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3d6b4f0614
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3d6b4f0614
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18844c7da7
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@@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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## [Unreleased]
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### Added
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- Add a new hyperparameter to the BoostAODE class, *alphablock*, to control the way α is computed, with the last model or with the ensmble built so far. Default value is *false*.
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## [1.0.6] 2024-11-23
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### Fixed
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@@ -12,7 +12,7 @@
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namespace bayesnet {
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Boost::Boost(bool predict_voting) : Ensemble(predict_voting)
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{
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validHyperparameters = { "order", "convergence", "convergence_best", "bisection", "threshold", "maxTolerance",
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validHyperparameters = { "alpha_block", "order", "convergence", "convergence_best", "bisection", "threshold", "maxTolerance",
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"predict_voting", "select_features", "block_update" };
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}
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void Boost::setHyperparameters(const nlohmann::json& hyperparameters_)
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@@ -26,6 +26,10 @@ namespace bayesnet {
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}
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hyperparameters.erase("order");
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}
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if (hyperparameters.contains("alpha_block")) {
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alpha_block = hyperparameters["alpha_block"];
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hyperparameters.erase("alpha_block");
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}
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if (hyperparameters.contains("convergence")) {
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convergence = hyperparameters["convergence"];
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hyperparameters.erase("convergence");
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@@ -66,6 +70,12 @@ namespace bayesnet {
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block_update = hyperparameters["block_update"];
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hyperparameters.erase("block_update");
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}
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if (block_update && alpha_block) {
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throw std::invalid_argument("alpha_block and block_update cannot be true at the same time");
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}
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if (block_update && !bisection) {
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throw std::invalid_argument("block_update needs bisection to be true");
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}
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Classifier::setHyperparameters(hyperparameters);
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}
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void Boost::buildModel(const torch::Tensor& weights)
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@@ -45,8 +45,8 @@ namespace bayesnet {
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std::string select_features_algorithm = Orders.DESC; // Selected feature selection algorithm
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FeatureSelect* featureSelector = nullptr;
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double threshold = -1;
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bool block_update = false;
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bool block_update = false; // if true, use block update algorithm, only meaningful if bisection is true
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bool alpha_block = false; // if true, the alpha is computed with the ensemble built so far and the new model
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};
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}
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#endif
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@@ -92,7 +92,25 @@ namespace bayesnet {
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model->fit(dataset, features, className, states, weights_, smoothing);
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alpha_t = 0.0;
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if (!block_update) {
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auto ypred = model->predict(X_train);
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torch::Tensor ypred;
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if (alpha_block) {
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//
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// Compute the prediction with the current ensemble + model
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//
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// Add the model to the ensemble
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n_models++;
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models.push_back(std::move(model));
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significanceModels.push_back(1);
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// Compute the prediction
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ypred = predict(X_train);
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// Remove the model from the ensemble
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model = std::move(models.back());
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models.pop_back();
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significanceModels.pop_back();
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n_models--;
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} else {
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ypred = model->predict(X_train);
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}
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// Step 3.1: Compute the classifier amout of say
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std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
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}
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@@ -130,6 +130,8 @@ TEST_CASE("Oddities", "[BoostAODE]")
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{ { "select_features","IWSS" }, { "threshold", 0.51 } },
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{ { "select_features","FCBF" }, { "threshold", 1e-8 } },
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{ { "select_features","FCBF" }, { "threshold", 1.01 } },
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{ { "alpha_block", true }, { "block_update", true } },
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{ { "bisection", false }, { "block_update", true } },
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};
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for (const auto& hyper : bad_hyper_fit.items()) {
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INFO("BoostAODE hyper: " << hyper.value().dump());
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@@ -137,7 +139,6 @@ TEST_CASE("Oddities", "[BoostAODE]")
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REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing), std::invalid_argument);
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}
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}
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TEST_CASE("Bisection Best", "[BoostAODE]")
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{
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auto clf = bayesnet::BoostAODE();
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@@ -180,7 +181,6 @@ TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
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auto score_last = clf.score(raw.X_test, raw.y_test);
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REQUIRE(score_last == Catch::Approx(0.976666689f).epsilon(raw.epsilon));
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}
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TEST_CASE("Block Update", "[BoostAODE]")
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{
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auto clf = bayesnet::BoostAODE();
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@@ -210,4 +210,19 @@ TEST_CASE("Block Update", "[BoostAODE]")
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// std::cout << note << std::endl;
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// }
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// std::cout << "Score " << score << std::endl;
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}
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TEST_CASE("Alphablock", "[BoostAODE]")
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{
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auto clf_alpha = bayesnet::BoostAODE();
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auto clf_no_alpha = bayesnet::BoostAODE();
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auto raw = RawDatasets("diabetes", true);
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clf_alpha.setHyperparameters({
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{"alpha_block", true},
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});
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clf_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
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clf_no_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
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auto score_alpha = clf_alpha.score(raw.X_test, raw.y_test);
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auto score_no_alpha = clf_no_alpha.score(raw.X_test, raw.y_test);
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REQUIRE(score_alpha == Catch::Approx(0.720779f).epsilon(raw.epsilon));
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REQUIRE(score_no_alpha == Catch::Approx(0.733766f).epsilon(raw.epsilon));
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}
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