Refactoring: move computeAUC and computeRocPoints from Utils to AbstractSolution. Update README

README.md

@@ -19,7 +19,7 @@ Using maven :
 <dependency>
   <groupId>be.cylab</groupId>
   <artifactId>java-wowa-training</artifactId>
-  <version>0.0.3</version>
+  <version>0.0.4</version>
 </dependency>
 ```
 
@@ -42,21 +42,27 @@ public static void main(String[] args) {
       
       
     TrainerParameters parameters = new TrainerParameters(logger, population_size, 
-         crossover_rate, mutation_rate, max_generation, selection_method, generation_population_method);
-    Trainer trainer = new Trainer(parameters);
+         crossover_rate, mutation_rate, max_generation, selection_method, 		 generation_population_method);
+    
     //Input data
-    List<double[]> data = new ArrayList<double[]>();
-    data.add(new double[] {0.1, 0.2, 0.3, 0.4});
-    data.add(new double[] {0.1, 0.8, 0.3, 0.4});
-    data.add(new double[] {0.2, 0.6, 0.3, 0.4});
-    data.add(new double[] {0.1, 0.2, 0.5, 0.8});
-    data.add(new double[] {0.5, 0.1, 0.2, 0.3});
-    data.add(new double[] {0.1, 0.1, 0.1, 0.1});
+    List<List<Double>> data = new ArrayList<>();
+    data.add(new ArrayList<>(Arrays.asList(0.1, 0.2, 0.3, 0.4)));
+    data.add(new ArrayList<>(Arrays.asList(0.1, 0.8, 0.3, 0.4)));
+    data.add(new ArrayList<>(Arrays.asList(0.2, 0.6, 0.3, 0.4)));
+    data.add(new ArrayList<>(Arrays.asList(0.1, 0.2, 0.5, 0.8)));
+    data.add(new ArrayList<>(Arrays.asList(0.5, 0.1, 0.2, 0.3)));
+    data.add(new ArrayList<>(Arrays.asList(0.1, 0.1, 0.1, 0.1)));
+
       
     //Expected aggregated value for each data vector
-    double[] expected = new double[] {0.1, 0.2, 0.3, 0.4, 0.5, 0.6};
+    List<Double> expected = new ArrayList<>(Arrays.asList(0.1, 0.2, 0.3, 0.4, 0.5, 0.6));
+    //Create object for the type of Solution (fitness score evaluation)
+    SolutionDistance solution_type = new SolutionDistance(data.get(0).size());
+    //Create trainer object
+    Trainer trainer = new Trainer(parameters, solution_type);
+
       
-    SolutionDistance solution = trainer.run(data, expected);
+    AbstractSolution solution = trainer.run(data, expected);
     //Display solution
     System.out.println(solution);
       
@@ -77,12 +83,27 @@ The **run** method returns a solution object, consisting of p weights and w weig
 ### Parameters description
 
 - **population_size** : size of the population in the algorithm. Suggested value : 100
+
 - **crossover_rate** : defines the percentage of population generated by crossover. Must be between 1 and 100. Suggested value : 60
+
 - **mutation_rate** : define the probability of random element change in the population. Must be between 1 and 100. Suggested value : 15
+
 - **selection_method** : Determine the method used to select element in the population (for generate the next generation). SELECTION_METHOD_RWS for Roulette Wheel Selection and SELECTION_METHOD_TOS for Tournament Selection.
+
 - **max_generation** : Determine the maximum number of iteration of the algorithm.
+
 - **generation_population_method**: Determine the method used to generate the initial population. POPULATION_INITIALIZATION_RANDOM for a full random initialization and POPULATION_INITIALIZATION_QUASI_RANDOM for a population with specific elements.
 
+### Solution type
+
+The algorithm is built to be used with different methods to evaluate the fitness score of each chromosome. Two different criteria are already implemented : *distance* and *AUC*. 
+
+- **Distance**: for each element in the population, the WOWA function us computed on all examples of the dataset. hen, the difference between the WOWA result just computed and the result given by the training dataset. All these differences are added to obtain the distance that is the fitness score of a chromosome.The smallest is the distance, the best is the chromosome.
+- **AUC*: the Area Under the Curve (AUC) fitness score is designed  for binary classification. The obtain the AUC, the Receiver Operating Characteristics (ROC) is built first. Concretely, the WOWA function is computed on all elements of the training dataset. Then, on these results, the ROC curve is built. The AUC of this ROC curve is the fitness score of an element. The biggest is the AUC, the best is the chromosome.
+
+It is possible to create new Solution type with new evaluation criterion. The new Solution type must inherit of *AbstractSolution* class and override the method *computeScoreTo*. It is also necessary to modify the method *createSolutionObject* method in the *Factory* class.
+
+
 ## References
 
 - [The WOWA operator : a review (V. Torra)](https://gitlab.cylab.be/cylab/wowa-training/raw/c3c3785c767ab8258df0fc585aec1e8d463851cd/doc/Torra%20-%202011%20-%20The%20WOWA%20Operator%20A%20Review.1007_978-3.pdf)

src/main/java/be/cylab/java/wowa/training/AbstractSolution.java

 package be.cylab.java.wowa.training;
 
+import be.cylab.java.roc.Roc;
+import be.cylab.java.roc.RocCoordinates;
+
+import java.text.DateFormat;
+import java.text.SimpleDateFormat;
 import java.util.ArrayList;
 import java.util.Arrays;
 import java.util.List;
 import java.util.Random;
+import java.util.Date;
+
 
 /**
  * Abstract class Solution.
@@ -91,6 +98,55 @@ public abstract class AbstractSolution
 
     }
 
+    /**
+     * @param data
+     * @param expected
+     * @return
+     */
+    final List<RocCoordinates> computeRocPoints(
+            final List<List<Double>> data,
+            final List<Double> expected,
+            final boolean save_on_csv) {
+        if (data.size() != expected.size()) {
+            throw new IllegalArgumentException(
+                    "Data and expected have different size");
+        }
+        boolean[] true_alert
+                = Utils.convertExpectedToBooleanArrayTrueAlert(expected);
+        double[] score = Utils.computeWOWAScoreWithData(this, data);
+        Roc roc = new Roc(score, true_alert);
+        List<RocCoordinates> coordinates = roc.computeRocPoints();
+        if (save_on_csv) {
+            DateFormat format = new SimpleDateFormat("yyyyMMdd_HH:mm:ss");
+            Date date = new Date();
+            be.cylab.java.roc.Utils.storeRocCoordinatesInCSVFile(coordinates,
+                    "RocCoordinates_" + format.format(date) + ".csv");
+        }
+        return coordinates;
+    }
+
+    /**
+     * @param data
+     * @param expected
+     * @return
+     */
+    final double computeAUC(
+            final List<List<Double>> data,
+            final List<Double> expected) {
+        if (data.size() != expected.size()) {
+            throw new IllegalArgumentException(
+                    "Data and expected have different size");
+        }
+        //Convert the array with expected values (0 or 1) to a boolean array.
+        boolean[] true_alert
+                = Utils.convertExpectedToBooleanArrayTrueAlert(expected);
+
+        //For each elements in dataset, compute the wowa function.
+        double[] score = Utils.computeWOWAScoreWithData(this, data);
+        Roc roc = new Roc(score, true_alert);
+        return roc.computeAUC();
+    }
+
     /**
      * @param solution
      * @return int

src/main/java/be/cylab/java/wowa/training/SolutionAUC.java

@@ -15,10 +15,14 @@ public class SolutionAUC extends AbstractSolution {
         super(weight_number);
     }
 
+    SolutionAUC(final int weight_number, final int seed) {
+        super(weight_number, seed);
+    }
+
     final void computeScoreTo(
             final List<List<Double>> data,
             final List<Double> expected) {
         this.fitness_score = 0.0;
-        this.fitness_score = -(Utils.computeAUC(this, data, expected));
+        this.fitness_score = -(super.computeAUC(data, expected));
     }
 }

src/main/java/be/cylab/java/wowa/training/Utils.java

 package be.cylab.java.wowa.training;
 
-import be.cylab.java.roc.Roc;
-import be.cylab.java.roc.RocCoordinates;
 import com.owlike.genson.GenericType;
 import com.owlike.genson.Genson;
 import info.debatty.java.aggregation.WOWA;
@@ -11,10 +9,7 @@ import java.io.IOException;
 import java.nio.charset.StandardCharsets;
 import java.nio.file.Files;
 import java.nio.file.Paths;
-import java.text.DateFormat;
-import java.text.SimpleDateFormat;
 import java.util.ArrayList;
-import java.util.Date;
 import java.util.List;
 import java.util.Random;
 
@@ -170,63 +165,14 @@ final class Utils {
         return json;
     }
 
-    /**
-     * @param solution
-     * @param data
-     * @param expected
-     * @return
-     */
-    public static double computeAUC(
-            final AbstractSolution solution,
-            final List<List<Double>> data,
-            final List<Double> expected) {
-        if (data.size() != expected.size()) {
-            throw new IllegalArgumentException(
-                    "Data and expected have different size");
-        }
-        //Convert the array with expected values (0 or 1) to a boolean array.
-        boolean[] true_alert = convertExpectedToBooleanArrayTrueAlert(expected);
-
-        //For each elements in dataset, compute the wowa function.
-        double[] score = computeWOWAScoreWithData(solution, data);
-        Roc roc = new Roc(score, true_alert);
-        return roc.computeAUC();
-    }
 
-    /**
-     * @param solution
-     * @param data
-     * @param expected
-     * @return
-     */
-    public static List<RocCoordinates> computeRocPoints(
-            final AbstractSolution solution,
-            final List<List<Double>> data,
-            final List<Double> expected,
-            final boolean save_on_csv) {
-        if (data.size() != expected.size()) {
-            throw new IllegalArgumentException(
-                    "Data and expected have different size");
-        }
-        boolean[] true_alert = convertExpectedToBooleanArrayTrueAlert(expected);
-        double[] score = computeWOWAScoreWithData(solution, data);
-        Roc roc = new Roc(score, true_alert);
-        List<RocCoordinates> coordinates = roc.computeRocPoints();
-        if (save_on_csv) {
-            DateFormat format = new SimpleDateFormat("yyyyMMdd_HH:mm:ss");
-            Date date = new Date();
-            be.cylab.java.roc.Utils.storeRocCoordinatesInCSVFile(coordinates,
-                    "RocCoordinates_" + format.format(date) + ".csv");
-        }
-        return coordinates;
-    }
 
     /**
      * @param solution
      * @param data
      * @return
      */
-    private static double[] computeWOWAScoreWithData(
+    static double[] computeWOWAScoreWithData(
             final AbstractSolution solution,
             final List<List<Double>> data) {
         double[] score = new double[data.size()];
@@ -246,7 +192,7 @@ final class Utils {
      * @param expected
      * @return
      */
-    private static boolean[] convertExpectedToBooleanArrayTrueAlert(
+    static boolean[] convertExpectedToBooleanArrayTrueAlert(
             final List<Double> expected) {
         boolean[] true_alert = new boolean[expected.size()];
         for (int i = 0; i < expected.size(); i++) {
@@ -268,7 +214,7 @@ final class Utils {
      * @param elements
      * @return
      */
-    public static double[] convertListDoubleToArrayDouble(
+    static double[] convertListDoubleToArrayDouble(
             final List<Double> elements) {
         Double[] w = new Double[elements.size()];
         w = elements.toArray(w);
@@ -281,7 +227,7 @@ final class Utils {
      * @param size
      * @return
      */
-    public static List<Double> initializeListWithZeroValues(
+    static List<Double> initializeListWithZeroValues(
             final int size) {
         List<Double> list = new ArrayList<>();
         for (int i = 0; i < size; i++) {