update to BayesNet 1.0.3

CMakeLists.txt

@@ -1,7 +1,7 @@
 cmake_minimum_required(VERSION 3.20)
 
 project(PyClassifiers
-  VERSION 1.0.0
+  VERSION 1.0.1
   DESCRIPTION "Python Classifiers Wrapper."
   HOMEPAGE_URL "https://github.com/rmontanana/pyclassifiers"
   LANGUAGES CXX
@@ -68,9 +68,9 @@ add_git_submodule("lib/BayesNet")
 
 # Subdirectories
 # --------------
-file(GLOB PyClassifiers_SOURCES CONFIGURE_DEPENDS ${PyClassifiers_SOURCE_DIR}/src/PyClassifiers/*.cc ${PyClassifiers_SOURCE_DIR}/src/PyClassifiers/*.hpp)
+file(GLOB PyClassifiers_SOURCES CONFIGURE_DEPENDS ${PyClassifiers_SOURCE_DIR}/src/*.cc ${PyClassifiers_SOURCE_DIR}/src/*.hpp)
 add_subdirectory(config)
-add_subdirectory(src/PyClassifiers)
+add_subdirectory(src)
 
 # Testing
 # -------

src/CMakeLists.txt

@@ -1,7 +1,7 @@
 include_directories(
     ${PyClassifiers_SOURCE_DIR}/lib/Files
     ${PyClassifiers_SOURCE_DIR}/lib/BayesNet/lib/json/include
-    ${PyClassifiers_SOURCE_DIR}/lib/BayesNet/src/BayesNet
+    ${PyClassifiers_SOURCE_DIR}/lib/BayesNet/src
     ${CMAKE_BINARY_DIR}/configured_files/include
     ${Python3_INCLUDE_DIRS}
     ${TORCH_INCLUDE_DIRS}

src/PyClassifier.h

@@ -24,9 +24,12 @@ namespace pywrap {
         PyClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override { return *this; };
         PyClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights) override { return *this; };
         torch::Tensor predict(torch::Tensor& X) override;
-        std::vector<int> predict(std::vector<std::vector<int >>& X) override { return std::vector<int>(); };
-        float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override { return 0.0; };
+        std::vector<int> predict(std::vector<std::vector<int >>& X) override { return std::vector<int>(); }; // Not implemented
+        torch::Tensor predict_proba(torch::Tensor& X) override { return torch::zeros({ 0, 0 }); } // Not implemented
+        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int >>& X) override { return std::vector<std::vector<double>>(); }; // Not implemented
+        float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override { return 0.0; }; // Not implemented
         float score(torch::Tensor& X, torch::Tensor& y) override;
+        int getClassNumStates() const override { return 0; };
         std::string version();
         std::string callMethodString(const std::string& method);
         int callMethodSumOfItems(const std::string& method) const;

tests/CMakeLists.txt

@@ -1,11 +1,11 @@
 if(ENABLE_TESTING)
     set(TEST_PYCLASSIFIERS "unit_tests_pyclassifiers")
     include_directories(
-        ${PyClassifiers_SOURCE_DIR}/src/PyClassifiers
+        ${PyClassifiers_SOURCE_DIR}/src/
         ${PyClassifiers_SOURCE_DIR}/lib/BayesNet/lib/json/include
         ${PyClassifiers_SOURCE_DIR}/lib/BayesNet/lib/Files
         ${PyClassifiers_SOURCE_DIR}/lib/BayesNet/lib/mdlp
-        ${PyClassifiers_SOURCE_DIR}/lib/BayesNet/src/BayesNet
+        ${PyClassifiers_SOURCE_DIR}/lib/BayesNet/src
         ${Python3_INCLUDE_DIRS}
         ${TORCH_INCLUDE_DIRS}
         ${CMAKE_BINARY_DIR}/configured_files/include

tests/TestPythonClassifiers.cc

@@ -26,36 +26,22 @@ TEST_CASE("Test Python Classifiers score", "[PyClassifiers]")
         {{"iris", "STree"}, 0.99333}, {{"iris", "ODTE"}, 0.98667}, {{"iris", "SVC"}, 0.97333}, {{"iris", "RandomForest"}, 1.0},
     };
 
-    std::string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
-    auto raw = RawDatasets(file_name, false);
-
-    SECTION("Test STree classifier (" + file_name + ")")
+    std::string name = GENERATE("ODTE", "STree", "SVC", "RandomForest");
+    map<std::string, pywrap::PyClassifier*> models = {
+        {"ODTE", new pywrap::ODTE()},
+        {"STree", new pywrap::STree()},
+        {"SVC", new pywrap::SVC()},
+        {"RandomForest", new pywrap::RandomForest()}
+    };
+    SECTION("Test Python Classifier " + name + " score ")
     {
-        auto clf = pywrap::STree();
-        clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
-        auto score = clf.score(raw.Xt, raw.yt);
-        REQUIRE(score == Catch::Approx(scores[{file_name, "STree"}]).epsilon(raw.epsilon));
-    }
-    SECTION("Test ODTE classifier (" + file_name + ")")
-    {
-        auto clf = pywrap::ODTE();
-        clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
-        auto score = clf.score(raw.Xt, raw.yt);
-        REQUIRE(score == Catch::Approx(scores[{file_name, "ODTE"}]).epsilon(raw.epsilon));
-    }
-    SECTION("Test SVC classifier (" + file_name + ")")
-    {
-        auto clf = pywrap::SVC();
-        clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
-        auto score = clf.score(raw.Xt, raw.yt);
-        REQUIRE(score == Catch::Approx(scores[{file_name, "SVC"}]).epsilon(raw.epsilon));
-    }
-    SECTION("Test RandomForest classifier (" + file_name + ")")
-    {
-        auto clf = pywrap::RandomForest();
-        clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
-        auto score = clf.score(raw.Xt, raw.yt);
-        REQUIRE(score == Catch::Approx(scores[{file_name, "RandomForest"}]).epsilon(raw.epsilon));
+        for (auto file_name : { "glass", "iris", "ecoli", "diabetes" }) {
+            auto raw = RawDatasets(file_name, false);
+            auto clf = models[name];
+            clf->fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
+            auto score = clf->score(raw.Xt, raw.yt);
+            REQUIRE(score == Catch::Approx(scores[{file_name, name}]).epsilon(raw.epsilon));
+        }
     }
 }
 TEST_CASE("Classifiers features", "[PyClassifiers]")
@@ -74,13 +60,13 @@ TEST_CASE("Get num features & num edges", "[PyClassifiers]")
     REQUIRE(clf.getNumberOfNodes() == 10);
     REQUIRE(clf.getNumberOfEdges() == 10);
 }
-TEST_CASE("XGBoost", "[PyClassifiers]")
-{
-    auto raw = RawDatasets("iris", true);
-    auto clf = pywrap::XGBoost();
-    clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
-    nlohmann::json hyperparameters = { "n_jobs=1" };
-    clf.setHyperparameters(hyperparameters);
-    auto score = clf.score(raw.Xt, raw.yt);
-    REQUIRE(score == Catch::Approx(0.98).epsilon(raw.epsilon));
-}
+// TEST_CASE("XGBoost", "[PyClassifiers]")
+// {
+//     auto raw = RawDatasets("iris", true);
+//     auto clf = pywrap::XGBoost();
+//     clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
+//     nlohmann::json hyperparameters = { "n_jobs=1" };
+//     clf.setHyperparameters(hyperparameters);
+//     auto score = clf.score(raw.Xt, raw.yt);
+//     REQUIRE(score == Catch::Approx(0.98).epsilon(raw.epsilon));
+// }