Add quiet mode to b_main

Reduce output when --quiet is set, not showing fold info

src/Platform/Experiment.cc

@@ -102,12 +102,12 @@ namespace platform {
         cout << data.dump(4) << endl;
     }
 
-    void Experiment::go(vector<string> filesToProcess)
+    void Experiment::go(vector<string> filesToProcess, bool quiet)
     {
         cout << "*** Starting experiment: " << title << " ***" << endl;
         for (auto fileName : filesToProcess) {
             cout << "- " << setw(20) << left << fileName << " " << right << flush;
-            cross_validation(fileName);
+            cross_validation(fileName, quiet);
             cout << endl;
         }
     }
@@ -132,7 +132,7 @@ namespace platform {
         cout << prefix << color << fold << Colors::RESET() << "(" << color << phase << Colors::RESET() << ")" << flush;
 
     }
-    void Experiment::cross_validation(const string& fileName)
+    void Experiment::cross_validation(const string& fileName, bool quiet)
     {
         auto datasets = platform::Datasets(discretized, Paths::datasets());
         // Get dataset
@@ -141,7 +141,9 @@ namespace platform {
         auto features = datasets.getFeatures(fileName);
         auto samples = datasets.getNSamples(fileName);
         auto className = datasets.getClassName(fileName);
-        cout << " (" << setw(5) << samples << "," << setw(3) << features.size() << ") " << flush;
+        if (!quiet) {
+            cout << " (" << setw(5) << samples << "," << setw(3) << features.size() << ") " << flush;
+        }
         // Prepare Result
         auto result = Result();
         auto [values, counts] = at::_unique(y);
@@ -159,7 +161,8 @@ namespace platform {
         Timer train_timer, test_timer;
         int item = 0;
         for (auto seed : randomSeeds) {
-            cout << "(" << seed << ") doing Fold: " << flush;
+            if (!quiet)
+                cout << "(" << seed << ") doing Fold: " << flush;
             Fold* fold;
             if (stratified)
                 fold = new StratifiedKFold(nfolds, y, seed);
@@ -180,10 +183,12 @@ namespace platform {
                 auto y_train = y.index({ train_t });
                 auto X_test = X.index({ "...", test_t });
                 auto y_test = y.index({ test_t });
-                showProgress(nfold + 1, getColor(clf->getStatus()), "a");
+                if (!quiet)
+                    showProgress(nfold + 1, getColor(clf->getStatus()), "a");
                 // Train model
                 clf->fit(X_train, y_train, features, className, states);
-                showProgress(nfold + 1, getColor(clf->getStatus()), "b");
+                if (!quiet)
+                    showProgress(nfold + 1, getColor(clf->getStatus()), "b");
                 nodes[item] = clf->getNumberOfNodes();
                 edges[item] = clf->getNumberOfEdges();
                 num_states[item] = clf->getNumberOfStates();
@@ -191,13 +196,15 @@ namespace platform {
                 // Score train
                 auto accuracy_train_value = clf->score(X_train, y_train);
                 // Test model
-                showProgress(nfold + 1, getColor(clf->getStatus()), "c");
+                if (!quiet)
+                    showProgress(nfold + 1, getColor(clf->getStatus()), "c");
                 test_timer.start();
                 auto accuracy_test_value = clf->score(X_test, y_test);
                 test_time[item] = test_timer.getDuration();
                 accuracy_train[item] = accuracy_train_value;
                 accuracy_test[item] = accuracy_test_value;
-                cout << "\b\b\b, " << flush;
+                if (!quiet)
+                    cout << "\b\b\b, " << flush;
                 // Store results and times in vector
                 result.addScoreTrain(accuracy_train_value);
                 result.addScoreTest(accuracy_test_value);
@@ -206,7 +213,8 @@ namespace platform {
                 item++;
                 clf.reset();
             }
-            cout << "end. " << flush;
+            if (!quiet)
+                cout << "end. " << flush;
             delete fold;
         }
         result.setScoreTest(torch::mean(accuracy_test).item<double>()).setScoreTrain(torch::mean(accuracy_train).item<double>());

src/Platform/Experiment.h

@@ -108,8 +108,8 @@ namespace platform {
         Experiment& setHyperparameters(const json& hyperparameters) { this->hyperparameters = hyperparameters; return *this; }
         string get_file_name();
         void save(const string& path);
-        void cross_validation(const string& fileName);
+        void cross_validation(const string& fileName, bool quiet);
-        void go(vector<string> filesToProcess);
+        void go(vector<string> filesToProcess, bool quiet);
         void show();
         void report();
     };

src/Platform/main.cc

@@ -30,6 +30,7 @@ argparse::ArgumentParser manageArguments()
     );
     program.add_argument("--title").default_value("").help("Experiment title");
     program.add_argument("--discretize").help("Discretize input dataset").default_value((bool)stoi(env.get("discretize"))).implicit_value(true);
+    program.add_argument("--quiet").help("Don't display detailed progress").default_value(false).implicit_value(true);
     program.add_argument("--save").help("Save result (always save if no dataset is supplied)").default_value(false).implicit_value(true);
     program.add_argument("--stratified").help("If Stratified KFold is to be done").default_value((bool)stoi(env.get("stratified"))).implicit_value(true);
     program.add_argument("-f", "--folds").help("Number of folds").default_value(stoi(env.get("n_folds"))).scan<'i', int>().action([](const string& value) {
@@ -55,7 +56,7 @@ int main(int argc, char** argv)
 {
     string file_name, model_name, title;
     json hyperparameters_json;
-    bool discretize_dataset, stratified, saveResults;
+    bool discretize_dataset, stratified, saveResults, quiet;
     vector<int> seeds;
     vector<string> filesToTest;
     int n_folds;
@@ -66,6 +67,7 @@ int main(int argc, char** argv)
         model_name = program.get<string>("model");
         discretize_dataset = program.get<bool>("discretize");
         stratified = program.get<bool>("stratified");
+        quiet = program.get<bool>("quiet");
         n_folds = program.get<int>("folds");
         seeds = program.get<vector<int>>("seeds");
         auto hyperparameters = program.get<string>("hyperparameters");
@@ -109,12 +111,13 @@ int main(int argc, char** argv)
     }
     platform::Timer timer;
     timer.start();
-    experiment.go(filesToTest);
+    experiment.go(filesToTest, quiet);
     experiment.setDuration(timer.getDuration());
     if (saveResults) {
         experiment.save(platform::Paths::results());
     }
-    experiment.report();
+    if (!quiet)
+        experiment.report();
     cout << "Done!" << endl;
     return 0;
 }