Update github workflow remove coverage report

* First approach

* 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

.github/workflows/build.yml

@@ -19,26 +19,29 @@ jobs:
           submodules: recursive
       - name: Install sonar-scanner and build-wrapper
         uses: SonarSource/sonarcloud-github-c-cpp@v2
+      - name: Install Python and Conan
+        run: |
+          sudo apt-get update
+          sudo apt-get -y install python3 python3-pip
+          pip3 install conan
       - name: Install lcov & gcovr
         run: |
           sudo apt-get -y install lcov
           sudo apt-get -y install gcovr
-      - name: Install Libtorch
+      - name: Setup Conan profileson
         run: |
-          wget https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-2.3.1%2Bcpu.zip
+          conan profile detect --force
-          unzip libtorch-cxx11-abi-shared-with-deps-2.3.1+cpu.zip
+          conan remote add cimmeria https://conan.rmontanana.es/artifactory/api/conan/Cimmeria
+      - name: Install dependencies with Conan
+        run: |
+          conan install . --build=missing -of build_debug -s build_type=Debug -o enable_testing=True
+      - name: Configure with CMake
+        run: |
+          cmake -S . -B build_debug -DCMAKE_TOOLCHAIN_FILE=build_debug/build/Debug/generators/conan_toolchain.cmake -DCMAKE_BUILD_TYPE=Debug -DENABLE_TESTING=ON
       - name: Tests & build-wrapper
         run: |
-          cmake -S . -B build -Wno-dev -DCMAKE_PREFIX_PATH=$(pwd)/libtorch -DCMAKE_BUILD_TYPE=Debug -DENABLE_TESTING=ON
+          build-wrapper-linux-x86-64 --out-dir ${{ env.BUILD_WRAPPER_OUT_DIR }} cmake --build build_debug --config Debug -j 4
-          build-wrapper-linux-x86-64 --out-dir ${{ env.BUILD_WRAPPER_OUT_DIR }} cmake --build build/ --config Debug
+          cp -r tests/datasets build_debug/tests/datasets
-          cmake --build build -j 4
+          cd build_debug/tests
-          cd build
+          ctest --output-on-failure -j 4
-          ctest -C Debug --output-on-failure -j 4
+          
-          gcovr -f ../src/CPPFImdlp.cpp -f ../src/Metrics.cpp -f ../src/BinDisc.cpp -f ../src/Discretizer.cpp --txt --sonarqube=coverage.xml
-      - name: Run sonar-scanner
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }}
-        run: |
-          sonar-scanner --define sonar.cfamily.compile-commands="${{ env.BUILD_WRAPPER_OUT_DIR }}" \
-                        --define sonar.coverageReportPaths=build/coverage.xml

.gitignore

@@ -39,4 +39,5 @@ build_release
 .idea
 cmake-*
 **/CMakeFiles
-**/gcovr-report
+**/gcovr-report
+CMakeUserPresets.json

CHANGELOG.md

@@ -5,6 +5,14 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [2.1.1] - 2025-07-17
+
+### Internal Changes
+
+- Updated Libtorch to version 2.7.1
+- Updated ArffFiles library to version 1.2.1
+- Enhance CMake configuration for better compatibility
+
 ## [2.1.0] - 2025-06-28
 
 ### Added

CMakeLists.txt

@@ -4,7 +4,7 @@ project(fimdlp
     LANGUAGES CXX
     DESCRIPTION "Discretization algorithm based on the paper by Fayyad & Irani Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning."
     HOMEPAGE_URL "https://github.com/rmontanana/mdlp"
-    VERSION 2.1.0
+    VERSION 2.1.1
 )
 set(CMAKE_CXX_STANDARD 17)
 cmake_policy(SET CMP0135 NEW)
@@ -15,7 +15,6 @@ find_package(Torch CONFIG REQUIRED)
 # Options
 # -------
 option(ENABLE_TESTING OFF)
-option(ENABLE_SAMPLE  OFF)
 option(COVERAGE       OFF)
 
 add_subdirectory(config)
@@ -26,20 +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(STATUS "Building sample")
-    message("Building sample")
+add_subdirectory(sample)
-    add_subdirectory(sample)
-endif()
 
 include_directories(
     ${fimdlp_SOURCE_DIR}/src

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"
-    ]
-}

CONAN_README.md

@@ -7,9 +7,11 @@ This directory contains the Conan package configuration for the fimdlp library.
 The package manages the following dependencies:
 
 ### Build Requirements
+
 - **libtorch/2.4.1** - PyTorch C++ library for tensor operations
 
 ### Test Requirements (when testing enabled)
+
 - **catch2/3.8.1** - Modern C++ testing framework
 - **arff-files** - ARFF file format support (included locally in tests/lib/Files/)
 
@@ -67,7 +69,7 @@ conan create . -o shared=True --profile:build=default --profile:host=default
 
 ```bash
 # Add Cimmeria remote
-conan remote add cimmeria <cimmeria-server-url>
+conan remote add cimmeria https://conan.rmontanana.es/artifactory/api/conan/Cimmeria
 
 # Login to Cimmeria
 conan remote login cimmeria <username>
@@ -150,4 +152,4 @@ conan create . -o enable_testing=True
 
 - C++17 compatible compiler
 - CMake 3.20 or later
-- Conan 2.0 or later
+- Conan 2.0 or later

Makefile

@@ -1,36 +1,44 @@
 SHELL := /bin/bash
-.DEFAULT_GOAL := build
+.DEFAULT_GOAL := help
-.PHONY: build install test
+.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
+define build_target
-	@echo ">>> Building the project for Release..."
+	@echo ">>> Building the project for $(1)..."
-	@if [ -d $(f_release) ]; then rm -fr $(f_release); fi
+	@if [ -d $(2) ]; then rm -fr $(2); fi
-	@conan install . --build=missing -of $(f_release) -s build_type=Release --profile:build=default --profile:host=default
+	@conan install . --build=missing -of $(2) -s build_type=$(1) $(4)
-	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 -S . -B $(2) -DCMAKE_TOOLCHAIN_FILE=$(2)/build/$(1)/generators/conan_toolchain.cmake -DCMAKE_BUILD_TYPE=$(1) -D$(3)
-	@cmake --build $(f_release) -j 8
+	@cmake --build $(2) --config $(1) -j 8
+endef
 
-install: ## Install the project
+debug: ## Build Debug version of the library
+	@$(call build_target,"Debug","$(f_debug)", "ENABLE_TESTING=ON", "-o enable_testing=True")
+
+release: ## Build Release version of the library
+	@$(call build_target,"Release","$(f_release)", "ENABLE_TESTING=OFF", "-o enable_testing=False")
+
+install: ## Install the library
 	@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 $(f_debug); fi
+	@$(MAKE) debug;
-	@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
 	@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; \
+	@echo ">>> Generating coverage report..."
+	@cd $(f_debug)/tests && $(lcov) --capture --directory ../ --demangle-cpp --ignore-errors source,source --ignore-errors mismatch --ignore-errors inconsistent --output-file coverage.info >/dev/null 2>&1; \
 	$(lcov) --remove coverage.info '/usr/*' --output-file coverage.info >/dev/null 2>&1; \
 	$(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)
@@ -40,11 +48,38 @@ test: ## Build Debug version and run tests
 	fi
 	@echo ">>> Updating coverage badge..."
 	@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 --pr:b=release -pr:h=release
+	conan create . --build=missing -tf "" -s:a build_type=Release 
-	# conan create . --build=missing -pr:b=debug -pr:h=debug
+	conan create . --build=missing -tf "" -s:a build_type=Debug -o "&:enable_testing=False"
-
+	@echo ">>> Done"
-
 
+help: ## Show help message
+	@IFS=$$'\n' ; \
+	help_lines=(`fgrep -h "##" $(MAKEFILE_LIST) | fgrep -v fgrep | sed -e 's/\\$$//' | sed -e 's/##/:/'`); \
+	printf "%s\n\n" "Usage: make [task]"; \
+	printf "%-20s %s\n" "task" "help" ; \
+	printf "%-20s %s\n" "------" "----" ; \
+	for help_line in $${help_lines[@]}; do \
+		IFS=$$':' ; \
+		help_split=($$help_line) ; \
+		help_command=`echo $${help_split[0]} | sed -e 's/^ *//' -e 's/ *$$//'` ; \
+		help_info=`echo $${help_split[2]} | sed -e 's/^ *//' -e 's/ *$$//'` ; \
+		printf '\033[36m'; \
+		printf "%-20s %s" $$help_command ; \
+		printf '\033[0m'; \
+		printf "%s\n" $$help_info; \
+	done

README.md

@@ -3,7 +3,7 @@
 [![Reliability Rating](https://sonarcloud.io/api/project_badges/measure?project=rmontanana_mdlp&metric=reliability_rating)](https://sonarcloud.io/summary/new_code?id=rmontanana_mdlp)
 [![Coverage Badge](https://img.shields.io/badge/Coverage-100,0%25-green)](html/index.html)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/rmontanana/mdlp)
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.14245443.svg)](https://doi.org/10.5281/zenodo.14245443)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.16025501.svg)](https://doi.org/10.5281/zenodo.16025501)
 
 # <img src="logo.png" alt="logo" width="50"/> mdlp
 
@@ -18,9 +18,7 @@ Other features:
 
 - Intervals with the same value of the variable are not taken into account for cutpoints.
 - Intervals have to have more than two examples to be evaluated (mdlp).
-
 - The algorithm returns the cut points for the variable.
-
 - The transform method uses the cut points returning its index in the following way:
 
         cut[i - 1] <= x < cut[i]

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
-

conanfile.py

@@ -51,10 +51,10 @@ class FimdlpConan(ConanFile):
     
     def requirements(self):
         # PyTorch dependency for tensor operations
-        self.requires("libtorch/2.7.0")
+        self.requires("libtorch/2.7.1")
         
     def build_requirements(self):
-        self.requires("arff-files/1.2.0") # for tests and sample
+        self.requires("arff-files/1.2.1") # for tests and sample
         if self.options.enable_testing: 
             self.test_requires("gtest/1.16.0")
     
@@ -108,4 +108,4 @@ class FimdlpConan(ConanFile):
             self.cpp_info.system_libs.append("pthread")  # Threading
         
         # Build information for consumers
-        self.cpp_info.builddirs = ["lib/cmake/fimdlp"]
+        self.cpp_info.builddirs = ["lib/cmake/fimdlp"]

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}
 )
 

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()) {

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&);

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");
             }

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);

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");
+    }
 }

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()
@@ -32,7 +41,7 @@ namespace mdlp {
         Discretizer* disc = new BinDisc(4, strategy_t::UNIFORM);
         auto version = disc->version();
         delete disc;
-        EXPECT_EQ("2.1.0", version);
+        EXPECT_EQ("2.1.1", version);
     }
     TEST(Discretizer, BinIrisUniform)
     {
@@ -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;
+    }
 }

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");
+    }
+
+
 }