Make some boostAODE tests

tests/TestBayesClassifier.cc

@@ -3,6 +3,8 @@
 #include <string>
 #include "TestUtils.h"
 #include "bayesnet/classifiers/TAN.h"
+#include "bayesnet/classifiers/KDB.h"
+#include "bayesnet/classifiers/KDBLd.h"
 
 
 TEST_CASE("Test Cannot build dataset with wrong data vector", "[Classifier]")
@@ -83,4 +85,20 @@ TEST_CASE("Not fitted model", "[Classifier]")
     REQUIRE_THROWS_WITH(model.predict_proba(raw.Xv), message);
     REQUIRE_THROWS_AS(model.score(raw.Xv, raw.yv), std::logic_error);
     REQUIRE_THROWS_WITH(model.score(raw.Xv, raw.yv), message);
+}
+TEST_CASE("KDB Graph", "[Classifier]")
+{
+    auto model = bayesnet::KDB(2);
+    auto raw = RawDatasets("iris", true);
+    model.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    auto graph = model.graph();
+    REQUIRE(graph.size() == 15);
+}
+TEST_CASE("KDBLd Graph", "[Classifier]")
+{
+    auto model = bayesnet::KDBLd(2);
+    auto raw = RawDatasets("iris", false);
+    model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
+    auto graph = model.graph();
+    REQUIRE(graph.size() == 15);
 }

tests/TestBoostAODE.cc

@@ -102,5 +102,50 @@ TEST_CASE("Order asc, desc & random", "[BoostAODE]")
 }
 TEST_CASE("Oddities", "[BoostAODE]")
 {
+    auto clf = bayesnet::BoostAODE();
+    auto raw = RawDatasets("iris", true);
+    auto bad_hyper = nlohmann::json{
+        { { "order", "duck" } },
+        { { "select_features", "duck" } },
+        { { "maxTolerance", 0 } },
+        { { "maxTolerance", 5 } },
+    };
+    for (const auto& hyper : bad_hyper.items()) {
+        INFO("BoostAODE hyper: " + hyper.value().dump());
+        REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
+    }
+    REQUIRE_THROWS_AS(clf.setHyperparameters({ {"maxTolerance", 0 } }), std::invalid_argument);
+    auto bad_hyper_fit = nlohmann::json{
+        { { "select_features","IWSS" }, { "threshold", -0.01 } },
+        { { "select_features","IWSS" }, { "threshold", 0.51 } },
+        { { "select_features","FCBF" }, { "threshold", 1e-8 } },
+        { { "select_features","FCBF" }, { "threshold", 1.01 } },
+    };
+    for (const auto& hyper : bad_hyper_fit.items()) {
+        INFO("BoostAODE hyper: " + hyper.value().dump());
+        clf.setHyperparameters(hyper.value());
+        REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv), std::invalid_argument);
+    }
+}
 
+TEST_CASE("Bisection", "[BoostAODE]")
+{
+    auto clf = bayesnet::BoostAODE();
+    auto raw = RawDatasets("mfeat-factors", true);
+    clf.setHyperparameters({
+        {"bisection", true},
+        {"maxTolerance", 3},
+        {"convergence", true},
+        });
+    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    REQUIRE(clf.getNumberOfNodes() == 217);
+    REQUIRE(clf.getNumberOfEdges() == 431);
+    REQUIRE(clf.getNotes().size() == 3);
+    REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 15 models eliminated");
+    REQUIRE(clf.getNotes()[1] == "Used features in train: 16 of 216");
+    REQUIRE(clf.getNotes()[2] == "Number of models: 1");
+    auto score = clf.score(raw.Xv, raw.yv);
+    auto scoret = clf.score(raw.Xt, raw.yt);
+    REQUIRE(score == Catch::Approx(1.0f).epsilon(raw.epsilon));
+    REQUIRE(scoret == Catch::Approx(1.0f).epsilon(raw.epsilon));
 }