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change boostaode ascending hyperparameter to order {asc,desc,rand}

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This commit is contained in:
Ricardo Montañana Gómez
2024-02-26 17:07:57 +01:00
parent 43dc79a345
commit fc3d63b7db
4 changed files with 59 additions and 19 deletions
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37
tests/TestBayesModels.cc
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@@ -17,7 +17,7 @@ const std::string ACTUAL_VERSION = "1.0.3";
TEST_CASE("Test Bayesian Classifiers score & version", "[BayesNet]")
{
map <pair<std::string, std::string>, float> scores = {
map <pair<std::string, std::string>, float> scores{
// Diabetes
{{"diabetes", "AODE"}, 0.811198}, {{"diabetes", "KDB"}, 0.852865}, {{"diabetes", "SPODE"}, 0.802083}, {{"diabetes", "TAN"}, 0.821615},
{{"diabetes", "AODELd"}, 0.8138f}, {{"diabetes", "KDBLd"}, 0.80208f}, {{"diabetes", "SPODELd"}, 0.78646f}, {{"diabetes", "TANLd"}, 0.8099f}, {{"diabetes", "BoostAODE"}, 0.83984f},
@@ -31,7 +31,7 @@ TEST_CASE("Test Bayesian Classifiers score & version", "[BayesNet]")
{{"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"}, 0.96f}, {{"iris", "TANLd"}, 0.97333f}, {{"iris", "BoostAODE"}, 0.98f}
};
std::map<std::string, bayesnet::BaseClassifier*> models = {
std::map<std::string, bayesnet::BaseClassifier*> models{
{"AODE", new bayesnet::AODE()}, {"AODELd", new bayesnet::AODELd()},
{"BoostAODE", new bayesnet::BoostAODE()},
{"KDB", new bayesnet::KDB(2)}, {"KDBLd", new bayesnet::KDBLd(2)},
@@ -104,7 +104,7 @@ TEST_CASE("BoostAODE test used features in train note and score", "[BayesNet]")
auto raw = RawDatasets("diabetes", true);
auto clf = bayesnet::BoostAODE(true);
clf.setHyperparameters({
{"ascending",true},
{"order", "asc"},
{"convergence", true},
{"repeatSparent",true},
{"select_features","CFS"},
@@ -168,8 +168,8 @@ TEST_CASE("Model predict_proba", "[BayesNet]")
{0, 1, 0},
{0, 1, 0}
});
std::map<std::string, std::vector<std::vector<double>>> res_prob = { {"TAN", res_prob_tan}, {"SPODE", res_prob_spode} , {"BoostAODEproba", res_prob_baode }, {"BoostAODEvoting", res_prob_voting } };
std::map<std::string, bayesnet::BaseClassifier*> models = { {"TAN", new bayesnet::TAN()}, {"SPODE", new bayesnet::SPODE(0)}, {"BoostAODEproba", new bayesnet::BoostAODE(false)}, {"BoostAODEvoting", new bayesnet::BoostAODE(true)} };
std::map<std::string, std::vector<std::vector<double>>> res_prob{ {"TAN", res_prob_tan}, {"SPODE", res_prob_spode} , {"BoostAODEproba", res_prob_baode }, {"BoostAODEvoting", res_prob_voting } };
std::map<std::string, bayesnet::BaseClassifier*> models{ {"TAN", new bayesnet::TAN()}, {"SPODE", new bayesnet::SPODE(0)}, {"BoostAODEproba", new bayesnet::BoostAODE(false)}, {"BoostAODEvoting", new bayesnet::BoostAODE(true)} };
int init_index = 78;
auto raw = RawDatasets("iris", true);
@@ -178,9 +178,9 @@ TEST_CASE("Model predict_proba", "[BayesNet]")
auto clf = models[model];
clf->fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
auto y_pred_proba = clf->predict_proba(raw.Xv);
auto yt_pred_proba = clf->predict_proba(raw.Xt);
auto y_pred = clf->predict(raw.Xv);
auto yt_pred = clf->predict(raw.Xt);
auto yt_pred_proba = clf->predict_proba(raw.Xt);
REQUIRE(y_pred.size() == yt_pred.size(0));
REQUIRE(y_pred.size() == y_pred_proba.size());
REQUIRE(y_pred.size() == yt_pred_proba.size(0));
@@ -193,6 +193,9 @@ TEST_CASE("Model predict_proba", "[BayesNet]")
REQUIRE(predictedClass == y_pred[i]);
// Check predict is coherent with predict_proba
REQUIRE(yt_pred_proba[i].argmax().item<int>() == y_pred[i]);
for (int j = 0; j < yt_pred_proba.size(1); j++) {
REQUIRE(yt_pred_proba[i][j].item<double>() == Catch::Approx(y_pred_proba[i][j]).epsilon(raw.epsilon));
}
}
// Check predict_proba values for vectors and tensors
for (int i = 0; i < res_prob.size(); i++) {
@@ -222,3 +225,25 @@ TEST_CASE("BoostAODE voting-proba", "[BayesNet]")
REQUIRE(pred_voting[83][2] == Catch::Approx(0.552091).epsilon(raw.epsilon));
REQUIRE(pred_proba[83][2] == Catch::Approx(0.546017).epsilon(raw.epsilon));
}
TEST_CASE("BoostAODE order asc, desc & random", "[BayesNet]")
{
auto raw = RawDatasets("glass", true);
std::map<std::string, double> scores{
{"asc", 0.83178f }, { "desc", 0.84579f }, { "rand", 0.83645f }
};
for (const std::string& order : { "asc", "desc", "rand" }) {
auto clf = bayesnet::BoostAODE();
clf.setHyperparameters({
{"order", order},
});
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
auto score = clf.score(raw.Xv, raw.yv);
auto scoret = clf.score(raw.Xt, raw.yt);
auto score2 = clf.score(raw.Xv, raw.yv);
auto scoret2 = clf.score(raw.Xt, raw.yt);
INFO("order: " + order);
REQUIRE(score == Catch::Approx(scores[order]).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
}
}