From 1326891d6a170e27c5e10c0c15ce38b5c72bcea2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana=20G=C3=B3mez?=
 <rmontanana@gmail.com>
Date: Tue, 9 Apr 2024 00:13:45 +0200
Subject: [PATCH] Fix previous tests of BoostAODE Due to the change of default
 values for hyperparameters in BoostAODE

---
 tests/TestBayesEnsemble.cc |  5 +++++
 tests/TestBayesModels.cc   | 26 +++++++++++++-------------
 tests/TestBoostAODE.cc     |  9 ++++++---
 3 files changed, 24 insertions(+), 16 deletions(-)

diff --git a/tests/TestBayesEnsemble.cc b/tests/TestBayesEnsemble.cc
index d009d11..79c3228 100644
--- a/tests/TestBayesEnsemble.cc
+++ b/tests/TestBayesEnsemble.cc
@@ -33,6 +33,11 @@ TEST_CASE("Show", "[Ensemble]")
 {
     auto clf = bayesnet::BoostAODE();
     auto raw = RawDatasets("iris", true);
+    clf.setHyperparameters({
+            {"bisection", false},
+            {"maxTolerance", 1},
+            {"convergence", false},
+        });
     clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
     std::vector<std::string> expected = {
         "class -> sepallength, sepalwidth, petallength, petalwidth, ",
diff --git a/tests/TestBayesModels.cc b/tests/TestBayesModels.cc
index 55a932f..8d071d8 100644
--- a/tests/TestBayesModels.cc
+++ b/tests/TestBayesModels.cc
@@ -115,15 +115,15 @@ TEST_CASE("Model predict_proba", "[Models]")
      {0.003135, 0.991799, 0.0050661}
         });
     auto res_prob_baode = std::vector<std::vector<double>>({
-        {0.00803291, 0.9676, 0.0243672},
-        {0.00398714, 0.945126, 0.050887},
-        {0.00398714, 0.945126, 0.050887},
-        {0.00398714, 0.945126, 0.050887},
-        {0.00189227, 0.859575, 0.138533},
-        {0.0118341, 0.442149, 0.546017},
-        {0.0216135, 0.785781, 0.192605},
-        {0.0204803, 0.844276, 0.135244},
-        {0.00576313, 0.961665, 0.0325716},
+        {0.0112349, 0.962274, 0.0264907},
+        {0.00371025, 0.950592, 0.0456973},
+        {0.00371025, 0.950592, 0.0456973},
+        {0.00371025, 0.950592, 0.0456973},
+        {0.00369275, 0.84967, 0.146637},
+        {0.0252205, 0.113564, 0.861215},
+        {0.0284828, 0.770524, 0.200993},
+        {0.0213182, 0.857189, 0.121493},
+        {0.00868436, 0.949494, 0.0418215}
         });
     auto res_prob_voting = std::vector<std::vector<double>>({
         {0, 1, 0},
@@ -131,8 +131,8 @@ TEST_CASE("Model predict_proba", "[Models]")
         {0, 1, 0},
         {0, 1, 0},
         {0, 1, 0},
-        {0, 0.447909, 0.552091},
-        {0, 0.811482, 0.188517},
+        {0, 0, 1},
+        {0, 1, 0},
         {0, 1, 0},
         {0, 1, 0}
         });
@@ -155,7 +155,7 @@ TEST_CASE("Model predict_proba", "[Models]")
         REQUIRE(y_pred.size() == raw.yv.size());
         REQUIRE(y_pred_proba[0].size() == 3);
         REQUIRE(yt_pred_proba.size(1) == y_pred_proba[0].size());
-        for (int i = 0; i < y_pred_proba.size(); ++i) {
+        for (int i = 0; i < 9; ++i) {
             auto maxElem = max_element(y_pred_proba[i].begin(), y_pred_proba[i].end());
             int predictedClass = distance(y_pred_proba[i].begin(), maxElem);
             REQUIRE(predictedClass == y_pred[i]);
@@ -166,7 +166,7 @@ TEST_CASE("Model predict_proba", "[Models]")
             }
         }
         // Check predict_proba values for vectors and tensors
-        for (int i = 0; i < res_prob.size(); i++) {
+        for (int i = 0; i < 9; i++) {
             REQUIRE(y_pred[i] == yt_pred[i].item<int>());
             for (int j = 0; j < 3; j++) {
                 REQUIRE(res_prob[model][i][j] == Catch::Approx(y_pred_proba[i + init_index][j]).epsilon(raw.epsilon));
diff --git a/tests/TestBoostAODE.cc b/tests/TestBoostAODE.cc
index b3281c7..73669b5 100644
--- a/tests/TestBoostAODE.cc
+++ b/tests/TestBoostAODE.cc
@@ -27,7 +27,7 @@ TEST_CASE("Feature_select IWSS", "[BoostAODE]")
     REQUIRE(clf.getNumberOfNodes() == 90);
     REQUIRE(clf.getNumberOfEdges() == 153);
     REQUIRE(clf.getNotes().size() == 2);
-    REQUIRE(clf.getNotes()[0] == "Used features in initialization: 5 of 9 with IWSS");
+    REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with IWSS");
     REQUIRE(clf.getNotes()[1] == "Number of models: 9");
 }
 TEST_CASE("Feature_select FCBF", "[BoostAODE]")
@@ -76,8 +76,8 @@ TEST_CASE("Voting vs proba", "[BoostAODE]")
     auto pred_voting = clf.predict_proba(raw.Xv);
     REQUIRE(score_proba == Catch::Approx(0.97333).epsilon(raw.epsilon));
     REQUIRE(score_voting == Catch::Approx(0.98).epsilon(raw.epsilon));
-    REQUIRE(pred_voting[83][2] == Catch::Approx(0.552091).epsilon(raw.epsilon));
-    REQUIRE(pred_proba[83][2] == Catch::Approx(0.546017).epsilon(raw.epsilon));
+    REQUIRE(pred_voting[83][2] == Catch::Approx(1.0).epsilon(raw.epsilon));
+    REQUIRE(pred_proba[83][2] == Catch::Approx(0.86121525).epsilon(raw.epsilon));
     REQUIRE(clf.dump_cpt() == "");
     REQUIRE(clf.topological_order() == std::vector<std::string>());
 }
@@ -91,6 +91,9 @@ TEST_CASE("Order asc, desc & random", "[BoostAODE]")
         auto clf = bayesnet::BoostAODE();
         clf.setHyperparameters({
             {"order", order},
+            {"bisection", false},
+            {"maxTolerance", 1},
+            {"convergence", false},
             });
         clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
         auto score = clf.score(raw.Xv, raw.yv);