Fix predict_proba in AdaBoost

src/experimental_clfs/AdaBoost.cpp

@@ -74,32 +74,53 @@ namespace bayesnet {
                 break;  // Stop boosting
             }
 
+            // Check for perfect classification BEFORE calculating alpha
+            if (weighted_error <= 1e-10) {
+                if (debug) std::cout << "  Perfect classification achieved (error=" << weighted_error << ")" << std::endl;
+
+                // For perfect classification, use a large but finite alpha
+                double alpha = 10.0 + std::log(static_cast<double>(n_classes - 1));
+
+                // Store the estimator and its weight
+                models.push_back(std::move(estimator));
+                alphas.push_back(alpha);
+
+                if (debug) {
+                    std::cout << "Iteration " << iter << ":" << std::endl;
+                    std::cout << "  Weighted error: " << weighted_error << std::endl;
+                    std::cout << "  Alpha (finite): " << alpha << std::endl;
+                    std::cout << "  Random guess error: " << random_guess_error << std::endl;
+                }
+
+                break;  // Stop training as we have a perfect classifier
+            }
+
             // Calculate alpha (estimator weight) using SAMME formula
             // alpha = log((1 - err) / err) + log(K - 1)
-            double alpha = std::log((1.0 - weighted_error) / weighted_error) +
+            // Clamp weighted_error to avoid division by zero and infinite alpha
+            double clamped_error = std::max(1e-15, std::min(1.0 - 1e-15, weighted_error));
+            double alpha = std::log((1.0 - clamped_error) / clamped_error) +
                 std::log(static_cast<double>(n_classes - 1));
 
+            // Clamp alpha to reasonable bounds to avoid numerical issues
+            alpha = std::max(-10.0, std::min(10.0, alpha));
+
             // Store the estimator and its weight
             models.push_back(std::move(estimator));
             alphas.push_back(alpha);
 
-            // Update sample weights
+            // Update sample weights (only if this is not the last iteration)
-            updateSampleWeights(models.back().get(), alpha);
+            if (iter < n_estimators - 1) {
-
+                updateSampleWeights(models.back().get(), alpha);
-            // Normalize weights
+                normalizeWeights();
-            normalizeWeights();
+            }
 
             if (debug) {
                 std::cout << "Iteration " << iter << ":" << std::endl;
                 std::cout << "  Weighted error: " << weighted_error << std::endl;
                 std::cout << "  Alpha: " << alpha << std::endl;
                 std::cout << "  Random guess error: " << random_guess_error << std::endl;
-            }
+                std::cout << "  Random guess error: " << random_guess_error << std::endl;
-
-            // Check for perfect classification
-            if (weighted_error < 1e-10) {
-                if (debug) std::cout << "  Perfect classification achieved, stopping" << std::endl;
-                break;
             }
         }
 

tests/TestAdaBoost.cpp

@@ -184,10 +184,9 @@ TEST_CASE("AdaBoost Basic Functionality", "[AdaBoost]")
             }
 
             // Check that predict_proba matches the expected predict value
-            // REQUIRE(pred == (p[0] > p[1] ? 0 : 1));
+            REQUIRE(pred == (p[0] > p[1] ? 0 : 1));
         }
         double accuracy = static_cast<double>(correct) / n_samples;
-        std::cout << "Probability accuracy: " << accuracy << std::endl;
         REQUIRE(accuracy > 0.99);  // Should achieve good accuracy on this simple dataset
     }
 }
@@ -711,6 +710,8 @@ TEST_CASE("AdaBoost SAMME Algorithm Validation", "[AdaBoost]")
         for (size_t i = 0; i < predictions.size(); i++) {
             int pred = predictions[i];
             auto probs = probabilities[i];
+            INFO("Sample " << i << ": predicted=" << pred
+                << ", probabilities=[" << probs[0] << ", " << probs[1] << "]");
 
             REQUIRE(pred == (probs[0] > probs[1] ? 0 : 1));
             REQUIRE(probs[0] + probs[1] == Catch::Approx(1.0).epsilon(1e-6));