547 lines
20 KiB
C++
547 lines
20 KiB
C++
// ***************************************************************
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// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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// SPDX-FileType: SOURCE
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// SPDX-License-Identifier: MIT
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// ***************************************************************
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#include <catch2/catch_test_macros.hpp>
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#include <catch2/catch_approx.hpp>
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#include <catch2/matchers/catch_matchers_string.hpp>
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#include <catch2/matchers/catch_matchers_vector.hpp>
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#include <torch/torch.h>
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#include <memory>
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#include <stdexcept>
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#include "experimental_clfs/AdaBoost.h"
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#include "experimental_clfs/DecisionTree.h"
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#include "common/TensorUtils.hpp"
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#include "TestUtils.h"
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using namespace bayesnet;
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using namespace Catch::Matchers;
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static const bool DEBUG = false;
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TEST_CASE("AdaBoost Construction", "[AdaBoost]")
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{
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SECTION("Default constructor")
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{
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REQUIRE_NOTHROW(AdaBoost());
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}
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SECTION("Constructor with parameters")
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{
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REQUIRE_NOTHROW(AdaBoost(100, 2));
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}
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SECTION("Constructor parameter access")
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{
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AdaBoost ada(75, 3);
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REQUIRE(ada.getNEstimators() == 75);
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REQUIRE(ada.getBaseMaxDepth() == 3);
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}
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}
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TEST_CASE("AdaBoost Hyperparameter Setting", "[AdaBoost]")
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{
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AdaBoost ada;
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SECTION("Set individual hyperparameters")
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{
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REQUIRE_NOTHROW(ada.setNEstimators(100));
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REQUIRE_NOTHROW(ada.setBaseMaxDepth(5));
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REQUIRE(ada.getNEstimators() == 100);
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REQUIRE(ada.getBaseMaxDepth() == 5);
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}
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SECTION("Set hyperparameters via JSON")
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{
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nlohmann::json params;
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params["n_estimators"] = 80;
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params["base_max_depth"] = 4;
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REQUIRE_NOTHROW(ada.setHyperparameters(params));
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}
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SECTION("Invalid hyperparameters should throw")
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{
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nlohmann::json params;
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// Negative n_estimators
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params["n_estimators"] = -1;
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REQUIRE_THROWS_AS(ada.setHyperparameters(params), std::invalid_argument);
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// Zero n_estimators
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params["n_estimators"] = 0;
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REQUIRE_THROWS_AS(ada.setHyperparameters(params), std::invalid_argument);
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// Negative base_max_depth
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params["n_estimators"] = 50;
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params["base_max_depth"] = -1;
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REQUIRE_THROWS_AS(ada.setHyperparameters(params), std::invalid_argument);
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// Zero base_max_depth
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params["base_max_depth"] = 0;
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REQUIRE_THROWS_AS(ada.setHyperparameters(params), std::invalid_argument);
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}
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}
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TEST_CASE("AdaBoost Basic Functionality", "[AdaBoost]")
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{
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// Create a simple dataset
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int n_samples = 20;
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int n_features = 2;
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std::vector<std::vector<int>> X(n_features, std::vector<int>(n_samples));
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std::vector<int> y(n_samples);
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// Simple pattern: class depends on first feature
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for (int i = 0; i < n_samples; i++) {
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X[0][i] = i < 10 ? 0 : 1;
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X[1][i] = i % 2;
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y[i] = X[0][i]; // Class equals first feature
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}
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std::vector<std::string> features = { "f1", "f2" };
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std::string className = "class";
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std::map<std::string, std::vector<int>> states;
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states["f1"] = { 0, 1 };
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states["f2"] = { 0, 1 };
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states["class"] = { 0, 1 };
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SECTION("Training with vector interface")
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{
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AdaBoost ada(10, 3); // 10 estimators, max_depth = 3
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REQUIRE_NOTHROW(ada.fit(X, y, features, className, states, Smoothing_t::NONE));
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// Check that we have the expected number of models
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auto weights = ada.getEstimatorWeights();
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REQUIRE(weights.size() <= 10); // Should be <= n_estimators
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REQUIRE(weights.size() > 0); // Should have at least one model
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// Check training errors
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auto errors = ada.getTrainingErrors();
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REQUIRE(errors.size() == weights.size());
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// All training errors should be less than 0.5 for this simple dataset
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for (double error : errors) {
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REQUIRE(error < 0.5);
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REQUIRE(error >= 0.0);
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}
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}
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SECTION("Prediction before fitting")
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{
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AdaBoost ada;
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REQUIRE_THROWS_WITH(ada.predict(X),
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ContainsSubstring("not been fitted"));
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REQUIRE_THROWS_WITH(ada.predict_proba(X),
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ContainsSubstring("not been fitted"));
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}
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SECTION("Prediction with vector interface")
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{
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AdaBoost ada(10, 3);
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ada.setDebug(DEBUG); // Enable debug to investigate
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ada.fit(X, y, features, className, states, Smoothing_t::NONE);
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auto predictions = ada.predict(X);
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REQUIRE(predictions.size() == static_cast<size_t>(n_samples));
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// Check accuracy
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int correct = 0;
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for (size_t i = 0; i < predictions.size(); i++) {
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if (predictions[i] == y[i]) correct++;
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}
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double accuracy = static_cast<double>(correct) / n_samples;
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REQUIRE(accuracy > 0.99); // Should achieve good accuracy on this simple dataset
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auto accuracy_computed = ada.score(X, y);
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REQUIRE(accuracy_computed == Catch::Approx(accuracy).epsilon(1e-6));
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}
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SECTION("Probability predictions with vector interface")
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{
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AdaBoost ada(10, 3);
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ada.setDebug(DEBUG); // ENABLE DEBUG HERE TOO
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ada.fit(X, y, features, className, states, Smoothing_t::NONE);
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auto proba = ada.predict_proba(X);
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REQUIRE(proba.size() == static_cast<size_t>(n_samples));
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REQUIRE(proba[0].size() == 2); // Two classes
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// Check probabilities sum to 1 and are valid
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auto predictions = ada.predict(X);
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int correct = 0;
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for (size_t i = 0; i < proba.size(); i++) {
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auto p = proba[i];
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auto pred = predictions[i];
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REQUIRE(p.size() == 2);
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REQUIRE(p[0] >= 0.0);
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REQUIRE(p[1] >= 0.0);
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double sum = p[0] + p[1];
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REQUIRE(sum == Catch::Approx(1.0).epsilon(1e-6));
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// compute the predicted class based on probabilities
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auto predicted_class = (p[0] > p[1]) ? 0 : 1;
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// compute accuracy based on predictions
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if (predicted_class == y[i]) {
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correct++;
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}
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INFO("Probability test - Sample " << i << ": pred=" << pred << ", probs=[" << p[0] << "," << p[1] << "], expected_from_probs=" << predicted_class);
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// Handle ties
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if (std::abs(p[0] - p[1]) < 1e-10) {
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INFO("Tie detected in probabilities");
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// Either prediction is valid in case of tie
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} else {
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// Check that predict_proba matches the expected predict value
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REQUIRE(pred == predicted_class);
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}
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}
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double accuracy = static_cast<double>(correct) / n_samples;
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REQUIRE(accuracy > 0.99); // Should achieve good accuracy on this simple dataset
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}
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}
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TEST_CASE("AdaBoost Tensor Interface", "[AdaBoost]")
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{
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auto raw = RawDatasets("iris", true);
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SECTION("Training with tensor format")
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{
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AdaBoost ada(20, 3);
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INFO("Dataset shape: " << raw.dataset.sizes());
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INFO("Features: " << raw.featurest.size());
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INFO("Samples: " << raw.nSamples);
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// AdaBoost expects dataset in format: features x samples, with labels as last row
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REQUIRE_NOTHROW(ada.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest, Smoothing_t::NONE));
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// Test prediction with tensor
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auto predictions = ada.predict(raw.Xt);
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REQUIRE(predictions.size(0) == raw.yt.size(0));
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// Calculate accuracy
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auto correct = torch::sum(predictions == raw.yt).item<int>();
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double accuracy = static_cast<double>(correct) / raw.yt.size(0);
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auto accuracy_computed = ada.score(raw.Xt, raw.yt);
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REQUIRE(accuracy_computed == Catch::Approx(accuracy).epsilon(1e-6));
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REQUIRE(accuracy > 0.97); // Should achieve good accuracy on Iris
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// Test probability predictions with tensor
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auto proba = ada.predict_proba(raw.Xt);
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REQUIRE(proba.size(0) == raw.yt.size(0));
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REQUIRE(proba.size(1) == 3); // Three classes in Iris
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// Check probabilities sum to 1
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auto prob_sums = torch::sum(proba, 1);
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for (int i = 0; i < prob_sums.size(0); i++) {
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REQUIRE(prob_sums[i].item<double>() == Catch::Approx(1.0).epsilon(1e-6));
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}
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}
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}
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TEST_CASE("AdaBoost SAMME Algorithm Validation", "[AdaBoost]")
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{
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auto raw = RawDatasets("iris", true);
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SECTION("Prediction consistency with probabilities")
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{
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AdaBoost ada(15, 3);
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ada.setDebug(DEBUG); // Enable debug for ALL instances
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ada.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest, Smoothing_t::NONE);
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auto predictions = ada.predict(raw.Xt);
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auto probabilities = ada.predict_proba(raw.Xt);
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REQUIRE(predictions.size(0) == probabilities.size(0));
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REQUIRE(probabilities.size(1) == 3); // Three classes in Iris
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// For each sample, predicted class should correspond to highest probability
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for (int i = 0; i < predictions.size(0); i++) {
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int predicted_class = predictions[i].item<int>();
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auto probs = probabilities[i];
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// Find class with highest probability
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auto max_prob_idx = torch::argmax(probs).item<int>();
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// Predicted class should match class with highest probability
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REQUIRE(predicted_class == max_prob_idx);
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// Probabilities should sum to 1
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double sum_probs = torch::sum(probs).item<double>();
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REQUIRE(sum_probs == Catch::Approx(1.0).epsilon(1e-6));
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// All probabilities should be non-negative
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for (int j = 0; j < 3; j++) {
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REQUIRE(probs[j].item<double>() >= 0.0);
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REQUIRE(probs[j].item<double>() <= 1.0);
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}
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}
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}
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SECTION("Weighted voting verification")
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{
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// Simple dataset where we can verify the weighted voting
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std::vector<std::vector<int>> X = { {0,0,1,1}, {0,1,0,1} };
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std::vector<int> y = { 0, 1, 1, 0 };
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std::vector<std::string> features = { "f1", "f2" };
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std::string className = "class";
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std::map<std::string, std::vector<int>> states;
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states["f1"] = { 0, 1 };
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states["f2"] = { 0, 1 };
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states["class"] = { 0, 1 };
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AdaBoost ada(5, 2);
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ada.setDebug(DEBUG); // Enable debug for detailed logging
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ada.fit(X, y, features, className, states, Smoothing_t::NONE);
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INFO("=== Final test verification ===");
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auto predictions = ada.predict(X);
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auto probabilities = ada.predict_proba(X);
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auto alphas = ada.getEstimatorWeights();
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INFO("Training info:");
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for (size_t i = 0; i < alphas.size(); i++) {
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INFO(" Model " << i << ": alpha=" << alphas[i]);
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}
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REQUIRE(predictions.size() == 4);
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REQUIRE(probabilities.size() == 4);
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REQUIRE(probabilities[0].size() == 2); // Two classes
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REQUIRE(alphas.size() > 0);
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// Verify that estimator weights are reasonable
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for (double alpha : alphas) {
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REQUIRE(alpha >= 0.0); // Alphas should be non-negative
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}
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// Verify prediction-probability consistency with detailed logging
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for (size_t i = 0; i < predictions.size(); i++) {
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int pred = predictions[i];
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auto probs = probabilities[i];
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INFO("Final check - Sample " << i << ": predicted=" << pred << ", probabilities=[" << probs[0] << "," << probs[1] << "]");
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// Handle the case where probabilities are exactly equal (tie)
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if (std::abs(probs[0] - probs[1]) < 1e-10) {
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INFO("Tie detected in probabilities - either prediction is valid");
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REQUIRE((pred == 0 || pred == 1));
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} else {
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// Normal case - prediction should match max probability
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int expected_pred = (probs[0] > probs[1]) ? 0 : 1;
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INFO("Expected prediction based on probs: " << expected_pred);
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REQUIRE(pred == expected_pred);
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}
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REQUIRE(probs[0] + probs[1] == Catch::Approx(1.0).epsilon(1e-6));
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}
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}
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SECTION("Empty models edge case")
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{
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AdaBoost ada(1, 1);
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ada.setDebug(DEBUG); // Enable debug for ALL instances
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// Try to predict before fitting
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std::vector<std::vector<int>> X = { {0}, {1} };
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REQUIRE_THROWS_WITH(ada.predict(X), ContainsSubstring("not been fitted"));
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REQUIRE_THROWS_WITH(ada.predict_proba(X), ContainsSubstring("not been fitted"));
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}
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}
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TEST_CASE("AdaBoost Debug - Simple Dataset Analysis", "[AdaBoost][debug]")
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{
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// Create the exact same simple dataset that was failing
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int n_samples = 20;
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int n_features = 2;
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std::vector<std::vector<int>> X(n_features, std::vector<int>(n_samples));
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std::vector<int> y(n_samples);
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// Simple pattern: class depends on first feature
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for (int i = 0; i < n_samples; i++) {
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X[0][i] = i < 10 ? 0 : 1;
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X[1][i] = i % 2;
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y[i] = X[0][i]; // Class equals first feature
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}
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std::vector<std::string> features = { "f1", "f2" };
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std::string className = "class";
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std::map<std::string, std::vector<int>> states;
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states["f1"] = { 0, 1 };
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states["f2"] = { 0, 1 };
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states["class"] = { 0, 1 };
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SECTION("Debug training process")
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{
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AdaBoost ada(5, 3); // Few estimators for debugging
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ada.setDebug(DEBUG);
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// This should work perfectly on this simple dataset
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REQUIRE_NOTHROW(ada.fit(X, y, features, className, states, Smoothing_t::NONE));
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// Get training details
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auto weights = ada.getEstimatorWeights();
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auto errors = ada.getTrainingErrors();
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INFO("Number of models trained: " << weights.size());
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INFO("Training errors: ");
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for (size_t i = 0; i < errors.size(); i++) {
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INFO(" Model " << i << ": error=" << errors[i] << ", weight=" << weights[i]);
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}
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// Should have at least one model
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REQUIRE(weights.size() > 0);
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REQUIRE(errors.size() == weights.size());
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// All training errors should be reasonable for this simple dataset
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for (double error : errors) {
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REQUIRE(error >= 0.0);
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REQUIRE(error < 0.5); // Should be better than random
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}
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// Test predictions
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auto predictions = ada.predict(X);
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REQUIRE(predictions.size() == static_cast<size_t>(n_samples));
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// Calculate accuracy
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int correct = 0;
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for (size_t i = 0; i < predictions.size(); i++) {
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if (predictions[i] == y[i]) correct++;
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INFO("Sample " << i << ": predicted=" << predictions[i] << ", actual=" << y[i]);
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}
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double accuracy = static_cast<double>(correct) / n_samples;
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INFO("Accuracy: " << accuracy);
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// Should achieve high accuracy on this perfectly separable dataset
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REQUIRE(accuracy >= 0.9); // Lower threshold for debugging
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// Test probability predictions
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auto proba = ada.predict_proba(X);
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REQUIRE(proba.size() == static_cast<size_t>(n_samples));
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// Verify probabilities are valid
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for (size_t i = 0; i < proba.size(); i++) {
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auto p = proba[i];
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REQUIRE(p.size() == 2);
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REQUIRE(p[0] >= 0.0);
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REQUIRE(p[1] >= 0.0);
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double sum = p[0] + p[1];
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REQUIRE(sum == Catch::Approx(1.0).epsilon(1e-6));
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// Predicted class should match highest probability
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int pred_class = predictions[i];
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// Handle ties
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if (std::abs(p[0] - p[1]) < 1e-10) {
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INFO("Tie detected - probabilities are equal");
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REQUIRE((pred_class == 0 || pred_class == 1));
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} else {
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REQUIRE(pred_class == (p[0] > p[1] ? 0 : 1));
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}
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}
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}
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SECTION("Compare with single DecisionTree")
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{
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// Test that AdaBoost performs at least as well as a single tree
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DecisionTree single_tree(3, 2, 1);
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single_tree.fit(X, y, features, className, states, Smoothing_t::NONE);
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auto tree_predictions = single_tree.predict(X);
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int tree_correct = 0;
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for (size_t i = 0; i < tree_predictions.size(); i++) {
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if (tree_predictions[i] == y[i]) tree_correct++;
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}
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double tree_accuracy = static_cast<double>(tree_correct) / n_samples;
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AdaBoost ada(5, 3);
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ada.setDebug(DEBUG);
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ada.fit(X, y, features, className, states, Smoothing_t::NONE);
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auto ada_predictions = ada.predict(X);
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int ada_correct = 0;
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for (size_t i = 0; i < ada_predictions.size(); i++) {
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if (ada_predictions[i] == y[i]) ada_correct++;
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}
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double ada_accuracy = static_cast<double>(ada_correct) / n_samples;
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INFO("DecisionTree accuracy: " << tree_accuracy);
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INFO("AdaBoost accuracy: " << ada_accuracy);
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// AdaBoost should perform at least as well as single tree
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// (allowing small tolerance for numerical differences)
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REQUIRE(ada_accuracy >= tree_accuracy - 0.1);
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}
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}
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TEST_CASE("AdaBoost Predict-Proba Consistency Fix", "[AdaBoost][consistency]")
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{
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// Simple binary classification dataset
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std::vector<std::vector<int>> X = { {0,0,1,1}, {0,1,0,1} };
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std::vector<int> y = { 0, 0, 1, 1 };
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std::vector<std::string> features = { "f1", "f2" };
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std::string className = "class";
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std::map<std::string, std::vector<int>> states;
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states["f1"] = { 0, 1 };
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states["f2"] = { 0, 1 };
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states["class"] = { 0, 1 };
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SECTION("Binary classification consistency")
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{
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AdaBoost ada(3, 2);
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ada.setDebug(DEBUG); // Enable debug output
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ada.fit(X, y, features, className, states, Smoothing_t::NONE);
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INFO("=== Debugging predict vs predict_proba consistency ===");
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// Get training info
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auto alphas = ada.getEstimatorWeights();
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auto errors = ada.getTrainingErrors();
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INFO("Training completed:");
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INFO(" Number of models: " << alphas.size());
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for (size_t i = 0; i < alphas.size(); i++) {
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INFO(" Model " << i << ": alpha=" << alphas[i] << ", error=" << errors[i]);
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}
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auto predictions = ada.predict(X);
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auto probabilities = ada.predict_proba(X);
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// Verify consistency for each sample
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for (size_t i = 0; i < predictions.size(); i++) {
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int predicted_class = predictions[i];
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auto probs = probabilities[i];
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INFO("Sample " << i << ":");
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INFO(" Features: [" << X[0][i] << ", " << X[1][i] << "]");
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INFO(" True class: " << y[i]);
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INFO(" Predicted class: " << predicted_class);
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INFO(" Probabilities: [" << probs[0] << ", " << probs[1] << "]");
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// The predicted class should be the one with highest probability
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int max_prob_class = (probs[0] > probs[1]) ? 0 : 1;
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INFO(" Max prob class: " << max_prob_class);
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// Handle tie case (when probabilities are equal)
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if (std::abs(probs[0] - probs[1]) < 1e-10) {
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INFO(" Tie detected - probabilities are equal");
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// In case of tie, either prediction is valid
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REQUIRE((predicted_class == 0 || predicted_class == 1));
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} else {
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REQUIRE(predicted_class == max_prob_class);
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}
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// Probabilities should sum to 1
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double sum_probs = probs[0] + probs[1];
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REQUIRE(sum_probs == Catch::Approx(1.0).epsilon(1e-6));
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// All probabilities should be valid
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REQUIRE(probs[0] >= 0.0);
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REQUIRE(probs[1] >= 0.0);
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REQUIRE(probs[0] <= 1.0);
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REQUIRE(probs[1] <= 1.0);
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}
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}
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} |