Java tutorial
/* * 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.shrawanraina; 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>() { 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()); } } }