commit inicial

bayesnet/utils/BayesMetrics.h

@@ -0,0 +1,62 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef BAYESNET_METRICS_H
+#define BAYESNET_METRICS_H
+#include <vector>
+#include <string>
+#include <torch/torch.h>
+namespace bayesnet {
+    class Metrics {
+    public:
+        Metrics() = default;
+        Metrics(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int classNumStates);
+        Metrics(const std::vector<std::vector<int>>& vsamples, const std::vector<int>& labels, const std::vector<std::string>& features, const std::string& className, const int classNumStates);
+        std::vector<int> SelectKBestWeighted(const torch::Tensor& weights, bool ascending = false, unsigned k = 0);
+        std::vector<std::pair<int, int>> SelectKPairs(const torch::Tensor& weights, std::vector<int>& featuresExcluded, bool ascending = false, unsigned k = 0);
+        std::vector<double> getScoresKBest() const;
+        std::vector<std::pair<std::pair<int, int>, double>> getScoresKPairs() const;
+        double mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
+        double conditionalMutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights);
+        torch::Tensor conditionalEdge(const torch::Tensor& weights);
+        std::vector<std::pair<int, int>> maximumSpanningTree(const std::vector<std::string>& features, const torch::Tensor& weights, const int root);
+        // Measured in nats (natural logarithm (log) base e)
+        // Elements of Information Theory, 2nd Edition, Thomas M. Cover, Joy A. Thomas p. 14
+        double entropy(const torch::Tensor& feature, const torch::Tensor& weights);
+        double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights);
+    protected:
+        torch::Tensor samples; // n+1xm torch::Tensor used to fit the model where samples[-1] is the y std::vector
+        std::string className;
+        std::vector<std::string> features;
+        template <class T>
+        std::vector<std::pair<T, T>> doCombinations(const std::vector<T>& source)
+        {
+            std::vector<std::pair<T, T>> result;
+            for (int i = 0; i < source.size() - 1; ++i) {
+                T temp = source[i];
+                for (int j = i + 1; j < source.size(); ++j) {
+                    result.push_back({ temp, source[j] });
+                }
+            }
+            return result;
+        }
+        template <class T>
+        T pop_first(std::vector<T>& v)
+        {
+            T temp = v[0];
+            v.erase(v.begin());
+            return temp;
+        }
+    private:
+        int classNumStates = 0;
+        std::vector<double> scoresKBest;
+        std::vector<int> featuresKBest; // sorted indices of the features
+        std::vector<std::pair<int, int>> pairsKBest; // sorted indices of the pairs
+        std::vector<std::pair<std::pair<int, int>, double>> scoresKPairs;
+        double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
+    };
+}
+#endif