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Add First BayesMetrics Tests

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Ricardo Montañana Gómez
2023-10-05 01:14:16 +02:00
parent 3448fb1299
commit 5f0676691c
4 changed files with 64 additions and 62 deletions
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53
tests/TestBayesModels.cc
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@@ -21,29 +21,30 @@ TEST_CASE("Test Bayesian Classifiers score", "[BayesNet]")
map <pair<string, string>, float> scores = {
// Diabetes
{{"diabetes", "AODE"}, 0.811198}, {{"diabetes", "KDB"}, 0.852865}, {{"diabetes", "SPODE"}, 0.802083}, {{"diabetes", "TAN"}, 0.821615},
{{"diabetes", "AODELd"}, 0.811198}, {{"diabetes", "KDBLd"}, 0.852865}, {{"diabetes", "SPODELd"}, 0.802083}, {{"diabetes", "TANLd"}, 0.821615}, {{"diabetes", "BoostAODE"}, 0.821615},
{{"diabetes", "AODELd"}, 0.8138f}, {{"diabetes", "KDBLd"}, 0.80208f}, {{"diabetes", "SPODELd"}, 0.78646f}, {{"diabetes", "TANLd"}, 0.8099f}, {{"diabetes", "BoostAODE"}, 0.83984f},
// Ecoli
{{"ecoli", "AODE"}, 0.889881}, {{"ecoli", "KDB"}, 0.889881}, {{"ecoli", "SPODE"}, 0.880952}, {{"ecoli", "TAN"}, 0.892857},
{{"ecoli", "AODELd"}, 0.889881}, {{"ecoli", "KDBLd"}, 0.889881}, {{"ecoli", "SPODELd"}, 0.880952}, {{"ecoli", "TANLd"}, 0.892857}, {{"ecoli", "BoostAODE"}, 0.892857},
{{"ecoli", "AODELd"}, 0.8869f}, {{"ecoli", "KDBLd"}, 0.875f}, {{"ecoli", "SPODELd"}, 0.84226f}, {{"ecoli", "TANLd"}, 0.86905f}, {{"ecoli", "BoostAODE"}, 0.89583f},
// Glass
{{"glass", "AODE"}, 0.78972}, {{"glass", "KDB"}, 0.827103}, {{"glass", "SPODE"}, 0.775701}, {{"glass", "TAN"}, 0.827103},
{{"glass", "AODELd"}, 0.78972}, {{"glass", "KDBLd"}, 0.827103}, {{"glass", "SPODELd"}, 0.775701}, {{"glass", "TANLd"}, 0.827103}, {{"glass", "BoostAODE"}, 0.827103},
{{"glass", "AODELd"}, 0.79439f}, {{"glass", "KDBLd"}, 0.85047f}, {{"glass", "SPODELd"}, 0.79439f}, {{"glass", "TANLd"}, 0.86449f}, {{"glass", "BoostAODE"}, 0.84579f},
// Iris
{{"iris", "AODE"}, 0.973333}, {{"iris", "KDB"}, 0.973333}, {{"iris", "SPODE"}, 0.973333}, {{"iris", "TAN"}, 0.973333},
{{"iris", "AODELd"}, 0.973333}, {{"iris", "KDBLd"}, 0.973333}, {{"iris", "SPODELd"}, 0.973333}, {{"iris", "TANLd"}, 0.973333}, {{"iris", "BoostAODE"}, 0.973333}
{{"iris", "AODELd"}, 0.973333}, {{"iris", "KDBLd"}, 0.973333}, {{"iris", "SPODELd"}, 0.96f}, {{"iris", "TANLd"}, 0.97333f}, {{"iris", "BoostAODE"}, 0.98f}
};
string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
auto [XCont, yCont, featuresCont, classNameCont, statesCont] = loadDataset(file_name, true, false);
auto [XDisc, yDisc, featuresDisc, className, statesDisc] = loadFile(file_name);
auto [XDisc, yDisc, featuresDisc, classNameDisc, statesDisc] = loadFile(file_name);
double epsilon = 1e-5;
SECTION("Test TAN classifier (" + file_name + ")")
{
auto clf = bayesnet::TAN();
clf.fit(XDisc, yDisc, featuresDisc, className, statesDisc);
clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
auto score = clf.score(XDisc, yDisc);
//scores[{file_name, "TAN"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "TAN"}]).epsilon(1e-6));
REQUIRE(score == Catch::Approx(scores[{file_name, "TAN"}]).epsilon(epsilon));
}
SECTION("Test TANLd classifier (" + file_name + ")")
{
@@ -51,16 +52,16 @@ TEST_CASE("Test Bayesian Classifiers score", "[BayesNet]")
clf.fit(XCont, yCont, featuresCont, classNameCont, statesCont);
auto score = clf.score(XCont, yCont);
//scores[{file_name, "TANLd"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "TANLd"}]).epsilon(1e-6));
REQUIRE(score == Catch::Approx(scores[{file_name, "TANLd"}]).epsilon(epsilon));
}
SECTION("Test KDB classifier (" + file_name + ")")
{
auto clf = bayesnet::KDB(2);
clf.fit(XDisc, yDisc, featuresDisc, className, statesDisc);
clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
auto score = clf.score(XDisc, yDisc);
//scores[{file_name, "KDB"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "KDB"
}]).epsilon(1e-6));
}]).epsilon(epsilon));
}
SECTION("Test KDBLd classifier (" + file_name + ")")
{
@@ -69,15 +70,15 @@ TEST_CASE("Test Bayesian Classifiers score", "[BayesNet]")
auto score = clf.score(XCont, yCont);
//scores[{file_name, "KDBLd"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "KDBLd"
}]).epsilon(1e-6));
}]).epsilon(epsilon));
}
SECTION("Test SPODE classifier (" + file_name + ")")
{
auto clf = bayesnet::SPODE(1);
clf.fit(XDisc, yDisc, featuresDisc, className, statesDisc);
clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
auto score = clf.score(XDisc, yDisc);
// scores[{file_name, "SPODE"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "SPODE"}]).epsilon(1e-6));
REQUIRE(score == Catch::Approx(scores[{file_name, "SPODE"}]).epsilon(epsilon));
}
SECTION("Test SPODELd classifier (" + file_name + ")")
{
@@ -85,31 +86,31 @@ TEST_CASE("Test Bayesian Classifiers score", "[BayesNet]")
clf.fit(XCont, yCont, featuresCont, classNameCont, statesCont);
auto score = clf.score(XCont, yCont);
// scores[{file_name, "SPODELd"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "SPODELd"}]).epsilon(1e-6));
REQUIRE(score == Catch::Approx(scores[{file_name, "SPODELd"}]).epsilon(epsilon));
}
SECTION("Test AODE classifier (" + file_name + ")")
{
auto clf = bayesnet::AODE();
clf.fit(XDisc, yDisc, featuresDisc, className, statesDisc);
clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
auto score = clf.score(XDisc, yDisc);
// scores[{file_name, "AODE"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "AODE"}]).epsilon(1e-6));
REQUIRE(score == Catch::Approx(scores[{file_name, "AODE"}]).epsilon(epsilon));
}
SECTION("Test AODELd classifier (" + file_name + ")")
{
auto clf = bayesnet::AODE();
auto clf = bayesnet::AODELd();
clf.fit(XCont, yCont, featuresCont, classNameCont, statesCont);
auto score = clf.score(XCont, yCont);
// scores[{file_name, "AODELd"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "AODELd"}]).epsilon(1e-6));
REQUIRE(score == Catch::Approx(scores[{file_name, "AODELd"}]).epsilon(epsilon));
}
SECTION("Test BoostAODE classifier (" + file_name + ")")
{
auto clf = bayesnet::BoostAODE();
clf.fit(XDisc, yDisc, featuresDisc, className, statesDisc);
clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
auto score = clf.score(XDisc, yDisc);
// scores[{file_name, "BoostAODE"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "BoostAODE"}]).epsilon(1e-6));
REQUIRE(score == Catch::Approx(scores[{file_name, "BoostAODE"}]).epsilon(epsilon));
}
// for (auto scores : scores) {
// cout << "{{\"" << scores.first.first << "\", \"" << scores.first.second << "\"}, " << scores.second << "}, ";
@@ -126,18 +127,18 @@ TEST_CASE("Models featuresDisc")
);
auto clf = bayesnet::TAN();
auto [XDisc, yDisc, featuresDisc, className, statesDisc] = loadFile("iris");
clf.fit(XDisc, yDisc, featuresDisc, className, statesDisc);
REQUIRE(clf.getNumberOfNodes() == 5);
auto [XDisc, yDisc, featuresDisc, classNameDisc, statesDisc] = loadFile("iris");
clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
REQUIRE(clf.getNumberOfNodes() == 6);
REQUIRE(clf.getNumberOfEdges() == 7);
REQUIRE(clf.show() == vector<string>{"class -> sepallength, sepalwidth, petallength, petalwidth, ", "petallength -> sepallength, ", "petalwidth -> ", "sepallength -> sepalwidth, ", "sepalwidth -> petalwidth, "});
REQUIRE(clf.graph("Test") == graph);
}
TEST_CASE("Get num featuresDisc & num edges")
{
auto [XDisc, yDisc, featuresDisc, className, statesDisc] = loadFile("iris");
auto [XDisc, yDisc, featuresDisc, classNameDisc, statesDisc] = loadFile("iris");
auto clf = bayesnet::KDB(2);
clf.fit(XDisc, yDisc, featuresDisc, className, statesDisc);
REQUIRE(clf.getNumberOfNodes() == 5);
clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
REQUIRE(clf.getNumberOfNodes() == 6);
REQUIRE(clf.getNumberOfEdges() == 8);
}