Line data    Source code

       1              : // ***************************************************************
       2              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
       3              : // SPDX-FileType: SOURCE
       4              : // SPDX-License-Identifier: MIT
       5              : // ***************************************************************
       6              : 
       7              : #ifndef BAYESNET_METRICS_H
       8              : #define BAYESNET_METRICS_H
       9              : #include <vector>
      10              : #include <string>
      11              : #include <torch/torch.h>
      12              : namespace bayesnet {
      13              :     class Metrics {
      14              :     public:
      15         4750 :         Metrics() = default;
      16              :         Metrics(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int classNumStates);
      17              :         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);
      18              :         std::vector<int> SelectKBestWeighted(const torch::Tensor& weights, bool ascending = false, unsigned k = 0);
      19              :         std::vector<double> getScoresKBest() const;
      20              :         double mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
      21              :         std::vector<float> conditionalEdgeWeights(std::vector<float>& weights); // To use in Python
      22              :         torch::Tensor conditionalEdge(const torch::Tensor& weights);
      23              :         std::vector<std::pair<int, int>> maximumSpanningTree(const std::vector<std::string>& features, const torch::Tensor& weights, const int root);
      24              :     protected:
      25              :         torch::Tensor samples; // n+1xm torch::Tensor used to fit the model where samples[-1] is the y std::vector
      26              :         std::string className;
      27              :         double entropy(const torch::Tensor& feature, const torch::Tensor& weights);
      28              :         std::vector<std::string> features;
      29              :         template <class T>
      30         2225 :         std::vector<std::pair<T, T>> doCombinations(const std::vector<T>& source)
      31              :         {
      32         2225 :             std::vector<std::pair<T, T>> result;
      33        11660 :             for (int i = 0; i < source.size(); ++i) {
      34         9435 :                 T temp = source[i];
      35        29517 :                 for (int j = i + 1; j < source.size(); ++j) {
      36        20082 :                     result.push_back({ temp, source[j] });
      37              :                 }
      38              :             }
      39         2225 :             return result;
      40            0 :         }
      41              :         template <class T>
      42          116 :         T pop_first(std::vector<T>& v)
      43              :         {
      44          116 :             T temp = v[0];
      45          116 :             v.erase(v.begin());
      46          116 :             return temp;
      47              :         }
      48              :     private:
      49              :         int classNumStates = 0;
      50              :         std::vector<double> scoresKBest;
      51              :         std::vector<int> featuresKBest; // sorted indices of the features
      52              :         double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
      53              :     };
      54              : }
      55              : #endif