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Complete json output compatible with benchmark

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This commit is contained in:
Ricardo Montañana Gómez
2023-07-26 19:01:39 +02:00
parent 6f7fb290b0
commit 3e954ba841
8 changed files with 88 additions and 16 deletions
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9
src/Platform/main.cc
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@@ -18,8 +18,6 @@
using namespace std;
int main(int argc, char** argv)
{
map<string, bool> datasets = {
@@ -60,6 +58,7 @@ int main(int argc, char** argv)
throw runtime_error("Model must be one of {AODE, KDB, SPODE, TAN}");
}
);
program.add_argument("--title").required().help("Experiment title");
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) {
@@ -79,7 +78,7 @@ int main(int argc, char** argv)
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;
string model_name, file_name, path, complete_file_name, title;
try {
program.parse_args(argc, argv);
file_name = program.get<string>("dataset");
@@ -91,6 +90,7 @@ int main(int argc, char** argv)
seed = program.get<int>("seed");
complete_file_name = path + file_name + ".arff";
class_last = datasets[file_name];
title = program.get<string>("title");
if (!file_exists(complete_file_name)) {
throw runtime_error("Data File " + path + file_name + ".arff" + " does not exist");
}
@@ -110,7 +110,8 @@ int main(int argc, char** argv)
else
fold = new KFold(n_folds, y.numel(), seed);
auto experiment = platform::Experiment();
experiment.setDiscretized(discretize_dataset).setModel(model_name).setPlatform("cpp");
experiment.setTitle(title).setLanguage("cpp").setLanguageVersion("1.0.0");
experiment.setDiscretized(discretize_dataset).setModel(model_name).setModelVersion("1...0").setPlatform("BayesNet");
experiment.setStratified(stratified).setNFolds(n_folds).addRandomSeed(seed).setScoreName("accuracy");
platform::Timer timer;
timer.start();