git-svn-id: https://svn.d4science.research-infrastructures.eu/gcube/trunk/data-analysis/EcologicalEngine@58857 82a268e6-3cf1-43bd-a215-b396298e98cf
This commit is contained in:
parent
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@ -1,3 +1,2 @@
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HSPEN=org.gcube.dataanalysis.ecoengine.models.ModelHSPEN
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AQUAMAPSNN=org.gcube.dataanalysis.ecoengine.models.ModelAquamapsNN
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AQUAMAPSNNNS=org.gcube.dataanalysis.ecoengine.models.ModelAquamapsNNNS
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AQUAMAPSNN=org.gcube.dataanalysis.ecoengine.models.ModelAquamapsNN
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@ -51,9 +51,7 @@ public class DistributionQualityAnalysis extends DataAnalysis {
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templates.add(TableTemplates.TESTSET);
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List<TableTemplates> templatesOccurrences = new ArrayList<TableTemplates>();
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templatesOccurrences.add(TableTemplates.OCCURRENCE_AQUAMAPS);
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templatesOccurrences.add(TableTemplates.TRAININGSET);
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templatesOccurrences.add(TableTemplates.TESTSET);
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templatesOccurrences.add(TableTemplates.HCAF);
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InputTable p1 = new InputTable(templatesOccurrences,"PositiveCasesTable","A Table containing positive cases");
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InputTable p2 = new InputTable(templatesOccurrences,"NegativeCasesTable","A Table containing negative cases");
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@ -51,8 +51,8 @@ public class HabitatRepresentativeness extends DataAnalysis {
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InputTable p1 = new InputTable(templates,"ProjectingAreaTable","A Table containing projecting area information");
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PrimitiveType p2 = new PrimitiveType(String.class.getName(), null, PrimitiveTypes.STRING, "ProjectingAreaFeaturesOptionalCondition","optional filter for taking area rows","oceanarea>0",true);
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InputTable p3 = new InputTable(templatesOccurrences,"PositiveCasesTable","A Table containing positive cases");
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InputTable p4 = new InputTable(templatesOccurrences,"NegativeCasesTable","A Table containing negative cases");
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InputTable p3 = new InputTable(templates,"PositiveCasesTable","A Table containing positive cases");
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InputTable p4 = new InputTable(templates,"NegativeCasesTable","A Table containing negative cases");
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// PrimitiveType p5 = new PrimitiveType(String.class.getName(), null, PrimitiveTypes.STRING, "FeaturesColumns","fetures columns names separated by comma","depthmean,depthmax,depthmin, sstanmean,sbtanmean,salinitymean,salinitybmean, primprodmean,iceconann,landdist,oceanarea");
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ColumnTypesList p5 = new ColumnTypesList ("PositiveCasesTable","FeaturesColumns", "Features columns", false);
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@ -230,6 +230,7 @@ public class HabitatRepresentativeness extends DataAnalysis {
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AnalysisLogger.getLogger().error("ALERT: AN ERROR OCCURRED DURING HRS CALCULATION : " + e.getLocalizedMessage());
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throw e;
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} finally {
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status=100;
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AnalysisLogger.getLogger().trace("COMPUTATION FINISHED ");
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}
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}
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@ -185,7 +185,7 @@ public class Neural_Network implements Serializable {
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return prop(input, 0);
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else
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System.out.println("Errore:numero di input non valido! ");
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System.out.println("Error : number of inputs not valid!");
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return null;
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}
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@ -225,7 +225,7 @@ public class Neural_Network implements Serializable {
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arrayout[i + 1] = trainprop(arrayout[i], i);
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return arrayout;
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} else
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System.out.println("Errore:numero di input non valido! ");
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System.out.println("Error : number of inputs not valid!");
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return null;
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}
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@ -308,7 +308,7 @@ public class Neural_Network implements Serializable {
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public void train(double[][] inputvet, double[][] correctoutputvet) {
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if (griglia[griglia.length - 1].length != correctoutputvet[0].length)
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System.out.println("Errore: il vettore degli output " + "NON ha una lunghezza pari " + "a quella dell'output " + "della rete");
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System.out.println("Error : the vector of outputs has not a lenght equal to the output of the network");
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else {
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double en = 2;
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int counter = 0;
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@ -320,9 +320,7 @@ public class Neural_Network implements Serializable {
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en += energy(this.propagate(inputvet[i]), correctoutputvet[i]);
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}
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System.out.println("errore: " + en);
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System.out.println("Learning Score: " + en);
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counter++;
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}
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System.out.println("Scarto Finale: " + en);
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@ -79,10 +79,10 @@ public class AquamapsNNSuitable extends AquamapsSuitable{
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}
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// double [] output = new double[1];
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float probability = (float) output[0];
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/*
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if (probability>0.1)
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AnalysisLogger.getLogger().debug(" Probability " + probability);
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*/
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// System.exit(0);
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return probability;
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}
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@ -44,7 +44,7 @@ public class ProduceTestMap {
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String finalDistributionKeyColumn = "csquarecode";
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// Train
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// ModelerT.train(ModelerT.getTrainingConfig(modelName, absenceTable, presenceTable, speciesCode, userName, neuralNetworkLayers, configPath));
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ModelerT.train(ModelerT.getTrainingConfig(modelName, absenceTable, presenceTable, speciesCode, userName, neuralNetworkLayers, configPath));
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// Generate
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GeneratorT.generate(GeneratorT.getGenerationConfig(numberOfResources, generatorName, envelopeTable, preprocessedTable, speciesCode, userName, csquareTable, finalDistributionTable, configPath));
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@ -0,0 +1,303 @@
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package org.gcube.dataanalysis.ecoengine.test.experiments.latimeria;
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import java.util.HashMap;
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import java.util.List;
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import org.gcube.dataanalysis.ecoengine.configuration.AlgorithmConfiguration;
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import org.gcube.dataanalysis.ecoengine.datatypes.PrimitiveType;
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import org.gcube.dataanalysis.ecoengine.evaluation.DiscrepancyAnalysis;
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import org.gcube.dataanalysis.ecoengine.interfaces.Evaluator;
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import org.gcube.dataanalysis.ecoengine.interfaces.Generator;
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import org.gcube.dataanalysis.ecoengine.interfaces.Modeler;
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import org.gcube.dataanalysis.ecoengine.processing.factories.EvaluatorsFactory;
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import org.gcube.dataanalysis.ecoengine.processing.factories.GeneratorsFactory;
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import org.gcube.dataanalysis.ecoengine.processing.factories.ModelersFactory;
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import org.gcube.dataanalysis.ecoengine.test.regression.Regressor;
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public class ExperimentsForLatimeria {
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static String absenceRandom = "absence_data_latimeria_random";
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static String absenceStatic = "absence_data_latimeria";
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static String aquamapsSuitableTable = "hspec_suitable_latimeria_chalumnae";
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static String aquamapsNativeTable = "hspec_native_latimeria_chalumnae";
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private static AlgorithmConfiguration configAquamapsSuitable() {
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AlgorithmConfiguration config = Regressor.getConfig();
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config.setNumberOfResources(5);
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config.setModel("AQUAMAPS_SUITABLE");
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config.setParam("DistributionTable",aquamapsSuitableTable );
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config.setParam("CsquarecodesTable", "hcaf_d");
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config.setParam("EnvelopeTable", "hspen_latimeria");
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config.setParam("OccurrencePointsTable", "occurrencecells");
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config.setParam("CreateTable", "true");
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return config;
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}
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private static AlgorithmConfiguration configAquamapsNative() {
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AlgorithmConfiguration config = Regressor.getConfig();
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config.setNumberOfResources(5);
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config.setModel("AQUAMAPS_NATIVE");
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config.setParam("DistributionTable", aquamapsNativeTable);
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config.setParam("CsquarecodesTable", "hcaf_d");
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config.setParam("EnvelopeTable", "hspen_latimeria");
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config.setParam("OccurrencePointsTable", "occurrencecells");
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config.setParam("CreateTable", "true");
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return config;
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}
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private static AlgorithmConfiguration configAquamapsNNSuitable(String tableName,String username) {
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AlgorithmConfiguration config = Regressor.getConfig();
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config.setNumberOfResources(5);
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config.setModel("AQUAMAPS_SUITABLE_NEURALNETWORK");
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config.setParam("DistributionTable", tableName);
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config.setParam("CsquarecodesTable", "hcaf_d");
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config.setParam("EnvelopeTable", "hspen_latimeria");
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config.setParam("OccurrencePointsTable", "occurrencecells");
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config.setParam("CreateTable", "true");
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config.setParam("SpeciesName", "Fis-30189");
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config.setParam("UserName", username);
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return config;
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}
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private static AlgorithmConfiguration configAquamapsNNNative(String tableName,String username) {
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AlgorithmConfiguration config = Regressor.getConfig();
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config.setNumberOfResources(5);
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config.setModel("AQUAMAPS_NATIVE_NEURALNETWORK");
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config.setParam("DistributionTable", tableName);
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config.setParam("CsquarecodesTable", "hcaf_d");
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config.setParam("EnvelopeTable", "hspen_latimeria");
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config.setParam("OccurrencePointsTable", "occurrencecells");
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config.setParam("CreateTable", "true");
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config.setParam("SpeciesName", "Fis-30189");
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config.setParam("UserName", username);
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return config;
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}
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public static AlgorithmConfiguration configSuitableNeuralNetworkTraining(String absenceTable,String username) {
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AlgorithmConfiguration config = Regressor.getConfig();
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config.setNumberOfResources(5);
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config.setModel("AQUAMAPSNN");
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config.setParam("AbsenceDataTable", absenceTable);
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config.setParam("PresenceDataTable", "presence_data_latimeria");
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config.setParam("SpeciesName", "Fis-30189");
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config.setParam("UserName",username);
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config.setParam("LayersNeurons", "100"+AlgorithmConfiguration.getListSeparator()+"2");
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return config;
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}
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public static AlgorithmConfiguration configNativeNeuralNetworkTraining(String absenceTable,String username) {
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AlgorithmConfiguration config = Regressor.getConfig();
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config.setNumberOfResources(5);
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config.setModel("AQUAMAPSNN");
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config.setParam("AbsenceDataTable", absenceTable);
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config.setParam("PresenceDataTable", "presence_data_latimeria");
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config.setParam("SpeciesName", "Fis-30189");
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config.setParam("UserName", username);
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config.setParam("LayersNeurons", "100");
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return config;
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}
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public static AlgorithmConfiguration configQualityAnalysis(String table,String absenceTable){
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AlgorithmConfiguration config = Regressor.getConfig();
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config.setNumberOfResources(1);
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config.setAgent("QUALITY_ANALYSIS");
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config.setParam("PositiveCasesTable","presence_data_latimeria");
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config.setParam("NegativeCasesTable",absenceTable);
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config.setParam("PositiveCasesTableKeyColumn","csquarecode");
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config.setParam("NegativeCasesTableKeyColumn","csquarecode");
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config.setParam("DistributionTable",table);
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config.setParam("DistributionTableKeyColumn","csquarecode");
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config.setParam("DistributionTableProbabilityColumn","probability");
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config.setParam("PositiveThreshold","0.8");
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config.setParam("NegativeThreshold","0.3");
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return config;
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}
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public static AlgorithmConfiguration configDiscrepancyAnalysis(String table1,String table2){
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AlgorithmConfiguration config = Regressor.getConfig();
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config.setNumberOfResources(1);
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config.setAgent("DISCREPANCY_ANALYSIS");
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config.setParam("FirstTable",table1);
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config.setParam("SecondTable",table2);
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config.setParam("FirstTableCsquareColumn","csquarecode");
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config.setParam("SecondTableCsquareColumn","csquarecode");
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config.setParam("FirstTableProbabilityColumn","probability");
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config.setParam("SecondTableProbabilityColumn","probability");
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config.setParam("ComparisonThreshold","0.1");
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return config;
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}
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public static AlgorithmConfiguration configHRSAnalysis(String projectiontable,String absenceTable){
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AlgorithmConfiguration config = Regressor.getConfig();
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config.setNumberOfResources(1);
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config.setAgent("HRS");
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config.setParam("ProjectingAreaTable", projectiontable);
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config.setParam("ProjectingAreaFeaturesOptionalCondition", "where oceanarea>0");
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config.setParam("FeaturesColumns", "depthmean"+AlgorithmConfiguration.getListSeparator()+"depthmax"+AlgorithmConfiguration.getListSeparator()+"depthmin"+AlgorithmConfiguration.getListSeparator()+" sstanmean"+AlgorithmConfiguration.getListSeparator()+"sbtanmean"+AlgorithmConfiguration.getListSeparator()+"salinitymean"+AlgorithmConfiguration.getListSeparator()+"salinitybmean"+AlgorithmConfiguration.getListSeparator()+" primprodmean"+AlgorithmConfiguration.getListSeparator()+"iceconann"+AlgorithmConfiguration.getListSeparator()+"landdist"+AlgorithmConfiguration.getListSeparator()+"oceanarea");
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config.setParam("PositiveCasesTable", "presence_data_latimeria");
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config.setParam("NegativeCasesTable", absenceTable);
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return config;
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}
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static String staticsuitable = "staticsuitable";
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static String randomsuitable = "randomsuitable";
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static String staticnative = "staticnative";
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static String randomnative = "randomnative";
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public static void trainNeuralNetworks() throws Exception{
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List<Modeler> modelers = null;
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modelers = ModelersFactory.getModelers(configSuitableNeuralNetworkTraining(absenceStatic,staticsuitable));
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modelers.get(0).init();
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Regressor.process(modelers.get(0));
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modelers = null;
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modelers = ModelersFactory.getModelers(configSuitableNeuralNetworkTraining(absenceRandom,randomsuitable));
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modelers.get(0).init();
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Regressor.process(modelers.get(0));
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modelers = null;
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modelers = ModelersFactory.getModelers(configNativeNeuralNetworkTraining(absenceStatic,staticnative));
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modelers.get(0).init();
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Regressor.process(modelers.get(0));
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modelers = null;
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modelers = ModelersFactory.getModelers(configNativeNeuralNetworkTraining(absenceRandom,randomnative));
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modelers.get(0).init();
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Regressor.process(modelers.get(0));
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modelers = null;
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}
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static String nntablesuitable = "hspec_suitable_neural_latimeria_chalumnae";
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static String nntablesuitableRandom = "hspec_suitable_neural_latimeria_chalumnae_random";
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static String nntablenative = "hspec_native_neural_latimeria_chalumnae";
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static String nntablenativeRandom = "hspec_native_neural_latimeria_chalumnae_random";
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public static void generateAquamapsNativeSuitableNeuralNetwokrs() throws Exception{
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List<Generator> generators = null;
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generators = GeneratorsFactory.getGenerators(configAquamapsNNSuitable(nntablesuitable,staticsuitable));
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generators.get(0).init();
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Regressor.process(generators.get(0));
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generators = null;
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generators = GeneratorsFactory.getGenerators(configAquamapsNNSuitable(nntablesuitableRandom,randomsuitable));
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generators.get(0).init();
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Regressor.process(generators.get(0));
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generators = null;
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generators = GeneratorsFactory.getGenerators(configAquamapsNNNative(nntablenative,staticnative));
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generators.get(0).init();
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Regressor.process(generators.get(0));
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generators = null;
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generators = GeneratorsFactory.getGenerators(configAquamapsNNNative(nntablenativeRandom,randomnative));
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generators.get(0).init();
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Regressor.process(generators.get(0));
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generators = null;
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}
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public static void generateAquamapsNativeSuitable() throws Exception{
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List<Generator> generators = null;
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generators = GeneratorsFactory.getGenerators(configAquamapsSuitable());
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generators.get(0).init();
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Regressor.process(generators.get(0));
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generators = null;
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generators = GeneratorsFactory.getGenerators(configAquamapsNative());
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generators.get(0).init();
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Regressor.process(generators.get(0));
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generators = null;
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}
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public static void calcdiscrepancy(String table1,String table2) throws Exception{
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List<Evaluator> evaluators = null;
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evaluators = EvaluatorsFactory.getEvaluators(configDiscrepancyAnalysis(table1, table2));
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evaluators.get(0).init();
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Regressor.process(evaluators.get(0));
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PrimitiveType output = (PrimitiveType) evaluators.get(0).getOutput();
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HashMap<String, String> out = (HashMap<String, String>)output.getContent();
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DiscrepancyAnalysis.visualizeResults(out);
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evaluators = null;
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}
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public static void calcquality(String table1,String absenceTable) throws Exception{
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List<Evaluator> evaluators = null;
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evaluators = EvaluatorsFactory.getEvaluators(configQualityAnalysis(table1, absenceTable));
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evaluators.get(0).init();
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Regressor.process(evaluators.get(0));
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PrimitiveType output = (PrimitiveType) evaluators.get(0).getOutput();
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HashMap<String, String> out = (HashMap<String, String>)output.getContent();
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DiscrepancyAnalysis.visualizeResults(out);
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evaluators = null;
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}
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static String hcaf= "hcaf_d";
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public static void calcHRS(String hcaf,String absenceTable) throws Exception{
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List<Evaluator> evaluators = null;
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evaluators = EvaluatorsFactory.getEvaluators(configHRSAnalysis(hcaf, absenceTable));
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evaluators.get(0).init();
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Regressor.process(evaluators.get(0));
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PrimitiveType output = (PrimitiveType) evaluators.get(0).getOutput();
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HashMap<String, String> out = (HashMap<String, String>)output.getContent();
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DiscrepancyAnalysis.visualizeResults(out);
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evaluators = null;
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}
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public static void main(String[] args) throws Exception{
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// generateAquamapsNativeSuitable();
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// trainNeuralNetworks();
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// generateAquamapsNativeSuitableNeuralNetwokrs();
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/*
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calcdiscrepancy(aquamapsSuitableTable, nntablesuitable);
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calcdiscrepancy(aquamapsSuitableTable, nntablesuitableRandom);
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calcdiscrepancy(aquamapsNativeTable, nntablenative);
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calcdiscrepancy(aquamapsNativeTable, nntablenativeRandom);
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*/
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/*
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calcquality(aquamapsSuitableTable, absenceStatic);
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calcquality(aquamapsSuitableTable, absenceRandom);
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calcquality(nntablesuitable, absenceStatic);
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calcquality(nntablesuitable, absenceRandom);
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calcquality(nntablesuitableRandom, absenceStatic);
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calcquality(nntablesuitableRandom, absenceRandom);
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calcquality(nntablenative, absenceStatic);
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calcquality(nntablenative, absenceRandom);
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calcquality(nntablenativeRandom, absenceStatic);
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calcquality(nntablenativeRandom, absenceRandom);
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*/
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calcHRS(hcaf,absenceStatic);
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calcHRS(hcaf,absenceRandom);
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}
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}
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