62 lines
2.6 KiB
C++
62 lines
2.6 KiB
C++
#define CATCH_CONFIG_MAIN // This tells Catch to provide a main() - only do
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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 <vector>
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#include <map>
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#include <string>
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#include "../src/KDB.h"
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#include "../src/TAN.h"
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#include "../src/SPODE.h"
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#include "../src/AODE.h"
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#include "utils.h"
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TEST_CASE("Test Bayesian Classifiers score", "[BayesNet]")
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{
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map <pair<string, string>, float> scores = {
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{{"diabetes", "AODE"}, 0.811198}, {{"diabetes", "KDB"}, 0.852865}, {{"diabetes", "SPODE"}, 0.802083}, {{"diabetes", "TAN"}, 0.821615},
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{{"ecoli", "AODE"}, 0.889881}, {{"ecoli", "KDB"}, 0.889881}, {{"ecoli", "SPODE"}, 0.880952}, {{"ecoli", "TAN"}, 0.892857},
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{{"glass", "AODE"}, 0.78972}, {{"glass", "KDB"}, 0.827103}, {{"glass", "SPODE"}, 0.775701}, {{"glass", "TAN"}, 0.827103},
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{{"iris", "AODE"}, 0.973333}, {{"iris", "KDB"}, 0.973333}, {{"iris", "SPODE"}, 0.973333}, {{"iris", "TAN"}, 0.973333}
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};
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string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
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auto[Xd, y, features, className, states] = loadFile(file_name);
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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(Xd, y, features, className, states);
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auto score = clf.score(Xd, y);
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//scores[{file_name, "TAN"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "TAN"}]).epsilon(1e-6));
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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(Xd, y, features, className, states);
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auto score = clf.score(Xd, y);
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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(1e-6));
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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(Xd, y, features, className, states);
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auto score = clf.score(Xd, y);
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// scores[{file_name, "SPODE"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "SPODE"}]).epsilon(1e-6));
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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(Xd, y, features, className, states);
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auto score = clf.score(Xd, y);
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// scores[{file_name, "AODE"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "AODE"}]).epsilon(1e-6));
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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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// }
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} |