content: /*
   * 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 org.apache.spark.api.java.*;
  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.evaluation.RegressionMetrics;
  import org.apache.spark.SparkConf;
  // $example off$
  
  public class JavaRegressionMetricsExample {
    public static void main(String[] args) {
      SparkConf conf = new SparkConf().setAppName("Java Regression Metrics Example");
      JavaSparkContext sc = new JavaSparkContext(conf);
      // $example on$
      // Load and parse the data
      String path = "data/mllib/sample_linear_regression_data.txt";
      JavaRDD<String> data = sc.textFile(path);
      JavaRDD<LabeledPoint> parsedData = data.map(
        new Function<String, LabeledPoint>() {
          public LabeledPoint call(String line) {
            String[] parts = line.split(" ");
            double[] v = new double[parts.length - 1];
            for (int i = 1; i < parts.length - 1; i++) {
              v[i - 1] = Double.parseDouble(parts[i].split(":")[1]);
            }
            return new LabeledPoint(Double.parseDouble(parts[0]), Vectors.dense(v));
          }
        }
      );
      parsedData.cache();
  
      // Building the model
      int numIterations = 100;
      final LinearRegressionModel model = LinearRegressionWithSGD.train(JavaRDD.toRDD(parsedData),
        numIterations);
  
      // Evaluate model on training examples and compute training error
      JavaRDD<Tuple2<Object, Object>> valuesAndPreds = parsedData.map(
        new Function<LabeledPoint, Tuple2<Object, Object>>() {
          public Tuple2<Object, Object> call(LabeledPoint point) {
            double prediction = model.predict(point.features());
            return new Tuple2<Object, Object>(prediction, point.label());
          }
        }
      );
  
      // Instantiate metrics object
      RegressionMetrics metrics = new RegressionMetrics(valuesAndPreds.rdd());
  
      // Squared error
      System.out.format("MSE = %f\n", metrics.meanSquaredError());
      System.out.format("RMSE = %f\n", metrics.rootMeanSquaredError());
  
      // R-squared
      System.out.format("R Squared = %f\n", metrics.r2());
  
      // Mean absolute error
      System.out.format("MAE = %f\n", metrics.meanAbsoluteError());
  
      // Explained variance
      System.out.format("Explained Variance = %f\n", metrics.explainedVariance());
  
      // Save and load model
      model.save(sc.sc(), "target/tmp/LogisticRegressionModel");
      LinearRegressionModel sameModel = LinearRegressionModel.load(sc.sc(),
        "target/tmp/LogisticRegressionModel");
      // $example off$
  
      sc.stop();
    }
  }
  