Fix CFS merit computation error
This commit is contained in:
@@ -11,7 +11,8 @@
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#include "TestUtils.h"
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#include "bayesnet/ensembles/XBA2DE.h"
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TEST_CASE("Normal test", "[XBA2DE]") {
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TEST_CASE("Normal test", "[XBA2DE]")
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{
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auto raw = RawDatasets("iris", true);
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auto clf = bayesnet::XBA2DE();
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clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
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@@ -25,37 +26,38 @@ TEST_CASE("Normal test", "[XBA2DE]") {
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REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(1.0f));
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REQUIRE(clf.graph().size() == 1);
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}
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TEST_CASE("Feature_select CFS", "[XBA2DE]") {
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TEST_CASE("Feature_select CFS", "[XBA2DE]")
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{
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auto raw = RawDatasets("glass", true);
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auto clf = bayesnet::XBA2DE();
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clf.setHyperparameters({{"select_features", "CFS"}});
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clf.setHyperparameters({ {"select_features", "CFS"} });
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clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 220);
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REQUIRE(clf.getNumberOfEdges() == 506);
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REQUIRE(clf.getNumberOfNodes() == 360);
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REQUIRE(clf.getNumberOfEdges() == 828);
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REQUIRE(clf.getNotes().size() == 2);
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REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 9 with CFS");
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REQUIRE(clf.getNotes()[1] == "Number of models: 22");
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REQUIRE(clf.getNotes()[0] == "Used features in initialization: 9 of 9 with CFS");
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REQUIRE(clf.getNotes()[1] == "Number of models: 36");
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REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.720930219));
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}
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TEST_CASE("Feature_select IWSS", "[XBA2DE]") {
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TEST_CASE("Feature_select IWSS", "[XBA2DE]")
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{
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auto raw = RawDatasets("glass", true);
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auto clf = bayesnet::XBA2DE();
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clf.setHyperparameters({{"select_features", "IWSS"}, {"threshold", 0.5}});
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clf.setHyperparameters({ {"select_features", "IWSS"}, {"threshold", 0.5} });
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clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 220);
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REQUIRE(clf.getNumberOfEdges() == 506);
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REQUIRE(clf.getNotes().size() == 4);
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REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with IWSS");
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REQUIRE(clf.getNotes()[1] == "Convergence threshold reached & 15 models eliminated");
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REQUIRE(clf.getNotes()[2] == "Pairs not used in train: 2");
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REQUIRE(clf.getNotes()[3] == "Number of models: 22");
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REQUIRE(clf.getNumberOfStates() == 5346);
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REQUIRE(clf.getNumberOfNodes() == 360);
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REQUIRE(clf.getNumberOfEdges() == 828);
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REQUIRE(clf.getNotes().size() == 2);
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REQUIRE(clf.getNotes()[0] == "Used features in initialization: 9 of 9 with IWSS");
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REQUIRE(clf.getNotes()[1] == "Number of models: 36");
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REQUIRE(clf.getNumberOfStates() == 8748);
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REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.72093));
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}
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TEST_CASE("Feature_select FCBF", "[XBA2DE]") {
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TEST_CASE("Feature_select FCBF", "[XBA2DE]")
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{
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auto raw = RawDatasets("glass", true);
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auto clf = bayesnet::XBA2DE();
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clf.setHyperparameters({{"select_features", "FCBF"}, {"threshold", 1e-7}});
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clf.setHyperparameters({ {"select_features", "FCBF"}, {"threshold", 1e-7} });
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clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 290);
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REQUIRE(clf.getNumberOfEdges() == 667);
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@@ -66,37 +68,39 @@ TEST_CASE("Feature_select FCBF", "[XBA2DE]") {
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REQUIRE(clf.getNotes()[2] == "Number of models: 29");
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REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.744186));
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}
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TEST_CASE("Test used features in train note and score", "[XBA2DE]") {
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TEST_CASE("Test used features in train note and score", "[XBA2DE]")
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{
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auto raw = RawDatasets("diabetes", true);
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auto clf = bayesnet::XBA2DE();
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clf.setHyperparameters({
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{"order", "asc"},
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{"convergence", true},
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{"select_features", "CFS"},
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});
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});
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clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 144);
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REQUIRE(clf.getNumberOfEdges() == 320);
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REQUIRE(clf.getNumberOfStates() == 5504);
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REQUIRE(clf.getNumberOfNodes() == 189);
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REQUIRE(clf.getNumberOfEdges() == 420);
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REQUIRE(clf.getNumberOfStates() == 7224);
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REQUIRE(clf.getNotes().size() == 2);
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REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 8 with CFS");
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REQUIRE(clf.getNotes()[1] == "Number of models: 16");
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REQUIRE(clf.getNotes()[0] == "Used features in initialization: 7 of 8 with CFS");
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REQUIRE(clf.getNotes()[1] == "Number of models: 21");
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auto score = clf.score(raw.Xv, raw.yv);
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auto scoret = clf.score(raw.Xt, raw.yt);
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REQUIRE(score == Catch::Approx(0.850260437f).epsilon(raw.epsilon));
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REQUIRE(scoret == Catch::Approx(0.850260437f).epsilon(raw.epsilon));
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REQUIRE(score == Catch::Approx(0.854166687f).epsilon(raw.epsilon));
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REQUIRE(scoret == Catch::Approx(0.854166687f).epsilon(raw.epsilon));
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}
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TEST_CASE("Order asc, desc & random", "[XBA2DE]") {
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TEST_CASE("Order asc, desc & random", "[XBA2DE]")
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{
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auto raw = RawDatasets("glass", true);
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std::map<std::string, double> scores{{"asc", 0.827103}, {"desc", 0.808411}, {"rand", 0.827103}};
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for (const std::string &order : {"asc", "desc", "rand"}) {
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std::map<std::string, double> scores{ {"asc", 0.827103}, {"desc", 0.808411}, {"rand", 0.827103} };
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for (const std::string& order : { "asc", "desc", "rand" }) {
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auto clf = bayesnet::XBA2DE();
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clf.setHyperparameters({
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{"order", order},
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{"bisection", false},
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{"maxTolerance", 1},
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{"convergence", true},
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});
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});
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clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
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auto score = clf.score(raw.Xv, raw.yv);
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auto scoret = clf.score(raw.Xt, raw.yt);
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@@ -105,7 +109,8 @@ TEST_CASE("Order asc, desc & random", "[XBA2DE]") {
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REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
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}
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}
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TEST_CASE("Oddities", "[XBA2DE]") {
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TEST_CASE("Oddities", "[XBA2DE]")
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{
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auto clf = bayesnet::XBA2DE();
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auto raw = RawDatasets("iris", true);
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auto bad_hyper = nlohmann::json{
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@@ -114,28 +119,28 @@ TEST_CASE("Oddities", "[XBA2DE]") {
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{{"maxTolerance", 0}},
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{{"maxTolerance", 7}},
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};
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for (const auto &hyper : bad_hyper.items()) {
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for (const auto& hyper : bad_hyper.items()) {
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INFO("XBA2DE hyper: " << hyper.value().dump());
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REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
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}
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REQUIRE_THROWS_AS(clf.setHyperparameters({{"maxTolerance", 0}}), std::invalid_argument);
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REQUIRE_THROWS_AS(clf.setHyperparameters({ {"maxTolerance", 0} }), std::invalid_argument);
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auto bad_hyper_fit = nlohmann::json{
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{{"select_features", "IWSS"}, {"threshold", -0.01}},
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{{"select_features", "IWSS"}, {"threshold", 0.51}},
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{{"select_features", "FCBF"}, {"threshold", 1e-8}},
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{{"select_features", "FCBF"}, {"threshold", 1.01}},
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};
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for (const auto &hyper : bad_hyper_fit.items()) {
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for (const auto& hyper : bad_hyper_fit.items()) {
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INFO("XBA2DE hyper: " << hyper.value().dump());
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clf.setHyperparameters(hyper.value());
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REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing),
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std::invalid_argument);
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std::invalid_argument);
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}
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auto bad_hyper_fit2 = nlohmann::json{
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{{"alpha_block", true}, {"block_update", true}},
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{{"bisection", false}, {"block_update", true}},
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};
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for (const auto &hyper : bad_hyper_fit2.items()) {
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for (const auto& hyper : bad_hyper_fit2.items()) {
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INFO("XBA2DE hyper: " << hyper.value().dump());
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REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
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}
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@@ -146,12 +151,13 @@ TEST_CASE("Oddities", "[XBA2DE]") {
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raw.features.pop_back();
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raw.features.pop_back();
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raw.features.pop_back();
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clf.setHyperparameters({{"select_features", "CFS"}, {"alpha_block", false}, {"block_update", false}});
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clf.setHyperparameters({ {"select_features", "CFS"}, {"alpha_block", false}, {"block_update", false} });
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clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
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REQUIRE(clf.getNotes().size() == 1);
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REQUIRE(clf.getNotes()[0] == "No features selected in initialization");
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}
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TEST_CASE("Bisection Best", "[XBA2DE]") {
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TEST_CASE("Bisection Best", "[XBA2DE]")
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{
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auto clf = bayesnet::XBA2DE();
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auto raw = RawDatasets("kdd_JapaneseVowels", true, 1200, true, false);
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clf.setHyperparameters({
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@@ -159,7 +165,7 @@ TEST_CASE("Bisection Best", "[XBA2DE]") {
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{"maxTolerance", 3},
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{"convergence", true},
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{"convergence_best", false},
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});
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});
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clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 330);
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REQUIRE(clf.getNumberOfEdges() == 836);
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@@ -173,7 +179,8 @@ TEST_CASE("Bisection Best", "[XBA2DE]") {
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REQUIRE(score == Catch::Approx(0.975).epsilon(raw.epsilon));
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REQUIRE(scoret == Catch::Approx(0.975).epsilon(raw.epsilon));
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}
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TEST_CASE("Bisection Best vs Last", "[XBA2DE]") {
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TEST_CASE("Bisection Best vs Last", "[XBA2DE]")
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{
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auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
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auto clf = bayesnet::XBA2DE();
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auto hyperparameters = nlohmann::json{
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@@ -193,7 +200,8 @@ TEST_CASE("Bisection Best vs Last", "[XBA2DE]") {
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auto score_last = clf.score(raw.X_test, raw.y_test);
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REQUIRE(score_last == Catch::Approx(0.99).epsilon(raw.epsilon));
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}
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TEST_CASE("Block Update", "[XBA2DE]") {
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TEST_CASE("Block Update", "[XBA2DE]")
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{
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auto clf = bayesnet::XBA2DE();
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auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
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clf.setHyperparameters({
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@@ -201,7 +209,7 @@ TEST_CASE("Block Update", "[XBA2DE]") {
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{"block_update", true},
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{"maxTolerance", 3},
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{"convergence", true},
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});
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});
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clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 120);
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REQUIRE(clf.getNumberOfEdges() == 304);
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@@ -221,13 +229,14 @@ TEST_CASE("Block Update", "[XBA2DE]") {
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/*}*/
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/*std::cout << "Score " << score << std::endl;*/
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}
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TEST_CASE("Alphablock", "[XBA2DE]") {
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TEST_CASE("Alphablock", "[XBA2DE]")
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{
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auto clf_alpha = bayesnet::XBA2DE();
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auto clf_no_alpha = bayesnet::XBA2DE();
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auto raw = RawDatasets("diabetes", true);
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clf_alpha.setHyperparameters({
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{"alpha_block", true},
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});
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});
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clf_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
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clf_no_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
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auto score_alpha = clf_alpha.score(raw.X_test, raw.y_test);
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