Begin Test Folding
This commit is contained in:
@@ -5,7 +5,7 @@ if(ENABLE_TESTING)
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include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
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include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
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include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
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set(TEST_SOURCES TestBayesModels.cc TestBayesNetwork.cc TestBayesMetrics.cc TestUtils.cc ${BayesNet_SOURCE_DIR}/src/Platform/Folding.cc ${BayesNet_SOURCES})
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set(TEST_SOURCES TestBayesModels.cc TestBayesNetwork.cc TestBayesMetrics.cc TestFolding.cc TestUtils.cc ${BayesNet_SOURCE_DIR}/src/Platform/Folding.cc ${BayesNet_SOURCES})
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add_executable(${TEST_MAIN} ${TEST_SOURCES})
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target_link_libraries(${TEST_MAIN} PUBLIC "${TORCH_LIBRARIES}" ArffFiles mdlp Catch2::Catch2WithMain)
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add_test(NAME ${TEST_MAIN} COMMAND ${TEST_MAIN})
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@@ -22,19 +22,13 @@ TEST_CASE("Metrics Test", "[BayesNet]")
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{"diabetes", 0.0345470614}
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};
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map<string, vector<pair<int, int>>> resultsMST = {
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{"glass", {{0,6}, {0,5}, {0,3}, {5,1}, {5,8}, {6,2}, {6,7}, {7,4}}},
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{"glass", {{0,6}, {0,5}, {0,3}, {6,2}, {6,7}, {5,1}, {5,8}, {5,4}}},
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{"iris", {{0,1},{0,2},{1,3}}},
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{"ecoli", {{0,1}, {0,2}, {1,5}, {1,3}, {5,6}, {5,4}}},
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{"diabetes", {{0,7}, {0,2}, {0,6}, {2,3}, {3,4}, {3,5}, {4,1}}}
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};
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auto [XDisc, yDisc, featuresDisc, classNameDisc, statesDisc] = loadDataset(file_name, true, true);
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int classNumStates = statesDisc.at(classNameDisc).size();
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auto yresized = torch::transpose(yDisc.view({ yDisc.size(0), 1 }), 0, 1);
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torch::Tensor dataset = torch::cat({ XDisc, yresized }, 0);
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int nSamples = dataset.size(1);
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double epsilon = 1e-5;
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torch::Tensor weights = torch::full({ nSamples }, 1.0 / nSamples, torch::kDouble);
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bayesnet::Metrics metrics(dataset, featuresDisc, classNameDisc, classNumStates);
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auto raw = RawDatasets(file_name, true);
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bayesnet::Metrics metrics(raw.dataset, raw.featurest, raw.classNamet, raw.classNumStates);
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SECTION("Test Constructor")
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{
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@@ -43,21 +37,21 @@ TEST_CASE("Metrics Test", "[BayesNet]")
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SECTION("Test SelectKBestWeighted")
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{
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vector<int> kBest = metrics.SelectKBestWeighted(weights, true, resultsKBest.at(file_name).first);
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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(dataset.index({ 1, "..." }), dataset.index({ 2, "..." }), weights);
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REQUIRE(result == Catch::Approx(resultsMI.at(file_name)).epsilon(epsilon));
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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(weights);
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auto result = metrics.maximumSpanningTree(featuresDisc, weights_matrix, 0);
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auto weights_matrix = metrics.conditionalEdge(raw.weights);
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auto result = metrics.maximumSpanningTree(raw.featurest, weights_matrix, 0);
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REQUIRE(result == resultsMST.at(file_name));
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}
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}
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@@ -34,83 +34,81 @@ TEST_CASE("Test Bayesian Classifiers score", "[BayesNet]")
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};
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string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
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auto [XCont, yCont, featuresCont, classNameCont, statesCont] = loadDataset(file_name, true, false);
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auto [XDisc, yDisc, featuresDisc, classNameDisc, statesDisc] = loadFile(file_name);
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double epsilon = 1e-5;
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auto raw = RawDatasets(file_name, false);
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SECTION("Test TAN classifier (" + file_name + ")")
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{
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auto clf = bayesnet::TAN();
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clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
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auto score = clf.score(XDisc, yDisc);
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clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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auto score = clf.score(raw.Xv, raw.yv);
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//scores[{file_name, "TAN"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "TAN"}]).epsilon(epsilon));
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REQUIRE(score == Catch::Approx(scores[{file_name, "TAN"}]).epsilon(raw.epsilon));
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}
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SECTION("Test TANLd classifier (" + file_name + ")")
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{
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auto clf = bayesnet::TANLd();
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clf.fit(XCont, yCont, featuresCont, classNameCont, statesCont);
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auto score = clf.score(XCont, yCont);
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clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
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auto score = clf.score(raw.Xt, raw.yt);
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//scores[{file_name, "TANLd"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "TANLd"}]).epsilon(epsilon));
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REQUIRE(score == Catch::Approx(scores[{file_name, "TANLd"}]).epsilon(raw.epsilon));
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}
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SECTION("Test KDB classifier (" + file_name + ")")
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{
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auto clf = bayesnet::KDB(2);
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clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
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auto score = clf.score(XDisc, yDisc);
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clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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auto score = clf.score(raw.Xv, raw.yv);
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//scores[{file_name, "KDB"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "KDB"
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}]).epsilon(epsilon));
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}]).epsilon(raw.epsilon));
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}
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SECTION("Test KDBLd classifier (" + file_name + ")")
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{
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auto clf = bayesnet::KDBLd(2);
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clf.fit(XCont, yCont, featuresCont, classNameCont, statesCont);
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auto score = clf.score(XCont, yCont);
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clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
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auto score = clf.score(raw.Xt, raw.yt);
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//scores[{file_name, "KDBLd"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "KDBLd"
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}]).epsilon(epsilon));
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}]).epsilon(raw.epsilon));
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}
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SECTION("Test SPODE classifier (" + file_name + ")")
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{
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auto clf = bayesnet::SPODE(1);
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clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
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auto score = clf.score(XDisc, yDisc);
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clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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auto score = clf.score(raw.Xv, raw.yv);
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// scores[{file_name, "SPODE"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "SPODE"}]).epsilon(epsilon));
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REQUIRE(score == Catch::Approx(scores[{file_name, "SPODE"}]).epsilon(raw.epsilon));
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}
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SECTION("Test SPODELd classifier (" + file_name + ")")
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{
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auto clf = bayesnet::SPODELd(1);
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clf.fit(XCont, yCont, featuresCont, classNameCont, statesCont);
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auto score = clf.score(XCont, yCont);
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clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
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auto score = clf.score(raw.Xt, raw.yt);
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// scores[{file_name, "SPODELd"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "SPODELd"}]).epsilon(epsilon));
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REQUIRE(score == Catch::Approx(scores[{file_name, "SPODELd"}]).epsilon(raw.epsilon));
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}
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SECTION("Test AODE classifier (" + file_name + ")")
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{
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auto clf = bayesnet::AODE();
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clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
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auto score = clf.score(XDisc, yDisc);
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clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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auto score = clf.score(raw.Xv, raw.yv);
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// scores[{file_name, "AODE"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "AODE"}]).epsilon(epsilon));
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REQUIRE(score == Catch::Approx(scores[{file_name, "AODE"}]).epsilon(raw.epsilon));
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}
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SECTION("Test AODELd classifier (" + file_name + ")")
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{
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auto clf = bayesnet::AODELd();
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clf.fit(XCont, yCont, featuresCont, classNameCont, statesCont);
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auto score = clf.score(XCont, yCont);
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clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
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auto score = clf.score(raw.Xt, raw.yt);
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// scores[{file_name, "AODELd"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "AODELd"}]).epsilon(epsilon));
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REQUIRE(score == Catch::Approx(scores[{file_name, "AODELd"}]).epsilon(raw.epsilon));
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}
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SECTION("Test BoostAODE classifier (" + file_name + ")")
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{
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auto clf = bayesnet::BoostAODE();
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clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
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auto score = clf.score(XDisc, yDisc);
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clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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auto score = clf.score(raw.Xv, raw.yv);
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// scores[{file_name, "BoostAODE"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "BoostAODE"}]).epsilon(epsilon));
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REQUIRE(score == Catch::Approx(scores[{file_name, "BoostAODE"}]).epsilon(raw.epsilon));
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}
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// for (auto scores : scores) {
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// cout << "{{\"" << scores.first.first << "\", \"" << scores.first.second << "\"}, " << scores.second << "}, ";
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@@ -125,10 +123,9 @@ TEST_CASE("Models features", "[BayesNet]")
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"sepallength -> sepalwidth", "sepalwidth [shape=circle] \n", "sepalwidth -> petalwidth", "}\n"
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}
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);
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auto raw = RawDatasets("iris", true);
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auto clf = bayesnet::TAN();
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auto [XDisc, yDisc, featuresDisc, classNameDisc, statesDisc] = loadFile("iris");
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clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
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clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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REQUIRE(clf.getNumberOfNodes() == 6);
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REQUIRE(clf.getNumberOfEdges() == 7);
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REQUIRE(clf.show() == vector<string>{"class -> sepallength, sepalwidth, petallength, petalwidth, ", "petallength -> sepallength, ", "petalwidth -> ", "sepallength -> sepalwidth, ", "sepalwidth -> petalwidth, "});
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@@ -136,9 +133,9 @@ TEST_CASE("Models features", "[BayesNet]")
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}
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TEST_CASE("Get num features & num edges", "[BayesNet]")
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{
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auto [XDisc, yDisc, featuresDisc, classNameDisc, statesDisc] = loadFile("iris");
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auto raw = RawDatasets("iris", true);
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auto clf = bayesnet::KDB(2);
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clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
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clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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REQUIRE(clf.getNumberOfNodes() == 6);
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REQUIRE(clf.getNumberOfEdges() == 8);
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}
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98
tests/TestFolding.cc
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98
tests/TestFolding.cc
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@@ -0,0 +1,98 @@
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#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 "TestUtils.h"
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#include "Folding.h"
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TEST_CASE("KFold Test", "[KFold]")
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{
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// Initialize a KFold object with k=5 and a seed of 19.
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string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
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auto raw = RawDatasets(file_name, true);
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int nFolds = 5;
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platform::KFold kfold(nFolds, raw.nSamples, 19);s
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int number = raw.nSamples * (kfold.getNumberOfFolds() - 1) / kfold.getNumberOfFolds();
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SECTION("Number of Folds")
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{
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REQUIRE(kfold.getNumberOfFolds() == nFolds);
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}
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SECTION("Fold Test")
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{
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// Test each fold's size and contents.
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for (int i = 0; i < nFolds; ++i) {
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auto [train_indices, test_indices] = kfold.getFold(i);
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bool result = train_indices.size() == number || train_indices.size() == number + 1;
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REQUIRE(result);
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REQUIRE(train_indices.size() + test_indices.size() == raw.nSamples);
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}
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}
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}
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map<int, int> counts(vector<int> y, vector<int> indices)
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{
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map<int, int> result;
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for (auto i = 0; i < indices.size(); ++i) {
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result[y[indices[i]]]++;
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}
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return result;
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}
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TEST_CASE("StratifiedKFold Test", "[StratifiedKFold]")
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{
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int nFolds = 3;
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// Initialize a StratifiedKFold object with k=3, using the y vector, and a seed of 17.
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string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
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auto raw = RawDatasets(file_name, true);
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platform::StratifiedKFold stratified_kfoldt(nFolds, raw.yt, 17);
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platform::StratifiedKFold stratified_kfoldv(nFolds, raw.yv, 17);
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int number = raw.nSamples * (stratified_kfold.getNumberOfFolds() - 1) / stratified_kfold.getNumberOfFolds();
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// SECTION("Number of Folds")
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// {
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// REQUIRE(stratified_kfold.getNumberOfFolds() == nFolds);
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// }
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SECTION("Fold Test")
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{
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// Test each fold's size and contents.
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auto counts = vector<int>(raw.classNumStates, 0);
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for (int i = 0; i < nFolds; ++i) {
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auto [train_indicest, test_indicest] = stratified_kfoldt.getFold(i);
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auto [train_indicesv, test_indicesv] = stratified_kfoldv.getFold(i);
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REQUIRE(train_indicest == train_indicesv);
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REQUIRE(test_indicest == test_indicesv);
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bool result = train_indices.size() == number || train_indices.size() == number + 1;
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REQUIRE(result);
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REQUIRE(train_indices.size() + test_indices.size() == raw.nSamples);
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auto train_t = torch::tensor(train_indices);
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auto ytrain = raw.yt.index({ train_t });
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cout << "dataset=" << file_name << endl;
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cout << "nSamples=" << raw.nSamples << endl;;
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cout << "number=" << number << endl;
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cout << "train_indices.size()=" << train_indices.size() << endl;
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cout << "test_indices.size()=" << test_indices.size() << endl;
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cout << "Class Name = " << raw.classNamet << endl;
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cout << "Features = ";
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for (const auto& item : raw.featurest) {
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cout << item << ", ";
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}
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cout << endl;
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cout << "Class States: ";
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for (const auto& item : raw.statest.at(raw.classNamet)) {
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cout << item << ", ";
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}
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cout << endl;
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// Check that the class labels have been equally assign to each fold
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for (const auto& idx : train_indices) {
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counts[ytrain[idx].item<int>()]++;
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}
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int j = 0;
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for (const auto& item : counts) {
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cout << "j=" << j++ << item << endl;
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}
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}
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REQUIRE(1 == 1);
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}
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}
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@@ -14,6 +14,29 @@ pair<vector<mdlp::labels_t>, map<string, int>> discretize(vector<mdlp::samples_t
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vector<mdlp::labels_t> discretizeDataset(vector<mdlp::samples_t>& X, mdlp::labels_t& y);
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tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vector<int>>> loadFile(const string& name);
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tuple<torch::Tensor, torch::Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(const string& name, bool class_last, bool discretize_dataset);
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#endif //TEST_UTILS_H
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#
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class RawDatasets {
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public:
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RawDatasets(const string& file_name, bool discretize)
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{
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// Xt can be either discretized or not
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tie(Xt, yt, featurest, classNamet, statest) = loadDataset(file_name, true, discretize);
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// Xv is always discretized
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tie(Xv, yv, featuresv, classNamev, statesv) = loadFile(file_name);
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auto yresized = torch::transpose(yt.view({ yt.size(0), 1 }), 0, 1);
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dataset = torch::cat({ Xt, yresized }, 0);
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nSamples = dataset.size(1);
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weights = torch::full({ nSamples }, 1.0 / nSamples, torch::kDouble);
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classNumStates = discretize ? statest.at(classNamet).size() : 0;
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}
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torch::Tensor Xt, yt, dataset, weights;
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vector<vector<int>> Xv;
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vector<int> yv;
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vector<string> featurest, featuresv;
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map<string, vector<int>> statest, statesv;
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string classNamet, classNamev;
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int nSamples, classNumStates;
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double epsilon = 1e-5;
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};
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#endif //TEST_UTILS_H
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