 * Supports both training (fit) and transformation (transform) operations
 */
public final class Pipeline implements Estimator<Pipeline, Pipeline>, 
                                     Transformer<Pipeline>, 
                                     Model<Pipeline> {
    
    /** Create empty pipeline */
    public Pipeline();
    
    /** Create pipeline from JSON representation */
    public Pipeline(String pipelineJson);
    
    /** Create pipeline from list of stages */
    public Pipeline(List<PipelineStage> stages);
    
    /** Add a stage to the end of the pipeline */
    public Pipeline appendStage(PipelineStage stage);
    
    /** Get immutable list of all pipeline stages */
    public List<PipelineStage> getStages();
    
    /** Check if pipeline contains any estimators that need training */
    public boolean needFit();
    
    /** Get pipeline parameters configuration */
    public Params getParams();
    
    /** Train pipeline on input data, producing trained pipeline */
    public Pipeline fit(TableEnvironment tEnv, Table input);
    
    /** Apply pipeline transformation to input data */
    public Table transform(TableEnvironment tEnv, Table input);
    
    /** Serialize pipeline to JSON string */
    public String toJson();
    
    /** Load pipeline from JSON string */
    public void loadJson(String json);
}

import org.apache.flink.ml.api.core.Pipeline;
import org.apache.flink.table.api.Table;

// Create and build pipeline
Pipeline pipeline = new Pipeline()
    .appendStage(new FeatureNormalizer())
    .appendStage(new LinearRegression())
    .appendStage(new PredictionTransformer());

// Check if training is needed
if (pipeline.needFit()) {
    // Train the pipeline
    Pipeline trainedPipeline = pipeline.fit(tableEnv, trainingData);
    
    // Apply to new data
    Table predictions = trainedPipeline.transform(tableEnv, testData);
}

// Serialize pipeline
String pipelineJson = pipeline.toJson();

// Load pipeline from JSON
Pipeline loadedPipeline = new Pipeline(pipelineJson);

/**
 * Base interface for pipeline components
 * All estimators, transformers, and models implement this interface
 */
public interface PipelineStage {
    /** Serialize stage to JSON representation */
    String toJson();
    
    /** Load stage configuration from JSON */
    void loadJson(String json);
}

/**
 * Machine learning estimators that train models from data
 * @param <E> The concrete estimator type
 * @param <M> The model type produced by this estimator
 */
public interface Estimator<E extends Estimator<E, M>, M extends Model<M>> 
                  extends PipelineStage {
    
    /** Train estimator on input data and produce a trained model */
    M fit(TableEnvironment tEnv, Table input);
}

public class MyLinearRegression implements Estimator<MyLinearRegression, MyLinearModel> {
    @Override
    public MyLinearModel fit(TableEnvironment tEnv, Table input) {
        // Training logic here
        return new MyLinearModel(/* trained parameters */);
    }
}

/**
 * Data transformation components that modify input data
 * @param <T> The concrete transformer type  
 */
public interface Transformer<T extends Transformer<T>> extends PipelineStage {
    
    /** Apply transformation to input data */
    Table transform(TableEnvironment tEnv, Table input);
}

public class FeatureNormalizer implements Transformer<FeatureNormalizer> {
    @Override
    public Table transform(TableEnvironment tEnv, Table input) {
        // Normalization logic here
        return normalizedTable;
    }
}

/**
 * Trained machine learning models
 * Models are transformers that have been produced by training an estimator
 * @param <M> The concrete model type
 */
public interface Model<M extends Model<M>> extends Transformer<M> {
    // Inherits transform() method from Transformer
    // Additional model-specific functionality can be added here
}

public class MyLinearModel implements Model<MyLinearModel> {
    private DenseVector weights;
    private double bias;
    
    public MyLinearModel(DenseVector weights, double bias) {
        this.weights = weights;
        this.bias = bias;
    }
    
    @Override
    public Table transform(TableEnvironment tEnv, Table input) {
        // Apply model to make predictions
        return predictionsTable;
    }
}

// Example execution flow
Pipeline pipeline = new Pipeline()
    .appendStage(preprocessor)      // Transformer - no training needed  
    .appendStage(featureSelector)   // Estimator - needs training
    .appendStage(classifier);       // Estimator - needs training

// Check if training needed (true, because of estimators)
boolean needsTraining = pipeline.needFit(); 

if (needsTraining) {
    // This will:
    // 1. Apply preprocessor transform
    // 2. Train featureSelector on preprocessed data  
    // 3. Apply featureSelector transform
    // 4. Train classifier on selected features
    Pipeline trainedPipeline = pipeline.fit(tEnv, trainingData);
    
    // Now apply full trained pipeline
    Table results = trainedPipeline.transform(tEnv, newData);
}