Fix BinDisc quantile mistakes (#9)

* Fix BinDisc quantile mistakes

* Fix FImdlp tests

* Fix tests, samples and remove uneeded support files

* Add coypright header to sources
Fix coverage report
Add coverage badge to README

* Update sonar github action

* Move sources to a folder and change ArffFiles files to library

* Add recursive submodules to github action

.github/workflows/build.yml

@@ -13,9 +13,10 @@ jobs:
     env:
       BUILD_WRAPPER_OUT_DIR: build_wrapper_output_directory # Directory where build-wrapper output will be placed
     steps:
-      - uses: actions/checkout@v4.1.6
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0 # Shallow clones should be disabled for a better relevancy of analysis
+          submodules: recursive
       - name: Install sonar-scanner and build-wrapper
         uses: SonarSource/sonarcloud-github-c-cpp@v2
       - name: Install lcov & gcovr
@@ -28,17 +29,16 @@ jobs:
           unzip libtorch-cxx11-abi-shared-with-deps-2.3.1+cpu.zip
       - name: Tests & build-wrapper
         run: |
-          cmake -S . -B build -Wno-dev -DCMAKE_PREFIX_PATH=$(pwd)/libtorch -DENABLE_TESTING=ON
+          cmake -S . -B build -Wno-dev -DCMAKE_PREFIX_PATH=$(pwd)/libtorch -DCMAKE_BUILD_TYPE=Debug -DENABLE_TESTING=ON
-          build-wrapper-linux-x86-64 --out-dir ${{ env.BUILD_WRAPPER_OUT_DIR }} cmake --build build/ --config Release
+          build-wrapper-linux-x86-64 --out-dir ${{ env.BUILD_WRAPPER_OUT_DIR }} cmake --build build/ --config Debug
+          cmake --build build -j 4
           cd build
-          make
+          ctest -C Debug --output-on-failure -j 4
-          ctest -C Release --output-on-failure --test-dir tests
+          gcovr -f ../src/CPPFImdlp.cpp -f ../src/Metrics.cpp -f ../src/BinDisc.cpp -f ../src/Discretizer.cpp --txt --sonarqube=coverage.xml
-          cd ..
-          gcovr -f CPPFImdlp.cpp -f Metrics.cpp -f BinDisc.cpp -f Discretizer.cpp --txt --sonarqube=coverage.xml
       - name: Run sonar-scanner
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }}
         run: |
-          sonar-scanner --define sonar.cfamily.build-wrapper-output="${{ env.BUILD_WRAPPER_OUT_DIR }}" \
+          sonar-scanner --define sonar.cfamily.compile-commands="${{ env.BUILD_WRAPPER_OUT_DIR }}" \
-                        --define sonar.coverageReportPaths=coverage.xml
+                        --define sonar.coverageReportPaths=build/coverage.xml

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "tests/lib/Files"]
+	path = tests/lib/Files
+	url = https://github.com/rmontanana/ArffFiles.git

CMakeLists.txt

@@ -1,11 +1,34 @@
 cmake_minimum_required(VERSION 3.20)
+
 project(mdlp)
 set(CMAKE_CXX_STANDARD 17)
+cmake_policy(SET CMP0135 NEW)
+
 find_package(Torch REQUIRED)
-include_directories(${TORCH_INCLUDE_DIRS})
+
-add_library(mdlp CPPFImdlp.cpp Metrics.cpp BinDisc.cpp Discretizer.cpp)
+set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG}  -fno-elide-constructors")
-target_link_libraries(mdlp "${TORCH_LIBRARIES}")
+set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3")
-add_subdirectory(sample)
+if (NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+    set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fno-default-inline")
+endif()
+
 if (ENABLE_TESTING)
+    MESSAGE("Debug mode")
+    enable_testing()
+    set(CODE_COVERAGE ON)
+    SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
     add_subdirectory(tests)
+else(ENABLE_TESTING)
+    MESSAGE("Release mode")
 endif(ENABLE_TESTING)
+
+
+add_subdirectory(sample)
+
+include_directories(
+    ${TORCH_INCLUDE_DIRS}
+    ${mdlp_SOURCE_DIR}/src
+)
+
+add_library(mdlp src/CPPFImdlp.cpp src/Metrics.cpp src/BinDisc.cpp src/Discretizer.cpp)
+target_link_libraries(mdlp "${TORCH_LIBRARIES}")

Makefile

@@ -1,13 +1,32 @@
 SHELL := /bin/bash
 .DEFAULT_GOAL := build
 .PHONY: build test
+lcov := lcov
 
 build: 
 	@if [ -d build_release ]; then rm -fr build_release; fi
 	@mkdir build_release
 	@cmake -B build_release -S . -DCMAKE_BUILD_TYPE=Release -DENABLE_TESTING=OFF
-	@cmake --build build_release
+	@cmake --build build_release -j 8
 
 test:
-	@echo "Testing..."
+	@if [ -d build_debug ]; then rm -fr build_debug; fi
-	@cd tests && ./test
+	@mkdir build_debug
+	@cmake -B build_debug -S . -DCMAKE_BUILD_TYPE=Debug -DENABLE_TESTING=ON
+	@cmake --build build_debug -j 8
+	@cd build_debug/tests && ctest --output-on-failure -j 8
+	@cd build_debug/tests && $(lcov) --capture --directory ../ --demangle-cpp --ignore-errors source,source --ignore-errors mismatch --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;
+	@genhtml build_debug/tests/coverage.info --demangle-cpp --output-directory build_debug/tests/coverage --title "Discretizer mdlp Coverage Report" -s -k -f --legend
+	@echo "* Coverage report is generated at build_debug/tests/coverage/index.html"
+	@which python || (echo ">>> Please install python"; exit 1)
+	@if [ ! -f build_debug/tests/coverage.info ]; then \
+		echo ">>> No coverage.info file found!"; \
+		exit 1; \
+	fi
+	@echo ">>> Updating coverage badge..."
+	@env python update_coverage.py build_debug/tests

README.md

@@ -1,6 +1,7 @@
 [![Build](https://github.com/rmontanana/mdlp/actions/workflows/build.yml/badge.svg)](https://github.com/rmontanana/mdlp/actions/workflows/build.yml)
 [![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=rmontanana_mdlp&metric=alert_status)](https://sonarcloud.io/summary/new_code?id=rmontanana_mdlp)
 [![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)
 
 # <img src="logo.png" alt="logo" width="50"/> mdlp
 
@@ -31,15 +32,14 @@ Other features:
 To run the sample, just execute the following commands:
 
 ```bash
-cmake -B build -S .
+make build
-cmake --build build
+build_release/sample/sample -f iris -m 2
-build/sample/sample -f iris -m 2
+build_release/sample/sample -h
-build/sample/sample -h
 ```
 
 ## Test
 
-To run the tests and see coverage (llvm & gcovr have to be installed), execute the following commands:
+To run the tests and see coverage (llvm with lcov and genhtml have to be installed), execute the following commands:
 
 ```bash
 make test

sample/CMakeLists.txt

@@ -2,5 +2,10 @@ set(CMAKE_CXX_STANDARD 17)
 
 set(CMAKE_BUILD_TYPE Debug)
 
-add_executable(sample sample.cpp ../tests/ArffFiles.cpp)
+include_directories(
+    ${mdlp_SOURCE_DIR}/src
+    ${mdlp_SOURCE_DIR}/tests/lib/Files
+)
+
+add_executable(sample sample.cpp )
 target_link_libraries(sample mdlp "${TORCH_LIBRARIES}")

sample/sample.cpp

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include <iostream>
 #include <vector>
 #include <iomanip>
@@ -6,10 +12,10 @@
 #include <cstring>
 #include <getopt.h>
 #include <torch/torch.h>
-#include "../Discretizer.h"
+#include <ArffFiles.hpp>
-#include "../CPPFImdlp.h"
+#include "Discretizer.h"
-#include "../BinDisc.h"
+#include "CPPFImdlp.h"
-#include "../tests/ArffFiles.h"
+#include "BinDisc.h"
 
 const string PATH = "tests/datasets/";
 
@@ -144,7 +150,7 @@ void process_file(const string& path, const string& file_name, bool class_last,
     auto result = test.fit_transform_t(Xt, yt);
     std::cout << "Transformed data (torch)...: " << std::endl;
     for (int i = 130; i < 135; i++) {
-        std::cout << std::fixed << std::setprecision(1) << Xt[i].item<float>() << " " << result[i].item<int>() << std::endl;
+        std::cout << std::fixed << std::setprecision(1) << Xt[i].item<mdlp::precision_t>() << " " << result[i].item<int>() << std::endl;
     }
     auto disc = mdlp::BinDisc(3);
     auto res_v = disc.fit_transform(X[0], y);
@@ -152,7 +158,7 @@ void process_file(const string& path, const string& file_name, bool class_last,
     auto res_t = disc.transform_t(Xt);
     std::cout << "Transformed data (BinDisc)...: " << std::endl;
     for (int i = 130; i < 135; i++) {
-        std::cout << std::fixed << std::setprecision(1) << Xt[i].item<float>() << " " << res_v[i] << " " << res_t[i].item<int>() << std::endl;
+        std::cout << std::fixed << std::setprecision(1) << Xt[i].item<mdlp::precision_t>() << " " << res_v[i] << " " << res_t[i].item<int>() << std::endl;
     }
 }
 

sonar-project.properties

@@ -3,7 +3,7 @@ sonar.organization=rmontanana
 
 # This is the name and version displayed in the SonarCloud UI.
 sonar.projectName=mdlp
-sonar.projectVersion=1.2.1
+sonar.projectVersion=2.0.0
 # sonar.test.exclusions=tests/**
 # sonar.tests=tests/
 # sonar.coverage.exclusions=tests/**,sample/**
@@ -11,4 +11,4 @@ sonar.projectVersion=1.2.1
 #sonar.sources=.
 
 # Encoding of the source code. Default is default system encoding
-sonar.sourceEncoding=UTF-8
+sonar.sourceEncoding=UTF-8

src/BinDisc.cpp

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include <algorithm>
 #include <cmath>
 #include "BinDisc.h"
@@ -24,8 +30,10 @@ namespace mdlp {
             return;
         }
         if (strategy == strategy_t::QUANTILE) {
+            direction = bound_dir_t::RIGHT;
             fit_quantile(X);
         } else if (strategy == strategy_t::UNIFORM) {
+            direction = bound_dir_t::RIGHT;
             fit_uniform(X);
         }
     }
@@ -46,31 +54,30 @@ namespace mdlp {
         }
         return linspc;
     }
-    size_t clip(const size_t n, size_t lower, size_t upper)
+    size_t clip(const size_t n, const size_t lower, const size_t upper)
     {
         return std::max(lower, std::min(n, upper));
     }
-    std::vector<precision_t> percentile(samples_t& data, std::vector<precision_t>& percentiles)
+    std::vector<precision_t> percentile(samples_t& data, const std::vector<precision_t>& percentiles)
     {
         // Implementation taken from https://dpilger26.github.io/NumCpp/doxygen/html/percentile_8hpp_source.html
         std::vector<precision_t> results;
         bool first = true;
         results.reserve(percentiles.size());
         for (auto percentile : percentiles) {
-            const size_t i = static_cast<size_t>(std::floor(static_cast<double>(data.size() - 1) * percentile / 100.));
+            const auto i = static_cast<size_t>(std::floor(static_cast<precision_t>(data.size() - 1) * percentile / 100.));
             const auto indexLower = clip(i, 0, data.size() - 2);
-            const double percentI = static_cast<double>(indexLower) / static_cast<double>(data.size() - 1);
+            const precision_t percentI = static_cast<precision_t>(indexLower) / static_cast<precision_t>(data.size() - 1);
-            const double fraction =
+            const precision_t fraction =
                 (percentile / 100.0 - percentI) /
-                (static_cast<double>(indexLower + 1) / static_cast<double>(data.size() - 1) - percentI);
+                (static_cast<precision_t>(indexLower + 1) / static_cast<precision_t>(data.size() - 1) - percentI);
-            const auto value = data[indexLower] + (data[indexLower + 1] - data[indexLower]) * fraction;
+            if (const auto value = data[indexLower] + (data[indexLower + 1] - data[indexLower]) * fraction; value != results.back() || first) // first needed as results.back() return is undefined for empty vectors
-            if (value != results.back() || first) // first needed as results.back() return is undefined for empty vectors
                 results.push_back(value);
             first = false;
         }
         return results;
     }
-    void BinDisc::fit_quantile(samples_t& X)
+    void BinDisc::fit_quantile(const samples_t& X)
     {
         auto quantiles = linspace(0.0, 100.0, n_bins + 1);
         auto data = X;
@@ -83,9 +90,9 @@ namespace mdlp {
         }
         cutPoints = percentile(data, quantiles);
     }
-    void BinDisc::fit_uniform(samples_t& X)
+    void BinDisc::fit_uniform(const samples_t& X)
     {
-        auto minmax = std::minmax_element(X.begin(), X.end());
+        auto [vmin, vmax] = std::minmax_element(X.begin(), X.end());
-        cutPoints = linspace(*minmax.first, *minmax.second, n_bins + 1);
+        cutPoints = linspace(*vmin, *vmax, n_bins + 1);
     }
 }

src/BinDisc.h

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #ifndef BINDISC_H
 #define BINDISC_H
 
@@ -18,8 +24,8 @@ namespace mdlp {
         void fit(samples_t& X_, labels_t& y) override;
         void fit(samples_t& X);
     private:
-        void fit_uniform(samples_t&);
+        void fit_uniform(const samples_t&);
-        void fit_quantile(samples_t&);
+        void fit_quantile(const samples_t&);
         int n_bins;
         strategy_t strategy;
     };

src/CPPFImdlp.cpp

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include <numeric>
 #include <algorithm>
 #include <set>
@@ -12,6 +18,7 @@ namespace mdlp {
         max_depth(max_depth_),
         proposed_cuts(proposed)
     {
+        direction = bound_dir_t::RIGHT;
     }
 
     size_t CPPFImdlp::compute_max_num_cut_points() const
@@ -20,11 +27,11 @@ namespace mdlp {
         if (proposed_cuts == 0) {
             return numeric_limits<size_t>::max();
         }
-        if (proposed_cuts < 0 || proposed_cuts > static_cast<float>(X.size())) {
+        if (proposed_cuts < 0 || proposed_cuts > static_cast<precision_t>(X.size())) {
             throw invalid_argument("wrong proposed num_cuts value");
         }
         if (proposed_cuts < 1)
-            return static_cast<size_t>(round(static_cast<float>(X.size()) * proposed_cuts));
+            return static_cast<size_t>(round(static_cast<precision_t>(X.size()) * proposed_cuts));
         return static_cast<size_t>(proposed_cuts); // The 2 extra cutpoints should not be considered here as this parameter is considered before they are added
     }
 
@@ -59,9 +66,9 @@ namespace mdlp {
             }
         }
         // Insert first & last X value to the cutpoints as them shall be ignored in transform
-        auto minmax = std::minmax_element(X.begin(), X.end());
+        auto [vmin, vmax] = std::minmax_element(X.begin(), X.end());
-        cutPoints.push_back(*minmax.second);
+        cutPoints.push_back(*vmax);
-        cutPoints.insert(cutPoints.begin(), *minmax.first);
+        cutPoints.insert(cutPoints.begin(), *vmin);
     }
 
     pair<precision_t, size_t> CPPFImdlp::valueCutPoint(size_t start, size_t cut, size_t end)

src/CPPFImdlp.h

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #ifndef CPPFIMDLP_H
 #define CPPFIMDLP_H
 

src/Discretizer.cpp

@@ -1,23 +1,26 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include "Discretizer.h"
 
 namespace mdlp {
+
     labels_t& Discretizer::transform(const samples_t& data)
     {
         discretizedData.clear();
         discretizedData.reserve(data.size());
-        // CutPoints always have more than two items
+        // CutPoints always have at least two items
         // Have to ignore first and last cut points provided
         auto first = cutPoints.begin() + 1;
         auto last = cutPoints.end() - 1;
+        auto bound = direction == bound_dir_t::LEFT ? std::lower_bound<std::vector<precision_t>::iterator, precision_t> : std::upper_bound<std::vector<precision_t>::iterator, precision_t>;
         for (const precision_t& item : data) {
-            auto upper = std::lower_bound(first, last, item);
+            auto pos = bound(first, last, item);
-            int number = upper - first;
+            auto number = pos - first;
-            /*
+            discretizedData.push_back(static_cast<label_t>(number));
-            OJO
-            */
-            if (number < 0)
-                throw std::runtime_error("number is less than 0 in discretizer::transform");
-            discretizedData.push_back(number);
         }
         return discretizedData;
     }
@@ -26,26 +29,26 @@ namespace mdlp {
         fit(X_, y_);
         return transform(X_);
     }
-    void Discretizer::fit_t(torch::Tensor& X_, torch::Tensor& y_)
+    void Discretizer::fit_t(const torch::Tensor& X_, const torch::Tensor& y_)
     {
         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);
         fit(X, y);
     }
-    torch::Tensor Discretizer::transform_t(torch::Tensor& X_)
+    torch::Tensor Discretizer::transform_t(const torch::Tensor& X_)
     {
         auto num_elements = X_.numel();
-        samples_t X(X_.data_ptr<float>(), X_.data_ptr<float>() + num_elements);
+        samples_t X(X_.data_ptr<precision_t>(), X_.data_ptr<precision_t>() + num_elements);
         auto result = transform(X);
-        return torch::tensor(result, torch::kInt32);
+        return torch::tensor(result, torch_label_t);
     }
-    torch::Tensor Discretizer::fit_transform_t(torch::Tensor& X_, torch::Tensor& y_)
+    torch::Tensor Discretizer::fit_transform_t(const torch::Tensor& X_, const torch::Tensor& y_)
     {
         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);
         auto result = fit_transform(X, y);
-        return torch::tensor(result, torch::kInt32);
+        return torch::tensor(result, torch_label_t);
     }
 }

src/Discretizer.h

@@ -1,12 +1,23 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #ifndef DISCRETIZER_H
 #define DISCRETIZER_H
 
 #include <string>
 #include <algorithm>
-#include <torch/torch.h>
 #include "typesFImdlp.h"
+#include <torch/torch.h>
 
 namespace mdlp {
+    enum class bound_dir_t {
+        LEFT,
+        RIGHT
+    };
+    const auto torch_label_t = torch::kInt32;
     class Discretizer {
     public:
         Discretizer() = default;
@@ -15,13 +26,14 @@ namespace mdlp {
         virtual void fit(samples_t& X_, labels_t& y_) = 0;
         labels_t& transform(const samples_t& data);
         labels_t& fit_transform(samples_t& X_, labels_t& y_);
-        void fit_t(torch::Tensor& X_, torch::Tensor& y_);
+        void fit_t(const torch::Tensor& X_, const torch::Tensor& y_);
-        torch::Tensor transform_t(torch::Tensor& X_);
+        torch::Tensor transform_t(const torch::Tensor& X_);
-        torch::Tensor fit_transform_t(torch::Tensor& X_, torch::Tensor& y_);
+        torch::Tensor fit_transform_t(const torch::Tensor& X_, const torch::Tensor& y_);
         static inline std::string version() { return "1.2.3"; };
     protected:
         labels_t discretizedData = labels_t();
         cutPoints_t cutPoints; // At least two cutpoints must be provided, the first and the last will be ignored in transform
+        bound_dir_t direction; // used in transform
     };
 }
 #endif

src/Metrics.cpp

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include "Metrics.h"
 #include <set>
 #include <cmath>

src/Metrics.h

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #ifndef CCMETRICS_H
 #define CCMETRICS_H
 

src/typesFImdlp.h

@@ -8,8 +8,9 @@
 using namespace std;
 namespace mdlp {
     typedef float precision_t;
+    typedef int label_t;
     typedef std::vector<precision_t> samples_t;
-    typedef std::vector<int> labels_t;
+    typedef std::vector<label_t> labels_t;
     typedef std::vector<size_t> indices_t;
     typedef std::vector<precision_t> cutPoints_t;
     typedef std::map<std::pair<int, int>, precision_t> cacheEnt_t;

tests/ArffFiles.cpp

@@ -1,132 +0,0 @@
-#include "ArffFiles.h"
-#include <fstream>
-#include <sstream>
-#include <map>
-
-using namespace std;
-
-ArffFiles::ArffFiles() = default;
-
-vector<string> ArffFiles::getLines() const
-{
-    return lines;
-}
-
-unsigned long int ArffFiles::getSize() const
-{
-    return lines.size();
-}
-
-vector<pair<string, string>> ArffFiles::getAttributes() const
-{
-    return attributes;
-}
-
-string ArffFiles::getClassName() const
-{
-    return className;
-}
-
-string ArffFiles::getClassType() const
-{
-    return classType;
-}
-
-vector<mdlp::samples_t>& ArffFiles::getX()
-{
-    return X;
-}
-
-vector<int>& ArffFiles::getY()
-{
-    return y;
-}
-
-void ArffFiles::load(const string& fileName, bool classLast)
-{
-    ifstream file(fileName);
-    if (!file.is_open()) {
-        throw invalid_argument("Unable to open file");
-    }
-    string line;
-    string keyword;
-    string attribute;
-    string type;
-    string type_w;
-    while (getline(file, line)) {
-        if (line.empty() || line[0] == '%' || line == "\r" || line == " ") {
-            continue;
-        }
-        if (line.find("@attribute") != string::npos || line.find("@ATTRIBUTE") != string::npos) {
-            stringstream ss(line);
-            ss >> keyword >> attribute;
-            type = "";
-            while (ss >> type_w)
-                type += type_w + " ";
-            attributes.emplace_back(trim(attribute), trim(type));
-            continue;
-        }
-        if (line[0] == '@') {
-            continue;
-        }
-        lines.push_back(line);
-    }
-    file.close();
-    if (attributes.empty())
-        throw invalid_argument("No attributes found");
-    if (classLast) {
-        className = get<0>(attributes.back());
-        classType = get<1>(attributes.back());
-        attributes.pop_back();
-    } else {
-        className = get<0>(attributes.front());
-        classType = get<1>(attributes.front());
-        attributes.erase(attributes.begin());
-    }
-    generateDataset(classLast);
-
-}
-
-void ArffFiles::generateDataset(bool classLast)
-{
-    X = vector<mdlp::samples_t>(attributes.size(), mdlp::samples_t(lines.size()));
-    auto yy = vector<string>(lines.size(), "");
-    int labelIndex = classLast ? static_cast<int>(attributes.size()) : 0;
-    for (size_t i = 0; i < lines.size(); i++) {
-        stringstream ss(lines[i]);
-        string value;
-        int pos = 0;
-        int xIndex = 0;
-        while (getline(ss, value, ',')) {
-            if (pos++ == labelIndex) {
-                yy[i] = value;
-            } else {
-                X[xIndex++][i] = stof(value);
-            }
-        }
-    }
-    y = factorize(yy);
-}
-
-string ArffFiles::trim(const string& source)
-{
-    string s(source);
-    s.erase(0, s.find_first_not_of(" '\n\r\t"));
-    s.erase(s.find_last_not_of(" '\n\r\t") + 1);
-    return s;
-}
-
-vector<int> ArffFiles::factorize(const vector<string>& labels_t)
-{
-    vector<int> yy;
-    yy.reserve(labels_t.size());
-    map<string, int> labelMap;
-    int i = 0;
-    for (const string& label : labels_t) {
-        if (labelMap.find(label) == labelMap.end()) {
-            labelMap[label] = i++;
-        }
-        yy.push_back(labelMap[label]);
-    }
-    return yy;
-}

tests/ArffFiles.h

@@ -1,35 +0,0 @@
-#ifndef ARFFFILES_H
-#define ARFFFILES_H
-
-#include <string>
-#include <vector>
-#include "../typesFImdlp.h"
-
-using namespace std;
-
-class ArffFiles {
-private:
-    vector<string> lines;
-    vector<pair<string, string>> attributes;
-    string className;
-    string classType;
-    vector<mdlp::samples_t> X;
-    vector<int> y;
-
-    void generateDataset(bool);
-
-public:
-    ArffFiles();
-    void load(const string&, bool = true);
-    vector<string> getLines() const;
-    unsigned long int getSize() const;
-    string getClassName() const;
-    string getClassType() const;
-    static string trim(const string&);
-    vector<mdlp::samples_t>& getX();
-    vector<int>& getY();
-    vector<pair<string, string>> getAttributes() const;
-    static vector<int> factorize(const vector<string>& labels_t);
-};
-
-#endif

tests/BinDisc_unittest.cpp

@@ -1,9 +1,15 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include <fstream>
 #include <string>
 #include <iostream>
 #include "gtest/gtest.h"
-#include "ArffFiles.h"
+#include <ArffFiles.hpp>
-#include "../BinDisc.h"
+#include "BinDisc.h"
 #include "Experiments.hpp"
 
 namespace mdlp {
@@ -75,7 +81,7 @@ namespace mdlp {
         EXPECT_NEAR(7.0, cuts.at(2), margin);
         EXPECT_NEAR(10.0, cuts.at(3), margin);
         auto labels = transform(X);
-        labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2 };
+        labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 2 };
         EXPECT_EQ(expected, labels);
     }
     TEST_F(TestBinDisc3Q, X10BinsQuantile)
@@ -89,7 +95,7 @@ namespace mdlp {
         EXPECT_NEAR(7.0, cuts.at(2), margin);
         EXPECT_NEAR(10.0, cuts.at(3), margin);
         auto labels = transform(X);
-        labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2 };
+        labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 2 };
         EXPECT_EQ(expected, labels);
     }
     TEST_F(TestBinDisc3U, X11BinsUniform)
@@ -237,7 +243,7 @@ namespace mdlp {
         EXPECT_NEAR(10.0, cuts.at(3), margin);
         EXPECT_NEAR(13.0, cuts.at(4), margin);
         auto labels = transform(X);
-        labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3 };
+        labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3 };
         EXPECT_EQ(expected, labels);
     }
     TEST_F(TestBinDisc4Q, X13BinsQuantile)
@@ -252,7 +258,7 @@ namespace mdlp {
         EXPECT_NEAR(10.0, cuts.at(3), margin);
         EXPECT_NEAR(13.0, cuts.at(4), margin);
         auto labels = transform(X);
-        labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3 };
+        labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3 };
         EXPECT_EQ(expected, labels);
     }
     TEST_F(TestBinDisc4U, X14BinsUniform)
@@ -297,7 +303,7 @@ namespace mdlp {
         EXPECT_NEAR(11.5, cuts.at(3), margin);
         EXPECT_NEAR(15.0, cuts.at(4), margin);
         auto labels = transform(X);
-        labels_t expected = { 3, 1, 3, 3, 1, 0, 3, 2, 2, 2, 1, 0, 0, 1, 0 };
+        labels_t expected = { 3, 2, 3, 3, 1, 0, 3, 2, 2, 2, 1, 0, 0, 1, 0 };
         EXPECT_EQ(expected, labels);
     }
     TEST_F(TestBinDisc4Q, X15BinsQuantile)
@@ -312,7 +318,7 @@ namespace mdlp {
         EXPECT_NEAR(11.5, cuts.at(3), margin);
         EXPECT_NEAR(15.0, cuts.at(4), margin);
         auto labels = transform(X);
-        labels_t expected = { 3, 3, 3, 3, 1, 0, 1, 2, 2, 2, 1, 0, 0, 1, 0 };
+        labels_t expected = { 3, 3, 3, 3, 1, 0, 2, 2, 2, 2, 1, 0, 0, 1, 0 };
         EXPECT_EQ(expected, labels);
     }
     TEST_F(TestBinDisc4U, RepeatedValuesUniform)
@@ -328,7 +334,7 @@ namespace mdlp {
         EXPECT_NEAR(3.0, cuts.at(3), margin);
         EXPECT_NEAR(4.0, cuts.at(4), margin);
         auto labels = transform(X);
-        labels_t expected = { 0, 0, 0, 0, 1, 1, 2, 2, 2, 3 };
+        labels_t expected = { 0, 1, 1, 1, 2, 2, 3, 3, 3, 3 };
         EXPECT_EQ(expected, labels);
     }
     TEST_F(TestBinDisc4Q, RepeatedValuesQuantile)
@@ -344,67 +350,55 @@ namespace mdlp {
         EXPECT_NEAR(3.0, cuts.at(3), margin);
         EXPECT_NEAR(4.0, cuts.at(4), margin);
         auto labels = transform(X);
-        labels_t expected = { 0, 0, 0, 0, 1, 1, 2, 2, 2, 3 };
+        labels_t expected = { 0, 1, 1, 1, 2, 2, 3, 3, 3, 3 };
         EXPECT_EQ(expected, labels);
     }
-    TEST_F(TestBinDisc4U, irisUniform)
-    {
-        ArffFiles file;
-        file.load(data_path + "iris.arff", true);
-        vector<samples_t>& X = file.getX();
-        fit(X[0]);
-        auto Xt = transform(X[0]);
-        labels_t expected = { 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 2, 2, 1, 2, 1, 2, 0, 2, 0, 0, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 0, 1, 1, 1, 2, 0, 1, 2, 1, 3, 2, 2, 3, 0, 3, 2, 3, 2, 2, 2, 1, 1, 2, 2, 3, 3, 1, 2, 1, 3, 2, 2, 3, 2, 1, 2, 3, 3, 3, 2, 2, 1, 3, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1 };
-        EXPECT_EQ(expected, Xt);
-        auto Xtt = fit_transform(X[0], file.getY());
-        EXPECT_EQ(expected, Xtt);
-        auto Xt_t = torch::tensor(X[0], torch::kFloat32);
-        auto y_t = torch::tensor(file.getY(), torch::kInt32);
-        auto Xtt_t = fit_transform_t(Xt_t, y_t);
-        for (int i = 0; i < expected.size(); i++)
-            EXPECT_EQ(expected[i], Xtt_t[i].item<int>());
-    }
-    TEST_F(TestBinDisc4Q, irisQuantile)
-    {
-        ArffFiles file;
-        file.load(data_path + "iris.arff", true);
-        vector<samples_t>& X = file.getX();
-        fit(X[0]);
-        auto Xt = transform(X[0]);
-        labels_t expected = { 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 2, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 3, 3, 3, 1, 3, 1, 2, 0, 3, 1, 0, 2, 2, 2, 1, 3, 1, 2, 2, 1, 2, 2, 2, 2, 3, 3, 3, 3, 2, 1, 1, 1, 2, 2, 1, 2, 3, 2, 1, 1, 1, 2, 2, 0, 1, 1, 1, 2, 1, 1, 2, 2, 3, 2, 3, 3, 0, 3, 3, 3, 3, 3, 3, 1, 2, 3, 3, 3, 3, 2, 3, 1, 3, 2, 3, 3, 2, 2, 3, 3, 3, 3, 3, 2, 2, 3, 2, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 2, 2 };
-        EXPECT_EQ(expected, Xt);
-        auto Xtt = fit_transform(X[0], file.getY());
-        EXPECT_EQ(expected, Xtt);
-        auto Xt_t = torch::tensor(X[0], torch::kFloat32);
-        auto y_t = torch::tensor(file.getY(), torch::kInt32);
-        auto Xtt_t = fit_transform_t(Xt_t, y_t);
-        for (int i = 0; i < expected.size(); i++)
-            EXPECT_EQ(expected[i], Xtt_t[i].item<int>());
-        fit_t(Xt_t, y_t);
-        auto Xt_t2 = transform_t(Xt_t);
-        for (int i = 0; i < expected.size(); i++)
-            EXPECT_EQ(expected[i], Xt_t2[i].item<int>());
-    }
     TEST(TestBinDiscGeneric, Fileset)
     {
         Experiments exps(data_path + "tests.txt");
         int num = 0;
         while (exps.is_next()) {
+            ++num;
             Experiment exp = exps.next();
-            std::cout << "Exp #: " << ++num << " From: " << exp.from_ << " To: " << exp.to_ << " Step: " << exp.step_ << " Bins: " << exp.n_bins_ << " Strategy: " << exp.strategy_ << std::endl;
+            BinDisc disc(exp.n_bins_, exp.strategy_[0] == 'Q' ? strategy_t::QUANTILE : strategy_t::UNIFORM);
-            BinDisc disc(exp.n_bins_, exp.strategy_ == "Q" ? strategy_t::QUANTILE : strategy_t::UNIFORM);
+            std::vector<precision_t> test;
-            std::vector<float> test;
+            if (exp.type_ == experiment_t::RANGE) {
-            for (float i = exp.from_; i < exp.to_; i += exp.step_) {
+                for (float i = exp.from_; i < exp.to_; i += exp.step_) {
-                test.push_back(i);
+                    test.push_back(i);
+                }
+            } else {
+                test = exp.dataset_;
             }
             // show_vector(test, "Test");
             auto empty = std::vector<int>();
             auto Xt = disc.fit_transform(test, empty);
             auto cuts = disc.getCutPoints();
             EXPECT_EQ(exp.discretized_data_.size(), Xt.size());
-            for (int i = 0; i < exp.discretized_data_.size(); ++i) {
+            auto flag = false;
-                if (exp.discretized_data_.at(i) != Xt.at(i)) {
+            size_t n_errors = 0;
-                    std::cout << "Error at " << i << " Expected: " << exp.discretized_data_.at(i) << " Got: " << Xt.at(i) << std::endl;
+            if (num < 40) {
+                //
+                // Check discretization of only the first 40 tests as after we cannot ensure the same codification due to precision problems
+                //
+                for (int i = 0; i < exp.discretized_data_.size(); ++i) {
+                    if (exp.discretized_data_.at(i) != Xt.at(i)) {
+                        if (!flag) {
+                            if (exp.type_ == experiment_t::RANGE)
+                                std::cout << "+Exp #: " << num << " From: " << exp.from_ << " To: " << exp.to_ << " Step: " << exp.step_ << " Bins: " << exp.n_bins_ << " Strategy: " << exp.strategy_ << std::endl;
+                            else {
+                                std::cout << "+Exp #: " << num << " strategy: " << exp.strategy_ << " " << " n_bins: " << exp.n_bins_ << " ";
+                                show_vector(exp.dataset_, "Dataset");
+                            }
+                            show_vector(cuts, "Cuts");
+                            std::cout << "Error at " << i << " test[i]=" << test.at(i) << " Expected: " << exp.discretized_data_.at(i) << " Got: " << Xt.at(i) << std::endl;
+                            flag = true;
+                            EXPECT_EQ(exp.discretized_data_.at(i), Xt.at(i));
+                        }
+                        n_errors++;
+                    }
+                }
+                if (flag) {
+                    std::cout << "*** Found " << n_errors << " mistakes in this experiment dataset" << std::endl;
                 }
             }
             EXPECT_EQ(exp.cutpoints_.size(), cuts.size());
@@ -412,5 +406,6 @@ namespace mdlp {
                 EXPECT_NEAR(exp.cutpoints_.at(i), cuts.at(i), margin);
             }
         }
+        std::cout << "* Number of experiments tested: " << num << std::endl;
     }
 }

tests/CMakeLists.txt

@@ -1,6 +1,3 @@
-cmake_minimum_required(VERSION 3.20)
-set(CMAKE_CXX_STANDARD 17)
-cmake_policy(SET CMP0135 NEW)
 include(FetchContent)
 include_directories(${GTEST_INCLUDE_DIRS})
 FetchContent_Declare(
@@ -11,28 +8,30 @@ FetchContent_Declare(
 set(gtest_force_shared_crt ON CACHE BOOL "" FORCE)
 FetchContent_MakeAvailable(googletest)
 
-find_package(Torch REQUIRED)
+include_directories(
+        ${TORCH_INCLUDE_DIRS}
+        ${mdlp_SOURCE_DIR}/src
+        ${mdlp_SOURCE_DIR}/tests/lib/Files
+)
 
-enable_testing()
+add_executable(Metrics_unittest ${mdlp_SOURCE_DIR}/src/Metrics.cpp Metrics_unittest.cpp)
-
-include_directories(${TORCH_INCLUDE_DIRS})
-
-add_executable(Metrics_unittest ../Metrics.cpp Metrics_unittest.cpp)
 target_link_libraries(Metrics_unittest GTest::gtest_main)
 target_compile_options(Metrics_unittest PRIVATE --coverage)
 target_link_options(Metrics_unittest PRIVATE --coverage)
 
-add_executable(FImdlp_unittest ../CPPFImdlp.cpp ArffFiles.cpp ../Metrics.cpp FImdlp_unittest.cpp ../Discretizer.cpp)
+add_executable(FImdlp_unittest FImdlp_unittest.cpp
+${mdlp_SOURCE_DIR}/src/CPPFImdlp.cpp ${mdlp_SOURCE_DIR}/src/Metrics.cpp  ${mdlp_SOURCE_DIR}/src/Discretizer.cpp)
 target_link_libraries(FImdlp_unittest GTest::gtest_main "${TORCH_LIBRARIES}")
 target_compile_options(FImdlp_unittest PRIVATE --coverage)
 target_link_options(FImdlp_unittest PRIVATE --coverage)
 
-add_executable(BinDisc_unittest ../BinDisc.cpp ArffFiles.cpp BinDisc_unittest.cpp ../Discretizer.cpp)
+add_executable(BinDisc_unittest BinDisc_unittest.cpp ${mdlp_SOURCE_DIR}/src/BinDisc.cpp  ${mdlp_SOURCE_DIR}/src/Discretizer.cpp)
 target_link_libraries(BinDisc_unittest GTest::gtest_main "${TORCH_LIBRARIES}")
 target_compile_options(BinDisc_unittest PRIVATE --coverage)
 target_link_options(BinDisc_unittest PRIVATE --coverage)
 
-add_executable(Discretizer_unittest ../BinDisc.cpp ../CPPFImdlp.cpp ArffFiles.cpp ../Metrics.cpp ../Discretizer.cpp Discretizer_unittest.cpp)
+add_executable(Discretizer_unittest Discretizer_unittest.cpp
+${mdlp_SOURCE_DIR}/src/BinDisc.cpp ${mdlp_SOURCE_DIR}/src/CPPFImdlp.cpp ${mdlp_SOURCE_DIR}/src/Metrics.cpp ${mdlp_SOURCE_DIR}/src/Discretizer.cpp )
 target_link_libraries(Discretizer_unittest GTest::gtest_main "${TORCH_LIBRARIES}")
 target_compile_options(Discretizer_unittest PRIVATE --coverage)
 target_link_options(Discretizer_unittest PRIVATE --coverage)

tests/Discretizer_unittest.cpp

@@ -1,11 +1,17 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include <fstream>
 #include <string>
 #include <iostream>
+#include <ArffFiles.hpp>
 #include "gtest/gtest.h"
-#include "ArffFiles.h"
+#include "Discretizer.h"
-#include "../Discretizer.h"
+#include "BinDisc.h"
-#include "../BinDisc.h"
+#include "CPPFImdlp.h"
-#include "../CPPFImdlp.h"
 
 namespace mdlp {
     const float margin = 1e-4;
@@ -20,7 +26,7 @@ namespace mdlp {
         return "../../tests/datasets/";
     }
     const std::string data_path = set_data_path();
-
+    const labels_t iris_quantile = { 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 2, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 3, 3, 3, 1, 3, 1, 2, 0, 3, 1, 0, 2, 2, 2, 1, 3, 1, 2, 2, 1, 2, 2, 2, 2, 3, 3, 3, 3, 2, 1, 1, 1, 2, 2, 1, 2, 3, 2, 1, 1, 1, 2, 2, 0, 1, 1, 1, 2, 1, 1, 2, 2, 3, 2, 3, 3, 0, 3, 3, 3, 3, 3, 3, 1, 2, 3, 3, 3, 3, 2, 3, 1, 3, 2, 3, 3, 2, 2, 3, 3, 3, 3, 3, 2, 2, 3, 2, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 2, 2 };
     TEST(Discretizer, Version)
     {
         Discretizer* disc = new BinDisc(4, strategy_t::UNIFORM);
@@ -29,7 +35,6 @@ namespace mdlp {
         std::cout << "Version computed: " << version;
         EXPECT_EQ("1.2.3", version);
     }
-
     TEST(Discretizer, BinIrisUniform)
     {
         ArffFiles file;
@@ -52,12 +57,198 @@ namespace mdlp {
         auto y = labels_t();
         disc->fit(X[0], y);
         auto Xt = disc->transform(X[0]);
-        labels_t expected = { 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 2, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 3, 3, 3, 1, 3, 1, 2, 0, 3, 1, 0, 2, 2, 2, 1, 3, 1, 2, 2, 1, 2, 2, 2, 2, 3, 3, 3, 3, 2, 1, 1, 1, 2, 2, 1, 2, 3, 2, 1, 1, 1, 2, 2, 0, 1, 1, 1, 2, 1, 1, 2, 2, 3, 2, 3, 3, 0, 3, 3, 3, 3, 3, 3, 1, 2, 3, 3, 3, 3, 2, 3, 1, 3, 2, 3, 3, 2, 2, 3, 3, 3, 3, 3, 2, 2, 3, 2, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 2, 2 };
         delete disc;
-        EXPECT_EQ(expected, Xt);
+        EXPECT_EQ(iris_quantile, Xt);
     }
+
+    TEST(Discretizer, BinIrisQuantileTorch)
+    {
+        ArffFiles file;
+        Discretizer* disc = new BinDisc(4, strategy_t::QUANTILE);
+        file.load(data_path + "iris.arff", true);
+        auto X = file.getX();
+        auto y = file.getY();
+        auto X_torch = torch::tensor(X[0], torch::kFloat32);
+        auto yt = torch::tensor(y, torch::kInt32);
+        disc->fit_t(X_torch, yt);
+        torch::Tensor Xt = disc->transform_t(X_torch);
+        delete disc;
+        EXPECT_EQ(iris_quantile.size(), Xt.size(0));
+        for (int i = 0; i < iris_quantile.size(); ++i) {
+            EXPECT_EQ(iris_quantile.at(i), Xt[i].item<int>());
+        }
+    }
+    TEST(Discretizer, BinIrisQuantileTorchFit_transform)
+    {
+        ArffFiles file;
+        Discretizer* disc = new BinDisc(4, strategy_t::QUANTILE);
+        file.load(data_path + "iris.arff", true);
+        auto X = file.getX();
+        auto y = file.getY();
+        auto X_torch = torch::tensor(X[0], torch::kFloat32);
+        auto yt = torch::tensor(y, torch::kInt32);
+        torch::Tensor Xt = disc->fit_transform_t(X_torch, yt);
+        delete disc;
+        EXPECT_EQ(iris_quantile.size(), Xt.size(0));
+        for (int i = 0; i < iris_quantile.size(); ++i) {
+            EXPECT_EQ(iris_quantile.at(i), Xt[i].item<int>());
+        }
+    }
+
     TEST(Discretizer, FImdlpIris)
     {
+        auto labelsq = {
+            1,
+                0,
+                0,
+                0,
+                0,
+                1,
+                0,
+                0,
+                0,
+                0,
+                1,
+                0,
+                0,
+                0,
+                2,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                0,
+                1,
+                0,
+                0,
+                0,
+                1,
+                1,
+                0,
+                0,
+                1,
+                1,
+                1,
+                0,
+                0,
+                1,
+                0,
+                0,
+                1,
+                0,
+                0,
+                0,
+                0,
+                1,
+                0,
+                1,
+                0,
+                1,
+                0,
+                3,
+                3,
+                3,
+                1,
+                3,
+                1,
+                2,
+                0,
+                3,
+                1,
+                0,
+                2,
+                2,
+                2,
+                1,
+                3,
+                1,
+                2,
+                2,
+                1,
+                2,
+                2,
+                2,
+                2,
+                3,
+                3,
+                3,
+                3,
+                2,
+                1,
+                1,
+                1,
+                2,
+                2,
+                1,
+                2,
+                3,
+                2,
+                1,
+                1,
+                1,
+                2,
+                2,
+                0,
+                1,
+                1,
+                1,
+                2,
+                1,
+                1,
+                2,
+                2,
+                3,
+                2,
+                3,
+                3,
+                0,
+                3,
+                3,
+                3,
+                3,
+                3,
+                3,
+                1,
+                2,
+                3,
+                3,
+                3,
+                3,
+                2,
+                3,
+                1,
+                3,
+                2,
+                3,
+                3,
+                2,
+                2,
+                3,
+                3,
+                3,
+                3,
+                3,
+                2,
+                2,
+                3,
+                2,
+                3,
+                2,
+                3,
+                3,
+                3,
+                2,
+                3,
+                3,
+                3,
+                2,
+                3,
+                2,
+                2,
+        };
         labels_t expected = {
             5, 3, 4, 4, 5, 5, 5, 5, 2, 4, 5, 5, 3, 3, 5, 5, 5, 5, 5, 5, 5, 5,
             5, 4, 5, 3, 5, 5, 5, 4, 4, 5, 5, 5, 4, 4, 5, 4, 3, 5, 5, 0, 4, 5,

tests/Experiments.hpp

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #ifndef EXPERIMENTS_HPP
 #define EXPERIMENTS_HPP
 #include<sstream>
@@ -6,14 +12,40 @@
 #include<fstream>
 #include<vector>
 #include<tuple>
-#include "../typesFImdlp.h"
+#include "typesFImdlp.h"
+
+template <typename T>
+void show_vector(const std::vector<T>& data, std::string title)
+{
+    std::cout << title << ": ";
+    std::string sep = "";
+    for (const auto& d : data) {
+        std::cout << sep << d;
+        sep = ", ";
+    }
+    std::cout << std::endl;
+}
+enum class experiment_t {
+    RANGE,
+    VECTOR
+};
 class Experiment {
 public:
-    Experiment(float from_, float to_, float step_, int n_bins, std::string strategy, std::vector<int> data_discretized, std::vector<float> cutpoints) :
+    Experiment(float from_, float to_, float step_, int n_bins, std::string strategy, std::vector<int> data_discretized, std::vector<mdlp::precision_t> cutpoints) :
-        from_{ from_ }, to_{ to_ }, step_{ step_ }, n_bins_{ n_bins }, strategy_{ strategy }, discretized_data_{ data_discretized }, cutpoints_{ cutpoints }
+        from_{ from_ }, to_{ to_ }, step_{ step_ }, n_bins_{ n_bins }, strategy_{ strategy }, discretized_data_{ data_discretized }, cutpoints_{ cutpoints }, type_{ experiment_t::RANGE }
     {
-        if (strategy != "Q" && strategy != "U") {
+        validate_strategy();
-            throw std::invalid_argument("Invalid strategy " + strategy);
+
+    }
+    Experiment(std::vector<mdlp::precision_t> dataset, int n_bins, std::string strategy, std::vector<int> data_discretized, std::vector<mdlp::precision_t> cutpoints) :
+        n_bins_{ n_bins }, strategy_{ strategy }, dataset_{ dataset }, discretized_data_{ data_discretized }, cutpoints_{ cutpoints }, type_{ experiment_t::VECTOR }
+    {
+        validate_strategy();
+    }
+    void validate_strategy()
+    {
+        if (strategy_ != "Q" && strategy_ != "U") {
+            throw std::invalid_argument("Invalid strategy " + strategy_);
         }
     }
     float from_;
@@ -21,8 +53,10 @@ public:
     float step_;
     int n_bins_;
     std::string strategy_;
+    std::vector<mdlp::precision_t> dataset_;
     std::vector<int> discretized_data_;
-    std::vector<float> cutpoints_;
+    std::vector<mdlp::precision_t> cutpoints_;
+    experiment_t type_;
 };
 class Experiments {
 public:
@@ -76,33 +110,30 @@ private:
     }
     Experiment parse_experiment(std::string& line)
     {
+        // Read experiment lines
+        std::string experiment, data, cuts, strategy;
+        std::getline(test_file, experiment);
+        std::getline(test_file, data);
+        std::getline(test_file, cuts);
+        // split data into variables
+        float from_, to_, step_;
+        int n_bins;
+        std::vector<mdlp::precision_t> dataset;
+        auto data_discretized = parse_vector<int>(data);
+        auto cutpoints = parse_vector<mdlp::precision_t>(cuts);
         if (line == "RANGE") {
-            std::getline(test_file, line);
+            tie(from_, to_, step_, n_bins, strategy) = parse_header(experiment);
-            auto [from_, to_, step_, n_bins, strategy] = parse_header(line);
+            return Experiment{ from_, to_, step_, n_bins, strategy, data_discretized, cutpoints };
-        } else {
-            std::getline(test_file, line);
-
         }
-        std::getline(test_file, line);
+        strategy = experiment.substr(0, 1);
-        auto data_discretized = parse_vector<int>(line);
+        n_bins = std::stoi(experiment.substr(1, 1));
-        std::getline(test_file, line);
+        data = experiment.substr(3, experiment.size() - 4);
-        auto cutpoints = parse_vector<float>(line);
+        dataset = parse_vector<mdlp::precision_t>(data);
-        return Experiment{ from_, to_, step_, n_bins, strategy, data_discretized, cutpoints };
+        return Experiment(dataset, n_bins, strategy, data_discretized, cutpoints);
     }
     std::ifstream test_file;
     std::string filename;
     std::string line;
     bool exp_end;
 };
-template <typename T>
-void show_vector(const std::vector<T>& data, std::string title)
-{
-    std::cout << title << ": ";
-    std::string sep = "";
-    for (const auto& d : data) {
-        std::cout << sep << d;
-        sep = ", ";
-    }
-    std::cout << std::endl;
-}
 #endif

tests/FImdlp_unittest.cpp

@@ -1,9 +1,15 @@
-#include "gtest/gtest.h"
+// ****************************************************************
-#include "../Metrics.h"
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
-#include "../CPPFImdlp.h"
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include <fstream>
 #include <iostream>
-#include "ArffFiles.h"
+#include <ArffFiles.hpp>
+#include "gtest/gtest.h"
+#include "Metrics.h"
+#include "CPPFImdlp.h"
 
 #define EXPECT_THROW_WITH_MESSAGE(stmt, etype, whatstring) EXPECT_THROW( \
 try { \

tests/Metrics_unittest.cpp

@@ -1,5 +1,11 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include "gtest/gtest.h"
-#include "../Metrics.h"
+#include "Metrics.h"
 
 namespace mdlp {
     class TestMetrics : public Metrics, public testing::Test {

tests/datasets/tests.txt

@@ -3,6 +3,9 @@
 # discretized data
 # cut points
 #
+#
+# Range experiments
+#
 RANGE
 0, 100, 1, 4, Q
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3
@@ -13,7 +16,7 @@ RANGE
 0.0, 12.25, 24.5, 36.75, 49.0
 RANGE
 0, 100, 1, 3, Q
-0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
 0.0, 33.0, 66.0, 99.0
 RANGE
 0, 50, 1, 3, Q
@@ -21,7 +24,7 @@ RANGE
 0.0, 16.33333, 32.66667, 49.0
 RANGE
 0, 10, 1, 3, Q
-0, 0, 0, 0, 1, 1, 1, 2, 2, 2
+0, 0, 0, 1, 1, 1, 2, 2, 2, 2
 0.0, 3.0, 6.0, 9.0
 RANGE
 0, 100, 1, 4, U
@@ -53,7 +56,7 @@ RANGE
 1.0, 3.66667, 6.33333, 9.0
 RANGE
 1, 11, 1, 3, Q
-0, 0, 0, 1, 1, 1, 1, 2, 2, 2
+0, 0, 0, 1, 1, 1, 2, 2, 2, 2
 1.0, 4.0, 7.0, 10.0
 RANGE
 1, 11, 1, 3, U
@@ -91,6 +94,9 @@ RANGE
 1, 15, 1, 3, U
 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2
 1.0, 5.33333, 9.66667, 14.0
+#
+# Vector experiments
+#
 VECTOR
 Q3[3.0, 1.0, 1.0, 3.0, 1.0, 1.0, 3.0, 1.0, 1.0]
 1, 0, 0, 1, 0, 0, 1, 0, 0
@@ -141,9 +147,76 @@ U3[15.0, 8.0, 12.0, 14.0, 6.0, 1.0, 13.0, 11.0, 10.0, 9.0, 7.0, 4.0, 3.0, 5.0, 2
 1.0, 5.66667, 10.33333, 15.0
 VECTOR
 Q3[0.0, 1.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0]
-0, 0, 0, 0, 1, 1, 2, 2, 2, 2
+0, 1, 1, 1, 1, 1, 2, 2, 2, 2
 0.0, 1.0, 3.0, 4.0
 VECTOR
 U3[0.0, 1.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0]
 0, 0, 0, 0, 1, 1, 2, 2, 2, 2
 0.0, 1.33333, 2.66667, 4.0
+#
+# Vector experiments with iris
+#
+VECTOR
+Q3[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9]
+0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 1, 2, 1, 2, 0, 2, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 2, 1, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1
+4.3, 5.4, 6.3, 7.9
+VECTOR
+U3[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9]
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 0, 1, 2, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 2, 1, 1, 2, 0, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1
+4.3, 5.5, 6.7, 7.9
+VECTOR
+Q4[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9]
+1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 2, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 3, 3, 3, 1, 3, 1, 2, 0, 3, 1, 0, 2, 2, 2, 1, 3, 1, 2, 2, 1, 2, 2, 2, 2, 3, 3, 3, 3, 2, 1, 1, 1, 2, 2, 1, 2, 3, 2, 1, 1, 1, 2, 2, 0, 1, 1, 1, 2, 1, 1, 2, 2, 3, 2, 3, 3, 0, 3, 3, 3, 3, 3, 3, 1, 2, 3, 3, 3, 3, 2, 3, 1, 3, 2, 3, 3, 2, 2, 3, 3, 3, 3, 3, 2, 2, 3, 2, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 2, 2
+4.3, 5.1, 5.8, 6.4, 7.9
+VECTOR
+U4[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9]
+0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 2, 2, 1, 2, 1, 2, 0, 2, 1, 0, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 0, 1, 1, 1, 2, 0, 1, 2, 1, 3, 2, 2, 3, 0, 3, 2, 3, 2, 2, 2, 1, 1, 2, 2, 3, 3, 1, 2, 1, 3, 2, 2, 3, 2, 2, 2, 3, 3, 3, 2, 2, 2, 3, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1
+4.3, 5.2, 6.1, 7.0, 7.9
+VECTOR
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+VECTOR
+Q3[0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1, 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6, 1.0, 1.3, 1.4, 1.0, 1.5, 1.0, 1.4, 1.3, 1.4, 1.5, 1.0, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1.0, 1.1, 1.0, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2, 1.0, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8, 2.2, 2.1, 1.7, 1.8, 1.8, 2.5, 2.0, 1.9, 2.1, 2.0, 2.4, 2.3, 1.8, 2.2, 2.3, 1.5, 2.3, 2.0, 2.0, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2.0, 2.2, 1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3, 1.9, 2.0, 2.3, 1.8]
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
+0.1, 0.86667, 1.6, 2.5
+VECTOR
+U3[0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1, 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6, 1.0, 1.3, 1.4, 1.0, 1.5, 1.0, 1.4, 1.3, 1.4, 1.5, 1.0, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1.0, 1.1, 1.0, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2, 1.0, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8, 2.2, 2.1, 1.7, 1.8, 1.8, 2.5, 2.0, 1.9, 2.1, 2.0, 2.4, 2.3, 1.8, 2.2, 2.3, 1.5, 2.3, 2.0, 2.0, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2.0, 2.2, 1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3, 1.9, 2.0, 2.3, 1.8]
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
+0.1, 0.9, 1.7, 2.5
+VECTOR
+Q4[0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1, 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6, 1.0, 1.3, 1.4, 1.0, 1.5, 1.0, 1.4, 1.3, 1.4, 1.5, 1.0, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1.0, 1.1, 1.0, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2, 1.0, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8, 2.2, 2.1, 1.7, 1.8, 1.8, 2.5, 2.0, 1.9, 2.1, 2.0, 2.4, 2.3, 1.8, 2.2, 2.3, 1.5, 2.3, 2.0, 2.0, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2.0, 2.2, 1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3, 1.9, 2.0, 2.3, 1.8]
+0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 3, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3
+0.1, 0.3, 1.3, 1.8, 2.5
+VECTOR
+U4[0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1, 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6, 1.0, 1.3, 1.4, 1.0, 1.5, 1.0, 1.4, 1.3, 1.4, 1.5, 1.0, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1.0, 1.1, 1.0, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2, 1.0, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8, 2.2, 2.1, 1.7, 1.8, 1.8, 2.5, 2.0, 1.9, 2.1, 2.0, 2.4, 2.3, 1.8, 2.2, 2.3, 1.5, 2.3, 2.0, 2.0, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2.0, 2.2, 1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3, 1.9, 2.0, 2.3, 1.8]
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 3, 3, 3, 2, 3, 3, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 2, 3, 3, 3, 2, 3, 2, 2, 2, 3, 2, 3, 3, 3, 2, 2, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2
+0.1, 0.7, 1.3, 1.9, 2.5

tests/k.cpp

@@ -1,32 +0,0 @@
-#include <iostream>
-#include <vector>
-#include <algorithm> // For std::lower_bound
-
-std::vector<int> searchsorted(const std::vector<float>& cuts, const std::vector<float>& data) {
-    std::vector<int> indices;
-    indices.reserve(data.size());
-
-    for (const float& value : data) {
-        // Find the first position in 'a' where 'value' could be inserted to maintain order
-        auto it = std::lower_bound(cuts.begin(), cuts.end(), value);
-        // Calculate the index
-        int index = it - cuts.begin();
-        indices.push_back(index);
-    }
-
-    return indices;
-}
-
-int main() {
-    std::vector<float> cuts = { 10.0 };
-    std::vector<float> data = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0 };
-
-    std::vector<int> result = searchsorted(cuts, data);
-
-    for (int idx : result) {
-        std::cout << idx << " ";
-    }
-
-    return 0;
-}
-

tests/t.cpp

@@ -1,102 +0,0 @@
-#include <iostream>
-#include <algorithm>
-#include <cmath>
-#include <vector>
-#include <string>
-
-typedef float precision_t;
-
-std::vector<int> transform(const std::vector<float> cutPoints, const std::vector<float>& data)
-{
-    std::vector<int> discretizedData;
-    discretizedData.reserve(data.size());
-    for (const float& item : data) {
-        auto upper = std::lower_bound(cutPoints.begin(), cutPoints.end(), item);
-        discretizedData.push_back(upper - cutPoints.begin());
-    }
-    return discretizedData;
-}
-template <typename T>
-void show_vector(const std::vector<T>& data, std::string title)
-{
-    std::cout << title << ": ";
-    std::string sep = "";
-    for (const auto& d : data) {
-        std::cout << sep << d;
-        sep = ", ";
-    }
-    std::cout << std::endl;
-}
-std::vector<precision_t> linspace(precision_t start, precision_t end, int num)
-{
-    if (start == end) {
-        return { start, end };
-    }
-    precision_t delta = (end - start) / static_cast<precision_t>(num - 1);
-    std::vector<precision_t> linspc;
-    for (size_t i = 0; i < num - 1; ++i) {
-        precision_t val = start + delta * static_cast<precision_t>(i);
-        linspc.push_back(val);
-    }
-    return linspc;
-}
-size_t clip(const size_t n, size_t lower, size_t upper)
-{
-    return std::max(lower, std::min(n, upper));
-}
-std::vector<precision_t> percentile(std::vector<precision_t>& data, std::vector<precision_t>& percentiles)
-{
-    // Implementation taken from https://dpilger26.github.io/NumCpp/doxygen/html/percentile_8hpp_source.html
-    std::vector<precision_t> results;
-    results.reserve(percentiles.size());
-    for (auto percentile : percentiles) {
-        const size_t i = static_cast<size_t>(std::floor(static_cast<double>(data.size() - 1) * percentile / 100.));
-        const auto indexLower = clip(i, 0, data.size() - 2);
-        const double percentI = static_cast<double>(indexLower) / static_cast<double>(data.size() - 1);
-        const double fraction =
-            (percentile / 100.0 - percentI) /
-            (static_cast<double>(indexLower + 1) / static_cast<double>(data.size() - 1) - percentI);
-        const auto value = data[indexLower] + (data[indexLower + 1] - data[indexLower]) * fraction;
-        if (value != results.back())
-            results.push_back(value);
-    }
-    return results;
-}
-int main()
-{
-    // std::vector<float> test;
-    // std::vector<float> cuts = { 0, 24.75, 49.5, 74.25, 10000 };
-    // for (int i = 0; i < 100; ++i) {
-    //     test.push_back(i);
-    // }
-    // auto Xt = transform(cuts, test);
-    // show_vector(Xt, "Discretized data:");
-    // std::vector<float> test2 = { 0,1,2,3,4,5,6,7,8,9,10,11 };
-    // std::vector<float> cuts2 = { 0,1,2,3,4,5,6,7,8,9 };
-    // auto Xt2 = transform(cuts2, test2);
-    // show_vector(Xt2, "discretized data2: ");
-    auto quantiles = linspace(0.0, 100.0, 3 + 1);
-    std::vector<float> data = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 };
-    std::vector<float> cutPoints;
-    std::sort(data.begin(), data.end());
-    cutPoints = percentile(data, quantiles);
-    cutPoints.push_back(std::numeric_limits<precision_t>::max());
-    data.push_back(15);
-    data.push_back(0);
-    cutPoints.pop_back();
-    cutPoints.erase(cutPoints.begin());
-    cutPoints.clear();
-    cutPoints.push_back(9.0);
-    auto Xt = transform(cutPoints, data);
-    show_vector(data, "Original data");
-    show_vector(Xt, "Discretized data");
-    show_vector(cutPoints, "Cutpoints");
-    return 0;
-}
-/*
-n_bins = 3
-data = [1,2,3,4,5,6,7,8,9,10]
-quantiles = np.linspace(0, 100, n_bins + 1)
-bin_edges = np.percentile(data, quantiles)
-
-*/

tests/test

@@ -1,15 +0,0 @@
-#!/bin/bash
-if [ -d build ] && [ "$1" != "run" ]; then
-	rm -fr build
-fi
-if [ -d gcovr-report ] ; then
-	rm -fr gcovr-report
-fi
-cmake -S . -B build -Wno-dev -DCMAKE_BUILD_TYPE=Debug -DCMAKE_CXX_FLAGS="--coverage" -DCMAKE_C_FLAGS="--coverage"
-cmake --build build
-cd build
-ctest --output-on-failure
-cd ..
-mkdir gcovr-report
-cd ..
-gcovr  --gcov-filter "CPPFImdlp.cpp" --gcov-filter "Metrics.cpp" --gcov-filter "BinDisc.cpp" --gcov-filter "Discretizer.cpp" --txt --sonarqube=tests/gcovr-report/coverage.xml --exclude-noncode-lines

tests/testKbins.py

@@ -1,412 +0,0 @@
-from scipy.io.arff import loadarff
-from sklearn.preprocessing import KBinsDiscretizer
-
-
-def test(clf, X, expected, title):
-    X = [[x] for x in X]
-    clf.fit(X)
-    computed = [int(x[0]) for x in clf.transform(X)]
-    print(f"{title}")
-    print(f"{computed=}")
-    print(f"{expected=}")
-    assert computed == expected
-    print("-" * 80)
-
-
-# Test Uniform Strategy
-clf3u = KBinsDiscretizer(
-    n_bins=3, encode="ordinal", strategy="uniform", subsample=200_000
-)
-clf3q = KBinsDiscretizer(
-    n_bins=3, encode="ordinal", strategy="quantile", subsample=200_000
-)
-clf4u = KBinsDiscretizer(
-    n_bins=4, encode="ordinal", strategy="uniform", subsample=200_000
-)
-clf4q = KBinsDiscretizer(
-    n_bins=4, encode="ordinal", strategy="quantile", subsample=200_000
-)
-#
-X = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
-labels = [0, 0, 0, 1, 1, 1, 2, 2, 2]
-test(clf3u, X, labels, title="Easy3BinsUniform")
-test(clf3q, X, labels, title="Easy3BinsQuantile")
-#
-X = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
-labels = [0, 0, 0, 1, 1, 1, 2, 2, 2, 2]
-# En C++ se obtiene el mismo resultado en ambos, no como aquí
-labels2 = [0, 0, 0, 1, 1, 1, 1, 2, 2, 2]
-test(clf3u, X, labels, title="X10BinsUniform")
-test(clf3q, X, labels2, title="X10BinsQuantile")
-#
-X = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0]
-labels = [0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2]
-# En C++ se obtiene el mismo resultado en ambos, no como aquí
-# labels2 = [0, 0, 0, 1, 1, 1, 1, 2, 2, 2]
-test(clf3u, X, labels, title="X11BinsUniform")
-test(clf3q, X, labels, title="X11BinsQuantile")
-#
-X = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
-labels = [0, 0, 0, 0, 0, 0]
-test(clf3u, X, labels, title="ConstantUniform")
-test(clf3q, X, labels, title="ConstantQuantile")
-#
-X = [3.0, 1.0, 1.0, 3.0, 1.0, 1.0, 3.0, 1.0, 1.0]
-labels = [2, 0, 0, 2, 0, 0, 2, 0, 0]
-labels2 = [1, 0, 0, 1, 0, 0, 1, 0, 0]  # igual que en C++
-test(clf3u, X, labels, title="EasyRepeatedUniform")
-test(clf3q, X, labels2, title="EasyRepeatedQuantile")
-#
-X = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0]
-labels = [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3]
-test(clf4u, X, labels, title="Easy4BinsUniform")
-test(clf4q, X, labels, title="Easy4BinsQuantile")
-#
-X = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0]
-labels = [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3]
-test(clf4u, X, labels, title="X13BinsUniform")
-test(clf4q, X, labels, title="X13BinsQuantile")
-#
-X = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0]
-labels = [0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3]
-test(clf4u, X, labels, title="X14BinsUniform")
-test(clf4q, X, labels, title="X14BinsQuantile")
-#
-X1 = [15.0, 8.0, 12.0, 14.0, 6.0, 1.0, 13.0, 11.0, 10.0, 9.0, 7.0, 4.0, 3.0, 5.0, 2.0]
-X2 = [15.0, 13.0, 12.0, 14.0, 6.0, 1.0, 8.0, 11.0, 10.0, 9.0, 7.0, 4.0, 3.0, 5.0, 2.0]
-labels1 = [3, 2, 3, 3, 1, 0, 3, 2, 2, 2, 1, 0, 0, 1, 0]
-labels2 = [3, 3, 3, 3, 1, 0, 2, 2, 2, 2, 1, 0, 0, 1, 0]
-test(clf4u, X1, labels1, title="X15BinsUniform")
-test(clf4q, X2, labels2, title="X15BinsQuantile")
-#
-X = [0.0, 1.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0]
-labels = [0, 1, 1, 1, 2, 2, 3, 3, 3, 3]
-test(clf4u, X, labels, title="RepeatedValuesUniform")
-test(clf4q, X, labels, title="RepeatedValuesQuantile")
-
-print(f"Uniform   {clf4u.bin_edges_=}")
-print(f"Quaintile {clf4q.bin_edges_=}")
-print("-" * 80)
-#
-data, meta = loadarff("tests/datasets/iris.arff")
-
-labelsu = [
-    0,
-    0,
-    0,
-    0,
-    0,
-    1,
-    0,
-    0,
-    0,
-    0,
-    1,
-    0,
-    0,
-    0,
-    1,
-    1,
-    1,
-    0,
-    1,
-    0,
-    1,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    1,
-    0,
-    1,
-    0,
-    0,
-    1,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    1,
-    0,
-    3,
-    2,
-    2,
-    1,
-    2,
-    1,
-    2,
-    0,
-    2,
-    0,
-    0,
-    1,
-    1,
-    1,
-    1,
-    2,
-    1,
-    1,
-    2,
-    1,
-    1,
-    1,
-    2,
-    1,
-    2,
-    2,
-    2,
-    2,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    2,
-    2,
-    1,
-    1,
-    1,
-    1,
-    1,
-    0,
-    1,
-    1,
-    1,
-    2,
-    0,
-    1,
-    2,
-    1,
-    3,
-    2,
-    2,
-    3,
-    0,
-    3,
-    2,
-    3,
-    2,
-    2,
-    2,
-    1,
-    1,
-    2,
-    2,
-    3,
-    3,
-    1,
-    2,
-    1,
-    3,
-    2,
-    2,
-    3,
-    2,
-    1,
-    2,
-    3,
-    3,
-    3,
-    2,
-    2,
-    1,
-    3,
-    2,
-    2,
-    1,
-    2,
-    2,
-    2,
-    1,
-    2,
-    2,
-    2,
-    2,
-    2,
-    2,
-    1,
-]
-labelsq = [
-    1,
-    0,
-    0,
-    0,
-    0,
-    1,
-    0,
-    0,
-    0,
-    0,
-    1,
-    0,
-    0,
-    0,
-    2,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    1,
-    0,
-    1,
-    0,
-    0,
-    0,
-    1,
-    1,
-    0,
-    0,
-    1,
-    1,
-    1,
-    0,
-    0,
-    1,
-    0,
-    0,
-    1,
-    0,
-    0,
-    0,
-    0,
-    1,
-    0,
-    1,
-    0,
-    1,
-    0,
-    3,
-    3,
-    3,
-    1,
-    3,
-    1,
-    2,
-    0,
-    3,
-    1,
-    0,
-    2,
-    2,
-    2,
-    1,
-    3,
-    1,
-    2,
-    2,
-    1,
-    2,
-    2,
-    2,
-    2,
-    3,
-    3,
-    3,
-    3,
-    2,
-    1,
-    1,
-    1,
-    2,
-    2,
-    1,
-    2,
-    3,
-    2,
-    1,
-    1,
-    1,
-    2,
-    2,
-    0,
-    1,
-    1,
-    1,
-    2,
-    1,
-    1,
-    2,
-    2,
-    3,
-    2,
-    3,
-    3,
-    0,
-    3,
-    3,
-    3,
-    3,
-    3,
-    3,
-    1,
-    2,
-    3,
-    3,
-    3,
-    3,
-    2,
-    3,
-    1,
-    3,
-    2,
-    3,
-    3,
-    2,
-    2,
-    3,
-    3,
-    3,
-    3,
-    3,
-    2,
-    2,
-    3,
-    2,
-    3,
-    2,
-    3,
-    3,
-    3,
-    2,
-    3,
-    3,
-    3,
-    2,
-    3,
-    2,
-    2,
-]
-# test(clf4u, data["sepallength"], labelsu, title="IrisUniform")
-# test(clf4q, data["sepallength"], labelsq, title="IrisQuantile")
-sepallength = [[x] for x in data["sepallength"]]
-clf4u.fit(sepallength)
-clf4q.fit(sepallength)
-computedu = clf4u.transform(sepallength)
-computedq = clf4q.transform(sepallength)
-wrongu = 0
-wrongq = 0
-for i in range(len(labelsu)):
-    if labelsu[i] != computedu[i]:
-        wrongu += 1
-    if labelsq[i] != computedq[i]:
-        wrongq += 1
-print(f"Iris sepallength diff. between BinDisc & sklearn::KBins Uniform  ={wrongu:3d}")
-print(f"Iris sepallength diff. between BinDisc & sklearn::KBins Quantile ={wrongq:3d}")

tests/tests_do.py

@@ -1,3 +1,9 @@
+# ***************************************************************
+# SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+# SPDX-FileType: SOURCE
+# SPDX-License-Identifier: MIT
+# ***************************************************************
+
 import json
 from sklearn.preprocessing import KBinsDiscretizer
 
@@ -6,6 +12,7 @@ with open("datasets/tests.txt") as f:
 
 data = [x.strip() for x in data if x[0] != "#"]
 
+errors = False
 for i in range(0, len(data), 4):
     experiment_type = data[i]
     print("Experiment:", data[i + 1])
@@ -28,23 +35,37 @@ for i in range(0, len(data), 4):
     expected_data = data[i + 2]
     cuts_data = data[i + 3]
     disc.fit(X)
+    #
+    # Normalize the cutpoints to remove numerical errors such as 33.0000000001
+    # instead of 33
+    #
+    for j in range(len(disc.bin_edges_[0])):
+        disc.bin_edges_[0][j] = round(disc.bin_edges_[0][j], 5)
     result = disc.transform(X)
     result = [int(x) for x in result.flatten()]
     expected = [int(x) for x in expected_data.split(",")]
+    #
+    # Check the Results
+    #
     assert len(result) == len(expected)
     for j in range(len(result)):
         if result[j] != expected[j]:
-            print("Error at", j, "Expected=", expected[j], "Result=", result[j])
+            print("* Error at", j, "Expected=", expected[j], "Result=", result[j])
+            errors = True
     expected_cuts = disc.bin_edges_[0]
     computed_cuts = [float(x) for x in cuts_data.split(",")]
     assert len(expected_cuts) == len(computed_cuts)
     for j in range(len(expected_cuts)):
         if round(expected_cuts[j], 5) != computed_cuts[j]:
             print(
-                "Error at",
+                "* Error at",
                 j,
                 "Expected=",
                 expected_cuts[j],
                 "Result=",
                 computed_cuts[j],
             )
+            errors = True
+if errors:
+    raise Exception("There were errors!")
+print("*** All tests run succesfully! ***")

tests/tests_generate.ipynb

@@ -6,7 +6,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.preprocessing import KBinsDiscretizer"
+    "from sklearn.preprocessing import KBinsDiscretizer\n",
+    "from sklearn.datasets import load_iris"
    ]
   },
   {
@@ -78,21 +79,43 @@
     "        sep = \", \"\n",
     "    file.write(\"\\n\")\n",
     "\n",
+    "def normalize_cuts(cuts):\n",
+    "    #\n",
+    "    # Normalize the cutpoints to remove numerical errors such as 33.0000000001\n",
+    "    # instead of 33\n",
+    "    #\n",
+    "    for k in range(cuts.shape[0]):\n",
+    "        for i in range(len(cuts[k])):\n",
+    "            cuts[k][i] = round(cuts[k][i], 5)\n",
+    "\n",
     "with open(\"datasets/tests.txt\", \"w\") as file:\n",
     "    file.write(\"#\\n\")\n",
     "    file.write(\"# from, to, step, #bins, Q/U\\n\")\n",
     "    file.write(\"# discretized data\\n\")\n",
     "    file.write(\"# cut points\\n\")\n",
     "    file.write(\"#\\n\")\n",
+    "    #\n",
+    "    # Range experiments\n",
+    "    #\n",
+    "    file.write(\"#\\n\")\n",
+    "    file.write(\"# Range experiments\\n\")\n",
+    "    file.write(\"#\\n\")\n",
     "    for experiment in experiments_range:\n",
     "        file.write(\"RANGE\\n\")\n",
     "        (from_, to_, step_, bins_, strategy) = experiment\n",
     "        disc = KBinsDiscretizer(n_bins=bins_, encode='ordinal', strategy='quantile' if strategy.strip() == \"Q\" else 'uniform')\n",
     "        data = [[x] for x in range(from_, to_, step_)]\n",
     "        disc.fit(data)\n",
+    "        normalize_cuts(disc.bin_edges_)\n",
     "        result = disc.transform(data)\n",
     "        file.write(f\"{from_}, {to_}, {step_}, {bins_}, {strategy}\\n\")\n",
     "        write_lists(file, result, disc.bin_edges_[0])\n",
+    "    #\n",
+    "    # Vector experiments\n",
+    "    #\n",
+    "    file.write(\"#\\n\")\n",
+    "    file.write(\"# Vector experiments\\n\")\n",
+    "    file.write(\"#\\n\")\n",
     "    for n_bins, experiment in experiments_vectors:\n",
     "        for strategy in [\"Q\", \"U\"]:\n",
     "            file.write(\"VECTOR\\n\")\n",
@@ -104,8 +127,61 @@
     "                strategy=\"quantile\" if strategy.strip() == \"Q\" else \"uniform\",\n",
     "            )\n",
     "            data = [[x] for x in experiment]\n",
-    "            result = disc.fit_transform(data)\n",
+    "            disc.fit(data)\n",
-    "            write_lists(file, result, disc.bin_edges_[0])"
+    "            normalize_cuts(disc.bin_edges_)\n",
+    "            result = disc.transform(data)\n",
+    "            write_lists(file, result, disc.bin_edges_[0])\n",
+    "    #\n",
+    "    # Vector experiments iris\n",
+    "    #\n",
+    "    file.write(\"#\\n\");\n",
+    "    file.write(\"# Vector experiments with iris\\n\");\n",
+    "    file.write(\"#\\n\");\n",
+    "    X, y = load_iris(return_X_y=True)\n",
+    "    for i in range(X.shape[1]):\n",
+    "        for n_bins in [3, 4]:\n",
+    "            for strategy in [\"Q\", \"U\"]:\n",
+    "                file.write(\"VECTOR\\n\")\n",
+    "                experiment = X[:, i]\n",
+    "                file.write(f\"{strategy}{n_bins}{experiment.tolist()}\\n\")\n",
+    "                disc = KBinsDiscretizer(\n",
+    "                    n_bins=n_bins,\n",
+    "                    encode=\"ordinal\",\n",
+    "                    strategy=\"quantile\" if strategy.strip() == \"Q\" else \"uniform\")\n",
+    "                data = [[x] for x in experiment]\n",
+    "                disc.fit(data)\n",
+    "                normalize_cuts(disc.bin_edges_)\n",
+    "                result = disc.transform(data)\n",
+    "                write_lists(file, result, disc.bin_edges_[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cut points:  [array([ 0., 33., 66., 99.])]\n",
+      "Mistaken transformed data disc.transform([[33]]) = [[0.]]\n",
+      "Reason of the mistake the cutpoint has decimals (double):  33.00000000000001\n"
+     ]
+    }
+   ],
+   "source": [
+    "#\n",
+    "# Proving the mistakes due to floating point precision\n",
+    "#\n",
+    "from sklearn.preprocessing import KBinsDiscretizer\n",
+    "\n",
+    "data = [[x] for x in range(100)]\n",
+    "disc = KBinsDiscretizer(n_bins=3, encode=\"ordinal\", strategy=\"quantile\")\n",
+    "disc.fit(data)\n",
+    "print(\"Cut points: \", disc.bin_edges_)\n",
+    "print(\"Mistaken transformed data disc.transform([[33]]) =\", disc.transform([[33]]))\n",
+    "print(\"Reason of the mistake the cutpoint has decimals (double): \", disc.bin_edges_[0][1])"
    ]
   }
  ],
@@ -125,7 +201,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.1.undefined"
   }
  },
  "nbformat": 4,

update_coverage.py

@@ -0,0 +1,38 @@
+# ***************************************************************
+# SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+# SPDX-FileType: SOURCE
+# SPDX-License-Identifier: MIT
+# ***************************************************************
+
+import subprocess
+import sys
+
+readme_file = "README.md"
+print("Updating coverage...")
+# Generate badge line
+output = subprocess.check_output(
+    "lcov --summary " + sys.argv[1] + "/coverage.info",
+    shell=True,
+)
+value = output.decode("utf-8").strip()
+percentage = 0
+for line in value.splitlines():
+    if "lines" in line:
+        percentage = float(line.split(":")[1].split("%")[0])
+        break
+print(f"Coverage: {percentage}%")
+if percentage < 90:
+    print("⛔Coverage is less than 90%. I won't update the badge.")
+    sys.exit(1)
+percentage_label = str(percentage).replace(".", ",")
+coverage_line = f"[![Coverage Badge](https://img.shields.io/badge/Coverage-{percentage_label}%25-green)](html/index.html)"
+# Update README.md
+with open(readme_file, "r") as f:
+    lines = f.readlines()
+with open(readme_file, "w") as f:
+    for line in lines:
+        if "img.shields.io/badge/Coverage" in line:
+            f.write(coverage_line + "\n")
+        else:
+            f.write(line)
+print(f"✅Coverage updated with value: {percentage}")