Complete refactor of XA1DE & XBAODE with new ExpClf class

src/experimental_clfs/ExpClf.cpp

@@ -0,0 +1,171 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "ExpClf.h"
+#include "TensorUtils.hpp"
+
+namespace platform {
+    ExpClf::ExpClf() : semaphore_{ CountingSemaphore::getInstance() }
+    {
+    }
+    void ExpClf::setHyperparameters(const nlohmann::json& hyperparameters)
+    {
+        if (!hyperparameters.empty()) {
+            throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
+        }
+    }
+    //
+    // Predict
+    //
+    std::vector<int> ExpClf::predict_spode(std::vector<std::vector<int>>& test_data, int parent)
+    {
+        int test_size = test_data[0].size();
+        int sample_size = test_data.size();
+        auto predictions = std::vector<int>(test_size);
+
+        int chunk_size = std::min(150, int(test_size / semaphore_.getMaxCount()) + 1);
+        std::vector<std::thread> threads;
+        auto worker = [&](const std::vector<std::vector<int>>& samples, int begin, int chunk, int sample_size, std::vector<int>& predictions) {
+            std::string threadName = "(V)PWorker-" + std::to_string(begin) + "-" + std::to_string(chunk);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+            std::vector<int> instance(sample_size);
+            for (int sample = begin; sample < begin + chunk; ++sample) {
+                for (int feature = 0; feature < sample_size; ++feature) {
+                    instance[feature] = samples[feature][sample];
+                }
+                predictions[sample] = aode_.predict_spode(instance, parent);
+            }
+            semaphore_.release();
+            };
+        for (int begin = 0; begin < test_size; begin += chunk_size) {
+            int chunk = std::min(chunk_size, test_size - begin);
+            semaphore_.acquire();
+            threads.emplace_back(worker, test_data, begin, chunk, sample_size, std::ref(predictions));
+        }
+        for (auto& thread : threads) {
+            thread.join();
+        }
+        return predictions;
+    }
+    torch::Tensor ExpClf::predict(torch::Tensor& X)
+    {
+        auto X_ = TensorUtils::to_matrix(X);
+        torch::Tensor y = torch::tensor(predict(X_));
+        return y;
+    }
+    torch::Tensor ExpClf::predict_proba(torch::Tensor& X)
+    {
+        auto X_ = TensorUtils::to_matrix(X);
+        auto probabilities = predict_proba(X_);
+        auto n_samples = X.size(1);
+        int n_classes = probabilities[0].size();
+        auto y = torch::zeros({ n_samples, n_classes });
+        for (int i = 0; i < n_samples; i++) {
+            for (int j = 0; j < n_classes; j++) {
+                y[i][j] = probabilities[i][j];
+            }
+        }
+        return y;
+    }
+    float ExpClf::score(torch::Tensor& X, torch::Tensor& y)
+    {
+        auto X_ = TensorUtils::to_matrix(X);
+        auto y_ = TensorUtils::to_vector<int>(y);
+        return score(X_, y_);
+    }
+    std::vector<std::vector<double>> ExpClf::predict_proba(const std::vector<std::vector<int>>& test_data)
+    {
+        int test_size = test_data[0].size();
+        int sample_size = test_data.size();
+        auto probabilities = std::vector<std::vector<double>>(test_size, std::vector<double>(aode_.statesClass()));
+
+        int chunk_size = std::min(150, int(test_size / semaphore_.getMaxCount()) + 1);
+        std::vector<std::thread> threads;
+        auto worker = [&](const std::vector<std::vector<int>>& samples, int begin, int chunk, int sample_size, std::vector<std::vector<double>>& predictions) {
+            std::string threadName = "(V)PWorker-" + std::to_string(begin) + "-" + std::to_string(chunk);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+
+            std::vector<int> instance(sample_size);
+            for (int sample = begin; sample < begin + chunk; ++sample) {
+                for (int feature = 0; feature < sample_size; ++feature) {
+                    instance[feature] = samples[feature][sample];
+                }
+                predictions[sample] = aode_.predict_proba(instance);
+            }
+            semaphore_.release();
+            };
+        for (int begin = 0; begin < test_size; begin += chunk_size) {
+            int chunk = std::min(chunk_size, test_size - begin);
+            semaphore_.acquire();
+            threads.emplace_back(worker, test_data, begin, chunk, sample_size, std::ref(probabilities));
+        }
+        for (auto& thread : threads) {
+            thread.join();
+        }
+        return probabilities;
+    }
+    std::vector<int> ExpClf::predict(std::vector<std::vector<int>>& test_data)
+    {
+        if (!fitted) {
+            throw std::logic_error(CLASSIFIER_NOT_FITTED);
+        }
+        auto probabilities = predict_proba(test_data);
+        std::vector<int> predictions(probabilities.size(), 0);
+
+        for (size_t i = 0; i < probabilities.size(); i++) {
+            predictions[i] = std::distance(probabilities[i].begin(), std::max_element(probabilities[i].begin(), probabilities[i].end()));
+        }
+
+        return predictions;
+    }
+    float ExpClf::score(std::vector<std::vector<int>>& test_data, std::vector<int>& labels)
+    {
+        Timer timer;
+        timer.start();
+        std::vector<int> predictions = predict(test_data);
+        int correct = 0;
+
+        for (size_t i = 0; i < predictions.size(); i++) {
+            if (predictions[i] == labels[i]) {
+                correct++;
+            }
+        }
+        if (debug) {
+            std::cout << "* Time to predict: " << timer.getDurationString() << std::endl;
+        }
+        return static_cast<float>(correct) / predictions.size();
+    }
+
+    //
+    // statistics
+    //
+    int ExpClf::getNumberOfNodes() const
+    {
+        return aode_.getNumberOfNodes();
+    }
+    int ExpClf::getNumberOfEdges() const
+    {
+        return aode_.getNumberOfEdges();
+    }
+    int ExpClf::getNumberOfStates() const
+    {
+        return aode_.getNumberOfStates();
+    }
+    int ExpClf::getClassNumStates() const
+    {
+        return aode_.statesClass();
+    }
+
+
+}