diff --git a/src/main/java/fr/univlille/sae/classification/model/ClassificationModel.java b/src/main/java/fr/univlille/sae/classification/model/ClassificationModel.java
index b2ecdbe8af0eb18f87da05049189dc42205bd4c0..3aa6c21d71195ee2c25ca3b280bc106db25c86ab 100644
--- a/src/main/java/fr/univlille/sae/classification/model/ClassificationModel.java
+++ b/src/main/java/fr/univlille/sae/classification/model/ClassificationModel.java
@@ -58,7 +58,7 @@ 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();
}
/**
diff --git a/src/main/java/fr/univlille/sae/classification/model/LoadableData.java b/src/main/java/fr/univlille/sae/classification/model/LoadableData.java
index 69088689a75b1f598a2523bb0c072253653d73c3..5cb828ff287e473800e532b56216a19e25eb22b0 100644
--- a/src/main/java/fr/univlille/sae/classification/model/LoadableData.java
+++ b/src/main/java/fr/univlille/sae/classification/model/LoadableData.java
@@ -12,7 +12,7 @@ import java.util.Map;
*/
public abstract class LoadableData {
- private static Set<String> classificationTypes;
+ private static Set<String> classificationTypes = new HashSet<>();
private static Map<String, Color> classification = new HashMap<>() ;
diff --git a/src/test/java/fr/univlille/sae/classification/knn/MethodKNNTest.java b/src/test/java/fr/univlille/sae/classification/knn/MethodKNNTest.java
index 946657490b368fe5667ceed11896e57a1f7bee91..cbeac973d125e7fde17fe27e2ff564ab5892e911 100644
--- a/src/test/java/fr/univlille/sae/classification/knn/MethodKNNTest.java
+++ b/src/test/java/fr/univlille/sae/classification/knn/MethodKNNTest.java
@@ -1,5 +1,6 @@
package fr.univlille.sae.classification.knn;
+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;
@@ -41,7 +42,7 @@ public class MethodKNNTest {
@Test
- public void testKVoisins_distance_euclidienne() throws IOException {
+ public void testKVoisins_distance_euclidienne() throws IOException, CsvRequiredFieldEmptyException {
model.loadData(csvTemp);
List<LoadableData> datas = model.getDatas();
@@ -59,7 +60,7 @@ public class MethodKNNTest {
@Test
- public void testKVoisins_distance_euclidienne_normalise() throws IOException {
+ public void testKVoisins_distance_euclidienne_normalise() throws IOException, CsvRequiredFieldEmptyException {
model.loadData(csvTemp);
List<LoadableData> datas = model.getDatas();
diff --git a/src/test/java/fr/univlille/sae/classification/model/ClassificationModelTest.java b/src/test/java/fr/univlille/sae/classification/model/ClassificationModelTest.java
index d3b5ab5a5667d95348db2a4c152a06126f7e44ca..359bcd19287adfecdc4c1a6912cfe720417f9e09 100644
--- a/src/test/java/fr/univlille/sae/classification/model/ClassificationModelTest.java
+++ b/src/test/java/fr/univlille/sae/classification/model/ClassificationModelTest.java
@@ -1,5 +1,10 @@
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 org.junit.jupiter.api.BeforeEach;
import org.junit.jupiter.api.Test;
@@ -7,6 +12,7 @@ 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;
@@ -18,16 +24,36 @@ 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
@@ -55,41 +81,42 @@ class ClassificationModelTest {
}
@Test
- void testLoadData() throws IOException {
+ void testLoadData() throws IOException, CsvRequiredFieldEmptyException, IllegalAccessException {
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
- void testClassifierDonnees() {
+ 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
@@ -97,4 +124,74 @@ 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);
+ });
+
+ }
+
+
}
diff --git a/src/test/java/fr/univlille/sae/classification/model/IrisTest.java b/src/test/java/fr/univlille/sae/classification/model/IrisTest.java
index 824be5f4bcf220de6165f03d342622ef2316bb37..f6e950e4bd3c8bbceb4dd7ab5e05cc1a55f83f7d 100644
--- a/src/test/java/fr/univlille/sae/classification/model/IrisTest.java
+++ b/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.getDataType("sepalWidth"));
@@ -47,4 +47,6 @@ class IrisTest {
void testToString() {
assertEquals("Iris{sepalLength=2.8, sepalWidth=3.0, petalLength=1.9, petalWidth=4.1}", iris.toString());
}
+
+ */
}
\ No newline at end of file