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/*
* 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();
}
}
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