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Begin Test Folding

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This commit is contained in:
Ricardo Montañana Gómez
2023-10-06 17:08:54 +02:00
parent b9e0028e9d
commit 17e079edd5
10 changed files with 250 additions and 55 deletions
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67
tests/TestBayesModels.cc
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@@ -34,83 +34,81 @@ TEST_CASE("Test Bayesian Classifiers score", "[BayesNet]")
};
string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
auto [XCont, yCont, featuresCont, classNameCont, statesCont] = loadDataset(file_name, true, false);
auto [XDisc, yDisc, featuresDisc, classNameDisc, statesDisc] = loadFile(file_name);
double epsilon = 1e-5;
auto raw = RawDatasets(file_name, false);
SECTION("Test TAN classifier (" + file_name + ")")
{
auto clf = bayesnet::TAN();
clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
auto score = clf.score(XDisc, yDisc);
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
auto score = clf.score(raw.Xv, raw.yv);
//scores[{file_name, "TAN"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "TAN"}]).epsilon(epsilon));
REQUIRE(score == Catch::Approx(scores[{file_name, "TAN"}]).epsilon(raw.epsilon));
}
SECTION("Test TANLd classifier (" + file_name + ")")
{
auto clf = bayesnet::TANLd();
clf.fit(XCont, yCont, featuresCont, classNameCont, statesCont);
auto score = clf.score(XCont, yCont);
clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
auto score = clf.score(raw.Xt, raw.yt);
//scores[{file_name, "TANLd"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "TANLd"}]).epsilon(epsilon));
REQUIRE(score == Catch::Approx(scores[{file_name, "TANLd"}]).epsilon(raw.epsilon));
}
SECTION("Test KDB classifier (" + file_name + ")")
{
auto clf = bayesnet::KDB(2);
clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
auto score = clf.score(XDisc, yDisc);
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
auto score = clf.score(raw.Xv, raw.yv);
//scores[{file_name, "KDB"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "KDB"
}]).epsilon(epsilon));
}]).epsilon(raw.epsilon));
}
SECTION("Test KDBLd classifier (" + file_name + ")")
{
auto clf = bayesnet::KDBLd(2);
clf.fit(XCont, yCont, featuresCont, classNameCont, statesCont);
auto score = clf.score(XCont, yCont);
clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
auto score = clf.score(raw.Xt, raw.yt);
//scores[{file_name, "KDBLd"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "KDBLd"
}]).epsilon(epsilon));
}]).epsilon(raw.epsilon));
}
SECTION("Test SPODE classifier (" + file_name + ")")
{
auto clf = bayesnet::SPODE(1);
clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
auto score = clf.score(XDisc, yDisc);
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
auto score = clf.score(raw.Xv, raw.yv);
// scores[{file_name, "SPODE"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "SPODE"}]).epsilon(epsilon));
REQUIRE(score == Catch::Approx(scores[{file_name, "SPODE"}]).epsilon(raw.epsilon));
}
SECTION("Test SPODELd classifier (" + file_name + ")")
{
auto clf = bayesnet::SPODELd(1);
clf.fit(XCont, yCont, featuresCont, classNameCont, statesCont);
auto score = clf.score(XCont, yCont);
clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
auto score = clf.score(raw.Xt, raw.yt);
// scores[{file_name, "SPODELd"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "SPODELd"}]).epsilon(epsilon));
REQUIRE(score == Catch::Approx(scores[{file_name, "SPODELd"}]).epsilon(raw.epsilon));
}
SECTION("Test AODE classifier (" + file_name + ")")
{
auto clf = bayesnet::AODE();
clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
auto score = clf.score(XDisc, yDisc);
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
auto score = clf.score(raw.Xv, raw.yv);
// scores[{file_name, "AODE"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "AODE"}]).epsilon(epsilon));
REQUIRE(score == Catch::Approx(scores[{file_name, "AODE"}]).epsilon(raw.epsilon));
}
SECTION("Test AODELd classifier (" + file_name + ")")
{
auto clf = bayesnet::AODELd();
clf.fit(XCont, yCont, featuresCont, classNameCont, statesCont);
auto score = clf.score(XCont, yCont);
clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
auto score = clf.score(raw.Xt, raw.yt);
// scores[{file_name, "AODELd"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "AODELd"}]).epsilon(epsilon));
REQUIRE(score == Catch::Approx(scores[{file_name, "AODELd"}]).epsilon(raw.epsilon));
}
SECTION("Test BoostAODE classifier (" + file_name + ")")
{
auto clf = bayesnet::BoostAODE();
clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
auto score = clf.score(XDisc, yDisc);
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
auto score = clf.score(raw.Xv, raw.yv);
// scores[{file_name, "BoostAODE"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "BoostAODE"}]).epsilon(epsilon));
REQUIRE(score == Catch::Approx(scores[{file_name, "BoostAODE"}]).epsilon(raw.epsilon));
}
// for (auto scores : scores) {
// cout << "{{\"" << scores.first.first << "\", \"" << scores.first.second << "\"}, " << scores.second << "}, ";
@@ -125,10 +123,9 @@ TEST_CASE("Models features", "[BayesNet]")
"sepallength -> sepalwidth", "sepalwidth [shape=circle] \n", "sepalwidth -> petalwidth", "}\n"
}
);
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::TAN();
auto [XDisc, yDisc, featuresDisc, classNameDisc, statesDisc] = loadFile("iris");
clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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, "});
@@ -136,9 +133,9 @@ TEST_CASE("Models features", "[BayesNet]")
}
TEST_CASE("Get num features & num edges", "[BayesNet]")
{
auto [XDisc, yDisc, featuresDisc, classNameDisc, statesDisc] = loadFile("iris");
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::KDB(2);
clf.fit(XDisc, yDisc, featuresDisc, classNameDisc, statesDisc);
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
REQUIRE(clf.getNumberOfNodes() == 6);
REQUIRE(clf.getNumberOfEdges() == 8);
}