BayesNet/CFS.cc.gcov.html at ae469b8146c2e68b1c1e06acc7d267e255389c45

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move test libraries to test folder

2024-04-30 00:52:09 +02:00

13 KiB

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LCOV - code coverage report

Current view:	top level - bayesnet/feature_selection - CFS.cc (source / functions)		Coverage	Total	Hit
Test:	coverage.info	Lines:	97.8 %	45	44
Test Date:	2024-04-29 20:48:03	Functions:	100.0 %	2	2

            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              : #include <limits>
       8              : #include "bayesnet/utils/bayesnetUtils.h"
       9              : #include "CFS.h"
      10              : namespace bayesnet {
      11           70 :     void CFS::fit()
      12              :     {
      13           70 :         initialize();
      14           70 :         computeSuLabels();
      15           70 :         auto featureOrder = argsort(suLabels); // sort descending order
      16           70 :         auto continueCondition = true;
      17           70 :         auto feature = featureOrder[0];
      18           70 :         selectedFeatures.push_back(feature);
      19           70 :         selectedScores.push_back(suLabels[feature]);
      20           70 :         featureOrder.erase(featureOrder.begin());
      21          398 :         while (continueCondition) {
      22          328 :             double merit = std::numeric_limits<double>::lowest();
      23          328 :             int bestFeature = -1;
      24         1929 :             for (auto feature : featureOrder) {
      25         1601 :                 selectedFeatures.push_back(feature);
      26              :                 // Compute merit with selectedFeatures
      27         1601 :                 auto meritNew = computeMeritCFS();
      28         1601 :                 if (meritNew > merit) {
      29          663 :                     merit = meritNew;
      30          663 :                     bestFeature = feature;
      31              :                 }
      32         1601 :                 selectedFeatures.pop_back();
      33              :             }
      34          328 :             if (bestFeature == -1) {
      35              :                 // meritNew has to be nan due to constant features
      36            0 :                 break;
      37              :             }
      38          328 :             selectedFeatures.push_back(bestFeature);
      39          328 :             selectedScores.push_back(merit);
      40          328 :             featureOrder.erase(remove(featureOrder.begin(), featureOrder.end(), bestFeature), featureOrder.end());
      41          328 :             continueCondition = computeContinueCondition(featureOrder);
      42              :         }
      43           70 :         fitted = true;
      44           70 :     }
      45          328 :     bool CFS::computeContinueCondition(const std::vector<int>& featureOrder)
      46              :     {
      47          328 :         if (selectedFeatures.size() == maxFeatures || featureOrder.size() == 0) {
      48           11 :             return false;
      49              :         }
      50          317 :         if (selectedScores.size() >= 5) {
      51              :             /*
      52              :             "To prevent the best first search from exploring the entire
      53              :             feature subset search space, a stopping criterion is imposed.
      54              :             The search will terminate if five consecutive fully expanded
      55              :             subsets show no improvement over the current best subset."
      56              :             as stated in Mark A.Hall Thesis
      57              :             */
      58          118 :             double item_ant = std::numeric_limits<double>::lowest();
      59          118 :             int num = 0;
      60          118 :             std::vector<double> lastFive(selectedScores.end() - 5, selectedScores.end());
      61          472 :             for (auto item : lastFive) {
      62          413 :                 if (item_ant == std::numeric_limits<double>::lowest()) {
      63          118 :                     item_ant = item;
      64              :                 }
      65          413 :                 if (item > item_ant) {
      66           59 :                     break;
      67              :                 } else {
      68          354 :                     num++;
      69          354 :                     item_ant = item;
      70              :                 }
      71              :             }
      72          118 :             if (num == 5) {
      73           59 :                 return false;
      74              :             }
      75          118 :         }
      76          258 :         return true;
      77              :     }
      78              : }

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13 KiB Raw Blame History

13 KiB

Raw Blame History