Fix report format

Reviewed-on: https://gitea.rmontanana.es:11000/rmontanana/BayesNet/pulls/4

.vscode/launch.json

@@ -25,9 +25,10 @@
             "program": "${workspaceFolder}/build/src/Platform/main",
             "args": [
                 "-m",
-                "SPODELd",
+                "BoostAODE",
                 "-p",
                 "/Users/rmontanana/Code/discretizbench/datasets",
+                "--discretize",
                 "--stratified",
                 "-d",
                 "iris"
@@ -45,6 +46,14 @@
             ],
             "cwd": "/Users/rmontanana/Code/discretizbench",
         },
+        {
+            "type": "lldb",
+            "request": "launch",
+            "name": "list",
+            "program": "${workspaceFolder}/build/src/Platform/list",
+            "args": [],
+            "cwd": "/Users/rmontanana/Code/discretizbench",
+        },
         {
             "name": "Build & debug active file",
             "type": "cppdbg",

.vscode/tasks.json

@@ -32,6 +32,29 @@
             ],
             "group": "build",
             "detail": "Task generated by Debugger."
+        },
+        {
+            "type": "cppbuild",
+            "label": "C/C++: g++ build active file",
+            "command": "/usr/bin/g++",
+            "args": [
+                "-fdiagnostics-color=always",
+                "-g",
+                "${file}",
+                "-o",
+                "${fileDirname}/${fileBasenameNoExtension}"
+            ],
+            "options": {
+                "cwd": "${fileDirname}"
+            },
+            "problemMatcher": [
+                "$gcc"
+            ],
+            "group": {
+                "kind": "build",
+                "isDefault": true
+            },
+            "detail": "Task generated by Debugger."
         }
     ]
 }

Makefile

@@ -15,7 +15,7 @@ dependency: ## Create a dependency graph diagram of the project (build/dependenc
 	cd build && cmake .. --graphviz=dependency.dot && dot -Tpng dependency.dot -o dependency.png
 
 build: ## Build the main and BayesNetSample
-	cmake --build build -t main -t BayesNetSample -j 32
+	cmake --build build -t main -t BayesNetSample -t manage -t list -j 32
 
 clean: ## Clean the debug info
 	@echo ">>> Cleaning Debug BayesNet ...";
@@ -35,7 +35,7 @@ release: ## Build a Release version of the project
 	@if [ -d ./build ]; then rm -rf ./build; fi
 	@mkdir build; 
 	cmake -S . -B build -D CMAKE_BUILD_TYPE=Release; \
-	cmake --build build -t main -t BayesNetSample -j 32;
+	cmake --build build -t main -t BayesNetSample -t manage -t list -j 32;
 	@echo ">>> Done";	
 
 test: ## Run tests

lib/Files/CMakeLists.txt

@@ -1,2 +1 @@
-add_library(ArffFiles ArffFiles.cc)
+add_library(ArffFiles ArffFiles.cc)
-#target_link_libraries(BayesNet "${TORCH_LIBRARIES}")

sample/sample.cc

@@ -141,43 +141,58 @@ int main(int argc, char** argv)
     /*
     * Begin Processing
     */
-    auto handler = ArffFiles();
+    auto ypred = torch::tensor({ 1,2,3,2,2,3,4,5,2,1 });
-    handler.load(complete_file_name, class_last);
+    auto y = torch::tensor({ 0,0,0,0,2,3,4,0,0,0 });
-    // Get Dataset X, y
+    auto weights = torch::ones({ 10 }, kDouble);
-    vector<mdlp::samples_t>& X = handler.getX();
+    auto mask = ypred == y;
-    mdlp::labels_t& y = handler.getY();
+    cout << "ypred:" << ypred << endl;
-    // Get className & Features
+    cout << "y:" << y << endl;
-    auto className = handler.getClassName();
+    cout << "weights:" << weights << endl;
-    vector<string> features;
+    cout << "mask:" << mask << endl;
-    auto attributes = handler.getAttributes();
+    double value_to_add = 0.5;
-    transform(attributes.begin(), attributes.end(), back_inserter(features),
+    weights += mask.to(torch::kDouble) * value_to_add;
-        [](const pair<string, string>& item) { return item.first; });
+    cout << "New weights:" << weights << endl;
-    // Discretize Dataset
+    auto masked_weights = weights * mask.to(weights.dtype());
-    auto [Xd, maxes] = discretize(X, y, features);
+    double sum_of_weights = masked_weights.sum().item<double>();
-    maxes[className] = *max_element(y.begin(), y.end()) + 1;
+    cout << "Sum of weights: " << sum_of_weights << endl;
-    map<string, vector<int>> states;
+    //weights.index_put_({ mask }, weights + 10);
-    for (auto feature : features) {
+    // auto handler = ArffFiles();
-        states[feature] = vector<int>(maxes[feature]);
+    // handler.load(complete_file_name, class_last);
-    }
+    // // Get Dataset X, y
-    states[className] = vector<int>(maxes[className]);
+    // vector<mdlp::samples_t>& X = handler.getX();
-    auto clf = platform::Models::instance()->create(model_name);
+    // mdlp::labels_t& y = handler.getY();
-    clf->fit(Xd, y, features, className, states);
+    // // Get className & Features
-    if (dump_cpt) {
+    // auto className = handler.getClassName();
-        cout << "--- CPT Tables ---" << endl;
+    // vector<string> features;
-        clf->dump_cpt();
+    // auto attributes = handler.getAttributes();
-    }
+    // transform(attributes.begin(), attributes.end(), back_inserter(features),
-    auto lines = clf->show();
+    //     [](const pair<string, string>& item) { return item.first; });
-    for (auto line : lines) {
+    // // Discretize Dataset
-        cout << line << endl;
+    // auto [Xd, maxes] = discretize(X, y, features);
-    }
+    // maxes[className] = *max_element(y.begin(), y.end()) + 1;
-    cout << "--- Topological Order ---" << endl;
+    // map<string, vector<int>> states;
-    auto order = clf->topological_order();
+    // for (auto feature : features) {
-    for (auto name : order) {
+    //     states[feature] = vector<int>(maxes[feature]);
-        cout << name << ", ";
+    // }
-    }
+    // states[className] = vector<int>(maxes[className]);
-    cout << "end." << endl;
+    // auto clf = platform::Models::instance()->create(model_name);
-    auto score = clf->score(Xd, y);
+    // clf->fit(Xd, y, features, className, states);
-    cout << "Score: " << score << endl;
+    // if (dump_cpt) {
+    //     cout << "--- CPT Tables ---" << endl;
+    //     clf->dump_cpt();
+    // }
+    // auto lines = clf->show();
+    // for (auto line : lines) {
+    //     cout << line << endl;
+    // }
+    // cout << "--- Topological Order ---" << endl;
+    // auto order = clf->topological_order();
+    // for (auto name : order) {
+    //     cout << name << ", ";
+    // }
+    // cout << "end." << endl;
+    // auto score = clf->score(Xd, y);
+    // cout << "Score: " << score << endl;
     // auto graph = clf->graph();
     // auto dot_file = model_name + "_" + file_name;
     // ofstream file(dot_file + ".dot");

src/BayesNet/AODE.cc

@@ -2,12 +2,14 @@
 
 namespace bayesnet {
     AODE::AODE() : Ensemble() {}
-    void AODE::buildModel()
+    void AODE::buildModel(const torch::Tensor& weights)
     {
         models.clear();
         for (int i = 0; i < features.size(); ++i) {
             models.push_back(std::make_unique<SPODE>(i));
         }
+        n_models = models.size();
+        significanceModels = vector<double>(n_models, 1.0);
     }
     vector<string> AODE::graph(const string& title) const
     {

src/BayesNet/AODE.h

@@ -5,7 +5,7 @@
 namespace bayesnet {
     class AODE : public Ensemble {
     protected:
-        void buildModel() override;
+        void buildModel(const torch::Tensor& weights) override;
     public:
         AODE();
         virtual ~AODE() {};

src/BayesNet/AODELd.cc

@@ -19,7 +19,7 @@ namespace bayesnet {
         return *this;
 
     }
-    void AODELd::buildModel()
+    void AODELd::buildModel(const torch::Tensor& weights)
     {
         models.clear();
         for (int i = 0; i < features.size(); ++i) {
@@ -27,7 +27,7 @@ namespace bayesnet {
         }
         n_models = models.size();
     }
-    void AODELd::trainModel()
+    void AODELd::trainModel(const torch::Tensor& weights)
     {
         for (const auto& model : models) {
             model->fit(Xf, y, features, className, states);

src/BayesNet/AODELd.h

@@ -8,8 +8,8 @@ namespace bayesnet {
     using namespace std;
     class AODELd : public Ensemble, public Proposal {
     protected:
-        void trainModel() override;
+        void trainModel(const torch::Tensor& weights) override;
-        void buildModel() override;
+        void buildModel(const torch::Tensor& weights) override;
     public:
         AODELd();
         AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_) override;

src/BayesNet/BaseClassifier.h

@@ -6,13 +6,14 @@ namespace bayesnet {
     using namespace std;
     class BaseClassifier {
     protected:
-        virtual void trainModel() = 0;
+        virtual void trainModel(const torch::Tensor& weights) = 0;
     public:
         // X is nxm vector, y is nx1 vector
         virtual BaseClassifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
         // X is nxm tensor, y is nx1 tensor
         virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
         virtual BaseClassifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights) = 0;
         virtual ~BaseClassifier() = default;
         torch::Tensor virtual predict(torch::Tensor& X) = 0;
         vector<int> virtual predict(vector<vector<int>>& X) = 0;

src/BayesNet/BayesMetrics.cc

@@ -21,6 +21,31 @@ namespace bayesnet {
         }
         samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
     }
+    vector<int> Metrics::SelectKBestWeighted(const torch::Tensor& weights, unsigned k)
+    {
+        auto n = samples.size(0) - 1;
+        if (k == 0) {
+            k = n;
+        }
+        // compute scores
+        scoresKBest.reserve(n);
+        auto label = samples.index({ -1, "..." });
+        for (int i = 0; i < n; ++i) {
+            scoresKBest.push_back(mutualInformation(label, samples.index({ i, "..." }), weights));
+            featuresKBest.push_back(i);
+        }
+        // sort & reduce scores and features
+        sort(featuresKBest.begin(), featuresKBest.end(), [&](int i, int j)
+            { return scoresKBest[i] > scoresKBest[j]; });
+        sort(scoresKBest.begin(), scoresKBest.end(), std::greater<double>());
+        featuresKBest.resize(k);
+        scoresKBest.resize(k);
+        return featuresKBest;
+    }
+    vector<double> Metrics::getScoresKBest() const
+    {
+        return scoresKBest;
+    }
     vector<pair<string, string>> Metrics::doCombinations(const vector<string>& source)
     {
         vector<pair<string, string>> result;
@@ -32,17 +57,18 @@ namespace bayesnet {
         }
         return result;
     }
-    torch::Tensor Metrics::conditionalEdge()
+    torch::Tensor Metrics::conditionalEdge(const torch::Tensor& weights)
     {
         auto result = vector<double>();
         auto source = vector<string>(features);
         source.push_back(className);
         auto combinations = doCombinations(source);
+        double totalWeight = weights.sum().item<double>();
         // Compute class prior
-        auto margin = torch::zeros({ classNumStates });
+        auto margin = torch::zeros({ classNumStates }, torch::kFloat);
         for (int value = 0; value < classNumStates; ++value) {
             auto mask = samples.index({ -1,  "..." }) == value;
-            margin[value] = mask.sum().item<float>() / samples.size(1);
+            margin[value] = mask.sum().item<double>() / samples.size(1);
         }
         for (auto [first, second] : combinations) {
             int index_first = find(features.begin(), features.end(), first) - features.begin();
@@ -52,8 +78,9 @@ namespace bayesnet {
                 auto mask = samples.index({ -1, "..." }) == value;
                 auto first_dataset = samples.index({ index_first, mask });
                 auto second_dataset = samples.index({ index_second, mask });
-                auto mi = mutualInformation(first_dataset, second_dataset);
+                auto weights_dataset = weights.index({ mask });
-                auto pb = margin[value].item<float>();
+                auto mi = mutualInformation(first_dataset, second_dataset, weights_dataset);
+                auto pb = margin[value].item<double>();
                 accumulated += pb * mi;
             }
             result.push_back(accumulated);
@@ -70,31 +97,32 @@ namespace bayesnet {
         return matrix;
     }
     // To use in Python
-    vector<float> Metrics::conditionalEdgeWeights()
+    vector<float> Metrics::conditionalEdgeWeights(vector<float>& weights_)
     {
-        auto matrix = conditionalEdge();
+        const torch::Tensor weights = torch::tensor(weights_);
+        auto matrix = conditionalEdge(weights);
         std::vector<float> v(matrix.data_ptr<float>(), matrix.data_ptr<float>() + matrix.numel());
         return v;
     }
-    double Metrics::entropy(const torch::Tensor& feature)
+    double Metrics::entropy(const torch::Tensor& feature, const torch::Tensor& weights)
     {
-        torch::Tensor counts = feature.bincount();
+        torch::Tensor counts = feature.bincount(weights);
-        int totalWeight = counts.sum().item<int>();
+        double totalWeight = counts.sum().item<double>();
         torch::Tensor probs = counts.to(torch::kFloat) / totalWeight;
         torch::Tensor logProbs = torch::log(probs);
         torch::Tensor entropy = -probs * logProbs;
         return entropy.nansum().item<double>();
     }
     // H(Y|X) = sum_{x in X} p(x) H(Y|X=x)
-    double Metrics::conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature)
+    double Metrics::conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights)
     {
         int numSamples = firstFeature.sizes()[0];
-        torch::Tensor featureCounts = secondFeature.bincount();
+        torch::Tensor featureCounts = secondFeature.bincount(weights);
         unordered_map<int, unordered_map<int, double>> jointCounts;
         double totalWeight = 0;
         for (auto i = 0; i < numSamples; i++) {
-            jointCounts[secondFeature[i].item<int>()][firstFeature[i].item<int>()] += 1;
+            jointCounts[secondFeature[i].item<int>()][firstFeature[i].item<int>()] += weights[i].item<double>();
-            totalWeight += 1;
+            totalWeight += weights[i].item<float>();
         }
         if (totalWeight == 0)
             return 0;
@@ -115,9 +143,9 @@ namespace bayesnet {
         return entropyValue;
     }
     // I(X;Y) = H(Y) - H(Y|X)
-    double Metrics::mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature)
+    double Metrics::mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights)
     {
-        return entropy(firstFeature) - conditionalEntropy(firstFeature, secondFeature);
+        return entropy(firstFeature, weights) - conditionalEntropy(firstFeature, secondFeature, weights);
     }
     /*
     Compute the maximum spanning tree considering the weights as distances

src/BayesNet/BayesMetrics.h

@@ -12,16 +12,20 @@ namespace bayesnet {
         vector<string> features;
         string className;
         int classNumStates = 0;
+        vector<double> scoresKBest;
+        vector<int> featuresKBest; // sorted indices of the features
+        double entropy(const Tensor& feature, const Tensor& weights);
+        double conditionalEntropy(const Tensor& firstFeature, const Tensor& secondFeature, const Tensor& weights);
+        vector<pair<string, string>> doCombinations(const vector<string>&);
     public:
         Metrics() = default;
-        Metrics(const Tensor&, const vector<string>&, const string&, const int);
+        Metrics(const torch::Tensor& samples, const vector<string>& features, const string& className, const int classNumStates);
-        Metrics(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&, const int);
+        Metrics(const vector<vector<int>>& vsamples, const vector<int>& labels, const vector<string>& features, const string& className, const int classNumStates);
-        double entropy(const Tensor&);
+        vector<int> SelectKBestWeighted(const torch::Tensor& weights, unsigned k = 0);
-        double conditionalEntropy(const Tensor&, const Tensor&);
+        vector<double> getScoresKBest() const;
-        double mutualInformation(const Tensor&, const Tensor&);
+        double mutualInformation(const Tensor& firstFeature, const Tensor& secondFeature, const Tensor& weights);
-        vector<float> conditionalEdgeWeights(); // To use in Python
+        vector<float> conditionalEdgeWeights(vector<float>& weights); // To use in Python
-        Tensor conditionalEdge();
+        Tensor conditionalEdge(const torch::Tensor& weights);
-        vector<pair<string, string>> doCombinations(const vector<string>&);
         vector<pair<int, int>> maximumSpanningTree(const vector<string>& features, const Tensor& weights, const int root);
     };
 }

src/BayesNet/BoostAODE.cc

@@ -0,0 +1,82 @@
+#include "BoostAODE.h"
+#include "BayesMetrics.h"
+
+namespace bayesnet {
+    BoostAODE::BoostAODE() : Ensemble() {}
+    void BoostAODE::buildModel(const torch::Tensor& weights)
+    {
+        // Models shall be built in trainModel
+    }
+    void BoostAODE::trainModel(const torch::Tensor& weights)
+    {
+        models.clear();
+        n_models = 0;
+        int max_models = .1 * n > 10 ? .1 * n : n;
+        Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
+        auto X_ = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
+        auto y_ = dataset.index({ -1, "..." });
+        bool exitCondition = false;
+        bool repeatSparent = false;
+        vector<int> featuresUsed;
+        // Step 0: Set the finish condition
+        // if not repeatSparent a finish condition is run out of features
+        // n_models == max_models
+        int numClasses = states[className].size();
+        while (!exitCondition) {
+            // Step 1: Build ranking with mutual information
+            auto featureSelection = metrics.SelectKBestWeighted(weights_, n); // Get all the features sorted
+            auto feature = featureSelection[0];
+            unique_ptr<Classifier> model;
+            if (!repeatSparent) {
+                if (n_models == 0) {
+                    models.resize(n); // Resize for n==nfeatures SPODEs
+                    significanceModels.resize(n);
+                }
+                bool found = false;
+                for (int i = 0; i < featureSelection.size(); ++i) {
+                    if (find(featuresUsed.begin(), featuresUsed.end(), i) != featuresUsed.end()) {
+                        continue;
+                    }
+                    found = true;
+                    feature = i;
+                    featuresUsed.push_back(feature);
+                    n_models++;
+                    break;
+                }
+                if (!found) {
+                    exitCondition = true;
+                    continue;
+                }
+            }
+            model = std::make_unique<SPODE>(feature);
+            model->fit(dataset, features, className, states, weights_);
+            auto ypred = model->predict(X_);
+            // Step 3.1: Compute the classifier amout of say
+            auto mask_wrong = ypred != y_;
+            auto masked_weights = weights_ * mask_wrong.to(weights_.dtype());
+            double wrongWeights = masked_weights.sum().item<double>();
+            double significance = wrongWeights == 0 ? 1 : 0.5 * log((1 - wrongWeights) / wrongWeights);
+            // Step 3.2: Update weights for next classifier
+            // Step 3.2.1: Update weights of wrong samples
+            weights_ += mask_wrong.to(weights_.dtype()) * exp(significance) * weights_;
+            // Step 3.3: Normalise the weights
+            double totalWeights = torch::sum(weights_).item<double>();
+            weights_ = weights_ / totalWeights;
+            // Step 3.4: Store classifier and its accuracy to weigh its future vote
+            if (!repeatSparent) {
+                models[feature] = std::move(model);
+                significanceModels[feature] = significance;
+            } else {
+                models.push_back(std::move(model));
+                significanceModels.push_back(significance);
+                n_models++;
+            }
+            exitCondition = n_models == max_models;
+        }
+        weights.copy_(weights_);
+    }
+    vector<string> BoostAODE::graph(const string& title) const
+    {
+        return Ensemble::graph(title);
+    }
+}

src/BayesNet/BoostAODE.h

@@ -0,0 +1,16 @@
+#ifndef BOOSTAODE_H
+#define BOOSTAODE_H
+#include "Ensemble.h"
+#include "SPODE.h"
+namespace bayesnet {
+    class BoostAODE : public Ensemble {
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights) override;
+    public:
+        BoostAODE();
+        virtual ~BoostAODE() {};
+        vector<string> graph(const string& title = "BoostAODE") const override;
+    };
+}
+#endif

src/BayesNet/CMakeLists.txt

@@ -3,5 +3,6 @@ include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
 include_directories(${BayesNet_SOURCE_DIR}/src/BayesNet)
 include_directories(${BayesNet_SOURCE_DIR}/src/Platform)
 add_library(BayesNet bayesnetUtils.cc Network.cc Node.cc BayesMetrics.cc Classifier.cc 
-    KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc TANLd.cc KDBLd.cc SPODELd.cc AODELd.cc Mst.cc Proposal.cc ${BayesNet_SOURCE_DIR}/src/Platform/Models.cc)
+    KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc TANLd.cc KDBLd.cc SPODELd.cc AODELd.cc BoostAODE.cc 
-target_link_libraries(BayesNet mdlp ArffFiles "${TORCH_LIBRARIES}")
+    Mst.cc Proposal.cc ${BayesNet_SOURCE_DIR}/src/Platform/Models.cc)
+target_link_libraries(BayesNet mdlp "${TORCH_LIBRARIES}")

src/BayesNet/Classifier.cc

@@ -5,7 +5,7 @@ namespace bayesnet {
     using namespace torch;
 
     Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
-    Classifier& Classifier::build(vector<string>& features, string className, map<string, vector<int>>& states)
+    Classifier& Classifier::build(vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights)
     {
         this->features = features;
         this->className = className;
@@ -16,12 +16,11 @@ namespace bayesnet {
         auto n_classes = states[className].size();
         metrics = Metrics(dataset, features, className, n_classes);
         model.initialize();
-        buildModel();
+        buildModel(weights);
-        trainModel();
+        trainModel(weights);
         fitted = true;
         return *this;
     }
-
     void Classifier::buildDataset(Tensor& ytmp)
     {
         try {
@@ -35,16 +34,17 @@ namespace bayesnet {
             exit(1);
         }
     }
-    void Classifier::trainModel()
+    void Classifier::trainModel(const torch::Tensor& weights)
     {
-        model.fit(dataset, features, className, states);
+        model.fit(dataset, weights, features, className, states);
     }
     // X is nxm where n is the number of features and m the number of samples
     Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states)
     {
         dataset = X;
         buildDataset(y);
-        return build(features, className, states);
+        const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
+        return build(features, className, states, weights);
     }
     // X is nxm where n is the number of features and m the number of samples
     Classifier& Classifier::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states)
@@ -55,12 +55,19 @@ namespace bayesnet {
         }
         auto ytmp = torch::tensor(y, kInt32);
         buildDataset(ytmp);
-        return build(features, className, states);
+        const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
+        return build(features, className, states, weights);
     }
     Classifier& Classifier::fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states)
     {
         this->dataset = dataset;
-        return build(features, className, states);
+        const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
+        return build(features, className, states, weights);
+    }
+    Classifier& Classifier::fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights)
+    {
+        this->dataset = dataset;
+        return build(features, className, states, weights);
     }
     void Classifier::checkFitParameters()
     {

src/BayesNet/Classifier.h

@@ -11,25 +11,26 @@ namespace bayesnet {
     class Classifier : public BaseClassifier {
     private:
         void buildDataset(torch::Tensor& y);
-        Classifier& build(vector<string>& features, string className, map<string, vector<int>>& states);
+        Classifier& build(vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights);
     protected:
         bool fitted;
-        Network model;
         int m, n; // m: number of samples, n: number of features
-        Tensor dataset; // (n+1)xm tensor
+        Network model;
         Metrics metrics;
         vector<string> features;
         string className;
         map<string, vector<int>> states;
+        Tensor dataset; // (n+1)xm tensor
         void checkFitParameters();
-        virtual void buildModel() = 0;
+        virtual void buildModel(const torch::Tensor& weights) = 0;
-        void trainModel() override;
+        void trainModel(const torch::Tensor& weights) override;
     public:
         Classifier(Network model);
         virtual ~Classifier() = default;
         Classifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
         Classifier& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
         Classifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        Classifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights) override;
         void addNodes();
         int getNumberOfNodes() const override;
         int getNumberOfEdges() const override;

src/BayesNet/Ensemble.cc

@@ -5,7 +5,7 @@ namespace bayesnet {
 
     Ensemble::Ensemble() : Classifier(Network()) {}
 
-    void Ensemble::trainModel()
+    void Ensemble::trainModel(const torch::Tensor& weights)
     {
         n_models = models.size();
         for (auto i = 0; i < n_models; ++i) {
@@ -18,9 +18,9 @@ namespace bayesnet {
         auto y_pred_ = y_pred.accessor<int, 2>();
         vector<int> y_pred_final;
         for (int i = 0; i < y_pred.size(0); ++i) {
-            vector<float> votes(y_pred.size(1), 0);
+            vector<double> votes(y_pred.size(1), 0);
             for (int j = 0; j < y_pred.size(1); ++j) {
-                votes[y_pred_[i][j]] += 1;
+                votes[y_pred_[i][j]] += significanceModels[j];
             }
             // argsort in descending order
             auto indices = argsort(votes);

src/BayesNet/Ensemble.h

@@ -14,7 +14,8 @@ namespace bayesnet {
     protected:
         unsigned n_models;
         vector<unique_ptr<Classifier>> models;
-        void trainModel() override;
+        vector<double> significanceModels;
+        void trainModel(const torch::Tensor& weights) override;
         vector<int> voting(Tensor& y_pred);
     public:
         Ensemble();

src/BayesNet/KDB.cc

@@ -4,7 +4,7 @@ namespace bayesnet {
     using namespace torch;
 
     KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta) {}
-    void KDB::buildModel()
+    void KDB::buildModel(const torch::Tensor& weights)
     {
         /*
         1. For each feature Xi, compute mutual information, I(X;C),
@@ -29,13 +29,13 @@ namespace bayesnet {
         // where C is the class.
         addNodes();
         const Tensor& y = dataset.index({ -1, "..." });
-        vector <float> mi;
+        vector<double> mi;
         for (auto i = 0; i < features.size(); i++) {
             Tensor firstFeature = dataset.index({ i, "..." });
-            mi.push_back(metrics.mutualInformation(firstFeature, y));
+            mi.push_back(metrics.mutualInformation(firstFeature, y, weights));
         }
         // 2. Compute class conditional mutual information I(Xi;XjIC), f or each
-        auto conditionalEdgeWeights = metrics.conditionalEdge();
+        auto conditionalEdgeWeights = metrics.conditionalEdge(weights);
         // 3. Let the used variable list, S, be empty.
         vector<int> S;
         // 4. Let the DAG network being constructed, BN, begin with a single

src/BayesNet/KDB.h

@@ -1,5 +1,6 @@
 #ifndef KDB_H
 #define KDB_H
+#include <torch/torch.h>
 #include "Classifier.h"
 #include "bayesnetUtils.h"
 namespace bayesnet {
@@ -11,7 +12,7 @@ namespace bayesnet {
         float theta;
         void add_m_edges(int idx, vector<int>& S, Tensor& weights);
     protected:
-        void buildModel() override;
+        void buildModel(const torch::Tensor& weights) override;
     public:
         explicit KDB(int k, float theta = 0.03);
         virtual ~KDB() {};

src/BayesNet/Network.cc

@@ -5,7 +5,6 @@
 namespace bayesnet {
     Network::Network() : features(vector<string>()), className(""), classNumStates(0), fitted(false) {}
     Network::Network(float maxT) : features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false) {}
-    Network::Network(float maxT, int smoothing) : laplaceSmoothing(smoothing), features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false) {}
     Network::Network(Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()), maxThreads(other.
         getmaxThreads()), fitted(other.fitted)
     {
@@ -104,8 +103,11 @@ namespace bayesnet {
     {
         return nodes;
     }
-    void Network::checkFitData(int n_samples, int n_features, int n_samples_y, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
+    void Network::checkFitData(int n_samples, int n_features, int n_samples_y, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states, const torch::Tensor& weights)
     {
+        if (weights.size(0) != n_samples) {
+            throw invalid_argument("Weights (" + to_string(weights.size(0)) + ") must have the same number of elements as samples (" + to_string(n_samples) + ") in Network::fit");
+        }
         if (n_samples != n_samples_y) {
             throw invalid_argument("X and y must have the same number of samples in Network::fit (" + to_string(n_samples) + " != " + to_string(n_samples_y) + ")");
         }
@@ -136,28 +138,29 @@ namespace bayesnet {
         classNumStates = nodes[className]->getNumStates();
     }
     // X comes in nxm, where n is the number of features and m the number of samples
-    void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
+    void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
     {
-        checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className, states);
+        checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className, states, weights);
         this->className = className;
         Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
         samples = torch::cat({ X , ytmp }, 0);
         for (int i = 0; i < featureNames.size(); ++i) {
             auto row_feature = X.index({ i, "..." });
         }
-        completeFit(states);
+        completeFit(states, weights);
     }
-    void Network::fit(const torch::Tensor& samples, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
+    void Network::fit(const torch::Tensor& samples, const torch::Tensor& weights, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
     {
-        checkFitData(samples.size(1), samples.size(0) - 1, samples.size(1), featureNames, className, states);
+        checkFitData(samples.size(1), samples.size(0) - 1, samples.size(1), featureNames, className, states, weights);
         this->className = className;
         this->samples = samples;
-        completeFit(states);
+        completeFit(states, weights);
     }
     // input_data comes in nxm, where n is the number of features and m the number of samples
-    void Network::fit(const vector<vector<int>>& input_data, const vector<int>& labels, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
+    void Network::fit(const vector<vector<int>>& input_data, const vector<int>& labels, const vector<float>& weights_, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
     {
-        checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className, states);
+        const torch::Tensor weights = torch::tensor(weights_, torch::kFloat64);
+        checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className, states, weights);
         this->className = className;
         // Build tensor of samples (nxm) (n+1 because of the class)
         samples = torch::zeros({ static_cast<int>(input_data.size() + 1), static_cast<int>(input_data[0].size()) }, torch::kInt32);
@@ -165,11 +168,12 @@ namespace bayesnet {
             samples.index_put_({ i, "..." }, torch::tensor(input_data[i], torch::kInt32));
         }
         samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
-        completeFit(states);
+        completeFit(states, weights);
     }
-    void Network::completeFit(const map<string, vector<int>>& states)
+    void Network::completeFit(const map<string, vector<int>>& states, const torch::Tensor& weights)
     {
         setStates(states);
+        laplaceSmoothing = 1.0 / samples.size(1); // To use in CPT computation
         int maxThreadsRunning = static_cast<int>(std::thread::hardware_concurrency() * maxThreads);
         if (maxThreadsRunning < 1) {
             maxThreadsRunning = 1;
@@ -182,7 +186,7 @@ namespace bayesnet {
         while (nextNodeIndex < nodes.size()) {
             unique_lock<mutex> lock(mtx);
             cv.wait(lock, [&activeThreads, &maxThreadsRunning]() { return activeThreads < maxThreadsRunning; });
-            threads.emplace_back([this, &nextNodeIndex, &mtx, &cv, &activeThreads]() {
+            threads.emplace_back([this, &nextNodeIndex, &mtx, &cv, &activeThreads, &weights]() {
                 while (true) {
                     unique_lock<mutex> lock(mtx);
                     if (nextNodeIndex >= nodes.size()) {
@@ -191,7 +195,7 @@ namespace bayesnet {
                     auto& pair = *std::next(nodes.begin(), nextNodeIndex);
                     ++nextNodeIndex;
                     lock.unlock();
-                    pair.second->computeCPT(samples, features, laplaceSmoothing);
+                    pair.second->computeCPT(samples, features, laplaceSmoothing, weights);
                     lock.lock();
                     nodes[pair.first] = std::move(pair.second);
                     lock.unlock();
@@ -343,7 +347,7 @@ namespace bayesnet {
         }
         // Normalize result
         double sum = accumulate(result.begin(), result.end(), 0.0);
-        transform(result.begin(), result.end(), result.begin(), [sum](double& value) { return value / sum; });
+        transform(result.begin(), result.end(), result.begin(), [sum](const double& value) { return value / sum; });
         return result;
     }
     vector<string> Network::show() const
@@ -431,6 +435,7 @@ namespace bayesnet {
     {
         for (auto& node : nodes) {
             cout << "* " << node.first << ": (" << node.second->getNumStates() << ") : " << node.second->getCPT().sizes() << endl;
+            cout << node.second->getCPT() << endl;
         }
     }
 }

src/BayesNet/Network.h

@@ -13,19 +13,18 @@ namespace bayesnet {
         int classNumStates;
         vector<string> features; // Including classname
         string className;
-        int laplaceSmoothing = 1;
+        double laplaceSmoothing;
         torch::Tensor samples; // nxm tensor used to fit the model
         bool isCyclic(const std::string&, std::unordered_set<std::string>&, std::unordered_set<std::string>&);
         vector<double> predict_sample(const vector<int>&);
         vector<double> predict_sample(const torch::Tensor&);
         vector<double> exactInference(map<string, int>&);
         double computeFactor(map<string, int>&);
-        void completeFit(const map<string, vector<int>>&);
+        void completeFit(const map<string, vector<int>>& states, const torch::Tensor& weights);
-        void checkFitData(int n_features, int n_samples, int n_samples_y, const vector<string>& featureNames, const string& className, const map<string, vector<int>>&);
+        void checkFitData(int n_features, int n_samples, int n_samples_y, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states, const torch::Tensor& weights);
         void setStates(const map<string, vector<int>>&);
     public:
         Network();
-        explicit Network(float, int);
         explicit Network(float);
         explicit Network(Network&);
         torch::Tensor& getSamples();
@@ -39,9 +38,9 @@ namespace bayesnet {
         int getNumEdges() const;
         int getClassNumStates() const;
         string getClassName() const;
-        void fit(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&, const map<string, vector<int>>&);
+        void fit(const vector<vector<int>>& input_data, const vector<int>& labels, const vector<float>& weights, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states);
-        void fit(const torch::Tensor&, const torch::Tensor&, const vector<string>&, const string&, const map<string, vector<int>>&);
+        void fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states);
-        void fit(const torch::Tensor&, const vector<string>&, const string&, const map<string, vector<int>>&);
+        void fit(const torch::Tensor& samples, const torch::Tensor& weights, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states);
         vector<int> predict(const vector<vector<int>>&); // Return mx1 vector of predictions
         torch::Tensor predict(const torch::Tensor&); // Return mx1 tensor of predictions
         torch::Tensor predict_tensor(const torch::Tensor& samples, const bool proba);

src/BayesNet/Node.cc

@@ -84,7 +84,7 @@ namespace bayesnet {
         }
         return result;
     }
-    void Node::computeCPT(const torch::Tensor& dataset, const vector<string>& features, const int laplaceSmoothing)
+    void Node::computeCPT(const torch::Tensor& dataset, const vector<string>& features, const double laplaceSmoothing, const torch::Tensor& weights)
     {
         dimensions.clear();
         // Get dimensions of the CPT
@@ -111,7 +111,7 @@ namespace bayesnet {
                 coordinates.push_back(dataset.index({ parent_index, n_sample }));
             }
             // Increment the count of the corresponding coordinate
-            cpTable.index_put_({ coordinates }, cpTable.index({ coordinates }) + 1);
+            cpTable.index_put_({ coordinates }, cpTable.index({ coordinates }) + weights.index({ n_sample }).item<double>());
         }
         // Normalize the counts
         cpTable = cpTable / cpTable.sum(0);

src/BayesNet/Node.h

@@ -26,7 +26,7 @@ namespace bayesnet {
         vector<Node*>& getParents();
         vector<Node*>& getChildren();
         torch::Tensor& getCPT();
-        void computeCPT(const torch::Tensor&, const vector<string>&, const int);
+        void computeCPT(const torch::Tensor& dataset, const vector<string>& features, const double laplaceSmoothing, const torch::Tensor& weights);
         int getNumStates() const;
         void setNumStates(int);
         unsigned minFill();

src/BayesNet/Proposal.cc

@@ -65,7 +65,8 @@ namespace bayesnet {
                 //Update new states of the feature/node
                 states[pFeatures[index]] = xStates;
             }
-            model.fit(pDataset, pFeatures, pClassName, states);
+            const torch::Tensor weights = torch::full({ pDataset.size(1) }, 1.0 / pDataset.size(1), torch::kDouble);
+            model.fit(pDataset, weights, pFeatures, pClassName, states);
         }
         return states;
     }

src/BayesNet/SPODE.cc

@@ -4,7 +4,7 @@ namespace bayesnet {
 
     SPODE::SPODE(int root) : Classifier(Network()), root(root) {}
 
-    void SPODE::buildModel()
+    void SPODE::buildModel(const torch::Tensor& weights)
     {
         // 0. Add all nodes to the model
         addNodes();

src/BayesNet/SPODE.h

@@ -7,7 +7,7 @@ namespace bayesnet {
     private:
         int root;
     protected:
-        void buildModel() override;
+        void buildModel(const torch::Tensor& weights) override;
     public:
         explicit SPODE(int root);
         virtual ~SPODE() {};

src/BayesNet/SPODELd.cc

@@ -21,7 +21,6 @@ namespace bayesnet {
     SPODELd& SPODELd::fit(torch::Tensor& dataset, vector<string>& features_, string className_, map<string, vector<int>>& states_)
     {
         Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
-        cout << "Xf " << Xf.sizes() << " dtype: " << Xf.dtype() << endl;
         y = dataset.index({ -1, "..." }).clone();
         // This first part should go in a Classifier method called fit_local_discretization o fit_float...
         features = features_;

src/BayesNet/TAN.cc

@@ -5,7 +5,7 @@ namespace bayesnet {
 
     TAN::TAN() : Classifier(Network()) {}
 
-    void TAN::buildModel()
+    void TAN::buildModel(const torch::Tensor& weights)
     {
         // 0. Add all nodes to the model
         addNodes();
@@ -15,15 +15,15 @@ namespace bayesnet {
         Tensor class_dataset = dataset.index({ -1, "..." });
         for (int i = 0; i < static_cast<int>(features.size()); ++i) {
             Tensor feature_dataset = dataset.index({ i, "..." });
-            auto mi_value = metrics.mutualInformation(class_dataset, feature_dataset);
+            auto mi_value = metrics.mutualInformation(class_dataset, feature_dataset, weights);
             mi.push_back({ i, mi_value });
         }
         sort(mi.begin(), mi.end(), [](const auto& left, const auto& right) {return left.second < right.second;});
         auto root = mi[mi.size() - 1].first;
         // 2. Compute mutual information between each feature and the class
-        auto weights = metrics.conditionalEdge();
+        auto weights_matrix = metrics.conditionalEdge(weights);
         // 3. Compute the maximum spanning tree
-        auto mst = metrics.maximumSpanningTree(features, weights, root);
+        auto mst = metrics.maximumSpanningTree(features, weights_matrix, root);
         // 4. Add edges from the maximum spanning tree to the model
         for (auto i = 0; i < mst.size(); ++i) {
             auto [from, to] = mst[i];

src/BayesNet/TAN.h

@@ -7,7 +7,7 @@ namespace bayesnet {
     class TAN : public Classifier {
     private:
     protected:
-        void buildModel() override;
+        void buildModel(const torch::Tensor& weights) override;
     public:
         TAN();
         virtual ~TAN() {};

src/BayesNet/bayesnetUtils.cc

@@ -4,7 +4,7 @@ namespace bayesnet {
     using namespace std;
     using namespace torch;
     // Return the indices in descending order
-    vector<int> argsort(vector<float>& nums)
+    vector<int> argsort(vector<double>& nums)
     {
         int n = nums.size();
         vector<int> indices(n);

src/BayesNet/bayesnetUtils.h

@@ -5,7 +5,7 @@
 namespace bayesnet {
     using namespace std;
     using namespace torch;
-    vector<int> argsort(vector<float>& nums);
+    vector<int> argsort(vector<double>& nums);
     vector<vector<int>> tensorToVector(Tensor& tensor);
 }
 #endif //BAYESNET_UTILS_H

src/Platform/CMakeLists.txt

@@ -6,5 +6,7 @@ include_directories(${BayesNet_SOURCE_DIR}/lib/argparse/include)
 include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
 add_executable(main main.cc Folding.cc platformUtils.cc Experiment.cc Datasets.cc Models.cc Report.cc)
 add_executable(manage manage.cc Results.cc Report.cc)
+add_executable(list list.cc platformUtils Datasets.cc)
 target_link_libraries(main BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}")
-target_link_libraries(manage "${TORCH_LIBRARIES}")
+target_link_libraries(manage "${TORCH_LIBRARIES}")
+target_link_libraries(list ArffFiles mdlp "${TORCH_LIBRARIES}")

src/Platform/Datasets.cc

@@ -24,75 +24,110 @@ namespace platform {
         transform(datasets.begin(), datasets.end(), back_inserter(result), [](const auto& d) { return d.first; });
         return result;
     }
-    vector<string> Datasets::getFeatures(string name)
+    vector<string> Datasets::getFeatures(const string& name) const
     {
-        if (datasets[name]->isLoaded()) {
+        if (datasets.at(name)->isLoaded()) {
-            return datasets[name]->getFeatures();
+            return datasets.at(name)->getFeatures();
         } else {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    map<string, vector<int>> Datasets::getStates(string name)
+    map<string, vector<int>> Datasets::getStates(const string& name) const
     {
-        if (datasets[name]->isLoaded()) {
+        if (datasets.at(name)->isLoaded()) {
-            return datasets[name]->getStates();
+            return datasets.at(name)->getStates();
         } else {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    string Datasets::getClassName(string name)
+    void Datasets::loadDataset(const string& name) const
     {
-        if (datasets[name]->isLoaded()) {
+        if (datasets.at(name)->isLoaded()) {
-            return datasets[name]->getClassName();
+            return;
+        } else {
+            datasets.at(name)->load();
+        }
+    }
+    string Datasets::getClassName(const string& name) const
+    {
+        if (datasets.at(name)->isLoaded()) {
+            return datasets.at(name)->getClassName();
         } else {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    int Datasets::getNSamples(string name)
+    int Datasets::getNSamples(const string& name) const
     {
-        if (datasets[name]->isLoaded()) {
+        if (datasets.at(name)->isLoaded()) {
-            return datasets[name]->getNSamples();
+            return datasets.at(name)->getNSamples();
         } else {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    pair<vector<vector<float>>&, vector<int>&> Datasets::getVectors(string name)
+    int Datasets::getNClasses(const string& name)
+    {
+        if (datasets.at(name)->isLoaded()) {
+            auto className = datasets.at(name)->getClassName();
+            if (discretize) {
+                auto states = getStates(name);
+                return states.at(className).size();
+            }
+            auto [Xv, yv] = getVectors(name);
+            return *max_element(yv.begin(), yv.end()) + 1;
+        } else {
+            throw invalid_argument("Dataset not loaded.");
+        }
+    }
+    vector<int> Datasets::getClassesCounts(const string& name) const
+    {
+        if (datasets.at(name)->isLoaded()) {
+            auto [Xv, yv] = datasets.at(name)->getVectors();
+            vector<int> counts(*max_element(yv.begin(), yv.end()) + 1);
+            for (auto y : yv) {
+                counts[y]++;
+            }
+            return counts;
+        } else {
+            throw invalid_argument("Dataset not loaded.");
+        }
+    }
+    pair<vector<vector<float>>&, vector<int>&> Datasets::getVectors(const string& name)
     {
         if (!datasets[name]->isLoaded()) {
             datasets[name]->load();
         }
         return datasets[name]->getVectors();
     }
-    pair<vector<vector<int>>&, vector<int>&> Datasets::getVectorsDiscretized(string name)
+    pair<vector<vector<int>>&, vector<int>&> Datasets::getVectorsDiscretized(const string& name)
     {
         if (!datasets[name]->isLoaded()) {
             datasets[name]->load();
         }
         return datasets[name]->getVectorsDiscretized();
     }
-    pair<torch::Tensor&, torch::Tensor&> Datasets::getTensors(string name)
+    pair<torch::Tensor&, torch::Tensor&> Datasets::getTensors(const string& name)
     {
         if (!datasets[name]->isLoaded()) {
             datasets[name]->load();
         }
         return datasets[name]->getTensors();
     }
-    bool Datasets::isDataset(const string& name)
+    bool Datasets::isDataset(const string& name) const
     {
         return datasets.find(name) != datasets.end();
     }
     Dataset::Dataset(const Dataset& dataset) : path(dataset.path), name(dataset.name), className(dataset.className), n_samples(dataset.n_samples), n_features(dataset.n_features), features(dataset.features), states(dataset.states), loaded(dataset.loaded), discretize(dataset.discretize), X(dataset.X), y(dataset.y), Xv(dataset.Xv), Xd(dataset.Xd), yv(dataset.yv), fileType(dataset.fileType)
     {
     }
-    string Dataset::getName()
+    string Dataset::getName() const
     {
         return name;
     }
-    string Dataset::getClassName()
+    string Dataset::getClassName() const
     {
         return className;
     }
-    vector<string> Dataset::getFeatures()
+    vector<string> Dataset::getFeatures() const
     {
         if (loaded) {
             return features;
@@ -100,7 +135,7 @@ namespace platform {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    int Dataset::getNFeatures()
+    int Dataset::getNFeatures() const
     {
         if (loaded) {
             return n_features;
@@ -108,7 +143,7 @@ namespace platform {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    int Dataset::getNSamples()
+    int Dataset::getNSamples() const
     {
         if (loaded) {
             return n_samples;
@@ -116,7 +151,7 @@ namespace platform {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    map<string, vector<int>> Dataset::getStates()
+    map<string, vector<int>> Dataset::getStates() const
     {
         if (loaded) {
             return states;

src/Platform/Datasets.h

@@ -29,15 +29,15 @@ namespace platform {
     public:
         Dataset(const string& path, const string& name, const string& className, bool discretize, fileType_t fileType) : path(path), name(name), className(className), discretize(discretize), loaded(false), fileType(fileType) {};
         explicit Dataset(const Dataset&);
-        string getName();
+        string getName() const;
-        string getClassName();
+        string getClassName() const;
-        vector<string> getFeatures();
+        vector<string> getFeatures() const;
-        map<string, vector<int>> getStates();
+        map<string, vector<int>> getStates() const;
         pair<vector<vector<float>>&, vector<int>&> getVectors();
         pair<vector<vector<int>>&, vector<int>&> getVectorsDiscretized();
         pair<torch::Tensor&, torch::Tensor&> getTensors();
-        int getNFeatures();
+        int getNFeatures() const;
-        int getNSamples();
+        int getNSamples() const;
         void load();
         const bool inline isLoaded() const { return loaded; };
     };
@@ -51,14 +51,17 @@ namespace platform {
     public:
         explicit Datasets(const string& path, bool discretize = false, fileType_t fileType = ARFF) : path(path), discretize(discretize), fileType(fileType) { load(); };
         vector<string> getNames();
-        vector<string> getFeatures(string name);
+        vector<string> getFeatures(const string& name) const;
-        int getNSamples(string name);
+        int getNSamples(const string& name) const;
-        string getClassName(string name);
+        string getClassName(const string& name) const;
-        map<string, vector<int>> getStates(string name);
+        int getNClasses(const string& name);
-        pair<vector<vector<float>>&, vector<int>&> getVectors(string name);
+        vector<int> getClassesCounts(const string& name) const;
-        pair<vector<vector<int>>&, vector<int>&> getVectorsDiscretized(string name);
+        map<string, vector<int>> getStates(const string& name) const;
-        pair<torch::Tensor&, torch::Tensor&> getTensors(string name);
+        pair<vector<vector<float>>&, vector<int>&> getVectors(const string& name);
-        bool isDataset(const string& name);
+        pair<vector<vector<int>>&, vector<int>&> getVectorsDiscretized(const string& name);
+        pair<torch::Tensor&, torch::Tensor&> getTensors(const string& name);
+        bool isDataset(const string& name) const;
+        void loadDataset(const string& name) const;
     };
 };
 

src/Platform/Models.h

@@ -10,6 +10,7 @@
 #include "KDBLd.h"
 #include "SPODELd.h"
 #include "AODELd.h"
+#include "BoostAODE.h"
 namespace platform {
     class Models {
     private:

src/Platform/Paths.h

@@ -1,5 +1,6 @@
 #ifndef PATHS_H
 #define PATHS_H
+#include <string>
 namespace platform {
     class Paths {
     public:

src/Platform/Report.cc

@@ -1,6 +1,9 @@
+#include <sstream>
+#include <locale>
 #include "Report.h"
 #include "BestResult.h"
 
+
 namespace platform {
     string headerLine(const string& text)
     {
@@ -10,20 +13,27 @@ namespace platform {
     }
     string Report::fromVector(const string& key)
     {
-        string result = "";
+        stringstream oss;
-
+        string sep = "";
+        oss << "[";
         for (auto& item : data[key]) {
-            result += to_string(item) + ", ";
+            oss << sep << item.get<double>();
+            sep = ", ";
         }
-        return "[" + result.substr(0, result.size() - 2) + "]";
+        oss << "]";
+        return oss.str();
     }
-    string fVector(const json& data)
+    string fVector(const string& title, const json& data, const int width, const int precision)
     {
-        string result = "";
+        stringstream oss;
+        string sep = "";
+        oss << title << "[";
         for (const auto& item : data) {
-            result += to_string(item) + ", ";
+            oss << sep << fixed << setw(width) << setprecision(precision) << item.get<double>();
+            sep = ", ";
         }
-        return "[" + result.substr(0, result.size() - 2) + "]";
+        oss << "]";
+        return oss.str();
     }
     void Report::show()
     {
@@ -31,21 +41,31 @@ namespace platform {
         body();
         footer();
     }
+    struct separated : numpunct<char> {
+        char do_decimal_point() const { return ','; }
+        char do_thousands_sep() const { return '.'; }
+        string do_grouping() const { return "\03"; }
+    };
     void Report::header()
     {
+        locale mylocale(cout.getloc(), new separated);
+        locale::global(mylocale);
+        cout.imbue(mylocale);
+        stringstream oss;
         cout << Colors::MAGENTA() << string(MAXL, '*') << endl;
         cout << headerLine("Report " + data["model"].get<string>() + " ver. " + data["version"].get<string>() + " with " + to_string(data["folds"].get<int>()) + " Folds cross validation and " + to_string(data["seeds"].size()) + " random seeds. " + data["date"].get<string>() + " " + data["time"].get<string>());
         cout << headerLine(data["title"].get<string>());
         cout << headerLine("Random seeds: " + fromVector("seeds") + " Stratified: " + (data["stratified"].get<bool>() ? "True" : "False"));
-        cout << headerLine("Execution took  " + to_string(data["duration"].get<float>()) + " seconds,   " + to_string(data["duration"].get<float>() / 3600) + " hours, on " + data["platform"].get<string>());
+        oss << "Execution took  " << setprecision(2) << fixed << data["duration"].get<float>() << " seconds,   " << data["duration"].get<float>() / 3600 << " hours, on " << data["platform"].get<string>();
+        cout << headerLine(oss.str());
         cout << headerLine("Score is " + data["score_name"].get<string>());
         cout << string(MAXL, '*') << endl;
         cout << endl;
     }
     void Report::body()
     {
-        cout << Colors::GREEN() << "Dataset                        Sampl. Feat. Cls Nodes   Edges   States  Score           Time               Hyperparameters" << endl;
+        cout << Colors::GREEN() << "Dataset                        Sampl. Feat. Cls Nodes     Edges     States    Score           Time               Hyperparameters" << endl;
-        cout << "============================== ====== ===== === ======= ======= ======= =============== ================== ===============" << endl;
+        cout << "============================== ====== ===== === ========= ========= ========= =============== ================== ===============" << endl;
         json lastResult;
         totalScore = 0;
         bool odd = true;
@@ -55,9 +75,9 @@ namespace platform {
             cout << setw(6) << right << r["samples"].get<int>() << " ";
             cout << setw(5) << right << r["features"].get<int>() << " ";
             cout << setw(3) << right << r["classes"].get<int>() << " ";
-            cout << setw(7) << setprecision(2) << fixed << r["nodes"].get<float>() << " ";
+            cout << setw(9) << setprecision(2) << fixed << r["nodes"].get<float>() << " ";
-            cout << setw(7) << setprecision(2) << fixed << r["leaves"].get<float>() << " ";
+            cout << setw(9) << setprecision(2) << fixed << r["leaves"].get<float>() << " ";
-            cout << setw(7) << setprecision(2) << fixed << r["depth"].get<float>() << " ";
+            cout << setw(9) << setprecision(2) << fixed << r["depth"].get<float>() << " ";
             cout << setw(8) << right << setprecision(6) << fixed << r["score"].get<double>() << "±" << setw(6) << setprecision(4) << fixed << r["score_std"].get<double>() << " ";
             cout << setw(11) << right << setprecision(6) << fixed << r["time"].get<double>() << "±" << setw(6) << setprecision(4) << fixed << r["time_std"].get<double>() << " ";
             try {
@@ -73,10 +93,10 @@ namespace platform {
         }
         if (data["results"].size() == 1) {
             cout << string(MAXL, '*') << endl;
-            cout << headerLine("Train scores: " + fVector(lastResult["scores_train"]));
+            cout << headerLine(fVector("Train scores: ", lastResult["scores_train"], 14, 12));
-            cout << headerLine("Test  scores: " + fVector(lastResult["scores_test"]));
+            cout << headerLine(fVector("Test  scores: ", lastResult["scores_test"], 14, 12));
-            cout << headerLine("Train  times: " + fVector(lastResult["times_train"]));
+            cout << headerLine(fVector("Train  times: ", lastResult["times_train"], 10, 3));
-            cout << headerLine("Test   times: " + fVector(lastResult["times_test"]));
+            cout << headerLine(fVector("Test   times: ", lastResult["times_test"], 10, 3));
             cout << string(MAXL, '*') << endl;
         }
     }
@@ -85,7 +105,9 @@ namespace platform {
         cout << Colors::MAGENTA() << string(MAXL, '*') << endl;
         auto score = data["score_name"].get<string>();
         if (score == BestResult::scoreName()) {
-            cout << headerLine(score + " compared to " + BestResult::title() + " .:  " + to_string(totalScore / BestResult::score()));
+            stringstream oss;
+            oss << score << " compared to " << BestResult::title() << " .:  " << totalScore / BestResult::score();
+            cout << headerLine(oss.str());
         }
         cout << string(MAXL, '*') << endl << Colors::RESET();
 

src/Platform/Report.h

@@ -6,7 +6,7 @@
 #include "Colors.h"
 
 using json = nlohmann::json;
-const int MAXL = 121;
+const int MAXL = 128;
 namespace platform {
     using namespace std;
     class Report {

src/Platform/list.cc

@@ -0,0 +1,57 @@
+#include <iostream>
+#include <locale>
+#include "Paths.h"
+#include "Colors.h"
+#include "Datasets.h"
+
+using namespace std;
+const int BALANCE_LENGTH = 75;
+
+struct separated : numpunct<char> {
+    char do_decimal_point() const { return ','; }
+    char do_thousands_sep() const { return '.'; }
+    string do_grouping() const { return "\03"; }
+};
+
+void outputBalance(const string& balance)
+{
+    auto temp = string(balance);
+    while (temp.size() > BALANCE_LENGTH - 1) {
+        auto part = temp.substr(0, BALANCE_LENGTH);
+        cout << part << endl;
+        cout << setw(48) << " ";
+        temp = temp.substr(BALANCE_LENGTH);
+    }
+    cout << temp << endl;
+}
+
+int main(int argc, char** argv)
+{
+    auto data = platform::Datasets(platform::Paths().datasets(), false);
+    locale mylocale(cout.getloc(), new separated);
+    locale::global(mylocale);
+    cout.imbue(mylocale);
+    cout << Colors::GREEN() << "Dataset                        Sampl. Feat. Cls. Balance" << endl;
+    string balanceBars = string(BALANCE_LENGTH, '=');
+    cout << "============================== ====== ===== === " << balanceBars << endl;
+    bool odd = true;
+    for (const auto& dataset : data.getNames()) {
+        auto color = odd ? Colors::CYAN() : Colors::BLUE();
+        cout << color << setw(30) << left << dataset << " ";
+        data.loadDataset(dataset);
+        auto nSamples = data.getNSamples(dataset);
+        cout << setw(6) << right << nSamples << " ";
+        cout << setw(5) << right << data.getFeatures(dataset).size() << " ";
+        cout << setw(3) << right << data.getNClasses(dataset) << " ";
+        stringstream oss;
+        string sep = "";
+        for (auto number : data.getClassesCounts(dataset)) {
+            oss << sep << setprecision(2) << fixed << (float)number / nSamples * 100.0 << "% (" << number << ")";
+            sep = " / ";
+        }
+        outputBalance(oss.str());
+        odd = !odd;
+    }
+    cout << Colors::RESET() << endl;
+    return 0;
+}

src/Platform/main.cc

@@ -103,7 +103,7 @@ int main(int argc, char** argv)
     */
     auto env = platform::DotEnv();
     auto experiment = platform::Experiment();
-    experiment.setTitle(title).setLanguage("cpp").setLanguageVersion("1.0.0");
+    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");
     for (auto seed : seeds) {

src/Platform/modelRegister.h

@@ -16,4 +16,6 @@ static platform::Registrar registrarA("AODE",
     [](void) -> bayesnet::BaseClassifier* { return new bayesnet::AODE();});
 static platform::Registrar registrarALD("AODELd",
     [](void) -> bayesnet::BaseClassifier* { return new bayesnet::AODELd();});
+static platform::Registrar registrarBA("BoostAODE",
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::BoostAODE();});
 #endif