Add json lib and json result generation

2023-07-26 17:49:03 +02:00
parent 49a49a9dcd
commit 6f7fb290b0
11 changed files with 325 additions and 325 deletions
--- a/src/Platform/main.cc
+++ b/src/Platform/main.cc
@@ -0,0 +1,124 @@
+#include <iostream>
+#include <string>
+#include <torch/torch.h>
+#include <thread>
+#include <argparse/argparse.hpp>
+#include "ArffFiles.h"
+#include "Network.h"
+#include "BayesMetrics.h"
+#include "CPPFImdlp.h"
+#include "KDB.h"
+#include "SPODE.h"
+#include "AODE.h"
+#include "TAN.h"
+#include "platformUtils.h"
+#include "Experiment.h"
+#include "Folding.h"
+
+
+using namespace std;
+
+
+
+int main(int argc, char** argv)
+{
+    map<string, bool> datasets = {
+            {"diabetes",           true},
+            {"ecoli",              true},
+            {"glass",              true},
+            {"iris",               true},
+            {"kdd_JapaneseVowels", false},
+            {"letter",             true},
+            {"liver-disorders",    true},
+            {"mfeat-factors",      true},
+    };
+    auto valid_datasets = vector<string>();
+    for (auto dataset : datasets) {
+        valid_datasets.push_back(dataset.first);
+    }
+    argparse::ArgumentParser program("BayesNetSample");
+    program.add_argument("-d", "--dataset")
+        .help("Dataset file name")
+        .action([valid_datasets](const std::string& value) {
+        if (find(valid_datasets.begin(), valid_datasets.end(), value) != valid_datasets.end()) {
+            return value;
+        }
+        throw runtime_error("file must be one of {diabetes, ecoli, glass, iris, kdd_JapaneseVowels, letter, liver-disorders, mfeat-factors}");
+            }
+    );
+    program.add_argument("-p", "--path")
+        .help("folder where the data files are located, default")
+        .default_value(string{ PATH }
+    );
+    program.add_argument("-m", "--model")
+        .help("Model to use {AODE, KDB, SPODE, TAN}")
+        .action([](const std::string& value) {
+        static const vector<string> choices = { "AODE", "KDB", "SPODE", "TAN" };
+        if (find(choices.begin(), choices.end(), value) != choices.end()) {
+            return value;
+        }
+        throw runtime_error("Model must be one of {AODE, KDB, SPODE, TAN}");
+            }
+    );
+    program.add_argument("--discretize").help("Discretize input dataset").default_value(false).implicit_value(true);
+    program.add_argument("--stratified").help("If Stratified KFold is to be done").default_value(false).implicit_value(true);
+    program.add_argument("-f", "--folds").help("Number of folds").default_value(5).scan<'i', int>().action([](const string& value) {
+        try {
+            auto k = stoi(value);
+            if (k < 2) {
+                throw runtime_error("Number of folds must be greater than 1");
+            }
+            return k;
+        }
+        catch (const runtime_error& err) {
+            throw runtime_error(err.what());
+        }
+        catch (...) {
+            throw runtime_error("Number of folds must be an integer");
+        }});
+    program.add_argument("-s", "--seed").help("Random seed").default_value(-1).scan<'i', int>();
+    bool class_last, discretize_dataset, stratified;
+    int n_folds, seed;
+    string model_name, file_name, path, complete_file_name;
+    try {
+        program.parse_args(argc, argv);
+        file_name = program.get<string>("dataset");
+        path = program.get<string>("path");
+        model_name = program.get<string>("model");
+        discretize_dataset = program.get<bool>("discretize");
+        stratified = program.get<bool>("stratified");
+        n_folds = program.get<int>("folds");
+        seed = program.get<int>("seed");
+        complete_file_name = path + file_name + ".arff";
+        class_last = datasets[file_name];
+        if (!file_exists(complete_file_name)) {
+            throw runtime_error("Data File " + path + file_name + ".arff" + " does not exist");
+        }
+    }
+    catch (const exception& err) {
+        cerr << err.what() << endl;
+        cerr << program;
+        exit(1);
+    }
+    /*
+    * Begin Processing
+    */
+    auto [X, y, features, className, states] = loadDataset(path, file_name, class_last, discretize_dataset);
+    Fold* fold;
+    if (stratified)
+        fold = new StratifiedKFold(n_folds, y, seed);
+    else
+        fold = new KFold(n_folds, y.numel(), seed);
+    auto experiment = platform::Experiment();
+    experiment.setDiscretized(discretize_dataset).setModel(model_name).setPlatform("cpp");
+    experiment.setStratified(stratified).setNFolds(n_folds).addRandomSeed(seed).setScoreName("accuracy");
+    platform::Timer timer;
+    timer.start();
+    auto result = platform::cross_validation(fold, model_name, X, y, features, className, states);
+    result.setDataset(file_name);
+    experiment.addResult(result);
+    experiment.setDuration(timer.getDuration());
+    experiment.save(path);
+    experiment.show();
+    return 0;
+}