Add quiet parameter

Add STree, Odte, SVC & RandomForest Classifiers
Remove using namespace ... in project

.gitignore

@@ -32,8 +32,7 @@
 *.out
 *.app
 build/**
-build_debug/**
+build_*/**
-build_release/**
 *.dSYM/**
 cmake-build*/**
 .idea

.vscode/launch.json

@@ -21,7 +21,7 @@
         {
             "type": "lldb",
             "request": "launch",
-            "name": "experiment",
+            "name": "experimentPy",
             "program": "${workspaceFolder}/build_debug/src/Platform/b_main",
             "args": [
                 "-m",
@@ -35,6 +35,23 @@
             ],
             "cwd": "/home/rmontanana/Code/discretizbench",
         },
+        {
+            "type": "lldb",
+            "request": "launch",
+            "name": "experimentBayes",
+            "program": "${workspaceFolder}/build_debug/src/Platform/b_main",
+            "args": [
+                "-m",
+                "TAN",
+                "--stratified",
+                "--discretize",
+                "-d",
+                "iris",
+                "--hyperparameters",
+                "{\"repeatSparent\": true, \"maxModels\": 12}"
+            ],
+            "cwd": "/home/rmontanana/Code/discretizbench",
+        },
         {
             "type": "lldb",
             "request": "launch",

Makefile

@@ -4,7 +4,7 @@ SHELL := /bin/bash
 
 f_release = build_release
 f_debug = build_debug
-app_targets = b_best b_list b_main b_manage
+app_targets = b_best b_list b_main b_manage b_grid
 test_targets = unit_tests_bayesnet unit_tests_platform
 n_procs = -j 16
 
@@ -35,11 +35,13 @@ dest ?= ${HOME}/bin
 install: ## Copy binary files to bin folder
 	@echo "Destination folder: $(dest)"
 	make buildr
+	@echo "*******************************************"
 	@echo ">>> Copying files to $(dest)"
-	@cp $(f_release)/src/Platform/b_main $(dest)
+	@echo "*******************************************"
-	@cp $(f_release)/src/Platform/b_list $(dest)
+	@for item in $(app_targets); do \
-	@cp $(f_release)/src/Platform/b_manage $(dest)
+		echo ">>> Copying $$item" ; \
-	@cp $(f_release)/src/Platform/b_best $(dest)
+		cp $(f_release)/src/Platform/$$item $(dest) ; \
+	done
 
 dependency: ## Create a dependency graph diagram of the project (build/dependency.png)
 	@echo ">>> Creating dependency graph diagram of the project...";

README.md

@@ -18,12 +18,24 @@ The best option is install the packages that the Linux distribution have in its
 sudo dnf install boost-devel
 ```
 
-If this is not possible and the compressed packaged is installed, the following environment variable has to be set:
+If this is not possible and the compressed packaged is installed, the following environment variable has to be set pointing to the folder where it was unzipped to:
 
 ```bash
 export BOOST_ROOT=/path/to/library/
 ```
 
+In some cases, it is needed to build the library, to do so:
+
+```bash
+cd /path/to/library
+mkdir own
+./bootstrap.sh --prefix=/path/to/library/own
+./b2 install
+export BOOST_ROOT=/path/to/library/own/
+```
+
+Don't forget to add the export BOOST_ROOT statement to .bashrc or wherever it is meant to be.
+
 ### libxlswriter
 
 ```bash

grid_stree.json

@@ -0,0 +1,162 @@
+{
+    "balance-scale": {
+        "C": 10000.0,
+        "gamma": 0.1,
+        "kernel": "rbf",
+        "max_iter": 10000
+    },
+    "balloons": {
+        "C": 7,
+        "gamma": 0.1,
+        "kernel": "rbf",
+        "max_iter": 10000
+    },
+    "breast-cancer-wisc-diag": {
+        "C": 0.2,
+        "max_iter": 10000
+    },
+    "breast-cancer-wisc-prog": {
+        "C": 0.2,
+        "max_iter": 10000
+    },
+    "breast-cancer-wisc": {},
+    "breast-cancer": {},
+    "cardiotocography-10clases": {},
+    "cardiotocography-3clases": {},
+    "conn-bench-sonar-mines-rocks": {},
+    "cylinder-bands": {},
+    "dermatology": {
+        "C": 55,
+        "max_iter": 10000
+    },
+    "echocardiogram": {
+        "C": 7,
+        "gamma": 0.1,
+        "kernel": "poly",
+        "max_features": "auto",
+        "max_iter": 10000
+    },
+    "fertility": {
+        "C": 0.05,
+        "max_features": "auto",
+        "max_iter": 10000
+    },
+    "haberman-survival": {},
+    "heart-hungarian": {
+        "C": 0.05,
+        "max_iter": 10000
+    },
+    "hepatitis": {
+        "C": 7,
+        "gamma": 0.1,
+        "kernel": "rbf",
+        "max_iter": 10000
+    },
+    "ilpd-indian-liver": {},
+    "ionosphere": {
+        "C": 7,
+        "gamma": 0.1,
+        "kernel": "rbf",
+        "max_iter": 10000
+    },
+    "iris": {},
+    "led-display": {},
+    "libras": {
+        "C": 0.08,
+        "max_iter": 10000
+    },
+    "low-res-spect": {
+        "C": 0.05,
+        "max_iter": 10000
+    },
+    "lymphography": {
+        "C": 0.05,
+        "max_iter": 10000
+    },
+    "mammographic": {},
+    "molec-biol-promoter": {
+        "C": 0.05,
+        "gamma": 0.1,
+        "kernel": "poly",
+        "max_iter": 10000
+    },
+    "musk-1": {
+        "C": 0.05,
+        "gamma": 0.1,
+        "kernel": "poly",
+        "max_iter": 10000
+    },
+    "oocytes_merluccius_nucleus_4d": {
+        "C": 8.25,
+        "gamma": 0.1,
+        "kernel": "poly"
+    },
+    "oocytes_merluccius_states_2f": {},
+    "oocytes_trisopterus_nucleus_2f": {},
+    "oocytes_trisopterus_states_5b": {
+        "C": 0.11,
+        "max_iter": 10000
+    },
+    "parkinsons": {},
+    "pima": {},
+    "pittsburg-bridges-MATERIAL": {
+        "C": 7,
+        "gamma": 0.1,
+        "kernel": "rbf",
+        "max_iter": 10000
+    },
+    "pittsburg-bridges-REL-L": {},
+    "pittsburg-bridges-SPAN": {
+        "C": 0.05,
+        "max_iter": 10000
+    },
+    "pittsburg-bridges-T-OR-D": {},
+    "planning": {
+        "C": 7,
+        "gamma": 10.0,
+        "kernel": "rbf",
+        "max_iter": 10000
+    },
+    "post-operative": {
+        "C": 55,
+        "degree": 5,
+        "gamma": 0.1,
+        "kernel": "poly",
+        "max_iter": 10000
+    },
+    "seeds": {
+        "C": 10000.0,
+        "max_iter": 10000
+    },
+    "statlog-australian-credit": {
+        "C": 0.05,
+        "max_features": "auto",
+        "max_iter": 10000
+    },
+    "statlog-german-credit": {},
+    "statlog-heart": {},
+    "statlog-image": {
+        "C": 7,
+        "max_iter": 10000
+    },
+    "statlog-vehicle": {},
+    "synthetic-control": {
+        "C": 0.55,
+        "max_iter": 10000
+    },
+    "tic-tac-toe": {
+        "C": 0.2,
+        "gamma": 0.1,
+        "kernel": "poly",
+        "max_iter": 10000
+    },
+    "vertebral-column-2clases": {},
+    "wine": {
+        "C": 0.55,
+        "max_iter": 10000
+    },
+    "zoo": {
+        "C": 0.1,
+        "max_iter": 10000
+    }
+}

sample/sample.cc

@@ -1,10 +1,10 @@
 #include <iostream>
 #include <torch/torch.h>
-#include <std::string>
+#include <string>
 #include <map>
 #include <argparse/argparse.hpp>
 #include <nlohmann/json.hpp>
-#include "ArffFiles.h"v
+#include "ArffFiles.h"
 #include "BayesMetrics.h"
 #include "CPPFImdlp.h"
 #include "Folding.h"

src/BayesNet/BaseClassifier.h

@@ -6,8 +6,6 @@
 namespace bayesnet {
     enum status_t { NORMAL, WARNING, ERROR };
     class BaseClassifier {
-    protected:
-        virtual void trainModel(const torch::Tensor& weights) = 0;
     public:
         // X is nxm std::vector, y is nx1 std::vector
         virtual BaseClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;
@@ -29,7 +27,11 @@ namespace bayesnet {
         virtual std::string getVersion() = 0;
         std::vector<std::string> virtual topological_order() = 0;
         void virtual dump_cpt()const = 0;
-        virtual void setHyperparameters(nlohmann::json& hyperparameters) = 0;
+        virtual void setHyperparameters(const nlohmann::json& hyperparameters) = 0;
+        std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }
+    protected:
+        virtual void trainModel(const torch::Tensor& weights) = 0;
+        std::vector<std::string> validHyperparameters;
     };
 }
 #endif

src/BayesNet/BoostAODE.cc

@@ -10,7 +10,11 @@
 #include "IWSS.h"
 
 namespace bayesnet {
-    BoostAODE::BoostAODE() : Ensemble() {}
+    BoostAODE::BoostAODE() : Ensemble()
+    {
+        validHyperparameters = { "repeatSparent", "maxModels", "ascending", "convergence", "threshold", "select_features" };
+
+    }
     void BoostAODE::buildModel(const torch::Tensor& weights)
     {
         // Models shall be built in trainModel
@@ -43,11 +47,8 @@ namespace bayesnet {
             y_train = y_;
         }
     }
-    void BoostAODE::setHyperparameters(nlohmann::json& hyperparameters)
+    void BoostAODE::setHyperparameters(const nlohmann::json& hyperparameters)
     {
-        // Check if hyperparameters are valid
-        const std::vector<std::string> validKeys = { "repeatSparent", "maxModels", "ascending", "convergence", "threshold", "select_features" };
-        checkHyperparameters(validKeys, hyperparameters);
         if (hyperparameters.contains("repeatSparent")) {
             repeatSparent = hyperparameters["repeatSparent"];
         }

src/BayesNet/BoostAODE.h

@@ -8,9 +8,9 @@ namespace bayesnet {
     class BoostAODE : public Ensemble {
     public:
         BoostAODE();
-        virtual ~BoostAODE() {};
+        virtual ~BoostAODE() = default;
         std::vector<std::string> graph(const std::string& title = "BoostAODE") const override;
-        void setHyperparameters(nlohmann::json& hyperparameters) override;
+        void setHyperparameters(const nlohmann::json& hyperparameters) override;
     protected:
         void buildModel(const torch::Tensor& weights) override;
         void trainModel(const torch::Tensor& weights) override;

src/BayesNet/Classifier.cc

@@ -153,18 +153,8 @@ namespace bayesnet {
     {
         model.dump_cpt();
     }
-    void Classifier::checkHyperparameters(const std::vector<std::string>& validKeys, nlohmann::json& hyperparameters)
+    void Classifier::setHyperparameters(const nlohmann::json& hyperparameters)
     {
-        for (const auto& item : hyperparameters.items()) {
+        //For classifiers that don't have hyperparameters
-            if (find(validKeys.begin(), validKeys.end(), item.key()) == validKeys.end()) {
-                throw std::invalid_argument("Hyperparameter " + item.key() + " is not valid");
-            }
-        }
-    }
-    void Classifier::setHyperparameters(nlohmann::json& hyperparameters)
-    {
-        // Check if hyperparameters are valid, default is no hyperparameters
-        const std::vector<std::string> validKeys = { };
-        checkHyperparameters(validKeys, hyperparameters);
     }
 }

src/BayesNet/Classifier.h

@@ -22,7 +22,6 @@ namespace bayesnet {
         void checkFitParameters();
         virtual void buildModel(const torch::Tensor& weights) = 0;
         void trainModel(const torch::Tensor& weights) override;
-        void checkHyperparameters(const std::vector<std::string>& validKeys, nlohmann::json& hyperparameters);
         void buildDataset(torch::Tensor& y);
     public:
         Classifier(Network model);
@@ -44,7 +43,7 @@ namespace bayesnet {
         std::vector<std::string> show() const override;
         std::vector<std::string> topological_order()  override;
         void dump_cpt() const override;
-        void setHyperparameters(nlohmann::json& hyperparameters) override;
+        void setHyperparameters(const nlohmann::json& hyperparameters) override; //For classifiers that don't have hyperparameters
     };
 }
 #endif

src/BayesNet/KDB.cc

@@ -1,12 +1,13 @@
 #include "KDB.h"
 
 namespace bayesnet {
-    KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta) {}
+    KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta)
-    void KDB::setHyperparameters(nlohmann::json& hyperparameters)
+    {
+        validHyperparameters = { "k", "theta" };
+
+    }
+    void KDB::setHyperparameters(const nlohmann::json& hyperparameters)
     {
-        // Check if hyperparameters are valid
-        const std::vector<std::string> validKeys = { "k", "theta" };
-        checkHyperparameters(validKeys, hyperparameters);
         if (hyperparameters.contains("k")) {
             k = hyperparameters["k"];
         }

src/BayesNet/KDB.h

@@ -13,8 +13,8 @@ namespace bayesnet {
         void buildModel(const torch::Tensor& weights) override;
     public:
         explicit KDB(int k, float theta = 0.03);
-        virtual ~KDB() {};
+        virtual ~KDB() = default;
-        void setHyperparameters(nlohmann::json& hyperparameters) override;
+        void setHyperparameters(const nlohmann::json& hyperparameters) override;
         std::vector<std::string> graph(const std::string& name = "KDB") const override;
     };
 }

src/BayesNet/SPODE.h

@@ -10,7 +10,7 @@ namespace bayesnet {
         void buildModel(const torch::Tensor& weights) override;
     public:
         explicit SPODE(int root);
-        virtual ~SPODE() {};
+        virtual ~SPODE() = default;
         std::vector<std::string> graph(const std::string& name = "SPODE") const override;
     };
 }

src/BayesNet/TAN.h

@@ -8,7 +8,7 @@ namespace bayesnet {
         void buildModel(const torch::Tensor& weights) override;
     public:
         TAN();
-        virtual ~TAN() {};
+        virtual ~TAN() = default;
         std::vector<std::string> graph(const std::string& name = "TAN") const override;
     };
 }

src/Platform/CMakeLists.txt

@@ -8,12 +8,14 @@ include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
 include_directories(${BayesNet_SOURCE_DIR}/lib/libxlsxwriter/include)
 include_directories(${Python3_INCLUDE_DIRS})
 
-add_executable(b_main b_main.cc Folding.cc Experiment.cc Datasets.cc Dataset.cc Models.cc ReportConsole.cc ReportBase.cc)
-add_executable(b_manage b_manage.cc Results.cc ManageResults.cc CommandParser.cc Result.cc ReportConsole.cc ReportExcel.cc ReportBase.cc Datasets.cc Dataset.cc ExcelFile.cc)
-add_executable(b_list b_list.cc Datasets.cc Dataset.cc)
 add_executable(b_best b_best.cc BestResults.cc Result.cc Statistics.cc BestResultsExcel.cc ReportExcel.cc ReportBase.cc Datasets.cc Dataset.cc ExcelFile.cc)
+add_executable(b_grid b_grid.cc GridSearch.cc GridData.cc HyperParameters.cc Folding.cc Datasets.cc Dataset.cc)
+add_executable(b_list b_list.cc Datasets.cc Dataset.cc)
+add_executable(b_main b_main.cc Folding.cc Experiment.cc Datasets.cc Dataset.cc Models.cc HyperParameters.cc ReportConsole.cc ReportBase.cc)
+add_executable(b_manage b_manage.cc Results.cc ManageResults.cc CommandParser.cc Result.cc ReportConsole.cc ReportExcel.cc ReportBase.cc Datasets.cc Dataset.cc ExcelFile.cc)
 
-target_link_libraries(b_main BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}" PyWrap)
-target_link_libraries(b_manage "${TORCH_LIBRARIES}" "${XLSXWRITER_LIB}" ArffFiles mdlp)
 target_link_libraries(b_best Boost::boost "${XLSXWRITER_LIB}" "${TORCH_LIBRARIES}" ArffFiles mdlp)
-target_link_libraries(b_list ArffFiles mdlp "${TORCH_LIBRARIES}")
+target_link_libraries(b_grid BayesNet  PyWrap)
+target_link_libraries(b_list ArffFiles mdlp "${TORCH_LIBRARIES}")
+target_link_libraries(b_main BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}" PyWrap)
+target_link_libraries(b_manage "${TORCH_LIBRARIES}" "${XLSXWRITER_LIB}" ArffFiles mdlp)

src/Platform/Experiment.cc

@@ -26,7 +26,6 @@ namespace platform {
         oss << std::put_time(timeinfo, "%H:%M:%S");
         return oss.str();
     }
-    Experiment::Experiment() : hyperparameters(json::parse("{}")) {}
     std::string Experiment::get_file_name()
     {
         std::string result = "results_" + score_name + "_" + model + "_" + platform + "_" + get_date() + "_" + get_time() + "_" + (stratified ? "1" : "0") + ".json";
@@ -134,7 +133,7 @@ namespace platform {
     }
     void Experiment::cross_validation(const std::string& fileName, bool quiet)
     {
-        auto datasets = platform::Datasets(discretized, Paths::datasets());
+        auto datasets = Datasets(discretized, Paths::datasets());
         // Get dataset
         auto [X, y] = datasets.getTensors(fileName);
         auto states = datasets.getStates(fileName);
@@ -148,7 +147,7 @@ namespace platform {
         auto result = Result();
         auto [values, counts] = at::_unique(y);
         result.setSamples(X.size(1)).setFeatures(X.size(0)).setClasses(values.size(0));
-        result.setHyperparameters(hyperparameters);
+        result.setHyperparameters(hyperparameters.get(fileName));
         // Initialize results std::vectors
         int nResults = nfolds * static_cast<int>(randomSeeds.size());
         auto accuracy_test = torch::zeros({ nResults }, torch::kFloat64);
@@ -171,9 +170,9 @@ namespace platform {
             for (int nfold = 0; nfold < nfolds; nfold++) {
                 auto clf = Models::instance()->create(model);
                 setModelVersion(clf->getVersion());
-                if (hyperparameters.size() != 0) {
+                auto valid = clf->getValidHyperparameters();
-                    clf->setHyperparameters(hyperparameters);
+                hyperparameters.check(valid, fileName);
-                }
+                clf->setHyperparameters(hyperparameters.get(fileName));
                 // Split train - test dataset
                 train_timer.start();
                 auto [train, test] = fold->getFold(nfold);

src/Platform/Experiment.h

@@ -3,29 +3,16 @@
 #include <torch/torch.h>
 #include <nlohmann/json.hpp>
 #include <string>
-#include <chrono>
 #include "Folding.h"
 #include "BaseClassifier.h"
+#include "HyperParameters.h"
 #include "TAN.h"
 #include "KDB.h"
 #include "AODE.h"
+#include "Timer.h"
 
 namespace platform {
     using json = nlohmann::json;
-    class Timer {
-    private:
-        std::chrono::high_resolution_clock::time_point begin;
-    public:
-        Timer() = default;
-        ~Timer() = default;
-        void start() { begin = std::chrono::high_resolution_clock::now(); }
-        double getDuration()
-        {
-            std::chrono::high_resolution_clock::time_point end = std::chrono::high_resolution_clock::now();
-            std::chrono::duration<double> time_span = std::chrono::duration_cast<std::chrono::duration<double >> (end - begin);
-            return time_span.count();
-        }
-    };
     class Result {
     private:
         std::string dataset, model_version;
@@ -80,17 +67,8 @@ namespace platform {
         const std::vector<double>& getTimesTest() const { return times_test; }
     };
     class Experiment {
-    private:
-        std::string title, model, platform, score_name, model_version, language_version, language;
-        bool discretized{ false }, stratified{ false };
-        std::vector<Result> results;
-        std::vector<int> randomSeeds;
-        json hyperparameters = "{}";
-        int nfolds{ 0 };
-        float duration{ 0 };
-        json build_json();
     public:
-        Experiment();
+        Experiment() = default;
         Experiment& setTitle(const std::string& title) { this->title = title; return *this; }
         Experiment& setModel(const std::string& model) { this->model = model; return *this; }
         Experiment& setPlatform(const std::string& platform) { this->platform = platform; return *this; }
@@ -104,13 +82,22 @@ namespace platform {
         Experiment& addResult(Result result) { results.push_back(result); return *this; }
         Experiment& addRandomSeed(int randomSeed) { randomSeeds.push_back(randomSeed); return *this; }
         Experiment& setDuration(float duration) { this->duration = duration; return *this; }
-        Experiment& setHyperparameters(const json& hyperparameters) { this->hyperparameters = hyperparameters; return *this; }
+        Experiment& setHyperparameters(const HyperParameters& hyperparameters_) { this->hyperparameters = hyperparameters_; return *this; }
         std::string get_file_name();
         void save(const std::string& path);
         void cross_validation(const std::string& fileName, bool quiet);
         void go(std::vector<std::string> filesToProcess, bool quiet);
         void show();
         void report();
+    private:
+        std::string title, model, platform, score_name, model_version, language_version, language;
+        bool discretized{ false }, stratified{ false };
+        std::vector<Result> results;
+        std::vector<int> randomSeeds;
+        HyperParameters hyperparameters;
+        int nfolds{ 0 };
+        float duration{ 0 };
+        json build_json();
     };
 }
 #endif

src/Platform/GridData.cc

@@ -0,0 +1,55 @@
+#include "GridData.h"
+#include <fstream>
+
+namespace platform {
+    GridData::GridData(const std::string& fileName)
+    {
+        std::ifstream resultData(fileName);
+        if (resultData.is_open()) {
+            grid = json::parse(resultData);
+        } else {
+            throw std::invalid_argument("Unable to open input file. [" + fileName + "]");
+        }
+    }
+    int GridData::computeNumCombinations(const json& line)
+    {
+        int numCombinations = 1;
+        for (const auto& item : line.items()) {
+            numCombinations *= item.value().size();
+        }
+        return numCombinations;
+    }
+    int GridData::getNumCombinations()
+    {
+        int numCombinations = 0;
+        for (const auto& line : grid) {
+            numCombinations += computeNumCombinations(line);
+        }
+        return numCombinations;
+    }
+    json GridData::generateCombinations(json::iterator index, const json::iterator last, std::vector<json>& output, json currentCombination)
+    {
+        if (index == last) {
+            // If we reached the end of input, store the current combination
+            output.push_back(currentCombination);
+            return  currentCombination;
+        }
+        const auto& key = index.key();
+        const auto& values = index.value();
+        for (const auto& value : values) {
+            auto combination = currentCombination;
+            combination[key] = value;
+            json::iterator nextIndex = index;
+            generateCombinations(++nextIndex, last, output, combination);
+        }
+        return currentCombination;
+    }
+    std::vector<json> GridData::getGrid()
+    {
+        auto result = std::vector<json>();
+        for (json line : grid) {
+            generateCombinations(line.begin(), line.end(), result, json({}));
+        }
+        return result;
+    }
+} /* namespace platform */

src/Platform/GridData.h

@@ -0,0 +1,22 @@
+#ifndef GRIDDATA_H
+#define GRIDDATA_H
+#include <string>
+#include <vector>
+#include <map>
+#include <nlohmann/json.hpp>
+
+namespace platform {
+    using json = nlohmann::json;
+    class GridData {
+    public:
+        explicit GridData(const std::string& fileName);
+        ~GridData() = default;
+        std::vector<json> getGrid();
+        int getNumCombinations();
+    private:
+        json generateCombinations(json::iterator index, const json::iterator last, std::vector<json>& output, json currentCombination);
+        int computeNumCombinations(const json& line);
+        json grid;
+    };
+} /* namespace platform */
+#endif /* GRIDDATA_H */

src/Platform/GridSearch.cc

@@ -0,0 +1,130 @@
+#include <iostream>
+#include <torch/torch.h>
+#include "GridSearch.h"
+#include "Models.h"
+#include "Paths.h"
+#include "Folding.h"
+#include "Colors.h"
+
+namespace platform {
+    GridSearch::GridSearch(struct ConfigGrid& config) : config(config)
+    {
+        this->config.output_file = config.path + "grid_" + config.model + "_output.json";
+        this->config.input_file = config.path + "grid_" + config.model + "_input.json";
+    }
+    void showProgressComb(const int num, const int total, const std::string& color)
+    {
+        int spaces = int(log(total) / log(10)) + 1;
+        int magic = 37 + 2 * spaces;
+        std::string prefix = num == 1 ? "" : string(magic, '\b') + string(magic + 1, ' ') + string(magic + 1, '\b');
+        std::cout << prefix << color << "(" << setw(spaces) << num << "/" << setw(spaces) << total << ") " << Colors::RESET() << flush;
+    }
+    void showProgressFold(int fold, const std::string& color, const std::string& phase)
+    {
+        std::string prefix = phase == "a" ? "" : "\b\b\b\b";
+        std::cout << prefix << color << fold << Colors::RESET() << "(" << color << phase << Colors::RESET() << ")" << flush;
+    }
+    std::string getColor(bayesnet::status_t status)
+    {
+        switch (status) {
+            case bayesnet::NORMAL:
+                return Colors::GREEN();
+            case bayesnet::WARNING:
+                return Colors::YELLOW();
+            case bayesnet::ERROR:
+                return Colors::RED();
+            default:
+                return Colors::RESET();
+        }
+    }
+    double GridSearch::processFile(std::string fileName, Datasets& datasets, HyperParameters& hyperparameters)
+    {
+        // Get dataset
+        auto [X, y] = datasets.getTensors(fileName);
+        auto states = datasets.getStates(fileName);
+        auto features = datasets.getFeatures(fileName);
+        auto samples = datasets.getNSamples(fileName);
+        auto className = datasets.getClassName(fileName);
+        double totalScore = 0.0;
+        int numItems = 0;
+        for (const auto& seed : config.seeds) {
+            if (!config.quiet)
+                std::cout << "(" << seed << ") doing Fold: " << flush;
+            Fold* fold;
+            if (config.stratified)
+                fold = new StratifiedKFold(config.n_folds, y, seed);
+            else
+                fold = new KFold(config.n_folds, y.size(0), seed);
+            double bestScore = 0.0;
+            for (int nfold = 0; nfold < config.n_folds; nfold++) {
+                auto clf = Models::instance()->create(config.model);
+                clf->setHyperparameters(hyperparameters.get(fileName));
+                auto [train, test] = fold->getFold(nfold);
+                auto train_t = torch::tensor(train);
+                auto test_t = torch::tensor(test);
+                auto X_train = X.index({ "...", train_t });
+                auto y_train = y.index({ train_t });
+                auto X_test = X.index({ "...", test_t });
+                auto y_test = y.index({ test_t });
+                // Train model
+                if (!config.quiet)
+                    showProgressFold(nfold + 1, getColor(clf->getStatus()), "a");
+                clf->fit(X_train, y_train, features, className, states);
+                // Test model
+                if (!config.quiet)
+                    showProgressFold(nfold + 1, getColor(clf->getStatus()), "b");
+                totalScore += clf->score(X_test, y_test);
+                numItems++;
+                if (!config.quiet)
+                    std::cout << "\b\b\b,  \b" << flush;
+            }
+            delete fold;
+        }
+        return numItems == 0 ? 0.0 : totalScore / numItems;
+    }
+    void GridSearch::go()
+    {
+        // Load datasets
+        auto datasets = Datasets(config.discretize, Paths::datasets());
+        // Create model
+        std::cout << "***************** Starting Gridsearch *****************" << std::endl;
+        std::cout << "input file=" << config.input_file << std::endl;
+        auto grid = GridData(config.input_file);
+        auto totalComb = grid.getNumCombinations();
+        std::cout << "* Doing " << totalComb << " combinations for each dataset/seed/fold" << std::endl;
+        // Generate hyperparameters grid & run gridsearch
+        // Check each combination of hyperparameters for each dataset and each seed
+        for (const auto& dataset : datasets.getNames()) {
+            if (!config.quiet)
+                std::cout << "- " << setw(20) << left << dataset << " " << right << flush;
+            int num = 0;
+            double bestScore = 0.0;
+            json bestHyperparameters;
+            for (const auto& hyperparam_line : grid.getGrid()) {
+                if (!config.quiet)
+                    showProgressComb(++num, totalComb, Colors::CYAN());
+                auto hyperparameters = platform::HyperParameters(datasets.getNames(), hyperparam_line);
+                double score = processFile(dataset, datasets, hyperparameters);
+                if (score > bestScore) {
+                    bestScore = score;
+                    bestHyperparameters = hyperparam_line;
+                }
+            }
+            if (!config.quiet) {
+                std::cout << "end." << " Score: " << setw(9) << setprecision(7) << fixed
+                    << bestScore << " [" << bestHyperparameters.dump() << "]" << std::endl;
+            }
+            results[dataset]["score"] = bestScore;
+            results[dataset]["hyperparameters"] = bestHyperparameters;
+        }
+        // Save results
+        save();
+        std::cout << "***************** Ending Gridsearch *******************" << std::endl;
+    }
+    void GridSearch::save() const
+    {
+        std::ofstream file(config.output_file);
+        file << results.dump(4);
+        file.close();
+    }
+} /* namespace platform */

src/Platform/GridSearch.h

@@ -0,0 +1,36 @@
+#ifndef GRIDSEARCH_H
+#define GRIDSEARCH_H
+#include <string>
+#include <vector>
+#include <nlohmann/json.hpp>
+#include "Datasets.h"
+#include "HyperParameters.h"
+#include "GridData.h"
+
+namespace platform {
+    using json = nlohmann::json;
+    struct ConfigGrid {
+        std::string model;
+        std::string score;
+        std::string path;
+        std::string input_file;
+        std::string output_file;
+        bool quiet;
+        bool discretize;
+        bool stratified;
+        int n_folds;
+        std::vector<int> seeds;
+    };
+    class GridSearch {
+    public:
+        explicit GridSearch(struct ConfigGrid& config);
+        void go();
+        void save() const;
+        ~GridSearch() = default;
+    private:
+        double processFile(std::string fileName, Datasets& datasets, HyperParameters& hyperparameters);
+        json results;
+        struct ConfigGrid config;
+    };
+} /* namespace platform */
+#endif /* GRIDSEARCH_H */

src/Platform/HyperParameters.cc

@@ -0,0 +1,55 @@
+#include "HyperParameters.h"
+#include <fstream>
+#include <sstream>
+#include <iostream>
+
+namespace platform {
+    HyperParameters::HyperParameters(const std::vector<std::string>& datasets, const json& hyperparameters_)
+    {
+        // Initialize all datasets with the given hyperparameters
+        for (const auto& item : datasets) {
+            hyperparameters[item] = hyperparameters_;
+        }
+    }
+    // https://www.techiedelight.com/implode-a-vector-of-strings-into-a-comma-separated-string-in-cpp/
+    std::string join(std::vector<std::string> const& strings, std::string delim)
+    {
+        std::stringstream ss;
+        std::copy(strings.begin(), strings.end(),
+            std::ostream_iterator<std::string>(ss, delim.c_str()));
+        return ss.str();
+    }
+    HyperParameters::HyperParameters(const std::vector<std::string>& datasets, const std::string& hyperparameters_file)
+    {
+        // Check if file exists
+        std::ifstream file(hyperparameters_file);
+        if (!file.is_open()) {
+            throw std::runtime_error("File " + hyperparameters_file + " not found");
+        }
+        // Check if file is a json
+        json input_hyperparameters = json::parse(file);
+        // Check if hyperparameters are valid
+        for (const auto& dataset : datasets) {
+            if (!input_hyperparameters.contains(dataset)) {
+                std::cerr << "*Warning: Dataset " << dataset << " not found in hyperparameters file" << " assuming default hyperparameters" << std::endl;
+                hyperparameters[dataset] = json({});
+                continue;
+            }
+            hyperparameters[dataset] = input_hyperparameters[dataset].get<json>();
+        }
+    }
+    void HyperParameters::check(const std::vector<std::string>& valid, const std::string& fileName)
+    {
+        json result = hyperparameters.at(fileName);
+        for (const auto& item : result.items()) {
+            if (find(valid.begin(), valid.end(), item.key()) == valid.end()) {
+                throw std::invalid_argument("Hyperparameter " + item.key() + " is not valid. Passed Hyperparameters are: "
+                    + result.dump(4) + "\n Valid hyperparameters are: {" + join(valid, ",") + "}");
+            }
+        }
+    }
+    json HyperParameters::get(const std::string& fileName)
+    {
+        return hyperparameters.at(fileName);
+    }
+} /* namespace platform */

src/Platform/HyperParameters.h

@@ -0,0 +1,23 @@
+#ifndef HYPERPARAMETERS_H
+#define HYPERPARAMETERS_H
+#include <string>
+#include <map>
+#include <vector>
+#include <nlohmann/json.hpp>
+
+namespace platform {
+    using json = nlohmann::json;
+    class HyperParameters {
+    public:
+        HyperParameters() = default;
+        explicit HyperParameters(const std::vector<std::string>& datasets, const json& hyperparameters_);
+        explicit HyperParameters(const std::vector<std::string>& datasets, const std::string& hyperparameters_file);
+        ~HyperParameters() = default;
+        bool notEmpty(const std::string& key) const { return !hyperparameters.at(key).empty(); }
+        void check(const std::vector<std::string>& valid, const std::string& fileName);
+        json get(const std::string& fileName);
+    private:
+        std::map<std::string, json> hyperparameters;
+    };
+} /* namespace platform */
+#endif /* HYPERPARAMETERS_H */

src/Platform/Paths.h

@@ -1,6 +1,7 @@
 #ifndef PATHS_H
 #define PATHS_H
 #include <string>
+#include <filesystem>
 #include "DotEnv.h"
 namespace platform {
     class Paths {
@@ -8,12 +9,22 @@ namespace platform {
         static std::string results() { return "results/"; }
         static std::string hiddenResults() { return "hidden_results/"; }
         static std::string excel() { return "excel/"; }
-        static std::string cfs() { return "cfs/"; }
+        static std::string grid() { return "grid/"; }
         static std::string datasets()
         {
             auto env = platform::DotEnv();
             return env.get("source_data");
         }
+        static void createPath(const std::string& path)
+        {
+            // Create directory if it does not exist
+            try {
+                std::filesystem::create_directory(path);
+            }
+            catch (std::exception& e) {
+                throw std::runtime_error("Could not create directory " + path);
+            }
+        }
         static std::string excelResults() { return "some_results.xlsx"; }
     };
 }

src/Platform/Timer.h

@@ -0,0 +1,34 @@
+#ifndef TIMER_H
+#define TIMER_H
+#include <chrono>
+#include <string>
+#include <sstream>
+
+namespace platform {
+    class Timer {
+    private:
+        std::chrono::high_resolution_clock::time_point begin;
+        std::chrono::high_resolution_clock::time_point end;
+    public:
+        Timer() = default;
+        ~Timer() = default;
+        void start() { begin = std::chrono::high_resolution_clock::now(); }
+        void stop() { end = std::chrono::high_resolution_clock::now(); }
+        double getDuration()
+        {
+            stop();
+            std::chrono::duration<double> time_span = std::chrono::duration_cast<std::chrono::duration<double >> (end - begin);
+            return time_span.count();
+        }
+        std::string getDurationString()
+        {
+            double duration = getDuration();
+            double durationShow = duration > 3600 ? duration / 3600 : duration > 60 ? duration / 60 : duration;
+            std::string durationUnit = duration > 3600 ? "h" : duration > 60 ? "m" : "s";
+            std::stringstream ss;
+            ss << std::setw(7) << std::setprecision(2) << std::fixed << durationShow << " " << durationUnit << " ";
+            return ss.str();
+        }
+    };
+} /* namespace platform */
+#endif /* TIMER_H */

src/Platform/b_best.cc

@@ -7,7 +7,7 @@
 
 argparse::ArgumentParser manageArguments(int argc, char** argv)
 {
-    argparse::ArgumentParser program("best");
+    argparse::ArgumentParser program("b_sbest");
     program.add_argument("-m", "--model").default_value("").help("Filter results of the selected model) (any for all models)");
     program.add_argument("-s", "--score").default_value("").help("Filter results of the score name supplied");
     program.add_argument("--build").help("build best score results file").default_value(false).implicit_value(true);

src/Platform/b_grid.cc

@@ -0,0 +1,81 @@
+#include <iostream>
+#include <argparse/argparse.hpp>
+#include "DotEnv.h"
+#include "Models.h"
+#include "modelRegister.h"
+#include "GridSearch.h"
+#include "Paths.h"
+#include "Timer.h"
+
+
+argparse::ArgumentParser manageArguments(std::string program_name)
+{
+    auto env = platform::DotEnv();
+    argparse::ArgumentParser program(program_name);
+    program.add_argument("-m", "--model")
+        .help("Model to use " + platform::Models::instance()->tostring())
+        .action([](const std::string& value) {
+        static const std::vector<std::string> choices = platform::Models::instance()->getNames();
+        if (find(choices.begin(), choices.end(), value) != choices.end()) {
+            return value;
+        }
+        throw std::runtime_error("Model must be one of " + platform::Models::instance()->tostring());
+            }
+    );
+    program.add_argument("--discretize").help("Discretize input datasets").default_value((bool)stoi(env.get("discretize"))).implicit_value(true);
+    program.add_argument("--quiet").help("Don't display detailed progress").default_value(false).implicit_value(true);
+    program.add_argument("--stratified").help("If Stratified KFold is to be done").default_value((bool)stoi(env.get("stratified"))).implicit_value(true);
+    program.add_argument("--score").help("Score used in gridsearch").default_value("accuracy");
+    program.add_argument("-f", "--folds").help("Number of folds").default_value(stoi(env.get("n_folds"))).scan<'i', int>().action([](const std::string& value) {
+        try {
+            auto k = stoi(value);
+            if (k < 2) {
+                throw std::runtime_error("Number of folds must be greater than 1");
+            }
+            return k;
+        }
+        catch (const runtime_error& err) {
+            throw std::runtime_error(err.what());
+        }
+        catch (...) {
+            throw std::runtime_error("Number of folds must be an integer");
+        }});
+    auto seed_values = env.getSeeds();
+    program.add_argument("-s", "--seeds").nargs(1, 10).help("Random seeds. Set to -1 to have pseudo random").scan<'i', int>().default_value(seed_values);
+    return program;
+}
+
+int main(int argc, char** argv)
+{
+    auto program = manageArguments("b_grid");
+    struct platform::ConfigGrid config;
+    try {
+        program.parse_args(argc, argv);
+        config.model = program.get<std::string>("model");
+        config.score = program.get<std::string>("score");
+        config.discretize = program.get<bool>("discretize");
+        config.stratified = program.get<bool>("stratified");
+        config.n_folds = program.get<int>("folds");
+        config.quiet = program.get<bool>("quiet");
+        config.seeds = program.get<std::vector<int>>("seeds");
+    }
+    catch (const exception& err) {
+        cerr << err.what() << std::endl;
+        cerr << program;
+        exit(1);
+    }
+    /*
+     * Begin Processing
+     */
+    auto env = platform::DotEnv();
+    platform::Paths::createPath(platform::Paths::grid());
+    config.path = platform::Paths::grid();
+    auto grid_search = platform::GridSearch(config);
+    platform::Timer timer;
+    timer.start();
+    grid_search.go();
+    std::cout << "Process took " << timer.getDurationString() << std::endl;
+    grid_search.save();
+    std::cout << "Done!" << std::endl;
+    return 0;
+}

src/Platform/b_main.cc

@@ -11,12 +11,14 @@
 
 using json = nlohmann::json;
 
-argparse::ArgumentParser manageArguments()
+argparse::ArgumentParser manageArguments(std::string program_name)
 {
     auto env = platform::DotEnv();
-    argparse::ArgumentParser program("main");
+    argparse::ArgumentParser program(program_name);
     program.add_argument("-d", "--dataset").default_value("").help("Dataset file name");
-    program.add_argument("--hyperparameters").default_value("{}").help("Hyperparamters passed to the model in Experiment");
+    program.add_argument("--hyperparameters").default_value("{}").help("Hyperparameters passed to the model in Experiment");
+    program.add_argument("--hyper-file").default_value("").help("Hyperparameters file name." \
+        "Mutually exclusive with hyperparameters. This file should contain hyperparameters for each dataset in json format.");
     program.add_argument("-m", "--model")
         .help("Model to use " + platform::Models::instance()->tostring())
         .action([](const std::string& value) {
@@ -53,13 +55,13 @@ argparse::ArgumentParser manageArguments()
 
 int main(int argc, char** argv)
 {
-    std::string file_name, model_name, title;
+    std::string file_name, model_name, title, hyperparameters_file;
     json hyperparameters_json;
     bool discretize_dataset, stratified, saveResults, quiet;
     std::vector<int> seeds;
     std::vector<std::string> filesToTest;
     int n_folds;
-    auto program = manageArguments();
+    auto program = manageArguments("b_main");
     try {
         program.parse_args(argc, argv);
         file_name = program.get<std::string>("dataset");
@@ -71,6 +73,10 @@ int main(int argc, char** argv)
         seeds = program.get<std::vector<int>>("seeds");
         auto hyperparameters = program.get<std::string>("hyperparameters");
         hyperparameters_json = json::parse(hyperparameters);
+        hyperparameters_file = program.get<std::string>("hyper-file");
+        if (hyperparameters_file != "" && hyperparameters != "{}") {
+            throw runtime_error("hyperparameters and hyper_file are mutually exclusive");
+        }
         title = program.get<std::string>("title");
         if (title == "" && file_name == "") {
             throw runtime_error("title is mandatory if dataset is not provided");
@@ -96,15 +102,22 @@ int main(int argc, char** argv)
         filesToTest = datasets.getNames();
         saveResults = true;
     }
+    platform::HyperParameters test_hyperparams;
+    if (hyperparameters_file != "") {
+        test_hyperparams = platform::HyperParameters(datasets.getNames(), hyperparameters_file);
+    } else {
+        test_hyperparams = platform::HyperParameters(datasets.getNames(), hyperparameters_json);
+    }
+
     /*
-    * Begin Processing
+     * Begin Processing
-    */
+     */
     auto env = platform::DotEnv();
     auto experiment = platform::Experiment();
     experiment.setTitle(title).setLanguage("cpp").setLanguageVersion("14.0.3");
     experiment.setDiscretized(discretize_dataset).setModel(model_name).setPlatform(env.get("platform"));
     experiment.setStratified(stratified).setNFolds(n_folds).setScoreName("accuracy");
-    experiment.setHyperparameters(hyperparameters_json);
+    experiment.setHyperparameters(test_hyperparams);
     for (auto seed : seeds) {
         experiment.addRandomSeed(seed);
     }

src/Platform/b_manage.cc

@@ -5,7 +5,7 @@
 
 argparse::ArgumentParser manageArguments(int argc, char** argv)
 {
-    argparse::ArgumentParser program("manage");
+    argparse::ArgumentParser program("b_manage");
     program.add_argument("-n", "--number").default_value(0).help("Number of results to show (0 = all)").scan<'i', int>();
     program.add_argument("-m", "--model").default_value("any").help("Filter results of the selected model)");
     program.add_argument("-s", "--score").default_value("any").help("Filter results of the score name supplied");

src/PyClassifiers/ODTE.cc

@@ -1,15 +1,12 @@
 #include "ODTE.h"
 
 namespace pywrap {
+    ODTE::ODTE() : PyClassifier("odte", "Odte")
+    {
+        validHyperparameters = { "n_jobs", "n_estimators", "random_state" };
+    }
     std::string ODTE::graph()
     {
         return callMethodString("graph");
     }
-    void ODTE::setHyperparameters(nlohmann::json& hyperparameters)
-    {
-        // Check if hyperparameters are valid
-        const std::vector<std::string> validKeys = { "n_jobs", "n_estimators", "random_state" };
-        checkHyperparameters(validKeys, hyperparameters);
-        this->hyperparameters = hyperparameters;
-    }
 } /* namespace pywrap */

src/PyClassifiers/ODTE.h

@@ -6,10 +6,9 @@
 namespace pywrap {
     class ODTE : public PyClassifier {
     public:
-        ODTE() : PyClassifier("odte", "Odte") {};
+        ODTE();
         ~ODTE() = default;
         std::string graph();
-        void setHyperparameters(nlohmann::json& hyperparameters) override;
     };
 } /* namespace pywrap */
 #endif /* ODTE_H */

src/PyClassifiers/PyClassifier.cc

@@ -81,19 +81,8 @@ namespace pywrap {
         float result = pyWrap->score(id, Xp, yp);
         return result;
     }
-    void PyClassifier::setHyperparameters(nlohmann::json& hyperparameters)
+    void PyClassifier::setHyperparameters(const nlohmann::json& hyperparameters)
     {
-        // Check if hyperparameters are valid, default is no hyperparameters
-        const std::vector<std::string> validKeys = { };
-        checkHyperparameters(validKeys, hyperparameters);
         this->hyperparameters = hyperparameters;
     }
-    void PyClassifier::checkHyperparameters(const std::vector<std::string>& validKeys, const nlohmann::json& hyperparameters)
-    {
-        for (const auto& item : hyperparameters.items()) {
-            if (find(validKeys.begin(), validKeys.end(), item.key()) == validKeys.end()) {
-                throw std::invalid_argument("Hyperparameter " + item.key() + " is not valid");
-            }
-        }
-    }
 } /* namespace pywrap */

src/PyClassifiers/PyClassifier.h

@@ -27,10 +27,10 @@ namespace pywrap {
         std::vector<int> predict(std::vector<std::vector<int >>& X) override { return std::vector<int>(); };
         float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override { return 0.0; };
         float score(torch::Tensor& X, torch::Tensor& y) override;
-        void setHyperparameters(nlohmann::json& hyperparameters) override;
         std::string version();
         std::string callMethodString(const std::string& method);
         std::string getVersion() override { return this->version(); };
+        // TODO: Implement these 3 methods
         int getNumberOfNodes()const override { return 0; };
         int getNumberOfEdges()const override { return 0; };
         int getNumberOfStates() const override { return 0; };
@@ -39,8 +39,8 @@ namespace pywrap {
         bayesnet::status_t getStatus() const override { return bayesnet::NORMAL; };
         std::vector<std::string> topological_order() override { return std::vector<std::string>(); }
         void dump_cpt() const override {};
+        void setHyperparameters(const nlohmann::json& hyperparameters) override;
     protected:
-        void checkHyperparameters(const std::vector<std::string>& validKeys, const nlohmann::json& hyperparameters);
         nlohmann::json hyperparameters;
         void trainModel(const torch::Tensor& weights) override {};
     private:

src/PyClassifiers/RandomForest.cc

@@ -1,11 +1,8 @@
 #include "RandomForest.h"
 
 namespace pywrap {
-    void RandomForest::setHyperparameters(nlohmann::json& hyperparameters)
+    RandomForest::RandomForest() : PyClassifier("sklearn.ensemble", "RandomForestClassifier", true)
     {
-        // Check if hyperparameters are valid
+        validHyperparameters = { "n_estimators", "n_jobs", "random_state" };
-        const std::vector<std::string> validKeys = { "n_estimators", "n_jobs", "random_state" };
-        checkHyperparameters(validKeys, hyperparameters);
-        this->hyperparameters = hyperparameters;
     }
 } /* namespace pywrap */

src/PyClassifiers/RandomForest.h

@@ -5,9 +5,8 @@
 namespace pywrap {
     class RandomForest : public PyClassifier {
     public:
-        RandomForest() : PyClassifier("sklearn.ensemble", "RandomForestClassifier", true) {};
+        RandomForest();
         ~RandomForest() = default;
-        void setHyperparameters(nlohmann::json& hyperparameters) override;
     };
 } /* namespace pywrap */
 #endif /* RANDOMFOREST_H */

src/PyClassifiers/STree.cc

@@ -1,15 +1,12 @@
 #include "STree.h"
 
 namespace pywrap {
+    STree::STree() : PyClassifier("stree", "Stree")
+    {
+        validHyperparameters = { "C", "kernel", "max_iter", "max_depth", "random_state", "multiclass_strategy", "gamma", "max_features", "degree" };
+    };
     std::string STree::graph()
     {
         return callMethodString("graph");
     }
-    void STree::setHyperparameters(nlohmann::json& hyperparameters)
-    {
-        // Check if hyperparameters are valid
-        const std::vector<std::string> validKeys = { "C", "kernel", "max_iter", "max_depth", "random_state", "multiclass_strategy" };
-        checkHyperparameters(validKeys, hyperparameters);
-        this->hyperparameters = hyperparameters;
-    }
 } /* namespace pywrap */

src/PyClassifiers/STree.h

@@ -6,10 +6,9 @@
 namespace pywrap {
     class STree : public PyClassifier {
     public:
-        STree() : PyClassifier("stree", "Stree") {};
+        STree();
         ~STree() = default;
         std::string graph();
-        void setHyperparameters(nlohmann::json& hyperparameters) override;
     };
 } /* namespace pywrap */
 #endif /* STREE_H */

src/PyClassifiers/SVC.cc

@@ -1,11 +1,8 @@
 #include "SVC.h"
 
 namespace pywrap {
-    void SVC::setHyperparameters(nlohmann::json& hyperparameters)
+    SVC::SVC() : PyClassifier("sklearn.svm", "SVC", true)
     {
-        // Check if hyperparameters are valid
+        validHyperparameters = { "C", "gamma", "kernel", "random_state" };
-        const std::vector<std::string> validKeys = { "C", "gamma", "kernel", "random_state" };
-        checkHyperparameters(validKeys, hyperparameters);
-        this->hyperparameters = hyperparameters;
     }
 } /* namespace pywrap */

src/PyClassifiers/SVC.h

@@ -5,10 +5,9 @@
 namespace pywrap {
     class SVC : public PyClassifier {
     public:
-        SVC() : PyClassifier("sklearn.svm", "SVC", true) {};
+        SVC();
         ~SVC() = default;
-        void setHyperparameters(nlohmann::json& hyperparameters) override;
     };
 
 } /* namespace pywrap */
-#endif /* STREE_H */
+#endif /* SVC_H */

stree_results.json

@@ -0,0 +1,835 @@
+[
+  {
+    "date": "2021-04-11",
+    "time": "18:46:29",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "balance-scale",
+    "accuracy": "0.97056",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{\"C\": 10000.0, \"gamma\": 0.1, \"kernel\": \"rbf\", \"max_iter\": 10000.0}",
+    "time_spent": "0.0135214",
+    "time_spent_std": "0.00111213",
+    "accuracy_std": "0.0150468",
+    "nodes": "7.0",
+    "leaves": "4.0",
+    "depth": "3.0"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:46:29",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "balloons",
+    "accuracy": "0.86",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{\"C\": 7, \"gamma\": 0.1, \"kernel\": \"rbf\", \"max_iter\": 10000.0}",
+    "time_spent": "0.000804768",
+    "time_spent_std": "7.74797e-05",
+    "accuracy_std": "0.285015",
+    "nodes": "3.0",
+    "leaves": "2.0",
+    "depth": "2.0"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:46:29",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "breast-cancer-wisc-diag",
+    "accuracy": "0.972764",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{\"C\": 0.2, \"max_iter\": 10000.0}",
+    "time_spent": "0.00380772",
+    "time_spent_std": "0.000638676",
+    "accuracy_std": "0.0173132",
+    "nodes": "3.24",
+    "leaves": "2.12",
+    "depth": "2.12"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:46:30",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "breast-cancer-wisc-prog",
+    "accuracy": "0.811128",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{\"C\": 0.2, \"max_iter\": 10000.0}",
+    "time_spent": "0.00767535",
+    "time_spent_std": "0.00148114",
+    "accuracy_std": "0.0584601",
+    "nodes": "5.84",
+    "leaves": "3.42",
+    "depth": "3.24"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:46:31",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "breast-cancer-wisc",
+    "accuracy": "0.966661",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{}",
+    "time_spent": "0.00652217",
+    "time_spent_std": "0.000726579",
+    "accuracy_std": "0.0139421",
+    "nodes": "8.88",
+    "leaves": "4.94",
+    "depth": "4.08"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:46:32",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "breast-cancer",
+    "accuracy": "0.734211",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{}",
+    "time_spent": "0.023475",
+    "time_spent_std": "0.00584447",
+    "accuracy_std": "0.0479774",
+    "nodes": "21.72",
+    "leaves": "11.36",
+    "depth": "5.86"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:49:08",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "cardiotocography-10clases",
+    "accuracy": "0.791487",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{}",
+    "time_spent": "3.10582",
+    "time_spent_std": "0.339218",
+    "accuracy_std": "0.0192082",
+    "nodes": "160.76",
+    "leaves": "80.88",
+    "depth": "22.86"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:50:01",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "cardiotocography-3clases",
+    "accuracy": "0.900613",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{}",
+    "time_spent": "1.05228",
+    "time_spent_std": "0.138768",
+    "accuracy_std": "0.0154004",
+    "nodes": "47.68",
+    "leaves": "24.34",
+    "depth": "8.84"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:50:01",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "conn-bench-sonar-mines-rocks",
+    "accuracy": "0.755528",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{}",
+    "time_spent": "0.011577",
+    "time_spent_std": "0.00341148",
+    "accuracy_std": "0.0678424",
+    "nodes": "6.08",
+    "leaves": "3.54",
+    "depth": "2.86"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:50:17",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "cylinder-bands",
+    "accuracy": "0.715049",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{}",
+    "time_spent": "0.301143",
+    "time_spent_std": "0.109773",
+    "accuracy_std": "0.0367646",
+    "nodes": "26.2",
+    "leaves": "13.6",
+    "depth": "6.82"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:50:19",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "dermatology",
+    "accuracy": "0.971833",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{\"C\": 55, \"max_iter\": 10000.0}",
+    "time_spent": "0.0377538",
+    "time_spent_std": "0.010726",
+    "accuracy_std": "0.0206883",
+    "nodes": "11.0",
+    "leaves": "6.0",
+    "depth": "6.0"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:50:19",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "echocardiogram",
+    "accuracy": "0.814758",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{\"C\": 7, \"gamma\": 0.1, \"kernel\": \"poly\", \"max_features\": \"auto\", \"max_iter\": 10000.0}",
+    "time_spent": "0.00333449",
+    "time_spent_std": "0.000964686",
+    "accuracy_std": "0.0998078",
+    "nodes": "7.0",
+    "leaves": "4.0",
+    "depth": "3.54"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:50:20",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "fertility",
+    "accuracy": "0.88",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{\"C\": 0.05, \"max_features\": \"auto\", \"max_iter\": 10000.0}",
+    "time_spent": "0.00090271",
+    "time_spent_std": "8.96446e-05",
+    "accuracy_std": "0.0547723",
+    "nodes": "1.0",
+    "leaves": "1.0",
+    "depth": "1.0"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:50:21",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "haberman-survival",
+    "accuracy": "0.735637",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{}",
+    "time_spent": "0.0171611",
+    "time_spent_std": "0.00334945",
+    "accuracy_std": "0.0434614",
+    "nodes": "23.4",
+    "leaves": "12.2",
+    "depth": "5.98"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:50:21",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "heart-hungarian",
+    "accuracy": "0.827522",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{\"C\": 0.05, \"max_iter\": 10000.0}",
+    "time_spent": "0.00493946",
+    "time_spent_std": "0.000738198",
+    "accuracy_std": "0.0505283",
+    "nodes": "10.16",
+    "leaves": "5.58",
+    "depth": "4.0"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:50:21",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "hepatitis",
+    "accuracy": "0.824516",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{\"C\": 7, \"gamma\": 0.1, \"kernel\": \"rbf\", \"max_iter\": 10000.0}",
+    "time_spent": "0.0021534",
+    "time_spent_std": "0.000133715",
+    "accuracy_std": "0.0738872",
+    "nodes": "3.0",
+    "leaves": "2.0",
+    "depth": "2.0"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:50:23",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "ilpd-indian-liver",
+    "accuracy": "0.723498",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{}",
+    "time_spent": "0.0345243",
+    "time_spent_std": "0.015789",
+    "accuracy_std": "0.0384886",
+    "nodes": "16.04",
+    "leaves": "8.52",
+    "depth": "5.28"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:50:24",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "ionosphere",
+    "accuracy": "0.953276",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{\"C\": 7, \"gamma\": 0.1, \"kernel\": \"rbf\", \"max_iter\": 10000.0}",
+    "time_spent": "0.00881722",
+    "time_spent_std": "0.000843108",
+    "accuracy_std": "0.0238537",
+    "nodes": "3.16",
+    "leaves": "2.08",
+    "depth": "2.08"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:50:24",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "iris",
+    "accuracy": "0.965333",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{}",
+    "time_spent": "0.00357342",
+    "time_spent_std": "0.000400509",
+    "accuracy_std": "0.0319444",
+    "nodes": "5.0",
+    "leaves": "3.0",
+    "depth": "3.0"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:50:36",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "led-display",
+    "accuracy": "0.703",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{}",
+    "time_spent": "0.222106",
+    "time_spent_std": "0.0116922",
+    "accuracy_std": "0.0291204",
+    "nodes": "47.16",
+    "leaves": "24.08",
+    "depth": "17.76"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:51:18",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "libras",
+    "accuracy": "0.788611",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{\"C\": 0.08, \"max_iter\": 10000.0}",
+    "time_spent": "0.841714",
+    "time_spent_std": "0.0830966",
+    "accuracy_std": "0.0516913",
+    "nodes": "82.28",
+    "leaves": "41.64",
+    "depth": "28.84"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:51:41",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "low-res-spect",
+    "accuracy": "0.883782",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{\"C\": 0.05, \"max_iter\": 10000.0}",
+    "time_spent": "0.446301",
+    "time_spent_std": "0.0411822",
+    "accuracy_std": "0.0324593",
+    "nodes": "27.4",
+    "leaves": "14.2",
+    "depth": "10.74"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:51:41",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "lymphography",
+    "accuracy": "0.835034",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{\"C\": 0.05, \"max_iter\": 10000.0}",
+    "time_spent": "0.00539465",
+    "time_spent_std": "0.000754365",
+    "accuracy_std": "0.0590649",
+    "nodes": "9.04",
+    "leaves": "5.02",
+    "depth": "4.48"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:51:43",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "mammographic",
+    "accuracy": "0.81915",
+    "norm": 1,
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+    "parameters": "{}",
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+    "time_spent_std": "0.00328533",
+    "accuracy_std": "0.0222517",
+    "nodes": "7.4",
+    "leaves": "4.2",
+    "depth": "4.0"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:51:43",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "molec-biol-promoter",
+    "accuracy": "0.767056",
+    "norm": 1,
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+    "parameters": "{\"C\": 0.05, \"gamma\": 0.1, \"kernel\": \"poly\", \"max_iter\": 10000.0}",
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+    "time_spent_std": "0.000105772",
+    "accuracy_std": "0.0910923",
+    "nodes": "3.0",
+    "leaves": "2.0",
+    "depth": "2.0"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:51:44",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "musk-1",
+    "accuracy": "0.916388",
+    "norm": 1,
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+    "parameters": "{\"C\": 0.05, \"gamma\": 0.1, \"kernel\": \"poly\", \"max_iter\": 10000.0}",
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+    "accuracy_std": "0.0275208",
+    "nodes": "3.0",
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+    "depth": "2.0"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:51:55",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "oocytes_merluccius_nucleus_4d",
+    "accuracy": "0.835125",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{\"C\": 8.25, \"gamma\": 0.1, \"kernel\": \"poly\"}",
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+    "nodes": "10.52",
+    "leaves": "5.76",
+    "depth": "4.42"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:52:04",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "oocytes_merluccius_states_2f",
+    "accuracy": "0.915365",
+    "norm": 1,
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+    "nodes": "18.04",
+    "leaves": "9.52",
+    "depth": "5.3"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:52:41",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "oocytes_trisopterus_nucleus_2f",
+    "accuracy": "0.800986",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{}",
+    "time_spent": "0.717113",
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+    "accuracy_std": "0.0218449",
+    "nodes": "29.88",
+    "leaves": "15.44",
+    "depth": "7.38"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:52:44",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "oocytes_trisopterus_states_5b",
+    "accuracy": "0.922249",
+    "norm": 1,
+    "stand": 0,
+    "parameters": "{\"C\": 0.11, \"max_iter\": 10000.0}",
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+    "nodes": "7.44",
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+    "depth": "3.6"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:52:44",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "parkinsons",
+    "accuracy": "0.882051",
+    "norm": 1,
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+    "parameters": "{}",
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+    "accuracy_std": "0.0478327",
+    "nodes": "8.48",
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+    "depth": "3.76"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:52:48",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "pima",
+    "accuracy": "0.766651",
+    "norm": 1,
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+    "parameters": "{}",
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+    "nodes": "17.4",
+    "leaves": "9.2",
+    "depth": "5.66"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:52:48",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "pittsburg-bridges-MATERIAL",
+    "accuracy": "0.867749",
+    "norm": 1,
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+    "parameters": "{\"C\": 7, \"gamma\": 0.1, \"kernel\": \"rbf\", \"max_iter\": 10000.0}",
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+    "nodes": "5.16",
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+    "depth": "3.02"
+  },
+  {
+    "date": "2021-04-11",
+    "time": "18:52:49",
+    "type": "crossval",
+    "classifier": "stree",
+    "dataset": "pittsburg-bridges-REL-L",
+    "accuracy": "0.632238",
+    "norm": 1,
+    "stand": 0,
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