diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index c4e4ab0..ed94eba 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -2,10 +2,12 @@ include_directories(
     ${PyClassifiers_SOURCE_DIR}/lib/Files
     ${PyClassifiers_SOURCE_DIR}/lib/BayesNet/lib/json/include
     ${PyClassifiers_SOURCE_DIR}/lib/BayesNet/src
+    ${PyClassifiers_SOURCE_DIR}/lib/BayesNet/src/classifiers
+    ${PyClassifiers_SOURCE_DIR}/lib/BayesNet/src/bayesian_network
+    ${PyClassifiers_SOURCE_DIR}/lib/BayesNet/src/utils
     ${CMAKE_BINARY_DIR}/configured_files/include
     ${Python3_INCLUDE_DIRS}
     ${TORCH_INCLUDE_DIRS}
 )
 add_library(PyClassifiers SHARED ${PyClassifiers_SOURCES})
-#target_link_libraries(PyClassifiers ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::boost Boost::python Boost::numpy xgboost::xgboost)
 target_link_libraries(PyClassifiers BayesNet ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::boost Boost::python Boost::numpy)
\ No newline at end of file
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 7206cc9..465f026 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -6,6 +6,9 @@ if(ENABLE_TESTING)
         ${PyClassifiers_SOURCE_DIR}/lib/BayesNet/lib/Files
         ${PyClassifiers_SOURCE_DIR}/lib/BayesNet/lib/mdlp
         ${PyClassifiers_SOURCE_DIR}/lib/BayesNet/src
+        ${PyClassifiers_SOURCE_DIR}/lib/BayesNet/src/classifiers
+        ${PyClassifiers_SOURCE_DIR}/lib/BayesNet/src/bayesian_network
+        ${PyClassifiers_SOURCE_DIR}/lib/BayesNet/src/utils
         ${Python3_INCLUDE_DIRS}
         ${TORCH_INCLUDE_DIRS}
         ${CMAKE_BINARY_DIR}/configured_files/include
diff --git a/tests/TestPythonClassifiers.cc b/tests/TestPythonClassifiers.cc
index 0407920..3856ec9 100644
--- a/tests/TestPythonClassifiers.cc
+++ b/tests/TestPythonClassifiers.cc
@@ -35,11 +35,12 @@ TEST_CASE("Test Python Classifiers score", "[PyClassifiers]")
     };
     SECTION("Test Python Classifier " + name + " score ")
     {
-        for (auto file_name : { "glass", "iris", "ecoli", "diabetes" }) {
+        for (std::string 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);
+            INFO("File: " + file_name + " Classifier: " + name + " Score: " + to_string(score));
             REQUIRE(score == Catch::Approx(scores[{file_name, name}]).epsilon(raw.epsilon));
         }
     }