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git-svn-id: https://svn.d4science.research-infrastructures.eu/gcube/trunk/data-analysis/EcologicalEngine@62248 82a268e6-3cf1-43bd-a215-b396298e98cf

This commit is contained in:
Gianpaolo Coro 2012-12-12 12:36:33 +00:00
parent 4d16880ef7
commit 6a342678db
851 changed files with 1850 additions and 1770 deletions
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2
cfg/transducerers.properties
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@ -1,7 +1,7 @@
BIOCLIMATE_HSPEC=org.gcube.dataanalysis.ecoengine.transducers.BioClimateHSPECTransducer
BIOCLIMATE_HCAF=org.gcube.dataanalysis.ecoengine.transducers.BioClimateHCAFTransducer
BIOCLIMATE_HSPEN=org.gcube.dataanalysis.ecoengine.transducers.BioClimateHSPENTransducer
#HCAF_INTERPOLATION=org.gcube.dataanalysis.ecoengine.transducers.InterpolationTransducer
HCAF_INTERPOLATION=org.gcube.dataanalysis.ecoengine.transducers.InterpolationTransducer
HCAF_FILTER=org.gcube.dataanalysis.ecoengine.transducers.simplequeryexecutors.HcafFilter
HSPEN_FILTER=org.gcube.dataanalysis.ecoengine.transducers.simplequeryexecutors.HspenFilter
ABSENCE_CELLS_FROM_AQUAMAPS=org.gcube.dataanalysis.ecoengine.transducers.simplequeryexecutors.MarineAbsencePointsFromAquamapsDistribution

2
cfg/userperspective.properties
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@ -1,6 +1,6 @@
ANOMALIES_DETECTION=DBSCAN,KMEANS,XMEANS
CLASSIFICATION=FEED_FORWARD_A_N_N_DISTRIBUTION
CLIMATE=BIOCLIMATE_HSPEC,BIOCLIMATE_HCAF,BIOCLIMATE_HSPEN
CLIMATE=BIOCLIMATE_HSPEC,BIOCLIMATE_HCAF,BIOCLIMATE_HSPEN,HCAF_INTERPOLATION
CORRELATION_ANALYSIS=HRS
DATA_CLUSTERING=DBSCAN,KMEANS,XMEANS
FILTERING=HCAF_FILTER,HSPEN_FILTER

2
deployDesktop.jardesc
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@ -1,6 +1,6 @@
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<jardesc>
<jar path="C:/Users/coro/Desktop/ecological-engine-1.5.0-SNAPSHOT.jar"/>
<jar path="C:/Users/coro/Desktop/ecological-engine-1.6.0-SNAPSHOT.jar"/>
<options buildIfNeeded="true" compress="true" descriptionLocation="/EcologicalEngine/deployDesktop.jardesc" exportErrors="true" exportWarnings="true" includeDirectoryEntries="false" overwrite="true" saveDescription="true" storeRefactorings="false" useSourceFolders="false"/>
<storedRefactorings deprecationInfo="true" structuralOnly="false"/>
<selectedProjects/>

1
src/main/java/org/gcube/contentmanagement/graphtools/abstracts/GenericStandaloneGraph.java
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@ -184,4 +184,5 @@ public abstract class GenericStandaloneGraph extends ApplicationFrame {
return dataset;
}
}

219
src/main/java/org/gcube/dataanalysis/ecoengine/models/testing/FeedForwardNNFile.java
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@ -1,11 +1,14 @@
package org.gcube.dataanalysis.ecoengine.models.testing;
import java.awt.Color;
import java.awt.Image;
import java.io.File;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import org.gcube.contentmanagement.graphtools.data.conversions.ImageTools;
import org.gcube.contentmanagement.lexicalmatcher.utils.AnalysisLogger;
import org.gcube.dataanalysis.ecoengine.configuration.AlgorithmConfiguration;
import org.gcube.dataanalysis.ecoengine.datatypes.ColumnType;
@ -25,10 +28,58 @@ import org.gcube.dataanalysis.ecoengine.models.ModelAquamapsNN;
import org.gcube.dataanalysis.ecoengine.models.cores.neuralnetworks.Neural_Network;
import org.gcube.dataanalysis.ecoengine.utils.DatabaseFactory;
import org.gcube.dataanalysis.ecoengine.utils.DatabaseUtils;
import org.jfree.chart.JFreeChart;
import org.jfree.chart.axis.NumberAxis;
import org.jfree.chart.plot.XYPlot;
import org.jfree.chart.renderer.xy.XYSplineRenderer;
import org.jfree.data.general.Dataset;
import org.jfree.data.xy.XYDataset;
import org.jfree.data.xy.XYSeriesCollection;
import org.jfree.ui.RectangleInsets;
public class FeedForwardNNFile extends ModelAquamapsNN {
final float frequency = 0.3f;// 1f;
int samplingRate = 32;
float timeShift = 1f / (float) samplingRate;
double[] generateSinSignal() {
double samples[] = new double[200];
float time = 0;
for (int i = 0; i < samples.length; i++) {
samples[i] = (float) Math.sin(2f * Math.PI * frequency * time);
time += timeShift;
}
return samples;
}
public static Image displaySignal(double[] signal, float timeshift) {
org.jfree.data.xy.XYSeries xyseries = new org.jfree.data.xy.XYSeries("signal");
float time = 0;
for (int i = 0; i < signal.length; i++) {
xyseries.add(time, signal[i]);
time = time + timeshift;
}
XYSeriesCollection collection = new XYSeriesCollection(xyseries);
NumberAxis numberaxis = new NumberAxis("X");
numberaxis.setAutoRangeIncludesZero(true);
NumberAxis numberaxis1 = new NumberAxis("Y");
numberaxis1.setAutoRangeIncludesZero(true);
XYSplineRenderer xysplinerenderer = new XYSplineRenderer();
XYPlot xyplot = new XYPlot((XYDataset) collection, numberaxis, numberaxis1, xysplinerenderer);
xyplot.setBackgroundPaint(Color.lightGray);
xyplot.setDomainGridlinePaint(Color.white);
xyplot.setRangeGridlinePaint(Color.white);
xyplot.setAxisOffset(new RectangleInsets(4D, 4D, 4D, 4D));
JFreeChart chart = new JFreeChart("Numeric Series", JFreeChart.DEFAULT_TITLE_FONT, xyplot, true);
Image image = ImageTools.toImage(chart.createBufferedImage(640, 480));
return image;
}
public class FeedForwardNNFile extends ModelAquamapsNN{
@Override
public String getName() {
return "FEED_FORWARD_ANN";
@ -38,33 +89,40 @@ public class FeedForwardNNFile extends ModelAquamapsNN{
public String getDescription() {
return "A Neural Networks to be trained on features of Real values";
}
@Override
public StatisticalType getOutput() {
HashMap<String,StatisticalType> map = new HashMap<String, StatisticalType>();
PrimitiveType p = new PrimitiveType(File.class.getName(), new File(fileName), PrimitiveTypes.FILE, "NeuralNetwork","Trained Neural Network");
PrimitiveType score = new PrimitiveType(String.class.getName(), ""+nn.en, PrimitiveTypes.STRING, "LearningScore","Learning Score");
HashMap<String, StatisticalType> map = new HashMap<String, StatisticalType>();
PrimitiveType p = new PrimitiveType(File.class.getName(), new File(fileName), PrimitiveTypes.FILE, "NeuralNetwork", "Trained Neural Network");
PrimitiveType score = new PrimitiveType(String.class.getName(), "" + nn.en, PrimitiveTypes.STRING, "LearningScore", "Learning Score");
List<TableTemplates> template = new ArrayList<TableTemplates>();
template.add(TableTemplates.GENERIC);
OutputTable outTable = new OutputTable(template, trainingDataSet, trainingDataSet, "Output table");
OutputTable outTable = new OutputTable(template, "Example Trained Table", trainingDataSet, "Output table");
map.put("NeuralNetwork", p);
map.put("LearningScore", score);
map.put("FormerInputTable", outTable);
PrimitiveType output = new PrimitiveType(HashMap.class.getName(), map, PrimitiveTypes.MAP, "ResultsMap","Results Map");
HashMap<String, Image> producedImages = new HashMap<String, Image>();
producedImages.put("Random img", displaySignal(generateSinSignal(), timeShift));
PrimitiveType images = new PrimitiveType("Random Images", producedImages, PrimitiveTypes.IMAGES, "Charts", "A map with keys and Images");
map.put("Images", images);
PrimitiveType output = new PrimitiveType(HashMap.class.getName(), map, PrimitiveTypes.MAP, "ResultsMap", "Results Map");
return output;
}
@Override
public float getStatus() {
if (status==100)
if (status == 100)
return status;
else if ((nn!=null) && (nn.status>0))
return nn.status*100f;
else if ((nn != null) && (nn.status > 0))
return nn.status * 100f;
else
return status;
return status;
}
protected static String TrainingDataSet = "TrainingDataSet";
protected String trainingDataSet;
protected static String TrainingDataSetColumns = "TrainingColumns";
@ -78,27 +136,27 @@ public class FeedForwardNNFile extends ModelAquamapsNN{
protected static String LearningThreshold = "LearningThreshold";
protected static String MaxIterations = "MaxIterations";
protected static String ModelName = "ModelName";
protected static String UserName= "UserName";
protected static String UserName = "UserName";
protected float learningThr;
protected int maxiter;
protected int maxiter;
@Override
public List<StatisticalType> getInputParameters() {
List<StatisticalType> parameters = new ArrayList<StatisticalType>();
List<TableTemplates> templatesOccurrences = new ArrayList<TableTemplates>();
templatesOccurrences.add(TableTemplates.GENERIC);
InputTable p1 = new InputTable(templatesOccurrences,TrainingDataSet,"a table containing real values colums for training the ANN");
InputTable p1 = new InputTable(templatesOccurrences, TrainingDataSet, "a table containing real values colums for training the ANN");
ColumnTypesList p2 = new ColumnTypesList(TrainingDataSet, TrainingDataSetColumns, "column names to use as features vectors", false);
ColumnType p3 = new ColumnType(TrainingDataSet, TrainingDataSetTargetColumn, "the column to use as target", "probability", false);
PrimitiveTypesList p4 = new PrimitiveTypesList(Integer.class.getName(), PrimitiveTypes.NUMBER,LayersNeurons,"a list of neurons number for each inner layer",true);
PrimitiveType p5 = new PrimitiveType(String.class.getName(), null, PrimitiveTypes.STRING, Reference,"the phenomenon this ANN is trying to model - can be a generic identifier. Put 1 for not specifying","1");
PrimitiveType p6 = new PrimitiveType(Float.class.getName(), null, PrimitiveTypes.NUMBER, LearningThreshold,"the learning threshold for this ANN","0.01");
PrimitiveType p7 = new PrimitiveType(Integer.class.getName(), null, PrimitiveTypes.NUMBER, MaxIterations,"the maximum number of iterations in the training","100");
PrimitiveType p11 = new PrimitiveType(String.class.getName(), null, PrimitiveTypes.STRING, ModelName,"The name of this Neural Network - insert without spaces","neuralnet_");
ServiceType p10 = new ServiceType(ServiceParameters.USERNAME, UserName,"LDAP username");
PrimitiveType p12 = new PrimitiveType(File.class.getName(), null, PrimitiveTypes.FILE, "PreviousNeuralNetwork","Previous Neural Network");
PrimitiveTypesList p4 = new PrimitiveTypesList(Integer.class.getName(), PrimitiveTypes.NUMBER, LayersNeurons, "a list of neurons number for each inner layer", true);
PrimitiveType p5 = new PrimitiveType(String.class.getName(), null, PrimitiveTypes.STRING, Reference, "the phenomenon this ANN is trying to model - can be a generic identifier. Put 1 for not specifying", "1");
PrimitiveType p6 = new PrimitiveType(Float.class.getName(), null, PrimitiveTypes.NUMBER, LearningThreshold, "the learning threshold for this ANN", "0.01");
PrimitiveType p7 = new PrimitiveType(Integer.class.getName(), null, PrimitiveTypes.NUMBER, MaxIterations, "the maximum number of iterations in the training", "100");
PrimitiveType p11 = new PrimitiveType(String.class.getName(), null, PrimitiveTypes.STRING, ModelName, "The name of this Neural Network - insert without spaces", "neuralnet_");
ServiceType p10 = new ServiceType(ServiceParameters.USERNAME, UserName, "LDAP username");
PrimitiveType p12 = new PrimitiveType(File.class.getName(), null, PrimitiveTypes.FILE, "PreviousNeuralNetwork", "Previous Neural Network");
parameters.add(p1);
parameters.add(p2);
parameters.add(p3);
@ -109,7 +167,7 @@ public class FeedForwardNNFile extends ModelAquamapsNN{
parameters.add(p11);
parameters.add(p10);
parameters.add(p12);
DatabaseType.addDefaultDBPars(parameters);
return parameters;
}
@ -119,105 +177,103 @@ public class FeedForwardNNFile extends ModelAquamapsNN{
AnalysisLogger.setLogger(config.getConfigPath() + AlgorithmConfiguration.defaultLoggerFile);
// init the database
try {
connection = DatabaseUtils.initDBSession(config);
connection = DatabaseUtils.initDBSession(config);
} catch (Exception e) {
e.printStackTrace();
AnalysisLogger.getLogger().trace("ERROR initializing connection");
}
fileName = config.getPersistencePath()+Neural_Network.generateNNName(config.getParam(Reference), config.getParam(UserName), config.getParam(ModelName));
fileName = config.getPersistencePath() + Neural_Network.generateNNName(config.getParam(Reference), config.getParam(UserName), config.getParam(ModelName));
trainingDataSet = config.getParam(TrainingDataSet);
trainingDataSetColumns = config.getParam(TrainingDataSetColumns);
trainingColumn = config.getParam(TrainingDataSetTargetColumn);
File previousfile = new File (config.getParam("PreviousNeuralNetwork"));
AnalysisLogger.getLogger().info("Previous File is "+previousfile.getAbsolutePath());
File previousfile = new File(config.getParam("PreviousNeuralNetwork"));
AnalysisLogger.getLogger().info("Previous File is " + previousfile.getAbsolutePath());
learningThr = Float.parseFloat(config.getParam(LearningThreshold));
maxiter = Integer.parseInt(config.getParam(MaxIterations));
String layersNeurons$ = config.getParam(LayersNeurons);
if ((layersNeurons$!=null)&&(layersNeurons$.length()>0))
{
String [] split = layersNeurons$.split(AlgorithmConfiguration.getListSeparator());
if ((layersNeurons$ != null) && (layersNeurons$.length() > 0)) {
String[] split = layersNeurons$.split(AlgorithmConfiguration.getListSeparator());
layersNeurons = new int[split.length];
boolean nullhyp=true;
for (int i = 0;i<split.length;i++){
boolean nullhyp = true;
for (int i = 0; i < split.length; i++) {
layersNeurons[i] = Integer.parseInt(split[i]);
if ((layersNeurons[i]>0)&&(nullhyp))
nullhyp=false;
if ((layersNeurons[i] > 0) && (nullhyp))
nullhyp = false;
}
if (nullhyp)
layersNeurons=null;
layersNeurons = null;
}
dbcolumns = "";
dbcolumnsList = trainingDataSetColumns.split(AlgorithmConfiguration.getListSeparator());
for (int i=0;i<dbcolumnsList.length;i++){
dbcolumns+=dbcolumnsList[i];
if (i<dbcolumnsList.length-1)
dbcolumns+=",";
for (int i = 0; i < dbcolumnsList.length; i++) {
dbcolumns += dbcolumnsList[i];
if (i < dbcolumnsList.length - 1)
dbcolumns += ",";
}
}
private String takeElementsQuery = "select %1$s from %2$s d order by %3$s";
protected Neural_Network nn;
protected double maxfactor=1;
protected double minfactor=0;
protected double maxfactor = 1;
protected double minfactor = 0;
@Override
public void train(AlgorithmConfiguration Input, Model previousModel) {
try {
// take all features input vectors
String query = String.format(takeElementsQuery, trainingColumn+","+dbcolumns,trainingDataSet,trainingColumn);
AnalysisLogger.getLogger().debug("Query to execute: "+query);
String query = String.format(takeElementsQuery, trainingColumn + "," + dbcolumns, trainingDataSet, trainingColumn);
AnalysisLogger.getLogger().debug("Query to execute: " + query);
List<Object> features = DatabaseFactory.executeSQLQuery(query, connection);
int numbOfFeatures = features.size();
//get reduction factor for normalizing the outputs
List<Object> maxmin = DatabaseFactory.executeSQLQuery("select max("+trainingColumn+"), min("+trainingColumn+") from "+trainingDataSet, connection);
maxfactor = Double.parseDouble(""+((Object[])maxmin.get(0))[0]);
minfactor = Double.parseDouble(""+((Object[])maxmin.get(0))[1]);
AnalysisLogger.getLogger().debug("Calculated max: "+maxfactor+" min: "+minfactor);
// get reduction factor for normalizing the outputs
List<Object> maxmin = DatabaseFactory.executeSQLQuery("select max(" + trainingColumn + "), min(" + trainingColumn + ") from " + trainingDataSet, connection);
maxfactor = Double.parseDouble("" + ((Object[]) maxmin.get(0))[0]);
minfactor = Double.parseDouble("" + ((Object[]) maxmin.get(0))[1]);
AnalysisLogger.getLogger().debug("Calculated max: " + maxfactor + " min: " + minfactor);
// setup Neural Network
int numberOfInputNodes = dbcolumnsList.length;
int numberOfOutputNodes = 1;
AnalysisLogger.getLogger().debug("Training the ANN with "+numbOfFeatures+" training data and "+numberOfInputNodes+" inputs");
if (layersNeurons!=null){
AnalysisLogger.getLogger().debug("Training the ANN with " + numbOfFeatures + " training data and " + numberOfInputNodes + " inputs");
if (layersNeurons != null) {
int[] innerLayers = Neural_Network.setupInnerLayers(layersNeurons);
nn = new Neural_Network(numberOfInputNodes, numberOfOutputNodes, innerLayers, Neural_Network.ACTIVATIONFUNCTION.SIGMOID);
}
else
} else
nn = new Neural_Network(numberOfInputNodes, numberOfOutputNodes, Neural_Network.ACTIVATIONFUNCTION.SIGMOID);
nn.maxfactor=maxfactor;
nn.minfactor=minfactor;
nn.maxfactor = maxfactor;
nn.minfactor = minfactor;
nn.setThreshold(learningThr);
nn.setCycles(maxiter);
AnalysisLogger.getLogger().debug("network parameters: M: "+maxfactor+", m: "+minfactor+", lt: "+learningThr+", it: "+maxiter);
AnalysisLogger.getLogger().debug("topology: "+nn.griglia.length+"X"+nn.griglia[0].length);
AnalysisLogger.getLogger().debug("network parameters: M: " + maxfactor + ", m: " + minfactor + ", lt: " + learningThr + ", it: " + maxiter);
AnalysisLogger.getLogger().debug("topology: " + nn.griglia.length + "X" + nn.griglia[0].length);
AnalysisLogger.getLogger().debug("Features preprocessing");
double[][] in = new double[numbOfFeatures][];
double[][] out = new double[numbOfFeatures][];
// build NN input
for (int i = 0; i < numbOfFeatures; i++) {
// out[i] = new double[0];
// out[i] = new double[0];
Object[] feats = (Object[]) features.get(i);
in[i] = Neural_Network.preprocessObjects(Arrays.copyOfRange((Object[]) features.get(i), 1, feats.length));
out[i] = Neural_Network.preprocessObjects(Arrays.copyOfRange((Object[]) features.get(i), 0, 1));
//apply reduction factor
// AnalysisLogger.getLogger().debug("Output Transformed from "+out[i][0]);
out[i][0] =nn.getCorrectValueForOutput(out[i][0]);
// AnalysisLogger.getLogger().debug("To "+out[i][0]);
// apply reduction factor
// AnalysisLogger.getLogger().debug("Output Transformed from "+out[i][0]);
out[i][0] = nn.getCorrectValueForOutput(out[i][0]);
// AnalysisLogger.getLogger().debug("To "+out[i][0]);
}
AnalysisLogger.getLogger().debug("Features were correctly preprocessed - Training");
// train the NN
nn.train(in, out);
AnalysisLogger.getLogger().debug("Saving Network");
save(fileName, nn);
AnalysisLogger.getLogger().debug("Done");
@ -225,9 +281,8 @@ public class FeedForwardNNFile extends ModelAquamapsNN{
e.printStackTrace();
AnalysisLogger.getLogger().error("ERROR during training");
}
status = 100f;
}
}

16
src/main/java/org/gcube/dataanalysis/ecoengine/transducers/InterpolationTransducer.java
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@ -1,6 +1,7 @@
package org.gcube.dataanalysis.ecoengine.transducers;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import org.gcube.dataanalysis.ecoengine.configuration.AlgorithmConfiguration;
@ -88,20 +89,27 @@ public class InterpolationTransducer implements Transducerer{
@Override
public StatisticalType getOutput() {
HashMap<String, StatisticalType> map = new HashMap<String, StatisticalType>();
List<TableTemplates> template = new ArrayList<TableTemplates>();
template.add(TableTemplates.HCAF);
TablesList p = new TablesList(template, "INTEPOLATED_HCAF_TABLE_LIST", "List of HCAF tables produced by the interpolation", false);
// TablesList p = new TablesList(template, "INTEPOLATED_HCAF_TABLE_LIST", "List of HCAF tables produced by the interpolation", false);
if ((producedtables!=null) &&(producedtables.length>0)){
int i=1;
for (String table:producedtables){
p.add(new OutputTable(template,table,table,"Interpolation point number "+i));
i++;
// p.add(new OutputTable(template,table,table,"Interpolation number "+i));
map.put("Interpolation "+i, new OutputTable(template,table,table,"Interpolation number "+i));
i++;
}
}
return p;
PrimitiveType output = new PrimitiveType(HashMap.class.getName(), map, PrimitiveTypes.MAP, "INTEPOLATED_HCAF_TABLE_LIST", "List of HCAF tables produced by the interpolation");
return output;
}

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target/apidocs/allclasses-frame.html
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@ -2,13 +2,13 @@
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All Classes (ecological-engine 1.6.0-SNAPSHOT API)
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ExampleDataBuiltGraph (ecological-engine 1.6.0-SNAPSHOT API)
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ExampleDerivative (ecological-engine 1.6.0-SNAPSHOT API)
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ExampleHistogram (ecological-engine 1.6.0-SNAPSHOT API)
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ExampleImage (ecological-engine 1.6.0-SNAPSHOT API)
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ExampleLines (ecological-engine 1.6.0-SNAPSHOT API)
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ExampleNormalDistribution (ecological-engine 1.6.0-SNAPSHOT API)
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ExamplePie (ecological-engine 1.6.0-SNAPSHOT API)
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ExampleTimeSeries (ecological-engine 1.6.0-SNAPSHOT API)
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ExampleTimeSeries2 (ecological-engine 1.6.0-SNAPSHOT API)
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Uses of Class org.gcube.contentmanagement.graphtools.examples.graphsTypes.ExampleLines (ecological-engine 1.6.0-SNAPSHOT API)
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