Fix CFS mistake

.vscode/launch.json

@@ -14,8 +14,9 @@
             "type": "lldb",
             "request": "launch",
             "name": "test",
-            "program": "${workspaceFolder}/build_debug/tests/unit_tests_bayesnet",
+            "program": "${workspaceFolder}/build_debug/tests/TestBayesNet",
             "args": [
+                "[FeatureSelection]"
                 //"-c=\"Metrics Test\"",
                 // "-s",
             ],

bayesnet/ensembles/BoostAODE.cc

@@ -145,6 +145,10 @@ namespace bayesnet {
         }
         featureSelector->fit();
         auto cfsFeatures = featureSelector->getFeatures();
+        auto scores = featureSelector->getScores();
+        for (int i = 0; i < cfsFeatures.size(); ++i) {
+            LOG_F(INFO, "Feature: %d Score: %f", cfsFeatures[i], scores[i]);
+        }
         for (const int& feature : cfsFeatures) {
             featuresUsed.push_back(feature);
             std::unique_ptr<Classifier> model = std::make_unique<SPODE>(feature);

bayesnet/feature_selection/CFS.cc

@@ -11,7 +11,7 @@ namespace bayesnet {
         auto feature = featureOrder[0];
         selectedFeatures.push_back(feature);
         selectedScores.push_back(suLabels[feature]);
-        selectedFeatures.erase(selectedFeatures.begin());
+        featureOrder.erase(featureOrder.begin());
         while (continueCondition) {
             double merit = std::numeric_limits<double>::lowest();
             int bestFeature = -1;

tests/TestFeatureSelection.cc

@@ -25,21 +25,21 @@ TEST_CASE("Features Selected", "[FeatureSelection]")
 
     auto raw = RawDatasets(file_name, true);
 
-    SECTION("Test features selected and size")
+    SECTION("Test features selected, scores and sizes")
     {
-        map<pair<std::string, std::string>, std::vector<int>> results = {
+        map<pair<std::string, std::string>, pair<std::vector<int>, std::vector<double>>> results = {
-            { {"glass", "CFS"}, { 2, 3, 6, 1, 8, 4 } },
+            { {"glass", "CFS"}, { { 2, 3, 6, 1, 8, 4 }, {0.365513, 0.42895, 0.369809, 0.298294, 0.240952, 0.200915} } },
-            { {"iris", "CFS"}, { 3, 2, 1, 0 } },
+            { {"iris", "CFS"}, { { 3, 2, 1, 0 }, {0.870521, 0.890375, 0.588155, 0.41843} } },
-            { {"ecoli", "CFS"}, { 5, 0, 4, 2, 1, 6 } },
+            { {"ecoli", "CFS"}, { { 5, 0, 4, 2, 1, 6 }, {0.512319, 0.565381, 0.486025, 0.41087, 0.331423, 0.266251} } },
-            { {"diabetes", "CFS"}, { 1, 5, 7, 6, 4, 2 } },
+            { {"diabetes", "CFS"}, { { 1, 5, 7, 6, 4, 2 }, {0.132858, 0.151209, 0.14244, 0.126591, 0.106028, 0.0825904} } },
-            { {"glass", "IWSS" }, { 2, 3, 5, 7, 6 } },
+            { {"glass", "IWSS" }, { { 2, 3, 5, 7, 6 }, {0.365513, 0.42895, 0.359907, 0.273784, 0.223346} } },
-            { {"iris", "IWSS"}, { 3, 2, 0 } },
+            { {"iris", "IWSS"}, { { 3, 2, 0 }, {0.870521, 0.890375, 0.585426} }},
-            { {"ecoli", "IWSS"}, { 5, 6, 0, 1, 4 } },
+            { {"ecoli", "IWSS"}, { { 5, 6, 0, 1, 4 }, {0.512319, 0.550978, 0.475025, 0.382607, 0.308203} } },
-            { {"diabetes", "IWSS"}, { 1, 5, 4, 7, 3 } },
+            { {"diabetes", "IWSS"}, { { 1, 5, 4, 7, 3 }, {0.132858, 0.151209, 0.136576, 0.122097, 0.0802232} } },
-            { {"glass", "FCBF" }, { 2, 3, 5, 7, 6 } },
+            { {"glass", "FCBF" }, { { 2, 3, 5, 7, 6 }, {0.365513, 0.304911, 0.302109, 0.281621, 0.253297} } },
-            { {"iris", "FCBF"}, { 3, 2 } },
+            { {"iris", "FCBF"}, {{ 3, 2 }, {0.870521, 0.816401} }},
-            { {"ecoli", "FCBF"}, { 5, 0, 1, 4, 2 } },
+            { {"ecoli", "FCBF"}, {{ 5, 0, 1, 4, 2 }, {0.512319, 0.350406, 0.260905, 0.203132, 0.11229} }},
-            { {"diabetes", "FCBF"}, { 1, 5, 7, 6 } }
+            { {"diabetes", "FCBF"}, {{ 1, 5, 7, 6 }, {0.132858, 0.083191, 0.0480135, 0.0224186} }}
         };
         double threshold;
         std::string selector;
@@ -52,68 +52,20 @@ TEST_CASE("Features Selected", "[FeatureSelection]")
             selector = item.first; threshold = item.second;
             bayesnet::FeatureSelect* featureSelector = build_selector(raw, selector, threshold);
             featureSelector->fit();
-            std::vector<int> selected = featureSelector->getFeatures();
             INFO("file_name: " << file_name << ", selector: " << selector);
-            REQUIRE(selected.size() == results.at({ file_name, selector }).size());
+            // Features
-            REQUIRE(selected == results.at({ file_name, selector }));
+            auto expected_features = results.at({ file_name, selector }).first;
+            std::vector<int> selected_features = featureSelector->getFeatures();
+            REQUIRE(selected_features.size() == expected_features.size());
+            REQUIRE(selected_features == expected_features);
+            // Scores
+            auto expected_scores = results.at({ file_name, selector }).second;
+            std::vector<double> selected_scores = featureSelector->getScores();
+            REQUIRE(selected_scores.size() == selected_features.size());
+            for (int i = 0; i < selected_scores.size(); i++) {
+                REQUIRE(selected_scores[i] == Catch::Approx(expected_scores[i]).epsilon(raw.epsilon));
+            }
             delete featureSelector;
         }
     }
-}
+}
-
-// TEST_CASE("Feature Selection Test", "[BayesNet]")
-// {
-//     std::string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
-//     std::string selector = GENERATE("CFS", "FCBF", "IWSS");
-//     map<std::string, pair<int, std::vector<int>>> resultsKBest = {
-//         {"glass", {7, { 0, 1, 7, 6, 3, 5, 2 }}},
-//         {"iris", {3, { 0, 3, 2 }} },
-//         {"ecoli", {6, { 2, 4, 1, 0, 6, 5 }}},
-//         {"diabetes", {2, { 7, 1 }}}
-//     };
-//     map<std::string, double> resultsMI = {
-//         {"glass", 0.12805398},
-//         {"iris", 0.3158139948},
-//         {"ecoli", 0.0089431099},
-//         {"diabetes", 0.0345470614}
-//     };
-//     map<pair<std::string, int>, std::vector<pair<int, int>>> resultsMST = {
-//         { {"glass", 0}, { {0, 6}, {0, 5}, {0, 3}, {5, 1}, {5, 8}, {5, 4}, {6, 2}, {6, 7} } },
-//         { {"glass", 1}, { {1, 5}, {5, 0}, {5, 8}, {5, 4}, {0, 6}, {0, 3}, {6, 2}, {6, 7} } },
-//         { {"iris", 0}, { {0, 1}, {0, 2}, {1, 3} } },
-//         { {"iris", 1}, { {1, 0}, {1, 3}, {0, 2} } },
-//         { {"ecoli", 0}, { {0, 1}, {0, 2}, {1, 5}, {1, 3}, {5, 6}, {5, 4} } },
-//         { {"ecoli", 1}, { {1, 0}, {1, 5}, {1, 3}, {5, 6}, {5, 4}, {0, 2} } },
-//         { {"diabetes", 0}, { {0, 7}, {0, 2}, {0, 6}, {2, 3}, {3, 4}, {3, 5}, {4, 1} } },
-//         { {"diabetes", 1}, { {1, 4}, {4, 3}, {3, 2}, {3, 5}, {2, 0}, {0, 7}, {0, 6} } }
-//     };
-//     auto raw = RawDatasets(file_name, true);
-//     FeatureSelect* featureSelector = build_selector(raw, selector);
-
-//     SECTION("Test Constructor")
-//     {
-//         REQUIRE(metrics.getScoresKBest().size() == 0);
-//     }
-
-//     SECTION("Test SelectKBestWeighted")
-//     {
-//         std::vector<int> kBest = metrics.SelectKBestWeighted(raw.weights, true, resultsKBest.at(file_name).first);
-//         REQUIRE(kBest.size() == resultsKBest.at(file_name).first);
-//         REQUIRE(kBest == resultsKBest.at(file_name).second);
-//     }
-
-//     SECTION("Test Mutual Information")
-//     {
-//         auto result = metrics.mutualInformation(raw.dataset.index({ 1, "..." }), raw.dataset.index({ 2, "..." }), raw.weights);
-//         REQUIRE(result == Catch::Approx(resultsMI.at(file_name)).epsilon(raw.epsilon));
-//     }
-
-//     SECTION("Test Maximum Spanning Tree")
-//     {
-//         auto weights_matrix = metrics.conditionalEdge(raw.weights);
-//         for (int i = 0; i < 2; ++i) {
-//             auto result = metrics.maximumSpanningTree(raw.featurest, weights_matrix, i);
-//             REQUIRE(result == resultsMST.at({ file_name, i }));
-//         }
-//     }
-// }