data-miner-manager-tester/src/main/java/org/gcube/portlets/user/dataminermanagertester/server/testconfig/FeedForwardAnnTest.java

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4.0 KiB
Java

package org.gcube.portlets.user.dataminermanagertester.server.testconfig;
import java.util.ArrayList;
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import org.gcube.data.analysis.dataminermanagercl.shared.data.OutputData;
import org.gcube.data.analysis.dataminermanagercl.shared.data.output.MapResource;
import org.gcube.data.analysis.dataminermanagercl.shared.data.output.Resource;
import org.gcube.data.analysis.dataminermanagercl.shared.parameters.ColumnListParameter;
import org.gcube.data.analysis.dataminermanagercl.shared.parameters.ColumnParameter;
import org.gcube.data.analysis.dataminermanagercl.shared.parameters.ListParameter;
import org.gcube.data.analysis.dataminermanagercl.shared.parameters.ObjectParameter;
import org.gcube.data.analysis.dataminermanagercl.shared.parameters.Parameter;
import org.gcube.data.analysis.dataminermanagercl.shared.parameters.TabularParameter;
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/**
*
* @author Giancarlo Panichi
*
*
*/
public class FeedForwardAnnTest implements DMTest {
private static Logger logger = LoggerFactory.getLogger(FeedForwardAnnTest.class);
private static final String id = "org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.modellers.FEED_FORWARD_ANN";
@Override
public String getId() {
return id;
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@Override
public void createRequest(Operator operator) {
TabularParameter trainingDataSet = new TabularParameter();
trainingDataSet.setName("TrainingDataSet");
trainingDataSet.setValue("http://data.d4science.org/K0ZoUlVDNW1hR0hDZWZucS9UQkJmUVRTeWRuRVBGUC9HbWJQNStIS0N6Yz0");
ColumnListParameter trainingColumns = new ColumnListParameter();
trainingColumns.setName("TrainingColumns");
trainingColumns.setValue("depthmin|depthmax|depthmean|depthsd");
ColumnParameter targetColumn = new ColumnParameter();
targetColumn.setName("TargetColumn");
targetColumn.setValue("sstanmean");
ListParameter layersNeurons = new ListParameter();
layersNeurons.setName("LayersNeurons");
layersNeurons.setValue("20");
ObjectParameter reference = new ObjectParameter();
reference.setName("Reference");
reference.setValue("1");
ObjectParameter learningThreshold = new ObjectParameter();
learningThreshold.setName("LearningThreshold");
learningThreshold.setValue("0.0001");
ObjectParameter maxIterations = new ObjectParameter();
maxIterations.setName("MaxIterations");
maxIterations.setValue("10");
ObjectParameter modelName = new ObjectParameter();
modelName.setName("ModelName");
modelName.setValue("trained_network");
List<Parameter> parameters = new ArrayList<>();
parameters.add(trainingDataSet);
parameters.add(trainingColumns);
parameters.add(targetColumn);
parameters.add(layersNeurons);
parameters.add(reference);
parameters.add(learningThreshold);
parameters.add(maxIterations);
parameters.add(modelName);
operator.setOperatorParameters(parameters);
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@Override
public String getResult(OutputData outputData) {
StringBuilder result=new StringBuilder();
logger.debug("Output: " + outputData);
Resource resource = outputData.getResource();
if (resource.isMap()) {
MapResource mapResource = (MapResource) resource;
for (String key : mapResource.getMap().keySet()) {
logger.debug("Entry: " + key + " = "
+ mapResource.getMap().get(key));
result.append("Entry: " + key + " = "
+ mapResource.getMap().get(key));
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return result.toString();
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@Override
public boolean isValidResult(OutputData outputData) {
boolean valid;
logger.debug("Output: " + outputData);
Resource resource = outputData.getResource();
if (resource.isMap()) {
MapResource mapResource = (MapResource) resource;
for (String key : mapResource.getMap().keySet()) {
logger.debug("Entry: " + key + " = "
+ mapResource.getMap().get(key));
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valid=true;
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valid=false;
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return valid;
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