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Add be_hyperparams hyperparameter to ODTE

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Ricardo Montañana Gómez
2024-08-02 12:56:23 +02:00
parent 4defb279b0
commit 7fbdcc1c3d
2 changed files with 20 additions and 1 deletions
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2
pyclfs/ODTE.cc
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@@ -3,7 +3,7 @@
namespace pywrap { namespace pywrap {
ODTE::ODTE() : PyClassifier("odte", "Odte") ODTE::ODTE() : PyClassifier("odte", "Odte")
{ {
validHyperparameters = { "n_jobs", "n_estimators", "random_state" }; validHyperparameters = { "n_jobs", "n_estimators", "random_state", "be_hyperparams" };
} }
int ODTE::getNumberOfNodes() const int ODTE::getNumberOfNodes() const
{ {

19
tests/TestPythonClassifiers.cc
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@@ -115,3 +115,22 @@ TEST_CASE("XGBoost", "[PyClassifiers]")
auto score = clf.score(raw.Xt, raw.yt); auto score = clf.score(raw.Xt, raw.yt);
REQUIRE(score == Catch::Approx(0.98).epsilon(raw.epsilon)); 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;
}