rmontanana/BayesNet
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Add tests for Classifier class

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This commit is contained in:
Ricardo Montañana Gómez 2024-04-08 01:25:14 +02:00
parent 9014649a0d
commit 50543e7929
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
6 changed files with 73 additions and 9 deletions
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2
bayesnet/BaseClassifier.h
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@ -30,7 +30,7 @@ namespace bayesnet {
virtual std::string getVersion() = 0;
std::vector<std::string> virtual topological_order() = 0;
std::vector<std::string> virtual getNotes() const = 0;
void virtual dump_cpt()const = 0;
std::string virtual dump_cpt()const = 0;
virtual void setHyperparameters(const nlohmann::json& hyperparameters) = 0;
std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }
protected:

10
bayesnet/classifiers/Classifier.cc
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@ -75,11 +75,11 @@ namespace bayesnet {
if (torch::is_floating_point(dataset)) {
throw std::invalid_argument("dataset (X, y) must be of type Integer");
}
if (n != features.size()) {
throw std::invalid_argument("Classifier: X " + std::to_string(n) + " and features " + std::to_string(features.size()) + " must have the same number of features");
if (dataset.size(0) - 1 != features.size()) {
throw std::invalid_argument("Classifier: X " + std::to_string(dataset.size(0) - 1) + " and features " + std::to_string(features.size()) + " must have the same number of features");
}
if (states.find(className) == states.end()) {
throw std::invalid_argument("className not found in states");
throw std::invalid_argument("class name not found in states");
}
for (auto feature : features) {
if (states.find(feature) == states.end()) {
@ -175,9 +175,9 @@ namespace bayesnet {
{
return model.topological_sort();
}
void Classifier::dump_cpt() const
std::string Classifier::dump_cpt() const
{
model.dump_cpt();
return model.dump_cpt();
}
void Classifier::setHyperparameters(const nlohmann::json& hyperparameters)
{

2
bayesnet/classifiers/Classifier.h
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@ -30,7 +30,7 @@ namespace bayesnet {
std::vector<std::string> show() const override;
std::vector<std::string> topological_order() override;
std::vector<std::string> getNotes() const override { return notes; }
void dump_cpt() const override;
std::string dump_cpt() const override;
void setHyperparameters(const nlohmann::json& hyperparameters) override; //For classifiers that don't have hyperparameters
protected:
bool fitted;

3
bayesnet/ensembles/Ensemble.h
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@ -25,8 +25,9 @@ namespace bayesnet {
{
return std::vector<std::string>();
}
void dump_cpt() const override
std::string dump_cpt() const override
{
return "";
}
protected:
torch::Tensor predict_average_voting(torch::Tensor& X);

63
tests/TestBayesClassifier.cc
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@ -20,4 +20,67 @@ TEST_CASE("Test Cannot build dataset with wrong data tensor", "[Classifier]")
auto yshort = torch::zeros({ 149 }, torch::kInt32);
REQUIRE_THROWS_AS(model.fit(raw.Xt, yshort, raw.featurest, raw.classNamet, raw.statest), std::runtime_error);
REQUIRE_THROWS_WITH(model.fit(raw.Xt, yshort, raw.featurest, raw.classNamet, raw.statest), "* Error in X and y dimensions *\nX dimensions: [4, 150]\ny dimensions: [149]");
}
TEST_CASE("Invalid data type", "[Classifier]")
{
auto model = bayesnet::TAN();
auto raw = RawDatasets("iris", false);
REQUIRE_THROWS_AS(model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest), std::invalid_argument);
REQUIRE_THROWS_WITH(model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest), "dataset (X, y) must be of type Integer");
}
TEST_CASE("Invalid number of features", "[Classifier]")
{
auto model = bayesnet::TAN();
auto raw = RawDatasets("iris", true);
auto Xt = torch::cat({ raw.Xt, torch::zeros({ 1, 150 }, torch::kInt32) }, 0);
REQUIRE_THROWS_AS(model.fit(Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest), std::invalid_argument);
REQUIRE_THROWS_WITH(model.fit(Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest), "Classifier: X 5 and features 4 must have the same number of features");
}
TEST_CASE("Invalid class name", "[Classifier]")
{
auto model = bayesnet::TAN();
auto raw = RawDatasets("iris", true);
REQUIRE_THROWS_AS(model.fit(raw.Xt, raw.yt, raw.featurest, "duck", raw.statest), std::invalid_argument);
REQUIRE_THROWS_WITH(model.fit(raw.Xt, raw.yt, raw.featurest, "duck", raw.statest), "class name not found in states");
}
TEST_CASE("Invalid feature name", "[Classifier]")
{
auto model = bayesnet::TAN();
auto raw = RawDatasets("iris", true);
auto statest = raw.statest;
statest.erase("petallength");
REQUIRE_THROWS_AS(model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, statest), std::invalid_argument);
REQUIRE_THROWS_WITH(model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, statest), "feature [petallength] not found in states");
}
TEST_CASE("Topological order", "[Classifier]")
{
auto model = bayesnet::TAN();
auto raw = RawDatasets("iris", true);
model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
auto order = model.topological_order();
REQUIRE(order.size() == 4);
REQUIRE(order[0] == "petallength");
REQUIRE(order[1] == "sepallength");
REQUIRE(order[2] == "sepalwidth");
REQUIRE(order[3] == "petalwidth");
}
TEST_CASE("Not fitted model", "[Classifier]")
{
auto model = bayesnet::TAN();
auto raw = RawDatasets("iris", true);
auto message = "Classifier has not been fitted";
// tensors
REQUIRE_THROWS_AS(model.predict(raw.Xt), std::logic_error);
REQUIRE_THROWS_WITH(model.predict(raw.Xt), message);
REQUIRE_THROWS_AS(model.predict_proba(raw.Xt), std::logic_error);
REQUIRE_THROWS_WITH(model.predict_proba(raw.Xt), message);
REQUIRE_THROWS_AS(model.score(raw.Xt, raw.yt), std::logic_error);
REQUIRE_THROWS_WITH(model.score(raw.Xt, raw.yt), message);
// vectors
REQUIRE_THROWS_AS(model.predict(raw.Xv), std::logic_error);
REQUIRE_THROWS_WITH(model.predict(raw.Xv), message);
REQUIRE_THROWS_AS(model.predict_proba(raw.Xv), std::logic_error);
REQUIRE_THROWS_WITH(model.predict_proba(raw.Xv), message);
REQUIRE_THROWS_AS(model.score(raw.Xv, raw.yv), std::logic_error);
REQUIRE_THROWS_WITH(model.score(raw.Xv, raw.yv), message);
}

2
tests/TestBayesModels.cc
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@ -246,7 +246,7 @@ TEST_CASE("BoostAODE voting-proba", "[Models]")
REQUIRE(score_voting == Catch::Approx(0.98).epsilon(raw.epsilon));
REQUIRE(pred_voting[83][2] == Catch::Approx(0.552091).epsilon(raw.epsilon));
REQUIRE(pred_proba[83][2] == Catch::Approx(0.546017).epsilon(raw.epsilon));
clf.dump_cpt();
REQUIRE(clf.dump_cpt() == "");
REQUIRE(clf.topological_order() == std::vector<std::string>());
}
TEST_CASE("AODE voting-proba", "[Models]")