From 3d6b4f0614f7a701a05f174cb6a4ed4d63abb3e8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana=20G=C3=B3mez?=
 <rmontanana@gmail.com>
Date: Sat, 14 Dec 2024 14:02:45 +0100
Subject: [PATCH] Implement the functionality of the hyperparameter alpha_block
 with test

---
 bayesnet/ensembles/BoostAODE.cc | 20 +++++++++++++++++++-
 tests/TestBoostAODE.cc          | 19 +++++++++++++++++--
 2 files changed, 36 insertions(+), 3 deletions(-)

diff --git a/bayesnet/ensembles/BoostAODE.cc b/bayesnet/ensembles/BoostAODE.cc
index 0638d78..b2ba9b6 100644
--- a/bayesnet/ensembles/BoostAODE.cc
+++ b/bayesnet/ensembles/BoostAODE.cc
@@ -92,7 +92,25 @@ namespace bayesnet {
                 model->fit(dataset, features, className, states, weights_, smoothing);
                 alpha_t = 0.0;
                 if (!block_update) {
-                    auto ypred = model->predict(X_train);
+                    torch::Tensor ypred;
+                    if (alpha_block) {
+                        //
+                        // Compute the prediction with the current ensemble + model
+                        //
+                        // Add the model to the ensemble
+                        n_models++;
+                        models.push_back(std::move(model));
+                        significanceModels.push_back(1);
+                        // Compute the prediction
+                        ypred = predict(X_train);
+                        // Remove the model from the ensemble
+                        model = std::move(models.back());
+                        models.pop_back();
+                        significanceModels.pop_back();
+                        n_models--;
+                    } else {
+                        ypred = model->predict(X_train);
+                    }
                     // Step 3.1: Compute the classifier amout of say
                     std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
                 }
diff --git a/tests/TestBoostAODE.cc b/tests/TestBoostAODE.cc
index 1a8a0f0..73d9048 100644
--- a/tests/TestBoostAODE.cc
+++ b/tests/TestBoostAODE.cc
@@ -130,6 +130,8 @@ TEST_CASE("Oddities", "[BoostAODE]")
         { { "select_features","IWSS" }, { "threshold", 0.51 } },
         { { "select_features","FCBF" }, { "threshold", 1e-8 } },
         { { "select_features","FCBF" }, { "threshold", 1.01 } },
+        { { "alpha_block", true }, { "block_update", true } },
+        { { "bisection", false }, { "block_update", true } },
     };
     for (const auto& hyper : bad_hyper_fit.items()) {
         INFO("BoostAODE hyper: " << hyper.value().dump());
@@ -137,7 +139,6 @@ TEST_CASE("Oddities", "[BoostAODE]")
         REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing), std::invalid_argument);
     }
 }
-
 TEST_CASE("Bisection Best", "[BoostAODE]")
 {
     auto clf = bayesnet::BoostAODE();
@@ -180,7 +181,6 @@ TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
     auto score_last = clf.score(raw.X_test, raw.y_test);
     REQUIRE(score_last == Catch::Approx(0.976666689f).epsilon(raw.epsilon));
 }
-
 TEST_CASE("Block Update", "[BoostAODE]")
 {
     auto clf = bayesnet::BoostAODE();
@@ -210,4 +210,19 @@ TEST_CASE("Block Update", "[BoostAODE]")
     //     std::cout << note << std::endl;
     // }
     // std::cout << "Score " << score << std::endl;
+}
+TEST_CASE("Alphablock", "[BoostAODE]")
+{
+    auto clf_alpha = bayesnet::BoostAODE();
+    auto clf_no_alpha = bayesnet::BoostAODE();
+    auto raw = RawDatasets("diabetes", true);
+    clf_alpha.setHyperparameters({
+        {"alpha_block", true},
+        });
+    clf_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
+    clf_no_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
+    auto score_alpha = clf_alpha.score(raw.X_test, raw.y_test);
+    auto score_no_alpha = clf_no_alpha.score(raw.X_test, raw.y_test);
+    REQUIRE(score_alpha == Catch::Approx(0.720779f).epsilon(raw.epsilon));
+    REQUIRE(score_no_alpha == Catch::Approx(0.733766f).epsilon(raw.epsilon));
 }
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