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Enhance predictProbaSample

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This commit is contained in:
Ricardo Montañana Gómez
2025-06-18 17:33:56 +02:00
parent 4e18dc87be
commit 41afa1b888
1 changed files with 51 additions and 22 deletions
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73
src/experimental_clfs/AdaBoost.cpp
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@@ -412,52 +412,81 @@ namespace bayesnet {
std::to_string(n) + " but got " + std::to_string(x.size(0)));
}
// Initialize class votes (same logic as predictSample)
// Initialize class votes with zeros
std::vector<double> class_votes(n_classes, 0.0);
double total_votes = 0.0;
// Accumulate weighted votes from all estimators (SAMME voting)
double total_alpha = 0.0;
if (debug) {
std::cout << "=== predictProbaSample Debug ===" << std::endl;
std::cout << "Number of models: " << models.size() << std::endl;
std::cout << "Number of classes: " << n_classes << std::endl;
}
// Accumulate votes from all estimators
for (size_t i = 0; i < models.size(); i++) {
if (alphas[i] <= 0) continue; // Skip estimators with zero or negative weight
double alpha = alphas[i];
// Skip invalid estimators
if (alpha <= 0 || !std::isfinite(alpha)) {
if (debug) std::cout << "Skipping model " << i << " (alpha=" << alpha << ")" << std::endl;
continue;
}
try {
// Get class prediction from this estimator (not probabilities!)
// Get class prediction from this estimator
int predicted_class = static_cast<DecisionTree*>(models[i].get())->predictSample(x);
// Add weighted vote for this class (SAMME algorithm)
if (debug) {
std::cout << "Model " << i << ": predicts class " << predicted_class
<< " with alpha " << alpha << std::endl;
}
// Add weighted vote for this class
if (predicted_class >= 0 && predicted_class < n_classes) {
class_votes[predicted_class] += alphas[i];
total_alpha += alphas[i];
class_votes[predicted_class] += alpha;
total_votes += alpha;
} else {
if (debug) std::cout << "Invalid class prediction: " << predicted_class << std::endl;
}
}
catch (const std::exception& e) {
std::cerr << "Error in estimator " << i << ": " << e.what() << std::endl;
if (debug) std::cout << "Error in model " << i << ": " << e.what() << std::endl;
continue;
}
}
if (debug) {
std::cout << "Total votes: " << total_votes << std::endl;
std::cout << "Class votes: [";
for (int j = 0; j < n_classes; j++) {
std::cout << class_votes[j];
if (j < n_classes - 1) std::cout << ", ";
}
std::cout << "]" << std::endl;
}
// Convert votes to probabilities
torch::Tensor class_probs = torch::zeros({ n_classes }, torch::kFloat);
if (total_alpha > 0) {
// Normalize votes to get probabilities
if (total_votes > 0) {
// Simple division to get probabilities
for (int j = 0; j < n_classes; j++) {
class_probs[j] = static_cast<float>(class_votes[j] / total_alpha);
class_probs[j] = static_cast<float>(class_votes[j] / total_votes);
}
} else {
// If no valid estimators, return uniform distribution
// If no valid votes, uniform distribution
if (debug) std::cout << "No valid votes, using uniform distribution" << std::endl;
class_probs.fill_(1.0f / n_classes);
}
// Ensure probabilities are valid (they should be already, but just in case)
class_probs = torch::clamp(class_probs, 0.0f, 1.0f);
// Verify they sum to 1 (they should, but normalize if needed due to floating point errors)
float sum_probs = torch::sum(class_probs).item<float>();
if (sum_probs > 1e-15f) {
class_probs = class_probs / sum_probs;
} else {
class_probs.fill_(1.0f / n_classes);
if (debug) {
std::cout << "Final probabilities: [";
for (int j = 0; j < n_classes; j++) {
std::cout << class_probs[j].item<float>();
if (j < n_classes - 1) std::cout << ", ";
}
std::cout << "]" << std::endl;
std::cout << "=== End predictProbaSample Debug ===" << std::endl;
}
return class_probs;