Begin AdaBoost integration

tests/CMakeLists.txt

@@ -12,9 +12,11 @@ if(ENABLE_TESTING)
         ${Bayesnet_INCLUDE_DIRS}
     )
     set(TEST_SOURCES_PLATFORM 
-        TestUtils.cpp TestPlatform.cpp TestResult.cpp TestScores.cpp TestDecisionTree.cpp
+        TestUtils.cpp TestPlatform.cpp TestResult.cpp TestScores.cpp TestDecisionTree.cpp TestAdaBoost.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/experimental_clfs/DecisionTree.cpp
+        ${Platform_SOURCE_DIR}/src/main/Scores.cpp 
+        ${Platform_SOURCE_DIR}/src/experimental_clfs/DecisionTree.cpp
+        ${Platform_SOURCE_DIR}/src/experimental_clfs/AdaBoost.cpp
     )
     add_executable(${TEST_PLATFORM} ${TEST_SOURCES_PLATFORM})
     target_link_libraries(${TEST_PLATFORM} PUBLIC "${TORCH_LIBRARIES}" fimdlp Catch2::Catch2WithMain bayesnet)

tests/TestAdaBoost.cpp

@@ -0,0 +1,707 @@
+// ***************************************************************
+// 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/AdaBoost.h"
+#include "experimental_clfs/DecisionTree.h"
+#include "TestUtils.h"
+
+using namespace bayesnet;
+using namespace Catch::Matchers;
+
+TEST_CASE("AdaBoost Construction", "[AdaBoost]")
+{
+    SECTION("Default constructor")
+    {
+        REQUIRE_NOTHROW(AdaBoost());
+    }
+
+    SECTION("Constructor with parameters")
+    {
+        REQUIRE_NOTHROW(AdaBoost(100, 2));
+    }
+
+    SECTION("Constructor parameter access")
+    {
+        AdaBoost ada(75, 3);
+        REQUIRE(ada.getNEstimators() == 75);
+        REQUIRE(ada.getBaseMaxDepth() == 3);
+    }
+}
+
+TEST_CASE("AdaBoost Hyperparameter Setting", "[AdaBoost]")
+{
+    AdaBoost ada;
+
+    SECTION("Set individual hyperparameters")
+    {
+        REQUIRE_NOTHROW(ada.setNEstimators(100));
+        REQUIRE_NOTHROW(ada.setBaseMaxDepth(5));
+
+        REQUIRE(ada.getNEstimators() == 100);
+        REQUIRE(ada.getBaseMaxDepth() == 5);
+    }
+
+    SECTION("Set hyperparameters via JSON")
+    {
+        nlohmann::json params;
+        params["n_estimators"] = 80;
+        params["base_max_depth"] = 4;
+
+        REQUIRE_NOTHROW(ada.setHyperparameters(params));
+    }
+
+    SECTION("Invalid hyperparameters should throw")
+    {
+        nlohmann::json params;
+
+        // Negative n_estimators
+        params["n_estimators"] = -1;
+        REQUIRE_THROWS_AS(ada.setHyperparameters(params), std::invalid_argument);
+
+        // Zero n_estimators
+        params["n_estimators"] = 0;
+        REQUIRE_THROWS_AS(ada.setHyperparameters(params), std::invalid_argument);
+
+        // Negative base_max_depth
+        params["n_estimators"] = 50;
+        params["base_max_depth"] = -1;
+        REQUIRE_THROWS_AS(ada.setHyperparameters(params), std::invalid_argument);
+
+        // Zero base_max_depth
+        params["base_max_depth"] = 0;
+        REQUIRE_THROWS_AS(ada.setHyperparameters(params), std::invalid_argument);
+    }
+}
+
+TEST_CASE("AdaBoost Basic Functionality", "[AdaBoost]")
+{
+    // 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")
+    {
+        AdaBoost ada(10, 3);  // 10 estimators, max_depth = 3
+        REQUIRE_NOTHROW(ada.fit(X, y, features, className, states, Smoothing_t::NONE));
+
+        // Check that we have the expected number of models
+        auto weights = ada.getEstimatorWeights();
+        REQUIRE(weights.size() <= 10);  // Should be <= n_estimators
+        REQUIRE(weights.size() > 0);    // Should have at least one model
+
+        // Check training errors
+        auto errors = ada.getTrainingErrors();
+        REQUIRE(errors.size() == weights.size());
+
+        // All training errors should be less than 0.5 for this simple dataset
+        for (double error : errors) {
+            REQUIRE(error < 0.5);
+            REQUIRE(error >= 0.0);
+        }
+    }
+
+    SECTION("Prediction before fitting")
+    {
+        AdaBoost ada;
+        REQUIRE_THROWS_WITH(ada.predict(X),
+            ContainsSubstring("not been fitted"));
+        REQUIRE_THROWS_WITH(ada.predict_proba(X),
+            ContainsSubstring("not been fitted"));
+    }
+
+    SECTION("Prediction with vector interface")
+    {
+        AdaBoost ada(10, 3);
+        ada.fit(X, y, features, className, states, Smoothing_t::NONE);
+
+        auto predictions = ada.predict(X);
+        REQUIRE(predictions.size() == static_cast<size_t>(n_samples));
+
+    }
+
+    SECTION("Probability predictions with vector interface")
+    {
+        AdaBoost ada(10, 3);
+        ada.fit(X, y, features, className, states, Smoothing_t::NONE);
+
+        auto proba = ada.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 are valid
+        auto predictions = ada.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];
+            REQUIRE(sum == Catch::Approx(1.0).epsilon(1e-6));
+
+            // Check that predict_proba matches the expected predict value
+            REQUIRE(pred == (p[0] > p[1] ? 0 : 1));
+        }
+    }
+}
+
+TEST_CASE("AdaBoost Tensor Interface", "[AdaBoost]")
+{
+    auto raw = RawDatasets("iris", true);
+
+    SECTION("Training with tensor format")
+    {
+        AdaBoost ada(20, 3);
+
+        INFO("Dataset shape: " << raw.dataset.sizes());
+        INFO("Features: " << raw.featurest.size());
+        INFO("Samples: " << raw.nSamples);
+
+        // AdaBoost expects dataset in format: features x samples, with labels as last row
+        REQUIRE_NOTHROW(ada.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest, Smoothing_t::NONE));
+
+        // Test prediction with tensor
+        auto predictions = ada.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 > 0.85);  // Should achieve good accuracy on Iris
+
+        // Test probability predictions with tensor
+        auto proba = ada.predict_proba(raw.Xt);
+        REQUIRE(proba.size(0) == raw.yt.size(0));
+        REQUIRE(proba.size(1) == 3);  // Three classes in Iris
+
+        // Check probabilities sum to 1
+        auto prob_sums = torch::sum(proba, 1);
+        for (int i = 0; i < prob_sums.size(0); i++) {
+            REQUIRE(prob_sums[i].item<double>() == Catch::Approx(1.0).epsilon(1e-6));
+        }
+    }
+}
+
+TEST_CASE("AdaBoost on Iris Dataset", "[AdaBoost][iris]")
+{
+    auto raw = RawDatasets("iris", true);
+
+    SECTION("Training with vector interface")
+    {
+        AdaBoost ada(30, 3);
+
+        REQUIRE_NOTHROW(ada.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv, Smoothing_t::NONE));
+
+        auto predictions = ada.predict(raw.Xv);
+        REQUIRE(predictions.size() == raw.yv.size());
+
+        // Calculate accuracy
+        int correct = 0;
+        for (size_t i = 0; i < predictions.size(); i++) {
+            if (predictions[i] == raw.yv[i]) correct++;
+        }
+        double accuracy = static_cast<double>(correct) / raw.yv.size();
+        REQUIRE(accuracy > 0.85);  // Should achieve good accuracy
+
+        // Test probability predictions
+        auto proba = ada.predict_proba(raw.Xv);
+        REQUIRE(proba.size() == raw.yv.size());
+        REQUIRE(proba[0].size() == 3);  // Three classes
+
+        // Verify estimator weights and errors
+        auto weights = ada.getEstimatorWeights();
+        auto errors = ada.getTrainingErrors();
+
+        REQUIRE(weights.size() == errors.size());
+        REQUIRE(weights.size() > 0);
+
+        // All weights should be positive (for non-zero error estimators)
+        for (double w : weights) {
+            REQUIRE(w >= 0.0);
+        }
+
+        // All errors should be less than 0.5 (better than random)
+        for (double e : errors) {
+            REQUIRE(e < 0.5);
+            REQUIRE(e >= 0.0);
+        }
+    }
+
+    SECTION("Different number of estimators")
+    {
+        std::vector<int> n_estimators = { 5, 15, 25 };
+
+        for (int n_est : n_estimators) {
+            AdaBoost ada(n_est, 2);
+            ada.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest, Smoothing_t::NONE);
+
+            auto predictions = ada.predict(raw.Xt);
+            REQUIRE(predictions.size(0) == raw.yt.size(0));
+
+            // Check that we don't exceed the specified number of estimators
+            auto weights = ada.getEstimatorWeights();
+            REQUIRE(static_cast<int>(weights.size()) <= n_est);
+        }
+    }
+
+    SECTION("Different base estimator depths")
+    {
+        std::vector<int> depths = { 1, 2, 4 };
+
+        for (int depth : depths) {
+            AdaBoost ada(15, depth);
+            ada.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest, Smoothing_t::NONE);
+
+            auto predictions = ada.predict(raw.Xt);
+            REQUIRE(predictions.size(0) == raw.yt.size(0));
+        }
+    }
+}
+
+TEST_CASE("AdaBoost Edge Cases", "[AdaBoost]")
+{
+    auto raw = RawDatasets("iris", true);
+
+    SECTION("Single estimator (depth 1 stump)")
+    {
+        AdaBoost ada(1, 1);  // Single decision stump
+        ada.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest, Smoothing_t::NONE);
+
+        auto predictions = ada.predict(raw.Xt);
+        REQUIRE(predictions.size(0) == raw.yt.size(0));
+
+        auto weights = ada.getEstimatorWeights();
+        REQUIRE(weights.size() == 1);
+    }
+
+    SECTION("Perfect classifier scenario")
+    {
+        // Create a perfectly separable dataset
+        std::vector<std::vector<int>> X = { {0,0,1,1}, {0,1,0,1} };
+        std::vector<int> y = { 0, 0, 1, 1 };
+        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 };
+
+        AdaBoost ada(10, 3);
+        ada.fit(X, y, features, className, states, Smoothing_t::NONE);
+
+        auto predictions = ada.predict(X);
+        REQUIRE(predictions.size() == 4);
+
+        // Should achieve perfect accuracy
+        int correct = 0;
+        for (size_t i = 0; i < predictions.size(); i++) {
+            if (predictions[i] == y[i]) correct++;
+        }
+        REQUIRE(correct == 4);
+
+        // Should stop early due to perfect classification
+        auto errors = ada.getTrainingErrors();
+        if (errors.size() > 0) {
+            REQUIRE(errors.back() < 1e-10);  // Very low error
+        }
+    }
+
+    SECTION("Small dataset")
+    {
+        // Very small dataset
+        std::vector<std::vector<int>> X = { {0,1}, {1,0} };
+        std::vector<int> y = { 0, 1 };
+        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 };
+
+        AdaBoost ada(5, 1);
+        REQUIRE_NOTHROW(ada.fit(X, y, features, className, states, Smoothing_t::NONE));
+
+        auto predictions = ada.predict(X);
+        REQUIRE(predictions.size() == 2);
+    }
+}
+
+TEST_CASE("AdaBoost Graph Visualization", "[AdaBoost]")
+{
+    // Simple dataset for visualization
+    std::vector<std::vector<int>> X = { {0,0,1,1}, {0,1,0,1} };
+    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")
+    {
+        AdaBoost ada(5, 2);
+        ada.fit(X, y, features, className, states, Smoothing_t::NONE);
+
+        auto graph_lines = ada.graph();
+
+        REQUIRE(graph_lines.size() > 2);
+        REQUIRE(graph_lines.front() == "digraph AdaBoost {");
+        REQUIRE(graph_lines.back() == "}");
+
+        // Should contain base estimator references
+        bool has_estimators = false;
+        for (const auto& line : graph_lines) {
+            if (line.find("Estimator") != std::string::npos) {
+                has_estimators = true;
+                break;
+            }
+        }
+        REQUIRE(has_estimators);
+
+        // Should contain alpha values
+        bool has_alpha = false;
+        for (const auto& line : graph_lines) {
+            if (line.find("α") != std::string::npos || line.find("alpha") != std::string::npos) {
+                has_alpha = true;
+                break;
+            }
+        }
+        REQUIRE(has_alpha);
+    }
+
+    SECTION("Graph with title")
+    {
+        AdaBoost ada(3, 1);
+        ada.fit(X, y, features, className, states, Smoothing_t::NONE);
+
+        auto graph_lines = ada.graph("XOR AdaBoost");
+
+        bool has_title = false;
+        for (const auto& line : graph_lines) {
+            if (line.find("label=\"XOR AdaBoost\"") != std::string::npos) {
+                has_title = true;
+                break;
+            }
+        }
+        REQUIRE(has_title);
+    }
+}
+
+TEST_CASE("AdaBoost with Weights", "[AdaBoost]")
+{
+    auto raw = RawDatasets("iris", true);
+
+    SECTION("Uniform weights")
+    {
+        AdaBoost ada(20, 3);
+        ada.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest, raw.weights, Smoothing_t::NONE);
+
+        auto predictions = ada.predict(raw.Xt);
+        REQUIRE(predictions.size(0) == raw.yt.size(0));
+
+        auto weights = ada.getEstimatorWeights();
+        REQUIRE(weights.size() > 0);
+    }
+
+    SECTION("Non-uniform weights")
+    {
+        auto weights = torch::ones({ raw.nSamples });
+        weights.index({ torch::indexing::Slice(0, 50) }) *= 3.0;  // Emphasize first class
+        weights = weights / weights.sum();
+
+        AdaBoost ada(15, 2);
+        ada.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest, weights, Smoothing_t::NONE);
+
+        auto predictions = ada.predict(raw.Xt);
+        REQUIRE(predictions.size(0) == raw.yt.size(0));
+
+        // Check that training completed successfully
+        auto estimator_weights = ada.getEstimatorWeights();
+        auto errors = ada.getTrainingErrors();
+
+        REQUIRE(estimator_weights.size() == errors.size());
+        REQUIRE(estimator_weights.size() > 0);
+    }
+}
+
+TEST_CASE("AdaBoost Input Dimension Validation", "[AdaBoost]")
+{
+    auto raw = RawDatasets("iris", true);
+
+    SECTION("Correct input dimensions")
+    {
+        AdaBoost ada(10, 2);
+        ada.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest, Smoothing_t::NONE);
+
+        // Test with correct tensor dimensions (features x samples)
+        REQUIRE_NOTHROW(ada.predict(raw.Xt));
+        REQUIRE_NOTHROW(ada.predict_proba(raw.Xt));
+
+        // Test with correct vector dimensions (features x samples)
+        REQUIRE_NOTHROW(ada.predict(raw.Xv));
+        REQUIRE_NOTHROW(ada.predict_proba(raw.Xv));
+    }
+
+    SECTION("Dimension consistency between interfaces")
+    {
+        AdaBoost ada(10, 2);
+        ada.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest, Smoothing_t::NONE);
+
+        // Get predictions from both interfaces
+        auto tensor_predictions = ada.predict(raw.Xt);
+        auto vector_predictions = ada.predict(raw.Xv);
+
+        // Should have same number of predictions
+        REQUIRE(tensor_predictions.size(0) == static_cast<int>(vector_predictions.size()));
+
+        // Test probability predictions
+        auto tensor_proba = ada.predict_proba(raw.Xt);
+        auto vector_proba = ada.predict_proba(raw.Xv);
+
+        REQUIRE(tensor_proba.size(0) == static_cast<int>(vector_proba.size()));
+        REQUIRE(tensor_proba.size(1) == static_cast<int>(vector_proba[0].size()));
+
+        // Verify predictions match between interfaces
+        for (int i = 0; i < tensor_predictions.size(0); i++) {
+            REQUIRE(tensor_predictions[i].item<int>() == vector_predictions[i]);
+
+            // Verify probabilities match between interfaces
+            for (int j = 0; j < tensor_proba.size(1); j++) {
+                REQUIRE(tensor_proba[i][j].item<double>() == Catch::Approx(vector_proba[i][j]).epsilon(1e-10));
+            }
+        }
+    }
+}
+
+TEST_CASE("AdaBoost Debug - Simple Dataset Analysis", "[AdaBoost][debug]")
+{
+    // Create the exact same simple dataset that was failing
+    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("Debug training process")
+    {
+        AdaBoost ada(5, 3);  // Few estimators for debugging
+
+        // This should work perfectly on this simple dataset
+        REQUIRE_NOTHROW(ada.fit(X, y, features, className, states, Smoothing_t::NONE));
+
+        // Get training details
+        auto weights = ada.getEstimatorWeights();
+        auto errors = ada.getTrainingErrors();
+
+        INFO("Number of models trained: " << weights.size());
+        INFO("Training errors: ");
+        for (size_t i = 0; i < errors.size(); i++) {
+            INFO("  Model " << i << ": error=" << errors[i] << ", weight=" << weights[i]);
+        }
+
+        // Should have at least one model
+        REQUIRE(weights.size() > 0);
+        REQUIRE(errors.size() == weights.size());
+
+        // All training errors should be reasonable for this simple dataset
+        for (double error : errors) {
+            REQUIRE(error >= 0.0);
+            REQUIRE(error < 0.5);  // Should be better than random
+        }
+
+        // Test predictions
+        auto predictions = ada.predict(X);
+        REQUIRE(predictions.size() == static_cast<size_t>(n_samples));
+
+        // Calculate accuracy
+        int correct = 0;
+        for (size_t i = 0; i < predictions.size(); i++) {
+            if (predictions[i] == y[i]) correct++;
+            INFO("Sample " << i << ": predicted=" << predictions[i] << ", actual=" << y[i]);
+        }
+        double accuracy = static_cast<double>(correct) / n_samples;
+        INFO("Accuracy: " << accuracy);
+
+        // Should achieve high accuracy on this perfectly separable dataset
+        REQUIRE(accuracy >= 0.9);  // Lower threshold for debugging
+
+        // Test probability predictions
+        auto proba = ada.predict_proba(X);
+        REQUIRE(proba.size() == static_cast<size_t>(n_samples));
+
+        // Verify probabilities are valid
+        for (size_t i = 0; i < proba.size(); i++) {
+            auto p = proba[i];
+            REQUIRE(p.size() == 2);
+            REQUIRE(p[0] >= 0.0);
+            REQUIRE(p[1] >= 0.0);
+            double sum = p[0] + p[1];
+            REQUIRE(sum == Catch::Approx(1.0).epsilon(1e-6));
+
+            // Predicted class should match highest probability
+            int pred_class = predictions[i];
+            REQUIRE(pred_class == (p[0] > p[1] ? 0 : 1));
+        }
+    }
+
+    SECTION("Compare with single DecisionTree")
+    {
+        // Test that AdaBoost performs at least as well as a single tree
+        DecisionTree single_tree(3, 2, 1);
+        single_tree.fit(X, y, features, className, states, Smoothing_t::NONE);
+        auto tree_predictions = single_tree.predict(X);
+
+        int tree_correct = 0;
+        for (size_t i = 0; i < tree_predictions.size(); i++) {
+            if (tree_predictions[i] == y[i]) tree_correct++;
+        }
+        double tree_accuracy = static_cast<double>(tree_correct) / n_samples;
+
+        AdaBoost ada(5, 3);
+        ada.fit(X, y, features, className, states, Smoothing_t::NONE);
+        auto ada_predictions = ada.predict(X);
+
+        int ada_correct = 0;
+        for (size_t i = 0; i < ada_predictions.size(); i++) {
+            if (ada_predictions[i] == y[i]) ada_correct++;
+        }
+        double ada_accuracy = static_cast<double>(ada_correct) / n_samples;
+
+        INFO("DecisionTree accuracy: " << tree_accuracy);
+        INFO("AdaBoost accuracy: " << ada_accuracy);
+
+        // AdaBoost should perform at least as well as single tree
+        // (allowing small tolerance for numerical differences)
+        REQUIRE(ada_accuracy >= tree_accuracy - 0.1);
+    }
+}
+
+TEST_CASE("AdaBoost SAMME Algorithm Validation", "[AdaBoost]")
+{
+    auto raw = RawDatasets("iris", true);
+
+    SECTION("Prediction consistency with probabilities")
+    {
+        AdaBoost ada(15, 3);
+        ada.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest, Smoothing_t::NONE);
+
+        auto predictions = ada.predict(raw.Xt);
+        auto probabilities = ada.predict_proba(raw.Xt);
+
+        REQUIRE(predictions.size(0) == probabilities.size(0));
+        REQUIRE(probabilities.size(1) == 3);  // Three classes in Iris
+
+        // For each sample, predicted class should correspond to highest probability
+        for (int i = 0; i < predictions.size(0); i++) {
+            int predicted_class = predictions[i].item<int>();
+            auto probs = probabilities[i];
+
+            // Find class with highest probability
+            auto max_prob_idx = torch::argmax(probs).item<int>();
+
+            // Predicted class should match class with highest probability
+            REQUIRE(predicted_class == max_prob_idx);
+
+            // Probabilities should sum to 1
+            double sum_probs = torch::sum(probs).item<double>();
+            REQUIRE(sum_probs == Catch::Approx(1.0).epsilon(1e-6));
+
+            // All probabilities should be non-negative
+            for (int j = 0; j < 3; j++) {
+                REQUIRE(probs[j].item<double>() >= 0.0);
+                REQUIRE(probs[j].item<double>() <= 1.0);
+            }
+        }
+    }
+
+    SECTION("Weighted voting verification")
+    {
+        // Simple dataset where we can verify the weighted voting
+        std::vector<std::vector<int>> X = { {0,0,1,1}, {0,1,0,1} };
+        std::vector<int> y = { 0, 1, 1, 0 };
+        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 };
+
+        AdaBoost ada(5, 2);
+        ada.fit(X, y, features, className, states, Smoothing_t::NONE);
+
+        auto predictions = ada.predict(X);
+        auto probabilities = ada.predict_proba(X);
+        auto alphas = ada.getEstimatorWeights();
+
+        REQUIRE(predictions.size() == 4);
+        REQUIRE(probabilities.size() == 4);
+        REQUIRE(probabilities[0].size() == 2);  // Two classes
+        REQUIRE(alphas.size() > 0);
+
+        // Verify that estimator weights are reasonable
+        for (double alpha : alphas) {
+            REQUIRE(alpha >= 0.0);  // Alphas should be non-negative
+        }
+
+        // Verify prediction-probability consistency
+        for (size_t i = 0; i < predictions.size(); i++) {
+            int pred = predictions[i];
+            auto probs = probabilities[i];
+
+            REQUIRE(pred == (probs[0] > probs[1] ? 0 : 1));
+            REQUIRE(probs[0] + probs[1] == Catch::Approx(1.0).epsilon(1e-6));
+        }
+    }
+
+    SECTION("Empty models edge case")
+    {
+        AdaBoost ada(1, 1);
+
+        // Try to predict before fitting
+        std::vector<std::vector<int>> X = { {0}, {1} };
+        REQUIRE_THROWS_WITH(ada.predict(X), ContainsSubstring("not been fitted"));
+        REQUIRE_THROWS_WITH(ada.predict_proba(X), ContainsSubstring("not been fitted"));
+    }
+}

tests/TestDecisionTree.cpp

@@ -39,6 +39,9 @@ TEST_CASE("DecisionTree Hyperparameter Setting", "[DecisionTree]")
         REQUIRE_NOTHROW(dt.setMaxDepth(10));
         REQUIRE_NOTHROW(dt.setMinSamplesSplit(5));
         REQUIRE_NOTHROW(dt.setMinSamplesLeaf(2));
+        REQUIRE(dt.getMaxDepth() == 10);
+        REQUIRE(dt.getMinSamplesSplit() == 5);
+        REQUIRE(dt.getMinSamplesLeaf() == 2);
     }
 
     SECTION("Set hyperparameters via JSON")
@@ -49,6 +52,9 @@ TEST_CASE("DecisionTree Hyperparameter Setting", "[DecisionTree]")
         params["min_samples_leaf"] = 2;
 
         REQUIRE_NOTHROW(dt.setHyperparameters(params));
+        REQUIRE(dt.getMaxDepth() == 7);
+        REQUIRE(dt.getMinSamplesSplit() == 4);
+        REQUIRE(dt.getMinSamplesLeaf() == 2);
     }
 
     SECTION("Invalid hyperparameters should throw")
@@ -164,7 +170,9 @@ TEST_CASE("DecisionTree on Iris Dataset", "[DecisionTree][iris]")
         // Calculate accuracy
         auto correct = torch::sum(predictions == raw.yt).item<int>();
         double accuracy = static_cast<double>(correct) / raw.yt.size(0);
+        double acurracy_computed = dt.score(raw.Xt, raw.yt);
         REQUIRE(accuracy > 0.97);  // Reasonable accuracy for Iris
+        REQUIRE(acurracy_computed == Catch::Approx(accuracy).epsilon(1e-6));
     }
 
     SECTION("Training with vector interface")