Fix and little enhancements on the Neural Networks model

git-svn-id: https://svn.d4science.research-infrastructures.eu/gcube/trunk/data-analysis/EcologicalEngine@101825 82a268e6-3cf1-43bd-a215-b396298e98cf

src/main/java/org/gcube/dataanalysis/ecoengine/evaluation/DiscrepancyAnalysis.java

@@ -164,6 +164,7 @@ public class DiscrepancyAnalysis extends DataAnalysis {
 		if (maxdiscrepancyPoint==null)
 			maxdiscrepancyPoint="-";
 		
+		AnalysisLogger.getLogger().debug("Discrepancy Calculation - Kappa values: " + "agreementA1B1 "+agreementA1B1 +" agreementA1B0 " + agreementA1B0 +"  agreementA0B1 "+agreementA0B1+" agreementA0B0 "+agreementA0B0);
 		double kappa = MathFunctions.cohensKappaForDichotomy(agreementA1B1, agreementA1B0, agreementA0B1, agreementA0B0);
 		AnalysisLogger.getLogger().debug("Discrepancy Calculation - Calculated Cohen's Kappa:" + kappa);
 		
@@ -228,7 +229,7 @@ public class DiscrepancyAnalysis extends DataAnalysis {
 				if (elements[3] != null)
 					probabilityPoint2 = Float.parseFloat(""+elements[3]);
 				float discrepancy = Math.abs(probabilityPoint2 - probabilityPoint1);
-				
+
 				if (discrepancy > threshold) {
 					errors.add(Math.abs(probabilityPoint2 - probabilityPoint1));
 					numberoferrors++;

src/main/java/org/gcube/dataanalysis/ecoengine/models/FeedForwardNN.java

@@ -192,7 +192,8 @@ public class FeedForwardNN extends ModelAquamapsNN{
 			AnalysisLogger.getLogger().debug("Features were correctly preprocessed - Training");
 			// train the NN
 			nn.train(in, out);
-			
+			learningscore=nn.en;
+			AnalysisLogger.getLogger().error("Final learning error: "+nn.en);
 			AnalysisLogger.getLogger().debug("Saving Network");
 			save(fileName, nn);
 			AnalysisLogger.getLogger().debug("Done");

src/main/java/org/gcube/dataanalysis/ecoengine/models/ModelAquamapsNN.java

@@ -6,6 +6,7 @@ import java.io.IOException;
 import java.io.ObjectOutputStream;
 import java.util.ArrayList;
 import java.util.HashMap;
+import java.util.LinkedHashMap;
 import java.util.List;
 import java.util.Map;
 
@@ -200,6 +201,8 @@ public class ModelAquamapsNN implements Model {
 			
 			// train the NN
 			nn.train(in, out);
+			learningscore=nn.en;
+			AnalysisLogger.getLogger().error("Final learning error: "+nn.en);
 			save(fileName, nn);
 			
 		} catch (Exception e) {
@@ -209,12 +212,16 @@ public class ModelAquamapsNN implements Model {
 		status = 100f;
 	}
 
-
+	
-
+	double learningscore =0;
 	@Override
 	public StatisticalType getOutput() {
+		LinkedHashMap<String, StatisticalType> map = new LinkedHashMap<String, StatisticalType>();
 		PrimitiveType p = new PrimitiveType(File.class.getName(), new File(fileName), PrimitiveTypes.FILE, "NeuralNetwork","Trained Neural Network");
-		return p;
+		map.put("Learning", new PrimitiveType(String.class.getName(), "" + learningscore, PrimitiveTypes.STRING, "Learning Score", ""));
+		map.put("NeuralNetwork", p);
+		PrimitiveType outputm = new PrimitiveType(LinkedHashMap.class.getName(), map, PrimitiveTypes.MAP, "ResultsMap", "Results Map");
+		return outputm;
 	}
 	
 	@Override

src/main/java/org/gcube/dataanalysis/ecoengine/models/cores/neuralnetworks/Neural_Network.java

@@ -363,7 +363,7 @@ public class Neural_Network implements Serializable {
 					enprec=en;
 			
 			}
-			System.out.println("Scarto Finale: " + en);
+			System.out.println("Final Error: " + en);
 			if (counter >= maxcycle)
 				AnalysisLogger.getLogger().debug("training incomplete: didn't manage to reduce the error under the thr!");
 			else
@@ -384,9 +384,9 @@ public class Neural_Network implements Serializable {
 	
 	public void writeout(double numero, double soglia) {
 		if (numero < soglia)
-			System.out.println("Uscita : " + 0);
+			System.out.println("Output : " + 0);
 		else
-			System.out.println("Uscita : " + 1);
+			System.out.println("Output : " + 1);
 	}
 
 	//classify
@@ -451,7 +451,7 @@ public class Neural_Network implements Serializable {
 
 		nn.train(in, out);
 		double[] dummy = { 0, 0 };
-		System.out.println("responso sul dummy: " + nn.propagate(dummy)[0]);
+		System.out.println("dummy test " + nn.propagate(dummy)[0]);
 
 		nn.writeout(nn.propagate(dummy)[0], 0.5);
 	

src/main/java/org/gcube/dataanalysis/ecoengine/test/regression/RegressionTestEvaluators.java

@@ -2,9 +2,9 @@ package org.gcube.dataanalysis.ecoengine.test.regression;
 
 import java.util.List;
 
+import org.gcube.contentmanagement.lexicalmatcher.utils.AnalysisLogger;
 import org.gcube.dataanalysis.ecoengine.configuration.AlgorithmConfiguration;
 import org.gcube.dataanalysis.ecoengine.interfaces.ComputationalAgent;
-import org.gcube.dataanalysis.ecoengine.interfaces.Evaluator;
 import org.gcube.dataanalysis.ecoengine.processing.factories.EvaluatorsFactory;
 
 public class RegressionTestEvaluators {
@@ -14,16 +14,18 @@ public class RegressionTestEvaluators {
 	 */
 
 	public static void main(String[] args) throws Exception {
-
+		AnalysisLogger.setLogger("./cfg/ALog.properties");
+		List<ComputationalAgent>  evaluators = null;
+		/*
 		List<ComputationalAgent>  evaluators = EvaluatorsFactory.getEvaluators(testConfig1());
 		evaluators.get(0).init();
 		Regressor.process(evaluators.get(0));
 		evaluators = null;
-		
+		*/
 		System.out.println("\n**********-------************\n");
 		
 		//test Discrepancy
-		evaluators = EvaluatorsFactory.getEvaluators(testConfig2());
+		evaluators = EvaluatorsFactory.getEvaluators(testMapsComparison());
 		evaluators.get(0).init();
 		Regressor.process(evaluators.get(0));
 		evaluators = null;
@@ -67,4 +69,32 @@ public class RegressionTestEvaluators {
 
 	}
 
+	private static AlgorithmConfiguration testMapsComparison() {
+
+		AlgorithmConfiguration config = Regressor.getConfig();
+		config.setNumberOfResources(1);
+		
+		config.setAgent("DISCREPANCY_ANALYSIS");
+		config.setParam("DatabaseUserName","utente");
+		config.setParam("DatabasePassword","d4science");
+		config.setParam("DatabaseURL","jdbc:postgresql://statistical-manager.d.d4science.org/testdb");
+		config.setParam("DatabaseDriver","org.postgresql.Driver");
+		config.setNumberOfResources(1);
+		
+		config.setParam("FirstTable", "rstr280e0453e8c7408c96edd49a8dcb5986");
+		config.setParam("SecondTable", "rstr11b3b436ddaf4ae5ae5227ea8e0658ba");
+		config.setParam("FirstTableCsquareColumn", "csquarecode");
+		config.setParam("SecondTableCsquareColumn", "csquarecode");
+		config.setParam("FirstTableProbabilityColumn", "f_probability");
+		config.setParam("SecondTableProbabilityColumn", "f_probability");
+		config.setParam("ComparisonThreshold", "0.5");
+		config.setParam("KThreshold", "0.5");
+		config.setParam("MaxSamples", "45000");
+		
+		
+		return config;
+
+	}
+		
+
 }