pr and mc

cnn.py

@@ -156,6 +156,8 @@ def run_kfold(X_train, X_test, y_train, y_test, params):
 
 	total_acc = 0
 	total_auc = 0
+	cm_tab = []
+	pr_tab = []
 	bs, ep = m.choose_batch_epochs(b,e)
 
 
@@ -171,19 +173,25 @@ def run_kfold(X_train, X_test, y_train, y_test, params):
 
 
 		# calculate area under the curve
-		y_pred = model.predict(X_test, batch_size = bs)
-		fpr, tpr, auc = ra.roc_auc(N_CLASSES, y_test, y_pred)
+		y_pred = model.predict(X_test[i], batch_size = bs)
+		fpr, tpr, auc = ra.roc_auc(N_CLASSES, y_test[i], y_pred)
 		total_auc += auc
 		print("Area under the curve:", auc)
 
+		# confusion matrix
+		cm_tab.append(confusion_matrix(y_test[i].argmax(axis=1), y_pred.argmax(axis=1)))
+
+		#pr curve (1 for each class)
 
+	#average of acc, auc, cm, pr
 	total_acc = total_acc/(N_SPLITS)
 	total_auc = total_acc/(N_SPLITS)
+	cm = sum([cm_tab[j] for j in range(N_SPLITS)])/5
+
 	print("Average accuracy: ", total_acc)
 	print("Average area under the curve: ", total_auc)
 
 
-	# confusion matrix
-	cm = confusion_matrix(y_test.argmax(axis=1), y_pred.argmax(axis=1))
 
-	return total_acc, total_auc, cm
+
+	return total_acc, total_auc, cm_tab, pr_tab