Add Ensemble tests

tests/TestBoostAODE.cc

@@ -0,0 +1,106 @@
+#include <type_traits>
+#include <catch2/catch_test_macros.hpp>
+#include <catch2/catch_approx.hpp>
+#include <catch2/generators/catch_generators.hpp>
+#include "bayesnet/ensembles/BoostAODE.h"
+#include "TestUtils.h"
+
+
+TEST_CASE("Feature_select CFS", "[BoostAODE]")
+{
+    auto raw = RawDatasets("glass", true);
+    auto clf = bayesnet::BoostAODE();
+    clf.setHyperparameters({ {"select_features", "CFS"} });
+    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    REQUIRE(clf.getNumberOfNodes() == 90);
+    REQUIRE(clf.getNumberOfEdges() == 153);
+    REQUIRE(clf.getNotes().size() == 2);
+    REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 9 with CFS");
+    REQUIRE(clf.getNotes()[1] == "Number of models: 9");
+}
+TEST_CASE("Feature_select IWSS", "[BoostAODE]")
+{
+    auto raw = RawDatasets("glass", true);
+    auto clf = bayesnet::BoostAODE();
+    clf.setHyperparameters({ {"select_features", "IWSS"}, {"threshold", 0.5 } });
+    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    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()[1] == "Number of models: 9");
+}
+TEST_CASE("Feature_select FCBF", "[BoostAODE]")
+{
+    auto raw = RawDatasets("glass", true);
+    auto clf = bayesnet::BoostAODE();
+    clf.setHyperparameters({ {"select_features", "FCBF"}, {"threshold", 1e-7 } });
+    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    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 FCBF");
+    REQUIRE(clf.getNotes()[1] == "Number of models: 9");
+}
+TEST_CASE("Test used features in train note and score", "[BoostAODE]")
+{
+    auto raw = RawDatasets("diabetes", true);
+    auto clf = bayesnet::BoostAODE(true);
+    clf.setHyperparameters({
+        {"order", "asc"},
+        {"convergence", true},
+        {"select_features","CFS"},
+        });
+    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    REQUIRE(clf.getNumberOfNodes() == 72);
+    REQUIRE(clf.getNumberOfEdges() == 120);
+    REQUIRE(clf.getNotes().size() == 2);
+    REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 8 with CFS");
+    REQUIRE(clf.getNotes()[1] == "Number of models: 8");
+    auto score = clf.score(raw.Xv, raw.yv);
+    auto scoret = clf.score(raw.Xt, raw.yt);
+    REQUIRE(score == Catch::Approx(0.80078).epsilon(raw.epsilon));
+    REQUIRE(scoret == Catch::Approx(0.80078).epsilon(raw.epsilon));
+}
+TEST_CASE("Voting vs proba", "[BoostAODE]")
+{
+    auto raw = RawDatasets("iris", true);
+    auto clf = bayesnet::BoostAODE(false);
+    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    auto score_proba = clf.score(raw.Xv, raw.yv);
+    auto pred_proba = clf.predict_proba(raw.Xv);
+    clf.setHyperparameters({
+        {"predict_voting",true},
+        });
+    auto score_voting = clf.score(raw.Xv, raw.yv);
+    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(clf.dump_cpt() == "");
+    REQUIRE(clf.topological_order() == std::vector<std::string>());
+}
+TEST_CASE("Order asc, desc & random", "[BoostAODE]")
+{
+    auto raw = RawDatasets("glass", true);
+    std::map<std::string, double> scores{
+        {"asc", 0.83645f }, { "desc", 0.84579f }, { "rand", 0.84112 }
+    };
+    for (const std::string& order : { "asc", "desc", "rand" }) {
+        auto clf = bayesnet::BoostAODE();
+        clf.setHyperparameters({
+            {"order", order},
+            });
+        clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+        auto score = clf.score(raw.Xv, raw.yv);
+        auto scoret = clf.score(raw.Xt, raw.yt);
+        INFO("BoostAODE order: " + order);
+        REQUIRE(score == Catch::Approx(scores[order]).epsilon(raw.epsilon));
+        REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
+    }
+}
+TEST_CASE("Oddities", "[BoostAODE]")
+{
+
+}