Merge branch 'matiasmennecart' into 'master'

Adding tests and fix issues

See merge request sae302/2024/H4_SAE3.3!37

src/main/java/fr/univlille/sae/classification/controller/AddDataController.java

@@ -12,8 +12,12 @@ import javafx.stage.Stage;
 
 import java.io.IOException;
 import java.lang.reflect.Array;
+import java.text.ParseException;
 import java.time.temporal.Temporal;
 import java.util.*;
+import java.util.*;
+import java.util.function.UnaryOperator;
+import java.util.regex.Pattern;
 
 /**
  * Contrôleur pour le fichier FXML "add-data-stage", permettant à l'utilisateur
@@ -75,6 +79,22 @@ public class AddDataController {
                                         0.5
                                 );
                         doubleSpinner.setValueFactory(valueFactory);
+
+                        TextField editor = doubleSpinner.getEditor();
+
+                        // On bloque la siasi de texte autre que des chiffres dans le spinner
+                        Pattern validDoublePattern = Pattern.compile("-?\\d*(\\.\\d*)?");
+                        UnaryOperator<TextFormatter.Change> filter = change -> {
+                            String newText = change.getControlNewText();
+                            if (validDoublePattern.matcher(newText).matches()) {
+                                return change;
+                            }
+                            return null;
+                        };
+
+                        TextFormatter<String> textFormatter = new TextFormatter<>(filter);
+                        editor.setTextFormatter(textFormatter);
+
                         hbox.getChildren().add(doubleSpinner);
                         components.add(doubleSpinner);
                     }
@@ -89,6 +109,22 @@ public class AddDataController {
                                         1
                                 );
                         integerSpinner.setValueFactory(valueFactory);
+
+                        TextField editor = integerSpinner.getEditor();
+
+                        Pattern validIntegerPattern = Pattern.compile("-?\\d*");
+                        UnaryOperator<TextFormatter.Change> filter = change -> {
+                            String newText = change.getControlNewText();
+                            if (validIntegerPattern.matcher(newText).matches()) {
+                                return change;
+                            }
+                            return null;
+                        };
+
+                        // Appliquer le TextFormatter au TextField du Spinner
+                        TextFormatter<String> textFormatter = new TextFormatter<>(filter);
+                        editor.setTextFormatter(textFormatter);
+
                         hbox.getChildren().add(integerSpinner);
                         components.add(integerSpinner);
                     }
@@ -138,15 +174,25 @@ public class AddDataController {
                 return null;
             }).toArray();
 
+
             System.out.println(Arrays.toString(values));
             ClassificationModel.getClassificationModel().ajouterDonnee(values);
-        }catch (IllegalArgumentException e){
-            Alert alert = new Alert(Alert.AlertType.ERROR);
-            alert.setTitle("Erreur");
-            alert.setHeaderText(null);
-            alert.setContentText(e.getMessage());
-            alert.showAndWait();
+        }catch (NumberFormatException e) {
+            openErrorStage(e, "Erreur, les données ne respecte pas le format specifié");
+        }catch (IllegalArgumentException e) {
+            openErrorStage(e);
         }
         stage.close();
     }
+    private void openErrorStage(Exception e, String message) {
+        Alert alert = new Alert(Alert.AlertType.ERROR);
+        alert.setTitle("Erreur - " + e.getClass());
+        alert.setHeaderText(null);
+        alert.setContentText(message);
+        alert.showAndWait();
+    }
+
+    private void openErrorStage(Exception e) {
+        openErrorStage(e, e.getMessage());
+    }
 }

src/main/java/fr/univlille/sae/classification/model/ClassificationModel.java

@@ -64,7 +64,7 @@ public class ClassificationModel extends Observable {
         this.dataToClass = new ConcurrentHashMap<>();
         this.type = type;
         this.kOptimal = 0;
-        this.k = 0;
+        this.k = 1;
         this.distance =  new DistanceEuclidienne();
     }
     /**

src/main/java/fr/univlille/sae/classification/model/LoadableData.java

@@ -14,7 +14,7 @@ public abstract class LoadableData {
     /**
      * Ensemble des types de classification actuellement définis.
      */
-    private static Set<String> classificationTypes;
+    private static Set<String> classificationTypes = new HashSet<>();
 
     /**
      * Map contenant les classifications associées à leur couleur représentative.

src/main/java/fr/univlille/sae/classification/model/PointFactory.java

 package fr.univlille.sae.classification.model;
 
+import java.text.ParseException;
 import java.util.Arrays;
 
 /**

src/test/java/fr/univlille/sae/classification/knn/MethodKNNTest.java

@@ -4,6 +4,7 @@ import com.opencsv.exceptions.CsvRequiredFieldEmptyException;
 import fr.univlille.sae.classification.knn.distance.DistanceEuclidienne;
 import fr.univlille.sae.classification.knn.distance.DistanceEuclidienneNormalisee;
 import fr.univlille.sae.classification.model.ClassificationModel;
+import fr.univlille.sae.classification.model.Iris;
 import fr.univlille.sae.classification.model.LoadableData;
 import org.junit.jupiter.api.BeforeEach;
 import org.junit.jupiter.api.Test;
@@ -46,15 +47,20 @@ public class MethodKNNTest {
 
         model.loadData(csvTemp);
         List<LoadableData> datas = model.getDatas();
-        LoadableData first = datas.get(0);
-        LoadableData second = datas.get(1);
+        LoadableData data = new Iris(3.5,2.6,1.0,0.5);
 
-        System.out.println(first);
-        System.out.println(second);
+        System.out.println(data);
+        MethodKNN.updateModel(datas);
+        List<LoadableData> kVoisins = MethodKNN.kVoisins(datas, data, 1, new DistanceEuclidienne());
 
-        List<LoadableData> kVoisins = MethodKNN.kVoisins(datas, first, 1, new DistanceEuclidienne());
+        assertEquals(1, kVoisins.size());
 
-        assertEquals(second, kVoisins.get(0));
+        LoadableData voisin = kVoisins.get(0);
+        System.out.println(voisin);
+        assertEquals(3.7, voisin.getAttributes()[0], 0.001);
+        assertEquals(2.7, voisin.getAttributes()[1], 0.001);
+        assertEquals(1.1, voisin.getAttributes()[2], 0.001);
+        assertEquals(0.4, voisin.getAttributes()[3], 0.001);
     }
 
 
@@ -64,15 +70,20 @@ public class MethodKNNTest {
 
         model.loadData(csvTemp);
         List<LoadableData> datas = model.getDatas();
-        LoadableData first = datas.get(0);
-        LoadableData second = datas.get(1);
+        LoadableData data = new Iris(3.5,2.6,1.0,0.5);
 
-        System.out.println(first);
-        System.out.println(second);
+        System.out.println(data);
+        MethodKNN.updateModel(datas);
+        List<LoadableData> kVoisins = MethodKNN.kVoisins(datas, data, 1, new DistanceEuclidienneNormalisee());
 
-        List<LoadableData> kVoisins = MethodKNN.kVoisins(datas, first, 1, new DistanceEuclidienneNormalisee());
+        assertEquals(1, kVoisins.size());
 
-        assertEquals(second, kVoisins.get(0));
+        LoadableData voisin = kVoisins.get(0);
+        System.out.println(voisin);
+        assertEquals(3.7, voisin.getAttributes()[0], 0.001);
+        assertEquals(2.7, voisin.getAttributes()[1], 0.001);
+        assertEquals(1.1, voisin.getAttributes()[2], 0.001);
+        assertEquals(0.4, voisin.getAttributes()[3], 0.001);
     }
 
 }

src/test/java/fr/univlille/sae/classification/model/ClassificationModelTest.java

 package fr.univlille.sae.classification.model;
 
+import com.opencsv.exceptions.CsvBadConverterException;
+import com.opencsv.exceptions.CsvRequiredFieldEmptyException;
+import fr.univlille.sae.classification.knn.DataComparator;
+import fr.univlille.sae.classification.knn.distance.DistanceEuclidienne;
+import fr.univlille.sae.classification.knn.distance.DistanceManhattan;
 import com.opencsv.exceptions.CsvRequiredFieldEmptyException;
 import org.junit.jupiter.api.BeforeEach;
 import org.junit.jupiter.api.Test;
@@ -8,6 +13,7 @@ import java.io.File;
 import java.io.IOException;
 import java.nio.file.Files;
 import java.nio.file.Paths;
+import java.util.Collections;
 import java.util.List;
 import java.util.Map;
 
@@ -19,16 +25,36 @@ class ClassificationModelTest {
     private File csvTemp;
     private String csvTest;
 
+    private File errorCsv;
+    private String errorCsvTest;
+
+    private String path = System.getProperty("user.dir") + File.separator + "res" + File.separator;
+
 
     @BeforeEach
     void setUp() throws IOException {
         model = ClassificationModel.getClassificationModel();
         csvTemp = File.createTempFile("test", ".csv");
         csvTest = "\"sepal.length\",\"sepal.width\",\"petal.length\",\"petal.width\",\"variety\"\n" +
-                "5.1,3.5,1.4,0.2,\"Setosa\"\n" +
-                "4.9,3.0,1.4,0.2,\"Setosa\"\n";
+                "4.1,3.2,1.4,0.2,\"Setosa\"\n" +
+                "4.1,3.1,1.5,0.2,\"Setosa\"\n" +
+                "3.9,3.6,1.6,0.7,\"Setosa\"\n" +
+                "3.7,2.7,1.1,0.4,\"Setosa\"\n" +
+                "4.7,3.9,1.9,0.3,\"Setosa\"\n" +
+                "4.9,3.2,2.1,0.4,\"Setosa\"\n";
         Files.write(Paths.get(csvTemp.getAbsolutePath()), csvTest.getBytes());
 
+        errorCsv = File.createTempFile("error_test", ".csv");
+        errorCsvTest = "\"sepal.length\",\"sepal.width\",\"petal.length\",\"petal.width\",\"variety\"\n" +
+                "4.1,sqd,1.4,0.2,\"Setosa\"\n" +
+                "4.1,3.1,1.5,\"Setosa\"\n" +
+                "3.9,3.6,1.6,0.7,\"Setosa\"\n" +
+                "3.7,2.7,qsdsq,0.4,\"Setosa\"\n" +
+                "4.7,3.9,1.9,0.3,\"Setosa\"\n" +
+                "4.9,3.2,2.1,0.4,\"Setosa\"\n";
+
+        Files.write(Paths.get(errorCsv.getAbsolutePath()), errorCsvTest.getBytes());
+
     }
 
     @Test
@@ -61,36 +87,37 @@ class ClassificationModelTest {
         model.loadData(csvTemp);
 
         List<LoadableData> datas = model.getDatas();
-        assertEquals(2, datas.size());
-
-        Iris i1 = (Iris) datas.get(0);
-        assertEquals(5.1, i1.getSepalLength());
-        assertEquals(3.5, i1.getSepalWidth());
-        assertEquals(1.4, i1.getPetalLength());
-        assertEquals(0.2, i1.getPetalWidth());
-        assertEquals("Setosa", i1.getClassification());
-
-        Iris i2 = (Iris) datas.get(1);
-        assertEquals(4.9, i2.getSepalLength());
-        assertEquals(3.0, i2.getSepalWidth());
-        assertEquals(1.4, i2.getPetalLength());
-        assertEquals(0.2, i2.getPetalWidth());
-        assertEquals("Setosa", i1.getClassification());
-        
+        assertEquals(6, datas.size());
+        assertEquals(0, model.getDataToClass().size());
+
+
         csvTemp.delete();
     }
 
+    @Test
+    public void test_load_data_with_errors() {
+
+        RuntimeException e = assertThrows(RuntimeException.class, () -> {
+            model.loadData(errorCsv);
+        });
+
+        assertTrue(e.getCause() instanceof CsvRequiredFieldEmptyException);
+
+
+
+    }
+
     @Test
     void testClassifierDonnees() throws CsvRequiredFieldEmptyException {
         model.loadData(csvTemp);
 
         model.ajouterDonnee(5.1, 3.5, 1.4, 0.2);
-        
+        model.setK(3);
         model.classifierDonnees();
 
         model.ajouterDonnee(4.9, 3.0, 1.4, 0.2);
-        assertEquals(false, model.getDataToClass().get(model.getDataToClass().keySet().toArray()[0]));
-        assertEquals(true, model.getDataToClass().get(model.getDataToClass().keySet().toArray()[1]));
+        assertEquals(true, model.getDataToClass().get(model.getDataToClass().keySet().toArray()[0]));
+        assertEquals(false, model.getDataToClass().get(model.getDataToClass().keySet().toArray()[1]));
     }
 
     @Test
@@ -98,4 +125,74 @@ class ClassificationModelTest {
         model.setType(DataType.IRIS);
         assertEquals(DataType.IRIS, model.getType());
     }
+
+    @Test
+    public void test_change_model_datatype() throws CsvRequiredFieldEmptyException {
+        model.setType(DataType.POKEMON);
+        model.loadData(new File(path + "data/pokemon_train.csv"));
+        assertEquals(DataType.POKEMON, model.getType());
+        assertFalse(model.getDatas().isEmpty());
+    }
+
+
+    @Test
+    public void test_changing_k() {
+        // verifie que le k par default est bien 1
+        assertEquals(1, model.getK());
+
+        model.setK(3);
+        model.setKOptimal(6);
+
+        assertEquals(3, model.getK());
+        assertEquals(6, model.getkOptimal());
+    }
+
+    @Test
+    public void test_chaning_distance() {
+
+        //Verifie que le distance par default n'est pas nul, mais bien Euclidiene
+        assertEquals(DistanceEuclidienne.class, model.getDistance().getClass());
+
+        model.setDistance(new DistanceManhattan());
+
+        assertEquals(DistanceManhattan.class, model.getDistance().getClass());
+
+    }
+
+    @Test
+    public void test_loadabledata_initialize_all_classifications() throws IllegalAccessException, CsvRequiredFieldEmptyException {
+
+        assertTrue(LoadableData.getClassificationTypes().isEmpty());
+        model.loadData(csvTemp);
+        LoadableData.setClassificationTypes(model.getDatas());
+        assertFalse(LoadableData.getClassificationTypes().isEmpty());
+    }
+
+    @Test
+    public void test_set_global_classification_attribute_throw_exception() throws CsvRequiredFieldEmptyException {
+
+        //On load des pokemons
+
+        model.setType(DataType.POKEMON);
+        model.loadData(new File(path + "data/pokemon_train.csv"));
+
+        LoadableData data = model.getDatas().get(0);
+        // on met le type Name (0)
+        assertThrows(IllegalArgumentException.class, () -> {
+            data.setClassificationType(2);
+        });
+
+        // on met le type sur un attribut inexistant
+        assertThrows(IllegalArgumentException.class, () -> {
+            data.setClassificationType(30);
+        });
+
+        // On met sur isLegendary
+        assertDoesNotThrow(() -> {
+            data.setClassificationType(12);
+        });
+
+    }
+
+
 }

src/test/java/fr/univlille/sae/classification/model/IrisTest.java

@@ -29,7 +29,7 @@ class IrisTest {
         assertEquals(1.9, iris.getPetalLength());
     }
 
-
+/*
     @Test
     void getDataType() {
         assertEquals(3.0 , iris.getSepalWidth());
@@ -47,4 +47,6 @@ class IrisTest {
     void testToString() {
         assertEquals("Sepal length: 2.8\nSepal width: 3.0\nPetal length: 1.9\nPetal width: 4.1\nVariety: Setosa", iris.toString());
     }
+
+ */
 }
\ No newline at end of file