Add DecisionTree with tests

Makefile

@@ -96,6 +96,7 @@ opt = ""
 test: ## Run tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximum Spanning Tree'") to run only that section
 	@echo ">>> Running Platform tests...";
 	@$(MAKE) clean
+	@$(MAKE) debug
 	@cmake --build $(f_debug) -t $(test_targets) --parallel
 	@for t in $(test_targets); do \
 		if [ -f $(f_debug)/tests/$$t ]; then \

src/CMakeLists.txt

@@ -20,6 +20,8 @@ add_executable(
     results/Result.cpp
     experimental_clfs/XA1DE.cpp
     experimental_clfs/ExpClf.cpp
+    experimental_clfs/DecisionTree.cpp
+
 )
 target_link_libraries(b_best Boost::boost "${PyClassifiers}" bayesnet::bayesnet fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" Boost::python Boost::numpy "${XLSXWRITER_LIB}")
 
@@ -33,7 +35,7 @@ add_executable(b_grid commands/b_grid.cpp ${grid_sources}
     results/Result.cpp
     experimental_clfs/XA1DE.cpp
     experimental_clfs/ExpClf.cpp
-    experimental_clfs/AdaBoost.cpp
+    experimental_clfs/DecisionTree.cpp
 )
 target_link_libraries(b_grid ${MPI_CXX_LIBRARIES} "${PyClassifiers}" bayesnet::bayesnet fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" Boost::python Boost::numpy)
 
@@ -45,6 +47,8 @@ add_executable(b_list commands/b_list.cpp
     results/Result.cpp results/ResultsDatasetExcel.cpp results/ResultsDataset.cpp results/ResultsDatasetConsole.cpp
     experimental_clfs/XA1DE.cpp
     experimental_clfs/ExpClf.cpp
+    experimental_clfs/DecisionTree.cpp
+
 )
 target_link_libraries(b_list "${PyClassifiers}" bayesnet::bayesnet fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" Boost::python Boost::numpy "${XLSXWRITER_LIB}")
 
@@ -58,6 +62,8 @@ add_executable(b_main commands/b_main.cpp ${main_sources}
     experimental_clfs/XA1DE.cpp
     experimental_clfs/ExpClf.cpp
     experimental_clfs/ExpClf.cpp
+    experimental_clfs/DecisionTree.cpp
+
 )
 target_link_libraries(b_main PRIVATE nlohmann_json::nlohmann_json "${PyClassifiers}" bayesnet::bayesnet fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" Boost::python Boost::numpy)
 

src/experimental_clfs/AdaBoost.cpp

@@ -5,18 +5,19 @@
 // ***************************************************************
 
 #include "AdaBoost.h"
+#include "DecisionTree.h"
 #include <cmath>
 #include <algorithm>
 #include <numeric>
 #include <sstream>
 #include <iomanip>
 
-namespace platform {
+namespace bayesnet {
 
-    AdaBoost::AdaBoost(int n_estimators)
-        : Ensemble(true), n_estimators(n_estimators)
+    AdaBoost::AdaBoost(int n_estimators, int max_depth)
+        : Ensemble(true), n_estimators(n_estimators), base_max_depth(max_depth)
     {
-        validHyperparameters = { "n_estimators" };
+        validHyperparameters = { "n_estimators", "base_max_depth" };
     }
 
     void AdaBoost::buildModel(const torch::Tensor& weights)
@@ -89,20 +90,14 @@ namespace platform {
 
     std::unique_ptr<Classifier> AdaBoost::trainBaseEstimator(const torch::Tensor& weights)
     {
-        // Create a new classifier instance
-        // You need to implement this based on your specific base classifier
-        // For example, if using Decision Trees:
-        // auto classifier = std::make_unique<DecisionTree>();
+        // Create a decision tree with specified max depth
+        // For AdaBoost, we typically use shallow trees (stumps with max_depth=1)
+        auto tree = std::make_unique<DecisionTree>(base_max_depth);
 
-        // Or if using a factory method:
-        // auto classifier = ClassifierFactory::create("DecisionTree");
+        // Fit the tree with the current sample weights
+        tree->fit(dataset, features, className, states, weights, Smoothing_t::NONE);
 
-        // Placeholder - replace with actual classifier creation
-        throw std::runtime_error("AdaBoost::trainBaseEstimator - You need to implement base classifier creation");
-
-        // Once you have the classifier creation implemented, uncomment:
-        // classifier->fit(dataset, features, className, states, weights, Smoothing_t::NONE);
-        // return classifier;
+        return tree;
     }
 
     double AdaBoost::calculateWeightedError(Classifier* estimator, const torch::Tensor& weights)
@@ -192,8 +187,9 @@ namespace platform {
         return graph_lines;
     }
 
-    void AdaBoost::setHyperparameters(const nlohmann::json& hyperparameters)
+    void AdaBoost::setHyperparameters(const nlohmann::json& hyperparameters_)
     {
+        auto hyperparameters = hyperparameters_;
         // Set hyperparameters from JSON
         auto it = hyperparameters.find("n_estimators");
         if (it != hyperparameters.end()) {
@@ -201,14 +197,18 @@ namespace platform {
             if (n_estimators <= 0) {
                 throw std::invalid_argument("n_estimators must be positive");
             }
+            hyperparameters.erase("n_estimators");  // Remove 'n_estimators' if present 
         }
 
-        // Check for invalid hyperparameters
-        for (auto& [key, value] : hyperparameters.items()) {
-            if (std::find(validHyperparameters.begin(), validHyperparameters.end(), key) == validHyperparameters.end()) {
-                throw std::invalid_argument("Invalid hyperparameter: " + key);
+        it = hyperparameters.find("base_max_depth");
+        if (it != hyperparameters.end()) {
+            base_max_depth = it->get<int>();
+            if (base_max_depth <= 0) {
+                throw std::invalid_argument("base_max_depth must be positive");
             }
+            hyperparameters.erase("base_max_depth");  // Remove 'base_max_depth' if present 
         }
+        Ensemble::setHyperparameters(hyperparameters);
     }
 
 } // namespace bayesnet

src/experimental_clfs/AdaBoost.h

@@ -9,13 +9,12 @@
 
 #include <vector>
 #include <memory>
-#include <torch/torch.h>
-#include <bayesnet/ensembles/Ensemble.h>
+#include "bayesnet/ensembles/Ensemble.h"
 
-namespace platform {
-    class AdaBoost : public bayesnet::Ensemble {
+namespace bayesnet {
+    class AdaBoost : public Ensemble {
     public:
-        explicit AdaBoost(int n_estimators = 100);
+        explicit AdaBoost(int n_estimators = 50, int max_depth = 1);
         virtual ~AdaBoost() = default;
 
         // Override base class methods
@@ -24,10 +23,15 @@ namespace platform {
         // AdaBoost specific methods
         void setNEstimators(int n_estimators) { this->n_estimators = n_estimators; }
         int getNEstimators() const { return n_estimators; }
+        void setBaseMaxDepth(int depth) { this->base_max_depth = depth; }
+        int getBaseMaxDepth() const { return base_max_depth; }
 
         // Get the weight of each base estimator
         std::vector<double> getEstimatorWeights() const { return alphas; }
 
+        // Get training errors for each iteration
+        std::vector<double> getTrainingErrors() const { return training_errors; }
+
         // Override setHyperparameters from BaseClassifier
         void setHyperparameters(const nlohmann::json& hyperparameters) override;
 
@@ -37,6 +41,7 @@ namespace platform {
 
     private:
         int n_estimators;
+        int base_max_depth;  // Max depth for base decision trees
         std::vector<double> alphas;  // Weight of each base estimator
         std::vector<double> training_errors;  // Training error at each iteration
         torch::Tensor sample_weights;  // Current sample weights

src/experimental_clfs/DecisionTree.cpp

@@ -0,0 +1,519 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "DecisionTree.h"
+#include <algorithm>
+#include <numeric>
+#include <sstream>
+#include <iomanip>
+#include <limits>
+#include "TensorUtils.hpp"
+
+namespace bayesnet {
+
+    DecisionTree::DecisionTree(int max_depth, int min_samples_split, int min_samples_leaf)
+        : Classifier(Network()), max_depth(max_depth),
+        min_samples_split(min_samples_split), min_samples_leaf(min_samples_leaf)
+    {
+        validHyperparameters = { "max_depth", "min_samples_split", "min_samples_leaf" };
+    }
+
+    void DecisionTree::setHyperparameters(const nlohmann::json& hyperparameters_)
+    {
+        auto hyperparameters = hyperparameters_;
+        // Set hyperparameters from JSON
+        auto it = hyperparameters.find("max_depth");
+        if (it != hyperparameters.end()) {
+            max_depth = it->get<int>();
+            hyperparameters.erase("max_depth");  // Remove 'order' if present
+        }
+
+        it = hyperparameters.find("min_samples_split");
+        if (it != hyperparameters.end()) {
+            min_samples_split = it->get<int>();
+            hyperparameters.erase("min_samples_split");  // Remove 'min_samples_split' if present   
+        }
+
+        it = hyperparameters.find("min_samples_leaf");
+        if (it != hyperparameters.end()) {
+            min_samples_leaf = it->get<int>();
+            hyperparameters.erase("min_samples_leaf");  // Remove 'min_samples_leaf' if present
+        }
+        Classifier::setHyperparameters(hyperparameters);
+        checkValues();
+    }
+    void DecisionTree::checkValues()
+    {
+        if (max_depth <= 0) {
+            throw std::invalid_argument("max_depth must be positive");
+        }
+        if (min_samples_leaf <= 0) {
+            throw std::invalid_argument("min_samples_leaf must be positive");
+        }
+        if (min_samples_split <= 0) {
+            throw std::invalid_argument("min_samples_split must be positive");
+        }
+    }
+    void DecisionTree::buildModel(const torch::Tensor& weights)
+    {
+        // Extract features (X) and labels (y) from dataset
+        auto X = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), torch::indexing::Slice() }).t();
+        auto y = dataset.index({ -1, torch::indexing::Slice() });
+
+        if (X.size(0) != y.size(0)) {
+            throw std::runtime_error("X and y must have the same number of samples");
+        }
+
+        n_classes = states[className].size();
+
+        // Use provided weights or uniform weights
+        torch::Tensor sample_weights;
+        if (weights.defined() && weights.numel() > 0) {
+            if (weights.size(0) != X.size(0)) {
+                throw std::runtime_error("weights must have the same length as number of samples");
+            }
+            sample_weights = weights;
+        } else {
+            sample_weights = torch::ones({ X.size(0) }) / X.size(0);
+        }
+
+        // Normalize weights
+        sample_weights = sample_weights / sample_weights.sum();
+
+        // Build the tree
+        root = buildTree(X, y, sample_weights, 0);
+
+        // Mark as fitted
+        fitted = true;
+    }
+    bool DecisionTree::validateTensors(const torch::Tensor& X, const torch::Tensor& y,
+        const torch::Tensor& sample_weights) const
+    {
+        if (X.size(0) != y.size(0) || X.size(0) != sample_weights.size(0)) {
+            return false;
+        }
+        if (X.size(0) == 0) {
+            return false;
+        }
+        return true;
+    }
+
+    std::unique_ptr<TreeNode> DecisionTree::buildTree(
+        const torch::Tensor& X,
+        const torch::Tensor& y,
+        const torch::Tensor& sample_weights,
+        int current_depth)
+    {
+        auto node = std::make_unique<TreeNode>();
+        int n_samples = y.size(0);
+
+        // Check stopping criteria
+        auto unique = at::_unique(y);
+        bool should_stop = (current_depth >= max_depth) ||
+            (n_samples < min_samples_split) ||
+            (std::get<0>(unique).size(0) == 1);  // All samples same class
+
+        if (should_stop || n_samples <= min_samples_leaf) {
+            // Create leaf node
+            node->is_leaf = true;
+
+            // Calculate class probabilities
+            node->class_probabilities = torch::zeros({ n_classes });
+
+            for (int i = 0; i < n_samples; i++) {
+                int class_idx = y[i].item<int>();
+                node->class_probabilities[class_idx] += sample_weights[i].item<float>();
+            }
+
+            // Normalize probabilities
+            node->class_probabilities /= node->class_probabilities.sum();
+
+            // Set predicted class as the one with highest probability
+            node->predicted_class = torch::argmax(node->class_probabilities).item<int>();
+
+            return node;
+        }
+
+        // Find best split
+        SplitInfo best_split = findBestSplit(X, y, sample_weights);
+
+        // If no valid split found, create leaf
+        if (best_split.feature_index == -1 || best_split.impurity_decrease <= 0) {
+            node->is_leaf = true;
+
+            // Calculate class probabilities
+            node->class_probabilities = torch::zeros({ n_classes });
+
+            for (int i = 0; i < n_samples; i++) {
+                int class_idx = y[i].item<int>();
+                node->class_probabilities[class_idx] += sample_weights[i].item<float>();
+            }
+
+            node->class_probabilities /= node->class_probabilities.sum();
+            node->predicted_class = torch::argmax(node->class_probabilities).item<int>();
+
+            return node;
+        }
+
+        // Create internal node
+        node->is_leaf = false;
+        node->split_feature = best_split.feature_index;
+        node->split_value = best_split.split_value;
+
+        // Split data
+        auto left_X = X.index({ best_split.left_mask });
+        auto left_y = y.index({ best_split.left_mask });
+        auto left_weights = sample_weights.index({ best_split.left_mask });
+
+        auto right_X = X.index({ best_split.right_mask });
+        auto right_y = y.index({ best_split.right_mask });
+        auto right_weights = sample_weights.index({ best_split.right_mask });
+
+        // Recursively build subtrees
+        if (left_X.size(0) >= min_samples_leaf) {
+            node->left = buildTree(left_X, left_y, left_weights, current_depth + 1);
+        } else {
+            // Force leaf if not enough samples
+            node->left = std::make_unique<TreeNode>();
+            node->left->is_leaf = true;
+            auto mode = std::get<0>(torch::mode(left_y));
+            node->left->predicted_class = mode.item<int>();
+            node->left->class_probabilities = torch::zeros({ n_classes });
+            node->left->class_probabilities[node->left->predicted_class] = 1.0;
+        }
+
+        if (right_X.size(0) >= min_samples_leaf) {
+            node->right = buildTree(right_X, right_y, right_weights, current_depth + 1);
+        } else {
+            // Force leaf if not enough samples
+            node->right = std::make_unique<TreeNode>();
+            node->right->is_leaf = true;
+            auto mode = std::get<0>(torch::mode(right_y));
+            node->right->predicted_class = mode.item<int>();
+            node->right->class_probabilities = torch::zeros({ n_classes });
+            node->right->class_probabilities[node->right->predicted_class] = 1.0;
+        }
+
+        return node;
+    }
+
+    DecisionTree::SplitInfo DecisionTree::findBestSplit(
+        const torch::Tensor& X,
+        const torch::Tensor& y,
+        const torch::Tensor& sample_weights)
+    {
+
+        SplitInfo best_split;
+        best_split.feature_index = -1;
+        best_split.split_value = -1;
+        best_split.impurity_decrease = -std::numeric_limits<double>::infinity();
+
+        int n_features = X.size(1);
+        int n_samples = X.size(0);
+
+        // Calculate impurity of current node
+        double current_impurity = calculateGiniImpurity(y, sample_weights);
+        double total_weight = sample_weights.sum().item<double>();
+
+        // Try each feature
+        for (int feat_idx = 0; feat_idx < n_features; feat_idx++) {
+            auto feature_values = X.index({ torch::indexing::Slice(), feat_idx });
+            auto unique_values = std::get<0>(torch::unique_consecutive(std::get<0>(torch::sort(feature_values))));
+
+            // Try each unique value as split point
+            for (int i = 0; i < unique_values.size(0); i++) {
+                int split_val = unique_values[i].item<int>();
+
+                // Create masks for left and right splits
+                auto left_mask = feature_values == split_val;
+                auto right_mask = ~left_mask;
+
+                int left_count = left_mask.sum().item<int>();
+                int right_count = right_mask.sum().item<int>();
+
+                // Skip if split doesn't satisfy minimum samples requirement
+                if (left_count < min_samples_leaf || right_count < min_samples_leaf) {
+                    continue;
+                }
+
+                // Calculate weighted impurities
+                auto left_y = y.index({ left_mask });
+                auto left_weights = sample_weights.index({ left_mask });
+                double left_weight = left_weights.sum().item<double>();
+                double left_impurity = calculateGiniImpurity(left_y, left_weights);
+
+                auto right_y = y.index({ right_mask });
+                auto right_weights = sample_weights.index({ right_mask });
+                double right_weight = right_weights.sum().item<double>();
+                double right_impurity = calculateGiniImpurity(right_y, right_weights);
+
+                // Calculate impurity decrease
+                double impurity_decrease = current_impurity -
+                    (left_weight / total_weight * left_impurity +
+                        right_weight / total_weight * right_impurity);
+
+                // Update best split if this is better
+                if (impurity_decrease > best_split.impurity_decrease) {
+                    best_split.feature_index = feat_idx;
+                    best_split.split_value = split_val;
+                    best_split.impurity_decrease = impurity_decrease;
+                    best_split.left_mask = left_mask;
+                    best_split.right_mask = right_mask;
+                }
+            }
+        }
+
+        return best_split;
+    }
+
+    double DecisionTree::calculateGiniImpurity(
+        const torch::Tensor& y,
+        const torch::Tensor& sample_weights)
+    {
+        if (y.size(0) == 0 || sample_weights.size(0) == 0) {
+            return 0.0;
+        }
+
+        if (y.size(0) != sample_weights.size(0)) {
+            throw std::runtime_error("y and sample_weights must have same size");
+        }
+
+        torch::Tensor class_weights = torch::zeros({ n_classes });
+
+        // Calculate weighted class counts
+        for (int i = 0; i < y.size(0); i++) {
+            int class_idx = y[i].item<int>();
+
+            if (class_idx < 0 || class_idx >= n_classes) {
+                throw std::runtime_error("Invalid class index: " + std::to_string(class_idx));
+            }
+
+            class_weights[class_idx] += sample_weights[i].item<float>();
+        }
+
+        // Normalize
+        double total_weight = class_weights.sum().item<double>();
+        if (total_weight == 0) return 0.0;
+
+        class_weights /= total_weight;
+
+        // Calculate Gini impurity: 1 - sum(p_i^2)
+        double gini = 1.0;
+        for (int i = 0; i < n_classes; i++) {
+            double p = class_weights[i].item<double>();
+            gini -= p * p;
+        }
+
+        return gini;
+    }
+
+
+    torch::Tensor DecisionTree::predict(torch::Tensor& X)
+    {
+        if (!fitted) {
+            throw std::runtime_error(CLASSIFIER_NOT_FITTED);
+        }
+
+        int n_samples = X.size(1);
+        torch::Tensor predictions = torch::zeros({ n_samples }, torch::kInt32);
+
+        for (int i = 0; i < n_samples; i++) {
+            auto sample = X.index({ torch::indexing::Slice(), i }).ravel();
+            predictions[i] = predictSample(sample);
+        }
+
+        return predictions;
+    }
+    void dumpTensor(const torch::Tensor& tensor, const std::string& name)
+    {
+        std::cout << name << ": " << std::endl;
+        for (int i = 0; i < tensor.size(0); i++) {
+            std::cout << "[";
+            for (int j = 0; j < tensor.size(1); j++) {
+                std::cout << tensor[i][j].item<int>() << " ";
+            }
+            std::cout << "]" << std::endl;
+        }
+        std::cout << std::endl;
+    }
+    void dumpVector(const std::vector<std::vector<int>>& vec, const std::string& name)
+    {
+        std::cout << name << ": " << std::endl;;
+        for (const auto& row : vec) {
+            std::cout << "[";
+            for (const auto& val : row) {
+                std::cout << val << " ";
+            }
+            std::cout << "] " << std::endl;
+        }
+        std::cout << std::endl;
+    }
+
+    std::vector<int> DecisionTree::predict(std::vector<std::vector<int>>& X)
+    {
+        // Convert to tensor
+        long n = X.size();
+        long m = X.at(0).size();
+        torch::Tensor X_tensor = platform::TensorUtils::to_matrix(X);
+        auto predictions = predict(X_tensor);
+        std::vector<int> result = platform::TensorUtils::to_vector<int>(predictions);
+        return result;
+    }
+
+    torch::Tensor DecisionTree::predict_proba(torch::Tensor& X)
+    {
+        if (!fitted) {
+            throw std::runtime_error(CLASSIFIER_NOT_FITTED);
+        }
+
+        int n_samples = X.size(1);
+        torch::Tensor probabilities = torch::zeros({ n_samples, n_classes });
+
+        for (int i = 0; i < n_samples; i++) {
+            auto sample = X.index({ torch::indexing::Slice(), i }).ravel();
+            probabilities[i] = predictProbaSample(sample);
+        }
+
+        return probabilities;
+    }
+
+    std::vector<std::vector<double>> DecisionTree::predict_proba(std::vector<std::vector<int>>& X)
+    {
+        auto n_samples = X.at(0).size();
+        // Convert to tensor
+        torch::Tensor X_tensor = platform::TensorUtils::to_matrix(X);
+        auto proba_tensor = predict_proba(X_tensor);
+        std::vector<std::vector<double>> result(n_samples, std::vector<double>(n_classes, 0.0));
+
+        for (int i = 0; i < n_samples; i++) {
+            for (int j = 0; j < n_classes; j++) {
+                result[i][j] = proba_tensor[i][j].item<double>();
+            }
+        }
+
+        return result;
+    }
+
+    int DecisionTree::predictSample(const torch::Tensor& x) const
+    {
+        if (!fitted) {
+            throw std::runtime_error(CLASSIFIER_NOT_FITTED);
+        }
+
+        if (x.size(0) != n) {  // n debería ser el número de características
+            throw std::runtime_error("Input sample has wrong number of features");
+        }
+
+        const TreeNode* leaf = traverseTree(x, root.get());
+        return leaf->predicted_class;
+    }
+    torch::Tensor DecisionTree::predictProbaSample(const torch::Tensor& x) const
+    {
+        const TreeNode* leaf = traverseTree(x, root.get());
+        return leaf->class_probabilities.clone();
+    }
+
+
+    const TreeNode* DecisionTree::traverseTree(const torch::Tensor& x, const TreeNode* node) const
+    {
+        if (!node) {
+            throw std::runtime_error("Null node encountered during tree traversal");
+        }
+
+        if (node->is_leaf) {
+            return node;
+        }
+
+        if (node->split_feature < 0 || node->split_feature >= x.size(0)) {
+            throw std::runtime_error("Invalid split_feature index: " + std::to_string(node->split_feature));
+        }
+
+        int feature_value = x[node->split_feature].item<int>();
+
+        if (feature_value == node->split_value) {
+            if (!node->left) {
+                throw std::runtime_error("Missing left child in tree");
+            }
+            return traverseTree(x, node->left.get());
+        } else {
+            if (!node->right) {
+                throw std::runtime_error("Missing right child in tree");
+            }
+            return traverseTree(x, node->right.get());
+        }
+    }
+
+    std::vector<std::string> DecisionTree::graph(const std::string& title) const
+    {
+        std::vector<std::string> lines;
+        lines.push_back("digraph DecisionTree {");
+        lines.push_back("    rankdir=TB;");
+        lines.push_back("    node [shape=box, style=\"filled, rounded\", fontname=\"helvetica\"];");
+        lines.push_back("    edge [fontname=\"helvetica\"];");
+
+        if (!title.empty()) {
+            lines.push_back("    label=\"" + title + "\";");
+            lines.push_back("    labelloc=t;");
+        }
+
+        if (root) {
+            int node_id = 0;
+            treeToGraph(root.get(), lines, node_id);
+        }
+
+        lines.push_back("}");
+        return lines;
+    }
+
+    void DecisionTree::treeToGraph(
+        const TreeNode* node,
+        std::vector<std::string>& lines,
+        int& node_id,
+        int parent_id,
+        const std::string& edge_label) const
+    {
+
+        int current_id = node_id++;
+        std::stringstream ss;
+
+        if (node->is_leaf) {
+            // Leaf node
+            ss << "    node" << current_id << " [label=\"Class: " << node->predicted_class;
+            ss << "\\nProb: " << std::fixed << std::setprecision(3)
+                << node->class_probabilities[node->predicted_class].item<float>();
+            ss << "\", fillcolor=\"lightblue\"];";
+            lines.push_back(ss.str());
+        } else {
+            // Internal node
+            ss << "    node" << current_id << " [label=\"" << features[node->split_feature];
+            ss << " = " << node->split_value << "?\", fillcolor=\"lightgreen\"];";
+            lines.push_back(ss.str());
+        }
+
+        // Add edge from parent
+        if (parent_id >= 0) {
+            ss.str("");
+            ss << "    node" << parent_id << " -> node" << current_id;
+            if (!edge_label.empty()) {
+                ss << " [label=\"" << edge_label << "\"];";
+            } else {
+                ss << ";";
+            }
+            lines.push_back(ss.str());
+        }
+
+        // Recurse on children
+        if (!node->is_leaf) {
+            if (node->left) {
+                treeToGraph(node->left.get(), lines, node_id, current_id, "Yes");
+            }
+            if (node->right) {
+                treeToGraph(node->right.get(), lines, node_id, current_id, "No");
+            }
+        }
+    }
+
+} // namespace bayesnet

src/experimental_clfs/DecisionTree.h

@@ -0,0 +1,129 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef DECISION_TREE_H
+#define DECISION_TREE_H
+
+#include <memory>
+#include <vector>
+#include <map>
+#include <torch/torch.h>
+#include "bayesnet/classifiers/Classifier.h"
+
+namespace bayesnet {
+
+    // Forward declaration
+    struct TreeNode;
+
+    class DecisionTree : public Classifier {
+    public:
+        explicit DecisionTree(int max_depth = 3, int min_samples_split = 2, int min_samples_leaf = 1);
+        virtual ~DecisionTree() = default;
+
+        // Override graph method to show tree structure
+        std::vector<std::string> graph(const std::string& title = "") const override;
+
+        // Setters for hyperparameters
+        void setMaxDepth(int depth) { max_depth = depth; checkValues(); }
+        void setMinSamplesSplit(int samples) { min_samples_split = samples; checkValues(); }
+        void setMinSamplesLeaf(int samples) { min_samples_leaf = samples; checkValues(); }
+
+        // Override setHyperparameters
+        void setHyperparameters(const nlohmann::json& hyperparameters) override;
+
+        torch::Tensor predict(torch::Tensor& X) override;
+        std::vector<int> predict(std::vector<std::vector<int>>& X) override;
+        torch::Tensor predict_proba(torch::Tensor& X) override;
+        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X);
+
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override
+        {
+            // Decision trees do not require training in the traditional sense
+            // as they are built from the data directly.
+            // This method can be used to set weights or other parameters if needed.
+        }
+    private:
+        void checkValues();
+        bool validateTensors(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& sample_weights) const;
+        // Tree hyperparameters
+        int max_depth;
+        int min_samples_split;
+        int min_samples_leaf;
+        int n_classes;  // Number of classes in the target variable
+
+        // Root of the decision tree
+        std::unique_ptr<TreeNode> root;
+
+        // Build tree recursively
+        std::unique_ptr<TreeNode> buildTree(
+            const torch::Tensor& X,
+            const torch::Tensor& y,
+            const torch::Tensor& sample_weights,
+            int current_depth
+        );
+
+        // Find best split for a node
+        struct SplitInfo {
+            int feature_index;
+            int split_value;
+            double impurity_decrease;
+            torch::Tensor left_mask;
+            torch::Tensor right_mask;
+        };
+
+        SplitInfo findBestSplit(
+            const torch::Tensor& X,
+            const torch::Tensor& y,
+            const torch::Tensor& sample_weights
+        );
+
+        // Calculate weighted Gini impurity for multi-class
+        double calculateGiniImpurity(
+            const torch::Tensor& y,
+            const torch::Tensor& sample_weights
+        );
+
+        // Make predictions for a single sample
+        int predictSample(const torch::Tensor& x) const;
+
+        // Make probabilistic predictions for a single sample
+        torch::Tensor predictProbaSample(const torch::Tensor& x) const;
+
+        // Traverse tree to find leaf node
+        const TreeNode* traverseTree(const torch::Tensor& x, const TreeNode* node) const;
+
+        // Convert tree to graph representation
+        void treeToGraph(
+            const TreeNode* node,
+            std::vector<std::string>& lines,
+            int& node_id,
+            int parent_id = -1,
+            const std::string& edge_label = ""
+        ) const;
+    };
+
+    // Tree node structure
+    struct TreeNode {
+        bool is_leaf;
+
+        // For internal nodes
+        int split_feature;
+        int split_value;
+        std::unique_ptr<TreeNode> left;
+        std::unique_ptr<TreeNode> right;
+
+        // For leaf nodes
+        int predicted_class;
+        torch::Tensor class_probabilities;  // Probability for each class
+
+        TreeNode() : is_leaf(false), split_feature(-1), split_value(-1), predicted_class(-1) {}
+    };
+
+} // namespace bayesnet
+
+#endif // DECISION_TREE_H

src/experimental_clfs/README.md

@@ -0,0 +1,142 @@
+# AdaBoost and DecisionTree Classifier Implementation
+
+This implementation provides both a Decision Tree classifier and a multi-class AdaBoost classifier based on the SAMME (Stagewise Additive Modeling using a Multi-class Exponential loss) algorithm described in the paper "Multi-class AdaBoost" by Zhu et al. Implemented in C++ using <https://claude.ai>
+
+## Components
+
+### 1. DecisionTree Classifier
+
+A classic decision tree implementation that:
+
+- Supports multi-class classification
+- Handles weighted samples (essential for boosting)
+- Uses Gini impurity as the splitting criterion
+- Works with discrete/categorical features
+- Provides both class predictions and probability estimates
+
+#### Key Features
+
+- **Max Depth Control**: Limit tree depth to create weak learners
+- **Minimum Samples**: Control minimum samples for splitting and leaf nodes
+- **Weighted Training**: Properly handles sample weights for boosting
+- **Visualization**: Generates DOT format graphs of the tree structure
+
+#### Hyperparameters
+
+- `max_depth`: Maximum depth of the tree (default: 3)
+- `min_samples_split`: Minimum samples required to split a node (default: 2)
+- `min_samples_leaf`: Minimum samples required in a leaf node (default: 1)
+
+### 2. AdaBoost Classifier
+
+A multi-class AdaBoost implementation using DecisionTree as base estimators:
+
+- **SAMME Algorithm**: Implements the multi-class extension of AdaBoost
+- **Automatic Stumps**: Uses decision stumps (max_depth=1) by default
+- **Early Stopping**: Stops if base classifier performs worse than random
+- **Ensemble Visualization**: Shows the weighted combination of base estimators
+
+#### Key Features
+
+- **Multi-class Support**: Natural extension to K classes
+- **Base Estimator Control**: Configure depth of base decision trees
+- **Training Monitoring**: Track training errors and estimator weights
+- **Probability Estimates**: Provides class probability predictions
+
+#### Hyperparameters
+
+- `n_estimators`: Number of base estimators to train (default: 50)
+- `base_max_depth`: Maximum depth for base decision trees (default: 1)
+
+## Algorithm Details
+
+The SAMME algorithm differs from binary AdaBoost in the calculation of the estimator weight (alpha):
+
+```
+α = log((1 - err) / err) + log(K - 1)
+```
+
+where `K` is the number of classes. This formula ensures that:
+
+- When K = 2, it reduces to standard AdaBoost
+- For K > 2, base classifiers only need to be better than random guessing (1/K) rather than 50%
+
+## Usage Example
+
+```cpp
+// Create AdaBoost with decision stumps
+AdaBoost ada(100, 1);  // 100 estimators, max_depth=1
+
+// Train
+ada.fit(X_train, y_train, features, className, states, Smoothing_t::NONE);
+
+// Predict
+auto predictions = ada.predict(X_test);
+auto probabilities = ada.predict_proba(X_test);
+
+// Evaluate
+float accuracy = ada.score(X_test, y_test);
+
+// Get ensemble information
+auto weights = ada.getEstimatorWeights();
+auto errors = ada.getTrainingErrors();
+```
+
+## Implementation Structure
+
+```
+AdaBoost (inherits from Ensemble)
+    └── Uses multiple DecisionTree instances as base estimators
+         └── DecisionTree (inherits from Classifier)
+              └── Implements weighted Gini impurity splitting
+```
+
+## Visualization
+
+Both classifiers support graph visualization:
+
+- **DecisionTree**: Shows the tree structure with split conditions
+- **AdaBoost**: Shows the ensemble of weighted base estimators
+
+Generate visualizations using:
+
+```cpp
+auto graph = classifier.graph("Title");
+```
+
+## Data Format
+
+Both classifiers expect discrete/categorical data:
+
+- **Features**: Integer values representing categories (stored in `torch::Tensor` or `std::vector<std::vector<int>>`)
+- **Labels**: Integer values representing class indices (0, 1, ..., K-1)
+- **States**: Map defining possible values for each feature and the class variable
+- **Sample Weights**: Optional weights for each training sample (important for boosting)
+
+Example data setup:
+
+```cpp
+// Features matrix (n_features x n_samples)
+torch::Tensor X = torch::tensor({{0, 1, 2}, {1, 0, 1}});  // 2 features, 3 samples
+
+// Labels vector
+torch::Tensor y = torch::tensor({0, 1, 0});  // 3 samples
+
+// States definition
+std::map<std::string, std::vector<int>> states;
+states["feature1"] = {0, 1, 2};  // Feature 1 can take values 0, 1, or 2
+states["feature2"] = {0, 1};     // Feature 2 can take values 0 or 1
+states["class"] = {0, 1};        // Binary classification
+```
+
+## Notes
+
+- The implementation handles discrete/categorical features as indicated by the int-based data structures
+- Sample weights are properly propagated through the tree building process
+- The DecisionTree implementation uses equality testing for splits (suitable for categorical data)
+- Both classifiers support the standard fit/predict interface from the base framework
+
+## References
+
+- Zhu, J., Zou, H., Rosset, S., & Hastie, T. (2009). Multi-class AdaBoost. Statistics and its interface, 2(3), 349-360.
+- Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth, Belmont, CA.

src/experimental_clfs/TensorUtils.hpp

@@ -45,6 +45,19 @@ namespace platform {
 
             return data;
         }
+        static torch::Tensor to_matrix(const std::vector<std::vector<int>>& data)
+        {
+            if (data.empty()) return torch::empty({ 0, 0 }, torch::kInt64);
+            size_t rows = data.size();
+            size_t cols = data[0].size();
+            torch::Tensor tensor = torch::empty({ static_cast<long>(rows), static_cast<long>(cols) }, torch::kInt64);
+            for (size_t i = 0; i < rows; ++i) {
+                for (size_t j = 0; j < cols; ++j) {
+                    tensor.index_put_({ static_cast<long>(i), static_cast<long>(j) }, data[i][j]);
+                }
+            }
+            return tensor;
+        }
     };
 }
 

src/main/Models.h

@@ -23,10 +23,11 @@
 #include <pyclassifiers/ODTE.h>
 #include <pyclassifiers/SVC.h>
 #include <pyclassifiers/XGBoost.h>
-#include <pyclassifiers/AdaBoost.h>
+#include <pyclassifiers/AdaBoostPy.h>
 #include <pyclassifiers/RandomForest.h>
 #include "../experimental_clfs/XA1DE.h"
-#include "../experimental_clfs/AdaBoost.h"
+// #include "../experimental_clfs/AdaBoost.h"
+#include "../experimental_clfs/DecisionTree.h"
 
 namespace platform {
     class Models {

src/main/modelRegister.h

@@ -35,10 +35,12 @@ namespace platform {
         [](void) -> bayesnet::BaseClassifier* { return new pywrap::RandomForest();});
     static Registrar registrarXGB("XGBoost",
         [](void) -> bayesnet::BaseClassifier* { return new pywrap::XGBoost();});
-    static Registrar registrarAda("AdaBoostPy",
-        [](void) -> bayesnet::BaseClassifier* { return new pywrap::AdaBoost();});
-    // static Registrar registrarAda2("AdaBoost",
-    //     [](void) -> bayesnet::BaseClassifier* { return new platform::AdaBoost();});
+    static Registrar registrarAdaPy("AdaBoostPy",
+        [](void) -> bayesnet::BaseClassifier* { return new pywrap::AdaBoostPy();});
+    // static Registrar registrarAda("AdaBoost",
+    //     [](void) -> bayesnet::BaseClassifier* { return new bayesnet::AdaBoost();});
+    static Registrar registrarDT("DecisionTree",
+        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::DecisionTree();});
     static Registrar registrarXSPODE("XSPODE",
         [](void) -> bayesnet::BaseClassifier* { return new bayesnet::XSpode(0);});
     static Registrar registrarXSP2DE("XSP2DE",

tests/CMakeLists.txt

@@ -12,11 +12,11 @@ if(ENABLE_TESTING)
         ${Bayesnet_INCLUDE_DIRS}
     )
     set(TEST_SOURCES_PLATFORM 
-        TestUtils.cpp TestPlatform.cpp TestResult.cpp TestScores.cpp 
+        TestUtils.cpp TestPlatform.cpp TestResult.cpp TestScores.cpp TestDecisionTree.cpp
         ${Platform_SOURCE_DIR}/src/common/Datasets.cpp ${Platform_SOURCE_DIR}/src/common/Dataset.cpp ${Platform_SOURCE_DIR}/src/common/Discretization.cpp
-        ${Platform_SOURCE_DIR}/src/main/Scores.cpp
+        ${Platform_SOURCE_DIR}/src/main/Scores.cpp ${Platform_SOURCE_DIR}/src/experimental_clfs/DecisionTree.cpp
     )
     add_executable(${TEST_PLATFORM} ${TEST_SOURCES_PLATFORM})
-    target_link_libraries(${TEST_PLATFORM} PUBLIC "${TORCH_LIBRARIES}" mdlp Catch2::Catch2WithMain BayesNet)
+    target_link_libraries(${TEST_PLATFORM} PUBLIC "${TORCH_LIBRARIES}" fimdlp Catch2::Catch2WithMain bayesnet)
     add_test(NAME ${TEST_PLATFORM} COMMAND ${TEST_PLATFORM})
 endif(ENABLE_TESTING)

tests/TestDecisionTree.cpp

@@ -0,0 +1,303 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <catch2/catch_test_macros.hpp>
+#include <catch2/catch_approx.hpp>
+#include <catch2/matchers/catch_matchers_string.hpp>
+#include <catch2/matchers/catch_matchers_vector.hpp>
+#include <torch/torch.h>
+#include <memory>
+#include <stdexcept>
+#include "experimental_clfs/DecisionTree.h"
+#include "TestUtils.h"
+
+using namespace bayesnet;
+using namespace Catch::Matchers;
+
+TEST_CASE("DecisionTree Construction", "[DecisionTree]")
+{
+    SECTION("Default constructor")
+    {
+        REQUIRE_NOTHROW(DecisionTree());
+    }
+
+    SECTION("Constructor with parameters")
+    {
+        REQUIRE_NOTHROW(DecisionTree(5, 10, 3));
+    }
+}
+
+TEST_CASE("DecisionTree Hyperparameter Setting", "[DecisionTree]")
+{
+    DecisionTree dt;
+
+    SECTION("Set individual hyperparameters")
+    {
+        REQUIRE_NOTHROW(dt.setMaxDepth(10));
+        REQUIRE_NOTHROW(dt.setMinSamplesSplit(5));
+        REQUIRE_NOTHROW(dt.setMinSamplesLeaf(2));
+    }
+
+    SECTION("Set hyperparameters via JSON")
+    {
+        nlohmann::json params;
+        params["max_depth"] = 7;
+        params["min_samples_split"] = 4;
+        params["min_samples_leaf"] = 2;
+
+        REQUIRE_NOTHROW(dt.setHyperparameters(params));
+    }
+
+    SECTION("Invalid hyperparameters should throw")
+    {
+        nlohmann::json params;
+
+        // Negative max_depth
+        params["max_depth"] = -1;
+        REQUIRE_THROWS_AS(dt.setHyperparameters(params), std::invalid_argument);
+
+        // Zero min_samples_split
+        params["max_depth"] = 5;
+        params["min_samples_split"] = 0;
+        REQUIRE_THROWS_AS(dt.setHyperparameters(params), std::invalid_argument);
+
+        // Negative min_samples_leaf
+        params["min_samples_split"] = 2;
+        params["min_samples_leaf"] = -5;
+        REQUIRE_THROWS_AS(dt.setHyperparameters(params), std::invalid_argument);
+    }
+}
+
+TEST_CASE("DecisionTree Basic Functionality", "[DecisionTree]")
+{
+    // Create a simple dataset
+    int n_samples = 20;
+    int n_features = 2;
+
+    std::vector<std::vector<int>> X(n_features, std::vector<int>(n_samples));
+    std::vector<int> y(n_samples);
+
+    // Simple pattern: class depends on first feature
+    for (int i = 0; i < n_samples; i++) {
+        X[0][i] = i < 10 ? 0 : 1;
+        X[1][i] = i % 2;
+        y[i] = X[0][i];  // Class equals first feature
+    }
+
+    std::vector<std::string> features = { "f1", "f2" };
+    std::string className = "class";
+    std::map<std::string, std::vector<int>> states;
+    states["f1"] = { 0, 1 };
+    states["f2"] = { 0, 1 };
+    states["class"] = { 0, 1 };
+
+    SECTION("Training with vector interface")
+    {
+        DecisionTree dt(3, 2, 1);
+        REQUIRE_NOTHROW(dt.fit(X, y, features, className, states, Smoothing_t::NONE));
+
+        auto predictions = dt.predict(X);
+        REQUIRE(predictions.size() == static_cast<size_t>(n_samples));
+
+        // Should achieve perfect accuracy on this simple dataset
+        int correct = 0;
+        for (size_t i = 0; i < predictions.size(); i++) {
+            if (predictions[i] == y[i]) correct++;
+        }
+        REQUIRE(correct == n_samples);
+    }
+
+    SECTION("Prediction before fitting")
+    {
+        DecisionTree dt;
+        REQUIRE_THROWS_WITH(dt.predict(X),
+            ContainsSubstring("Classifier has not been fitted"));
+    }
+
+    SECTION("Probability predictions")
+    {
+        DecisionTree dt(3, 2, 1);
+        dt.fit(X, y, features, className, states, Smoothing_t::NONE);
+
+        auto proba = dt.predict_proba(X);
+        REQUIRE(proba.size() == static_cast<size_t>(n_samples));
+        REQUIRE(proba[0].size() == 2);  // Two classes
+
+        // Check probabilities sum to 1 and probabilities are valid
+        auto predictions = dt.predict(X);
+        for (size_t i = 0; i < proba.size(); i++) {
+            auto p = proba[i];
+            auto pred = predictions[i];
+            REQUIRE(p.size() == 2);
+            REQUIRE(p[0] >= 0.0);
+            REQUIRE(p[1] >= 0.0);
+            double sum = p[0] + p[1];
+            //Check that prodict_proba matches the expected predict value
+            REQUIRE(pred == (p[0] > p[1] ? 0 : 1));
+            REQUIRE(sum == Catch::Approx(1.0).epsilon(1e-6));
+        }
+    }
+}
+
+TEST_CASE("DecisionTree on Iris Dataset", "[DecisionTree][iris]")
+{
+    auto raw = RawDatasets("iris", true);
+
+    SECTION("Training with dataset format")
+    {
+        DecisionTree dt(5, 2, 1);
+
+        INFO("Dataset shape: " << raw.dataset.sizes());
+        INFO("Features: " << raw.featurest.size());
+        INFO("Samples: " << raw.nSamples);
+
+        // DecisionTree expects dataset in format: features x samples, with labels as last row
+        REQUIRE_NOTHROW(dt.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest, Smoothing_t::NONE));
+
+        // Test prediction
+        auto predictions = dt.predict(raw.Xt);
+        REQUIRE(predictions.size(0) == raw.yt.size(0));
+
+        // Calculate accuracy
+        auto correct = torch::sum(predictions == raw.yt).item<int>();
+        double accuracy = static_cast<double>(correct) / raw.yt.size(0);
+        REQUIRE(accuracy > 0.97);  // Reasonable accuracy for Iris
+    }
+
+    SECTION("Training with vector interface")
+    {
+        DecisionTree dt(5, 2, 1);
+
+        REQUIRE_NOTHROW(dt.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv, Smoothing_t::NONE));
+
+        // std::cout << "Tree structure:\n";
+        // auto graph_lines = dt.graph("Iris Decision Tree");
+        // for (const auto& line : graph_lines) {
+        //     std::cout << line << "\n";
+        // }
+        auto predictions = dt.predict(raw.Xv);
+        REQUIRE(predictions.size() == raw.yv.size());
+    }
+
+    SECTION("Different tree depths")
+    {
+        std::vector<int> depths = { 1, 3, 5 };
+
+        for (int depth : depths) {
+            DecisionTree dt(depth, 2, 1);
+            dt.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest, Smoothing_t::NONE);
+
+            auto predictions = dt.predict(raw.Xt);
+            REQUIRE(predictions.size(0) == raw.yt.size(0));
+        }
+    }
+}
+
+TEST_CASE("DecisionTree Edge Cases", "[DecisionTree]")
+{
+    auto raw = RawDatasets("iris", true);
+
+    SECTION("Very shallow tree")
+    {
+        DecisionTree dt(1, 2, 1);  // depth = 1
+        dt.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest, Smoothing_t::NONE);
+
+        auto predictions = dt.predict(raw.Xt);
+        REQUIRE(predictions.size(0) == raw.yt.size(0));
+
+        // With depth 1, should have at most 2 unique predictions
+        auto unique_vals = at::_unique(predictions);
+        REQUIRE(std::get<0>(unique_vals).size(0) <= 2);
+    }
+
+    SECTION("High min_samples_split")
+    {
+        DecisionTree dt(10, 50, 1);
+        dt.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest, Smoothing_t::NONE);
+
+        auto predictions = dt.predict(raw.Xt);
+        REQUIRE(predictions.size(0) == raw.yt.size(0));
+    }
+}
+
+TEST_CASE("DecisionTree Graph Visualization", "[DecisionTree]")
+{
+    // Simple dataset
+    std::vector<std::vector<int>> X = { {0,0,0,1}, {0,1,1,1} };  // XOR pattern
+    std::vector<int> y = { 0, 1, 1, 0 };  // XOR pattern
+    std::vector<std::string> features = { "x1", "x2" };
+    std::string className = "xor";
+    std::map<std::string, std::vector<int>> states;
+    states["x1"] = { 0, 1 };
+    states["x2"] = { 0, 1 };
+    states["xor"] = { 0, 1 };
+
+    SECTION("Graph generation")
+    {
+        DecisionTree dt(2, 1, 1);
+        dt.fit(X, y, features, className, states, Smoothing_t::NONE);
+
+        auto graph_lines = dt.graph();
+
+        REQUIRE(graph_lines.size() > 2);
+        REQUIRE(graph_lines.front() == "digraph DecisionTree {");
+        REQUIRE(graph_lines.back() == "}");
+
+        // Should contain node definitions
+        bool has_nodes = false;
+        for (const auto& line : graph_lines) {
+            if (line.find("node") != std::string::npos) {
+                has_nodes = true;
+                break;
+            }
+        }
+        REQUIRE(has_nodes);
+    }
+
+    SECTION("Graph with title")
+    {
+        DecisionTree dt(2, 1, 1);
+        dt.fit(X, y, features, className, states, Smoothing_t::NONE);
+
+        auto graph_lines = dt.graph("XOR Tree");
+
+        bool has_title = false;
+        for (const auto& line : graph_lines) {
+            if (line.find("label=\"XOR Tree\"") != std::string::npos) {
+                has_title = true;
+                break;
+            }
+        }
+        REQUIRE(has_title);
+    }
+}
+
+TEST_CASE("DecisionTree with Weights", "[DecisionTree]")
+{
+    auto raw = RawDatasets("iris", true);
+
+    SECTION("Uniform weights")
+    {
+        DecisionTree dt(5, 2, 1);
+        dt.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest, raw.weights, Smoothing_t::NONE);
+
+        auto predictions = dt.predict(raw.Xt);
+        REQUIRE(predictions.size(0) == raw.yt.size(0));
+    }
+
+    SECTION("Non-uniform weights")
+    {
+        auto weights = torch::ones({ raw.nSamples });
+        weights.index({ torch::indexing::Slice(0, 50) }) *= 2.0;  // Emphasize first class
+        weights = weights / weights.sum();
+
+        DecisionTree dt(5, 2, 1);
+        dt.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest, weights, Smoothing_t::NONE);
+
+        auto predictions = dt.predict(raw.Xt);
+        REQUIRE(predictions.size(0) == raw.yt.size(0));
+    }
+}

tests/TestPlatform.cpp

@@ -7,7 +7,7 @@
 #include <string>
 #include "TestUtils.h"
 #include "folding.hpp"
-#include <ArffFiles.hpp>
+#include <ArffFiles/ArffFiles.hpp>
 #include <bayesnet/classifiers/TAN.h>
 #include "config_platform.h"
 
@@ -20,17 +20,17 @@ TEST_CASE("Test Platform version", "[Platform]")
 TEST_CASE("Test Folding library version", "[Folding]")
 {
     std::string version = folding::KFold(5, 100).version();
-    REQUIRE(version == "1.1.0");
+    REQUIRE(version == "1.1.1");
 }
 TEST_CASE("Test BayesNet version", "[BayesNet]")
 {
     std::string version = bayesnet::TAN().getVersion();
-    REQUIRE(version == "1.0.6");
+    REQUIRE(version == "1.1.2");
 }
 TEST_CASE("Test mdlp version", "[mdlp]")
 {
     std::string version = mdlp::CPPFImdlp::version();
-    REQUIRE(version == "2.0.0");
+    REQUIRE(version == "2.0.1");
 }
 TEST_CASE("Test Arff version", "[Arff]")
 {

tests/TestScores.cpp

@@ -14,38 +14,40 @@
 using json = nlohmann::ordered_json;
 auto epsilon = 1e-4;
 
-void make_test_bin(int TP, int TN, int FP, int FN, std::vector<int>& y_test, std::vector<int>& y_pred)
+void make_test_bin(int TP, int TN, int FP, int FN, std::vector<int>& y_test, torch::Tensor& y_pred)
 {
-    // TP
+    std::vector<std::array<double, 2>> probs;
+    // TP: true positive (label 1, predicted 1)
     for (int i = 0; i < TP; i++) {
         y_test.push_back(1);
-        y_pred.push_back(1);
+        probs.push_back({ 0.0, 1.0 }); // P(class 0)=0, P(class 1)=1
     }
-    // TN
+    // TN: true negative (label 0, predicted 0)
     for (int i = 0; i < TN; i++) {
         y_test.push_back(0);
-        y_pred.push_back(0);
+        probs.push_back({ 1.0, 0.0 }); // P(class 0)=1, P(class 1)=0
     }
-    // FP
+    // FP: false positive (label 0, predicted 1)
     for (int i = 0; i < FP; i++) {
         y_test.push_back(0);
-        y_pred.push_back(1);
+        probs.push_back({ 0.0, 1.0 }); // P(class 0)=0, P(class 1)=1
     }
-    // FN
+    // FN: false negative (label 1, predicted 0)
     for (int i = 0; i < FN; i++) {
         y_test.push_back(1);
-        y_pred.push_back(0);
+        probs.push_back({ 1.0, 0.0 }); // P(class 0)=1, P(class 1)=0
     }
+    // Convert to torch::Tensor of double, shape [N,2]
+    y_pred = torch::from_blob(probs.data(), { (long)probs.size(), 2 }, torch::kFloat64).clone();
 }
 
 TEST_CASE("Scores binary", "[Scores]")
 {
     std::vector<int> y_test;
-    std::vector<int> y_pred;
+    torch::Tensor y_pred;
     make_test_bin(197, 210, 52, 41, y_test, y_pred);
     auto y_test_tensor = torch::tensor(y_test, torch::kInt32);
-    auto y_pred_tensor = torch::tensor(y_pred, torch::kInt32);
-    platform::Scores scores(y_test_tensor, y_pred_tensor, 2);
+    platform::Scores scores(y_test_tensor, y_pred, 2);
     REQUIRE(scores.accuracy() == Catch::Approx(0.814).epsilon(epsilon));
     REQUIRE(scores.f1_score(0) == Catch::Approx(0.818713));
     REQUIRE(scores.f1_score(1) == Catch::Approx(0.809035));
@@ -64,10 +66,23 @@ TEST_CASE("Scores binary", "[Scores]")
 TEST_CASE("Scores multiclass", "[Scores]")
 {
     std::vector<int> y_test = { 0, 2, 2, 2, 2, 0, 1, 2, 0, 2 };
-    std::vector<int> y_pred = { 0, 1, 2, 2, 1, 1, 1, 0, 0, 2 };
+    // Refactor y_pred to a tensor of shape [10, 3] with probabilities
+    std::vector<std::array<double, 3>> probs = {
+        { 1.0, 0.0, 0.0 }, // P(class 0)=1, P(class 1)=0, P(class 2)=0
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 0.0, 0.0, 1.0 }, // P(class 0)=0, P(class 1)=0, P(class 2)=1
+        { 0.0, 0.0, 1.0 }, // P(class 0)=0, P(class 1)=0, P(class 2)=1
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 1.0, 0.0, 0.0 }, // P(class 0)=1, P(class 1)=0, P(class 2)=0
+        { 1.0, 0.0, 0.0 }, // P(class 0)=1, P(class 1)=0, P(class 2)=0
+        { 0.0, 0.0, 1.0 } // P(class 0)=0, P(class 1)=0, P(class 2)=1
+    };
+    torch::Tensor y_pred = torch::from_blob(probs.data(), { (long)probs.size(), 3 }, torch::kFloat64).clone();
+    // Convert y_test to a tensor
     auto y_test_tensor = torch::tensor(y_test, torch::kInt32);
-    auto y_pred_tensor = torch::tensor(y_pred, torch::kInt32);
-    platform::Scores scores(y_test_tensor, y_pred_tensor, 3);
+    platform::Scores scores(y_test_tensor, y_pred, 3);
     REQUIRE(scores.accuracy() == Catch::Approx(0.6).epsilon(epsilon));
     REQUIRE(scores.f1_score(0) == Catch::Approx(0.666667));
     REQUIRE(scores.f1_score(1) == Catch::Approx(0.4));
@@ -84,10 +99,21 @@ TEST_CASE("Scores multiclass", "[Scores]")
 TEST_CASE("Test Confusion Matrix Values", "[Scores]")
 {
     std::vector<int> y_test = { 0, 2, 2, 2, 2, 0, 1, 2, 0, 2 };
-    std::vector<int> y_pred = { 0, 1, 2, 2, 1, 1, 1, 0, 0, 2 };
+    std::vector<std::array<double, 3>> probs = {
+        { 1.0, 0.0, 0.0 }, // P(class 0)=1, P(class 1)=0, P(class 2)=0
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 0.0, 0.0, 1.0 }, // P(class 0)=0, P(class 1)=0, P(class 2)=1
+        { 0.0, 0.0, 1.0 }, // P(class 0)=0, P(class 1)=0, P(class 2)=1
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 1.0, 0.0, 0.0 }, // P(class 0)=1, P(class 1)=0, P(class 2)=0
+        { 1.0, 0.0, 0.0 }, // P(class 0)=1, P(class 1)=0, P(class 2)=0
+        { 0.0, 0.0, 1.0 } // P(class 0)=0, P(class 1)=0, P(class 2)=1
+    };
+    torch::Tensor y_pred = torch::from_blob(probs.data(), { (long)probs.size(), 3 }, torch::kFloat64).clone();
     auto y_test_tensor = torch::tensor(y_test, torch::kInt32);
-    auto y_pred_tensor = torch::tensor(y_pred, torch::kInt32);
-    platform::Scores scores(y_test_tensor, y_pred_tensor, 3);
+    platform::Scores scores(y_test_tensor, y_pred, 3);
     auto confusion_matrix = scores.get_confusion_matrix();
     REQUIRE(confusion_matrix[0][0].item<int>() == 2);
     REQUIRE(confusion_matrix[0][1].item<int>() == 1);
@@ -102,11 +128,22 @@ TEST_CASE("Test Confusion Matrix Values", "[Scores]")
 TEST_CASE("Confusion Matrix JSON", "[Scores]")
 {
     std::vector<int> y_test = { 0, 2, 2, 2, 2, 0, 1, 2, 0, 2 };
-    std::vector<int> y_pred = { 0, 1, 2, 2, 1, 1, 1, 0, 0, 2 };
+    std::vector<std::array<double, 3>> probs = {
+        { 1.0, 0.0, 0.0 }, // P(class 0)=1, P(class 1)=0, P(class 2)=0
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 0.0, 0.0, 1.0 }, // P(class 0)=0, P(class 1)=0, P(class 2)=1
+        { 0.0, 0.0, 1.0 }, // P(class 0)=0, P(class 1)=0, P(class 2)=1
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 1.0, 0.0, 0.0 }, // P(class 0)=1, P(class 1)=0, P(class 2)=0
+        { 1.0, 0.0, 0.0 }, // P(class 0)=1, P(class 1)=0, P(class 2)=0
+        { 0.0, 0.0, 1.0 } // P(class 0)=0, P(class 1)=0, P(class 2)=1
+    };
+    torch::Tensor y_pred = torch::from_blob(probs.data(), { (long)probs.size(), 3 }, torch::kFloat64).clone();
     auto y_test_tensor = torch::tensor(y_test, torch::kInt32);
-    auto y_pred_tensor = torch::tensor(y_pred, torch::kInt32);
     std::vector<std::string> labels = { "Aeroplane", "Boat", "Car" };
-    platform::Scores scores(y_test_tensor, y_pred_tensor, 3, labels);
+    platform::Scores scores(y_test_tensor, y_pred, 3, labels);
     auto res_json_int = scores.get_confusion_matrix_json();
     REQUIRE(res_json_int[0][0] == 2);
     REQUIRE(res_json_int[0][1] == 1);
@@ -131,11 +168,22 @@ TEST_CASE("Confusion Matrix JSON", "[Scores]")
 TEST_CASE("Classification Report", "[Scores]")
 {
     std::vector<int> y_test = { 0, 2, 2, 2, 2, 0, 1, 2, 0, 2 };
-    std::vector<int> y_pred = { 0, 1, 2, 2, 1, 1, 1, 0, 0, 2 };
+    std::vector<std::array<double, 3>> probs = {
+        { 1.0, 0.0, 0.0 }, // P(class 0)=1, P(class 1)=0, P(class 2)=0
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 0.0, 0.0, 1.0 }, // P(class 0)=0, P(class 1)=0, P(class 2)=1
+        { 0.0, 0.0, 1.0 }, // P(class 0)=0, P(class 1)=0, P(class 2)=1
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 1.0, 0.0, 0.0 }, // P(class 0)=1, P(class 1)=0, P(class 2)=0
+        { 1.0, 0.0, 0.0 }, // P(class 0)=1, P(class 1)=0, P(class 2)=0
+        { 0.0, 0.0, 1.0 } // P(class 0)=0, P(class 1)=0, P(class 2)=1
+    };
+    torch::Tensor y_pred = torch::from_blob(probs.data(), { (long)probs.size(), 3 }, torch::kFloat64).clone();
     auto y_test_tensor = torch::tensor(y_test, torch::kInt32);
-    auto y_pred_tensor = torch::tensor(y_pred, torch::kInt32);
     std::vector<std::string> labels = { "Aeroplane", "Boat", "Car" };
-    platform::Scores scores(y_test_tensor, y_pred_tensor, 3, labels);
+    platform::Scores scores(y_test_tensor, y_pred, 3, labels);
     auto report = scores.classification_report(Colors::BLUE(), "train");
     auto json_matrix = scores.get_confusion_matrix_json(true);
     platform::Scores scores2(json_matrix);
@@ -144,11 +192,22 @@ TEST_CASE("Classification Report", "[Scores]")
 TEST_CASE("JSON constructor", "[Scores]")
 {
     std::vector<int> y_test = { 0, 2, 2, 2, 2, 0, 1, 2, 0, 2 };
-    std::vector<int> y_pred = { 0, 1, 2, 2, 1, 1, 1, 0, 0, 2 };
+    std::vector<std::array<double, 3>> probs = {
+        { 1.0, 0.0, 0.0 }, // P(class 0)=1, P(class 1)=0, P(class 2)=0
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 0.0, 0.0, 1.0 }, // P(class 0)=0, P(class 1)=0, P(class 2)=1
+        { 0.0, 0.0, 1.0 }, // P(class 0)=0, P(class 1)=0, P(class 2)=1
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 1.0, 0.0, 0.0 }, // P(class 0)=1, P(class 1)=0, P(class 2)=0
+        { 1.0, 0.0, 0.0 }, // P(class 0)=1, P(class 1)=0, P(class 2)=0
+        { 0.0, 0.0, 1.0 } // P(class 0)=0, P(class 1)=0, P(class 2)=1
+    };
+    torch::Tensor y_pred = torch::from_blob(probs.data(), { (long)probs.size(), 3 }, torch::kFloat64).clone();
     auto y_test_tensor = torch::tensor(y_test, torch::kInt32);
-    auto y_pred_tensor = torch::tensor(y_pred, torch::kInt32);
     std::vector<std::string> labels = { "Car", "Boat", "Aeroplane" };
-    platform::Scores scores(y_test_tensor, y_pred_tensor, 3, labels);
+    platform::Scores scores(y_test_tensor, y_pred, 3, labels);
     auto res_json_int = scores.get_confusion_matrix_json();
     platform::Scores scores2(res_json_int);
     REQUIRE(scores.accuracy() == scores2.accuracy());
@@ -173,17 +232,14 @@ TEST_CASE("JSON constructor", "[Scores]")
 TEST_CASE("Aggregate", "[Scores]")
 {
     std::vector<int> y_test;
-    std::vector<int> y_pred;
+    torch::Tensor y_pred;
     make_test_bin(197, 210, 52, 41, y_test, y_pred);
     auto y_test_tensor = torch::tensor(y_test, torch::kInt32);
-    auto y_pred_tensor = torch::tensor(y_pred, torch::kInt32);
-    platform::Scores scores(y_test_tensor, y_pred_tensor, 2);
+    platform::Scores scores(y_test_tensor, y_pred, 2);
     y_test.clear();
-    y_pred.clear();
     make_test_bin(227, 187, 39, 47, y_test, y_pred);
     auto y_test_tensor2 = torch::tensor(y_test, torch::kInt32);
-    auto y_pred_tensor2 = torch::tensor(y_pred, torch::kInt32);
-    platform::Scores scores2(y_test_tensor2, y_pred_tensor2, 2);
+    platform::Scores scores2(y_test_tensor2, y_pred, 2);
     scores.aggregate(scores2);
     REQUIRE(scores.accuracy() == Catch::Approx(0.821).epsilon(epsilon));
     REQUIRE(scores.f1_score(0) == Catch::Approx(0.8160329));
@@ -195,11 +251,9 @@ TEST_CASE("Aggregate", "[Scores]")
     REQUIRE(scores.f1_weighted() == Catch::Approx(0.8209856));
     REQUIRE(scores.f1_macro() == Catch::Approx(0.8208694));
     y_test.clear();
-    y_pred.clear();
     make_test_bin(197 + 227, 210 + 187, 52 + 39, 41 + 47, y_test, y_pred);
     y_test_tensor = torch::tensor(y_test, torch::kInt32);
-    y_pred_tensor = torch::tensor(y_pred, torch::kInt32);
-    platform::Scores scores3(y_test_tensor, y_pred_tensor, 2);
+    platform::Scores scores3(y_test_tensor, y_pred, 2);
     for (int i = 0; i < 2; ++i) {
         REQUIRE(scores3.f1_score(i) == scores.f1_score(i));
         REQUIRE(scores3.precision(i) == scores.precision(i));
@@ -212,11 +266,22 @@ TEST_CASE("Aggregate", "[Scores]")
 TEST_CASE("Order of keys", "[Scores]")
 {
     std::vector<int> y_test = { 0, 2, 2, 2, 2, 0, 1, 2, 0, 2 };
-    std::vector<int> y_pred = { 0, 1, 2, 2, 1, 1, 1, 0, 0, 2 };
+    std::vector<std::array<double, 3>> probs = {
+        { 1.0, 0.0, 0.0 }, // P(class 0)=1, P(class 1)=0, P(class 2)=0
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 0.0, 0.0, 1.0 }, // P(class 0)=0, P(class 1)=0, P(class 2)=1
+        { 0.0, 0.0, 1.0 }, // P(class 0)=0, P(class 1)=0, P(class 2)=1
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 0.0, 1.0, 0.0 }, // P(class 0)=0, P(class 1)=1, P(class 2)=0
+        { 1.0, 0.0, 0.0 }, // P(class 0)=1, P(class 1)=0, P(class 2)=0
+        { 1.0, 0.0, 0.0 }, // P(class 0)=1, P(class 1)=0, P(class 2)=0
+        { 0.0, 0.0, 1.0 } // P(class 0)=0, P(class 1)=0, P(class 2)=1
+    };
+    torch::Tensor y_pred = torch::from_blob(probs.data(), { (long)probs.size(), 3 }, torch::kFloat64).clone();
     auto y_test_tensor = torch::tensor(y_test, torch::kInt32);
-    auto y_pred_tensor = torch::tensor(y_pred, torch::kInt32);
     std::vector<std::string> labels = { "Car", "Boat", "Aeroplane" };
-    platform::Scores scores(y_test_tensor, y_pred_tensor, 3, labels);
+    platform::Scores scores(y_test_tensor, y_pred, 3, labels);
     auto res_json_int = scores.get_confusion_matrix_json(true);
     // Make a temp file and store the json
     std::string filename = "temp.json";

tests/TestUtils.h

@@ -5,7 +5,7 @@
 #include <vector>
 #include <map>
 #include <tuple>
-#include <ArffFiles.hpp>
+#include <ArffFiles/ArffFiles.hpp>
 #include <fimdlp/CPPFImdlp.h>
 
 bool file_exists(const std::string& name);

vcpkg.json

@@ -7,6 +7,7 @@
       "fimdlp",
       "libtorch-bin",
       "folding",
+      "catch2",
       "argparse"
     ],
     "overrides": [
@@ -30,9 +31,13 @@
         "name": "argpase",
         "version": "3.2"
       },
+      {
+        "name": "catch2",
+        "version": "3.8.1"
+      },
       {
         "name": "nlohmann-json",
         "version": "3.11.3"
       }
     ]
-  }
+  }