Fix conan (#10)

* Fix debug conan build target * Add viewcoverage and fix coverage generation * Add more tests to cover new integrity checks * Add tests to accomplish 100% * Fix conan-create makefile target
2025-08-15 07:25:56 +00:00 · 2025-07-02 20:09:34 +02:00
parent c1759ba1ce
commit 6d8b55a808
15 changed files with 322 additions and 165 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -39,4 +39,5 @@ build_release
 .idea
 cmake-*
 **/CMakeFiles
-**/gcovr-report
+**/gcovr-report
+CMakeUserPresets.json
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -10,12 +10,11 @@ set(CMAKE_CXX_STANDARD 17)
 cmake_policy(SET CMP0135 NEW)

 # Find dependencies
-find_package(Torch REQUIRED)
+find_package(Torch CONFIG REQUIRED)

 # Options
 # -------
 option(ENABLE_TESTING OFF)
-option(ENABLE_SAMPLE  OFF)
 option(COVERAGE       OFF)

 add_subdirectory(config)
@@ -26,21 +25,24 @@ if (NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
    set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fno-default-inline")
 endif()

+if (CMAKE_BUILD_TYPE STREQUAL "Debug")
+    message(STATUS "Debug mode")
+else()
+    message(STATUS "Release mode")
+endif()
+
 if (ENABLE_TESTING)
-    message("Debug mode")
-    
+    message(STATUS "Testing is enabled")
    enable_testing()
    set(CODE_COVERAGE ON)
    set(GCC_COVERAGE_LINK_FLAGS "${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
    add_subdirectory(tests)
 else()
-    message("Release mode")
+    message(STATUS "Testing is disabled")
 endif()

-if (ENABLE_SAMPLE)
-    message("Building sample")
-    add_subdirectory(sample)
-endif()
+message(STATUS "Building sample")
+add_subdirectory(sample)

 include_directories(
    ${fimdlp_SOURCE_DIR}/src
@@ -62,11 +64,10 @@ write_basic_package_version_file(
 install(TARGETS fimdlp
        EXPORT fimdlpTargets
        ARCHIVE DESTINATION lib
-        LIBRARY DESTINATION lib
-        CONFIGURATIONS Release)
+        LIBRARY DESTINATION lib)

-install(DIRECTORY src/ DESTINATION include/fimdlp FILES_MATCHING CONFIGURATIONS Release PATTERN "*.h")
-install(FILES ${CMAKE_BINARY_DIR}/configured_files/include/config.h DESTINATION include/fimdlp CONFIGURATIONS Release)
+install(DIRECTORY src/ DESTINATION include/fimdlp FILES_MATCHING PATTERN "*.h")
+install(FILES ${CMAKE_BINARY_DIR}/configured_files/include/config.h DESTINATION include/fimdlp)

 install(EXPORT fimdlpTargets
        FILE fimdlpTargets.cmake
--- a/CMakeUserPresets.json
+++ b/CMakeUserPresets.json
@@ -1,11 +0,0 @@
-{
-    "version": 4,
-    "vendor": {
-        "conan": {}
-    },
-    "include": [
-        "build_release/build/Release/generators/CMakePresets.json",
-        "build_debug/build/Debug/generators/CMakePresets.json",
-        "build/Release/generators/CMakePresets.json"
-    ]
-}
--- a/58
+++ b/58
@@ -1,28 +1,34 @@
 SHELL := /bin/bash
-.DEFAULT_GOAL := build
-.PHONY: build install test
+.DEFAULT_GOAL := release
+.PHONY: debug release install test conan-create viewcoverage
 lcov := lcov

 f_debug = build_debug
 f_release = build_release
+genhtml = genhtml
+docscdir = docs

-build: ## Build the project for Release
-	@echo ">>> Building the project for Release..."
-	@if [ -d $(f_release) ]; then rm -fr $(f_release); fi
-	@conan install . --build=missing -of $(f_release) -s build_type=Release --profile:build=default --profile:host=default
-	cmake -S . -B $(f_release) -DCMAKE_TOOLCHAIN_FILE=$(f_release)/build/Release/generators/conan_toolchain.cmake -DCMAKE_BUILD_TYPE=Release -DENABLE_TESTING=OFF -DENABLE_SAMPLE=OFF
-	@cmake --build $(f_release) -j 8
+define build_target
+	@echo ">>> Building the project for $(1)..."
+	@if [ -d $(2) ]; then rm -fr $(2); fi
+	@conan install . --build=missing -of $(2) -s build_type=$(1)
+	@cmake -S . -B $(2) -DCMAKE_TOOLCHAIN_FILE=$(2)/build/$(1)/generators/conan_toolchain.cmake -DCMAKE_BUILD_TYPE=$(1) -D$(3)
+	@cmake --build $(2) --config $(1) -j 8
+endef
+
+debug: ## Build Debug version of the library
+	@$(call build_target,"Debug","$(f_debug)", "ENABLE_TESTING=ON")
+
+release: ## Build Release version of the library
+	@$(call build_target,"Release","$(f_release)", "ENABLE_TESTING=OFF")

 install: ## Install the project
 	@echo ">>> Installing the project..."
-	@cmake --build build_release --target install -j 8		
+	@cmake --build $(f_release) --target install -j 8		

 test: ## Build Debug version and run tests
 	@echo ">>> Building Debug version and running tests..."
-	@if [ -d $(f_debug) ]; then rm -fr $(f_debug); fi
-	@conan install . --build=missing -of $(f_debug) -s build_type=Debug
-	@cmake -B $(f_debug) -S . -DCMAKE_BUILD_TYPE=Debug -DCMAKE_TOOLCHAIN_FILE=$(f_debug)/build/Debug/generators/conan_toolchain.cmake -DENABLE_TESTING=ON -DENABLE_SAMPLE=ON
-	@cmake --build $(f_debug) -j 8
+	@$(MAKE) debug;
 	@cp -r tests/datasets $(f_debug)/tests/datasets
 	@cd $(f_debug)/tests && ctest --output-on-failure -j 8
 	@cd $(f_debug)/tests && $(lcov) --capture --directory ../ --demangle-cpp --ignore-errors source,source --ignore-errors mismatch --output-file coverage.info >/dev/null 2>&1; \
@@ -30,7 +36,8 @@ test: ## Build Debug version and run tests
 	$(lcov) --remove coverage.info 'lib/*' --output-file coverage.info >/dev/null 2>&1; \
 	$(lcov) --remove coverage.info 'libtorch/*' --output-file coverage.info >/dev/null 2>&1; \
 	$(lcov) --remove coverage.info 'tests/*' --output-file coverage.info >/dev/null 2>&1; \
-	$(lcov) --remove coverage.info 'gtest/*' --output-file coverage.info >/dev/null 2>&1;
+	$(lcov) --remove coverage.info 'gtest/*' --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info '*/.conan2/*' --ignore-errors unused --output-file coverage.info >/dev/null 2>&1;
 	@genhtml $(f_debug)/tests/coverage.info --demangle-cpp --output-directory $(f_debug)/tests/coverage --title "Discretizer mdlp Coverage Report" -s -k -f --legend
 	@echo "* Coverage report is generated at $(f_debug)/tests/coverage/index.html"
 	@which python || (echo ">>> Please install python"; exit 1)
@@ -39,4 +46,25 @@ test: ## Build Debug version and run tests
 		exit 1; \
 	fi
 	@echo ">>> Updating coverage badge..."
-	@env python update_coverage.py $(f_debug)/tests
+	@env python update_coverage.py $(f_debug)/tests
+	@echo ">>> Done"
+
+viewcoverage: ## View the html coverage report
+	@which $(genhtml) >/dev/null || (echo ">>> Please install lcov (genhtml not found)"; exit 1)
+	@if [ ! -d $(docscdir)/coverage ]; then mkdir -p $(docscdir)/coverage; fi
+	@if [ ! -f $(f_debug)/tests/coverage.info ]; then \
+		echo ">>> No coverage.info file found. Run make coverage first!"; \
+		exit 1; \
+	fi
+	@$(genhtml) $(f_debug)/tests/coverage.info --demangle-cpp --output-directory $(docscdir)/coverage --title "FImdlp Coverage Report" -s -k -f --legend >/dev/null 2>&1;
+	@xdg-open $(docscdir)/coverage/index.html || open $(docscdir)/coverage/index.html 2>/dev/null
+	@echo ">>> Done";
+
+conan-create: ## Create the conan package
+	@echo ">>> Creating the conan package..."
+	conan create . --build=missing -tf "" -s:a build_type=Release 
+	conan create . --build=missing -tf "" -s:a build_type=Debug -o "&:enable_testing=False"
+	@echo ">>> Done"
+
+
+
--- a/build_conan/CMakeCache.txt
+++ b/build_conan/CMakeCache.txt
@@ -1,101 +0,0 @@
-# This is the CMakeCache file.
-# For build in directory: /home/rmontanana/Code/mdlp/build_conan
-# It was generated by CMake: /usr/bin/cmake
-# You can edit this file to change values found and used by cmake.
-# If you do not want to change any of the values, simply exit the editor.
-# If you do want to change a value, simply edit, save, and exit the editor.
-# The syntax for the file is as follows:
-# KEY:TYPE=VALUE
-# KEY is the name of a variable in the cache.
-# TYPE is a hint to GUIs for the type of VALUE, DO NOT EDIT TYPE!.
-# VALUE is the current value for the KEY.
-
-########################
-# EXTERNAL cache entries
-########################
-
-//No help, variable specified on the command line.
-CMAKE_BUILD_TYPE:UNINITIALIZED=Release
-
-//Value Computed by CMake.
-CMAKE_FIND_PACKAGE_REDIRECTS_DIR:STATIC=/home/rmontanana/Code/mdlp/build_conan/CMakeFiles/pkgRedirects
-
-//Value Computed by CMake
-CMAKE_PROJECT_DESCRIPTION:STATIC=Discretization algorithm based on the paper by Fayyad & Irani Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning.
-
-//Value Computed by CMake
-CMAKE_PROJECT_HOMEPAGE_URL:STATIC=https://github.com/rmontanana/mdlp
-
-//Value Computed by CMake
-CMAKE_PROJECT_NAME:STATIC=fimdlp
-
-//Value Computed by CMake
-CMAKE_PROJECT_VERSION:STATIC=2.1.0
-
-//Value Computed by CMake
-CMAKE_PROJECT_VERSION_MAJOR:STATIC=2
-
-//Value Computed by CMake
-CMAKE_PROJECT_VERSION_MINOR:STATIC=1
-
-//Value Computed by CMake
-CMAKE_PROJECT_VERSION_PATCH:STATIC=0
-
-//Value Computed by CMake
-CMAKE_PROJECT_VERSION_TWEAK:STATIC=
-
-//No help, variable specified on the command line.
-CMAKE_TOOLCHAIN_FILE:UNINITIALIZED=conan_toolchain.cmake
-
-//Value Computed by CMake
-fimdlp_BINARY_DIR:STATIC=/home/rmontanana/Code/mdlp/build_conan
-
-//Value Computed by CMake
-fimdlp_IS_TOP_LEVEL:STATIC=ON
-
-//Value Computed by CMake
-fimdlp_SOURCE_DIR:STATIC=/home/rmontanana/Code/mdlp
-
-
-########################
-# INTERNAL cache entries
-########################
-
-//This is the directory where this CMakeCache.txt was created
-CMAKE_CACHEFILE_DIR:INTERNAL=/home/rmontanana/Code/mdlp/build_conan
-//Major version of cmake used to create the current loaded cache
-CMAKE_CACHE_MAJOR_VERSION:INTERNAL=3
-//Minor version of cmake used to create the current loaded cache
-CMAKE_CACHE_MINOR_VERSION:INTERNAL=30
-//Patch version of cmake used to create the current loaded cache
-CMAKE_CACHE_PATCH_VERSION:INTERNAL=8
-//Path to CMake executable.
-CMAKE_COMMAND:INTERNAL=/usr/bin/cmake
-//Path to cpack program executable.
-CMAKE_CPACK_COMMAND:INTERNAL=/usr/bin/cpack
-//Path to ctest program executable.
-CMAKE_CTEST_COMMAND:INTERNAL=/usr/bin/ctest
-//Path to cache edit program executable.
-CMAKE_EDIT_COMMAND:INTERNAL=/usr/bin/ccmake
-//Name of external makefile project generator.
-CMAKE_EXTRA_GENERATOR:INTERNAL=
-//Name of generator.
-CMAKE_GENERATOR:INTERNAL=Unix Makefiles
-//Generator instance identifier.
-CMAKE_GENERATOR_INSTANCE:INTERNAL=
-//Name of generator platform.
-CMAKE_GENERATOR_PLATFORM:INTERNAL=
-//Name of generator toolset.
-CMAKE_GENERATOR_TOOLSET:INTERNAL=
-//Source directory with the top level CMakeLists.txt file for this
-// project
-CMAKE_HOME_DIRECTORY:INTERNAL=/home/rmontanana/Code/mdlp
-//number of local generators
-CMAKE_NUMBER_OF_MAKEFILES:INTERNAL=1
-//Platform information initialized
-CMAKE_PLATFORM_INFO_INITIALIZED:INTERNAL=1
-//Path to CMake installation.
-CMAKE_ROOT:INTERNAL=/usr/share/cmake
-//uname command
-CMAKE_UNAME:INTERNAL=/usr/bin/uname
-
--- a/sample/CMakeLists.txt
+++ b/sample/CMakeLists.txt
@@ -1,14 +1,10 @@
 set(CMAKE_CXX_STANDARD 17)

-set(CMAKE_BUILD_TYPE Debug)
-
 find_package(arff-files REQUIRED)

 include_directories(
    ${fimdlp_SOURCE_DIR}/src
-    ${fimdlp_SOURCE_DIR}/tests/lib/Files
    ${CMAKE_BINARY_DIR}/configured_files/include
-    ${libtorch_INCLUDE_DIRS_RELEASE}
    ${arff-files_INCLUDE_DIRS}
 )

--- a/src/BinDisc.cpp
+++ b/src/BinDisc.cpp
@@ -49,7 +49,7 @@ namespace mdlp {
        // Note: y parameter is validated but not used in binning strategy
        fit(X);
    }
-    std::vector<precision_t> linspace(precision_t start, precision_t end, int num)
+    std::vector<precision_t> BinDisc::linspace(precision_t start, precision_t end, int num)
    {
        // Input validation
        if (num < 2) {
@@ -77,7 +77,7 @@ namespace mdlp {
    {
        return std::max(lower, std::min(n, upper));
    }
-    std::vector<precision_t> percentile(samples_t& data, const std::vector<precision_t>& percentiles)
+    std::vector<precision_t> BinDisc::percentile(samples_t& data, const std::vector<precision_t>& percentiles)
    {
        // Input validation
        if (data.empty()) {
--- a/src/BinDisc.h
+++ b/src/BinDisc.h
@@ -23,6 +23,9 @@ namespace mdlp {
        // y is included for compatibility with the Discretizer interface
        void fit(samples_t& X_, labels_t& y) override;
        void fit(samples_t& X);
+    protected:
+        std::vector<precision_t> linspace(precision_t start, precision_t end, int num);
+        std::vector<precision_t> percentile(samples_t& data, const std::vector<precision_t>& percentiles);
    private:
        void fit_uniform(const samples_t&);
        void fit_quantile(const samples_t&);
--- a/src/CPPFImdlp.h
+++ b/src/CPPFImdlp.h
@@ -39,8 +39,8 @@ namespace mdlp {
        size_t getCandidate(size_t, size_t);
        size_t compute_max_num_cut_points() const;
        pair<precision_t, size_t> valueCutPoint(size_t, size_t, size_t);
-    private:
-        inline precision_t safe_X_access(size_t idx) const {
+        inline precision_t safe_X_access(size_t idx) const
+        {
            if (idx >= indices.size()) {
                throw std::out_of_range("Index out of bounds for indices array");
            }
@@ -50,7 +50,8 @@ namespace mdlp {
            }
            return X[real_idx];
        }
-        inline label_t safe_y_access(size_t idx) const {
+        inline label_t safe_y_access(size_t idx) const
+        {
            if (idx >= indices.size()) {
                throw std::out_of_range("Index out of bounds for indices array");
            }
@@ -60,7 +61,8 @@ namespace mdlp {
            }
            return y[real_idx];
        }
-        inline size_t safe_subtract(size_t a, size_t b) const {
+        inline size_t safe_subtract(size_t a, size_t b) const
+        {
            if (b > a) {
                throw std::underflow_error("Subtraction would cause underflow");
            }
--- a/src/Discretizer.cpp
+++ b/src/Discretizer.cpp
@@ -17,7 +17,7 @@ namespace mdlp {
        if (cutPoints.size() < 2) {
            throw std::runtime_error("Discretizer not fitted yet or no valid cut points found");
        }
-        
+
        discretizedData.clear();
        discretizedData.reserve(data.size());
        // CutPoints always have at least two items
@@ -40,9 +40,6 @@ namespace mdlp {
    void Discretizer::fit_t(const torch::Tensor& X_, const torch::Tensor& y_)
    {
        // Validate tensor properties for security
-        if (!X_.is_contiguous() || !y_.is_contiguous()) {
-            throw std::invalid_argument("Tensors must be contiguous");
-        }
        if (X_.sizes().size() != 1 || y_.sizes().size() != 1) {
            throw std::invalid_argument("Only 1D tensors supported");
        }
@@ -58,7 +55,7 @@ namespace mdlp {
        if (X_.numel() == 0) {
            throw std::invalid_argument("Tensors cannot be empty");
        }
-        
+
        auto num_elements = X_.numel();
        samples_t X(X_.data_ptr<precision_t>(), X_.data_ptr<precision_t>() + num_elements);
        labels_t y(y_.data_ptr<int>(), y_.data_ptr<int>() + num_elements);
@@ -67,9 +64,6 @@ namespace mdlp {
    torch::Tensor Discretizer::transform_t(const torch::Tensor& X_)
    {
        // Validate tensor properties for security
-        if (!X_.is_contiguous()) {
-            throw std::invalid_argument("Tensor must be contiguous");
-        }
        if (X_.sizes().size() != 1) {
            throw std::invalid_argument("Only 1D tensors supported");
        }
@@ -79,7 +73,7 @@ namespace mdlp {
        if (X_.numel() == 0) {
            throw std::invalid_argument("Tensor cannot be empty");
        }
-        
+
        auto num_elements = X_.numel();
        samples_t X(X_.data_ptr<precision_t>(), X_.data_ptr<precision_t>() + num_elements);
        auto result = transform(X);
@@ -88,9 +82,6 @@ namespace mdlp {
    torch::Tensor Discretizer::fit_transform_t(const torch::Tensor& X_, const torch::Tensor& y_)
    {
        // Validate tensor properties for security
-        if (!X_.is_contiguous() || !y_.is_contiguous()) {
-            throw std::invalid_argument("Tensors must be contiguous");
-        }
        if (X_.sizes().size() != 1 || y_.sizes().size() != 1) {
            throw std::invalid_argument("Only 1D tensors supported");
        }
@@ -106,7 +97,7 @@ namespace mdlp {
        if (X_.numel() == 0) {
            throw std::invalid_argument("Tensors cannot be empty");
        }
-        
+
        auto num_elements = X_.numel();
        samples_t X(X_.data_ptr<precision_t>(), X_.data_ptr<precision_t>() + num_elements);
        labels_t y(y_.data_ptr<int>(), y_.data_ptr<int>() + num_elements);
--- a/test_package/CMakeUserPresets.json
+++ b/test_package/CMakeUserPresets.json
@@ -4,6 +4,7 @@
        "conan": {}
    },
    "include": [
-        "build/gcc-14-x86_64-gnu17-release/generators/CMakePresets.json"
+        "build/gcc-14-x86_64-gnu17-release/generators/CMakePresets.json",
+        "build/gcc-14-x86_64-gnu17-debug/generators/CMakePresets.json"
    ]
 }
--- a/tests/BinDisc_unittest.cpp
+++ b/tests/BinDisc_unittest.cpp
@@ -11,6 +11,16 @@
 #include <ArffFiles.hpp>
 #include "BinDisc.h"
 #include "Experiments.hpp"
+#include <cmath>
+
+#define EXPECT_THROW_WITH_MESSAGE(stmt, etype, whatstring) EXPECT_THROW( \
+try { \
+stmt; \
+} catch (const etype& ex) { \
+EXPECT_EQ(whatstring, std::string(ex.what())); \
+throw; \
+} \
+, etype)

 namespace mdlp {
    const float margin = 1e-4;
@@ -400,4 +410,64 @@ namespace mdlp {
        }
        // std::cout << "* Number of experiments tested: " << num << std::endl;
    }
+
+    TEST_F(TestBinDisc3U, FitDataSizeTooSmall)
+    {
+        // Test when data size is smaller than n_bins
+        samples_t X = { 1.0, 2.0 }; // Only 2 elements for 3 bins
+        EXPECT_THROW_WITH_MESSAGE(fit(X), std::invalid_argument, "Input data size must be at least equal to n_bins");
+    }
+
+    TEST_F(TestBinDisc3Q, FitDataSizeTooSmall)
+    {
+        // Test when data size is smaller than n_bins
+        samples_t X = { 1.0, 2.0 }; // Only 2 elements for 3 bins
+        EXPECT_THROW_WITH_MESSAGE(fit(X), std::invalid_argument, "Input data size must be at least equal to n_bins");
+    }
+
+    TEST_F(TestBinDisc3U, FitWithYEmptyX)
+    {
+        // Test fit(X, y) with empty X
+        samples_t X = {};
+        labels_t y = { 1, 2, 3 };
+        EXPECT_THROW_WITH_MESSAGE(fit(X, y), std::invalid_argument, "X cannot be empty");
+    }
+
+    TEST_F(TestBinDisc3U, LinspaceInvalidNumPoints)
+    {
+        // Test linspace with num < 2
+        EXPECT_THROW_WITH_MESSAGE(linspace(0.0f, 1.0f, 1), std::invalid_argument, "Number of points must be at least 2 for linspace");
+    }
+
+    TEST_F(TestBinDisc3U, LinspaceNaNValues)
+    {
+        // Test linspace with NaN values
+        float nan_val = std::numeric_limits<float>::quiet_NaN();
+        EXPECT_THROW_WITH_MESSAGE(linspace(nan_val, 1.0f, 3), std::invalid_argument, "Start and end values cannot be NaN");
+        EXPECT_THROW_WITH_MESSAGE(linspace(0.0f, nan_val, 3), std::invalid_argument, "Start and end values cannot be NaN");
+    }
+
+    TEST_F(TestBinDisc3U, LinspaceInfiniteValues)
+    {
+        // Test linspace with infinite values
+        float inf_val = std::numeric_limits<float>::infinity();
+        EXPECT_THROW_WITH_MESSAGE(linspace(inf_val, 1.0f, 3), std::invalid_argument, "Start and end values cannot be infinite");
+        EXPECT_THROW_WITH_MESSAGE(linspace(0.0f, inf_val, 3), std::invalid_argument, "Start and end values cannot be infinite");
+    }
+
+    TEST_F(TestBinDisc3U, PercentileEmptyData)
+    {
+        // Test percentile with empty data
+        samples_t empty_data = {};
+        std::vector<precision_t> percentiles = { 25.0f, 50.0f, 75.0f };
+        EXPECT_THROW_WITH_MESSAGE(percentile(empty_data, percentiles), std::invalid_argument, "Data cannot be empty for percentile calculation");
+    }
+
+    TEST_F(TestBinDisc3U, PercentileEmptyPercentiles)
+    {
+        // Test percentile with empty percentiles
+        samples_t data = { 1.0f, 2.0f, 3.0f };
+        std::vector<precision_t> empty_percentiles = {};
+        EXPECT_THROW_WITH_MESSAGE(percentile(data, empty_percentiles), std::invalid_argument, "Percentiles cannot be empty");
+    }
 }
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -1,6 +1,7 @@

 find_package(arff-files REQUIRED)
 find_package(GTest REQUIRED)
+find_package(Torch CONFIG REQUIRED)

 include_directories(
        ${libtorch_INCLUDE_DIRS_DEBUG}
--- a/tests/Discretizer_unittest.cpp
+++ b/tests/Discretizer_unittest.cpp
@@ -13,6 +13,15 @@
 #include "BinDisc.h"
 #include "CPPFImdlp.h"

+#define EXPECT_THROW_WITH_MESSAGE(stmt, etype, whatstring) EXPECT_THROW( \
+try { \
+stmt; \
+} catch (const etype& ex) { \
+EXPECT_EQ(whatstring, std::string(ex.what())); \
+throw; \
+} \
+, etype)
+
 namespace mdlp {
    const float margin = 1e-4;
    static std::string set_data_path()
@@ -270,4 +279,110 @@ namespace mdlp {
            EXPECT_EQ(computed[i], expected[i]);
        }
    }
+
+    TEST(Discretizer, TransformEmptyData)
+    {
+        Discretizer* disc = new BinDisc(4, strategy_t::UNIFORM);
+        samples_t empty_data = {};
+        EXPECT_THROW_WITH_MESSAGE(disc->transform(empty_data), std::invalid_argument, "Data for transformation cannot be empty");
+        delete disc;
+    }
+
+    TEST(Discretizer, TransformNotFitted)
+    {
+        Discretizer* disc = new BinDisc(4, strategy_t::UNIFORM);
+        samples_t data = { 1.0f, 2.0f, 3.0f };
+        EXPECT_THROW_WITH_MESSAGE(disc->transform(data), std::runtime_error, "Discretizer not fitted yet or no valid cut points found");
+        delete disc;
+    }
+
+    TEST(Discretizer, TensorValidationFit)
+    {
+        Discretizer* disc = new BinDisc(4, strategy_t::UNIFORM);
+
+        auto X = torch::tensor({ 1.0f, 2.0f, 3.0f }, torch::kFloat32);
+        auto y = torch::tensor({ 1, 2, 3 }, torch::kInt32);
+
+        // Test non-1D tensors
+        auto X_2d = torch::tensor({ {1.0f, 2.0f}, {3.0f, 4.0f} }, torch::kFloat32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_t(X_2d, y), std::invalid_argument, "Only 1D tensors supported");
+
+        auto y_2d = torch::tensor({ {1, 2}, {3, 4} }, torch::kInt32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_t(X, y_2d), std::invalid_argument, "Only 1D tensors supported");
+
+        // Test wrong tensor types
+        auto X_int = torch::tensor({ 1, 2, 3 }, torch::kInt32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_t(X_int, y), std::invalid_argument, "X tensor must be Float32 type");
+
+        auto y_float = torch::tensor({ 1.0f, 2.0f, 3.0f }, torch::kFloat32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_t(X, y_float), std::invalid_argument, "y tensor must be Int32 type");
+
+        // Test mismatched sizes
+        auto y_short = torch::tensor({ 1, 2 }, torch::kInt32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_t(X, y_short), std::invalid_argument, "X and y tensors must have same number of elements");
+
+        // Test empty tensors
+        auto X_empty = torch::tensor({}, torch::kFloat32);
+        auto y_empty = torch::tensor({}, torch::kInt32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_t(X_empty, y_empty), std::invalid_argument, "Tensors cannot be empty");
+
+        delete disc;
+    }
+
+    TEST(Discretizer, TensorValidationTransform)
+    {
+        Discretizer* disc = new BinDisc(4, strategy_t::UNIFORM);
+
+        // First fit with valid data
+        auto X_fit = torch::tensor({ 1.0f, 2.0f, 3.0f, 4.0f }, torch::kFloat32);
+        auto y_fit = torch::tensor({ 1, 2, 3, 4 }, torch::kInt32);
+        disc->fit_t(X_fit, y_fit);
+
+        // Test non-1D tensor
+        auto X_2d = torch::tensor({ {1.0f, 2.0f}, {3.0f, 4.0f} }, torch::kFloat32);
+        EXPECT_THROW_WITH_MESSAGE(disc->transform_t(X_2d), std::invalid_argument, "Only 1D tensors supported");
+
+        // Test wrong tensor type
+        auto X_int = torch::tensor({ 1, 2, 3 }, torch::kInt32);
+        EXPECT_THROW_WITH_MESSAGE(disc->transform_t(X_int), std::invalid_argument, "X tensor must be Float32 type");
+
+        // Test empty tensor
+        auto X_empty = torch::tensor({}, torch::kFloat32);
+        EXPECT_THROW_WITH_MESSAGE(disc->transform_t(X_empty), std::invalid_argument, "Tensor cannot be empty");
+
+        delete disc;
+    }
+
+    TEST(Discretizer, TensorValidationFitTransform)
+    {
+        Discretizer* disc = new BinDisc(4, strategy_t::UNIFORM);
+
+        auto X = torch::tensor({ 1.0f, 2.0f, 3.0f }, torch::kFloat32);
+        auto y = torch::tensor({ 1, 2, 3 }, torch::kInt32);
+
+        // Test non-1D tensors
+        auto X_2d = torch::tensor({ {1.0f, 2.0f}, {3.0f, 4.0f} }, torch::kFloat32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_transform_t(X_2d, y), std::invalid_argument, "Only 1D tensors supported");
+
+        auto y_2d = torch::tensor({ {1, 2}, {3, 4} }, torch::kInt32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_transform_t(X, y_2d), std::invalid_argument, "Only 1D tensors supported");
+
+        // Test wrong tensor types
+        auto X_int = torch::tensor({ 1, 2, 3 }, torch::kInt32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_transform_t(X_int, y), std::invalid_argument, "X tensor must be Float32 type");
+
+        auto y_float = torch::tensor({ 1.0f, 2.0f, 3.0f }, torch::kFloat32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_transform_t(X, y_float), std::invalid_argument, "y tensor must be Int32 type");
+
+        // Test mismatched sizes
+        auto y_short = torch::tensor({ 1, 2 }, torch::kInt32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_transform_t(X, y_short), std::invalid_argument, "X and y tensors must have same number of elements");
+
+        // Test empty tensors
+        auto X_empty = torch::tensor({}, torch::kFloat32);
+        auto y_empty = torch::tensor({}, torch::kInt32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_transform_t(X_empty, y_empty), std::invalid_argument, "Tensors cannot be empty");
+
+        delete disc;
+    }
 }
--- a/tests/FImdlp_unittest.cpp
+++ b/tests/FImdlp_unittest.cpp
@@ -167,6 +167,15 @@ namespace mdlp {
        indices = { 1, 2, 0 };
    }

+    TEST_F(TestFImdlp, SortIndicesOutOfBounds)
+    {
+        // Test for out of bounds exception in sortIndices
+        samples_t X_long = { 1.0f, 2.0f, 3.0f };
+        labels_t y_short = { 1, 2 };
+        EXPECT_THROW_WITH_MESSAGE(sortIndices(X_long, y_short), std::out_of_range, "Index out of bounds in sort comparison");
+    }
+
+
    TEST_F(TestFImdlp, TestShortDatasets)
    {
        vector<precision_t> computed;
@@ -364,4 +373,55 @@ namespace mdlp {
            EXPECT_EQ(computed_ft[i], expected[i]);
        }
    }
+    TEST_F(TestFImdlp, SafeXAccessIndexOutOfBounds)
+    {
+        // Test safe_X_access with index out of bounds for indices array
+        X = { 1.0f, 2.0f, 3.0f };
+        y = { 1, 2, 3 };
+        indices = { 0, 1 }; // shorter than expected
+
+        // This should trigger the first exception in safe_X_access (idx >= indices.size())
+        EXPECT_THROW_WITH_MESSAGE(safe_X_access(2), std::out_of_range, "Index out of bounds for indices array");
+    }
+
+    TEST_F(TestFImdlp, SafeXAccessXOutOfBounds)
+    {
+        // Test safe_X_access with real_idx out of bounds for X array
+        X = { 1.0f, 2.0f }; // shorter array
+        y = { 1, 2, 3 };
+        indices = { 0, 1, 5 }; // indices[2] = 5 is out of bounds for X
+
+        // This should trigger the second exception in safe_X_access (real_idx >= X.size())
+        EXPECT_THROW_WITH_MESSAGE(safe_X_access(2), std::out_of_range, "Index out of bounds for X array");
+    }
+
+    TEST_F(TestFImdlp, SafeYAccessIndexOutOfBounds)
+    {
+        // Test safe_y_access with index out of bounds for indices array
+        X = { 1.0f, 2.0f, 3.0f };
+        y = { 1, 2, 3 };
+        indices = { 0, 1 }; // shorter than expected
+
+        // This should trigger the first exception in safe_y_access (idx >= indices.size())
+        EXPECT_THROW_WITH_MESSAGE(safe_y_access(2), std::out_of_range, "Index out of bounds for indices array");
+    }
+
+    TEST_F(TestFImdlp, SafeYAccessYOutOfBounds)
+    {
+        // Test safe_y_access with real_idx out of bounds for y array
+        X = { 1.0f, 2.0f, 3.0f };
+        y = { 1, 2 }; // shorter array
+        indices = { 0, 1, 5 }; // indices[2] = 5 is out of bounds for y
+
+        // This should trigger the second exception in safe_y_access (real_idx >= y.size())
+        EXPECT_THROW_WITH_MESSAGE(safe_y_access(2), std::out_of_range, "Index out of bounds for y array");
+    }
+
+    TEST_F(TestFImdlp, SafeSubtractUnderflow)
+    {
+        // Test safe_subtract with underflow condition (b > a)
+        EXPECT_THROW_WITH_MESSAGE(safe_subtract(3, 5), std::underflow_error, "Subtraction would cause underflow");
+    }
+
+
 }