SVMClassifier/multiclass__strategy_8cpp_source.html at gh-pages

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SVM Classifier C++ 1.0.0
High-performance Support Vector Machine classifier with scikit-learn compatible API
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    1#include "svm_classifier/multiclass_strategy.hpp"
    2#include "svm.h"        // libsvm
    3#include "linear.h"     // liblinear
    4#include <algorithm>
    5#include <unordered_map>
    6#include <unordered_set>
    7#include <chrono>
    8#include <cmath>

   10namespace svm_classifier {

  // OneVsRestStrategy Implementation
  OneVsRestStrategy::OneVsRestStrategy()
      : library_type_(SVMLibrary::LIBLINEAR)
  {
  }

  OneVsRestStrategy::~OneVsRestStrategy()
  {
      cleanup_models();
  }

  TrainingMetrics OneVsRestStrategy::fit(const torch::Tensor& X,
      const torch::Tensor& y,
      const KernelParameters& params,
      DataConverter& converter)
  {
      cleanup_models();

      auto start_time = std::chrono::high_resolution_clock::now();

      // Store parameters and determine library type
      params_ = params;
      library_type_ = get_svm_library(params.get_kernel_type());

      // Extract unique classes
      auto y_cpu = y.to(torch::kCPU);
      auto unique_classes_tensor = torch::unique(y_cpu);
      classes_.clear();

      for (int i = 0; i < unique_classes_tensor.size(0); ++i) {
          classes_.push_back(unique_classes_tensor[i].item<int>());
      }

      std::sort(classes_.begin(), classes_.end());

      // Handle binary classification case
      if (classes_.size() <= 2) {
          // For binary classification, train a single classifier
          classes_.resize(2); // Ensure we have exactly 2 classes

          auto binary_y = y;
          if (classes_.size() == 1) {
              // Edge case: only one class, create dummy binary problem
              classes_.push_back(classes_[0] + 1);
              binary_y = torch::cat({ y, torch::full({1}, classes_[1], y.options()) });
              auto dummy_x = torch::zeros({ 1, X.size(1) }, X.options());
              auto extended_X = torch::cat({ X, dummy_x });

              double training_time = train_binary_classifier(extended_X, binary_y, params, converter, 0);
          } else {
              double training_time = train_binary_classifier(X, binary_y, params, converter, 0);
          }
      } else {
          // Multiclass case: train one classifier per class
          if (library_type_ == SVMLibrary::LIBSVM) {
              svm_models_.resize(classes_.size());
          } else {
              linear_models_.resize(classes_.size());
          }

          double total_training_time = 0.0;

          for (size_t i = 0; i < classes_.size(); ++i) {
              auto binary_y = create_binary_labels(y, classes_[i]);
              total_training_time += train_binary_classifier(X, binary_y, params, converter, i);
          }
      }

      auto end_time = std::chrono::high_resolution_clock::now();
      auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time);

      is_trained_ = true;

      TrainingMetrics metrics;
      metrics.training_time = duration.count() / 1000.0;
      metrics.status = TrainingStatus::SUCCESS;

      return metrics;
  }

  std::vector<int> OneVsRestStrategy::predict(const torch::Tensor& X, DataConverter& converter)
  {
      if (!is_trained_) {
          throw std::runtime_error("Model is not trained");
      }

      auto decision_values = decision_function(X, converter);
      std::vector<int> predictions;
      predictions.reserve(X.size(0));

      for (const auto& decision_row : decision_values) {
          // Find the class with maximum decision value
          auto max_it = std::max_element(decision_row.begin(), decision_row.end());
          int predicted_class_idx = std::distance(decision_row.begin(), max_it);
          predictions.push_back(classes_[predicted_class_idx]);
      }

      return predictions;
  }

  std::vector<std::vector<double>> OneVsRestStrategy::predict_proba(const torch::Tensor& X,
      DataConverter& converter)
  {
      if (!supports_probability()) {
          throw std::runtime_error("Probability prediction not supported for current configuration");
      }

      if (!is_trained_) {
          throw std::runtime_error("Model is not trained");
      }

      std::vector<std::vector<double>> probabilities;
      probabilities.reserve(X.size(0));

      for (int i = 0; i < X.size(0); ++i) {
          auto sample = X[i];
          std::vector<double> sample_probs;
          sample_probs.reserve(classes_.size());

          if (library_type_ == SVMLibrary::LIBSVM) {
              for (size_t j = 0; j < classes_.size(); ++j) {
                  if (svm_models_[j]) {
                      auto sample_node = converter.to_svm_node(sample);
                      double prob_estimates[2];
                      svm_predict_probability(svm_models_[j].get(), sample_node, prob_estimates);
                      sample_probs.push_back(prob_estimates[0]); // Probability of positive class
                  } else {
                      sample_probs.push_back(0.0);
                  }
              }
          } else {
              for (size_t j = 0; j < classes_.size(); ++j) {
                  if (linear_models_[j]) {
                      auto sample_node = converter.to_feature_node(sample);
                      double prob_estimates[2];
                      predict_probability(linear_models_[j].get(), sample_node, prob_estimates);
                      sample_probs.push_back(prob_estimates[0]); // Probability of positive class
                  } else {
                      sample_probs.push_back(0.0);
                  }
              }
          }

          // Normalize probabilities
          double sum = std::accumulate(sample_probs.begin(), sample_probs.end(), 0.0);
          if (sum > 0.0) {
              for (auto& prob : sample_probs) {
                  prob /= sum;
              }
          } else {
              // Uniform distribution if all probabilities are zero
              std::fill(sample_probs.begin(), sample_probs.end(), 1.0 / classes_.size());
          }

          probabilities.push_back(sample_probs);
      }

      return probabilities;
  }

  std::vector<std::vector<double>> OneVsRestStrategy::decision_function(const torch::Tensor& X,
      DataConverter& converter)
  {
      if (!is_trained_) {
          throw std::runtime_error("Model is not trained");
      }

      std::vector<std::vector<double>> decision_values;
      decision_values.reserve(X.size(0));

      for (int i = 0; i < X.size(0); ++i) {
          auto sample = X[i];
          std::vector<double> sample_decisions;
          sample_decisions.reserve(classes_.size());

          if (library_type_ == SVMLibrary::LIBSVM) {
              for (size_t j = 0; j < classes_.size(); ++j) {
                  if (svm_models_[j]) {
                      auto sample_node = converter.to_svm_node(sample);
                      double decision_value;
                      svm_predict_values(svm_models_[j].get(), sample_node, &decision_value);
                      sample_decisions.push_back(decision_value);
                  } else {
                      sample_decisions.push_back(0.0);
                  }
              }
          } else {
              for (size_t j = 0; j < classes_.size(); ++j) {
                  if (linear_models_[j]) {
                      auto sample_node = converter.to_feature_node(sample);
                      double decision_value;
                      predict_values(linear_models_[j].get(), sample_node, &decision_value);
                      sample_decisions.push_back(decision_value);
                  } else {
                      sample_decisions.push_back(0.0);
                  }
              }
          }

          decision_values.push_back(sample_decisions);
      }

      return decision_values;
  }

  bool OneVsRestStrategy::supports_probability() const
  {
      if (!is_trained_) {
          return params_.get_probability();
      }

      // Check if any model supports probability
      if (library_type_ == SVMLibrary::LIBSVM) {
          for (const auto& model : svm_models_) {
              if (model && svm_check_probability_model(model.get())) {
                  return true;
              }
          }
      } else {
          for (const auto& model : linear_models_) {
              if (model && check_probability_model(model.get())) {
                  return true;
              }
          }
      }

      return false;
  }

  torch::Tensor OneVsRestStrategy::create_binary_labels(const torch::Tensor& y, int positive_class)
  {
      auto binary_labels = torch::ones_like(y) * (-1); // Initialize with -1 (negative class)
      auto positive_mask = (y == positive_class);
      binary_labels.masked_fill_(positive_mask, 1); // Set positive class to +1

      return binary_labels;
  }

  double OneVsRestStrategy::train_binary_classifier(const torch::Tensor& X,
      const torch::Tensor& y_binary,
      const KernelParameters& params,
      DataConverter& converter,
      int class_idx)
  {
      auto start_time = std::chrono::high_resolution_clock::now();

      if (library_type_ == SVMLibrary::LIBSVM) {
          // Use libsvm
          auto problem = converter.to_svm_problem(X, y_binary);

          // Setup SVM parameters
          svm_parameter svm_params;
          svm_params.svm_type = C_SVC;

          switch (params.get_kernel_type()) {
              case KernelType::RBF:
                  svm_params.kernel_type = RBF;
                  break;
              case KernelType::POLYNOMIAL:
                  svm_params.kernel_type = POLY;
                  break;
              case KernelType::SIGMOID:
                  svm_params.kernel_type = SIGMOID;
                  break;
              default:
                  throw std::runtime_error("Invalid kernel type for libsvm");
          }

          svm_params.degree = params.get_degree();
          svm_params.gamma = (params.get_gamma() == -1.0) ? 1.0 / X.size(1) : params.get_gamma();
          svm_params.coef0 = params.get_coef0();
          svm_params.cache_size = params.get_cache_size();
          svm_params.eps = params.get_tolerance();
          svm_params.C = params.get_C();
          svm_params.nr_weight = 0;
          svm_params.weight_label = nullptr;
          svm_params.weight = nullptr;
          svm_params.nu = 0.5;
          svm_params.p = 0.1;
          svm_params.shrinking = 1;
          svm_params.probability = params.get_probability() ? 1 : 0;

          // Check parameters
          const char* error_msg = svm_check_parameter(problem.get(), &svm_params);
          if (error_msg) {
              throw std::runtime_error("SVM parameter error: " + std::string(error_msg));
          }

          // Train model
          auto model = svm_train(problem.get(), &svm_params);
          if (!model) {
              throw std::runtime_error("Failed to train SVM model");
          }

          svm_models_[class_idx] = std::unique_ptr<svm_model>(model);

      } else {
          // Use liblinear
          auto problem = converter.to_linear_problem(X, y_binary);

          // Setup linear parameters
          parameter linear_params;
          linear_params.solver_type = L2R_L2LOSS_SVC_DUAL; // Default solver for C-SVC
          linear_params.C = params.get_C();
          linear_params.eps = params.get_tolerance();
          linear_params.nr_weight = 0;
          linear_params.weight_label = nullptr;
          linear_params.weight = nullptr;
          linear_params.p = 0.1;
          linear_params.nu = 0.5;
          linear_params.init_sol = nullptr;
          linear_params.regularize_bias = 0;

          // Check parameters
          const char* error_msg = check_parameter(problem.get(), &linear_params);
          if (error_msg) {
              throw std::runtime_error("Linear parameter error: " + std::string(error_msg));
          }

          // Train model
          auto model = train(problem.get(), &linear_params);
          if (!model) {
              throw std::runtime_error("Failed to train linear model");
          }

          linear_models_[class_idx] = std::unique_ptr<::model>(model);
      }

      auto end_time = std::chrono::high_resolution_clock::now();
      auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time);

      return duration.count() / 1000.0;
  }

  void OneVsRestStrategy::cleanup_models()
  {
      for (auto& model : svm_models_) {
          if (model) {
              svm_free_and_destroy_model(&model);
          }
      }
      svm_models_.clear();

      for (auto& model : linear_models_) {
          if (model) {
              free_and_destroy_model(&model);
          }
      }
      linear_models_.clear();

      is_trained_ = false;
  }

  // OneVsOneStrategy Implementation
  OneVsOneStrategy::OneVsOneStrategy()
      : library_type_(SVMLibrary::LIBLINEAR)
  {
  }

  OneVsOneStrategy::~OneVsOneStrategy()
  {
      cleanup_models();
  }

  TrainingMetrics OneVsOneStrategy::fit(const torch::Tensor& X,
      const torch::Tensor& y,
      const KernelParameters& params,
      DataConverter& converter)
  {
      cleanup_models();

      auto start_time = std::chrono::high_resolution_clock::now();

      // Store parameters and determine library type
      params_ = params;
      library_type_ = get_svm_library(params.get_kernel_type());

      // Extract unique classes
      auto y_cpu = y.to(torch::kCPU);
      auto unique_classes_tensor = torch::unique(y_cpu);
      classes_.clear();

      for (int i = 0; i < unique_classes_tensor.size(0); ++i) {
          classes_.push_back(unique_classes_tensor[i].item<int>());
      }

      std::sort(classes_.begin(), classes_.end());

      // Generate all class pairs
      class_pairs_.clear();
      for (size_t i = 0; i < classes_.size(); ++i) {
          for (size_t j = i + 1; j < classes_.size(); ++j) {
              class_pairs_.emplace_back(classes_[i], classes_[j]);
          }
      }

      // Initialize model storage
      if (library_type_ == SVMLibrary::LIBSVM) {
          svm_models_.resize(class_pairs_.size());
      } else {
          linear_models_.resize(class_pairs_.size());
      }

      double total_training_time = 0.0;

      // Train one classifier for each class pair
      for (size_t i = 0; i < class_pairs_.size(); ++i) {
          auto [class1, class2] = class_pairs_[i];
          total_training_time += train_pairwise_classifier(X, y, class1, class2, params, converter, i);
      }

      auto end_time = std::chrono::high_resolution_clock::now();
      auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time);

      is_trained_ = true;

      TrainingMetrics metrics;
      metrics.training_time = duration.count() / 1000.0;
      metrics.status = TrainingStatus::SUCCESS;

      return metrics;
  }

  std::vector<int> OneVsOneStrategy::predict(const torch::Tensor& X, DataConverter& converter)
  {
      if (!is_trained_) {
          throw std::runtime_error("Model is not trained");
      }

      auto decision_values = decision_function(X, converter);
      return vote_predictions(decision_values);
  }

  std::vector<std::vector<double>> OneVsOneStrategy::predict_proba(const torch::Tensor& X,
      DataConverter& converter)
  {
      // OvO probability estimation is more complex and typically done via
      // pairwise coupling (Hastie & Tibshirani, 1998)
      // For simplicity, we'll use decision function values and normalize

      auto decision_values = decision_function(X, converter);
      std::vector<std::vector<double>> probabilities;
      probabilities.reserve(X.size(0));

      for (const auto& decision_row : decision_values) {
          std::vector<double> class_scores(classes_.size(), 0.0);

          // Aggregate decision values for each class
          for (size_t i = 0; i < class_pairs_.size(); ++i) {
              auto [class1, class2] = class_pairs_[i];
              double decision = decision_row[i];

              auto it1 = std::find(classes_.begin(), classes_.end(), class1);
              auto it2 = std::find(classes_.begin(), classes_.end(), class2);

              if (it1 != classes_.end() && it2 != classes_.end()) {
                  size_t idx1 = std::distance(classes_.begin(), it1);
                  size_t idx2 = std::distance(classes_.begin(), it2);

                  if (decision > 0) {
                      class_scores[idx1] += 1.0;
                  } else {
                      class_scores[idx2] += 1.0;
                  }
              }
          }

          // Convert scores to probabilities
          double sum = std::accumulate(class_scores.begin(), class_scores.end(), 0.0);
          if (sum > 0.0) {
              for (auto& score : class_scores) {
                  score /= sum;
              }
          } else {
              std::fill(class_scores.begin(), class_scores.end(), 1.0 / classes_.size());
          }

          probabilities.push_back(class_scores);
      }

      return probabilities;
  }

  std::vector<std::vector<double>> OneVsOneStrategy::decision_function(const torch::Tensor& X,
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