rmontanana/BayesNet
rmontanana/BayesNet
1
0
Fork 0
Code Issues 2 Pull Requests Packages Projects Releases 3 Wiki Activity

Change BoostAODE tests results because folding upgrade

Browse Source
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
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

23
tests/TestBoostAODE.cc
View File

@ -45,7 +45,7 @@ TEST_CASE("Feature_select FCBF", "[BoostAODE]")
REQUIRE(clf.getNumberOfNodes() == 90);
REQUIRE(clf.getNumberOfEdges() == 153);
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");
}
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");
auto score = clf.score(raw.Xv, raw.yv);
auto scoret = clf.score(raw.Xt, raw.yt);
REQUIRE(score == Catch::Approx(0.80078).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.80078).epsilon(raw.epsilon));
REQUIRE(score == Catch::Approx(0.809895813).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.809895813).epsilon(raw.epsilon));
}
TEST_CASE("Voting vs proba", "[BoostAODE]")
{
@ -149,15 +149,14 @@ TEST_CASE("Bisection Best", "[BoostAODE]")
{"convergence_best", false},
});
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
REQUIRE(clf.getNumberOfNodes() == 75);
REQUIRE(clf.getNumberOfEdges() == 135);
REQUIRE(clf.getNotes().size() == 2);
REQUIRE(clf.getNotes().at(0) == "Convergence threshold reached & 9 models eliminated");
REQUIRE(clf.getNotes().at(1) == "Number of models: 5");
REQUIRE(clf.getNumberOfNodes() == 210);
REQUIRE(clf.getNumberOfEdges() == 378);
REQUIRE(clf.getNotes().size() == 1);
REQUIRE(clf.getNotes().at(0) == "Number of models: 14");
auto score = 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(scoret == Catch::Approx(1.0f).epsilon(raw.epsilon));
REQUIRE(score == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
}
TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
{
@ -172,13 +171,13 @@ TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
clf.setHyperparameters(hyperparameters);
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);
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
hyperparameters["convergence_best"] = false;
clf.setHyperparameters(hyperparameters);
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);
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]")