119 lines
5.2 KiB
C++
119 lines
5.2 KiB
C++
#include <catch2/catch_test_macros.hpp>
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#include <catch2/catch_approx.hpp>
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#include <catch2/generators/catch_generators.hpp>
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#include "bayesnet/utils/BayesMetrics.h"
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#include "bayesnet/feature_selection/CFS.h"
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#include "bayesnet/feature_selection/FCBF.h"
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#include "bayesnet/feature_selection/IWSS.h"
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#include "TestUtils.h"
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bayesnet::FeatureSelect* build_selector(RawDatasets& raw, std::string selector, double threshold)
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{
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if (selector == "CFS") {
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return new bayesnet::CFS(raw.dataset, raw.featuresv, raw.classNamev, raw.featuresv.size(), raw.classNumStates, raw.weights);
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} else if (selector == "FCBF") {
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return new bayesnet::FCBF(raw.dataset, raw.featuresv, raw.classNamev, raw.featuresv.size(), raw.classNumStates, raw.weights, threshold);
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} else if (selector == "IWSS") {
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return new bayesnet::IWSS(raw.dataset, raw.featuresv, raw.classNamev, raw.featuresv.size(), raw.classNumStates, raw.weights, threshold);
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}
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return nullptr;
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}
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TEST_CASE("Features Selected", "[FeatureSelection]")
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{
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std::string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
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auto raw = RawDatasets(file_name, true);
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SECTION("Test features selected and size")
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{
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map<pair<std::string, std::string>, std::vector<int>> results = {
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{ {"glass", "CFS"}, { 2, 3, 6, 1, 8, 4 } },
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{ {"iris", "CFS"}, { 3, 2, 1, 0 } },
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{ {"ecoli", "CFS"}, { 5, 0, 4, 2, 1, 6 } },
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{ {"diabetes", "CFS"}, { 1, 5, 7, 6, 4, 2 } },
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{ {"glass", "IWSS" }, { 2, 3, 5, 7, 6 } },
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{ {"iris", "IWSS"}, { 3, 2, 0 } },
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{ {"ecoli", "IWSS"}, { 5, 6, 0, 1, 4 } },
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{ {"diabetes", "IWSS"}, { 1, 5, 4, 7, 3 } },
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{ {"glass", "FCBF" }, { 2, 3, 5, 7, 6 } },
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{ {"iris", "FCBF"}, { 3, 2 } },
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{ {"ecoli", "FCBF"}, { 5, 0, 1, 4, 2 } },
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{ {"diabetes", "FCBF"}, { 1, 5, 7, 6 } }
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};
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double threshold;
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std::string selector;
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std::vector<std::pair<std::string, double>> selectors = {
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{ "CFS", 0.0 },
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{ "IWSS", 0.5 },
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{ "FCBF", 1e-7 }
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};
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for (const auto item : selectors) {
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selector = item.first; threshold = item.second;
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bayesnet::FeatureSelect* featureSelector = build_selector(raw, selector, threshold);
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featureSelector->fit();
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std::vector<int> selected = featureSelector->getFeatures();
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INFO("file_name: " << file_name << ", selector: " << selector);
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REQUIRE(selected.size() == results.at({ file_name, selector }).size());
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REQUIRE(selected == results.at({ file_name, selector }));
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delete featureSelector;
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}
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}
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}
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// TEST_CASE("Feature Selection Test", "[BayesNet]")
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// {
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// std::string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
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// std::string selector = GENERATE("CFS", "FCBF", "IWSS");
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// map<std::string, pair<int, std::vector<int>>> resultsKBest = {
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// {"glass", {7, { 0, 1, 7, 6, 3, 5, 2 }}},
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// {"iris", {3, { 0, 3, 2 }} },
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// {"ecoli", {6, { 2, 4, 1, 0, 6, 5 }}},
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// {"diabetes", {2, { 7, 1 }}}
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// };
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// map<std::string, double> resultsMI = {
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// {"glass", 0.12805398},
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// {"iris", 0.3158139948},
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// {"ecoli", 0.0089431099},
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// {"diabetes", 0.0345470614}
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// };
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// map<pair<std::string, int>, std::vector<pair<int, int>>> resultsMST = {
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// { {"glass", 0}, { {0, 6}, {0, 5}, {0, 3}, {5, 1}, {5, 8}, {5, 4}, {6, 2}, {6, 7} } },
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// { {"glass", 1}, { {1, 5}, {5, 0}, {5, 8}, {5, 4}, {0, 6}, {0, 3}, {6, 2}, {6, 7} } },
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// { {"iris", 0}, { {0, 1}, {0, 2}, {1, 3} } },
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// { {"iris", 1}, { {1, 0}, {1, 3}, {0, 2} } },
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// { {"ecoli", 0}, { {0, 1}, {0, 2}, {1, 5}, {1, 3}, {5, 6}, {5, 4} } },
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// { {"ecoli", 1}, { {1, 0}, {1, 5}, {1, 3}, {5, 6}, {5, 4}, {0, 2} } },
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// { {"diabetes", 0}, { {0, 7}, {0, 2}, {0, 6}, {2, 3}, {3, 4}, {3, 5}, {4, 1} } },
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// { {"diabetes", 1}, { {1, 4}, {4, 3}, {3, 2}, {3, 5}, {2, 0}, {0, 7}, {0, 6} } }
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// };
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// auto raw = RawDatasets(file_name, true);
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// FeatureSelect* featureSelector = build_selector(raw, selector);
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// SECTION("Test Constructor")
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// {
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// REQUIRE(metrics.getScoresKBest().size() == 0);
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// }
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// SECTION("Test SelectKBestWeighted")
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// {
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// std::vector<int> kBest = metrics.SelectKBestWeighted(raw.weights, true, resultsKBest.at(file_name).first);
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// REQUIRE(kBest.size() == resultsKBest.at(file_name).first);
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// REQUIRE(kBest == resultsKBest.at(file_name).second);
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// }
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// SECTION("Test Mutual Information")
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// {
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// auto result = metrics.mutualInformation(raw.dataset.index({ 1, "..." }), raw.dataset.index({ 2, "..." }), raw.weights);
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// REQUIRE(result == Catch::Approx(resultsMI.at(file_name)).epsilon(raw.epsilon));
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// }
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// SECTION("Test Maximum Spanning Tree")
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// {
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// auto weights_matrix = metrics.conditionalEdge(raw.weights);
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// for (int i = 0; i < 2; ++i) {
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// auto result = metrics.maximumSpanningTree(raw.featurest, weights_matrix, i);
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// REQUIRE(result == resultsMST.at({ file_name, i }));
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// }
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// }
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// }
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