/*
* Cloud9: A Hadoop toolkit for working with big data
*
* Licensed 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 edu.umd.cloud9.example.pagerank;
import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.Arrays;
import java.util.PriorityQueue;
import java.util.Set;
import org.apache.commons.cli.CommandLine;
import org.apache.commons.cli.CommandLineParser;
import org.apache.commons.cli.GnuParser;
import org.apache.commons.cli.HelpFormatter;
import org.apache.commons.cli.OptionBuilder;
import org.apache.commons.cli.Options;
import org.apache.commons.cli.ParseException;
import org.apache.commons.collections15.Transformer;
import org.apache.hadoop.util.ToolRunner;
import edu.uci.ics.jung.algorithms.cluster.WeakComponentClusterer;
import edu.uci.ics.jung.algorithms.importance.Ranking;
import edu.uci.ics.jung.algorithms.scoring.PageRankWithPriors;
import edu.uci.ics.jung.graph.DirectedSparseGraph;
/**
* <p>
* Program that computes personalized PageRank for a graph using the <a
* href="http://jung.sourceforge.net/">JUNG</a> package (2.0 alpha1). Program takes two command-line
* arguments: the first is a file containing the graph data, and the second is the random jump
* factor (a typical setting is 0.15).
* </p>
*
* <p>
* The graph should be represented as an adjacency list. Each line should have at least one token;
* tokens should be tab delimited. The first token represents the unique id of the source node;
* subsequent tokens represent its link targets (i.e., outlinks from the source node). For
* completeness, there should be a line representing all nodes, even nodes without outlinks (those
* lines will simply contain one token, the source node id).
* </p>
*
* @author Jimmy Lin
*/
public class SequentialPersonalizedPageRank {
private SequentialPersonalizedPageRank() {}
private static final String INPUT = "input";
private static final String JUMP = "jump";
private static final String SOURCE = "source";
@SuppressWarnings({ "static-access" })
public static void main(String[] args) throws IOException {
Options options = new Options();
options.addOption(OptionBuilder.withArgName("path").hasArg()
.withDescription("input path").create(INPUT));
options.addOption(OptionBuilder.withArgName("val").hasArg()
.withDescription("random jump factor").create(JUMP));
options.addOption(OptionBuilder.withArgName("node").hasArg()
.withDescription("source node (i.e., destination of the random jump)").create(SOURCE));
CommandLine cmdline = null;
CommandLineParser parser = new GnuParser();
try {
cmdline = parser.parse(options, args);
} catch (ParseException exp) {
System.err.println("Error parsing command line: " + exp.getMessage());
System.exit(-1);
}
if (!cmdline.hasOption(INPUT) || !cmdline.hasOption(SOURCE)) {
System.out.println("args: " + Arrays.toString(args));
HelpFormatter formatter = new HelpFormatter();
formatter.setWidth(120);
formatter.printHelp(SequentialPersonalizedPageRank.class.getName(), options);
ToolRunner.printGenericCommandUsage(System.out);
System.exit(-1);
}
String infile = cmdline.getOptionValue(INPUT);
final String source = cmdline.getOptionValue(SOURCE);
float alpha = cmdline.hasOption(JUMP) ? Float.parseFloat(cmdline.getOptionValue(JUMP)) : 0.15f;
int edgeCnt = 0;
DirectedSparseGraph<String, Integer> graph = new DirectedSparseGraph<String, Integer>();
BufferedReader data = new BufferedReader(new InputStreamReader(new FileInputStream(infile)));
String line;
while ((line = data.readLine()) != null) {
line.trim();
String[] arr = line.split("\\t");
for (int i = 1; i < arr.length; i++) {
graph.addEdge(new Integer(edgeCnt++), arr[0], arr[i]);
}
}
data.close();
if (!graph.containsVertex(source)) {
System.err.println("Error: source node not found in the graph!");
System.exit(-1);
}
WeakComponentClusterer<String, Integer> clusterer = new WeakComponentClusterer<String, Integer>();
Set<Set<String>> components = clusterer.transform(graph);
int numComponents = components.size();
System.out.println("Number of components: " + numComponents);
System.out.println("Number of edges: " + graph.getEdgeCount());
System.out.println("Number of nodes: " + graph.getVertexCount());
System.out.println("Random jump factor: " + alpha);
// Compute personalized PageRank.
PageRankWithPriors<String, Integer> ranker = new PageRankWithPriors<String, Integer>(graph,
new Transformer<String, Double>() {
@Override
public Double transform(String vertex) {
return vertex.equals(source) ? 1.0 : 0;
}
}, alpha);
ranker.evaluate();
// Use priority queue to sort vertices by PageRank values.
PriorityQueue<Ranking<String>> q = new PriorityQueue<Ranking<String>>();
int i = 0;
for (String pmid : graph.getVertices()) {
q.add(new Ranking<String>(i++, ranker.getVertexScore(pmid), pmid));
}
// Print PageRank values.
System.out.println("\nPageRank of nodes, in descending order:");
Ranking<String> r = null;
while ((r = q.poll()) != null) {
System.out.println(r.rankScore + "\t" + r.getRanked());
}
}
}