Fix CFS mistake
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e55365c41c
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46064262FD9A7ADE
3
.vscode/launch.json
vendored
3
.vscode/launch.json
vendored
@ -14,8 +14,9 @@
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"type": "lldb",
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"request": "launch",
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"name": "test",
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"program": "${workspaceFolder}/build_debug/tests/unit_tests_bayesnet",
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"program": "${workspaceFolder}/build_debug/tests/TestBayesNet",
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"args": [
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"[FeatureSelection]"
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//"-c=\"Metrics Test\"",
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// "-s",
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],
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@ -145,6 +145,10 @@ namespace bayesnet {
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}
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featureSelector->fit();
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auto cfsFeatures = featureSelector->getFeatures();
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auto scores = featureSelector->getScores();
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for (int i = 0; i < cfsFeatures.size(); ++i) {
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LOG_F(INFO, "Feature: %d Score: %f", cfsFeatures[i], scores[i]);
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}
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for (const int& feature : cfsFeatures) {
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featuresUsed.push_back(feature);
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std::unique_ptr<Classifier> model = std::make_unique<SPODE>(feature);
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@ -11,7 +11,7 @@ namespace bayesnet {
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auto feature = featureOrder[0];
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selectedFeatures.push_back(feature);
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selectedScores.push_back(suLabels[feature]);
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selectedFeatures.erase(selectedFeatures.begin());
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featureOrder.erase(featureOrder.begin());
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while (continueCondition) {
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double merit = std::numeric_limits<double>::lowest();
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int bestFeature = -1;
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@ -25,21 +25,21 @@ TEST_CASE("Features Selected", "[FeatureSelection]")
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auto raw = RawDatasets(file_name, true);
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SECTION("Test features selected and size")
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SECTION("Test features selected, scores and sizes")
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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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map<pair<std::string, std::string>, pair<std::vector<int>, std::vector<double>>> results = {
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{ {"glass", "CFS"}, { { 2, 3, 6, 1, 8, 4 }, {0.365513, 0.42895, 0.369809, 0.298294, 0.240952, 0.200915} } },
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{ {"iris", "CFS"}, { { 3, 2, 1, 0 }, {0.870521, 0.890375, 0.588155, 0.41843} } },
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{ {"ecoli", "CFS"}, { { 5, 0, 4, 2, 1, 6 }, {0.512319, 0.565381, 0.486025, 0.41087, 0.331423, 0.266251} } },
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{ {"diabetes", "CFS"}, { { 1, 5, 7, 6, 4, 2 }, {0.132858, 0.151209, 0.14244, 0.126591, 0.106028, 0.0825904} } },
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{ {"glass", "IWSS" }, { { 2, 3, 5, 7, 6 }, {0.365513, 0.42895, 0.359907, 0.273784, 0.223346} } },
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{ {"iris", "IWSS"}, { { 3, 2, 0 }, {0.870521, 0.890375, 0.585426} }},
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{ {"ecoli", "IWSS"}, { { 5, 6, 0, 1, 4 }, {0.512319, 0.550978, 0.475025, 0.382607, 0.308203} } },
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{ {"diabetes", "IWSS"}, { { 1, 5, 4, 7, 3 }, {0.132858, 0.151209, 0.136576, 0.122097, 0.0802232} } },
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{ {"glass", "FCBF" }, { { 2, 3, 5, 7, 6 }, {0.365513, 0.304911, 0.302109, 0.281621, 0.253297} } },
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{ {"iris", "FCBF"}, {{ 3, 2 }, {0.870521, 0.816401} }},
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{ {"ecoli", "FCBF"}, {{ 5, 0, 1, 4, 2 }, {0.512319, 0.350406, 0.260905, 0.203132, 0.11229} }},
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{ {"diabetes", "FCBF"}, {{ 1, 5, 7, 6 }, {0.132858, 0.083191, 0.0480135, 0.0224186} }}
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};
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double threshold;
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std::string selector;
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@ -52,68 +52,20 @@ TEST_CASE("Features Selected", "[FeatureSelection]")
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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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// Features
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auto expected_features = results.at({ file_name, selector }).first;
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std::vector<int> selected_features = featureSelector->getFeatures();
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REQUIRE(selected_features.size() == expected_features.size());
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REQUIRE(selected_features == expected_features);
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// Scores
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auto expected_scores = results.at({ file_name, selector }).second;
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std::vector<double> selected_scores = featureSelector->getScores();
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REQUIRE(selected_scores.size() == selected_features.size());
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for (int i = 0; i < selected_scores.size(); i++) {
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REQUIRE(selected_scores[i] == Catch::Approx(expected_scores[i]).epsilon(raw.epsilon));
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}
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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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}
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