rmontanana/BayesNet
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Change BoostAODE tests results because folding upgrade

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This commit is contained in:
Ricardo Montañana Gómez 2024-05-12 20:23:05 +02:00
parent c0fc107abb
commit 56b62a67cc
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
1 changed files with 11 additions and 12 deletions
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23
tests/TestBoostAODE.cc
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@ -45,7 +45,7 @@ TEST_CASE("Feature_select FCBF", "[BoostAODE]")
REQUIRE(clf.getNumberOfNodes() == 90); REQUIRE(clf.getNumberOfNodes() == 90);
REQUIRE(clf.getNumberOfEdges() == 153); REQUIRE(clf.getNumberOfEdges() == 153);
REQUIRE(clf.getNotes().size() == 2); REQUIRE(clf.getNotes().size() == 2);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 5 of 9 with FCBF"); REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with FCBF");
REQUIRE(clf.getNotes()[1] == "Number of models: 9"); REQUIRE(clf.getNotes()[1] == "Number of models: 9");
} }
TEST_CASE("Test used features in train note and score", "[BoostAODE]") TEST_CASE("Test used features in train note and score", "[BoostAODE]")
@ -65,8 +65,8 @@ TEST_CASE("Test used features in train note and score", "[BoostAODE]")
REQUIRE(clf.getNotes()[1] == "Number of models: 8"); REQUIRE(clf.getNotes()[1] == "Number of models: 8");
auto score = clf.score(raw.Xv, raw.yv); auto score = clf.score(raw.Xv, raw.yv);
auto scoret = clf.score(raw.Xt, raw.yt); auto scoret = clf.score(raw.Xt, raw.yt);
REQUIRE(score == Catch::Approx(0.80078).epsilon(raw.epsilon)); REQUIRE(score == Catch::Approx(0.809895813).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.80078).epsilon(raw.epsilon)); REQUIRE(scoret == Catch::Approx(0.809895813).epsilon(raw.epsilon));
} }
TEST_CASE("Voting vs proba", "[BoostAODE]") TEST_CASE("Voting vs proba", "[BoostAODE]")
{ {
@ -149,15 +149,14 @@ TEST_CASE("Bisection Best", "[BoostAODE]")
{"convergence_best", false}, {"convergence_best", false},
}); });
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states); clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
REQUIRE(clf.getNumberOfNodes() == 75); REQUIRE(clf.getNumberOfNodes() == 210);
REQUIRE(clf.getNumberOfEdges() == 135); REQUIRE(clf.getNumberOfEdges() == 378);
REQUIRE(clf.getNotes().size() == 2); REQUIRE(clf.getNotes().size() == 1);
REQUIRE(clf.getNotes().at(0) == "Convergence threshold reached & 9 models eliminated"); REQUIRE(clf.getNotes().at(0) == "Number of models: 14");
REQUIRE(clf.getNotes().at(1) == "Number of models: 5");
auto score = clf.score(raw.X_test, raw.y_test); auto score = clf.score(raw.X_test, raw.y_test);
auto scoret = clf.score(raw.X_test, raw.y_test); auto scoret = clf.score(raw.X_test, raw.y_test);
REQUIRE(score == Catch::Approx(1.0f).epsilon(raw.epsilon)); REQUIRE(score == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(1.0f).epsilon(raw.epsilon)); REQUIRE(scoret == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
} }
TEST_CASE("Bisection Best vs Last", "[BoostAODE]") TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
{ {
@ -172,13 +171,13 @@ TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
clf.setHyperparameters(hyperparameters); clf.setHyperparameters(hyperparameters);
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states); clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
auto score_best = clf.score(raw.X_test, raw.y_test); auto score_best = clf.score(raw.X_test, raw.y_test);
REQUIRE(score_best == Catch::Approx(0.993355f).epsilon(raw.epsilon)); REQUIRE(score_best == Catch::Approx(0.980000019f).epsilon(raw.epsilon));
// Now we will set the hyperparameter to use the last accuracy // Now we will set the hyperparameter to use the last accuracy
hyperparameters["convergence_best"] = false; hyperparameters["convergence_best"] = false;
clf.setHyperparameters(hyperparameters); clf.setHyperparameters(hyperparameters);
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states); clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
auto score_last = clf.score(raw.X_test, raw.y_test); auto score_last = clf.score(raw.X_test, raw.y_test);
REQUIRE(score_last == Catch::Approx(0.996678f).epsilon(raw.epsilon)); REQUIRE(score_last == Catch::Approx(0.976666689f).epsilon(raw.epsilon));
} }
TEST_CASE("Block Update", "[BoostAODE]") TEST_CASE("Block Update", "[BoostAODE]")