Java tutorial
/* * WekaClassifierMultiAttribute - created 7/02/2009 - 11:46:52 PM * Copyright Daniel McEnnis, see license.txt */ /* * This file is part of GraphRAT. * * GraphRAT is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GraphRAT is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with GraphRAT. If not, see <http://www.gnu.org/licenses/>. */ package org.mcennis.graphrat.algorithm.machinelearning; import java.util.*; import java.util.logging.Level; import java.util.logging.Logger; import org.dynamicfactory.descriptors.Properties; import org.mcennis.graphrat.graph.Graph; import org.mcennis.graphrat.actor.Actor; import org.mcennis.graphrat.algorithm.Algorithm; import org.mcennis.graphrat.algorithm.AlgorithmMacros; import org.mcennis.graphrat.descriptors.IODescriptor; import org.mcennis.graphrat.descriptors.IODescriptor.Type; import org.mcennis.graphrat.descriptors.IODescriptorFactory; import org.dynamicfactory.descriptors.*; import org.dynamicfactory.property.Property; import org.mcennis.graphrat.link.Link; import org.mcennis.graphrat.link.LinkFactory; import org.dynamicfactory.model.ModelShell; import org.mcennis.graphrat.query.ActorQuery; import org.mcennis.graphrat.query.ActorQueryFactory; import org.mcennis.graphrat.query.LinkQuery; import org.mcennis.graphrat.query.LinkQuery.LinkEnd; import org.mcennis.graphrat.query.LinkQueryFactory; import org.mcennis.graphrat.query.actor.ActorByMode; import org.mcennis.graphrat.query.link.LinkByRelation; import weka.classifiers.Classifier; import weka.core.Attribute; import weka.core.FastVector; import weka.core.Instance; import weka.core.Instances; /** * * @author Daniel McEnnis */ public class ClassifySingleAttribute extends ModelShell implements Algorithm { PropertiesInternal parameter = PropertiesFactory.newInstance().create(); LinkedList<IODescriptor> input = new LinkedList<IODescriptor>(); LinkedList<IODescriptor> output = new LinkedList<IODescriptor>(); public ClassifySingleAttribute() { ParameterInternal name = ParameterFactory.newInstance().create("AlgorithmClass", String.class); SyntaxObject syntax = SyntaxCheckerFactory.newInstance().create(1, 1, null, String.class); name.setRestrictions(syntax); name.add("Weka Classifier Multi-Attribute"); parameter.add(name); name = ParameterFactory.newInstance().create("Name", String.class); syntax = SyntaxCheckerFactory.newInstance().create(1, Integer.MAX_VALUE, null, String.class); name.setRestrictions(syntax); name.add("Weka Classifier Multi-Attribute"); parameter.add(name); name = ParameterFactory.newInstance().create("Category", String.class); syntax = SyntaxCheckerFactory.newInstance().create(1, 1, null, String.class); name.setRestrictions(syntax); name.add("Machine Learning"); parameter.add(name); name = ParameterFactory.newInstance().create("LinkFilter", LinkQuery.class); syntax = SyntaxCheckerFactory.newInstance().create(0, 1, null, LinkQuery.class); name.setRestrictions(syntax); parameter.add(name); name = ParameterFactory.newInstance().create("ActorFilter", ActorQuery.class); syntax = SyntaxCheckerFactory.newInstance().create(0, 1, null, ActorQuery.class); name.setRestrictions(syntax); parameter.add(name); name = ParameterFactory.newInstance().create("SourceAppendGraphID", Boolean.class); syntax = SyntaxCheckerFactory.newInstance().create(1, 1, null, Boolean.class); name.setRestrictions(syntax); name.add(false); parameter.add(name); name = ParameterFactory.newInstance().create("DestinationAppendGraphID", Boolean.class); syntax = SyntaxCheckerFactory.newInstance().create(1, 1, null, Boolean.class); name.setRestrictions(syntax); name.add(false); parameter.add(name); name = ParameterFactory.newInstance().create("GroundMode", String.class); syntax = SyntaxCheckerFactory.newInstance().create(1, 1, null, String.class); name.setRestrictions(syntax); name.add("tag"); parameter.add(name); name = ParameterFactory.newInstance().create("TargetMode", String.class); syntax = SyntaxCheckerFactory.newInstance().create(1, 1, null, String.class); name.setRestrictions(syntax); name.add("tag"); parameter.add(name); name = ParameterFactory.newInstance().create("Relation", String.class); syntax = SyntaxCheckerFactory.newInstance().create(1, 1, null, String.class); name.setRestrictions(syntax); name.add("tag"); parameter.add(name); name = ParameterFactory.newInstance().create("SourceProperty", String.class); syntax = SyntaxCheckerFactory.newInstance().create(1, 1, null, String.class); name.setRestrictions(syntax); name.add("Property"); parameter.add(name); name = ParameterFactory.newInstance().create("ClassifierProperty", String.class); syntax = SyntaxCheckerFactory.newInstance().create(1, 1, null, String.class); name.setRestrictions(syntax); name.add("Property"); parameter.add(name); name = ParameterFactory.newInstance().create("LinkEnd", LinkEnd.class); syntax = SyntaxCheckerFactory.newInstance().create(1, 1, null, LinkEnd.class); name.setRestrictions(syntax); name.add(LinkEnd.SOURCE); parameter.add(name); } public void execute(Graph g) { // construct the queries to be used ActorByMode groundMode = (ActorByMode) ActorQueryFactory.newInstance().create("ActorByMode"); groundMode.buildQuery((String) parameter.get("GroundMode").get(), ".*", false); ActorByMode targetMode = (ActorByMode) ActorQueryFactory.newInstance().create("ActorByMode"); targetMode.buildQuery((String) parameter.get("TargetMode").get(), ".*", false); LinkByRelation groundTruth = (LinkByRelation) LinkQueryFactory.newInstance().create("LinkByRelation"); groundTruth.buildQuery((String) parameter.get("Relation").get(), false); // build a list of new artists TreeSet<Actor> artists = new TreeSet<Actor>(); artists.addAll(AlgorithmMacros.filterActor(parameter, g, targetMode.execute(g, artists, null))); // collect the instance variables from the properties to be the Property classifierProperty = g.getProperty( AlgorithmMacros.getSourceID(parameter, g, (String) parameter.get("ClassifierProperty").get())); if (!classifierProperty.getValue().isEmpty()) { Classifier classifier = (Classifier) classifierProperty.getValue().get(0); Iterator<Actor> users = AlgorithmMacros.filterActor(parameter, g, groundMode, null, null); Instances dataSet = null; boolean firstEntry = true; while (users.hasNext()) { TreeSet<Actor> user = new TreeSet<Actor>(); user.add(users.next()); Property property = user.first().getProperty( AlgorithmMacros.getSourceID(parameter, g, (String) parameter.get("SourceProperty").get())); if (property.getPropertyClass().getName().contentEquals(Instance.class.getName())) { List values = property.getValue(); if (!values.isEmpty()) { // get the existing instance Instance object = (Instance) values.get(0); if (firstEntry) { firstEntry = false; Instances current = object.dataset(); FastVector attributes = new FastVector(); for (int j = 0; j < current.numAttributes(); ++j) { attributes.addElement(current.attribute(j)); } FastVector targetNames = new FastVector(); Iterator<Actor> artistIt = targetMode.executeIterator(g, null, null); while (artistIt.hasNext()) { targetNames.addElement(artistIt.next().getID()); } Attribute classValue = new Attribute("TargetID", targetNames); attributes.addElement(classValue); dataSet = new Instances("Training", attributes, 1000); dataSet.setClassIndex(dataSet.numAttributes() - 1); } // for every artist, create a temporary artist classifer double[] content = new double[object.numAttributes() + 1]; for (int j = 0; j < object.numAttributes() + 1; ++j) { content[j] = object.value(j); } Instance base = new Instance(1.0, content); try { double strength = classifier.classifyInstance(base); if (!Double.isNaN(strength)) { String id = dataSet.classAttribute().value((int) strength); Actor target = g.getActor((String) parameter.get("TargetMode").get(), id); Link link = LinkFactory.newInstance() .create((String) parameter.get("Relation").get()); if ((LinkEnd) parameter.get("LinkEnd").get() == LinkEnd.SOURCE) { link.set(user.first(), strength, target); } else { link.set(target, strength, user.first()); } g.add(link); } } catch (Exception ex) { Logger.getLogger(ClassifyPerActor.class.getName()).log(Level.SEVERE, null, ex); } } } } } } public ClassifySingleAttribute prototype() { return new ClassifySingleAttribute(); } public List<IODescriptor> getInputType() { return input; } public List<IODescriptor> getOutputType() { return output; } public Properties getParameter() { return parameter; } public Parameter getParameter(String param) { return parameter.get(param); } public void init(Properties map) { if (parameter.check(map)) { parameter.merge(map); IODescriptor desc = IODescriptorFactory.newInstance().create(Type.ACTOR, (String) parameter.get("Name").get(), (String) parameter.get("GroundMode").get(), null, null, "", false); input.add(desc); desc = IODescriptorFactory.newInstance().create(Type.ACTOR, (String) parameter.get("Name").get(), (String) parameter.get("TargetMode").get(), null, null, "", false); input.add(desc); desc = IODescriptorFactory.newInstance().create(Type.LINK, (String) parameter.get("Name").get(), (String) parameter.get("Relation").get(), null, null, "", false); output.add(desc); desc = IODescriptorFactory.newInstance().create(Type.ACTOR_PROPERTY, (String) parameter.get("Name").get(), (String) parameter.get("GroundMode").get(), null, (String) parameter.get("SourceProperty").get(), "", (Boolean) parameter.get("SourceAppendGraphID").get()); input.add(desc); desc = IODescriptorFactory.newInstance().create(Type.GRAPH_PROPERTY, (String) parameter.get("Name").get(), (String) parameter.get("TargetMode").get(), null, (String) parameter.get("ClassifierProperty").get(), "", (Boolean) parameter.get("DestinationAppendGraphID").get()); input.add(desc); } } }