From d39a17089ef13e7430dc603a0e8a39ad76c5a6d9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana=20G=C3=B3mez?= Date: Mon, 26 Feb 2024 20:29:08 +0100 Subject: [PATCH 01/11] Begin implementing predict_single hyperparameter in BoostAODE --- CHANGELOG.md | 3 ++- src/BoostAODE.cc | 31 ++++++++++++++++++++++++++++--- src/BoostAODE.h | 8 ++++++-- tests/TestBayesModels.cc | 22 ++++++++++++++++++++-- 4 files changed, 56 insertions(+), 8 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 8bea3e3..47d09a5 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,7 +9,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Added -- Change _ascending_ hyperparameter to _order_ with these possible values _{"asc", "desc", "rand"}_ +- Change _ascending_ hyperparameter to _order_ with these possible values _{"asc", "desc", "rand"}_, Default is _"desc"_. +- Add the _predict_single_ hyperparameter to control if only the last model created is used to predict in boost training or the whole ensemble (all the models built so far). Default is true. ## [1.0.3] diff --git a/src/BoostAODE.cc b/src/BoostAODE.cc index 9f11f02..10eb694 100644 --- a/src/BoostAODE.cc +++ b/src/BoostAODE.cc @@ -10,7 +10,10 @@ namespace bayesnet { BoostAODE::BoostAODE(bool predict_voting) : Ensemble(predict_voting) { - validHyperparameters = { "repeatSparent", "maxModels", "order", "convergence", "threshold", "select_features", "tolerance", "predict_voting" }; + validHyperparameters = { + "repeatSparent", "maxModels", "order", "convergence", "threshold", + "select_features", "tolerance", "predict_voting", "predict_single" + }; } void BoostAODE::buildModel(const torch::Tensor& weights) @@ -69,6 +72,10 @@ namespace bayesnet { convergence = hyperparameters["convergence"]; hyperparameters.erase("convergence"); } + if (hyperparameters.contains("predict_single")) { + predict_single = hyperparameters["predict_single"]; + hyperparameters.erase("predict_single"); + } if (hyperparameters.contains("threshold")) { threshold = hyperparameters["threshold"]; hyperparameters.erase("threshold"); @@ -116,7 +123,6 @@ namespace bayesnet { featureSelector->fit(); auto cfsFeatures = featureSelector->getFeatures(); for (const int& feature : cfsFeatures) { - // std::cout << "Feature: [" << feature << "] " << feature << " " << features.at(feature) << std::endl; featuresUsed.insert(feature); std::unique_ptr model = std::make_unique(feature); model->fit(dataset, features, className, states, weights_); @@ -128,8 +134,22 @@ namespace bayesnet { delete featureSelector; return featuresUsed; } + torch::Tensor BoostAODE::ensemble_predict(torch::Tensor& X, SPODE* model) + { + if (initialize_prob_table) { + initialize_prob_table = false; + prob_table = model->predict_proba(X) * 1.0; + } else { + prob_table += model->predict_proba(X) * 1.0; + } + // prob_table doesn't store probabilities but the sum of them + // to have them we need to divide by the sum of the significances but we + // don't need them to predict label values + return prob_table.argmax(1); + } void BoostAODE::trainModel(const torch::Tensor& weights) { + initialize_prob_table = true; fitted = true; // Algorithm based on the adaboost algorithm for classification // as explained in Ensemble methods (Zhi-Hua Zhou, 2012) @@ -181,7 +201,12 @@ namespace bayesnet { std::unique_ptr model; model = std::make_unique(feature); model->fit(dataset, features, className, states, weights_); - auto ypred = model->predict(X_train); + torch::Tensor ypred; + if (predict_single) { + ypred = model->predict(X_train); + } else { + ypred = ensemble_predict(X_train, dynamic_cast(model.get())); + } // Step 3.1: Compute the classifier amout of say auto mask_wrong = ypred != y_train; auto mask_right = ypred == y_train; diff --git a/src/BoostAODE.h b/src/BoostAODE.h index 7119194..c58bc3e 100644 --- a/src/BoostAODE.h +++ b/src/BoostAODE.h @@ -15,17 +15,21 @@ namespace bayesnet { void buildModel(const torch::Tensor& weights) override; void trainModel(const torch::Tensor& weights) override; private: + std::unordered_set initializeModels(); + torch::Tensor ensemble_predict(torch::Tensor& X, SPODE* model); torch::Tensor dataset_; torch::Tensor X_train, y_train, X_test, y_test; - std::unordered_set initializeModels(); // Hyperparameters bool repeatSparent = false; // if true, a feature can be selected more than once int maxModels = 0; int tolerance = 0; + bool predict_single = true; // wether the last model is used to predict in training or the whole ensemble std::string order_algorithm; // order to process the KBest features asc, desc, rand bool convergence = false; //if true, stop when the model does not improve bool selectFeatures = false; // if true, use feature selection - std::string select_features_algorithm = ""; // Selected feature selection algorithm + std::string select_features_algorithm = "desc"; // Selected feature selection algorithm + bool initialize_prob_table; // if true, initialize the prob_table with the first model (used in train) + torch::Tensor prob_table; // Table of probabilities for ensemble predicting if predict_single is false FeatureSelect* featureSelector = nullptr; double threshold = -1; }; diff --git a/tests/TestBayesModels.cc b/tests/TestBayesModels.cc index 0234747..c88f571 100644 --- a/tests/TestBayesModels.cc +++ b/tests/TestBayesModels.cc @@ -240,10 +240,28 @@ TEST_CASE("BoostAODE order asc, desc & random", "[BayesNet]") 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)); } } +TEST_CASE("BoostAODE predict_single", "[BayesNet]") +{ + + auto raw = RawDatasets("glass", true); + std::map scores{ + {true, 0.84579f }, { false, 0.81308f } + }; + for (const bool kind : { true, false}) { + auto clf = bayesnet::BoostAODE(); + clf.setHyperparameters({ + {"predict_single", kind}, {"order", "desc" }, + }); + 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); + INFO("kind: " + std::string(kind ? "true" : "false")); + REQUIRE(score == Catch::Approx(scores[kind]).epsilon(raw.epsilon)); + REQUIRE(scoret == Catch::Approx(scores[kind]).epsilon(raw.epsilon)); + } +} -- 2.45.2 From f10d0daf2ee9d1d88db1105bcee38e44686d1598 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana=20G=C3=B3mez?= Date: Tue, 27 Feb 2024 10:16:20 +0100 Subject: [PATCH 02/11] Update test --- CMakeLists.txt | 7 +++++++ tests/TestBayesModels.cc | 2 +- 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 9d42041..acd389f 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -36,6 +36,13 @@ option(CODE_COVERAGE "Collect coverage from test library" OFF) set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake/modules ${CMAKE_MODULE_PATH}) include(AddGitSubmodule) +if (CMAKE_BUILD_TYPE STREQUAL "Debug") + MESSAGE("Debug mode") + set(ENABLE_TESTING ON) + set(CODE_COVERAGE ON) +endif (CMAKE_BUILD_TYPE STREQUAL "Debug") + + if (CODE_COVERAGE) enable_testing() include(CodeCoverage) diff --git a/tests/TestBayesModels.cc b/tests/TestBayesModels.cc index c88f571..db318ad 100644 --- a/tests/TestBayesModels.cc +++ b/tests/TestBayesModels.cc @@ -250,7 +250,7 @@ TEST_CASE("BoostAODE predict_single", "[BayesNet]") auto raw = RawDatasets("glass", true); std::map scores{ - {true, 0.84579f }, { false, 0.81308f } + {true, 0.84579f }, { false, 0.80841f } }; for (const bool kind : { true, false}) { auto clf = bayesnet::BoostAODE(); -- 2.45.2 From 903b1433388fd63f5e752eb63fd67f05c0d9873c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ricardo=20Montan=CC=83ana?= Date: Tue, 27 Feb 2024 13:06:13 +0100 Subject: [PATCH 03/11] Refactor library structure and add sample --- CHANGELOG.md | 5 ++ CMakeLists.txt | 3 +- Makefile | 10 +++- sample/CMakeLists.txt | 14 +++++ sample/sample.cc | 62 ++++++++++++++++++++ src/CMakeLists.txt | 11 +++- src/{ => bayesian_network}/Network.cc | 0 src/{ => bayesian_network}/Network.h | 0 src/{ => bayesian_network}/Node.cc | 0 src/{ => bayesian_network}/Node.h | 0 src/{ => classifiers}/Classifier.cc | 0 src/{ => classifiers}/Classifier.h | 0 src/{ => classifiers}/KDB.cc | 0 src/{ => classifiers}/KDB.h | 0 src/{ => classifiers}/KDBLd.cc | 0 src/{ => classifiers}/KDBLd.h | 0 src/{ => classifiers}/Proposal.cc | 0 src/{ => classifiers}/Proposal.h | 0 src/{ => classifiers}/SPODE.cc | 0 src/{ => classifiers}/SPODE.h | 0 src/{ => classifiers}/SPODELd.cc | 0 src/{ => classifiers}/SPODELd.h | 0 src/{ => classifiers}/TAN.cc | 0 src/{ => classifiers}/TAN.h | 0 src/{ => classifiers}/TANLd.cc | 0 src/{ => classifiers}/TANLd.h | 0 src/{ => ensembles}/AODE.cc | 0 src/{ => ensembles}/AODE.h | 0 src/{ => ensembles}/AODELd.cc | 0 src/{ => ensembles}/AODELd.h | 0 src/{ => ensembles}/BoostAODE.cc | 0 src/{ => ensembles}/BoostAODE.h | 0 src/{ => ensembles}/Ensemble.cc | 0 src/{ => ensembles}/Ensemble.h | 0 src/{ => feature_selection}/CFS.cc | 0 src/{ => feature_selection}/CFS.h | 0 src/{ => feature_selection}/FCBF.cc | 0 src/{ => feature_selection}/FCBF.h | 0 src/{ => feature_selection}/FeatureSelect.cc | 0 src/{ => feature_selection}/FeatureSelect.h | 0 src/{ => feature_selection}/IWSS.cc | 0 src/{ => feature_selection}/IWSS.h | 0 src/{ => utils}/BayesMetrics.cc | 0 src/{ => utils}/BayesMetrics.h | 0 src/{ => utils}/Mst.cc | 0 src/{ => utils}/Mst.h | 0 src/{ => utils}/bayesnetUtils.cc | 0 src/{ => utils}/bayesnetUtils.h | 0 tests/CMakeLists.txt | 7 ++- 49 files changed, 104 insertions(+), 8 deletions(-) create mode 100644 sample/CMakeLists.txt create mode 100644 sample/sample.cc rename src/{ => bayesian_network}/Network.cc (100%) rename src/{ => bayesian_network}/Network.h (100%) rename src/{ => bayesian_network}/Node.cc (100%) rename src/{ => bayesian_network}/Node.h (100%) rename src/{ => classifiers}/Classifier.cc (100%) rename src/{ => classifiers}/Classifier.h (100%) rename src/{ => classifiers}/KDB.cc (100%) rename src/{ => classifiers}/KDB.h (100%) rename src/{ => classifiers}/KDBLd.cc (100%) rename src/{ => classifiers}/KDBLd.h (100%) rename src/{ => classifiers}/Proposal.cc (100%) rename src/{ => classifiers}/Proposal.h (100%) rename src/{ => classifiers}/SPODE.cc (100%) rename src/{ => classifiers}/SPODE.h (100%) rename src/{ => classifiers}/SPODELd.cc (100%) rename src/{ => classifiers}/SPODELd.h (100%) rename src/{ => classifiers}/TAN.cc (100%) rename src/{ => classifiers}/TAN.h (100%) rename src/{ => classifiers}/TANLd.cc (100%) rename src/{ => classifiers}/TANLd.h (100%) rename src/{ => ensembles}/AODE.cc (100%) rename src/{ => ensembles}/AODE.h (100%) rename src/{ => ensembles}/AODELd.cc (100%) rename src/{ => ensembles}/AODELd.h (100%) rename src/{ => ensembles}/BoostAODE.cc (100%) rename src/{ => ensembles}/BoostAODE.h (100%) rename src/{ => ensembles}/Ensemble.cc (100%) rename src/{ => ensembles}/Ensemble.h (100%) rename src/{ => feature_selection}/CFS.cc (100%) rename src/{ => feature_selection}/CFS.h (100%) rename src/{ => feature_selection}/FCBF.cc (100%) rename src/{ => feature_selection}/FCBF.h (100%) rename src/{ => feature_selection}/FeatureSelect.cc (100%) rename src/{ => feature_selection}/FeatureSelect.h (100%) rename src/{ => feature_selection}/IWSS.cc (100%) rename src/{ => feature_selection}/IWSS.h (100%) rename src/{ => utils}/BayesMetrics.cc (100%) rename src/{ => utils}/BayesMetrics.h (100%) rename src/{ => utils}/Mst.cc (100%) rename src/{ => utils}/Mst.h (100%) rename src/{ => utils}/bayesnetUtils.cc (100%) rename src/{ => utils}/bayesnetUtils.h (100%) diff --git a/CHANGELOG.md b/CHANGELOG.md index 47d09a5..fe8c2cd 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -11,6 +11,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Change _ascending_ hyperparameter to _order_ with these possible values _{"asc", "desc", "rand"}_, Default is _"desc"_. - Add the _predict_single_ hyperparameter to control if only the last model created is used to predict in boost training or the whole ensemble (all the models built so far). Default is true. +- sample app to show how to use the library (make sample) + +### Changed + +- Change the library structure adding folders for each group of classes (classifiers, ensembles, etc). ## [1.0.3] diff --git a/CMakeLists.txt b/CMakeLists.txt index acd389f..69156f7 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -65,10 +65,9 @@ add_git_submodule("lib/json") # -------------- add_subdirectory(config) add_subdirectory(lib/Files) +add_subdirectory(sample) add_subdirectory(src) -file(GLOB BayesNet_SOURCES CONFIGURE_DEPENDS ${BayesNet_SOURCE_DIR}/src/*.cc) - # Testing # ------- if (ENABLE_TESTING) diff --git a/Makefile b/Makefile index 2cda612..a7b6725 100644 --- a/Makefile +++ b/Makefile @@ -1,11 +1,11 @@ SHELL := /bin/bash .DEFAULT_GOAL := help -.PHONY: coverage setup help buildr buildd test clean debug release +.PHONY: coverage setup help buildr buildd test clean debug release sample f_release = build_release f_debug = build_debug app_targets = BayesNet -test_targets = unit_tests_bayesnet +test_targets = unit_tests_bayesnet n_procs = -j 16 define ClearTests @@ -59,6 +59,12 @@ release: ## Build a Release version of the project @if [ -d ./$(f_release) ]; then rm -rf ./$(f_release); fi @mkdir $(f_release); @cmake -S . -B $(f_release) -D CMAKE_BUILD_TYPE=Release + @echo ">>> Done"; + +sample: ## Build sample + @echo ">>> Building Sample..."; + cmake --build $(f_release) -t bayesnet_sample $(n_procs) + $(f_release)/sample/bayesnet_sample tests/data/iris.arff @echo ">>> Done"; opt = "" diff --git a/sample/CMakeLists.txt b/sample/CMakeLists.txt new file mode 100644 index 0000000..b56a20c --- /dev/null +++ b/sample/CMakeLists.txt @@ -0,0 +1,14 @@ +include_directories( + ${BayesNet_SOURCE_DIR}/src + ${BayesNet_SOURCE_DIR}/src/classifiers + ${BayesNet_SOURCE_DIR}/src/ensembles + ${BayesNet_SOURCE_DIR}/src/bayesian_network + ${BayesNet_SOURCE_DIR}/src/utils + ${BayesNet_SOURCE_DIR}/src/feature_selection + ${BayesNet_SOURCE_DIR}/lib/Files + ${BayesNet_SOURCE_DIR}/lib/mdlp + ${BayesNet_SOURCE_DIR}/lib/json/include + ${CMAKE_BINARY_DIR}/configured_files/include +) +add_executable(bayesnet_sample sample.cc) +target_link_libraries(bayesnet_sample ArffFiles BayesNet) \ No newline at end of file diff --git a/sample/sample.cc b/sample/sample.cc new file mode 100644 index 0000000..54b8639 --- /dev/null +++ b/sample/sample.cc @@ -0,0 +1,62 @@ +#include "ArffFiles.h" +#include "CPPFImdlp.h" +#include "BoostAODE.h" + +std::vector discretizeDataset(std::vector& X, mdlp::labels_t& y) +{ + std::vector Xd; + auto fimdlp = mdlp::CPPFImdlp(); + for (int i = 0; i < X.size(); i++) { + fimdlp.fit(X[i], y); + mdlp::labels_t& xd = fimdlp.transform(X[i]); + Xd.push_back(xd); + } + return Xd; +} +tuple, std::string, map>> loadDataset(const std::string& name, bool class_last) +{ + auto handler = ArffFiles(); + handler.load(name, class_last); + // Get Dataset X, y + std::vector& X = handler.getX(); + mdlp::labels_t& y = handler.getY(); + // Get className & Features + auto className = handler.getClassName(); + std::vector features; + auto attributes = handler.getAttributes(); + transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; }); + torch::Tensor Xd; + auto states = map>(); + auto Xr = discretizeDataset(X, y); + Xd = torch::zeros({ static_cast(Xr.size()), static_cast(Xr[0].size()) }, torch::kInt32); + for (int i = 0; i < features.size(); ++i) { + states[features[i]] = std::vector(*max_element(Xr[i].begin(), Xr[i].end()) + 1); + auto item = states.at(features[i]); + iota(begin(item), end(item), 0); + Xd.index_put_({ i, "..." }, torch::tensor(Xr[i], torch::kInt32)); + } + states[className] = std::vector(*max_element(y.begin(), y.end()) + 1); + iota(begin(states.at(className)), end(states.at(className)), 0); + return { Xd, torch::tensor(y, torch::kInt32), features, className, states }; +} + +int main(int argc, char* argv[]) +{ + if (argc < 2) { + std::cerr << "Usage: " << argv[0] << " " << std::endl; + return 1; + } + std::string file_name = argv[1]; + torch::Tensor X, y; + std::vector features; + std::string className; + map> states; + auto clf = bayesnet::BoostAODE(false); // false for not using voting in predict + std::cout << "Library version: " << clf.getVersion() << std::endl; + tie(X, y, features, className, states) = loadDataset(file_name, true); + clf.fit(X, y, features, className, states); + auto score = clf.score(X, y); + std::cout << "File: " << file_name << " score: " << score << std::endl; + return 0; +} + diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt index 461c6a9..e798319 100644 --- a/src/CMakeLists.txt +++ b/src/CMakeLists.txt @@ -4,10 +4,15 @@ include_directories( ${BayesNet_SOURCE_DIR}/lib/folding ${BayesNet_SOURCE_DIR}/lib/json/include ${BayesNet_SOURCE_DIR}/src + ${BayesNet_SOURCE_DIR}/src/feature_selection + ${BayesNet_SOURCE_DIR}/src/bayesian_network + ${BayesNet_SOURCE_DIR}/src/classifiers + ${BayesNet_SOURCE_DIR}/src/ensembles + ${BayesNet_SOURCE_DIR}/src/utils ${CMAKE_BINARY_DIR}/configured_files/include ) -add_library(BayesNet bayesnetUtils.cc Network.cc Node.cc BayesMetrics.cc Classifier.cc - KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc TANLd.cc KDBLd.cc SPODELd.cc AODELd.cc BoostAODE.cc - Mst.cc Proposal.cc CFS.cc FCBF.cc IWSS.cc FeatureSelect.cc ) +file(GLOB_RECURSE Sources "*.cc") + +add_library(BayesNet ${Sources}) target_link_libraries(BayesNet mdlp "${TORCH_LIBRARIES}") \ No newline at end of file diff --git a/src/Network.cc b/src/bayesian_network/Network.cc similarity index 100% rename from src/Network.cc rename to src/bayesian_network/Network.cc diff --git a/src/Network.h b/src/bayesian_network/Network.h similarity index 100% rename from src/Network.h rename to src/bayesian_network/Network.h diff --git a/src/Node.cc b/src/bayesian_network/Node.cc similarity index 100% rename from src/Node.cc rename to src/bayesian_network/Node.cc diff --git a/src/Node.h b/src/bayesian_network/Node.h similarity index 100% rename from src/Node.h rename to src/bayesian_network/Node.h diff --git a/src/Classifier.cc b/src/classifiers/Classifier.cc similarity index 100% rename from src/Classifier.cc rename to src/classifiers/Classifier.cc diff --git a/src/Classifier.h b/src/classifiers/Classifier.h similarity index 100% rename from src/Classifier.h rename to src/classifiers/Classifier.h diff --git a/src/KDB.cc b/src/classifiers/KDB.cc similarity index 100% rename from src/KDB.cc rename to src/classifiers/KDB.cc diff --git a/src/KDB.h b/src/classifiers/KDB.h similarity index 100% rename from src/KDB.h rename to src/classifiers/KDB.h diff --git a/src/KDBLd.cc b/src/classifiers/KDBLd.cc similarity index 100% rename from src/KDBLd.cc rename to src/classifiers/KDBLd.cc diff --git a/src/KDBLd.h b/src/classifiers/KDBLd.h similarity index 100% rename from src/KDBLd.h rename to src/classifiers/KDBLd.h diff --git a/src/Proposal.cc b/src/classifiers/Proposal.cc similarity index 100% rename from src/Proposal.cc rename to src/classifiers/Proposal.cc diff --git a/src/Proposal.h b/src/classifiers/Proposal.h similarity index 100% rename from src/Proposal.h rename to src/classifiers/Proposal.h diff --git a/src/SPODE.cc b/src/classifiers/SPODE.cc similarity index 100% rename from src/SPODE.cc rename to src/classifiers/SPODE.cc diff --git a/src/SPODE.h b/src/classifiers/SPODE.h similarity index 100% rename from src/SPODE.h rename to src/classifiers/SPODE.h diff --git a/src/SPODELd.cc b/src/classifiers/SPODELd.cc similarity index 100% rename from src/SPODELd.cc rename to src/classifiers/SPODELd.cc diff --git a/src/SPODELd.h b/src/classifiers/SPODELd.h similarity index 100% rename from src/SPODELd.h rename to src/classifiers/SPODELd.h diff --git a/src/TAN.cc b/src/classifiers/TAN.cc similarity index 100% rename from src/TAN.cc rename to src/classifiers/TAN.cc diff --git a/src/TAN.h b/src/classifiers/TAN.h similarity index 100% rename from src/TAN.h rename to src/classifiers/TAN.h diff --git a/src/TANLd.cc b/src/classifiers/TANLd.cc similarity index 100% rename from src/TANLd.cc rename to src/classifiers/TANLd.cc diff --git a/src/TANLd.h b/src/classifiers/TANLd.h similarity index 100% rename from src/TANLd.h rename to src/classifiers/TANLd.h diff --git a/src/AODE.cc b/src/ensembles/AODE.cc similarity index 100% rename from src/AODE.cc rename to src/ensembles/AODE.cc diff --git a/src/AODE.h b/src/ensembles/AODE.h similarity index 100% rename from src/AODE.h rename to src/ensembles/AODE.h diff --git a/src/AODELd.cc b/src/ensembles/AODELd.cc similarity index 100% rename from src/AODELd.cc rename to src/ensembles/AODELd.cc diff --git a/src/AODELd.h b/src/ensembles/AODELd.h similarity index 100% rename from src/AODELd.h rename to src/ensembles/AODELd.h diff --git a/src/BoostAODE.cc b/src/ensembles/BoostAODE.cc similarity index 100% rename from src/BoostAODE.cc rename to src/ensembles/BoostAODE.cc diff --git a/src/BoostAODE.h b/src/ensembles/BoostAODE.h similarity index 100% rename from src/BoostAODE.h rename to src/ensembles/BoostAODE.h diff --git a/src/Ensemble.cc b/src/ensembles/Ensemble.cc similarity index 100% rename from src/Ensemble.cc rename to src/ensembles/Ensemble.cc diff --git a/src/Ensemble.h b/src/ensembles/Ensemble.h similarity index 100% rename from src/Ensemble.h rename to src/ensembles/Ensemble.h diff --git a/src/CFS.cc b/src/feature_selection/CFS.cc similarity index 100% rename from src/CFS.cc rename to src/feature_selection/CFS.cc diff --git a/src/CFS.h b/src/feature_selection/CFS.h similarity index 100% rename from src/CFS.h rename to src/feature_selection/CFS.h diff --git a/src/FCBF.cc b/src/feature_selection/FCBF.cc similarity index 100% rename from src/FCBF.cc rename to src/feature_selection/FCBF.cc diff --git a/src/FCBF.h b/src/feature_selection/FCBF.h similarity index 100% rename from src/FCBF.h rename to src/feature_selection/FCBF.h diff --git a/src/FeatureSelect.cc b/src/feature_selection/FeatureSelect.cc similarity index 100% rename from src/FeatureSelect.cc rename to src/feature_selection/FeatureSelect.cc diff --git a/src/FeatureSelect.h b/src/feature_selection/FeatureSelect.h similarity index 100% rename from src/FeatureSelect.h rename to src/feature_selection/FeatureSelect.h diff --git a/src/IWSS.cc b/src/feature_selection/IWSS.cc similarity index 100% rename from src/IWSS.cc rename to src/feature_selection/IWSS.cc diff --git a/src/IWSS.h b/src/feature_selection/IWSS.h similarity index 100% rename from src/IWSS.h rename to src/feature_selection/IWSS.h diff --git a/src/BayesMetrics.cc b/src/utils/BayesMetrics.cc similarity index 100% rename from src/BayesMetrics.cc rename to src/utils/BayesMetrics.cc diff --git a/src/BayesMetrics.h b/src/utils/BayesMetrics.h similarity index 100% rename from src/BayesMetrics.h rename to src/utils/BayesMetrics.h diff --git a/src/Mst.cc b/src/utils/Mst.cc similarity index 100% rename from src/Mst.cc rename to src/utils/Mst.cc diff --git a/src/Mst.h b/src/utils/Mst.h similarity index 100% rename from src/Mst.h rename to src/utils/Mst.h diff --git a/src/bayesnetUtils.cc b/src/utils/bayesnetUtils.cc similarity index 100% rename from src/bayesnetUtils.cc rename to src/utils/bayesnetUtils.cc diff --git a/src/bayesnetUtils.h b/src/utils/bayesnetUtils.h similarity index 100% rename from src/bayesnetUtils.h rename to src/utils/bayesnetUtils.h diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index efccc48..630beab 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -2,13 +2,18 @@ if(ENABLE_TESTING) set(TEST_BAYESNET "unit_tests_bayesnet") include_directories( ${BayesNet_SOURCE_DIR}/src - ${BayesNet_SOURCE_DIR}/src/Platform + ${BayesNet_SOURCE_DIR}/src/feature_selection + ${BayesNet_SOURCE_DIR}/src/bayesian_network + ${BayesNet_SOURCE_DIR}/src/classifiers + ${BayesNet_SOURCE_DIR}/src/utils + ${BayesNet_SOURCE_DIR}/src/ensembles ${BayesNet_SOURCE_DIR}/lib/Files ${BayesNet_SOURCE_DIR}/lib/mdlp ${BayesNet_SOURCE_DIR}/lib/folding ${BayesNet_SOURCE_DIR}/lib/json/include ${CMAKE_BINARY_DIR}/configured_files/include ) + file(GLOB_RECURSE BayesNet_SOURCES "${BayesNet_SOURCE_DIR}/src/*.cc") set(TEST_SOURCES_BAYESNET TestBayesModels.cc TestBayesNetwork.cc TestBayesMetrics.cc TestUtils.cc ${BayesNet_SOURCES}) add_executable(${TEST_BAYESNET} ${TEST_SOURCES_BAYESNET}) target_link_libraries(${TEST_BAYESNET} PUBLIC "${TORCH_LIBRARIES}" ArffFiles mdlp Catch2::Catch2WithMain ) -- 2.45.2 From 8bccc3e4bcaffba3c463586610cb6044cbe14902 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana=20G=C3=B3mez?= Date: Tue, 27 Feb 2024 14:24:58 +0100 Subject: [PATCH 04/11] Update boostaode algorithm explain --- docs/BoostAODE.docx | Bin 18270 -> 0 bytes docs/BoostAODE.md | 44 ++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 44 insertions(+) delete mode 100644 docs/BoostAODE.docx create mode 100644 docs/BoostAODE.md diff --git a/docs/BoostAODE.docx 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(boolean)**: Permite que se repitan variables del dataset como padre de un SPODE. Valor por defecto _false_. +- **_maxModels_ (int)**: número máximo de modelos (SPODEs) a construir. Este hiperparámetro únicamente es tenido en cuenta si “repeatSparent” se establece como verdadero. Valor por defecto 0. +- **order ({"asc", "desc", "rand"})**: Establece el orden (ascendente/descendente/aleatorio) en el que se procesarán las variables del dataset para elegir los padres de los SPODEs. Valor por defecto _"desc"_. +- **_convergence_ (boolean)**: Establece si se utilizará la convergencia del resultado como condición de finalización. En caso de establecer este hiperparámetro como verdadero, el conjunto de datos de entrenamiento que se pasa al modelo se divide en dos conjuntos uno que servirá como datos de entrenamiento y otro de test (por lo que la partición de test original pasará a ser en este caso una partición de validación). La partición se realiza tomando como la primera partición generada por un proceso de generación de 5 particiones estratificadas y con una semilla prefijada. La condición de salida es que la diferencia entre la precisión obtenida por el modelo actual, y la obtenida por el modelo anterior sea mayor que 1e-4, en caso contrario se sumará uno al número de modelos que empeora el resultado (ver siguiente hiperparámetro). Valor por defecto false. +- **_tolerance_ (int)**: Establece el número máximo de modelos que pueden empeorar el resultado sin suponer una condición de finalización. Valor por defecto 0. +- **_select_features_ ({“IWSS”, “FCBF”, “CFS”, “”})**: Selecciona en caso de establecerlo, el método de selección de variables que se utilizará para construir unos modelos iniciales para el ensemble que se incluirán sin tener en cuenta ninguna de las otras condiciones de salida. Estos modelos tampoco actualizan o utilizan los pesos que utiliza el algoritmo de Boosting y su significancia se establece en 1. +- **_threshold_ (double)**: Establece el valor necesario para los algoritmos IWSS y FCBF para poder funcionar. Los valores aceptados son: + - IWSS: \[0, 0.5\] + - FCBF: \[1e-7, 1\] +- **_predict_voting_ (boolean)**: Establece si el algoritmo de BoostAODE utilizará la votación de los modelos para predecir el resultado. En caso de establecerlo como falso, se utilizará la media ponderada de las probabilidades de cada predicción de los modelos. Valor por defecto _true_. +- **_predict_single_ (boolean)**: Establece si el algoritmo de BoostAODE utilizará la predicción de un solo modelo en el proceso de aprendizaje. En caso de establecerlo como falso, se utilizarán todos los modelos entrenados hasta ese momento para calcular la predicción necesaria para actualizar los pesos en el proceso de aprendizaje. Valor por defecto _true_. + +## Funcionamiento + +El algoritmo realiza los siguientes pasos: + +- Si se ha establecido select_features se crean tantos SPODEs como variables selecciona el algoritmo correspondiente y se marcan como utilizadas estas variables +- Se establecen los pesos iniciales de los ejemplos como 1/m +- Bucle principal de entrenamiento + - Ordena las variables por orden de información mutua con la variable clase y se procesan en orden ascente o descendente según valor del hiperparámetro. En caso de ser aleatorio se barajan las variables + - Si no se ha establecido la repetición de padres, se marca la variable como utilizada + - Crea un Spode utilizando como padre la variable seleccionada + - Entrena el modelo y calcula la variable clase correspondiente al dataset de entenamiento. El cálculo lo podrá hacer utilizando el último modelo entrenado o el conjunto de modelos entrenados hasta ese momento. + - Actualiza los pesos asociados a los ejemplos de acuerdo a la expresión: + + - **wi · eαt** (si el ejemplo ha sido mal clasificado) + + - **wi · e-αt** (si el ejemplo ha sido bien clasificado) + + - Establece la significancia del modelo como αt + - Si se ha establecido el hiperparámetro de convergencia, se calcula el valor de la precisión sobre el dataset de test que hemos separado en un paso inicial + - Condiciones de salida: + - εt > 0,5 => se penaliza a los ejemplos mal clasificados + - Número de modelos con precisión peor mayor que tolerancia y convergencia establecida + - No hay más variables para crear modelos y no repeatSparent + - Número de modelos > maxModels si repeatSparent está establecido -- 2.45.2 From 272dbad4f3fa0ef667add394f4c5bebe958532b6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana=20G=C3=B3mez?= Date: Tue, 27 Feb 2024 17:16:26 +0100 Subject: [PATCH 05/11] Update README and docs --- .vscode/launch.json | 109 +------------------------------------------- Makefile | 3 +- README.md | 12 ++++- docs/BoostAODE.md | 6 +-- 4 files changed, 18 insertions(+), 112 deletions(-) diff --git a/.vscode/launch.json b/.vscode/launch.json index a384091..65760c3 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -5,102 +5,10 @@ "type": "lldb", "request": "launch", "name": "sample", - "program": "${workspaceFolder}/build_debug/sample/BayesNetSample", + "program": "${workspaceFolder}/build_release/sample/bayesnet_sample", "args": [ - "-d", - "iris", - "-m", - "TANLd", - "-s", - "271", - "-p", - "/Users/rmontanana/Code/discretizbench/datasets/", + "${workspaceFolder}/tests/data/glass.arff" ], - //"cwd": "${workspaceFolder}/build/sample/", - }, - { - "type": "lldb", - "request": "launch", - "name": "experimentPy", - "program": "${workspaceFolder}/build_debug/src/Platform/b_main", - "args": [ - "-m", - "STree", - "--stratified", - "-d", - "iris", - //"--discretize" - // "--hyperparameters", - // "{\"repeatSparent\": true, \"maxModels\": 12}" - ], - "cwd": "${workspaceFolder}/../discretizbench", - }, - { - "type": "lldb", - "request": "launch", - "name": "gridsearch", - "program": "${workspaceFolder}/build_debug/src/Platform/b_grid", - "args": [ - "-m", - "KDB", - "--discretize", - "--continue", - "glass", - "--only", - "--compute" - ], - "cwd": "${workspaceFolder}/../discretizbench", - }, - { - "type": "lldb", - "request": "launch", - "name": "experimentBayes", - "program": "${workspaceFolder}/build_debug/src/Platform/b_main", - "args": [ - "-m", - "TAN", - "--stratified", - "--discretize", - "-d", - "iris", - "--hyperparameters", - "{\"repeatSparent\": true, \"maxModels\": 12}" - ], - "cwd": "/home/rmontanana/Code/discretizbench", - }, - { - "type": "lldb", - "request": "launch", - "name": "best", - "program": "${workspaceFolder}/build_debug/src/Platform/b_best", - "args": [ - "-m", - "BoostAODE", - "-s", - "accuracy", - "--build", - ], - "cwd": "${workspaceFolder}/../discretizbench", - }, - { - "type": "lldb", - "request": "launch", - "name": "manage", - "program": "${workspaceFolder}/build_debug/src/Platform/b_manage", - "args": [ - "-n", - "20" - ], - "cwd": "${workspaceFolder}/../discretizbench", - }, - { - "type": "lldb", - "request": "launch", - "name": "list", - "program": "${workspaceFolder}/build_debug/src/Platform/b_list", - "args": [], - //"cwd": "/Users/rmontanana/Code/discretizbench", - "cwd": "${workspaceFolder}/../discretizbench", }, { "type": "lldb", @@ -112,19 +20,6 @@ // "-s", ], "cwd": "${workspaceFolder}/build_debug/tests", - }, - { - "name": "Build & debug active file", - "type": "cppdbg", - "request": "launch", - "program": "${workspaceFolder}/build_debug/bayesnet", - "args": [], - "stopAtEntry": false, - "cwd": "${workspaceFolder}", - "environment": [], - "externalConsole": false, - "MIMode": "lldb", - "preLaunchTask": "CMake: build" } ] } \ No newline at end of file diff --git a/Makefile b/Makefile index a7b6725..d376787 100644 --- a/Makefile +++ b/Makefile @@ -61,10 +61,11 @@ release: ## Build a Release version of the project @cmake -S . -B $(f_release) -D CMAKE_BUILD_TYPE=Release @echo ">>> Done"; +fname = "tests/data/iris.arff" sample: ## Build sample @echo ">>> Building Sample..."; cmake --build $(f_release) -t bayesnet_sample $(n_procs) - $(f_release)/sample/bayesnet_sample tests/data/iris.arff + $(f_release)/sample/bayesnet_sample $(fname) @echo ">>> Done"; opt = "" diff --git a/README.md b/README.md index ee21fa4..2dd4bb3 100644 --- a/README.md +++ b/README.md @@ -19,4 +19,14 @@ make test make coverage ``` -## 1. Introduction +### Sample app + +```bash +make release +make sample +make sample fname=tests/data/glass.arff +``` + +## Models + +### [BoostAODE](docs/BoostAODE.md) diff --git a/docs/BoostAODE.md b/docs/BoostAODE.md index 648b224..431399d 100644 --- a/docs/BoostAODE.md +++ b/docs/BoostAODE.md @@ -6,9 +6,9 @@ El algoritmo se basa en el funcionamiento del algoritmo AdaBoost, y utilizando u Los hiperparámetros que están definidos en el algoritmo son: -- **_repeatSparent_ (boolean)**: Permite que se repitan variables del dataset como padre de un SPODE. Valor por defecto _false_. -- **_maxModels_ (int)**: número máximo de modelos (SPODEs) a construir. Este hiperparámetro únicamente es tenido en cuenta si “repeatSparent” se establece como verdadero. Valor por defecto 0. -- **order ({"asc", "desc", "rand"})**: Establece el orden (ascendente/descendente/aleatorio) en el que se procesarán las variables del dataset para elegir los padres de los SPODEs. Valor por defecto _"desc"_. +- **_repeatSparent_ (boolean)**: Permite que se repitan variables del dataset como padre de un _SPODE_. Valor por defecto _false_. +- **_maxModels_ (int)**: número máximo de modelos (SPODEs) a construir. Este hiperparámetro únicamente es tenido en cuenta si _**repeatSparent**_ se establece como verdadero. Valor por defecto 0. +- **_order_ ({"asc", "desc", "rand"})**: Establece el orden (ascendente/descendente/aleatorio) en el que se procesarán las variables del dataset para elegir los padres de los SPODEs. Valor por defecto _"desc"_. - **_convergence_ (boolean)**: Establece si se utilizará la convergencia del resultado como condición de finalización. En caso de establecer este hiperparámetro como verdadero, el conjunto de datos de entrenamiento que se pasa al modelo se divide en dos conjuntos uno que servirá como datos de entrenamiento y otro de test (por lo que la partición de test original pasará a ser en este caso una partición de validación). La partición se realiza tomando como la primera partición generada por un proceso de generación de 5 particiones estratificadas y con una semilla prefijada. La condición de salida es que la diferencia entre la precisión obtenida por el modelo actual, y la obtenida por el modelo anterior sea mayor que 1e-4, en caso contrario se sumará uno al número de modelos que empeora el resultado (ver siguiente hiperparámetro). Valor por defecto false. - **_tolerance_ (int)**: Establece el número máximo de modelos que pueden empeorar el resultado sin suponer una condición de finalización. Valor por defecto 0. - **_select_features_ ({“IWSS”, “FCBF”, “CFS”, “”})**: Selecciona en caso de establecerlo, el método de selección de variables que se utilizará para construir unos modelos iniciales para el ensemble que se incluirán sin tener en cuenta ninguna de las otras condiciones de salida. Estos modelos tampoco actualizan o utilizan los pesos que utiliza el algoritmo de Boosting y su significancia se establece en 1. -- 2.45.2 From 20669dd1610963c47fdac433d5fa72759065eb7e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana=20G=C3=B3mez?= Date: Tue, 27 Feb 2024 20:29:01 +0100 Subject: [PATCH 06/11] Translate BoostAODE.md to English --- docs/BoostAODE.md | 93 ++++++++++++++++++++++++++++++----------------- 1 file changed, 59 insertions(+), 34 deletions(-) diff --git a/docs/BoostAODE.md b/docs/BoostAODE.md index 431399d..b5a7b09 100644 --- a/docs/BoostAODE.md +++ b/docs/BoostAODE.md @@ -1,44 +1,69 @@ -# Descripción de funcionamiento del algoritmo BoostAODE +# BoostAODE Algorithm Operation -El algoritmo se basa en el funcionamiento del algoritmo AdaBoost, y utilizando una serie de hiperparámetros se activan cada una de las posibles alternativas. +The algorithm is based on the AdaBoost algorithm with some new proposals that can be activated using the following hyperparameters. -## Hiperparámetros +## Hyperparameters -Los hiperparámetros que están definidos en el algoritmo son: +The hyperparameters defined in the algorithm are: -- **_repeatSparent_ (boolean)**: Permite que se repitan variables del dataset como padre de un _SPODE_. Valor por defecto _false_. -- **_maxModels_ (int)**: número máximo de modelos (SPODEs) a construir. Este hiperparámetro únicamente es tenido en cuenta si _**repeatSparent**_ se establece como verdadero. Valor por defecto 0. -- **_order_ ({"asc", "desc", "rand"})**: Establece el orden (ascendente/descendente/aleatorio) en el que se procesarán las variables del dataset para elegir los padres de los SPODEs. Valor por defecto _"desc"_. -- **_convergence_ (boolean)**: Establece si se utilizará la convergencia del resultado como condición de finalización. En caso de establecer este hiperparámetro como verdadero, el conjunto de datos de entrenamiento que se pasa al modelo se divide en dos conjuntos uno que servirá como datos de entrenamiento y otro de test (por lo que la partición de test original pasará a ser en este caso una partición de validación). La partición se realiza tomando como la primera partición generada por un proceso de generación de 5 particiones estratificadas y con una semilla prefijada. La condición de salida es que la diferencia entre la precisión obtenida por el modelo actual, y la obtenida por el modelo anterior sea mayor que 1e-4, en caso contrario se sumará uno al número de modelos que empeora el resultado (ver siguiente hiperparámetro). Valor por defecto false. -- **_tolerance_ (int)**: Establece el número máximo de modelos que pueden empeorar el resultado sin suponer una condición de finalización. Valor por defecto 0. -- **_select_features_ ({“IWSS”, “FCBF”, “CFS”, “”})**: Selecciona en caso de establecerlo, el método de selección de variables que se utilizará para construir unos modelos iniciales para el ensemble que se incluirán sin tener en cuenta ninguna de las otras condiciones de salida. Estos modelos tampoco actualizan o utilizan los pesos que utiliza el algoritmo de Boosting y su significancia se establece en 1. -- **_threshold_ (double)**: Establece el valor necesario para los algoritmos IWSS y FCBF para poder funcionar. Los valores aceptados son: - - IWSS: \[0, 0.5\] - - FCBF: \[1e-7, 1\] -- **_predict_voting_ (boolean)**: Establece si el algoritmo de BoostAODE utilizará la votación de los modelos para predecir el resultado. En caso de establecerlo como falso, se utilizará la media ponderada de las probabilidades de cada predicción de los modelos. Valor por defecto _true_. -- **_predict_single_ (boolean)**: Establece si el algoritmo de BoostAODE utilizará la predicción de un solo modelo en el proceso de aprendizaje. En caso de establecerlo como falso, se utilizarán todos los modelos entrenados hasta ese momento para calcular la predicción necesaria para actualizar los pesos en el proceso de aprendizaje. Valor por defecto _true_. +- ***repeatSparent*** (*boolean*): Allows dataset variables to be repeated as parents of an *SPODE*. Default value: *false*. -## Funcionamiento +- ***maxModels*** (*int*): Maximum number of models (*SPODEs*) to build. This hyperparameter is only taken into account if ***repeatSparent*** is set to *true*. Default value: *0*. -El algoritmo realiza los siguientes pasos: +- ***order*** (*{"asc", "desc", "rand"}*): Sets the order (ascending/descending/random) in which dataset variables will be processed to choose the parents of the *SPODEs*. Default value: *"desc"*. -- Si se ha establecido select_features se crean tantos SPODEs como variables selecciona el algoritmo correspondiente y se marcan como utilizadas estas variables -- Se establecen los pesos iniciales de los ejemplos como 1/m -- Bucle principal de entrenamiento - - Ordena las variables por orden de información mutua con la variable clase y se procesan en orden ascente o descendente según valor del hiperparámetro. En caso de ser aleatorio se barajan las variables - - Si no se ha establecido la repetición de padres, se marca la variable como utilizada - - Crea un Spode utilizando como padre la variable seleccionada - - Entrena el modelo y calcula la variable clase correspondiente al dataset de entenamiento. El cálculo lo podrá hacer utilizando el último modelo entrenado o el conjunto de modelos entrenados hasta ese momento. - - Actualiza los pesos asociados a los ejemplos de acuerdo a la expresión: +- ***convergence*** (*boolean*): Sets whether the convergence of the result will be used as a termination condition. If this hyperparameter is set to true, the training dataset passed to the model is divided into two sets, one serving as training data and the other as a test set (so the original test partition will become a validation partition in this case). The partition is made by taking the first partition generated by a process of generating a 5 fold partition with stratification using a predetermined seed. The exit condition used in this *convergence* is that the difference between the accuracy obtained by the current model and that obtained by the previous model is greater than *1e-4*; otherwise, one will be added to the number of models that worsen the result (see next hyperparameter). Default value: *false*. - - **wi · eαt** (si el ejemplo ha sido mal clasificado) +- ***tolerance*** (*int*): Sets the maximum number of models that can worsen the result without constituting a termination condition. Default value: *0*. - - **wi · e-αt** (si el ejemplo ha sido bien clasificado) +- ***select_features*** (*{"IWSS", "FCBF", "CFS", ""}*): Selects the variable selection method to be used to build initial models for the ensemble that will be included without considering any of the other exit conditions. These models also do not update or use the weights used by the Boosting algorithm, and their significance is set to 1. - - Establece la significancia del modelo como αt - - Si se ha establecido el hiperparámetro de convergencia, se calcula el valor de la precisión sobre el dataset de test que hemos separado en un paso inicial - - Condiciones de salida: - - εt > 0,5 => se penaliza a los ejemplos mal clasificados - - Número de modelos con precisión peor mayor que tolerancia y convergencia establecida - - No hay más variables para crear modelos y no repeatSparent - - Número de modelos > maxModels si repeatSparent está establecido +- ***threshold*** (*double*): Sets the necessary value for the IWSS and FCBF algorithms to function. Accepted values are: + - IWSS: $threshold \in [0, 0.5]$ + - FCBF: $threshold \in [10^{-7}, 1]$ + + Default value is *-1* so every time any of those algorithms are called, the threshold has to be set to the desired value. + +- ***predict_voting*** (*boolean*): Sets whether the algorithm will use *model voting* to predict the result. If set to false, the weighted average of the probabilities of each model's prediction will be used. Default value: *true*. + +- ***predict_single*** (*boolean*): Sets whether the algorithm will use single-model prediction in the learning process. If set to *false*, all models trained up to that point will be used to calculate the prediction necessary to update the weights in the learning process. Default value: *true*. + +## Operation + +The algorithm performs the following steps: + +1. **Initialization** + + - If ***select_features*** is set, as many *SPODEs* are created as variables selected by the corresponding feature selection algorithm, and these variables are marked as used. + + - Initial weights of the examples are set to *1/m*. + +1. **Main Training Loop:** + + - Variables are sorted by mutual information order with the class variable and processed in ascending, descending or random order, according to the value of the *order* hyperparameter. If it is random, the variables are shuffled. + + - If the parent repetition is not established, the variable is marked as used. + + - A *SPODE* is created using the selected variable as the parent. + + - The model is trained, and the class variable corresponding to the training dataset is calculated. The calculation can be done using the last trained model or the set of models trained up to that point, according to the value of the *predict_single* hyperparameter. + + - The weights associated with the examples are updated using this expression: + + - wi · eαt (if the example has been misclassified) + + - wi · e-αt (if the example has been correctly classified) + + - The model significance is set as αt. + + - If the ***convergence*** hyperparameter is set, the accuracy value on the test dataset that we separated in an initial step is calculated. + +1. **Exit Conditions:** + + - εt > 0.5 => misclassified examples are penalized. + + - Number of models with worse accuracy greater than ***tolerance*** and ***convergence*** established. + + - There are no more variables to create models, and ***repeatSparent*** is not set. + + - Number of models > ***maxModels*** if ***repeatSparent*** is set. -- 2.45.2 From d6af1ffe8e3a25854d3c3b5dba85f870dc4ea806 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana=20G=C3=B3mez?= Date: Wed, 28 Feb 2024 11:51:37 +0100 Subject: [PATCH 07/11] Update gcovr config and fix some warnings --- .clang-tidy | 1 + CMakeLists.txt | 10 +++++----- gcovr.cfg | 2 +- src/bayesian_network/Network.cc | 2 +- src/feature_selection/FeatureSelect.cc | 1 - src/feature_selection/IWSS.cc | 2 +- tests/TestBayesModels.cc | 2 ++ 7 files changed, 11 insertions(+), 9 deletions(-) diff --git a/.clang-tidy b/.clang-tidy index 81f7209..ef88702 100644 --- a/.clang-tidy +++ b/.clang-tidy @@ -5,6 +5,7 @@ Checks: '-*, cppcoreguidelines-*, modernize-*, performance-*, + -modernize-use-nodiscard, -cppcoreguidelines-pro-type-vararg, -modernize-use-trailing-return-type, -bugprone-exception-escape' diff --git a/CMakeLists.txt b/CMakeLists.txt index 69156f7..0babab8 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -44,11 +44,11 @@ endif (CMAKE_BUILD_TYPE STREQUAL "Debug") if (CODE_COVERAGE) - enable_testing() - include(CodeCoverage) - MESSAGE("Code coverage enabled") - set(CMAKE_CXX_FLAGS " ${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage -O0 -g") - SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage") + enable_testing() + include(CodeCoverage) + MESSAGE("Code coverage enabled") + set(CMAKE_CXX_FLAGS " ${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage -O0 -g") + SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage") endif (CODE_COVERAGE) if (ENABLE_CLANG_TIDY) diff --git a/gcovr.cfg b/gcovr.cfg index 89e0877..01ad57e 100644 --- a/gcovr.cfg +++ b/gcovr.cfg @@ -1,4 +1,4 @@ filter = src/ exclude-directories = build_debug/lib/ print-summary = yes -sort-percentage = yes +sort = uncovered-percent diff --git a/src/bayesian_network/Network.cc b/src/bayesian_network/Network.cc index 32e6ecf..0357ba6 100644 --- a/src/bayesian_network/Network.cc +++ b/src/bayesian_network/Network.cc @@ -71,7 +71,7 @@ namespace bayesnet { for (Node* child : nodes[nodeId]->getChildren()) { if (visited.find(child->getName()) == visited.end() && isCyclic(child->getName(), visited, recStack)) return true; - else if (recStack.find(child->getName()) != recStack.end()) + if (recStack.find(child->getName()) != recStack.end()) return true; } } diff --git a/src/feature_selection/FeatureSelect.cc b/src/feature_selection/FeatureSelect.cc index b8300a5..fba1228 100644 --- a/src/feature_selection/FeatureSelect.cc +++ b/src/feature_selection/FeatureSelect.cc @@ -50,7 +50,6 @@ namespace bayesnet { } double FeatureSelect::computeMeritCFS() { - double result; double rcf = 0; for (auto feature : selectedFeatures) { rcf += suLabels[feature]; diff --git a/src/feature_selection/IWSS.cc b/src/feature_selection/IWSS.cc index 4fd11ea..e63bf6b 100644 --- a/src/feature_selection/IWSS.cc +++ b/src/feature_selection/IWSS.cc @@ -28,7 +28,7 @@ namespace bayesnet { selectedFeatures.push_back(feature); // Compute merit with selectedFeatures auto meritNew = computeMeritCFS(); - double delta = merit != 0.0 ? abs(merit - meritNew) / merit : 0.0; + double delta = merit != 0.0 ? std::abs(merit - meritNew) / merit : 0.0; if (meritNew > merit || delta < threshold) { if (meritNew > merit) { merit = meritNew; diff --git a/tests/TestBayesModels.cc b/tests/TestBayesModels.cc index db318ad..8488f05 100644 --- a/tests/TestBayesModels.cc +++ b/tests/TestBayesModels.cc @@ -224,6 +224,8 @@ TEST_CASE("BoostAODE voting-proba", "[BayesNet]") 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.topological_order() == std::vector()); } TEST_CASE("BoostAODE order asc, desc & random", "[BayesNet]") { -- 2.45.2 From 78d7ea7c4de9334da34aa214b4063c8486b42960 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana=20G=C3=B3mez?= Date: Sun, 3 Mar 2024 22:56:01 +0100 Subject: [PATCH 08/11] Add predict_single proposal detailed info --- docs/BoostAODE.md | 4 +++- docs/BoostAODE_train_predict.odp | Bin 0 -> 56004 bytes docs/BoostAODE_train_predict.pdf | Bin 0 -> 77384 bytes 3 files changed, 3 insertions(+), 1 deletion(-) create mode 100644 docs/BoostAODE_train_predict.odp create mode 100644 docs/BoostAODE_train_predict.pdf diff --git a/docs/BoostAODE.md b/docs/BoostAODE.md index b5a7b09..23f5bd3 100644 --- a/docs/BoostAODE.md +++ b/docs/BoostAODE.md @@ -54,7 +54,7 @@ The algorithm performs the following steps: - wi · e-αt (if the example has been correctly classified) - - The model significance is set as αt. + - The model significance is set to αt. - If the ***convergence*** hyperparameter is set, the accuracy value on the test dataset that we separated in an initial step is calculated. @@ -67,3 +67,5 @@ The algorithm performs the following steps: - There are no more variables to create models, and ***repeatSparent*** is not set. - Number of models > ***maxModels*** if ***repeatSparent*** is set. + +### [Proposal for *predict_single = false*](./BoostAODE_train_predict.pdf) diff --git a/docs/BoostAODE_train_predict.odp b/docs/BoostAODE_train_predict.odp new file mode 100644 index 0000000000000000000000000000000000000000..4931f0c6a9772e65ef294a17c26f7657f2a9363c GIT binary patch literal 56004 zcmbSy1C(V;(r($dZQE9t?dq~^+qSFAwr$(CtIIaJ>h+y}X4b6t-?=mIoptuwJ96)g zjLbL@u}^%F@>0McC;$Ku002MY4b_>+Dog1B0090tem4PFn_C+@x!W1*+u2!}8|pim z+uG2(*cj2->N}b{(%IS>+ZfpzI$ImtIMLZT7(4zx|Lqz1zcGb?fcP8J-+cc0!TxVxZA8(SrbT zC0$j@+FQV^)u?l%6)qkcvSM=r;%rr_P&GB7Dz#k8yeRGCTYX?fL6Q@ zpmt2Wb0qLcVGfWs1*iZ3Bn$bPv}5m2Czu6fO^naO4b$kW20F6}8$~@0ed++CglP@M>USw69`dB=}{T3lNeKOpVRL8vpv*fiQw3z}y8?-hou+N2QnsLLFEn zpP2&nFLrG~xnQ2L<^s4e3Cf2Jwe0O^<@P6qtl^ zL6tYfo&+?zAj&nMBv1k#G!A%Cls{!99yAgo=m4h~_WP+0U#lb_i>7xoCM`)TNi#M% zqXZ#KJ1!l_aY(-@!D3g>5%-D^X00Kx4s(X7cg3ogo8p;Xda0vS`U4ZQK63YJYIsk_ zD}0Y0I2w6Cwp9ufBPdCB0GFD@4S9e@EG;n_A-pdvs#kkD469XH1}wLC3iyl@bjln% zy={-vn)p6fx{ZKb4kROQ8iuKr#1Uu_ir@z!veZkn5G>fKsg=LIfZt+Vi6)jrDB3td 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zg{n3J+}dn=b9FYgv9ok0zNt;i<^*{5he1dX9|(fFD3BDfE=nweg3Ewb(kBJt3yp|A zMIMQi$72-b!T;nDcsT;_DQHPKoSYmI4a$^r-$E=2D>#_TAGS2Nb#Xf?D1pWh Date: Tue, 5 Mar 2024 11:05:11 +0100 Subject: [PATCH 09/11] Replace constant strings in BoostAODE --- src/ensembles/BoostAODE.cc | 32 +++++++++++++++++++++----------- 1 file changed, 21 insertions(+), 11 deletions(-) diff --git a/src/ensembles/BoostAODE.cc b/src/ensembles/BoostAODE.cc index 10eb694..7bc32b8 100644 --- a/src/ensembles/BoostAODE.cc +++ b/src/ensembles/BoostAODE.cc @@ -8,6 +8,16 @@ #include "folding.hpp" namespace bayesnet { + struct { + std::string CFS = "CFS"; + std::string FCBF = "FCBF"; + std::string IWSS = "IWSS"; + }SelectFeatures; + struct { + std::string ASC = "asc"; + std::string DESC = "desc"; + std::string RAND = "rand"; + }Orders; BoostAODE::BoostAODE(bool predict_voting) : Ensemble(predict_voting) { validHyperparameters = { @@ -61,10 +71,10 @@ namespace bayesnet { hyperparameters.erase("maxModels"); } if (hyperparameters.contains("order")) { - std::vector algos = { "asc", "desc", "rand" }; + std::vector algos = { Orders.ASC, Orders.DESC, Orders.RAND }; order_algorithm = hyperparameters["order"]; if (std::find(algos.begin(), algos.end(), order_algorithm) == algos.end()) { - throw std::invalid_argument("Invalid order algorithm, valid values [asc, desc, rand]"); + throw std::invalid_argument("Invalid order algorithm, valid values [" + Orders.ASC + ", " + Orders.DESC + ", " + Orders.RAND + "]"); } hyperparameters.erase("order"); } @@ -90,11 +100,11 @@ namespace bayesnet { } if (hyperparameters.contains("select_features")) { auto selectedAlgorithm = hyperparameters["select_features"]; - std::vector algos = { "IWSS", "FCBF", "CFS" }; + std::vector algos = { SelectFeatures.IWSS, SelectFeatures.CFS, SelectFeatures.CFS }; selectFeatures = true; select_features_algorithm = selectedAlgorithm; if (std::find(algos.begin(), algos.end(), selectedAlgorithm) == algos.end()) { - throw std::invalid_argument("Invalid selectFeatures value, valid values [IWSS, FCBF, CFS]"); + throw std::invalid_argument("Invalid selectFeatures value, valid values [" + SelectFeatures.IWSS + ", " + SelectFeatures.CFS + ", " + SelectFeatures.FCBF + "]"); } hyperparameters.erase("select_features"); } @@ -107,16 +117,16 @@ namespace bayesnet { std::unordered_set featuresUsed; torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64); int maxFeatures = 0; - if (select_features_algorithm == "CFS") { + if (select_features_algorithm == SelectFeatures.CFS) { featureSelector = new CFS(dataset, features, className, maxFeatures, states.at(className).size(), weights_); - } else if (select_features_algorithm == "IWSS") { + } else if (select_features_algorithm == SelectFeatures.IWSS) { if (threshold < 0 || threshold >0.5) { - throw std::invalid_argument("Invalid threshold value for IWSS [0, 0.5]"); + throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.IWSS + " [0, 0.5]"); } featureSelector = new IWSS(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold); - } else if (select_features_algorithm == "FCBF") { + } else if (select_features_algorithm == SelectFeatures.FCBF) { if (threshold < 1e-7 || threshold > 1) { - throw std::invalid_argument("Invalid threshold value [1e-7, 1]"); + throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.FCBF + " [1e-7, 1]"); } featureSelector = new FCBF(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold); } @@ -174,12 +184,12 @@ namespace bayesnet { // n_models == maxModels // epsilon sub t > 0.5 => inverse the weights policy // validation error is not decreasing - bool ascending = order_algorithm == "asc"; + bool ascending = order_algorithm == Orders.ASC; std::mt19937 g{ 173 }; while (!exitCondition) { // Step 1: Build ranking with mutual information auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted - if (order_algorithm == "rand") { + if (order_algorithm == Orders.RAND) { std::shuffle(featureSelection.begin(), featureSelection.end(), g); } auto feature = featureSelection[0]; -- 2.45.2 From 0ee3eaed5380128e614b0734c0d410c3de023f2e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ricardo=20Montan=CC=83ana?= Date: Tue, 5 Mar 2024 12:10:58 +0100 Subject: [PATCH 10/11] Update select features models significance --- docs/BoostAODE.md | 2 +- src/ensembles/BoostAODE.cc | 62 ++++++++++++++++++++++++++------------ 2 files changed, 43 insertions(+), 21 deletions(-) diff --git a/docs/BoostAODE.md b/docs/BoostAODE.md index 23f5bd3..6a32ab6 100644 --- a/docs/BoostAODE.md +++ b/docs/BoostAODE.md @@ -16,7 +16,7 @@ The hyperparameters defined in the algorithm are: - ***tolerance*** (*int*): Sets the maximum number of models that can worsen the result without constituting a termination condition. Default value: *0*. -- ***select_features*** (*{"IWSS", "FCBF", "CFS", ""}*): Selects the variable selection method to be used to build initial models for the ensemble that will be included without considering any of the other exit conditions. These models also do not update or use the weights used by the Boosting algorithm, and their significance is set to 1. +- ***select_features*** (*{"IWSS", "FCBF", "CFS", ""}*): Selects the variable selection method to be used to build initial models for the ensemble that will be included without considering any of the other exit conditions. Once the models of the selected variables are built, the algorithm will update the weights using the ensemble and set the significance of all the models built with the same alpha_t. Default value: *""*. - ***threshold*** (*double*): Sets the necessary value for the IWSS and FCBF algorithms to function. Accepted values are: - IWSS: $threshold \in [0, 0.5]$ diff --git a/src/ensembles/BoostAODE.cc b/src/ensembles/BoostAODE.cc index 7bc32b8..bc75dfb 100644 --- a/src/ensembles/BoostAODE.cc +++ b/src/ensembles/BoostAODE.cc @@ -1,6 +1,7 @@ #include #include #include +#include #include "BoostAODE.h" #include "CFS.h" #include "FCBF.h" @@ -112,6 +113,33 @@ namespace bayesnet { throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump()); } } + std::tuple update_weights(torch::Tensor& ytrain, torch::Tensor& ypred, torch::Tensor& weights) + { + bool terminate = false; + double alpha_t = 0; + auto mask_wrong = ypred != ytrain; + auto mask_right = ypred == ytrain; + auto masked_weights = weights * mask_wrong.to(weights.dtype()); + double epsilon_t = masked_weights.sum().item(); + if (epsilon_t > 0.5) { + // Inverse the weights policy (plot ln(wt)) + // "In each round of AdaBoost, there is a sanity check to ensure that the current base + // learner is better than random guess" (Zhi-Hua Zhou, 2012) + terminate = true; + } else { + double wt = (1 - epsilon_t) / epsilon_t; + alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt); + // Step 3.2: Update weights for next classifier + // Step 3.2.1: Update weights of wrong samples + weights += mask_wrong.to(weights.dtype()) * exp(alpha_t) * weights; + // Step 3.2.2: Update weights of right samples + weights += mask_right.to(weights.dtype()) * exp(-alpha_t) * weights; + // Step 3.3: Normalise the weights + double totalWeights = torch::sum(weights).item(); + weights = weights / totalWeights; + } + return { weights, alpha_t, terminate }; + } std::unordered_set BoostAODE::initializeModels() { std::unordered_set featuresUsed; @@ -161,19 +189,29 @@ namespace bayesnet { { initialize_prob_table = true; fitted = true; + double alpha_t = 0; // Algorithm based on the adaboost algorithm for classification // as explained in Ensemble methods (Zhi-Hua Zhou, 2012) + torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64); + bool exitCondition = false; std::unordered_set featuresUsed; if (selectFeatures) { featuresUsed = initializeModels(); + auto ypred = predict(X_train); + std::tie(weights_, alpha_t, exitCondition) = update_weights(y_train, ypred, weights_); + // Update significance of the models + for (int i = 0; i < n_models; ++i) { + significanceModels[i] = alpha_t; + } + if (exitCondition) { + return; + } } bool resetMaxModels = false; if (maxModels == 0) { maxModels = .1 * n > 10 ? .1 * n : n; resetMaxModels = true; // Flag to unset maxModels } - torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64); - bool exitCondition = false; // Variables to control the accuracy finish condition double priorAccuracy = 0.0; double delta = 1.0; @@ -218,26 +256,10 @@ namespace bayesnet { ypred = ensemble_predict(X_train, dynamic_cast(model.get())); } // Step 3.1: Compute the classifier amout of say - auto mask_wrong = ypred != y_train; - auto mask_right = ypred == y_train; - auto masked_weights = weights_ * mask_wrong.to(weights_.dtype()); - double epsilon_t = masked_weights.sum().item(); - if (epsilon_t > 0.5) { - // Inverse the weights policy (plot ln(wt)) - // "In each round of AdaBoost, there is a sanity check to ensure that the current base - // learner is better than random guess" (Zhi-Hua Zhou, 2012) + std::tie(weights_, alpha_t, exitCondition) = update_weights(y_train, ypred, weights_); + if (exitCondition) { break; } - double wt = (1 - epsilon_t) / epsilon_t; - double alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt); - // Step 3.2: Update weights for next classifier - // Step 3.2.1: Update weights of wrong samples - weights_ += mask_wrong.to(weights_.dtype()) * exp(alpha_t) * weights_; - // Step 3.2.2: Update weights of right samples - weights_ += mask_right.to(weights_.dtype()) * exp(-alpha_t) * weights_; - // Step 3.3: Normalise the weights - double totalWeights = torch::sum(weights_).item(); - weights_ = weights_ / totalWeights; // Step 3.4: Store classifier and its accuracy to weigh its future vote featuresUsed.insert(feature); models.push_back(std::move(model)); -- 2.45.2 From d858e26e4bcc47e493dcb92e2cf955d95116cc85 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana=20G=C3=B3mez?= Date: Wed, 6 Mar 2024 17:04:16 +0100 Subject: [PATCH 11/11] Update version number and Changelog --- CHANGELOG.md | 3 ++- CMakeLists.txt | 2 +- docs/BoostAODE.md | 2 +- src/ensembles/BoostAODE.cc | 8 ++++---- 4 files changed, 8 insertions(+), 7 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index fe8c2cd..dc0a6a0 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,7 +5,7 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). -## [Unreleased] +## [1.0.4] ### Added @@ -16,6 +16,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Changed - Change the library structure adding folders for each group of classes (classifiers, ensembles, etc). +- The significances of the models generated under the feature selection algorithm are now computed after all the models have been generated and an αt value is computed and assigned to each model. ## [1.0.3] diff --git a/CMakeLists.txt b/CMakeLists.txt index 0babab8..327f09b 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,7 +1,7 @@ cmake_minimum_required(VERSION 3.20) project(BayesNet - VERSION 1.0.3 + VERSION 1.0.4 DESCRIPTION "Bayesian Network and basic classifiers Library." HOMEPAGE_URL "https://github.com/rmontanana/bayesnet" LANGUAGES CXX diff --git a/docs/BoostAODE.md b/docs/BoostAODE.md index 6a32ab6..a04cc0e 100644 --- a/docs/BoostAODE.md +++ b/docs/BoostAODE.md @@ -16,7 +16,7 @@ The hyperparameters defined in the algorithm are: - ***tolerance*** (*int*): Sets the maximum number of models that can worsen the result without constituting a termination condition. Default value: *0*. -- ***select_features*** (*{"IWSS", "FCBF", "CFS", ""}*): Selects the variable selection method to be used to build initial models for the ensemble that will be included without considering any of the other exit conditions. Once the models of the selected variables are built, the algorithm will update the weights using the ensemble and set the significance of all the models built with the same alpha_t. Default value: *""*. +- ***select_features*** (*{"IWSS", "FCBF", "CFS", ""}*): Selects the variable selection method to be used to build initial models for the ensemble that will be included without considering any of the other exit conditions. Once the models of the selected variables are built, the algorithm will update the weights using the ensemble and set the significance of all the models built with the same αt. Default value: *""*. - ***threshold*** (*double*): Sets the necessary value for the IWSS and FCBF algorithms to function. Accepted values are: - IWSS: $threshold \in [0, 0.5]$ diff --git a/src/ensembles/BoostAODE.cc b/src/ensembles/BoostAODE.cc index bc75dfb..e8a5166 100644 --- a/src/ensembles/BoostAODE.cc +++ b/src/ensembles/BoostAODE.cc @@ -181,17 +181,17 @@ namespace bayesnet { prob_table += model->predict_proba(X) * 1.0; } // prob_table doesn't store probabilities but the sum of them - // to have them we need to divide by the sum of the significances but we - // don't need them to predict label values + // to have them we need to divide by the sum of the "weights" used to + // consider the results obtanined in the model's predict_proba. return prob_table.argmax(1); } void BoostAODE::trainModel(const torch::Tensor& weights) { + // Algorithm based on the adaboost algorithm for classification + // as explained in Ensemble methods (Zhi-Hua Zhou, 2012) initialize_prob_table = true; fitted = true; double alpha_t = 0; - // Algorithm based on the adaboost algorithm for classification - // as explained in Ensemble methods (Zhi-Hua Zhou, 2012) torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64); bool exitCondition = false; std::unordered_set featuresUsed; -- 2.45.2