Add Ensemble tests

tests/TestBayesEnsemble.cc

@@ -0,0 +1,104 @@
+#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("Topological Order", "[Ensemble]")
+{
+    auto raw = RawDatasets("glass", true);
+    auto clf = bayesnet::BoostAODE();
+    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    auto order = clf.topological_order();
+    REQUIRE(order.size() == 0);
+}
+TEST_CASE("Dump CPT", "[Ensemble]")
+{
+    auto raw = RawDatasets("glass", true);
+    auto clf = bayesnet::BoostAODE();
+    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    auto dump = clf.dump_cpt();
+    REQUIRE(dump == "");
+}
+TEST_CASE("Number of States", "[Ensemble]")
+{
+    auto clf = bayesnet::BoostAODE();
+    auto raw = RawDatasets("iris", true);
+    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    REQUIRE(clf.getNumberOfStates() == 76);
+}
+TEST_CASE("Show", "[Ensemble]")
+{
+    auto clf = bayesnet::BoostAODE();
+    auto raw = RawDatasets("iris", true);
+    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    std::vector<std::string> expected = {
+        "class -> sepallength, sepalwidth, petallength, petalwidth, ",
+        "petallength -> sepallength, sepalwidth, petalwidth, ",
+        "petalwidth -> ",
+        "sepallength -> ",
+        "sepalwidth -> ",
+        "class -> sepallength, sepalwidth, petallength, petalwidth, ",
+        "petallength -> ",
+        "petalwidth -> sepallength, sepalwidth, petallength, ",
+        "sepallength -> ",
+        "sepalwidth -> ",
+        "class -> sepallength, sepalwidth, petallength, petalwidth, ",
+        "petallength -> ",
+        "petalwidth -> ",
+        "sepallength -> sepalwidth, petallength, petalwidth, ",
+        "sepalwidth -> ",
+        "class -> sepallength, sepalwidth, petallength, petalwidth, ",
+        "petallength -> ",
+        "petalwidth -> ",
+        "sepallength -> ",
+        "sepalwidth -> sepallength, petallength, petalwidth, ",
+    };
+    auto show = clf.show();
+    REQUIRE(show.size() == expected.size());
+    for (size_t i = 0; i < show.size(); i++)
+        REQUIRE(show[i] == expected[i]);
+}
+TEST_CASE("Graph", "[Ensemble]")
+{
+    auto clf = bayesnet::BoostAODE();
+    auto raw = RawDatasets("iris", true);
+    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    auto graph = clf.graph();
+    REQUIRE(graph.size() == 56);
+}
+TEST_CASE("Compute ArgMax", "[Ensemble]")
+{
+    class TestEnsemble : public bayesnet::BoostAODE {
+    public:
+        TestEnsemble() : bayesnet::BoostAODE() {}
+        torch::Tensor compute_arg_max(torch::Tensor& X) { return Ensemble::compute_arg_max(X); }
+        std::vector<int> compute_arg_max(std::vector<std::vector<double>>& X) { return Ensemble::compute_arg_max(X); }
+    };
+    TestEnsemble clf;
+    std::vector<std::vector<double>> X = {
+        {0.1f, 0.2f, 0.3f},
+        {0.4f, 0.9f, 0.6f},
+        {0.7f, 0.8f, 0.9f},
+        {0.5f, 0.2f, 0.1f},
+        {0.3f, 0.7f, 0.2f},
+        {0.5f, 0.5f, 0.2f}
+    };
+    std::vector<int> expected = { 2, 1, 2, 0, 1, 0 };
+    auto argmax = clf.compute_arg_max(X);
+    REQUIRE(argmax.size() == expected.size());
+    REQUIRE(argmax == expected);
+    auto Xt = torch::zeros({ 6, 3 }, torch::kFloat32);
+    Xt[0][0] = 0.1f; Xt[0][1] = 0.2f; Xt[0][2] = 0.3f;
+    Xt[1][0] = 0.4f; Xt[1][1] = 0.9f; Xt[1][2] = 0.6f;
+    Xt[2][0] = 0.7f; Xt[2][1] = 0.8f; Xt[2][2] = 0.9f;
+    Xt[3][0] = 0.5f; Xt[3][1] = 0.2f; Xt[3][2] = 0.1f;
+    Xt[4][0] = 0.3f; Xt[4][1] = 0.7f; Xt[4][2] = 0.2f;
+    Xt[5][0] = 0.5f; Xt[5][1] = 0.5f; Xt[5][2] = 0.2f;
+    auto argmaxt = clf.compute_arg_max(Xt);
+    REQUIRE(argmaxt.size(0) == expected.size());
+    for (int i = 0; i < argmaxt.size(0); i++)
+        REQUIRE(argmaxt[i].item<int>() == expected[i]);
+}