/*
* 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.mllib.examples;
import java.util.regex.Pattern;
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.LogisticRegressionWithSGD;
import org.apache.spark.mllib.classification.LogisticRegressionModel;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.regression.LabeledPoint;
/**
* Logistic regression based classification using ML Lib.
*/
public final class JavaLR {
static class ParsePoint implements Function<String, LabeledPoint> {
private static final Pattern COMMA = Pattern.compile(",");
private static final Pattern SPACE = Pattern.compile(" ");
@Override
public LabeledPoint call(String line) {
String[] parts = COMMA.split(line);
double y = Double.parseDouble(parts[0]);
String[] tok = SPACE.split(parts[1]);
double[] x = new double[tok.length];
for (int i = 0; i < tok.length; ++i) {
x[i] = Double.parseDouble(tok[i]);
}
return new LabeledPoint(y, Vectors.dense(x));
}
}
public static void main(String[] args) {
if (args.length != 4) {
System.err.println("Usage: JavaLR <master> <input_dir> <step_size> <niters>");
System.exit(1);
}
JavaSparkContext sc = new JavaSparkContext(args[0], "JavaLR",
System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaLR.class));
JavaRDD<String> lines = sc.textFile(args[1]);
JavaRDD<LabeledPoint> points = lines.map(new ParsePoint()).cache();
double stepSize = Double.parseDouble(args[2]);
int iterations = Integer.parseInt(args[3]);
// Another way to configure LogisticRegression
//
// LogisticRegressionWithSGD lr = new LogisticRegressionWithSGD();
// lr.optimizer().setNumIterations(iterations)
// .setStepSize(stepSize)
// .setMiniBatchFraction(1.0);
// lr.setIntercept(true);
// LogisticRegressionModel model = lr.train(points.rdd());
LogisticRegressionModel model = LogisticRegressionWithSGD.train(points.rdd(),
iterations, stepSize);
System.out.print("Final w: " + model.weights());
System.exit(0);
}
}