learnBLOG.java

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import java.io.File;
import java.io.PrintStream;
import java.util.Vector;
import java.util.regex.Pattern;

import edu.tum.cs.srl.Database;
import edu.tum.cs.srl.Signature;
import edu.tum.cs.srl.bayesnets.ABL;
import edu.tum.cs.srl.bayesnets.BLOGModel;
import edu.tum.cs.srl.bayesnets.learning.CPTLearner;
import edu.tum.cs.srl.bayesnets.learning.DomainLearner;

public class learnBLOG {        
        
        public static enum Mode {
                BLOG, ABL
        }
        
        public static void learn(Mode mode, String[] args) {
                try {
                        String acronym = mode == Mode.ABL ? "ABL" : "BLOG";
                        
                        boolean showBN = false, learnDomains = false, ignoreUndefPreds = false, toMLN = false, debug = false, uniformDefault = false;
                        String blogFile = null, bifFile = null, dbFile = null, outFileBLOG = null, outFileNetwork = null;
                        boolean noNormalization = false;
                        for(int i = 0; i < args.length; i++) {
                                if(args[i].equals("-s"))
                                        showBN = true;
                                else if(args[i].equals("-d"))
                                        learnDomains = true;
                                else if(args[i].equals("-i"))
                                        ignoreUndefPreds = true;
                                else if(args[i].equals("-b"))
                                        blogFile = args[++i];
                                else if(args[i].equals("-x"))
                                        bifFile = args[++i];
                                else if(args[i].equals("-t"))
                                        dbFile = args[++i];
                                else if(args[i].equals("-ob"))
                                        outFileBLOG = args[++i];
                                else if(args[i].equals("-ox"))
                                        outFileNetwork = args[++i];
                                else if(args[i].equals("-mln"))
                                        toMLN = true;
                                else if(args[i].equals("-nn"))
                                        noNormalization = true;                                 
                                else if(args[i].equals("-ud"))
                                        uniformDefault = true;                                  
                                else if(args[i].equals("-debug"))
                                        debug = true;                                   
                        }                       
                        if(bifFile == null || dbFile == null || outFileBLOG == null || outFileNetwork == null) {
                                System.out.println("\n usage: learn" + acronym + " [-b <" + acronym + " file>] <-x <network file>> <-t <training db pattern>> <-ob <" + acronym + " output>> <-ox <network output>> [-s] [-d]\n\n"+
                                                                 "    -b      " + acronym + " file from which to read function signatures\n" +
                                                             "    -s      show learned fragment network\n" +
                                                             "    -d      learn domains\n" + 
                                                             "    -i      ignore data on predicates not defined in the model\n" +
                                                             "    -ud     apply uniform distribution by default (for CPT columns with no examples)\n" +
                                                             "    -nn     no normalization (i.e. keep counts in CPTs)\n" +
                                                             "    -mln    convert learnt model to a Markov logic network\n" +
                                                             "    -debug  output debug information\n");
                                return;
                        }
                        // create a BLOG model
                        BLOGModel bn;
                        if(mode == Mode.BLOG) {
                                if(blogFile != null)
                                        bn = new BLOGModel(blogFile, bifFile);
                                else
                                        bn = new BLOGModel(bifFile);
                        }
                        else {
                                if(blogFile != null)
                                        bn = new ABL(blogFile, bifFile);
                                else
                                        bn = new BLOGModel(bifFile);
                        }
                        
                        // prepare it for learning
                        bn.prepareForLearning();
                        
                        System.out.println("Signatures:");
                        for(Signature sig : bn.getSignatures()) {
                                System.out.println("  " + sig);
                        }

                        // read the training databases
                        System.out.println("Reading data...");
                        Vector<Database> dbs = new Vector<Database>();
                        String regex = new File(dbFile).getName();
                        Pattern p = Pattern.compile( regex );
                        File directory = new File(dbFile).getParentFile();
                        if(directory == null || !directory.exists())
                                directory = new File(".");
                        System.out.printf("Searching for '%s' in '%s'...\n", regex, directory);
                        for (File file : directory.listFiles()) { 
                                if(p.matcher(file.getName()).matches()) {
                                        Database db = new Database(bn);
                                        System.out.printf("reading %s...\n", file.getAbsolutePath());
                                        db.readBLOGDB(file.getPath(), ignoreUndefPreds);
                                        dbs.add(db);
                                }
                        }
                        
                        // check domains for overlaps and merge if necessary
                        System.out.println("Checking domains...");
                        for(Database db : dbs)
                                db.checkDomains(true);
                        
                        // learn domains
                        if(learnDomains) {
                                System.out.println("Learning domains...");
                                DomainLearner domLearner = new DomainLearner(bn);
                                for(Database db : dbs) {                                        
                                        domLearner.learn(db);                                   
                                }
                                domLearner.finish();
                        }
                        System.out.println("Domains:");
                        for(Signature sig : bn.getSignatures()) {       
                                System.out.println("  " + sig.functionName + ": " + sig.returnType + " ");
                        }
                        // learn parameters
                        boolean learnParams = true;
                        if(learnParams) {
                                System.out.println("Learning parameters...");
                                CPTLearner cptLearner = new CPTLearner(bn, uniformDefault, debug);
                                //cptLearner.setUniformDefault(true);
                                for(Database db : dbs)
                                        cptLearner.learnTyped(db, true, true);
                                if(!noNormalization)
                                        cptLearner.finish();
                                // write learnt BLOG/ABL model
                                System.out.println("Writing "+ acronym + " output to " + outFileBLOG + "...");
                                PrintStream out = new PrintStream(new File(outFileBLOG));
                                bn.write(out);                  
                                out.close();
                                // write parameters to Bayesian network template
                                System.out.println("Writing network output to " + outFileNetwork + "...");
                                bn.save(outFileNetwork);
                        }
                        // write MLN
                        if(toMLN) {
                                String filename = outFileBLOG + ".mln";
                                System.out.println("Writing MLN " + filename);
                                PrintStream out = new PrintStream(new File(outFileBLOG + ".mln"));
                                bn.toMLN(out, false, false, false);
                        }
                        // show bayesian network
                        if(showBN) {
                                System.out.println("Showing Bayesian network...");
                                bn.show();
                        }
                }
                catch(Exception e) {
                        e.printStackTrace();
                }               
        }
        
        public static void main(String[] args) {
                learn(Mode.BLOG, args);
        }
}

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srldb
Author(s): Dominik Jain, Stefan Waldherr, Moritz Tenorth
autogenerated on Fri Jan 11 09:58:29 2013