git-svn-id: https://svn.d4science.research-infrastructures.eu/gcube/trunk/data-analysis/EcologicalEngine@57560 82a268e6-3cf1-43bd-a215-b396298e98cf
This commit is contained in:
parent
fec0f3ac4d
commit
8941ec3652
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@ -1,2 +1,3 @@
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DBSCAN=org.gcube.dataanalysis.ecoengine.clustering.DBScan
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KMEANS=org.gcube.dataanalysis.ecoengine.clustering.KMeans
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KMEANS=org.gcube.dataanalysis.ecoengine.clustering.KMeans
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XMEANS=org.gcube.dataanalysis.ecoengine.clustering.XMeansWrapper
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@ -0,0 +1,10 @@
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5.1,3.5
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4.9,3.0
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4.7,3.2
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4.6,3.1
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5.0,3.6
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5.4,3.9
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4.6,3.4
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5.0,3.4
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4.4,2.9
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4.9,3.1
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21
pom.xml
21
pom.xml
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@ -151,6 +151,27 @@
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<artifactId>xstream</artifactId>
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<version>1.3.1</version>
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</dependency>
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<dependency>
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<groupId>net.sf.squirrel-sql.thirdparty-non-maven</groupId>
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<artifactId>java-cup</artifactId>
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<version>0.11a</version>
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</dependency>
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<dependency>
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<groupId>nz.ac.waikato.cms.weka</groupId>
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<artifactId>weka-dev</artifactId>
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<version>3.7.6</version>
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</dependency>
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<dependency>
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<groupId>nz.ac.waikato.cms.weka</groupId>
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<artifactId>XMeans</artifactId>
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<version>1.0.3</version>
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</dependency>
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<dependency>
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<groupId>org.pentaho.pentaho-commons</groupId>
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<artifactId>pentaho-package-manager</artifactId>
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<name>Pentaho Package Manager</name>
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<version>1.0.0</version>
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</dependency>
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</dependencies>
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<repositories>
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<repository>
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@ -46,15 +46,18 @@ public class DBScan implements Clusterer{
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protected String FeaturesColumnNames;
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protected float status;
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protected SessionFactory dbHibConnection;
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protected double[][] samplesVector;
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public static String clusterColumn = "clusterid";
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public static String clusterColumnType = "character varying";
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public static String outliersColumn = "outlier";
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public static String outliersColumnType = "boolean";
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protected boolean initrapidminer = true;
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public static void mainCluster(String[] args) throws Exception{
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String coordinates [] = {
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"55.973798,-55.297853",
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"57.279043,-57.055666",
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@ -162,8 +165,8 @@ public class DBScan implements Clusterer{
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@Override
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public void init() throws Exception {
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if (config!=null)
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status = 0;
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if ((config!=null) && (initrapidminer))
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config.initRapidMiner();
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AnalysisLogger.getLogger().debug("Initialized Rapid Miner ");
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AnalysisLogger.getLogger().debug("Initializing Database Connection");
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@ -212,20 +215,16 @@ public class DBScan implements Clusterer{
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FeaturesColumnNames=config.getParam("FeaturesColumnNames");
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this.config=config;
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}
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}
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protected void getSamples() throws Exception{
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System.out.println("->"+DatabaseUtils.getColumnsElementsStatement(OccurrencePointsTable, FeaturesColumnNames, ""));
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// System.out.println("->"+DatabaseUtils.getColumnsElementsStatement(OccurrencePointsTable, FeaturesColumnNames, ""));
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FeaturesColumnNames=FeaturesColumnNames.replace(AlgorithmConfiguration.listSeparator, ",");
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List<Object> samples = DatabaseFactory.executeSQLQuery(DatabaseUtils.getColumnsElementsStatement(OccurrencePointsTable, FeaturesColumnNames, ""), dbHibConnection);
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String [] elements = FeaturesColumnNames.split(",");
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int dimensions = elements.length;
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int nSamples = samples.size();
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double[][] samplesVector = new double[nSamples][dimensions];
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samplesVector = new double[nSamples][dimensions];
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int ir=0;
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for (Object row:samples){
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Object[] rowArr = (Object[]) row;
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@ -310,7 +309,7 @@ public class DBScan implements Clusterer{
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//take cluster element indexes
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int npoints = c.getExampleIds().size();
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AnalysisLogger.getLogger().debug("DBScan: Analyzing Cluster ->"+id+" with "+npoints);
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if (npoints==minpoints)
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if (npoints<minpoints)
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outlier=true;
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int k=0;
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@ -23,10 +23,6 @@ import com.rapidminer.tools.OperatorService;
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public class KMeans extends DBScan{
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public static String clusterColumn = "clusterid";
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public static String clusterColumnType = "character varying";
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public static String outliersColumn = "outlier";
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public static String outliersColumnType = "boolean";
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private String kk;
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private String maxRuns;
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private String maxOptimizations;
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@ -130,7 +126,7 @@ public class KMeans extends DBScan{
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PrimitiveType p4 = new PrimitiveType(Integer.class.getName(), null, PrimitiveTypes.NUMBER, "k","Expected Number of Clusters","3");
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PrimitiveType p5 = new PrimitiveType(Integer.class.getName(), null, PrimitiveTypes.NUMBER, "max_runs","Max runs of the clustering procedure","10");
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PrimitiveType p12 = new PrimitiveType(Integer.class.getName(), null, PrimitiveTypes.NUMBER, "max_optimization_steps","Max number of internal optimization steps","5");
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PrimitiveType p13 = new PrimitiveType(Integer.class.getName(), null, PrimitiveTypes.NUMBER, "min_points","Minimum number of points to define an outlier set","2");
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PrimitiveType p13 = new PrimitiveType(Integer.class.getName(), null, PrimitiveTypes.NUMBER, "min_points","Number of points which define an outlier set","2");
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parameters.add(p1);
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parameters.add(p2);
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@ -144,6 +140,7 @@ public class KMeans extends DBScan{
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parameters.add(p10);
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parameters.add(p11);
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parameters.add(p12);
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parameters.add(p13);
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return parameters;
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}
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File diff suppressed because it is too large
Load Diff
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@ -0,0 +1,302 @@
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package org.gcube.dataanalysis.ecoengine.clustering;
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import java.io.ByteArrayInputStream;
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import java.io.File;
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import java.io.InputStream;
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import java.util.ArrayList;
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import java.util.List;
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import org.gcube.contentmanagement.lexicalmatcher.utils.AnalysisLogger;
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import org.gcube.dataanalysis.ecoengine.configuration.AlgorithmConfiguration;
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import org.gcube.dataanalysis.ecoengine.datatypes.DatabaseType;
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import org.gcube.dataanalysis.ecoengine.datatypes.InputTable;
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import org.gcube.dataanalysis.ecoengine.datatypes.PrimitiveType;
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import org.gcube.dataanalysis.ecoengine.datatypes.PrimitiveTypesList;
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import org.gcube.dataanalysis.ecoengine.datatypes.ServiceType;
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import org.gcube.dataanalysis.ecoengine.datatypes.StatisticalType;
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import org.gcube.dataanalysis.ecoengine.datatypes.enumtypes.DatabaseParameters;
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import org.gcube.dataanalysis.ecoengine.datatypes.enumtypes.PrimitiveTypes;
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import org.gcube.dataanalysis.ecoengine.datatypes.enumtypes.ServiceParameters;
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import org.gcube.dataanalysis.ecoengine.datatypes.enumtypes.TableTemplates;
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import org.gcube.dataanalysis.ecoengine.utils.DatabaseFactory;
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import org.gcube.dataanalysis.ecoengine.utils.DatabaseUtils;
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import org.gcube.dataanalysis.ecoengine.utils.Transformations;
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import com.rapidminer.example.Attribute;
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import com.rapidminer.example.Attributes;
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import com.rapidminer.example.Example;
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import com.rapidminer.example.ExampleSet;
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import com.rapidminer.operator.IOContainer;
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import com.rapidminer.operator.IOObject;
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import com.rapidminer.operator.clustering.Cluster;
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import com.rapidminer.operator.clustering.ClusterModel;
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import com.rapidminer.tools.OperatorService;
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import weka.clusterers.ClusterEvaluation;
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import weka.core.DenseInstance;
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import weka.core.Instance;
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import weka.core.Instances;
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import weka.core.converters.ArffSaver;
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import weka.core.converters.CSVLoader;
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public class XMeansWrapper extends DBScan {
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private String maxIterations;
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private String minClusters;
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private String maxClusters;
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public XMeansWrapper(){
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super();
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initrapidminer=false;
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}
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public static void main1(String[] args) throws Exception {
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args = new String[2];
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args[0] = "input.csv";
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args[1] = "c:/tmp/output.arff";
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// load CSV
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CSVLoader loader = new CSVLoader();
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loader.setSource(new File(args[0]));
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Instances data = loader.getDataSet();
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// save ARFF
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ArffSaver saver = new ArffSaver();
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saver.setInstances(data);
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saver.setFile(new File(args[1]));
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// saver.setDestination(new File(args[1]));
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saver.writeBatch();
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}
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public class CSV2Arff {
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/**
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* takes 2 arguments: - CSV input file - ARFF output file
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*/
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}
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public static void main(String[] args) throws Exception {
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XMeans xmeans = new XMeans();
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// xmeans.setInputCenterFile(new File("./clusterinput.arf"));
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// String[] options = {"-I 10","-M 1000","-J 1000","-L 2","-H 50","-B 1.0","-use-kdtree no","-N clusterinput.arf","-O clusterout.txt","-U 3"};
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// String[] options = {"-I 10","-M 1000","-J 1000","-L 2","-H 50","-B 1.0","-use-kdtree no","-t clusterinput.arf","-O clusterout.txt","-U 3"};
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// String optionsS = "-t c:/tmp/output.arff -O c:/tmp/clusterout.arff";
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String optionsS = "-t c:/tmp/output.arff";
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String[] options = optionsS.split(" ");
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String elements = "ciao,tutti\n5.1,3.5\n4.9,3.0\n4.7,3.2\n4.6,3.1\n5.0,3.6\n5.4,3.9\n4.6,3.4\n5.0,3.4\n4.4,2.9\n4.9,3.1\n";
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// xmeans.setInputCenterFile(new File("./clusterinput.arf"));
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CSVLoader loader = new CSVLoader();
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InputStream tis = new ByteArrayInputStream(elements.getBytes("UTF-8"));
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loader.setSource(tis);
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Instances id = loader.getDataSet();
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System.out.println("ids: "+id.numInstances());
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System.exit(0);
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xmeans.buildClusterer(id);
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// xmeans.main(options);
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// ClusterEvaluation.evaluateClusterer(xmeans, options);
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/*
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* String[] opts = xmeans.getOptions(); for (int i=0;i<opts.length;i++){ System.out.println("options: "+opts[i]); }
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*/
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System.out.println(ClusterEvaluation.evaluateClusterer(xmeans, options));
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// ClusterEvaluation.evaluateClusterer(xmeans, options);
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System.out.println("*************");
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Instances is = xmeans.getClusterCenters();
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for (Instance i : is) {
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DenseInstance di = (DenseInstance) i;
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System.out.println("Attributes: " + i.numAttributes());
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System.out.print("->" + di.toString(0));
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System.out.println(" " + di.toString(1));
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// System.out.println(i);
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System.out.println("-------------------------------");
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}
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System.out.println(xmeans.m_Bic);
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// System.out.println(xmeans.clusterInstance(instance));
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int[] ii = xmeans.m_ClusterAssignments;
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for (int ix : ii)
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System.out.print(ix + " ");
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// xmeans.main(options);
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}
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@Override
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public List<StatisticalType> getInputParameters() {
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List<StatisticalType> parameters = new ArrayList<StatisticalType>();
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List<TableTemplates> templateOccs = new ArrayList<TableTemplates>();
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templateOccs.add(TableTemplates.GENERIC);
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InputTable p1 = new InputTable(templateOccs, "OccurrencePointsTable", "Occurrence Points Table", "occurrences");
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PrimitiveTypesList p2 = new PrimitiveTypesList(PrimitiveTypes.STRING, "FeaturesColumnNames", "Column Names for the features", false);
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ServiceType p3 = new ServiceType(ServiceParameters.RANDOMSTRING, "OccurrencePointsClusterTable", "Table name of the distribution", "occCluster_");
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PrimitiveType p4 = new PrimitiveType(Integer.class.getName(), null, PrimitiveTypes.NUMBER, "maxIterations", "XMeans max number of overall iterations of the clustering learning", "10");
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PrimitiveType p5 = new PrimitiveType(Integer.class.getName(), null, PrimitiveTypes.NUMBER, "minClusters", "Minimum number of expected clusters", "1");
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PrimitiveType p12 = new PrimitiveType(Integer.class.getName(), null, PrimitiveTypes.NUMBER, "maxClusters", "Maximum number of clusters to produce", "50");
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PrimitiveType p13 = new PrimitiveType(Integer.class.getName(), null, PrimitiveTypes.NUMBER, "min_points", "Number of points which define an outlier set", "2");
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DatabaseType p6 = new DatabaseType(DatabaseParameters.DATABASEUSERNAME, "DatabaseUserName", "db user name");
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DatabaseType p7 = new DatabaseType(DatabaseParameters.DATABASEPASSWORD, "DatabasePassword", "db password");
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DatabaseType p8 = new DatabaseType(DatabaseParameters.DATABASEDRIVER, "DatabaseDriver", "db driver");
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DatabaseType p9 = new DatabaseType(DatabaseParameters.DATABASEURL, "DatabaseURL", "db url");
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DatabaseType p10 = new DatabaseType(DatabaseParameters.DATABASEDIALECT, "DatabaseDialect", "db dialect");
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DatabaseType p11 = new DatabaseType(DatabaseParameters.DATABASETABLESPACE, "DatabaseTableSpace", "db dialect");
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parameters.add(p1);
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parameters.add(p2);
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parameters.add(p3);
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parameters.add(p4);
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parameters.add(p5);
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parameters.add(p6);
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parameters.add(p7);
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parameters.add(p8);
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parameters.add(p9);
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parameters.add(p10);
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parameters.add(p11);
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parameters.add(p12);
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parameters.add(p13);
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return parameters;
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}
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@Override
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public String getDescription() {
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return "Clustering with XMeans Algorithm: X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region. ";
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}
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@Override
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public void setConfiguration(AlgorithmConfiguration config) {
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if (config != null) {
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maxIterations = config.getParam("maxIterations");
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minClusters = config.getParam("minClusters");
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maxClusters = config.getParam("maxClusters");
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minPoints = config.getParam("min_points");
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OccurrencePointsTable = config.getParam("OccurrencePointsTable").toLowerCase();
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OccurrencePointsClusterTable = config.getParam("OccurrencePointsClusterTable").toLowerCase();
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FeaturesColumnNames = config.getParam("FeaturesColumnNames");
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this.config = config;
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}
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}
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@Override
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public void compute() throws Exception {
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if ((config == null) || maxIterations == null || minClusters == null || maxClusters == null) {
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throw new Exception("XMeans: Error incomplete parameters");
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}
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if ((samplesVector != null) && (samplesVector.length > 0)) {
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AnalysisLogger.getLogger().debug("XMeans: Setting up the cluster");
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CSVLoader loader = new CSVLoader();
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StringBuffer sb = new StringBuffer();
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for (int i = -1; i < samplesVector.length; i++) {
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for (int j = 0; j < samplesVector[0].length; j++) {
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if (i==-1)
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sb.append("F"+j);
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else
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sb.append(samplesVector[i][j]);
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if (j < samplesVector[0].length - 1) {
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sb.append(",");
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} else
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sb.append("\n");
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}
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}
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InputStream tis = new ByteArrayInputStream(sb.toString().getBytes("UTF-8"));
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loader.setSource(tis);
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Instances id = loader.getDataSet();
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XMeans xmeans = new XMeans();
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xmeans.setMaxIterations(Integer.parseInt(maxIterations));
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xmeans.setMinNumClusters(Integer.parseInt(minClusters));
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xmeans.setMaxNumClusters(Integer.parseInt(maxClusters));
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xmeans.buildClusterer(id);
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status = 50f;
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// do clustering
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AnalysisLogger.getLogger().debug("XMeans: Clustering ...");
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Instances is = xmeans.getClusterCenters();
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int nClusters = is.numInstances();
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// take results
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AnalysisLogger.getLogger().debug("XMeans: Found "+nClusters+" Centroids");
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for (Instance i : is) {
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DenseInstance di = (DenseInstance) i;
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int nCluster = di.numAttributes();
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for (int k = 0; k < nCluster; k++) {
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AnalysisLogger.getLogger().debug(di.toString(k));
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}
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AnalysisLogger.getLogger().debug("-------------------------------");
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}
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int[] clusteringAssignments = xmeans.m_ClusterAssignments;
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int[] counters = new int[nClusters];
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for (int cluster:clusteringAssignments){
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counters[cluster]++;
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}
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AnalysisLogger.getLogger().debug("XMeans: Building Table");
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BuildClusterTable(clusteringAssignments, counters);
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} else
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AnalysisLogger.getLogger().debug("XMeans: Warning - Empty Training Set");
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shutdown();
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status = 100f;
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}
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protected void BuildClusterTable(int[] clusteringAssignments, int[] counters) throws Exception {
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String columnsNames = FeaturesColumnNames + "," + clusterColumn + "," + outliersColumn;
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int minpoints = Integer.parseInt(minPoints);
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AnalysisLogger.getLogger().debug("Analyzing Cluster ->" + " minpoints " + minpoints);
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StringBuffer bufferRows = new StringBuffer();
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int nrows = samplesVector.length;
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int ncols = samplesVector[0].length;
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AnalysisLogger.getLogger().debug("Analyzing Cluster ->" + "Building Rows to Insert");
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for (int k = 0; k < nrows; k++) {
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bufferRows.append("(");
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int cindex = clusteringAssignments[k];
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boolean isoutlier = (counters[cindex]<minpoints);
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for (int j = 0; j < ncols; j++) {
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bufferRows.append(samplesVector[k][j]);
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bufferRows.append(",");
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}
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bufferRows.append(cindex + "," + isoutlier + ")");
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if (k < nrows - 1) {
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bufferRows.append(",");
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||||
}
|
||||
}
|
||||
|
||||
//TO-DO: insert row at chunks
|
||||
|
||||
AnalysisLogger.getLogger().debug("Analyzing Cluster ->" + "Inserting rows");
|
||||
|
||||
if (bufferRows.length() > 0) {
|
||||
AnalysisLogger.getLogger().debug("XMeans: Writing into DB");
|
||||
AnalysisLogger.getLogger().debug(DatabaseUtils.insertFromBuffer(OccurrencePointsClusterTable, columnsNames, bufferRows));
|
||||
DatabaseFactory.executeSQLUpdate(DatabaseUtils.insertFromBuffer(OccurrencePointsClusterTable, columnsNames, bufferRows), dbHibConnection);
|
||||
AnalysisLogger.getLogger().debug("XMeans: Finished with writing into DB");
|
||||
} else
|
||||
AnalysisLogger.getLogger().debug("XMeans: Nothing to write in the buffer");
|
||||
|
||||
status = 95f;
|
||||
AnalysisLogger.getLogger().debug("XMeans: Status: " + status);
|
||||
|
||||
}
|
||||
|
||||
}
|
||||
|
|
@ -3,6 +3,8 @@ package org.gcube.dataanalysis.ecoengine.test.regression;
|
|||
import java.util.List;
|
||||
|
||||
import org.gcube.dataanalysis.ecoengine.configuration.AlgorithmConfiguration;
|
||||
import org.gcube.dataanalysis.ecoengine.datatypes.PrimitiveType;
|
||||
import org.gcube.dataanalysis.ecoengine.datatypes.enumtypes.PrimitiveTypes;
|
||||
import org.gcube.dataanalysis.ecoengine.interfaces.Clusterer;
|
||||
import org.gcube.dataanalysis.ecoengine.processing.factories.ClusterersFactory;
|
||||
|
||||
|
|
@ -15,12 +17,22 @@ public class RegressionTestClusterers {
|
|||
public static void main(String[] args) throws Exception {
|
||||
|
||||
System.out.println("TEST 1");
|
||||
|
||||
List<Clusterer> clus = ClusterersFactory.getClusterers(testConfigLocal());
|
||||
List<Clusterer> clus;
|
||||
|
||||
clus = ClusterersFactory.getClusterers(testConfigLocal());
|
||||
clus.get(0).init();
|
||||
Regressor.process(clus.get(0));
|
||||
clus = null;
|
||||
|
||||
clus = ClusterersFactory.getClusterers(testConfigLocal2());
|
||||
clus.get(0).init();
|
||||
Regressor.process(clus.get(0));
|
||||
clus = null;
|
||||
|
||||
clus = ClusterersFactory.getClusterers(testConfigLocal3());
|
||||
clus.get(0).init();
|
||||
Regressor.process(clus.get(0));
|
||||
clus = null;
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -30,11 +42,45 @@ public static void main(String[] args) throws Exception {
|
|||
config.setNumberOfResources(1);
|
||||
config.setAgent("DBSCAN");
|
||||
config.setParam("OccurrencePointsTable","presence_basking_cluster");
|
||||
config.setParam("FeaturesColumnNames","centerlat,centerlong");
|
||||
config.setParam("OccurrencePointsClusterTable","occCluster_2");
|
||||
config.setParam("FeaturesColumnNames","centerlat"+AlgorithmConfiguration.getListSeparator()+"centerlong");
|
||||
config.setParam("OccurrencePointsClusterTable","occcluster_dbscan");
|
||||
config.setParam("epsilon","10");
|
||||
config.setParam("minPoints","1");
|
||||
|
||||
return config;
|
||||
}
|
||||
|
||||
|
||||
private static AlgorithmConfiguration testConfigLocal2() {
|
||||
|
||||
AlgorithmConfiguration config = Regressor.getConfig();
|
||||
config.setNumberOfResources(1);
|
||||
config.setAgent("KMEANS");
|
||||
config.setParam("OccurrencePointsTable","presence_basking_cluster");
|
||||
config.setParam("FeaturesColumnNames","centerlat"+AlgorithmConfiguration.getListSeparator()+"centerlong");
|
||||
config.setParam("OccurrencePointsClusterTable","occcluster_kmeans");
|
||||
config.setParam("k","50");
|
||||
config.setParam("max_runs","10");
|
||||
config.setParam("max_optimization_steps","10");
|
||||
config.setParam("min_points","2");
|
||||
|
||||
return config;
|
||||
}
|
||||
|
||||
|
||||
private static AlgorithmConfiguration testConfigLocal3() {
|
||||
|
||||
AlgorithmConfiguration config = Regressor.getConfig();
|
||||
config.setNumberOfResources(1);
|
||||
config.setAgent("XMEANS");
|
||||
config.setParam("OccurrencePointsTable","presence_basking_cluster");
|
||||
config.setParam("FeaturesColumnNames","centerlat"+AlgorithmConfiguration.getListSeparator()+"centerlong");
|
||||
config.setParam("OccurrencePointsClusterTable","occcluster_xmeans");
|
||||
config.setParam("maxIterations","1000");
|
||||
config.setParam("minClusters","10");
|
||||
config.setParam("maxClusters","50");
|
||||
config.setParam("min_points","10");
|
||||
|
||||
return config;
|
||||
}
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue