Tests XSpode & XBAODE
This commit is contained in:
@@ -10,14 +10,14 @@ if(ENABLE_TESTING)
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)
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file(GLOB_RECURSE BayesNet_SOURCES "${BayesNet_SOURCE_DIR}/bayesnet/*.cc")
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add_executable(TestBayesNet TestBayesNetwork.cc TestBayesNode.cc TestBayesClassifier.cc
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TestBayesModels.cc TestBayesMetrics.cc TestFeatureSelection.cc TestBoostAODE.cc TestXBAODE.cc TestA2DE.cc TestWA2DE.cc
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TestUtils.cc TestBayesEnsemble.cc TestModulesVersions.cc TestBoostA2DE.cc TestMST.cc ${BayesNet_SOURCES})
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TestBayesModels.cc TestBayesMetrics.cc TestFeatureSelection.cc TestBoostAODE.cc TestXBAODE.cc TestA2DE.cc
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TestUtils.cc TestBayesEnsemble.cc TestModulesVersions.cc TestBoostA2DE.cc TestMST.cc TestXSPODE.cc ${BayesNet_SOURCES})
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target_link_libraries(TestBayesNet PUBLIC "${TORCH_LIBRARIES}" fimdlp PRIVATE Catch2::Catch2WithMain)
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add_test(NAME BayesNetworkTest COMMAND TestBayesNet)
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add_test(NAME A2DE COMMAND TestBayesNet "[A2DE]")
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add_test(NAME WA2DE COMMAND TestBayesNet "[WA2DE]")
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add_test(NAME BoostA2DE COMMAND TestBayesNet "[BoostA2DE]")
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add_test(NAME BoostAODE COMMAND TestBayesNet "[BoostAODE]")
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add_test(NAME XSPODE COMMAND TestBayesNet "[XSPODE]")
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add_test(NAME XBAODE COMMAND TestBayesNet "[XBAODE]")
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add_test(NAME Classifier COMMAND TestBayesNet "[Classifier]")
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add_test(NAME Ensemble COMMAND TestBayesNet "[Ensemble]")
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@@ -1,31 +0,0 @@
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// ***************************************************************
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// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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// SPDX-FileType: SOURCE
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// SPDX-License-Identifier: MIT
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// ***************************************************************
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#include <type_traits>
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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 "bayesnet/ensembles/WA2DE.h"
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#include "TestUtils.h"
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TEST_CASE("Fit and Score", "[WA2DE]")
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{
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auto raw = RawDatasets("iris", true);
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auto clf = bayesnet::WA2DE();
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clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
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REQUIRE(clf.score(raw.Xt, raw.yt) == Catch::Approx(0.6333333333333333).epsilon(raw.epsilon));
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}
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TEST_CASE("Test graph", "[WA2DE]")
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{
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auto raw = RawDatasets("iris", true);
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auto clf = bayesnet::WA2DE();
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clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
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auto graph = clf.graph("BayesNet WA2DE");
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REQUIRE(graph.size() == 2);
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REQUIRE(graph[0] == "BayesNet WA2DE");
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REQUIRE(graph[1] == "Graph visualization not implemented.");
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}
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@@ -4,88 +4,94 @@
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// SPDX-License-Identifier: MIT
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// ***************************************************************
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#include <type_traits>
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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 <catch2/catch_test_macros.hpp>
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#include <catch2/generators/catch_generators.hpp>
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#include <catch2/matchers/catch_matchers.hpp>
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#include "bayesnet/ensembles/XBAODE.h"
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#include "TestUtils.h"
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#include "bayesnet/ensembles/XBAODE.h"
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TEST_CASE("Normal test", "[XBAODE]")
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{
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auto raw = RawDatasets("iris", true);
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auto clf = bayesnet::XBAODE();
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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() == 20);
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REQUIRE(clf.getNumberOfEdges() == 112);
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REQUIRE(clf.getNotes().size() == 1);
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TEST_CASE("Normal test", "[XBAODE]") {
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auto raw = RawDatasets("iris", true);
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auto clf = bayesnet::XBAODE();
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clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states,
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raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 20);
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REQUIRE(clf.getNumberOfEdges() == 36);
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REQUIRE(clf.getNotes().size() == 1);
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REQUIRE(clf.getVersion() == "0.9.7");
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REQUIRE(clf.getNotes()[0] == "Number of models: 4");
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REQUIRE(clf.getNumberOfStates() == 256);
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REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.933333));
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}
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//TEST_CASE("Feature_select CFS", "[XBAODE]")
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//{
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// auto raw = RawDatasets("glass", true);
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// auto clf = bayesnet::XBAODE();
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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() == 97);
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// REQUIRE(clf.getNumberOfEdges() == 153);
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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: 9");
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//}
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// TEST_CASE("Feature_select IWSS", "[XBAODE]")
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// {
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// auto raw = RawDatasets("glass", true);
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// auto clf = bayesnet::XBAODE();
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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() == 90);
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// REQUIRE(clf.getNumberOfEdges() == 153);
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// REQUIRE(clf.getNotes().size() == 2);
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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] == "Number of models: 9");
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// }
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// TEST_CASE("Feature_select FCBF", "[XBAODE]")
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// {
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// auto raw = RawDatasets("glass", true);
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// auto clf = bayesnet::XBAODE();
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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() == 90);
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// REQUIRE(clf.getNumberOfEdges() == 153);
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// REQUIRE(clf.getNotes().size() == 2);
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// REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with FCBF");
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// REQUIRE(clf.getNotes()[1] == "Number of models: 9");
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// }
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// TEST_CASE("Test used features in train note and score", "[XBAODE]")
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// {
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// auto raw = RawDatasets("diabetes", true);
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// auto clf = bayesnet::XBAODE(true);
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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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// clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
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// REQUIRE(clf.getNumberOfNodes() == 72);
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// REQUIRE(clf.getNumberOfEdges() == 120);
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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: 8");
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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(0.809895813).epsilon(raw.epsilon));
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// REQUIRE(scoret == Catch::Approx(0.809895813).epsilon(raw.epsilon));
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// }
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TEST_CASE("Feature_select CFS", "[XBAODE]") {
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auto raw = RawDatasets("glass", true);
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auto clf = bayesnet::XBAODE();
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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,
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raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 90);
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REQUIRE(clf.getNumberOfEdges() == 171);
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REQUIRE(clf.getNotes().size() == 2);
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REQUIRE(clf.getNotes()[0] ==
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"Used features in initialization: 6 of 9 with CFS");
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REQUIRE(clf.getNotes()[1] == "Number of models: 9");
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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", "[XBAODE]") {
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auto raw = RawDatasets("glass", true);
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auto clf = bayesnet::XBAODE();
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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,
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raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 90);
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REQUIRE(clf.getNumberOfEdges() == 171);
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REQUIRE(clf.getNotes().size() == 2);
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REQUIRE(clf.getNotes()[0] ==
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"Used features in initialization: 4 of 9 with IWSS");
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REQUIRE(clf.getNotes()[1] == "Number of models: 9");
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REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.697674394));
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}
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TEST_CASE("Feature_select FCBF", "[XBAODE]") {
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auto raw = RawDatasets("glass", true);
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auto clf = bayesnet::XBAODE();
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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,
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raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 90);
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REQUIRE(clf.getNumberOfEdges() == 171);
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REQUIRE(clf.getNotes().size() == 2);
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REQUIRE(clf.getNotes()[0] ==
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"Used features in initialization: 4 of 9 with FCBF");
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REQUIRE(clf.getNotes()[1] == "Number of models: 9");
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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("Test used features in train note and score", "[XBAODE]")
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{
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auto raw = RawDatasets("diabetes", true);
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auto clf = bayesnet::XBAODE();
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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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clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states,
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raw.smoothing); REQUIRE(clf.getNumberOfNodes() == 72);
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REQUIRE(clf.getNumberOfEdges() == 136);
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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: 8");
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auto score = clf.score(raw.Xv, raw.yv); auto scoret = clf.score(raw.Xt, raw.yt);
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REQUIRE(score == Catch::Approx(0.819010437f).epsilon(raw.epsilon));
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REQUIRE(scoret == Catch::Approx(0.819010437f).epsilon(raw.epsilon));
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}
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// TEST_CASE("Voting vs proba", "[XBAODE]")
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// {
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// auto raw = RawDatasets("iris", true);
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// auto clf = bayesnet::XBAODE(false);
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// clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
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// auto score_proba = clf.score(raw.Xv, raw.yv);
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// auto pred_proba = clf.predict_proba(raw.Xv);
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// clf.setHyperparameters({
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// clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states,
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// raw.smoothing); auto score_proba = clf.score(raw.Xv, raw.yv); auto
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// pred_proba = clf.predict_proba(raw.Xv); clf.setHyperparameters({
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// {"predict_voting",true},
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// });
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// auto score_voting = clf.score(raw.Xv, raw.yv);
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@@ -93,9 +99,9 @@ TEST_CASE("Normal test", "[XBAODE]")
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// REQUIRE(score_proba == Catch::Approx(0.97333).epsilon(raw.epsilon));
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// REQUIRE(score_voting == Catch::Approx(0.98).epsilon(raw.epsilon));
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// REQUIRE(pred_voting[83][2] == Catch::Approx(1.0).epsilon(raw.epsilon));
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// REQUIRE(pred_proba[83][2] == Catch::Approx(0.86121525).epsilon(raw.epsilon));
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// REQUIRE(clf.dump_cpt() == "");
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// REQUIRE(clf.topological_order() == std::vector<std::string>());
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// REQUIRE(pred_proba[83][2] ==
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// Catch::Approx(0.86121525).epsilon(raw.epsilon)); REQUIRE(clf.dump_cpt()
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// == ""); REQUIRE(clf.topological_order() == std::vector<std::string>());
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// }
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// TEST_CASE("Order asc, desc & random", "[XBAODE]")
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// {
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@@ -111,10 +117,9 @@ TEST_CASE("Normal test", "[XBAODE]")
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// {"maxTolerance", 1},
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// {"convergence", false},
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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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// INFO("XBAODE order: " << order);
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// clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states,
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// raw.smoothing); auto score = clf.score(raw.Xv, raw.yv); auto scoret =
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// clf.score(raw.Xt, raw.yt); INFO("XBAODE order: " << order);
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// REQUIRE(score == Catch::Approx(scores[order]).epsilon(raw.epsilon));
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// REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
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// }
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@@ -131,10 +136,11 @@ TEST_CASE("Normal test", "[XBAODE]")
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// };
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// for (const auto& hyper : bad_hyper.items()) {
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// INFO("XBAODE hyper: " << hyper.value().dump());
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// REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
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// REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()),
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// 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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// auto bad_hyper_fit = nlohmann::json{
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// REQUIRE_THROWS_AS(clf.setHyperparameters({ {"maxTolerance", 0 } }),
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// std::invalid_argument); 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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@@ -143,7 +149,8 @@ TEST_CASE("Normal test", "[XBAODE]")
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// for (const auto& hyper : bad_hyper_fit.items()) {
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// INFO("XBAODE 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), std::invalid_argument);
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// REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features,
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// raw.className, raw.states, raw.smoothing), std::invalid_argument);
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// }
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// auto bad_hyper_fit2 = nlohmann::json{
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@@ -152,7 +159,8 @@ TEST_CASE("Normal test", "[XBAODE]")
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// };
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// for (const auto& hyper : bad_hyper_fit2.items()) {
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// INFO("XBAODE hyper: " << hyper.value().dump());
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// REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
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// REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()),
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// std::invalid_argument);
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// }
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// }
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// TEST_CASE("Bisection Best", "[XBAODE]")
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@@ -165,8 +173,8 @@ TEST_CASE("Normal test", "[XBAODE]")
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// {"convergence", true},
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// {"convergence_best", false},
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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() == 210);
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// clf.fit(raw.X_train, raw.y_train, raw.features, raw.className,
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// raw.states, raw.smoothing); REQUIRE(clf.getNumberOfNodes() == 210);
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// REQUIRE(clf.getNumberOfEdges() == 378);
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// REQUIRE(clf.getNotes().size() == 1);
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// REQUIRE(clf.getNotes().at(0) == "Number of models: 14");
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@@ -186,15 +194,17 @@ TEST_CASE("Normal test", "[XBAODE]")
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// {"convergence_best", true},
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// };
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// clf.setHyperparameters(hyperparameters);
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// clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
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// auto score_best = clf.score(raw.X_test, raw.y_test);
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// REQUIRE(score_best == Catch::Approx(0.980000019f).epsilon(raw.epsilon));
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// clf.fit(raw.X_train, raw.y_train, raw.features, raw.className,
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// raw.states, raw.smoothing); auto score_best = clf.score(raw.X_test,
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// raw.y_test); REQUIRE(score_best ==
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// Catch::Approx(0.980000019f).epsilon(raw.epsilon));
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// // Now we will set the hyperparameter to use the last accuracy
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// hyperparameters["convergence_best"] = false;
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// clf.setHyperparameters(hyperparameters);
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// clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
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// auto score_last = clf.score(raw.X_test, raw.y_test);
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// REQUIRE(score_last == Catch::Approx(0.976666689f).epsilon(raw.epsilon));
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// clf.fit(raw.X_train, raw.y_train, raw.features, raw.className,
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// raw.states, raw.smoothing); auto score_last = clf.score(raw.X_test,
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// raw.y_test); REQUIRE(score_last ==
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// Catch::Approx(0.976666689f).epsilon(raw.epsilon));
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// }
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// TEST_CASE("Block Update", "[XBAODE]")
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// {
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@@ -206,20 +216,21 @@ TEST_CASE("Normal test", "[XBAODE]")
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// {"maxTolerance", 3},
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// {"convergence", true},
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// });
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// clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
// REQUIRE(clf.getNumberOfNodes() == 868);
|
||||
// clf.fit(raw.X_train, raw.y_train, raw.features, raw.className,
|
||||
// raw.states, raw.smoothing); REQUIRE(clf.getNumberOfNodes() == 868);
|
||||
// REQUIRE(clf.getNumberOfEdges() == 1724);
|
||||
// REQUIRE(clf.getNotes().size() == 3);
|
||||
// REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 15 models eliminated");
|
||||
// REQUIRE(clf.getNotes()[1] == "Used features in train: 19 of 216");
|
||||
// REQUIRE(clf.getNotes()[2] == "Number of models: 4");
|
||||
// auto score = clf.score(raw.X_test, raw.y_test);
|
||||
// auto scoret = clf.score(raw.X_test, raw.y_test);
|
||||
// REQUIRE(score == Catch::Approx(0.99f).epsilon(raw.epsilon));
|
||||
// REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 15 models
|
||||
// eliminated"); REQUIRE(clf.getNotes()[1] == "Used features in train: 19 of
|
||||
// 216"); REQUIRE(clf.getNotes()[2] == "Number of models: 4"); auto score =
|
||||
// clf.score(raw.X_test, raw.y_test); auto scoret = clf.score(raw.X_test,
|
||||
// raw.y_test); REQUIRE(score == Catch::Approx(0.99f).epsilon(raw.epsilon));
|
||||
// REQUIRE(scoret == Catch::Approx(0.99f).epsilon(raw.epsilon));
|
||||
// //
|
||||
// // std::cout << "Number of nodes " << clf.getNumberOfNodes() << std::endl;
|
||||
// // std::cout << "Number of edges " << clf.getNumberOfEdges() << std::endl;
|
||||
// // std::cout << "Number of nodes " << clf.getNumberOfNodes() <<
|
||||
// std::endl;
|
||||
// // std::cout << "Number of edges " << clf.getNumberOfEdges() <<
|
||||
// std::endl;
|
||||
// // std::cout << "Notes size " << clf.getNotes().size() << std::endl;
|
||||
// // for (auto note : clf.getNotes()) {
|
||||
// // std::cout << note << std::endl;
|
||||
@@ -234,10 +245,11 @@ TEST_CASE("Normal test", "[XBAODE]")
|
||||
// clf_alpha.setHyperparameters({
|
||||
// {"alpha_block", true},
|
||||
// });
|
||||
// clf_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
// clf_no_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
// auto score_alpha = clf_alpha.score(raw.X_test, raw.y_test);
|
||||
// auto score_no_alpha = clf_no_alpha.score(raw.X_test, raw.y_test);
|
||||
// REQUIRE(score_alpha == Catch::Approx(0.720779f).epsilon(raw.epsilon));
|
||||
// REQUIRE(score_no_alpha == Catch::Approx(0.733766f).epsilon(raw.epsilon));
|
||||
// clf_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className,
|
||||
// raw.states, raw.smoothing); clf_no_alpha.fit(raw.X_train, raw.y_train,
|
||||
// raw.features, raw.className, raw.states, raw.smoothing); auto score_alpha
|
||||
// = clf_alpha.score(raw.X_test, raw.y_test); auto score_no_alpha =
|
||||
// clf_no_alpha.score(raw.X_test, raw.y_test); REQUIRE(score_alpha ==
|
||||
// Catch::Approx(0.720779f).epsilon(raw.epsilon)); REQUIRE(score_no_alpha ==
|
||||
// Catch::Approx(0.733766f).epsilon(raw.epsilon));
|
||||
// }
|
||||
|
126
tests/TestXSPODE.cc
Normal file
126
tests/TestXSPODE.cc
Normal file
@@ -0,0 +1,126 @@
|
||||
// ***************************************************************
|
||||
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
|
||||
// SPDX-FileType: SOURCE
|
||||
// SPDX-License-Identifier: MIT
|
||||
// ***************************************************************
|
||||
|
||||
#include <catch2/catch_test_macros.hpp>
|
||||
#include <catch2/catch_approx.hpp>
|
||||
#include <catch2/matchers/catch_matchers.hpp>
|
||||
#include <stdexcept>
|
||||
#include "bayesnet/classifiers/XSPODE.h"
|
||||
#include "TestUtils.h"
|
||||
|
||||
TEST_CASE("fit vector test", "[XSPODE]") {
|
||||
auto raw = RawDatasets("iris", true);
|
||||
auto scores = std::vector<float>({0.966667, 0.9333333, 0.966667, 0.966667});
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
auto clf = bayesnet::XSpode(i);
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states,
|
||||
raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 5);
|
||||
REQUIRE(clf.getNumberOfEdges() == 9);
|
||||
REQUIRE(clf.getNotes().size() == 0);
|
||||
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(scores.at(i)));
|
||||
}
|
||||
}
|
||||
TEST_CASE("fit dataset test", "[XSPODE]") {
|
||||
auto raw = RawDatasets("iris", true);
|
||||
auto scores = std::vector<float>({0.966667, 0.9333333, 0.966667, 0.966667});
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
auto clf = bayesnet::XSpode(i);
|
||||
clf.fit(raw.dataset, raw.features, raw.className, raw.states,
|
||||
raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 5);
|
||||
REQUIRE(clf.getNumberOfEdges() == 9);
|
||||
REQUIRE(clf.getNotes().size() == 0);
|
||||
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(scores.at(i)));
|
||||
}
|
||||
}
|
||||
TEST_CASE("tensors dataset predict & predict_proba", "[XSPODE]") {
|
||||
auto raw = RawDatasets("iris", true);
|
||||
auto scores = std::vector<float>({0.966667, 0.9333333, 0.966667, 0.966667});
|
||||
auto probs_expected = std::vector<std::vector<float>>({
|
||||
{0.999017, 0.000306908, 0.000676449},
|
||||
{0.99831, 0.00119304, 0.000497099},
|
||||
{0.998432, 0.00078416, 0.00078416},
|
||||
{0.998801, 0.000599438, 0.000599438}
|
||||
});
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
auto clf = bayesnet::XSpode(i);
|
||||
clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states,
|
||||
raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 5);
|
||||
REQUIRE(clf.getNumberOfEdges() == 9);
|
||||
REQUIRE(clf.getNotes().size() == 0);
|
||||
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(scores.at(i)));
|
||||
// Get the first 4 lines of X_test to do predict_proba
|
||||
auto X_reduced = raw.X_test.slice(1, 0, 4);
|
||||
auto proba = clf.predict_proba(X_reduced);
|
||||
for (int p = 0; p < 3; ++p) {
|
||||
REQUIRE(proba[0][p].item<double>() == Catch::Approx(probs_expected.at(i).at(p)));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
TEST_CASE("mfeat-factors dataset test", "[XSPODE]") {
|
||||
auto raw = RawDatasets("mfeat-factors", true);
|
||||
auto scores = std::vector<float>({0.9825, 0.9775, 0.9775, 0.99});
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
auto clf = bayesnet::XSpode(i);
|
||||
clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 217);
|
||||
REQUIRE(clf.getNumberOfEdges() == 433);
|
||||
REQUIRE(clf.getNotes().size() == 0);
|
||||
REQUIRE(clf.getNumberOfStates() == 652320);
|
||||
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(scores.at(i)));
|
||||
}
|
||||
}
|
||||
TEST_CASE("Laplace predict", "[XSPODE]") {
|
||||
auto raw = RawDatasets("iris", true);
|
||||
auto scores = std::vector<float>({0.966666639, 1.0f, 0.933333337, 1.0f});
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
auto clf = bayesnet::XSpode(0);
|
||||
clf.setHyperparameters({ {"parent", i} });
|
||||
clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, bayesnet::Smoothing_t::LAPLACE);
|
||||
REQUIRE(clf.getNumberOfNodes() == 5);
|
||||
REQUIRE(clf.getNumberOfEdges() == 9);
|
||||
REQUIRE(clf.getNotes().size() == 0);
|
||||
REQUIRE(clf.getNumberOfStates() == 64);
|
||||
REQUIRE(clf.getNFeatures() == 4);
|
||||
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(scores.at(i)));
|
||||
}
|
||||
}
|
||||
TEST_CASE("Not fitted model predict", "[XSPODE]")
|
||||
{
|
||||
auto raw = RawDatasets("iris", true);
|
||||
auto clf = bayesnet::XSpode(0);
|
||||
REQUIRE_THROWS_AS(clf.predict(std::vector<int>({1,2,3})), std::logic_error);
|
||||
}
|
||||
TEST_CASE("Test instance predict", "[XSPODE]")
|
||||
{
|
||||
auto raw = RawDatasets("iris", true);
|
||||
auto clf = bayesnet::XSpode(0);
|
||||
clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, bayesnet::Smoothing_t::ORIGINAL);
|
||||
REQUIRE(clf.predict(std::vector<int>({1,2,3,4})) == 1);
|
||||
REQUIRE(clf.score(raw.Xv, raw.yv) == Catch::Approx(0.973333359f));
|
||||
// Cestnik is not defined in the classifier so it should imply alpha_ = 0
|
||||
clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, bayesnet::Smoothing_t::CESTNIK);
|
||||
REQUIRE(clf.predict(std::vector<int>({1,2,3,4})) == 0);
|
||||
REQUIRE(clf.score(raw.Xv, raw.yv) == Catch::Approx(0.973333359f));
|
||||
}
|
||||
TEST_CASE("Test to_string and fitx", "[XSPODE]")
|
||||
{
|
||||
auto raw = RawDatasets("iris", true);
|
||||
auto clf = bayesnet::XSpode(0);
|
||||
auto weights = torch::full({raw.Xt.size(1)}, 1.0 / raw.Xt.size(1), torch::kFloat64);
|
||||
clf.fitx(raw.Xt, raw.yt, weights, bayesnet::Smoothing_t::ORIGINAL);
|
||||
REQUIRE(clf.getNumberOfNodes() == 5);
|
||||
REQUIRE(clf.getNumberOfEdges() == 9);
|
||||
REQUIRE(clf.getNotes().size() == 0);
|
||||
REQUIRE(clf.getNumberOfStates() == 64);
|
||||
REQUIRE(clf.getNFeatures() == 4);
|
||||
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.966666639f));
|
||||
REQUIRE(clf.to_string().size() == 1966);
|
||||
REQUIRE(clf.graph("Not yet implemented") == std::vector<std::string>({"Not yet implemented"}));
|
||||
}
|
Reference in New Issue
Block a user