Hi i'm trying to run SimpleKmeanClustering Code, from Github to see how clustering works, I'm able to complile the code on my windows Eclipse.
I made a jar of my project, i want to run it on a single node Hadoop cluster(CHD-4.2.1), with mahout installed on it. The mahout examples run fine on this cluster, so no issues regarding installation.
I use the following command in command Promt to run my jar, i'm not sure if i'm trying in right way.
user@INFPH01463U:~$ mahout jar /home/user/apurv/Kmean.jar tryout.SimpleKMeansClustering
I got corresponding error
MAHOUT_LOCAL is not set; adding HADOOP_CONF_DIR to classpath. Running on hadoop, using /usr/lib/hadoop/bin/hadoop and HADOOP_CONF_DIR=/etc/hadoop/conf MAHOUT-JOB: /usr/lib/mahout/mahout-examples-0.7-cdh4.3.0-job.jar 13/06/06 14:42:18 WARN driver.MahoutDriver: Unable to add class: jar java.lang.ClassNotFoundException: jar at java.net.URLClassLoader$1.run(URLClassLoader.java:202) at java.security.AccessController.doPrivileged(Native Method) at java.net.URLClassLoader.findClass(URLClassLoader.java:190) at java.lang.ClassLoader.loadClass(ClassLoader.java:306) at java.lang.ClassLoader.loadClass(ClassLoader.java:247) at java.lang.Class.forName0(Native Method) at java.lang.Class.forName(Class.java:169) at org.apache.mahout.driver.MahoutDriver.addClass(MahoutDriver.java:236) at org.apache.mahout.driver.MahoutDriver.main(MahoutDriver.java:128) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) at java.lang.reflect.Method.invoke(Method.java:597) at org.apache.hadoop.util.RunJar.main(RunJar.java:208) 13/06/06 14:42:18 WARN driver.MahoutDriver: No jar.props found on classpath, will use command-line arguments only Unknown program 'jar' chosen. Valid program names are: arff.vector: : Generate Vectors from an ARFF file or directory baumwelch: : Baum-Welch algorithm for unsupervised HMM training canopy: : Canopy clustering cat: : Print a file or resource as the logistic regression models would see it
cleansvd: : Cleanup and verification of SVD output clusterdump: : Dump cluster output to text clusterpp: : Groups Clustering Output In Clusters cmdump: : Dump confusion matrix in HTML or text formats
cvb: : LDA via Collapsed Variation Bayes (0th deriv. approx)
cvb0_local: : LDA via Collapsed Variation Bayes, in memory locally.
dirichlet: : Dirichlet Clustering eigencuts: : Eigencuts spectral clustering evaluateFactorization: : compute RMSE and MAE of a rating matrix factorization against probes fkmeans: : Fuzzy K-means clustering fpg: : Frequent Pattern Growth hmmpredict: : Generate random sequence of observations by given HMM itemsimilarity: : Compute the item-item-similarities for item-based collaborative filtering kmeans: : K-means clustering lucene.vector: : Generate Vectors from a Lucene index matrixdump: : Dump matrix in CSV format matrixmult: : Take the product of two matrices meanshift: : Mean Shift clustering minhash: : Run Minhash clustering parallelALS: : ALS-WR factorization of a rating matrix recommendfactorized: : Compute recommendations using the factorization of a rating matrix
recommenditembased: : Compute recommendations using item-based collaborative filtering regexconverter: : Convert text files on a per line basis based on regular expressions rowid: : Map SequenceFile to {SequenceFile, SequenceFile} rowsimilarity: : Compute the pairwise similarities of the rows of a matrix runAdaptiveLogistic: : Score new production data using a probably trained and validated AdaptivelogisticRegression model runlogistic: : Run a logistic regression model against CSV data seq2encoded: : Encoded Sparse Vector generation from Text sequence files seq2sparse: : Sparse Vector generation from Text sequence files seqdirectory: : Generate sequence files (of Text) from a directory seqdumper: : Generic Sequence File dumper seqmailarchives: : Creates SequenceFile from a directory containing gzipped mail archives seqwiki: : Wikipedia xml dump to sequence file spectralkmeans: : Spectral k-means clustering split: : Split Input data into test and train sets splitDataset: : split a rating dataset into training and probe parts ssvd: : Stochastic SVD svd: : Lanczos Singular Value Decomposition testnb: : Test the Vector-based Bayes classifier trainAdaptiveLogistic: : Train an AdaptivelogisticRegression model trainlogistic: : Train a logistic regression using stochastic gradient descent trainnb: : Train the Vector-based Bayes classifier transpose: : Take the transpose of a matrix validateAdaptiveLogistic: : Validate an AdaptivelogisticRegression model against hold-out data set vecdist: : Compute the distances between a set of Vectors (or Cluster or Canopy, they must fit in memory) and a list of Vectors vectordump: : Dump vectors from a sequence file to text viterbi: : Viterbi decoding of hidden states from given output states sequence 13/06/06 14:42:18 INFO driver.MahoutDriver: Program took 2 ms (Minutes: 3.3333333333333335E-5)
Here is my code which i'm using:
Code
package tryout;
import java.io.File;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
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.math.RandomAccessSparseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
import org.apache.mahout.clustering.kmeans.Kluster;
import org.apache.mahout.clustering.classify.WeightedVectorWritable;
import org.apache.mahout.clustering.kmeans.KMeansDriver;
import org.apache.mahout.common.distance.EuclideanDistanceMeasure;
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);
@SuppressWarnings("deprecation")
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("testdata");
if (!testData.exists()) {
testData.mkdir();
}
testData = new File("testdata/points");
if (!testData.exists()) {
testData.mkdir();
}
Configuration conf = new Configuration();
FileSystem fs = FileSystem.get(conf);
writePointsToFile(vectors, "testdata/points/file1", fs, conf);
Path path = new Path("testdata/clusters/part-00000");
@SuppressWarnings("deprecation")
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("testdata/points"), new Path("testdata/clusters"),
new Path("output"), new EuclideanDistanceMeasure(), 0.001, 10,
true,0.0, false);
@SuppressWarnings("deprecation")
SequenceFile.Reader reader = new SequenceFile.Reader(fs,new Path("output/" + Kluster.CLUSTERED_POINTS_DIR+ "/part-m-00000"), conf);
IntWritable key = new IntWritable();
WeightedVectorWritable value = new WeightedVectorWritable();
while (reader.next(key, value)) {
System.out.println(value.toString() + " belongs to cluster " + key.toString());
}
reader.close();
}
}
Can anyone Guide me for this...
