From 2e95e8999d8bfe2dd222fcf7e95644df426add05 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana=20G=C3=B3mez?=
 <rmontanana@gmail.com>
Date: Sun, 3 Dec 2023 12:37:25 +0100
Subject: [PATCH] Complete nested gridsearch

---
 src/Platform/GridSearch.cc | 54 ++++++++++++++++++++++++++++++--------
 1 file changed, 43 insertions(+), 11 deletions(-)

diff --git a/src/Platform/GridSearch.cc b/src/Platform/GridSearch.cc
index 9eed00c..49c3a71 100644
--- a/src/Platform/GridSearch.cc
+++ b/src/Platform/GridSearch.cc
@@ -172,8 +172,9 @@ namespace platform {
         auto states = datasets.getStates(fileName);
         auto features = datasets.getFeatures(fileName);
         auto className = datasets.getClassName(fileName);
-        double bestScore = 0.0;
-        json bestHyperparameters;
+        int spcs_combinations = int(log(combinations.size()) / log(10)) + 1;
+        double goatScore = 0.0;
+        json goatHyperparameters;
         // for dataset // for seed // for fold // for hyperparameters // for nested fold
         for (const auto& seed : config.seeds) {
             Fold* fold;
@@ -182,9 +183,11 @@ namespace platform {
             else
                 fold = new KFold(config.n_folds, y.size(0), seed);
             double bestScore = 0.0;
+            json bestHyperparameters;
             std::cout << "(" << seed << ") doing Fold: " << flush;
             for (int nfold = 0; nfold < config.n_folds; nfold++) {
-                std::cout << nfold + 1 << flush;
+                if (!config.quiet)
+                    std::cout << Colors::GREEN() << nfold + 1 << " " << flush;
                 // First level fold
                 auto [train, test] = fold->getFold(nfold);
                 auto train_t = torch::tensor(train);
@@ -195,9 +198,12 @@ namespace platform {
                 auto y_test = y.index({ test_t });
                 auto num = 0;
                 json result_fold;
-                double score = 0.0;
+                double hypScore = 0.0;
+                double bestHypScore = 0.0;
+                json bestHypHyperparameters;
                 for (const auto& hyperparam_line : combinations) {
-                    std::cout << "[" << ++num << "/" << combinations.size() << "] " << std::flush;
+                    std::cout << "[" << setw(spcs_combinations) << ++num << "/" << setw(spcs_combinations)
+                        << combinations.size() << "] " << std::flush;
                     Fold* nested_fold;
                     if (config.stratified)
                         nested_fold = new StratifiedKFold(config.nested, y_train, seed);
@@ -221,21 +227,47 @@ namespace platform {
                         // Train model
                         if (!config.quiet)
                             showProgressFold(n_nested_fold + 1, getColor(clf->getStatus()), "a");
-                        // clf->fit(X_nexted_train, y_nested_train, features, className, states);
+                        clf->fit(X_nexted_train, y_nested_train, features, className, states);
                         // Test model
                         if (!config.quiet)
                             showProgressFold(n_nested_fold + 1, getColor(clf->getStatus()), "b");
-                        // score += clf->score(X_nested_test, y_nested_test);
-                        score = 0.0;
+                        hypScore += clf->score(X_nested_test, y_nested_test);
+                        if (!config.quiet)
+                            std::cout << "\b\b\b,  \b" << flush;
                     }
+                    std::cout << string(3 * config.nested + 2 * spcs_combinations + 4, '\b') << flush;
                     delete nested_fold;
-                    score = score / config.nested;
-
+                    hypScore /= config.nested;
+                    if (hypScore > bestHypScore) {
+                        bestHypScore = hypScore;
+                        bestHypHyperparameters = hyperparam_line;
+                    }
                 }
+                // Build Classifier with selected hyperparameters
+                auto clf = Models::instance()->create(config.model);
+                clf->setHyperparameters(bestHypHyperparameters);
+                // Train model
+                if (!config.quiet)
+                    showProgressFold(nfold + 1, getColor(clf->getStatus()), "a");
+                clf->fit(X_train, y_train, features, className, states);
+                // Test model
+                if (!config.quiet)
+                    showProgressFold(nfold + 1, getColor(clf->getStatus()), "b");
+                double score = clf->score(X_test, y_test);
+                if (!config.quiet)
+                    std::cout << "\b\b\b\b\b,  \b" << flush;
+                if (score > bestScore) {
+                    bestScore = score;
+                    bestHyperparameters = bestHypHyperparameters;
+                }
+            }
+            if (bestScore > goatScore) {
+                goatScore = bestScore;
+                goatHyperparameters = bestHyperparameters;
             }
             delete fold;
         }
-        return { bestScore, bestHyperparameters };
+        return { goatScore, goatHyperparameters };
     }
     vector<std::string> GridSearch::processDatasets(Datasets& datasets)
     {