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Update test

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This commit is contained in:
Ricardo Montañana Gómez
2024-08-02 19:11:14 +02:00
parent 7fbdcc1c3d
commit a3e00d2485
2 changed files with 28 additions and 26 deletions
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52
tests/TestPythonClassifiers.cc
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@@ -17,11 +17,11 @@ TEST_CASE("Test Python Classifiers score", "[PyClassifiers]")
{
map <pair<std::string, std::string>, float> scores = {
// Diabetes
{{"diabetes", "STree"}, 0.81641}, {{"diabetes", "ODTE"}, 0.84635}, {{"diabetes", "SVC"}, 0.76823}, {{"diabetes", "RandomForest"}, 1.0},
{{"diabetes", "STree"}, 0.81641}, {{"diabetes", "ODTE"}, 0.854166687}, {{"diabetes", "SVC"}, 0.76823}, {{"diabetes", "RandomForest"}, 1.0},
// Ecoli
{{"ecoli", "STree"}, 0.8125}, {{"ecoli", "ODTE"}, 0.84821}, {{"ecoli", "SVC"}, 0.89583}, {{"ecoli", "RandomForest"}, 1.0},
{{"ecoli", "STree"}, 0.8125}, {{"ecoli", "ODTE"}, 0.875}, {{"ecoli", "SVC"}, 0.89583}, {{"ecoli", "RandomForest"}, 1.0},
// Glass
{{"glass", "STree"}, 0.57009}, {{"glass", "ODTE"}, 0.77103}, {{"glass", "SVC"}, 0.35514}, {{"glass", "RandomForest"}, 1.0},
{{"glass", "STree"}, 0.57009}, {{"glass", "ODTE"}, 0.76168227}, {{"glass", "SVC"}, 0.35514}, {{"glass", "RandomForest"}, 1.0},
// Iris
{{"iris", "STree"}, 0.99333}, {{"iris", "ODTE"}, 0.98667}, {{"iris", "SVC"}, 0.97333}, {{"iris", "RandomForest"}, 1.0},
};
@@ -33,10 +33,10 @@ TEST_CASE("Test Python Classifiers score", "[PyClassifiers]")
{"RandomForest", new pywrap::RandomForest()}
};
map<std::string, std::string> versions = {
{"ODTE", "0.3.6"},
{"ODTE", "1.0.0"},
{"STree", "1.3.2"},
{"SVC", "1.5.0"},
{"RandomForest", "1.5.0"}
{"SVC", "1.5.1"},
{"RandomForest", "1.5.1"}
};
auto clf = models[name];
@@ -68,8 +68,10 @@ TEST_CASE("Classifiers features", "[PyClassifiers]")
}
TEST_CASE("Get num features & num edges", "[PyClassifiers]")
{
auto estimators = nlohmann::json::parse("{ \"n_estimators\": 10 }");
auto raw = RawDatasets("iris", false);
auto clf = pywrap::ODTE();
clf.setHyperparameters(estimators);
clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
REQUIRE(clf.getNumberOfNodes() == 50);
REQUIRE(clf.getNumberOfEdges() == 30);
@@ -115,22 +117,22 @@ TEST_CASE("XGBoost", "[PyClassifiers]")
auto score = clf.score(raw.Xt, raw.yt);
REQUIRE(score == Catch::Approx(0.98).epsilon(raw.epsilon));
}
TEST_CASE("XGBoost predict proba", "[PyClassifiers]")
{
auto raw = RawDatasets("iris", true);
auto clf = pywrap::XGBoost();
clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
nlohmann::json hyperparameters = { "n_jobs=1" };
clf.setHyperparameters(hyperparameters);
auto predict = clf.predict(raw.Xt);
// for (int row = 0; row < predict.size(0); row++) {
// auto sum = 0.0;
// for (int col = 0; col < predict.size(1); col++) {
// std::cout << std::setw(12) << std::setprecision(10) << predict[row][col].item<double>() << " ";
// sum += predict[row][col].item<int>();
// }
// std::cout << std::endl;
// // REQUIRE(sum == Catch::Approx(1.0).epsilon(raw.epsilon));
// }
std::cout << predict << std::endl;
}
// TEST_CASE("XGBoost predict proba", "[PyClassifiers]")
// {
// auto raw = RawDatasets("iris", true);
// auto clf = pywrap::XGBoost();
// clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
// // nlohmann::json hyperparameters = { "n_jobs=1" };
// // clf.setHyperparameters(hyperparameters);
// auto predict = clf.predict(raw.Xt);
// for (int row = 0; row < predict.size(0); row++) {
// auto sum = 0.0;
// for (int col = 0; col < predict.size(1); col++) {
// std::cout << std::setw(12) << std::setprecision(10) << predict[row][col].item<double>() << " ";
// sum += predict[row][col].item<int>();
// }
// std::cout << std::endl;
// // REQUIRE(sum == Catch::Approx(1.0).epsilon(raw.epsilon));
// }
// std::cout << predict << std::endl;
// }

2
tests/data/glass.arff
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@@ -114,7 +114,7 @@
@attribute 'Ca' real
@attribute 'Ba' real
@attribute 'Fe' real
@attribute 'Type' {'build wind float', 'build wind non-float', 'vehic wind float', 'vehic wind non-float', containers, tableware, headlamps}
@attribute 'Type' { 'build wind float', 'build wind non-float', 'vehic wind float', 'vehic wind non-float', containers, tableware, headlamps}
@data
1.51793,12.79,3.5,1.12,73.03,0.64,8.77,0,0,'build wind float'
1.51643,12.16,3.52,1.35,72.89,0.57,8.53,0,0,'vehic wind float'