Separate commands from modules in folders

CMakeLists.txt

@@ -78,6 +78,7 @@ message("XLSXWRITER_LIB=${XLSXWRITER_LIB}")
 
 # Subdirectories
 # --------------
+set(Platform_MODULES ${Platform_SOURCE_DIR}/src/Platform/modules/)
 add_subdirectory(config)
 add_subdirectory(src/Platform)
 add_subdirectory(sample)

sample/CMakeLists.txt

@@ -10,5 +10,5 @@ include_directories(
     ${Platform_SOURCE_DIR}/lib/PyClassifiers/lib/BayesNet/lib/json/include
     ${CMAKE_BINARY_DIR}/configured_files/include
 )
-add_executable(PlatformSample sample.cc ${Platform_SOURCE_DIR}/src/Platform/Models.cc) 
+add_executable(PlatformSample sample.cc ${Platform_MODULES}/Models.cc) 
 target_link_libraries(PlatformSample PyClassifiers ArffFiles mdlp "${TORCH_LIBRARIES}")

src/Platform/CMakeLists.txt

@@ -4,7 +4,7 @@ include_directories(
     ${Platform_SOURCE_DIR}/lib/PyClassifiers/lib/BayesNet/lib/mdlp
     ${Platform_SOURCE_DIR}/lib/PyClassifiers/lib/BayesNet/lib/json/include
     ${Platform_SOURCE_DIR}/lib/PyClassifiers/src/PyClassifiers
-    ${Platform_SOURCE_DIR}/src/Platform
+    ${Platform_MODULES}
     ${Platform_SOURCE_DIR}/lib/Files
     ${Platform_SOURCE_DIR}/lib/mdlp
     ${Platform_SOURCE_DIR}/lib/argparse/include
@@ -15,11 +15,19 @@ include_directories(
     ${CMAKE_BINARY_DIR}/configured_files/include
 )
 
-add_executable(b_best b_best.cc BestResults.cc Result.cc Statistics.cc BestResultsExcel.cc ReportExcel.cc ReportBase.cc Datasets.cc Dataset.cc ExcelFile.cc)
-add_executable(b_grid b_grid.cc GridSearch.cc GridData.cc HyperParameters.cc Datasets.cc Dataset.cc Models.cc)
-add_executable(b_list b_list.cc Datasets.cc Dataset.cc)
-add_executable(b_main b_main.cc Experiment.cc Datasets.cc Dataset.cc Models.cc HyperParameters.cc ReportConsole.cc ReportBase.cc)
-add_executable(b_manage b_manage.cc Results.cc ManageResults.cc CommandParser.cc Result.cc ReportConsole.cc ReportExcel.cc ReportBase.cc Datasets.cc Dataset.cc ExcelFile.cc)
+set(best_sources BestResults.cc Result.cc Statistics.cc BestResultsExcel.cc ReportExcel.cc ReportBase.cc Datasets.cc Dataset.cc ExcelFile.cc)
+list(TRANSFORM best_sources PREPEND ${Platform_MODULES})
+add_executable(b_best b_best.cc ${best_sources})
+set(grid_sources GridSearch.cc GridData.cc HyperParameters.cc Datasets.cc Dataset.cc Models.cc)
+list(TRANSFORM grid_sources PREPEND ${Platform_MODULES})
+add_executable(b_grid b_grid.cc ${grid_sources})
+add_executable(b_list b_list.cc ${Platform_MODULES}Datasets.cc ${Platform_MODULES}Dataset.cc)
+set(main_sources Experiment.cc Datasets.cc Dataset.cc Models.cc HyperParameters.cc ReportConsole.cc ReportBase.cc)
+list(TRANSFORM main_sources PREPEND ${Platform_MODULES})
+add_executable(b_main b_main.cc ${main_sources})
+set(manage_sources Results.cc ManageResults.cc CommandParser.cc Result.cc ReportConsole.cc ReportExcel.cc ReportBase.cc Datasets.cc Dataset.cc ExcelFile.cc)
+list(TRANSFORM manage_sources PREPEND ${Platform_MODULES})
+add_executable(b_manage b_manage.cc ${manage_sources})
 
 target_link_libraries(b_best Boost::boost "${XLSXWRITER_LIB}" "${TORCH_LIBRARIES}" ArffFiles mdlp)
 target_link_libraries(b_grid PyClassifiers ${MPI_CXX_LIBRARIES} ArffFiles)

tests/CMakeLists.txt

@@ -7,7 +7,7 @@ if(ENABLE_TESTING)
         ${Platform_SOURCE_DIR}/lib/Files
         ${CMAKE_BINARY_DIR}/configured_files/include
     )
-    set(TEST_SOURCES_PLATFORM TestUtils.cc)
+    set(TEST_SOURCES_PLATFORM TestUtils.cc TestPlatform.cc)
     add_executable(${TEST_PLATFORM} ${TEST_SOURCES_PLATFORM})
     target_link_libraries(${TEST_PLATFORM} PUBLIC "${TORCH_LIBRARIES}" ArffFiles mdlp Catch2::Catch2WithMain)
     add_test(NAME ${TEST_PLATFORM} COMMAND ${TEST_PLATFORM})

tests/TestPlatform.cc

@@ -0,0 +1,70 @@
+#define CATCH_CONFIG_MAIN  // This tells Catch to provide a main() - only do
+#include <catch2/catch_test_macros.hpp>
+#include <catch2/catch_approx.hpp>
+#include <catch2/generators/catch_generators.hpp>
+#include <vector>
+#include <map>
+#include <string>
+#include "TestUtils.h"
+
+TEST_CASE("Test Python Classifiers score", "[PyClassifiers]")
+{
+    map <pair<std::string, std::string>, float> scores = {
+        // Diabetes
+        {{"diabetes", "STree"}, 0.81641}, {{"diabetes", "ODTE"}, 0.84635}, {{"diabetes", "SVC"}, 0.76823}, {{"diabetes", "RandomForest"}, 1.0},
+        // Ecoli
+        {{"ecoli", "STree"}, 0.8125}, {{"ecoli", "ODTE"}, 0.84821}, {{"ecoli", "SVC"}, 0.89583}, {{"ecoli", "RandomForest"}, 1.0},
+        // Glass
+        {{"glass", "STree"}, 0.57009}, {{"glass", "ODTE"}, 0.77103}, {{"glass", "SVC"}, 0.35514}, {{"glass", "RandomForest"}, 1.0},
+        // Iris
+        {{"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 + ")")
+    {
+        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));
+    }
+}
+TEST_CASE("Classifiers features", "[PyClassifiers]")
+{
+    auto raw = RawDatasets("iris", true);
+    auto clf = pywrap::STree();
+    clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
+    REQUIRE(clf.getNumberOfNodes() == 3);
+    REQUIRE(clf.getNumberOfEdges() == 2);
+}
+TEST_CASE("Get num features & num edges", "[PyClassifiers]")
+{
+    auto raw = RawDatasets("iris", true);
+    auto clf = pywrap::ODTE();
+    clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
+    REQUIRE(clf.getNumberOfNodes() == 10);
+    REQUIRE(clf.getNumberOfEdges() == 10);
+}