Add AUC to performance metrics

src/processing.py

@@ -1,5 +1,5 @@
 from numpy import mean
-from sklearn.metrics import confusion_matrix, accuracy_score
+from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score
 from sklearn.model_selection import cross_val_score
 from sklearn.naive_bayes import GaussianNB
 from sklearn.neighbors import KNeighborsClassifier
@@ -27,29 +27,33 @@ def predict_data(data, target, model):
         data = scale(data)
     accuracy_scores = []
     confusion_matrices = []
+    auc = []
     for train_index, test_index in split_k_sets(data):
         model.fit(data.iloc[train_index], target.iloc[train_index])
         prediction = model.predict(data.iloc[test_index])
         accuracy_scores.append(accuracy_score(target.iloc[test_index], prediction))
         confusion_matrices.append(confusion_matrix(target.iloc[test_index], prediction))
+        auc.append(roc_auc_score(target.iloc[test_index], prediction))
     cv_score = cross_val_score(model, data, target, cv=10)
     evaluate_performance(
         confusion_matrix=mean(confusion_matrices, axis=0),
         accuracy=mean(accuracy_scores),
         cv_score=mean(cv_score),
+        auc=mean(auc),
     )
 
 
-def evaluate_performance(confusion_matrix, accuracy, cv_score):
+def evaluate_performance(confusion_matrix, accuracy, cv_score, auc):
     print("Accuracy Score: " + str(accuracy))
     print("Confusion matrix: ")
     print(str(confusion_matrix))
     print("Cross validation score: " + str(cv_score))
+    print("AUC: " + str(auc))
 
 
 def main():
     data, target = parse_data(source="data/mamografia.csv", action="drop")
-    predict_data(data=data, target=target, model="svc")
+    predict_data(data=data, target=target, model="gnb")
 
 
 if __name__ == "__main__":