spark MLlib Classification and regression 学习

二分类：SVMs，logistic regression，decision trees，random forests，gradient-boosted trees，naive Bayes

多分类：             logistic regression，decision trees，random forests，                                        naive Bayes

归回：      linear least regression，    decision tress，random forests，gradient-boosted trees，                       isotonic regression。

一。Linear models

　　

　　　

　　　　

　　classification (SVMs, logistic regression)

　　　

package ML.ClassificationAndRegression;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.mllib.classification.SVMModel;
import org.apache.spark.mllib.classification.SVMWithSGD;
import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics;
import org.apache.spark.mllib.optimization.L1Updater;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.util.MLUtils;
import org.apache.spark.rdd.RDD;
import scala.Tuple2;

/**
 * TODO
 *
 * @ClassName: SVMClassifier
 * @author: DingH
 * @since: 2019/4/9 10:28
 */
public class SVMClassifier {
    public static void main(String[] args) {
        SparkConf conf = new SparkConf().setAppName("SVM Classifier Example").setMaster("local");
        JavaSparkContext jsc = new JavaSparkContext(conf);
        String path = "D:\IdeaProjects\SimpleApp\src\main\resources\data\mllib\sample_libsvm_data.txt";
        JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(), path).toJavaRDD();

        // Split initial RDD into two... [60% training data, 40% testing data].
        JavaRDD<LabeledPoint> train = data.sample(false, 0.6, 11L);
        train.cache();
        final JavaRDD<LabeledPoint> test = data.subtract(train);

        //Run training algorithm to build the model
        int numsIterations = 100;
        SVMWithSGD svm = new SVMWithSGD();
        svm.optimizer().setNumIterations(200).setRegParam(0.01).setUpdater(new L1Updater());
        final SVMModel model1 = svm.run(train.rdd());
//        final SVMModel model1 = SVMWithSGD.train(train.rdd(), numsIterations);

        model1.clearThreshold();

        JavaRDD<Tuple2<Object, Object>> scoraAndLables = test.map(new Function<LabeledPoint, Tuple2<Object, Object>>() {
            public Tuple2<Object, Object> call(LabeledPoint p) throws Exception {
                double predict = model1.predict(p.features());
                return new Tuple2<Object, Object>(predict, p.label());
            }
        });

        BinaryClassificationMetrics metrics = new BinaryClassificationMetrics(scoraAndLables.rdd());

        double areaUnderROC = metrics.areaUnderROC();

        System.out.println("Area under ROC = " + areaUnderROC);

        model1.save(jsc.sc(),"D:\IdeaProjects\SimpleApp\src\main\java\MLModel");
        SVMModel model = SVMModel.load(jsc.sc(), "D:\IdeaProjects\SimpleApp\src\main\java\MLModel");

    }
}

package ML.ClassificationAndRegression;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.ml.classification.MultiClassSummarizer;
import org.apache.spark.mllib.classification.LogisticRegressionModel;
import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS;
import org.apache.spark.mllib.classification.LogisticRegressionWithSGD;
import org.apache.spark.mllib.evaluation.MulticlassMetrics;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.util.MLUtils;
import scala.Tuple2;

/**
 * TODO
 *
 * @ClassName: LogistiRegression
 * @author: DingH
 * @since: 2019/4/9 11:08
 */
public class LogistiRegression {
    public static void main(String[] args) {
        SparkConf conf = new SparkConf().setMaster("local").setAppName("LogisticRegression");
        JavaSparkContext jsc = new JavaSparkContext(conf);
        String path = "D:\IdeaProjects\SimpleApp\src\main\resources\data\mllib\sample_libsvm_data.txt";
        JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(), path).toJavaRDD();

        JavaRDD<LabeledPoint>[] split = data.randomSplit(new double[]{0.6, 0.4}, 11L);
        JavaRDD<LabeledPoint> training = split[0].cache();
        final JavaRDD<LabeledPoint> test = split[1];

        final LogisticRegressionModel model = new LogisticRegressionWithLBFGS().setNumClasses(10).run(training.rdd());
        JavaRDD<Tuple2<Object, Object>> predictionAndLabels = test.map(new Function<LabeledPoint, Tuple2<Object, Object>>() {
            public Tuple2<Object, Object> call(LabeledPoint labeledPoint) throws Exception {
                double predict = model.predict(labeledPoint.features());
                return new Tuple2<Object, Object>(predict, labeledPoint.label());
            }
        });

        MulticlassMetrics metrics = new MulticlassMetrics(predictionAndLabels.rdd());

        double precision = metrics.precision();
        System.out.println("Precision = " + precision);

        // Save and load model
//        model.save(jsc.sc(), "myModelPath");
//        LogisticRegressionModel sameModel = LogisticRegressionModel.load(jsc.sc(), "myModelPath");
    }
}

　　linear regression (least squares, Lasso, ridge)

　

package ML.ClassificationAndRegression;

import org.apache.hadoop.yarn.webapp.hamlet.Hamlet;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaDoubleRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.regression.LinearRegressionModel;
import org.apache.spark.mllib.regression.LinearRegressionWithSGD;
import org.apache.spark.mllib.util.MLUtils;
import scala.Tuple2;

/**
 * TODO
 *
 * @ClassName: Regression
 * @author: DingH
 * @since: 2019/4/9 11:21
 */
public class Regression {
    public static void main(String[] args) {
        SparkConf conf = new SparkConf().setAppName("Regression").setMaster("local");
        JavaSparkContext jsc = new JavaSparkContext(conf);
        String path = "D:\IdeaProjects\SimpleApp\src\main\resources\data\mllib\ridge-data\lpsa.data";
        JavaRDD<String> data = jsc.textFile(path);

        JavaRDD<LabeledPoint> parsedData = data.map(new Function<String, LabeledPoint>() {
            public LabeledPoint call(String line) throws Exception {
                String[] split = line.split(",");
                String[] features = split[1].split(" ");
                double[] v = new double[features.length];
                for (int i = 0; i < features.length - 1; i++) {
                    v[i] = Double.parseDouble(features[i]);
                }

                return new LabeledPoint(Double.parseDouble(split[0]), Vectors.dense(v));
            }
        }).cache();

        final LinearRegressionModel model = LinearRegressionWithSGD.train(parsedData.rdd(), 100);

        JavaRDD<Tuple2<Double, Double>> valuesAndLabels = parsedData.map(new Function<LabeledPoint, Tuple2<Double, Double>>() {
            public Tuple2<Double, Double> call(LabeledPoint labeledPoint) throws Exception {
                double predict = model.predict(labeledPoint.features());
                return new Tuple2<Double, Double>(predict, labeledPoint.label());
            }
        });

        Double MSE = new JavaDoubleRDD(valuesAndLabels.map(
                new Function<Tuple2<Double, Double>, Object>() {
                    public Object call(Tuple2<Double, Double> dat) throws Exception {
                        return Math.pow(dat._1 - dat._2, 2.0);
                    }
                }
        ).rdd()).mean();
        System.out.println("training Mean Squared Error = " + MSE);

        // Save and load model
//        model.save(jsc.sc(), "myModelPath");
//        LinearRegressionModel sameModel = LinearRegressionModel.load(jsc.sc(), "myModelPath");
    }
}

二。Decision Trees.

　

problem specification parameters:   algo, numClasses, categoricalFeaturesInfo

stopping criteria : maxDepth, minInfoGain, minInstancePerNode

tunnable parameters: maxBins, impurity, 

package ML.DT;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.tree.DecisionTree;
import org.apache.spark.mllib.tree.model.DecisionTreeModel;
import org.apache.spark.mllib.util.MLUtils;
import scala.Tuple2;

import java.util.HashMap;

/**
 * TODO
 *
 * @ClassName: classification
 * @author: DingH
 * @since: 2019/4/9 16:11
 */
public class classification {
    public static void main(String[] args) {
        SparkConf conf = new SparkConf().setAppName("DTclassification").setMaster("local");
        JavaSparkContext jsc = new JavaSparkContext(conf);

        String path = "D:\IdeaProjects\SimpleApp\src\main\resources\data\mllib\sample_libsvm_data.txt";
        JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(), path).toJavaRDD();

        JavaRDD<LabeledPoint>[] split = data.randomSplit(new double[]{0.7, 0.3}, 11L);
        JavaRDD<LabeledPoint> trainningData = split[0];
        JavaRDD<LabeledPoint> test = split[1];

        int numsClasses = 2;
        HashMap<Integer, Integer> categoricalFeaturesInfo  = new HashMap<Integer, Integer>();
        String impurity = "gini";
        int maxDepth  = 1;
        int maxbins = 32;

        final DecisionTreeModel model = DecisionTree.trainClassifier(trainningData, numsClasses,categoricalFeaturesInfo, impurity, maxDepth,maxbins);
        JavaPairRDD<Double, Double> predictionAndLable = test.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
            public Tuple2<Double, Double> call(LabeledPoint labeledPoint) throws Exception {
                return new Tuple2<Double, Double>(model.predict(labeledPoint.features()), labeledPoint.label());
            }
        });

        double testErr = predictionAndLable.filter(new Function<Tuple2<Double, Double>, Boolean>() {
            public Boolean call(Tuple2<Double, Double> doubleDoubleTuple2) throws Exception {
                return !doubleDoubleTuple2._1().equals(doubleDoubleTuple2._2());
            }
        }).count() * 1.0 / test.count();

        System.out.println("Test Error: " + testErr);
        System.out.println("Learned classification tree model:
" + model.toDebugString());

        // Save and load model
//        model.save(jsc.sc(), "target/tmp/myDecisionTreeClassificationModel");
//        DecisionTreeModel sameModel = DecisionTreeModel.load(jsc.sc(), "target/tmp/myDecisionTreeClassificationModel");


    }
}

package ML.DT;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.tree.DecisionTree;
import org.apache.spark.mllib.tree.model.DecisionTreeModel;
import org.apache.spark.mllib.util.MLUtils;
import scala.Tuple2;

import java.util.HashMap;
import java.util.Map;

/**
 * TODO
 *
 * @ClassName: Regression
 * @author: DingH
 * @since: 2019/4/9 16:33
 */
public class Regression {
    public static void main(String[] args) {
        SparkConf sparkConf = new SparkConf().setAppName("JavaDecisionTreeRegressionExample").setMaster("local");
        JavaSparkContext jsc = new JavaSparkContext(sparkConf);

        // Load and parse the data file.
        String datapath = "D:\IdeaProjects\SimpleApp\src\main\resources\data\mllib\sample_libsvm_data.txt";
        JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(), datapath).toJavaRDD();
        // Split the data into training and test sets (30% held out for testing)
        JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.7, 0.3});
        JavaRDD<LabeledPoint> trainingData = splits[0];
        JavaRDD<LabeledPoint> testData = splits[1];

        // Set parameters.
        // Empty categoricalFeaturesInfo indicates all features are continuous.
        Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
        String impurity = "variance";
        Integer maxDepth = 5;
        Integer maxBins = 32;

        // Train a DecisionTree model.
        final DecisionTreeModel model = DecisionTree.trainRegressor(trainingData, categoricalFeaturesInfo, impurity, maxDepth, maxBins);

        // Evaluate model on test instances and compute test error
        JavaPairRDD<Double, Double> predictionAndLabel = testData.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
            public Tuple2<Double, Double> call(LabeledPoint p) {
                return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
            }
        });

        Double testMSE = predictionAndLabel.map(new Function<Tuple2<Double, Double>, Double>() {
            public Double call(Tuple2<Double, Double> pl) {
                Double diff = pl._1() - pl._2();
                return diff * diff;
            }
        }).reduce(new Function2<Double, Double, Double>() {
            public Double call(Double a, Double b) {
                return a + b;
            }
        }) / data.count();

        System.out.println("Test Mean Squared Error: " + testMSE);
        System.out.println("Learned regression tree model:
" + model.toDebugString());

        // Save and load model
//        model.save(jsc.sc(), "target/tmp/myDecisionTreeRegressionModel");
//        DecisionTreeModel sameModel = DecisionTreeModel.load(jsc.sc(), "target/tmp/myDecisionTreeRegressionModel");
    }
}

三。Random Forests

　　样本随机，特征随机  

　　featureSubsetStrategy - Number of features to consider for splits at each node. Supported: "auto", "all", "sqrt", "log2", "onethird". If "auto" is set, this parameter is set based on numTrees: if numTrees == 1, set to "all"; if numTrees > 1 (forest) set to "sqrt".

package ML.RF;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.tree.RandomForest;
import org.apache.spark.mllib.tree.model.RandomForestModel;
import org.apache.spark.mllib.util.MLUtils;
import scala.Tuple2;

import java.util.HashMap;

/**
 * TODO
 *
 * @ClassName: classification
 * @author: DingH
 * @since: 2019/4/9 16:58
 */
public class classification {
    public static void main(String[] args) {
        SparkConf sparkConf = new SparkConf().setMaster("local").setAppName("JavaRandomForestClassificationExample");
        JavaSparkContext jsc = new JavaSparkContext(sparkConf);

        // Load and parse the data file.
        String datapath = "D:\IdeaProjects\SimpleApp\src\main\resources\data\mllib\sample_libsvm_data.txt";
        JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(), datapath).toJavaRDD();

        // Split the data into training and test sets (30% held out for testing)
        JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.7, 0.3});
        JavaRDD<LabeledPoint> trainingData = splits[0];
        JavaRDD<LabeledPoint> testData = splits[1];

        // Train a RandomForest model.
        // Empty categoricalFeaturesInfo indicates all features are continuous.
        Integer numClasses = 2;
        HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
        Integer numTrees = 3; // Use more in practice.
        String featureSubsetStrategy = "auto"; // Let the algorithm choose.
        String impurity = "gini";
        Integer maxDepth = 5;
        Integer maxBins = 32;
        Integer seed = 12345;

        final RandomForestModel model = RandomForest.trainClassifier(trainingData, numClasses,categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins, seed);

        // Evaluate model on test instances and compute test error
        JavaPairRDD<Double, Double> predictionAndLabel = testData.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
            public Tuple2<Double, Double> call(LabeledPoint p) {
              return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
            }
          });

        Double testErr =
          1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
            public Boolean call(Tuple2<Double, Double> pl) {
              return !pl._1().equals(pl._2());
            }
          }).count() / testData.count();

        System.out.println("Test Error: " + testErr);
        System.out.println("Learned classification forest model:
" + model.toDebugString());

        // Save and load model
//        model.save(jsc.sc(), "target/tmp/myRandomForestClassificationModel");
//        RandomForestModel sameModel = RandomForestModel.load(jsc.sc(),"target/tmp/myRandomForestClassificationModel");
    }

}

package ML.RF;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.tree.RandomForest;
import org.apache.spark.mllib.tree.model.RandomForestModel;
import org.apache.spark.mllib.util.MLUtils;
import scala.Tuple2;

import java.util.HashMap;
import java.util.Map;

/**
 * TODO
 *
 * @ClassName: regression
 * @author: DingH
 * @since: 2019/4/9 17:50
 */
public class regression {
    public static void main(String[] args) {
        SparkConf sparkConf = new SparkConf().setMaster("local").setAppName("JavaRandomForestRegressionExample");
        JavaSparkContext jsc = new JavaSparkContext(sparkConf);
        // Load and parse the data file.
        String datapath = "D:\IdeaProjects\SimpleApp\src\main\resources\data\mllib\sample_libsvm_data.txt";
        JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(), datapath).toJavaRDD();

        // Split the data into training and test sets (30% held out for testing)
        JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.7, 0.3});
        JavaRDD<LabeledPoint> trainingData = splits[0];
        JavaRDD<LabeledPoint> testData = splits[1];

        // Set parameters.
        // Empty categoricalFeaturesInfo indicates all features are continuous.
        Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
        Integer numTrees = 3; // Use more in practice.
        String featureSubsetStrategy = "auto"; // Let the algorithm choose.
        String impurity = "variance";
        Integer maxDepth = 4;
        Integer maxBins = 32;
        Integer seed = 12345;
        // Train a RandomForest model.
        final RandomForestModel model = RandomForest.trainRegressor(trainingData,categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins, seed);

        // Evaluate model on test instances and compute test error
        JavaPairRDD<Double, Double> predictionAndLabel = testData.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
            public Tuple2<Double, Double> call(LabeledPoint p) {
              return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
            }
          });
        Double testMSE = predictionAndLabel.map(new Function<Tuple2<Double, Double>, Double>() {
            public Double call(Tuple2<Double, Double> pl) {
              Double diff = pl._1() - pl._2();
              return diff * diff;
            }
          }).reduce(new Function2<Double, Double, Double>() {
            public Double call(Double a, Double b) {
              return a + b;
            }
          }) / testData.count();

        System.out.println("Test Mean Squared Error: " + testMSE);
        System.out.println("Learned regression forest model:
" + model.toDebugString());

        // Save and load model
        model.save(jsc.sc(), "target/tmp/myRandomForestRegressionModel");
        RandomForestModel sameModel = RandomForestModel.load(jsc.sc(),
          "target/tmp/myRandomForestRegressionModel");
    }
}

四。Gradient-Boosted Trees

　　

Usage tips: loss, numIterations, learningRate,  algo

BoostingStrategy.validationTol

package ML.GradientBoostedTrees;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.tree.GradientBoostedTrees;
import org.apache.spark.mllib.tree.configuration.BoostingStrategy;
import org.apache.spark.mllib.tree.model.GradientBoostedTreesModel;
import org.apache.spark.mllib.util.MLUtils;
import scala.Tuple2;

import java.util.HashMap;
import java.util.Map;

/**
 * TODO
 *
 * @ClassName: classification
 * @author: DingH
 * @since: 2019/4/9 17:56
 */
public class classification {
    public static void main(String[] args) {
        SparkConf sparkConf = new SparkConf().setMaster("local").setAppName("JavaGradientBoostedTreesClassificationExample");
        JavaSparkContext jsc = new JavaSparkContext(sparkConf);

        // Load and parse the data file.
        String datapath = "D:\IdeaProjects\SimpleApp\src\main\resources\data\mllib\sample_libsvm_data.txt";
        JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(), datapath).toJavaRDD();

        // Split the data into training and test sets (30% held out for testing)
        JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.7, 0.3});
        JavaRDD<LabeledPoint> trainingData = splits[0];
        JavaRDD<LabeledPoint> testData = splits[1];

        // Train a GradientBoostedTrees model.
        // The defaultParams for Classification use LogLoss by default.
        BoostingStrategy boostingStrategy = BoostingStrategy.defaultParams("Classification");
        boostingStrategy.setNumIterations(3); // Note: Use more iterations in practice.
        boostingStrategy.getTreeStrategy().setNumClasses(2);
        boostingStrategy.getTreeStrategy().setMaxDepth(5);
        // Empty categoricalFeaturesInfo indicates all features are continuous.
        Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
        boostingStrategy.treeStrategy().setCategoricalFeaturesInfo(categoricalFeaturesInfo);

        final GradientBoostedTreesModel model =
          GradientBoostedTrees.train(trainingData, boostingStrategy);

        // Evaluate model on test instances and compute test error
        JavaPairRDD<Double, Double> predictionAndLabel =
          testData.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
            public Tuple2<Double, Double> call(LabeledPoint p) {
              return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
            }
          });
        Double testErr =
          1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
            public Boolean call(Tuple2<Double, Double> pl) {
              return !pl._1().equals(pl._2());
            }
          }).count() / testData.count();
        System.out.println("Test Error: " + testErr);
        System.out.println("Learned classification GBT model:
" + model.toDebugString());

        // Save and load model
//        model.save(jsc.sc(), "target/tmp/myGradientBoostingClassificationModel");
//        GradientBoostedTreesModel sameModel = GradientBoostedTreesModel.load(jsc.sc(),
//          "target/tmp/myGradientBoostingClassificationModel");
    }
}

五。naive Bayes

　　model type : "multinomial","bernouli"

　　

package ML.ClassificationAndRegression.NaiveBayes;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.mllib.classification.NaiveBayes;
import org.apache.spark.mllib.classification.NaiveBayesModel;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.util.MLUtils;
import scala.Tuple2;

/**
 * TODO
 *
 * @ClassName: example
 * @author: DingH
 * @since: 2019/4/10 10:04
 */
public class example {
    public static void main(String[] args) {
        SparkConf conf = new SparkConf().setMaster("local").setAppName("naiveBayesExample");
        JavaSparkContext jsc = new JavaSparkContext(conf);

        String path = "D:\IdeaProjects\SimpleApp\src\main\resources\data\mllib\sample_libsvm_data.txt";
        JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(), path).toJavaRDD();

        JavaRDD<LabeledPoint>[] split = data.randomSplit(new double[]{0.6, 0.4}, 12345L);
        JavaRDD<LabeledPoint> train = split[0];
        JavaRDD<LabeledPoint> test = split[1];

        final NaiveBayesModel model = NaiveBayes.train(train.rdd(), 1.0, "multinomial");

        JavaRDD<Tuple2<Double, Double>> predictionAndLabel = test.map(new Function<LabeledPoint, Tuple2<Double, Double>>() {
            public Tuple2<Double, Double> call(LabeledPoint labeledPoint) throws Exception {
                double predict = model.predict(labeledPoint.features());
                return new Tuple2<Double, Double>(predict, labeledPoint.label());
            }
        });

        double accuracy = predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
            public Boolean call(Tuple2<Double, Double> doubleDoubleTuple2) throws Exception {
                return doubleDoubleTuple2._1().equals(doubleDoubleTuple2._2());
            }
        }).count() / (double) test.count();

        System.out.println("acucuracy is : " + accuracy);

        NaiveBayesModel model1 = NaiveBayesModel.load(jsc.sc(), "");


    }
}

六。isotonic regression

　　

　　

package ML.ClassificationAndRegression.IsotonicRegression;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaDoubleRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.mllib.regression.IsotonicRegression;
import org.apache.spark.mllib.regression.IsotonicRegressionModel;
import scala.Tuple2;
import scala.Tuple3;

/**
 * TODO
 *
 * @ClassName: example
 * @author: DingH
 * @since: 2019/4/10 10:31
 */
public class example {
    public static void main(String[] args) {
        SparkConf conf = new SparkConf().setMaster("local").setAppName("isotonicRegressionExample");
        JavaSparkContext jsc = new JavaSparkContext(conf);

        String path = "D:\IdeaProjects\SimpleApp\src\main\resources\data\mllib\sample_isotonic_regression_data.txt";
        JavaRDD<String> data = jsc.textFile(path);

        JavaRDD<Tuple3<Double, Double, Double>> parsedData = data.map(new Function<String, Tuple3<Double, Double, Double>>() {
            public Tuple3<Double, Double, Double> call(String s) throws Exception {
                String[] strings = s.split(",");
                return new Tuple3<Double, Double, Double>(Double.parseDouble(strings[0]), Double.parseDouble(strings[1]), 1.0);
            }
        });

        JavaRDD<Tuple3<Double, Double, Double>>[] split = parsedData.randomSplit(new double[]{0.7, 0.3}, 1234L);
        JavaRDD<Tuple3<Double, Double, Double>> train = split[0];
        JavaRDD<Tuple3<Double, Double, Double>> test = split[1];

        final IsotonicRegressionModel model = new IsotonicRegression().setIsotonic(true).run(train);

        JavaRDD<Tuple2<Double, Double>> preditionAndLabel = test.map(new Function<Tuple3<Double, Double, Double>, Tuple2<Double, Double>>() {
            public Tuple2<Double, Double> call(Tuple3<Double, Double, Double> doubleDoubleDoubleTuple3) throws Exception {
                double predict = model.predict(doubleDoubleDoubleTuple3._1());
                return new Tuple2<Double, Double>(predict, doubleDoubleDoubleTuple3._2());
            }
        });

        Double MSE = new JavaDoubleRDD(preditionAndLabel.map(new Function<Tuple2<Double, Double>, Object>() {
            public Object call(Tuple2<Double, Double> doubleDoubleTuple2) throws Exception {

                return Math.pow(doubleDoubleTuple2._1() - doubleDoubleTuple2._2(), 2.0);
            }
        }).rdd()).mean();

        System.out.println("Mean Squared Error = " + MSE);
    }
}