Fix json key automatic ordering error when creating Score from json

src/best/BestResults.h

@@ -2,7 +2,7 @@
 
 #include <string>
 #include <nlohmann/json.hpp>
-using json = nlohmann::json;
+using json = nlohmann::ordered_json;
 namespace platform {
     class BestResults {
     public:

src/best/BestResultsExcel.h

@@ -5,7 +5,7 @@
 #include <nlohmann/json.hpp>
 #include "reports/ExcelFile.h"
 
-using json = nlohmann::json;
+using json = nlohmann::ordered_json;
 
 namespace platform {
 

src/best/Statistics.h

@@ -5,7 +5,7 @@
 #include <map>
 #include <nlohmann/json.hpp>
 
-using json = nlohmann::json;
+using json = nlohmann::ordered_json;
 
 namespace platform {
     struct WTL {

src/commands/b_grid.cpp

@@ -13,7 +13,7 @@
 #include "grid/GridSearch.h"
 #include "config.h"
 
-using json = nlohmann::json;
+using json = nlohmann::ordered_json;
 const int MAXL = 133;
 
 void assignModel(argparse::ArgumentParser& parser)
@@ -29,7 +29,7 @@ void assignModel(argparse::ArgumentParser& parser)
         }
         throw std::runtime_error("Model must be one of " + models->toString());
             }
-    );
+        );
 }
 void add_compute_args(argparse::ArgumentParser& program)
 {
@@ -54,23 +54,23 @@ void add_compute_args(argparse::ArgumentParser& program)
         catch (...) {
             throw std::runtime_error("Number of nested folds must be an integer");
         }});
-    program.add_argument("--score").help("Score used in gridsearch").default_value("accuracy");
-    program.add_argument("-f", "--folds").help("Number of folds").default_value(stoi(env.get("n_folds"))).scan<'i', int>().action([](const std::string& value) {
-        try {
-            auto k = stoi(value);
-            if (k < 2) {
-                throw std::runtime_error("Number of folds must be greater than 1");
+        program.add_argument("--score").help("Score used in gridsearch").default_value("accuracy");
+        program.add_argument("-f", "--folds").help("Number of folds").default_value(stoi(env.get("n_folds"))).scan<'i', int>().action([](const std::string& value) {
+            try {
+                auto k = stoi(value);
+                if (k < 2) {
+                    throw std::runtime_error("Number of folds must be greater than 1");
+                }
+                return k;
             }
-            return k;
-        }
-        catch (const runtime_error& err) {
-            throw std::runtime_error(err.what());
-        }
-        catch (...) {
-            throw std::runtime_error("Number of folds must be an integer");
-        }});
-    auto seed_values = env.getSeeds();
-    program.add_argument("-s", "--seeds").nargs(1, 10).help("Random seeds. Set to -1 to have pseudo random").scan<'i', int>().default_value(seed_values);
+            catch (const runtime_error& err) {
+                throw std::runtime_error(err.what());
+            }
+            catch (...) {
+                throw std::runtime_error("Number of folds must be an integer");
+            }});
+            auto seed_values = env.getSeeds();
+            program.add_argument("-s", "--seeds").nargs(1, 10).help("Random seeds. Set to -1 to have pseudo random").scan<'i', int>().default_value(seed_values);
 }
 std::string headerLine(const std::string& text, int utf = 0)
 {

src/commands/b_main.cpp

@@ -10,7 +10,7 @@
 #include "config.h"
 
 
-using json = nlohmann::json;
+using json = nlohmann::ordered_json;
 
 void manageArguments(argparse::ArgumentParser& program)
 {

src/grid/GridData.h

@@ -6,7 +6,7 @@
 #include <nlohmann/json.hpp>
 
 namespace platform {
-    using json = nlohmann::json;
+    using json = nlohmann::ordered_json;
     const std::string ALL_DATASETS = "all";
     class GridData {
     public:

src/grid/GridSearch.h

@@ -10,7 +10,7 @@
 #include "GridData.h"
 
 namespace platform {
-    using json = nlohmann::json;
+    using json = nlohmann::ordered_json;
     struct ConfigGrid {
         std::string model;
         std::string score;

src/main/Experiment.cpp

@@ -5,7 +5,7 @@
 #include "Scores.h"
 #include "Experiment.h"
 namespace platform {
-    using json = nlohmann::json;
+    using json = nlohmann::ordered_json;
 
     void Experiment::saveResult()
     {

src/main/Experiment.h

@@ -9,7 +9,7 @@
 #include "results/Result.h"
 
 namespace platform {
-    using json = nlohmann::json;
+    using json = nlohmann::ordered_json;
 
     class Experiment {
     public:

src/main/HyperParameters.h

@@ -6,7 +6,7 @@
 #include <nlohmann/json.hpp>
 
 namespace platform {
-    using json = nlohmann::json;
+    using json = nlohmann::ordered_json;
     class HyperParameters {
     public:
         HyperParameters() = default;

src/main/PartialResult.h

@@ -4,7 +4,7 @@
 #include <nlohmann/json.hpp>
 
 namespace platform {
-    using json = nlohmann::json;
+    using json = nlohmann::ordered_json;
     class PartialResult {
 
     public:

src/main/Scores.cpp

@@ -37,11 +37,16 @@ namespace platform {
             }
             i++;
         }
-        // Compute accuracy with the confusion matrix
+        compute_accuracy_value();
+    }
+    void Scores::compute_accuracy_value()
+    {
+        accuracy_value = 0;
         for (int i = 0; i < num_classes; i++) {
             accuracy_value += confusion_matrix[i][i].item<int>();
         }
         accuracy_value /= total;
+        accuracy_value = std::min(accuracy_value, 1.0f);
     }
     void Scores::init_confusion_matrix()
     {
@@ -59,8 +64,7 @@ namespace platform {
             throw std::invalid_argument("The number of classes must be the same");
         confusion_matrix += a.confusion_matrix;
         total += a.total;
-        accuracy_value += a.accuracy_value;
-        accuracy_value /= 2;
+        compute_accuracy_value();
     }
     float Scores::accuracy()
     {
@@ -71,6 +75,7 @@ namespace platform {
         // Compute f1_score in a one vs rest fashion
         auto precision_value = precision(num_class);
         auto recall_value = recall(num_class);
+        if (precision_value + recall_value == 0) return 0; // Avoid division by zero (0/0 = 0)
         return 2 * precision_value * recall_value / (precision_value + recall_value);
     }
     float Scores::f1_weighted()
@@ -94,6 +99,7 @@ namespace platform {
         int tp = confusion_matrix[num_class][num_class].item<int>();
         int fp = confusion_matrix.index({ "...", num_class }).sum().item<int>() - tp;
         int fn = confusion_matrix[num_class].sum().item<int>() - tp;
+        if (tp + fp == 0) return 0; // Avoid division by zero (0/0 = 0
         return float(tp) / (tp + fp);
     }
     float Scores::recall(int num_class)
@@ -101,6 +107,7 @@ namespace platform {
         int tp = confusion_matrix[num_class][num_class].item<int>();
         int fp = confusion_matrix.index({ "...", num_class }).sum().item<int>() - tp;
         int fn = confusion_matrix[num_class].sum().item<int>() - tp;
+        if (tp + fn == 0) return 0; // Avoid division by zero (0/0 = 0
         return float(tp) / (tp + fn);
     }
     std::string Scores::classification_report_line(std::string label, float precision, float recall, float f1_score, int support)

src/main/Scores.h

@@ -5,7 +5,7 @@
 #include <torch/torch.h>
 #include <nlohmann/json.hpp>
 namespace platform {
-    using json = nlohmann::json;
+    using json = nlohmann::ordered_json;
     class Scores {
     public:
         Scores(torch::Tensor& y_test, torch::Tensor& y_pred, int num_classes, std::vector<std::string> labels = {});
@@ -24,6 +24,7 @@ namespace platform {
         std::string classification_report_line(std::string label, float precision, float recall, float f1_score, int support);
         void init_confusion_matrix();
         void init_default_labels();
+        void compute_accuracy_value();
         int num_classes;
         float accuracy_value;
         int total;

src/manage/ResultsManager.h

@@ -5,7 +5,7 @@
 #include <nlohmann/json.hpp>
 #include "results/Result.h"
 namespace platform {
-    using json = nlohmann::json;
+    using json = nlohmann::ordered_json;
     enum class SortType {
         ASC = 0,
         DESC = 1,

src/reports/DatasetsConsole.h

@@ -6,7 +6,7 @@
 #include "ReportsPaged.h"
 
 namespace platform {
-    using json = nlohmann::json;
+    using json = nlohmann::ordered_json;
 
 
     class DatasetsConsole : public ReportsPaged {

src/reports/DatasetsExcel.h

@@ -3,7 +3,7 @@
 #include <nlohmann/json.hpp>
 #include "reports/ExcelFile.h"
 
-using json = nlohmann::json;
+using json = nlohmann::ordered_json;
 
 namespace platform {
 

src/reports/ReportBase.h

@@ -6,7 +6,7 @@
 #include "common/Paths.h"
 #include "common/Symbols.h"
 
-using json = nlohmann::json;
+using json = nlohmann::ordered_json;
 namespace platform {
 
     class ReportBase {

src/reports/ReportsPaged.h

@@ -5,7 +5,7 @@
 #include <nlohmann/json.hpp>
 
 namespace platform {
-    using json = nlohmann::json;
+    using json = nlohmann::ordered_json;
 
     class ReportsPaged {
     public:

src/results/Result.h

@@ -9,7 +9,7 @@
 #include "main/PartialResult.h"
 
 namespace platform {
-    using json = nlohmann::json;
+    using json = nlohmann::ordered_json;
 
     class Result {
     public:

src/results/ResultsDataset.h

@@ -5,7 +5,7 @@
 #include <nlohmann/json.hpp>
 #include "results/Result.h"
 namespace platform {
-    using json = nlohmann::json;
+    using json = nlohmann::ordered_json;
     class ResultsDataset {
     public:
         ResultsDataset(const std::string& dataset, const std::string& model, const std::string& score);

src/results/ResultsDatasetExcel.h

@@ -3,7 +3,7 @@
 #include <nlohmann/json.hpp>
 #include "reports/ExcelFile.h"
 
-using json = nlohmann::json;
+using json = nlohmann::ordered_json;
 
 namespace platform {
 

tests/TestPlatform.cpp

@@ -19,12 +19,12 @@ TEST_CASE("Test Platform version", "[Platform]")
 TEST_CASE("Test Folding library version", "[Folding]")
 {
     std::string version = folding::KFold(5, 100).version();
-    REQUIRE(version == "1.0.1");
+    REQUIRE(version == "1.1.0");
 }
 TEST_CASE("Test BayesNet version", "[BayesNet]")
 {
     std::string version = bayesnet::TAN().getVersion();
-    REQUIRE(version == "1.0.4.1");
+    REQUIRE(version == "1.0.5.1");
 }
 TEST_CASE("Test mdlp version", "[mdlp]")
 {

tests/TestScores.cpp

@@ -10,6 +10,7 @@
 #include "main/Scores.h"
 #include "config.h"
 
+using json = nlohmann::ordered_json;
 auto epsilon = 1e-4;
 
 void make_test_bin(int TP, int TN, int FP, int FN, std::vector<int>& y_test, std::vector<int>& y_pred)
@@ -157,7 +158,7 @@ TEST_CASE("JSON constructor", "[Scores]")
     std::vector<int> y_pred = { 0, 1, 2, 2, 1, 1, 1, 0, 0, 2 };
     auto y_test_tensor = torch::tensor(y_test, torch::kInt32);
     auto y_pred_tensor = torch::tensor(y_pred, torch::kInt32);
-    std::vector<std::string> labels = { "Aeroplane", "Boat", "Car" };
+    std::vector<std::string> labels = { "Car", "Boat", "Aeroplane" };
     platform::Scores scores(y_test_tensor, y_pred_tensor, 3, labels);
     auto res_json_int = scores.get_confusion_matrix_json();
     platform::Scores scores2(res_json_int);
@@ -218,4 +219,33 @@ TEST_CASE("Aggregate", "[Scores]")
     REQUIRE(scores3.f1_weighted() == scores.f1_weighted());
     REQUIRE(scores3.f1_macro() == scores.f1_macro());
     REQUIRE(scores3.accuracy() == scores.accuracy());
+}
+TEST_CASE("Order of keys", "[Scores]")
+{
+    std::vector<int> y_test = { 0, 2, 2, 2, 2, 0, 1, 2, 0, 2 };
+    std::vector<int> y_pred = { 0, 1, 2, 2, 1, 1, 1, 0, 0, 2 };
+    auto y_test_tensor = torch::tensor(y_test, torch::kInt32);
+    auto y_pred_tensor = torch::tensor(y_pred, torch::kInt32);
+    std::vector<std::string> labels = { "Car", "Boat", "Aeroplane" };
+    platform::Scores scores(y_test_tensor, y_pred_tensor, 3, labels);
+    auto res_json_int = scores.get_confusion_matrix_json(true);
+    // Make a temp file and store the json
+    std::string filename = "temp.json";
+    std::ofstream file(filename);
+    file << res_json_int;
+    file.close();
+    // Read the json from the file
+    std::ifstream file2(filename);
+    json res_json_int2;
+    file2 >> res_json_int2;
+    file2.close();
+    // Remove the temp file
+    std::remove(filename.c_str());
+    platform::Scores scores2(res_json_int2);
+    REQUIRE(scores.accuracy() == scores2.accuracy());
+    for (int i = 0; i < 2; ++i) {
+        REQUIRE(scores.f1_score(i) == scores2.f1_score(i));
+        REQUIRE(scores.precision(i) == scores2.precision(i));
+        REQUIRE(scores.recall(i) == scores2.recall(i));
+    }
 }