Refactor aggregate score to a constructor

src/main/Scores.cpp

@@ -40,6 +40,15 @@ namespace platform {
         }
         compute_accuracy_value();
     }
+    static Score Scores::create_aggregate(json& data, std::string key)
+    {
+        auto scores = Scores(result[key][0]);
+        for (int i = 1; i < result[key].size(); i++) {
+            auto score = Scores(result[key][i]);
+            scores.aggregate(score);
+        }
+        return scores;
+    }
     void Scores::compute_accuracy_value()
     {
         accuracy_value = 0;

src/main/Scores.h

@@ -10,6 +10,7 @@ namespace platform {
     public:
         Scores(torch::Tensor& y_test, torch::Tensor& y_pred, int num_classes, std::vector<std::string> labels = {});
         explicit Scores(json& confusion_matrix_);
+        static Score create_aggregate(json& data, std::string key);
         float accuracy();
         float f1_score(int num_class);
         float f1_weighted();

src/reports/ReportConsole.cpp

@@ -186,13 +186,13 @@ namespace platform {
         int lines_header = 0;
         std::string color_line;
         std::string suffix = "";
-        auto scores = aggregateScore(result, "confusion_matrices");
+        auto scores = Scores::create_aggregate(result, "confusion_matrices");
         auto output_test = scores.classification_report(color, "Test");
         int maxLine = (*std::max_element(output_test.begin(), output_test.end(), [](const std::string& a, const std::string& b) { return a.size() < b.size(); })).size();
         bool train_data = result.find("confusion_matrices_train") != result.end();
         std::vector<std::string> output_train;
         if (train_data) {
-            auto scores_train = aggregateScore(result, "confusion_matrices_train");
+            auto scores_train = Scores::create_aggregate(result, "confusion_matrices_train");
             output_train = scores_train.classification_report(color, "Train");
         }
         oss << Colors::BLUE();