Fix mistake in github action

Add fit_transform methods

.github/workflows/build.yml

@@ -22,15 +22,19 @@ jobs:
         run: |
           sudo apt-get -y install lcov
           sudo apt-get -y install gcovr
+      - name: Install Libtorch
+        run: |
+          wget https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-2.3.1%2Bcpu.zip
+          unzip libtorch-cxx11-abi-shared-with-deps-2.3.1+cpu.zip
       - name: Tests & build-wrapper
         run: |
-          cmake -S . -B build -Wno-dev 
+          cmake -S . -B build -Wno-dev -DCMAKE_PREFIX_PATH=$(pwd)/libtorch
           build-wrapper-linux-x86-64 --out-dir ${{ env.BUILD_WRAPPER_OUT_DIR }} cmake --build build/ --config Release
           cd build
           make
           ctest -C Release --output-on-failure --test-dir tests
           cd ..
-          gcovr -f CPPFImdlp.cpp -f Metrics.cpp  -f BinDisc.cpp --txt --sonarqube=coverage.xml
+          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 }}

.vscode/launch.json

@@ -8,15 +8,10 @@
             "name": "C++ Launch config",
             "type": "cppdbg",
             "request": "launch",
-            "program": "${workspaceFolder}/build/sample/sample",
-            "cwd": "${workspaceFolder}/build/sample",
-            "args": [
-                "-f",
-                "glass"
-            ],
-            "targetArchitecture": "arm64",
+            "program": "${workspaceFolder}/tests/build/Metrics_unittest",
+            "cwd": "${workspaceFolder}/tests/build",
+            "args": [],
             "launchCompleteCommand": "exec-run",
-            "preLaunchTask": "CMake: build",
             "stopAtEntry": false,
             "linux": {
                 "MIMode": "gdb",

.vscode/settings.json

@@ -5,5 +5,105 @@
     },
     "C_Cpp.default.configurationProvider": "ms-vscode.cmake-tools",
     "cmake.configureOnOpen": true,
-    "sonarlint.pathToCompileCommands": "${workspaceFolder}/build/compile_commands.json"
+    "sonarlint.pathToCompileCommands": "${workspaceFolder}/build/compile_commands.json",
+    "files.associations": {
+        "*.rmd": "markdown",
+        "*.py": "python",
+        "vector": "cpp",
+        "__bit_reference": "cpp",
+        "__bits": "cpp",
+        "__config": "cpp",
+        "__debug": "cpp",
+        "__errc": "cpp",
+        "__hash_table": "cpp",
+        "__locale": "cpp",
+        "__mutex_base": "cpp",
+        "__node_handle": "cpp",
+        "__nullptr": "cpp",
+        "__split_buffer": "cpp",
+        "__string": "cpp",
+        "__threading_support": "cpp",
+        "__tuple": "cpp",
+        "array": "cpp",
+        "atomic": "cpp",
+        "bitset": "cpp",
+        "cctype": "cpp",
+        "chrono": "cpp",
+        "clocale": "cpp",
+        "cmath": "cpp",
+        "compare": "cpp",
+        "complex": "cpp",
+        "concepts": "cpp",
+        "cstdarg": "cpp",
+        "cstddef": "cpp",
+        "cstdint": "cpp",
+        "cstdio": "cpp",
+        "cstdlib": "cpp",
+        "cstring": "cpp",
+        "ctime": "cpp",
+        "cwchar": "cpp",
+        "cwctype": "cpp",
+        "exception": "cpp",
+        "initializer_list": "cpp",
+        "ios": "cpp",
+        "iosfwd": "cpp",
+        "istream": "cpp",
+        "limits": "cpp",
+        "locale": "cpp",
+        "memory": "cpp",
+        "mutex": "cpp",
+        "new": "cpp",
+        "optional": "cpp",
+        "ostream": "cpp",
+        "ratio": "cpp",
+        "sstream": "cpp",
+        "stdexcept": "cpp",
+        "streambuf": "cpp",
+        "string": "cpp",
+        "string_view": "cpp",
+        "system_error": "cpp",
+        "tuple": "cpp",
+        "type_traits": "cpp",
+        "typeinfo": "cpp",
+        "unordered_map": "cpp",
+        "variant": "cpp",
+        "algorithm": "cpp",
+        "iostream": "cpp",
+        "iomanip": "cpp",
+        "numeric": "cpp",
+        "set": "cpp",
+        "__tree": "cpp",
+        "deque": "cpp",
+        "list": "cpp",
+        "map": "cpp",
+        "unordered_set": "cpp",
+        "any": "cpp",
+        "condition_variable": "cpp",
+        "forward_list": "cpp",
+        "fstream": "cpp",
+        "stack": "cpp",
+        "thread": "cpp",
+        "__memory": "cpp",
+        "filesystem": "cpp",
+        "*.toml": "toml",
+        "utility": "cpp",
+        "span": "cpp",
+        "*.tcc": "cpp",
+        "bit": "cpp",
+        "charconv": "cpp",
+        "cinttypes": "cpp",
+        "codecvt": "cpp",
+        "functional": "cpp",
+        "iterator": "cpp",
+        "memory_resource": "cpp",
+        "random": "cpp",
+        "source_location": "cpp",
+        "format": "cpp",
+        "numbers": "cpp",
+        "semaphore": "cpp",
+        "stop_token": "cpp",
+        "text_encoding": "cpp",
+        "typeindex": "cpp",
+        "valarray": "cpp"
+    }
 }

BinDisc.cpp

@@ -7,7 +7,8 @@
 
 namespace mdlp {
 
-    BinDisc::BinDisc(int n_bins, strategy_t strategy) : n_bins{ n_bins }, strategy{ strategy }
+    BinDisc::BinDisc(int n_bins, strategy_t strategy) :
+        Discretizer(), n_bins{ n_bins }, strategy{ strategy }
     {
         if (n_bins < 3) {
             throw std::invalid_argument("n_bins must be greater than 2");
@@ -16,6 +17,7 @@ namespace mdlp {
     BinDisc::~BinDisc() = default;
     void BinDisc::fit(samples_t& X)
     {
+        // y is included for compatibility with the Discretizer interface
         cutPoints.clear();
         if (X.empty()) {
             cutPoints.push_back(std::numeric_limits<precision_t>::max());
@@ -27,6 +29,10 @@ namespace mdlp {
             fit_uniform(X);
         }
     }
+    void BinDisc::fit(samples_t& X, labels_t& y)
+    {
+        fit(X);
+    }
     std::vector<precision_t> linspace(precision_t start, precision_t end, int num)
     {
         // Doesn't include end point as it is not needed
@@ -90,49 +96,4 @@ namespace mdlp {
         // Remove first as it is not needed
         cutPoints.erase(cutPoints.begin());
     }
-    labels_t& BinDisc::transform(const samples_t& X)
-    {
-        discretizedData.clear();
-        discretizedData.reserve(X.size());
-        for (const precision_t& item : X) {
-            auto upper = std::upper_bound(cutPoints.begin(), cutPoints.end(), item);
-            discretizedData.push_back(upper - cutPoints.begin());
-        }
-        return discretizedData;
-    }
-}
-// void BinDisc::fit_quantile(samples_t& X)
-    // {
-    //     cutPoints.clear();
-    //     if (X.empty()) {
-    //         cutPoints.push_back(std::numeric_limits<float>::max());
-    //         return;
-    //     }
-    //     samples_t data = X;
-    //     std::sort(data.begin(), data.end());
-    //     float min_val = data.front();
-    //     float max_val = data.back();
-    //     // Handle case of all data points having the same value
-    //     if (min_val == max_val) {
-    //         cutPoints.push_back(std::numeric_limits<float>::max());
-    //         return;
-    //     }
-    //     int first = X.size() / n_bins;
-    //     cutPoints.push_back(data.at(first - 1));
-    //     int bins_done = 1;
-    //     int prev = first - 1;
-    //     while (bins_done < n_bins) {
-    //         int next = first * (bins_done + 1) - 1;
-    //         while (next < X.size() && data.at(next) == data[prev]) {
-    //             ++next;
-    //         }
-    //         if (next == X.size() || bins_done == n_bins - 1) {
-    //             cutPoints.push_back(std::numeric_limits<float>::max());
-    //             break;
-    //         } else {
-    //             cutPoints.push_back(data[next]);
-    //             bins_done++;
-    //             prev = next;
-    //         }
-    //     }
-    // }
+}

BinDisc.h

@@ -2,30 +2,27 @@
 #define BINDISC_H
 
 #include "typesFImdlp.h"
+#include "Discretizer.h"
 #include <string>
 
 namespace mdlp {
-
     enum class strategy_t {
         UNIFORM,
         QUANTILE
     };
-    class BinDisc {
+    class BinDisc : public Discretizer {
     public:
         BinDisc(int n_bins = 3, strategy_t strategy = strategy_t::UNIFORM);
         ~BinDisc();
-        void fit(samples_t&);
-        inline cutPoints_t getCutPoints() const { return cutPoints; };
-        labels_t& transform(const samples_t&);
-        static inline std::string version() { return "1.0.0"; };
+        // y is included for compatibility with the Discretizer interface
+        void fit(samples_t& X_, labels_t& y) override;
+        void fit(samples_t& X);
     private:
         void fit_uniform(samples_t&);
         void fit_quantile(samples_t&);
         void normalizeCutPoints();
         int n_bins;
         strategy_t strategy;
-        labels_t discretizedData = labels_t();
-        cutPoints_t cutPoints;
     };
 }
 #endif

CMakeLists.txt

@@ -1,13 +1,9 @@
 cmake_minimum_required(VERSION 3.20)
 project(mdlp)
-
-if (POLICY CMP0135)
-    cmake_policy(SET CMP0135 NEW)
-endif ()
-
-set(CMAKE_CXX_STANDARD 11)
-
-add_library(mdlp CPPFImdlp.cpp Metrics.cpp)
+set(CMAKE_CXX_STANDARD 17)
+find_package(Torch REQUIRED)
+include_directories(${TORCH_INCLUDE_DIRS})
+add_library(mdlp CPPFImdlp.cpp Metrics.cpp BinDisc.cpp Discretizer.cpp)
+target_link_libraries(mdlp "${TORCH_LIBRARIES}")
 add_subdirectory(sample)
-add_subdirectory(tests)
-
+add_subdirectory(tests)

CPPFImdlp.cpp

@@ -6,16 +6,14 @@
 
 namespace mdlp {
 
-    CPPFImdlp::CPPFImdlp(size_t min_length_, int max_depth_, float proposed) : min_length(min_length_),
+    CPPFImdlp::CPPFImdlp(size_t min_length_, int max_depth_, float proposed) :
+        Discretizer(),
+        min_length(min_length_),
         max_depth(max_depth_),
         proposed_cuts(proposed)
     {
     }
 
-    CPPFImdlp::CPPFImdlp() = default;
-
-    CPPFImdlp::~CPPFImdlp() = default;
-
     size_t CPPFImdlp::compute_max_num_cut_points() const
     {
         // Set the actual maximum number of cut points as a number or as a percentage of the number of samples
@@ -208,14 +206,5 @@ namespace mdlp {
         }
         cutPoints.erase(cutPoints.begin() + static_cast<long>(maxEntropyIdx));
     }
-    labels_t& CPPFImdlp::transform(const samples_t& data)
-    {
-        discretizedData.clear();
-        discretizedData.reserve(data.size());
-        for (const precision_t& item : data) {
-            auto upper = std::upper_bound(cutPoints.begin(), cutPoints.end(), item);
-            discretizedData.push_back(upper - cutPoints.begin());
-        }
-        return discretizedData;
-    }
+
 }

CPPFImdlp.h

@@ -6,18 +6,16 @@
 #include <utility>
 #include <string>
 #include "Metrics.h"
+#include "Discretizer.h"
 
 namespace mdlp {
-    class CPPFImdlp {
+    class CPPFImdlp : public Discretizer {
     public:
-        CPPFImdlp();
-        CPPFImdlp(size_t, int, float);
-        ~CPPFImdlp();
-        void fit(samples_t&, labels_t&);
-        inline cutPoints_t getCutPoints() const { return cutPoints; };
-        labels_t& transform(const samples_t&);
+        CPPFImdlp() = default;
+        CPPFImdlp(size_t min_length_, int max_depth_, float proposed);
+        virtual ~CPPFImdlp() = default;
+        void fit(samples_t& X_, labels_t& y_) override;
         inline int get_depth() const { return depth; };
-        static inline std::string version() { return "1.1.3"; };
     protected:
         size_t min_length = 3;
         int depth = 0;
@@ -27,9 +25,7 @@ namespace mdlp {
         samples_t X = samples_t();
         labels_t y = labels_t();
         Metrics metrics = Metrics(y, indices);
-        cutPoints_t cutPoints;
         size_t num_cut_points = numeric_limits<size_t>::max();
-        labels_t discretizedData = labels_t();
         static indices_t sortIndices(samples_t&, labels_t&);
         void computeCutPoints(size_t, size_t, int);
         void resizeCutPoints();

Discretizer.cpp

@@ -0,0 +1,41 @@
+#include "Discretizer.h"
+
+namespace mdlp {
+    labels_t& Discretizer::transform(const samples_t& data)
+    {
+        discretizedData.clear();
+        discretizedData.reserve(data.size());
+        for (const precision_t& item : data) {
+            auto upper = std::upper_bound(cutPoints.begin(), cutPoints.end(), item);
+            discretizedData.push_back(upper - cutPoints.begin());
+        }
+        return discretizedData;
+    }
+    labels_t& Discretizer::fit_transform(samples_t& X_, labels_t& y_)
+    {
+        fit(X_, y_);
+        return transform(X_);
+    }
+    void Discretizer::fit_t(torch::Tensor& X_, 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<int64_t>(), y_.data_ptr<int64_t>() + num_elements);
+        fit(X, y);
+    }
+    torch::Tensor Discretizer::transform_t(torch::Tensor& X_)
+    {
+        auto num_elements = X_.numel();
+        samples_t X(X_.data_ptr<float>(), X_.data_ptr<float>() + num_elements);
+        auto result = transform(X);
+        return torch::tensor(result, torch::kInt64);
+    }
+    torch::Tensor Discretizer::fit_transform_t(torch::Tensor& X_, 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<int64_t>(), y_.data_ptr<int64_t>() + num_elements);
+        auto result = fit_transform(X, y);
+        return torch::tensor(result, torch::kInt64);
+    }
+}

Discretizer.h

@@ -0,0 +1,27 @@
+#ifndef DISCRETIZER_H
+#define DISCRETIZER_H
+
+#include <string>
+#include <algorithm>
+#include <torch/torch.h>
+#include "typesFImdlp.h"
+
+namespace mdlp {
+    class Discretizer {
+    public:
+        Discretizer() = default;
+        virtual ~Discretizer() = default;
+        inline cutPoints_t getCutPoints() const { return cutPoints; };
+        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_);
+        torch::Tensor transform_t(torch::Tensor& X_);
+        torch::Tensor fit_transform_t(torch::Tensor& X_, torch::Tensor& y_);
+        static inline std::string version() { return "1.2.1"; };
+    protected:
+        labels_t discretizedData = labels_t();
+        cutPoints_t cutPoints;
+    };
+}
+#endif

Metrics.cpp

@@ -4,8 +4,8 @@
 
 using namespace std;
 namespace mdlp {
-    Metrics::Metrics(labels_t& y_, indices_t& indices_): y(y_), indices(indices_),
-        numClasses(computeNumClasses(0, indices.size()))
+    Metrics::Metrics(labels_t& y_, indices_t& indices_) : y(y_), indices(indices_),
+        numClasses(computeNumClasses(0, indices_.size()))
     {
     }
 

README.md

@@ -23,12 +23,10 @@ The algorithm returns the cut points for the variable.
 To run the sample, just execute the following commands:
 
 ```bash
-cd sample
-cmake -B build
-cd build
-make
-./sample -f iris -m 2
-./sample -h
+cmake -B build -S .
+cmake --build build
+build/sample/sample -f iris -m 2
+build/sample/sample -h
 ```
 
 ## Test

sample/.vscode/launch.json

@@ -1,21 +0,0 @@
-{
-    "version": "0.2.0",
-    "configurations": [
-        {
-            "name": "lldb puro",
-            "type": "cppdbg",
-            // "targetArchitecture": "arm64",
-            "request": "launch",
-            "program": "${workspaceRoot}/build/sample",
-            "args": [
-                "-f",
-                "iris"
-            ],
-            "stopAtEntry": false,
-            "cwd": "${workspaceRoot}/build/",
-            "environment": [],
-            "externalConsole": false,
-            "MIMode": "lldb"
-        },
-    ]
-}

sample/CMakeLists.txt

@@ -1,5 +1,6 @@
-set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD 17)
 
 set(CMAKE_BUILD_TYPE Debug)
 
-add_executable(sample sample.cpp ../tests/ArffFiles.cpp ../Metrics.cpp ../CPPFImdlp.cpp)
+add_executable(sample sample.cpp ../tests/ArffFiles.cpp)
+target_link_libraries(sample mdlp "${TORCH_LIBRARIES}")

sample/sample.cpp

@@ -5,13 +5,13 @@
 #include <algorithm>
 #include <cstring>
 #include <getopt.h>
+#include <torch/torch.h>
+#include "../Discretizer.h"
 #include "../CPPFImdlp.h"
+#include "../BinDisc.h"
 #include "../tests/ArffFiles.h"
 
-using namespace std;
-using namespace mdlp;
-
-const string PATH = "../../tests/datasets/";
+const string PATH = "tests/datasets/";
 
 /* print a description of all supported options */
 void usage(const char* path)
@@ -20,17 +20,17 @@ void usage(const char* path)
     const char* basename = strrchr(path, '/');
     basename = basename ? basename + 1 : path;
 
-    cout << "usage: " << basename << "[OPTION]" << endl;
-    cout << "  -h, --help\t\t Print this help and exit." << endl;
-    cout
+    std::cout << "usage: " << basename << "[OPTION]" << std::endl;
+    std::cout << "  -h, --help\t\t Print this help and exit." << std::endl;
+    std::cout
         << "  -f, --file[=FILENAME]\t {all, diabetes, glass, iris, kdd_JapaneseVowels, letter, liver-disorders, mfeat-factors, test}."
-        << endl;
-    cout << "  -p, --path[=FILENAME]\t folder where the arff dataset is located, default " << PATH << endl;
-    cout << "  -m, --max_depth=INT\t max_depth pased to discretizer. Default = MAX_INT" << endl;
-    cout
+        << std::endl;
+    std::cout << "  -p, --path[=FILENAME]\t folder where the arff dataset is located, default " << PATH << std::endl;
+    std::cout << "  -m, --max_depth=INT\t max_depth pased to discretizer. Default = MAX_INT" << std::endl;
+    std::cout
         << "  -c, --max_cutpoints=FLOAT\t percentage of lines expressed in decimal or integer number or cut points. Default = 0 -> any"
-        << endl;
-    cout << "  -n, --min_length=INT\t interval min_length pased to discretizer. Default = 3" << endl;
+        << std::endl;
+    std::cout << "  -n, --min_length=INT\t interval min_length pased to discretizer. Default = 3" << std::endl;
 }
 
 tuple<string, string, int, int, float> parse_arguments(int argc, char** argv)
@@ -96,56 +96,79 @@ void process_file(const string& path, const string& file_name, bool class_last,
     file.load(path + file_name + ".arff", class_last);
     const auto attributes = file.getAttributes();
     const auto items = file.getSize();
-    cout << "Number of lines: " << items << endl;
-    cout << "Attributes: " << endl;
+    std::cout << "Number of lines: " << items << std::endl;
+    std::cout << "Attributes: " << std::endl;
     for (auto attribute : attributes) {
-        cout << "Name: " << get<0>(attribute) << " Type: " << get<1>(attribute) << endl;
+        std::cout << "Name: " << get<0>(attribute) << " Type: " << get<1>(attribute) << std::endl;
     }
-    cout << "Class name: " << file.getClassName() << endl;
-    cout << "Class type: " << file.getClassType() << endl;
-    cout << "Data: " << endl;
-    vector<samples_t>& X = file.getX();
-    labels_t& y = file.getY();
+    std::cout << "Class name: " << file.getClassName() << std::endl;
+    std::cout << "Class type: " << file.getClassType() << std::endl;
+    std::cout << "Data: " << std::endl;
+    std::vector<mdlp::samples_t>& X = file.getX();
+    mdlp::labels_t& y = file.getY();
     for (int i = 0; i < 5; i++) {
         for (auto feature : X) {
-            cout << fixed << setprecision(1) << feature[i] << " ";
+            std::cout << fixed << setprecision(1) << feature[i] << " ";
         }
-        cout << y[i] << endl;
+        std::cout << y[i] << std::endl;
     }
     auto test = mdlp::CPPFImdlp(min_length, max_depth, max_cutpoints);
     size_t total = 0;
     for (auto i = 0; i < attributes.size(); i++) {
         auto min_max = minmax_element(X[i].begin(), X[i].end());
-        cout << "Cut points for feature " << get<0>(attributes[i]) << ": [" << setprecision(3);
+        std::cout << "Cut points for feature " << get<0>(attributes[i]) << ": [" << setprecision(3);
         test.fit(X[i], y);
         auto cut_points = test.getCutPoints();
         for (auto item : cut_points) {
-            cout << item;
+            std::cout << item;
             if (item != cut_points.back())
-                cout << ", ";
+                std::cout << ", ";
         }
         total += test.getCutPoints().size();
-        cout << "]" << endl;
-        cout << "Min: " << *min_max.first << " Max: " << *min_max.second << endl;
-        cout << "--------------------------" << endl;
+        std::cout << "]" << std::endl;
+        std::cout << "Min: " << *min_max.first << " Max: " << *min_max.second << std::endl;
+        std::cout << "--------------------------" << std::endl;
+    }
+    std::cout << "Total cut points ...: " << total << std::endl;
+    std::cout << "Total feature states: " << total + attributes.size() << std::endl;
+    std::cout << "Version ............: " << test.version() << std::endl;
+    std::cout << "Transformed data (vector)..: " << std::endl;
+    test.fit(X[0], y);
+    auto data = test.transform(X[0]);
+    for (int i = 130; i < 135; i++) {
+        std::cout << std::fixed << std::setprecision(1) << X[0][i] << " " << data[i] << std::endl;
+    }
+    auto Xt = torch::tensor(X[0], torch::kFloat32);
+    auto yt = torch::tensor(y, torch::kInt64);
+    //test.fit_t(Xt, yt);
+    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<int64_t>() << std::endl;
+    }
+    auto disc = mdlp::BinDisc(3);
+    auto res_v = disc.fit_transform(X[0], y);
+    disc.fit_t(Xt, yt);
+    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<int64_t>() << std::endl;
     }
-    cout << "Total cut points ...: " << total << endl;
-    cout << "Total feature states: " << total + attributes.size() << endl;
 }
 
 void process_all_files(const map<string, bool>& datasets, const string& path, int max_depth, int min_length,
     float max_cutpoints)
 {
-    cout << "Results: " << "Max_depth: " << max_depth << "  Min_length: " << min_length << "  Max_cutpoints: "
-        << max_cutpoints << endl << endl;
+    std::cout << "Results: " << "Max_depth: " << max_depth << "  Min_length: " << min_length << "  Max_cutpoints: "
+        << max_cutpoints << std::endl << std::endl;
     printf("%-20s %4s %4s\n", "Dataset", "Feat", "Cuts Time(ms)");
     printf("==================== ==== ==== ========\n");
     for (const auto& dataset : datasets) {
         ArffFiles file;
         file.load(path + dataset.first + ".arff", dataset.second);
         auto attributes = file.getAttributes();
-        vector<samples_t>& X = file.getX();
-        labels_t& y = file.getY();
+        std::vector<mdlp::samples_t>& X = file.getX();
+        mdlp::labels_t& y = file.getY();
         size_t timing = 0;
         size_t cut_points = 0;
         for (auto i = 0; i < attributes.size(); i++) {
@@ -163,7 +186,7 @@ void process_all_files(const map<string, bool>& datasets, const string& path, in
 
 int main(int argc, char** argv)
 {
-    map<string, bool> datasets = {
+    std::map<std::string, bool> datasets = {
             {"diabetes",           true},
             {"glass",              true},
             {"iris",               true},
@@ -173,14 +196,14 @@ int main(int argc, char** argv)
             {"mfeat-factors",      true},
             {"test",               true}
     };
-    string file_name;
-    string path;
+    std::string file_name;
+    std::string path;
     int max_depth;
     int min_length;
     float max_cutpoints;
     tie(file_name, path, max_depth, min_length, max_cutpoints) = parse_arguments(argc, argv);
     if (datasets.find(file_name) == datasets.end() && file_name != "all") {
-        cout << "Invalid file name: " << file_name << endl;
+        std::cout << "Invalid file name: " << file_name << std::endl;
         usage(argv[0]);
         exit(1);
     }
@@ -188,10 +211,10 @@ int main(int argc, char** argv)
         process_all_files(datasets, path, max_depth, min_length, max_cutpoints);
     else {
         process_file(path, file_name, datasets[file_name], max_depth, min_length, max_cutpoints);
-        cout << "File name ....: " << file_name << endl;
-        cout << "Max depth ....: " << max_depth << endl;
-        cout << "Min length ...: " << min_length << endl;
-        cout << "Max cutpoints : " << max_cutpoints << endl;
+        std::cout << "File name ....: " << file_name << std::endl;
+        std::cout << "Max depth ....: " << max_depth << std::endl;
+        std::cout << "Min length ...: " << min_length << std::endl;
+        std::cout << "Max cutpoints : " << max_cutpoints << std::endl;
     }
     return 0;
 }

tests/BinDisc_unittest.cpp

@@ -37,12 +37,13 @@ namespace mdlp {
     TEST_F(TestBinDisc3U, Easy3BinsUniform)
     {
         samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 };
-        fit(X);
+        auto y = labels_t();
+        fit(X, y);
         auto cuts = getCutPoints();
-        EXPECT_NEAR(3.66667, cuts[0], margin);
-        EXPECT_NEAR(6.33333, cuts[1], margin);
-        EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
-        EXPECT_EQ(3, cuts.size());
+        ASSERT_EQ(3, cuts.size());
+        EXPECT_NEAR(3.66667, cuts.at(0), margin);
+        EXPECT_NEAR(6.33333, cuts.at(1), margin);
+        EXPECT_EQ(numeric_limits<float>::max(), cuts.at(2));
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2 };
         EXPECT_EQ(expected, labels);
@@ -52,10 +53,10 @@ namespace mdlp {
         samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 };
         fit(X);
         auto cuts = getCutPoints();
+        ASSERT_EQ(3, cuts.size());
         EXPECT_NEAR(3.666667, cuts[0], margin);
         EXPECT_NEAR(6.333333, cuts[1], margin);
         EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
-        EXPECT_EQ(3, cuts.size());
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2 };
         EXPECT_EQ(expected, labels);
@@ -65,10 +66,10 @@ namespace mdlp {
         samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0 };
         fit(X);
         auto cuts = getCutPoints();
+        ASSERT_EQ(3, cuts.size());
         EXPECT_EQ(4.0, cuts[0]);
         EXPECT_EQ(7.0, cuts[1]);
         EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
-        EXPECT_EQ(3, cuts.size());
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 2 };
         EXPECT_EQ(expected, labels);
@@ -78,10 +79,10 @@ namespace mdlp {
         samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0 };
         fit(X);
         auto cuts = getCutPoints();
+        ASSERT_EQ(3, cuts.size());
         EXPECT_EQ(4, cuts[0]);
         EXPECT_EQ(7, cuts[1]);
         EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
-        EXPECT_EQ(3, cuts.size());
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 2 };
         EXPECT_EQ(expected, labels);
@@ -91,10 +92,10 @@ namespace mdlp {
         samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0 };
         fit(X);
         auto cuts = getCutPoints();
+        ASSERT_EQ(3, cuts.size());
         EXPECT_NEAR(4.33333, cuts[0], margin);
         EXPECT_NEAR(7.66667, cuts[1], margin);
         EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
-        EXPECT_EQ(3, cuts.size());
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2 };
         EXPECT_EQ(expected, labels);
@@ -104,10 +105,10 @@ namespace mdlp {
         samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0 };
         fit(X);
         auto cuts = getCutPoints();
+        ASSERT_EQ(3, cuts.size());
         EXPECT_NEAR(4.33333, cuts[0], margin);
         EXPECT_NEAR(7.66667, cuts[1], margin);
         EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
-        EXPECT_EQ(3, cuts.size());
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2 };
         EXPECT_EQ(expected, labels);
@@ -117,8 +118,8 @@ namespace mdlp {
         samples_t X = { 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
         fit(X);
         auto cuts = getCutPoints();
+        ASSERT_EQ(1, cuts.size());
         EXPECT_EQ(numeric_limits<float>::max(), cuts[0]);
-        EXPECT_EQ(1, cuts.size());
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 0, 0, 0 };
         EXPECT_EQ(expected, labels);
@@ -128,8 +129,8 @@ namespace mdlp {
         samples_t X = { 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
         fit(X);
         auto cuts = getCutPoints();
-        EXPECT_EQ(numeric_limits<float>::max(), cuts[0]);
         EXPECT_EQ(1, cuts.size());
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[0]);
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 0, 0, 0 };
         EXPECT_EQ(expected, labels);
@@ -139,16 +140,16 @@ namespace mdlp {
         samples_t X = {};
         fit(X);
         auto cuts = getCutPoints();
-        EXPECT_EQ(numeric_limits<float>::max(), cuts[0]);
         EXPECT_EQ(1, cuts.size());
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[0]);
     }
     TEST_F(TestBinDisc3Q, EmptyQuantile)
     {
         samples_t X = {};
         fit(X);
         auto cuts = getCutPoints();
-        EXPECT_EQ(numeric_limits<float>::max(), cuts[0]);
         EXPECT_EQ(1, cuts.size());
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[0]);
     }
     TEST(TestBinDisc3, ExceptionNumberBins)
     {
@@ -159,44 +160,38 @@ namespace mdlp {
         samples_t X = { 3.0, 1.0, 1.0, 3.0, 1.0, 1.0, 3.0, 1.0, 1.0 };
         fit(X);
         auto cuts = getCutPoints();
+        ASSERT_EQ(3, cuts.size());
         EXPECT_NEAR(1.66667, cuts[0], margin);
         EXPECT_NEAR(2.33333, cuts[1], margin);
         EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
-        EXPECT_EQ(3, cuts.size());
         auto labels = transform(X);
         labels_t expected = { 2, 0, 0, 2, 0, 0, 2, 0, 0 };
         EXPECT_EQ(expected, labels);
-        EXPECT_EQ(3.0, X[0]); // X is not modified
+        ASSERT_EQ(3.0, X[0]); // X is not modified
     }
     TEST_F(TestBinDisc3Q, EasyRepeated)
     {
         samples_t X = { 3.0, 1.0, 1.0, 3.0, 1.0, 1.0, 3.0, 1.0, 1.0 };
         fit(X);
         auto cuts = getCutPoints();
-        std::cout << "cuts: ";
-        for (auto cut : cuts) {
-            std::cout << cut << " ";
-        }
-        std::cout << std::endl;
-        std::cout << std::string(80, '-') << std::endl;
+        EXPECT_EQ(2, cuts.size());
         EXPECT_NEAR(1.66667, cuts[0], margin);
         EXPECT_EQ(numeric_limits<float>::max(), cuts[1]);
-        EXPECT_EQ(2, cuts.size());
         auto labels = transform(X);
         labels_t expected = { 1, 0, 0, 1, 0, 0, 1, 0, 0 };
         EXPECT_EQ(expected, labels);
-        EXPECT_EQ(3.0, X[0]); // X is not modified
+        ASSERT_EQ(3.0, X[0]); // X is not modified
     }
     TEST_F(TestBinDisc4U, Easy4BinsUniform)
     {
         samples_t 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 };
         fit(X);
         auto cuts = getCutPoints();
-        EXPECT_EQ(3.75, cuts[0]);
+        EXPECT_EQ(4, cuts.size());
+        ASSERT_EQ(3.75, cuts[0]);
         EXPECT_EQ(6.5, cuts[1]);
         EXPECT_EQ(9.25, cuts[2]);
         EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
-        EXPECT_EQ(4, cuts.size());
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3 };
         EXPECT_EQ(expected, labels);
@@ -206,11 +201,11 @@ namespace mdlp {
         samples_t 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 };
         fit(X);
         auto cuts = getCutPoints();
-        EXPECT_EQ(3.75, cuts[0]);
+        EXPECT_EQ(4, cuts.size());
+        ASSERT_EQ(3.75, cuts[0]);
         EXPECT_EQ(6.5, cuts[1]);
         EXPECT_EQ(9.25, cuts[2]);
         EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
-        EXPECT_EQ(4, cuts.size());
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3 };
         EXPECT_EQ(expected, labels);
@@ -220,11 +215,11 @@ namespace mdlp {
         samples_t 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 };
         fit(X);
         auto cuts = getCutPoints();
+        EXPECT_EQ(4, cuts.size());
         EXPECT_EQ(4.0, cuts[0]);
         EXPECT_EQ(7.0, cuts[1]);
         EXPECT_EQ(10.0, cuts[2]);
         EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
-        EXPECT_EQ(4, cuts.size());
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3 };
         EXPECT_EQ(expected, labels);
@@ -234,11 +229,11 @@ namespace mdlp {
         samples_t 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 };
         fit(X);
         auto cuts = getCutPoints();
+        EXPECT_EQ(4, cuts.size());
         EXPECT_EQ(4.0, cuts[0]);
         EXPECT_EQ(7.0, cuts[1]);
         EXPECT_EQ(10.0, cuts[2]);
         EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
-        EXPECT_EQ(4, cuts.size());
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3 };
         EXPECT_EQ(expected, labels);
@@ -248,11 +243,11 @@ namespace mdlp {
         samples_t 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 };
         fit(X);
         auto cuts = getCutPoints();
+        EXPECT_EQ(4, cuts.size());
         EXPECT_EQ(4.25, cuts[0]);
         EXPECT_EQ(7.5, cuts[1]);
         EXPECT_EQ(10.75, cuts[2]);
         EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
-        EXPECT_EQ(4, cuts.size());
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3 };
         EXPECT_EQ(expected, labels);
@@ -262,11 +257,11 @@ namespace mdlp {
         samples_t 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 };
         fit(X);
         auto cuts = getCutPoints();
+        EXPECT_EQ(4, cuts.size());
         EXPECT_EQ(4.25, cuts[0]);
         EXPECT_EQ(7.5, cuts[1]);
         EXPECT_EQ(10.75, cuts[2]);
         EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
-        EXPECT_EQ(4, cuts.size());
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3 };
         EXPECT_EQ(expected, labels);
@@ -276,11 +271,11 @@ namespace mdlp {
         samples_t X = { 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 };
         fit(X);
         auto cuts = getCutPoints();
+        EXPECT_EQ(4, cuts.size());
         EXPECT_EQ(4.5, cuts[0]);
         EXPECT_EQ(8, cuts[1]);
         EXPECT_EQ(11.5, cuts[2]);
         EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
-        EXPECT_EQ(4, cuts.size());
         auto labels = transform(X);
         labels_t expected = { 3, 2, 3, 3, 1, 0, 3, 2, 2, 2, 1, 0, 0, 1, 0 };
         EXPECT_EQ(expected, labels);
@@ -290,11 +285,11 @@ namespace mdlp {
         samples_t X = { 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 };
         fit(X);
         auto cuts = getCutPoints();
+        EXPECT_EQ(4, cuts.size());
         EXPECT_EQ(4.5, cuts[0]);
         EXPECT_EQ(8, cuts[1]);
         EXPECT_EQ(11.5, cuts[2]);
         EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
-        EXPECT_EQ(4, cuts.size());
         auto labels = transform(X);
         labels_t expected = { 3, 3, 3, 3, 1, 0, 2, 2, 2, 2, 1, 0, 0, 1, 0 };
         EXPECT_EQ(expected, labels);
@@ -305,11 +300,11 @@ namespace mdlp {
         //               0    1     2   3    4    5    6    7    8    9
         fit(X);
         auto cuts = getCutPoints();
+        EXPECT_EQ(4, cuts.size());
         EXPECT_EQ(1.0, cuts[0]);
         EXPECT_EQ(2.0, cuts[1]);
-        EXPECT_EQ(3.0, cuts[2]);
+        ASSERT_EQ(3.0, cuts[2]);
         EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
-        EXPECT_EQ(4, cuts.size());
         auto labels = transform(X);
         labels_t expected = { 0, 1, 1, 1, 2, 2, 3, 3, 3, 3 };
         EXPECT_EQ(expected, labels);
@@ -320,10 +315,10 @@ namespace mdlp {
         //               0    1     2   3    4    5    6    7    8    9
         fit(X);
         auto cuts = getCutPoints();
+        ASSERT_EQ(3, cuts.size());
         EXPECT_EQ(2.0, cuts[0]);
-        EXPECT_EQ(3.0, cuts[1]);
+        ASSERT_EQ(3.0, cuts[1]);
         EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
-        EXPECT_EQ(3, cuts.size());
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 0, 1, 1, 2, 2, 2, 2 };
         EXPECT_EQ(expected, labels);
@@ -337,6 +332,13 @@ namespace mdlp {
         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::kInt64);
+        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<int64_t>());
     }
     TEST_F(TestBinDisc4Q, irisQuantile)
     {
@@ -347,5 +349,16 @@ namespace mdlp {
         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::kInt64);
+        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<int64_t>());
+        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<int64_t>());
     }
 }

tests/CMakeLists.txt

@@ -1,10 +1,8 @@
 cmake_minimum_required(VERSION 3.20)
-set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD 17)
+cmake_policy(SET CMP0135 NEW)
 include(FetchContent)
-
 include_directories(${GTEST_INCLUDE_DIRS})
-
-
 FetchContent_Declare(
         googletest
         URL https://github.com/google/googletest/archive/03597a01ee50ed33e9dfd640b249b4be3799d395.zip
@@ -13,22 +11,35 @@ FetchContent_Declare(
 set(gtest_force_shared_crt ON CACHE BOOL "" FORCE)
 FetchContent_MakeAvailable(googletest)
 
+find_package(Torch REQUIRED)
+
 enable_testing()
 
+include_directories(${TORCH_INCLUDE_DIRS})
+
 add_executable(Metrics_unittest ../Metrics.cpp Metrics_unittest.cpp)
-add_executable(FImdlp_unittest ../CPPFImdlp.cpp ArffFiles.cpp ../Metrics.cpp FImdlp_unittest.cpp)
-add_executable(BinDisc_unittest ../BinDisc.cpp ArffFiles.cpp BinDisc_unittest.cpp)
 target_link_libraries(Metrics_unittest GTest::gtest_main)
-target_link_libraries(FImdlp_unittest GTest::gtest_main)
-target_link_libraries(BinDisc_unittest GTest::gtest_main)
 target_compile_options(Metrics_unittest PRIVATE --coverage)
-target_compile_options(FImdlp_unittest PRIVATE --coverage)
-target_compile_options(BinDisc_unittest PRIVATE --coverage)
 target_link_options(Metrics_unittest PRIVATE --coverage)
+
+add_executable(FImdlp_unittest ../CPPFImdlp.cpp ArffFiles.cpp ../Metrics.cpp FImdlp_unittest.cpp ../Discretizer.cpp)
+target_link_libraries(FImdlp_unittest GTest::gtest_main "${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)
+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)
+target_link_libraries(Discretizer_unittest GTest::gtest_main "${TORCH_LIBRARIES}")
+target_compile_options(Discretizer_unittest PRIVATE --coverage)
+target_link_options(Discretizer_unittest PRIVATE --coverage)
+
 include(GoogleTest)
+
 gtest_discover_tests(Metrics_unittest)
 gtest_discover_tests(FImdlp_unittest)
-gtest_discover_tests(BinDisc_unittest)
+gtest_discover_tests(BinDisc_unittest)
+gtest_discover_tests(Discretizer_unittest)

tests/Discretizer_unittest.cpp

@@ -0,0 +1,74 @@
+#include <fstream>
+#include <string>
+#include <iostream>
+#include "gtest/gtest.h"
+#include "ArffFiles.h"
+#include "../Discretizer.h"
+#include "../BinDisc.h"
+#include "../CPPFImdlp.h"
+
+namespace mdlp {
+    const float margin = 1e-4;
+    static std::string set_data_path()
+    {
+        std::string path = "../datasets/";
+        std::ifstream file(path + "iris.arff");
+        if (file.is_open()) {
+            file.close();
+            return path;
+        }
+        return "../../tests/datasets/";
+    }
+    const std::string data_path = set_data_path();
+
+    TEST(Discretizer, BinIrisUniform)
+    {
+        ArffFiles file;
+        Discretizer* disc = new BinDisc(4, strategy_t::UNIFORM);
+        file.load(data_path + "iris.arff", true);
+        vector<samples_t>& X = file.getX();
+        auto y = labels_t();
+        disc->fit(X[0], y);
+        auto Xt = disc->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 };
+        delete disc;
+        EXPECT_EQ(expected, Xt);
+    }
+    TEST(Discretizer, BinIrisQuantile)
+    {
+        ArffFiles file;
+        Discretizer* disc = new BinDisc(4, strategy_t::QUANTILE);
+        file.load(data_path + "iris.arff", true);
+        vector<samples_t>& X = file.getX();
+        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);
+    }
+    TEST(Discretizer, FImdlpIris)
+    {
+        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,
+            5, 3, 5, 4, 5, 4, 4, 4, 4, 0, 1, 1, 4, 0, 2, 0, 0, 3, 0, 2, 2, 4,
+            3, 0, 0, 0, 4, 1, 0, 1, 2, 3, 1, 3, 2, 0, 0, 0, 0, 0, 3, 5, 4, 0,
+            3, 0, 0, 3, 0, 0, 0, 3, 2, 2, 0, 1, 4, 0, 3, 2, 3, 3, 0, 2, 0, 5,
+            4, 0, 3, 0, 1, 4, 3, 5, 0, 0, 4, 1, 1, 0, 4, 4, 1, 3, 1, 3, 1, 5,
+            1, 1, 0, 3, 5, 4, 3, 4, 4, 4, 0, 4, 4, 3, 0, 3, 5, 3
+        };
+        ArffFiles file;
+        Discretizer* disc = new CPPFImdlp();
+        file.load(data_path + "iris.arff", true);
+        vector<samples_t>& X = file.getX();
+        labels_t& y = file.getY();
+        disc->fit(X[1], y);
+        auto computed = disc->transform(X[1]);
+        delete disc;
+        EXPECT_EQ(computed.size(), expected.size());
+        for (unsigned long i = 0; i < computed.size(); i++) {
+            EXPECT_EQ(computed[i], expected[i]);
+        }
+    }
+}

tests/FImdlp_unittest.cpp

@@ -345,10 +345,15 @@ namespace mdlp {
         vector<samples_t>& X = file.getX();
         labels_t& y = file.getY();
         fit(X[1], y);
-        auto computed = transform(X[1]);
-        EXPECT_EQ(computed.size(), expected.size());
-        for (unsigned long i = 0; i < computed.size(); i++) {
-            EXPECT_EQ(computed[i], expected[i]);
-        }
+        // auto computed = transform(X[1]);
+        // EXPECT_EQ(computed.size(), expected.size());
+        // for (unsigned long i = 0; i < computed.size(); i++) {
+        //     EXPECT_EQ(computed[i], expected[i]);
+        // }
+        // auto computed_ft = fit_transform(X[1], y);
+        // EXPECT_EQ(computed_ft.size(), expected.size());
+        // for (unsigned long i = 0; i < computed_ft.size(); i++) {
+        //     EXPECT_EQ(computed_ft[i], expected[i]);
+        // }
     }
 }

tests/Metrics_unittest.cpp

@@ -2,13 +2,13 @@
 #include "../Metrics.h"
 
 namespace mdlp {
-    class TestMetrics: public Metrics, public testing::Test {
+    class TestMetrics : public Metrics, public testing::Test {
     public:
         labels_t y_ = { 1, 1, 1, 1, 1, 2, 2, 2, 2, 2 };
         indices_t indices_ = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
-        precision_t precision = 0.000001f;
+        precision_t precision = 1e-6;
 
-        TestMetrics(): Metrics(y_, indices_) {};
+        TestMetrics() : Metrics(y_, indices_) {};
 
         void SetUp() override
         {

tests/test

@@ -1,18 +1,15 @@
 #!/bin/bash
-if [ -d build ] ; then
+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 
+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
-#lcov --capture --directory ./ --output-file lcoverage/main_coverage.info
-#lcov --remove lcoverage/main_coverage.info 'v1/*' '/Applications/*' '*/tests/*' --output-file lcoverage/main_coverage.info -q
-#lcov --list lcoverage/main_coverage.info
 cd ..
-gcovr  --gcov-filter "CPPFImdlp.cpp" --gcov-filter "Metrics.cpp" --gcov-filter "BinDisc.cpp" --txt --sonarqube=tests/gcovr-report/coverage.xml --exclude-noncode-lines
+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

@@ -89,6 +89,7 @@ print(f"Quaintile {clf4q.bin_edges_=}")
 print("-" * 80)
 #
 data, meta = loadarff("tests/datasets/iris.arff")
+
 labelsu = [
     0,
     0,
@@ -117,12 +118,12 @@ labelsu = [
     0,
     0,
     0,
-    1,
-    1,
+    0,
+    0,
     0,
     0,
     1,
-    1,
+    0,
     1,
     0,
     0,
@@ -149,11 +150,11 @@ labelsu = [
     2,
     0,
     2,
-    1,
+    0,
     0,
     1,
     1,
-    2,
+    1,
     1,
     2,
     1,
@@ -161,9 +162,9 @@ labelsu = [
     2,
     1,
     1,
+    1,
     2,
-    2,
-    2,
+    1,
     2,
     2,
     2,
@@ -181,7 +182,7 @@ labelsu = [
     1,
     1,
     1,
-    2,
+    1,
     1,
     0,
     1,
@@ -217,14 +218,14 @@ labelsu = [
     2,
     3,
     2,
-    2,
+    1,
     2,
     3,
     3,
     3,
     2,
     2,
-    2,
+    1,
     3,
     2,
     2,
@@ -393,12 +394,19 @@ labelsq = [
     2,
     2,
 ]
-test(clf4u, data["sepallength"], labelsu, title="IrisUniform")
-test(clf4q, data["sepallength"], labelsq, title="IrisQuantile")
-# print("Labels")
-# print(labels)
-# print("Expected")
-# print(expected)
-# for i in range(len(labels)):
-#     if labels[i] != expected[i]:
-#         print(f"Error at {i} {labels[i]} != {expected[i]}")
+# 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}")