我正在学习Mahout并阅读“Mahout in Action”。
当我尝试在第7章SimpleKMeansClustering.java中运行示例代码时,弹出了一个异常:
线程“main”中的异常java.io.IOException:错误的值类:0.0:null不是org.apache.hadoop.io.SequenceFile $ Reader.next中的类org.apache.mahout.clustering.WeightedPropertyVectorWritable(SequenceFile) .java:1874)在SimpleKMeansClustering.main(SimpleKMeansClustering.java:95)
我在mahout-0.5上成功了这个代码,但在mahout-0.6上我看到了这个异常。 即使我将目录名从clusters-0更改为clusters-0-final,我仍然面临此异常。
KMeansDriver.run(conf, vectors, new Path(canopyCentroids, "clusters-0-final"), clusterOutput, new TanimotoDistanceMeasure(), 0.01, 20, true, false);//First, I changed this path.
SequenceFile.Reader reader = new SequenceFile.Reader(fs, new Path("output/clusters/clusteredPoints/part-m-00000"), conf);//I double checked this folder and filename.
IntWritable key = new IntWritable();
WeightedVectorWritable value = new WeightedVectorWritable();
int i=0;
while(reader.next(key, value)) {
System.out.println(value.toString() + " belongs to cluster " + key.toString());
i++;
}
System.out.println(i);
reader.close();
有没有人对这个例外有任何想法?我一直试图解决它很长时间,并没有任何想法。互联网上的消息来源很少。
提前致谢
答案 0 :(得分:4)
为了使这个例子在Mahout 0.6中起作用,添加
import org.apache.mahout.clustering.WeightedPropertyVectorWritable;
导入并替换行:
WeightedVectorWritable value = new WeightedVectorWritable();
通过
WeightedPropertyVectorWritable value = new WeightedPropertyVectorWritable();
这是因为Mahout 0.6代码将聚类输出值写入新类型WeightedPropertyVectorWritable。
答案 1 :(得分:3)
对于它可能涉及的人,这里是mahout 0.9的工作MiA样本:
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.SequenceFile;
import org.apache.hadoop.io.Text;
import org.apache.mahout.clustering.Cluster;
import org.apache.mahout.clustering.classify.WeightedPropertyVectorWritable;
import org.apache.mahout.clustering.kmeans.KMeansDriver;
import org.apache.mahout.clustering.kmeans.Kluster;
import org.apache.mahout.common.distance.EuclideanDistanceMeasure;
import org.apache.mahout.math.RandomAccessSparseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
import java.io.File;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
public class SimpleKMeansClustering {
public static final double[][] points = {
{1, 1}, {2, 1}, {1, 2},
{2, 2}, {3, 3}, {8, 8},
{9, 8}, {8, 9}, {9, 9}};
public static void writePointsToFile(List<Vector> points,
String fileName,
FileSystem fs,
Configuration conf) throws IOException {
Path path = new Path(fileName);
SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf,
path, LongWritable.class, VectorWritable.class);
long recNum = 0;
VectorWritable vec = new VectorWritable();
for (Vector point : points) {
vec.set(point);
writer.append(new LongWritable(recNum++), vec);
}
writer.close();
}
public static List<Vector> getPoints(double[][] raw) {
List<Vector> points = new ArrayList<Vector>();
for (int i = 0; i < raw.length; i++) {
double[] fr = raw[i];
Vector vec = new RandomAccessSparseVector(fr.length);
vec.assign(fr);
points.add(vec);
}
return points;
}
public static void main(String args[]) throws Exception {
int k = 2;
List<Vector> vectors = getPoints(points);
File testData = new File("clustering/testdata");
if (!testData.exists()) {
testData.mkdir();
}
testData = new File("clustering/testdata/points");
if (!testData.exists()) {
testData.mkdir();
}
Configuration conf = new Configuration();
FileSystem fs = FileSystem.get(conf);
writePointsToFile(vectors, "clustering/testdata/points/file1", fs, conf);
Path path = new Path("clustering/testdata/clusters/part-00000");
SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf, path, Text.class, Kluster.class);
for (int i = 0; i < k; i++) {
Vector vec = vectors.get(i);
Kluster cluster = new Kluster(vec, i, new EuclideanDistanceMeasure());
writer.append(new Text(cluster.getIdentifier()), cluster);
}
writer.close();
KMeansDriver.run(conf,
new Path("clustering/testdata/points"),
new Path("clustering/testdata/clusters"),
new Path("clustering/output"),
0.001,
10,
true,
0,
true);
SequenceFile.Reader reader = new SequenceFile.Reader(fs,
new Path("clustering/output/" + Cluster.CLUSTERED_POINTS_DIR + "/part-m-0"), conf);
IntWritable key = new IntWritable();
WeightedPropertyVectorWritable value = new WeightedPropertyVectorWritable();
while (reader.next(key, value)) {
System.out.println(value.toString() + " belongs to cluster " + key.toString());
}
reader.close();
}
}
答案 2 :(得分:2)
本书中的示例适用于mahout 05,但有以下小改动:
(1)正确设置路径:
KMeansDriver.run(conf, new Path("testdata/points"), new Path("testdata/clusters"), new Path("testdata/output"), new EuclideanDistanceMeasure(), 0.001, 10, true, false);
和
SequenceFile.Reader reader = new SequenceFile.Reader(fs, new Path("testdata/output/clusteredPoints/part-m-0"), conf);
(2)如果您没有安装HADOOP,则需要将KMeansDriver.run()调用的最后一个参数从“false”更改为“true”。
KMeansDriver.run(conf, new Path("testdata/points"), new Path("testdata/clusters"), new Path("testdata/output"), new EuclideanDistanceMeasure(), 0.001, 10, true, true);
然后该示例有效。
答案 3 :(得分:0)
替换
import org.apache.mahout.clustering.WeightedVectorWritable;
与
import org.apache.mahout.clustering.classify.WeightedVectorWritable;