/**
* 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.hama.examples;
import java.io.IOException;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.IOUtils;
import org.apache.hama.HamaConfiguration;
import org.apache.hama.bsp.BSPJob;
import org.apache.hama.bsp.FileOutputFormat;
import org.apache.hama.bsp.TextOutputFormat;
import org.apache.hama.ml.regression.GradientDescentBSP;
import org.apache.hama.ml.regression.LogisticRegressionModel;
import org.apache.hama.ml.regression.RegressionModel;
import org.apache.hama.ml.regression.VectorDoubleFileInputFormat;
import org.apache.hama.ml.writable.VectorWritable;
/**
* A {@link GradientDescentBSP} job example
*/
public class GradientDescentExample {
private static final Path TMP_OUTPUT = new Path("/tmp/gd");
public static void main(String[] args) throws InterruptedException,
IOException, ClassNotFoundException {
if (!(args.length == 1 || args.length == 2)) {
System.out.println("USAGE: <INPUT_PATH> [<REGRESSION_MODEL>]");
return;
}
// BSP job configuration
HamaConfiguration conf = new HamaConfiguration();
conf.setFloat(GradientDescentBSP.ALPHA, 0.002f);
conf.setFloat(GradientDescentBSP.COST_THRESHOLD, 0.5f);
conf.setInt(GradientDescentBSP.ITERATIONS_THRESHOLD, 300);
conf.setInt(GradientDescentBSP.INITIAL_THETA_VALUES, 10);
if (args.length == 2 && args[1] != null) {
if (args[1].equals("logistic")) {
conf.setClass(GradientDescentBSP.REGRESSION_MODEL_CLASS,
LogisticRegressionModel.class, RegressionModel.class);
} else if (args[1].equals("linear")) {
// do nothing as 'linear' is default
} else {
throw new RuntimeException(new StringBuilder(
"unsupported RegressionModel").append(args[1])
.append(", use 'logistic' or 'linear'").toString());
}
}
BSPJob bsp = new BSPJob(conf, GradientDescentExample.class);
// Set the job name
bsp.setJobName("Gradient Descent Example");
bsp.setBspClass(GradientDescentBSP.class);
bsp.setInputFormat(VectorDoubleFileInputFormat.class);
bsp.setInputPath(new Path(args[0]));
bsp.setInputKeyClass(VectorWritable.class);
bsp.setInputValueClass(DoubleWritable.class);
bsp.setOutputKeyClass(VectorWritable.class);
bsp.setOutputValueClass(DoubleWritable.class);
bsp.setOutputFormat(TextOutputFormat.class);
FileOutputFormat.setOutputPath(bsp, TMP_OUTPUT);
long startTime = System.currentTimeMillis();
if (bsp.waitForCompletion(true)) {
printOutput(conf);
System.out.println("Job Finished in "
+ (System.currentTimeMillis() - startTime) / 1000.0 + " seconds");
}
}
static void printOutput(HamaConfiguration conf) throws IOException {
FileSystem fs = FileSystem.get(conf);
FileStatus[] files = fs.listStatus(TMP_OUTPUT);
for (int i = 0; i < files.length; i++) {
if (files[i].getLen() > 0) {
FSDataInputStream in = fs.open(files[i].getPath());
IOUtils.copyBytes(in, System.out, conf, false);
in.close();
break;
}
}
fs.delete(TMP_OUTPUT, true);
}
}