spark-gpu-master/examples/src/main/java/org/apache/spark/examples/mllib/JavaIsotonicRegressionExample.java
/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.apache.spark.examples.mllib;

// $example on$
import scala.Tuple2;
import scala.Tuple3;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.api.java.JavaDoubleRDD;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.mllib.regression.IsotonicRegression;
import org.apache.spark.mllib.regression.IsotonicRegressionModel;
// $example off$
import org.apache.spark.SparkConf;

public class JavaIsotonicRegressionExample {
  public static void main(String[] args) {
    SparkConf sparkConf = new SparkConf().setAppName("JavaIsotonicRegressionExample");
    JavaSparkContext jsc = new JavaSparkContext(sparkConf);
    // $example on$
    JavaRDD<String> data = jsc.textFile("data/mllib/sample_isotonic_regression_data.txt");

    // Create label, feature, weight tuples from input data with weight set to default value 1.0.
    JavaRDD<Tuple3<Double, Double, Double>> parsedData = data.map(
      new Function<String, Tuple3<Double, Double, Double>>() {
        public Tuple3<Double, Double, Double> call(String line) {
          String[] parts = line.split(",");
          return new Tuple3<>(new Double(parts[0]), new Double(parts[1]), 1.0);
        }
      }
    );

    // Split data into training (60%) and test (40%) sets.
    JavaRDD<Tuple3<Double, Double, Double>>[] splits = parsedData.randomSplit(new double[]{0.6, 0.4}, 11L);
    JavaRDD<Tuple3<Double, Double, Double>> training = splits[0];
    JavaRDD<Tuple3<Double, Double, Double>> test = splits[1];

    // Create isotonic regression model from training data.
    // Isotonic parameter defaults to true so it is only shown for demonstration
    final IsotonicRegressionModel model = new IsotonicRegression().setIsotonic(true).run(training);

    // Create tuples of predicted and real labels.
    JavaPairRDD<Double, Double> predictionAndLabel = test.mapToPair(
      new PairFunction<Tuple3<Double, Double, Double>, Double, Double>() {
        @Override
        public Tuple2<Double, Double> call(Tuple3<Double, Double, Double> point) {
          Double predictedLabel = model.predict(point._2());
          return new Tuple2<Double, Double>(predictedLabel, point._1());
        }
      }
    );

    // Calculate mean squared error between predicted and real labels.
    Double meanSquaredError = new JavaDoubleRDD(predictionAndLabel.map(
      new Function<Tuple2<Double, Double>, Object>() {
        @Override
        public Object call(Tuple2<Double, Double> pl) {
          return Math.pow(pl._1() - pl._2(), 2);
        }
      }
    ).rdd()).mean();
    System.out.println("Mean Squared Error = " + meanSquaredError);

    // Save and load model
    model.save(jsc.sc(), "target/tmp/myIsotonicRegressionModel");
    IsotonicRegressionModel sameModel = IsotonicRegressionModel.load(jsc.sc(), "target/tmp/myIsotonicRegressionModel");
    // $example off$
  }
}
