Upgrade version number to 1.0.5

Fix dependency graph Remove loguru library
Add check dependencies in make diagrams endpoint
2024-04-20 18:00:40 +02:00 · 2024-04-19 19:47:37 +02:00 · 2024-04-19 18:29:43 +02:00 · 2024-04-19 14:33:00 +02:00 · 2024-04-15 10:26:50 +00:00
15 changed files with 629 additions and 3555 deletions
--- a/.clang-uml
+++ b/.clang-uml
@@ -0,0 +1,39 @@
+compilation_database_dir: build_debug
+output_directory: diagrams
+diagrams:
+  BayesNet:
+    type: class
+    glob:
+      - bayesnet/*.h
+      - bayesnet/classifiers/*.h
+      - bayesnet/classifiers/*.cc
+      - bayesnet/ensembles/*.h
+      - bayesnet/ensembles/*.cc
+      - bayesnet/feature_selection/*.h
+      - bayesnet/feature_selection/*.cc
+      - bayesnet/network/*.h
+      - bayesnet/network/*.cc
+      - bayesnet/utils/*.h
+      - bayesnet/utils/*.cc
+    include:
+      # Only include entities from the following namespaces
+      namespaces:
+        - bayesnet
+    exclude:
+      access:
+        - private
+    plantuml:
+      style:
+        # Apply this style to all classes in the diagram
+        class: "#aliceblue;line:blue;line.dotted;text:blue"
+        # Apply this style to all packages in the diagram
+        package: "#back:grey"
+        # Make all template instantiation relations point upwards and draw them
+        # as green and dotted lines
+        instantiation: "up[#green,dotted]"
+      cmd: "/usr/bin/plantuml -tsvg \"diagrams/{}.puml\""
+      before:
+        - 'title clang-uml class diagram model'
+    mermaid:
+      before:
+        - 'classDiagram'
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -0,0 +1,12 @@
+name: CI
+on: push
+
+jobs:
+  tests:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - run: sudo apt-get install ninja-build cmake
+    - run: ninja --version
+    - run: cmake --version
+    - run: g++ --version
--- 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]
+## [1.0.5] 2024-04-20

 ### Added

@@ -16,6 +16,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Badges of coverage and code quality (codacy) in README.md. Coverage badge is updated with *make viewcoverage*
 - Tests to reach 97% of coverage.
 - Copyright header to source files.
+- Diagrams to README.md: UML class diagram & dependency diagram
+- Action to create diagrams to Makefile: *make diagrams*

 ### Changed

--- a/CMakeGraphVizOptions.cmake
+++ b/CMakeGraphVizOptions.cmake
@@ -0,0 +1,5 @@
+# Set the default graph title
+set(GRAPHVIZ_GRAPH_NAME "BayesNet dependency graph")
+
+set(GRAPHVIZ_SHARED_LIBS OFF)
+set(GRAPHVIZ_STATIC_LIBS ON)
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,7 +1,7 @@
 cmake_minimum_required(VERSION 3.20)

 project(BayesNet
-  VERSION 1.0.4.1
+  VERSION 1.0.5
  DESCRIPTION "Bayesian Network and basic classifiers Library."
  HOMEPAGE_URL "https://github.com/rmontanana/bayesnet"
  LANGUAGES CXX
--- a/20
+++ b/20
@@ -1,11 +1,15 @@
 SHELL := /bin/bash
 .DEFAULT_GOAL := help
-.PHONY: viewcoverage coverage setup help install uninstall buildr buildd test clean debug release sample updatebadge
+.PHONY: viewcoverage coverage setup help install uninstall diagrams buildr buildd test clean debug release sample updatebadge

 f_release = build_release
 f_debug = build_debug
+f_diagrams = diagrams
 app_targets = BayesNet
 test_targets = TestBayesNet
+clang-uml = clang-uml
+plantuml = plantuml
+dot = dot
 n_procs = -j 16

 define ClearTests
@@ -31,11 +35,21 @@ setup: ## Install dependencies for tests and coverage
 		pip install gcovr; \
 		sudo dnf install lcov;\
 	fi
+	@echo "* You should install plantuml & graphviz for the diagrams"

-dependency: ## Create a dependency graph diagram of the project (build/dependency.png)
+diagrams: ## Create an UML class diagram & depnendency of the project (diagrams/BayesNet.png)
+	@which $(plantuml) || (echo ">>> Please install plantuml"; exit 1)
+	@which $(dot) || (echo ">>> Please install graphviz"; exit 1)
+	@which $(clang-uml) || (echo ">>> Please install clang-uml"; exit 1)
+	@export PLANTUML_LIMIT_SIZE=16384
+	@echo ">>> Creating UML class diagram of the project...";
+	@$(clang-uml) -p 
+	@cd $(f_diagrams); \
+	$(plantuml) -tsvg BayesNet.puml
 	@echo ">>> Creating dependency graph diagram of the project...";
 	$(MAKE) debug
-	cd $(f_debug) && cmake .. --graphviz=dependency.dot && dot -Tpng dependency.dot -o dependency.png
+	cd $(f_debug) && cmake .. --graphviz=dependency.dot 
+	@$(dot) -Tsvg $(f_debug)/dependency.dot.BayesNet -o $(f_diagrams)/dependency.svg

 buildd: ## Build the debug targets
 	cmake --build $(f_debug) -t $(app_targets) $(n_procs)
--- a/README.md
+++ b/README.md
@@ -48,3 +48,13 @@ make sample fname=tests/data/glass.arff
 ## Models

 ### [BoostAODE](docs/BoostAODE.md)
+
+## Diagrams
+
+### UML Class Diagram
+
+![BayesNet UML Class Diagram](diagrams/BayesNet.svg)
+
+### Dependency Diagram
+
+![BayesNet Dependency Diagram](diagrams/dependency.svg)
--- a/bayesnet/ensembles/BoostAODE.cc
+++ b/bayesnet/ensembles/BoostAODE.cc
@@ -14,8 +14,6 @@
 #include "bayesnet/feature_selection/IWSS.h"
 #include "BoostAODE.h"

-#include "bayesnet/utils/loguru.cpp"
-
 namespace bayesnet {

    BoostAODE::BoostAODE(bool predict_voting) : Ensemble(predict_voting)
@@ -186,7 +184,6 @@ namespace bayesnet {
        significanceModels = std::vector<double>(k, 1.0);
        // 4. Move first n classifiers to models_bak
        // backup the first n_models - k models (if n_models == k, don't backup any)
-        VLOG_SCOPE_F(1, "upd_weights_block n_models=%d k=%d", n_models, k);
        for (int i = 0; i < n_models - k; ++i) {
            model = std::move(models[0]);
            models.erase(models.begin());
@@ -251,9 +248,6 @@ namespace bayesnet {
        featureSelector->fit();
        auto cfsFeatures = featureSelector->getFeatures();
        auto scores = featureSelector->getScores();
-        for (int i = 0; i < cfsFeatures.size(); ++i) {
-            LOG_F(INFO, "Feature: %d Score: %f", cfsFeatures[i], scores[i]);
-        }
        for (const int& feature : cfsFeatures) {
            featuresUsed.push_back(feature);
            std::unique_ptr<Classifier> model = std::make_unique<SPODE>(feature);
@@ -268,12 +262,6 @@ namespace bayesnet {
    }
    void BoostAODE::trainModel(const torch::Tensor& weights)
    {
-        //
-        // Logging setup
-        //
-        loguru::set_thread_name("BoostAODE");
-        loguru::g_stderr_verbosity = loguru::Verbosity_OFF;;
-        loguru::add_file("boostAODE.log", loguru::Truncate, loguru::Verbosity_MAX);
        // Algorithm based on the adaboost algorithm for classification
        // as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
        fitted = true;
@@ -292,11 +280,6 @@ namespace bayesnet {
            if (finished) {
                return;
            }
-            LOG_F(INFO, "Initial models: %d", n_models);
-            LOG_F(INFO, "Significances: ");
-            for (int i = 0; i < n_models; ++i) {
-                LOG_F(INFO, "i=%d significance=%f", i, significanceModels[i]);
-            }
        }
        int numItemsPack = 0; // The counter of the models inserted in the current pack
        // Variables to control the accuracy finish condition
@@ -313,7 +296,6 @@ namespace bayesnet {
        while (!finished) {
            // Step 1: Build ranking with mutual information
            auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
-            VLOG_SCOPE_F(1, "featureSelection.size: %zu featuresUsed.size: %zu", featureSelection.size(), featuresUsed.size());
            if (order_algorithm == Orders.RAND) {
                std::shuffle(featureSelection.begin(), featureSelection.end(), g);
            }
@@ -324,7 +306,6 @@ namespace bayesnet {
            );
            int k = pow(2, tolerance);
            int counter = 0; // The model counter of the current pack
-            VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, featureSelection.size());
            while (counter++ < k && featureSelection.size() > 0) {
                auto feature = featureSelection[0];
                featureSelection.erase(featureSelection.begin());
@@ -336,10 +317,6 @@ namespace bayesnet {
                    auto ypred = model->predict(X_train);
                    // Step 3.1: Compute the classifier amout of say
                    std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
-                    if (finished) {
-                        VLOG_SCOPE_F(2, "** epsilon_t > 0.5 **");
-                        break;
-                    }
                }
                // Step 3.4: Store classifier and its accuracy to weigh its future vote
                numItemsPack++;
@@ -347,7 +324,6 @@ namespace bayesnet {
                models.push_back(std::move(model));
                significanceModels.push_back(alpha_t);
                n_models++;
-                VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %zu", numItemsPack, n_models, featuresUsed.size());
            }
            if (block_update) {
                std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);
@@ -357,15 +333,12 @@ namespace bayesnet {
                double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
                if (priorAccuracy == 0) {
                    priorAccuracy = accuracy;
-                    VLOG_SCOPE_F(3, "First accuracy: %f", priorAccuracy);
                } else {
                    improvement = accuracy - priorAccuracy;
                }
                if (improvement < convergence_threshold) {
-                    VLOG_SCOPE_F(3, "(improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
                    tolerance++;
                } else {
-                    VLOG_SCOPE_F(3, "*(improvement>=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
                    tolerance = 0; // Reset the counter if the model performs better
                    numItemsPack = 0;
                }
@@ -373,20 +346,17 @@ namespace bayesnet {
                priorAccuracy = std::max(accuracy, priorAccuracy);
                // priorAccuracy = accuracy;
            }
-            VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", tolerance, featuresUsed.size(), features.size());
            finished = finished || tolerance > maxTolerance || featuresUsed.size() == features.size();
        }
        if (tolerance > maxTolerance) {
            if (numItemsPack < n_models) {
                notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
-                VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);
                for (int i = 0; i < numItemsPack; ++i) {
                    significanceModels.pop_back();
                    models.pop_back();
                    n_models--;
                }
            } else {
-                VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
                notes.push_back("Convergence threshold reached & 0 models eliminated");
            }
        }
--- a/bayesnet/utils/loguru.cpp
+++ b/bayesnet/utils/loguru.cpp
--- a/bayesnet/utils/loguru.hpp
+++ b/bayesnet/utils/loguru.hpp
--- a/diagrams/BayesNet.pdf
+++ b/diagrams/BayesNet.pdf
--- a/diagrams/BayesNet.puml
+++ b/diagrams/BayesNet.puml
@@ -0,0 +1,412 @@
+@startuml
+title clang-uml class diagram model
+class "bayesnet::Metrics" as C_0000736965376885623323
+class C_0000736965376885623323 #aliceblue;line:blue;line.dotted;text:blue {
+Metrics() = default : void
+Metrics(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int classNumStates) : void
+Metrics(const std::vector<std::vector<int>> & vsamples, const std::vector<int> & labels, const std::vector<std::string> & features, const std::string & className, const int classNumStates) : void
+..
+SelectKBestWeighted(const torch::Tensor & weights, bool ascending = false, unsigned int k = 0) : std::vector<int>
+conditionalEdge(const torch::Tensor & weights) : torch::Tensor
+conditionalEdgeWeights(std::vector<float> & weights) : std::vector<float>
+#doCombinations<T>(const std::vector<T> & source) : std::vector<std::pair<T, T> >
+#entropy(const torch::Tensor & feature, const torch::Tensor & weights) : double
+getScoresKBest() const : std::vector<double>
+maximumSpanningTree(const std::vector<std::string> & features, const torch::Tensor & weights, const int root) : std::vector<std::pair<int,int>>
+mutualInformation(const torch::Tensor & firstFeature, const torch::Tensor & secondFeature, const torch::Tensor & weights) : double
+#pop_first<T>(std::vector<T> & v) : T
+__
+#className : std::string
+#features : std::vector<std::string>
+#samples : torch::Tensor
+}
+class "bayesnet::Node" as C_0001303524929067080934
+class C_0001303524929067080934 #aliceblue;line:blue;line.dotted;text:blue {
+Node(const std::string &) : void
+..
+addChild(Node *) : void
+addParent(Node *) : void
+clear() : void
+computeCPT(const torch::Tensor & dataset, const std::vector<std::string> & features, const double laplaceSmoothing, const torch::Tensor & weights) : void
+getCPT() : torch::Tensor &
+getChildren() : std::vector<Node *> &
+getFactorValue(std::map<std::string,int> &) : float
+getName() const : std::string
+getNumStates() const : int
+getParents() : std::vector<Node *> &
+graph(const std::string & clasName) : std::vector<std::string>
+minFill() : unsigned int
+removeChild(Node *) : void
+removeParent(Node *) : void
+setNumStates(int) : void
+__
+}
+class "bayesnet::Network" as C_0001186707649890429575
+class C_0001186707649890429575 #aliceblue;line:blue;line.dotted;text:blue {
+Network() : void
+Network(float) : void
+Network(const Network &) : void
+~Network() = default : void
+..
+addEdge(const std::string &, const std::string &) : void
+addNode(const std::string &) : void
+dump_cpt() const : std::string
+fit(const torch::Tensor & samples, const torch::Tensor & weights, const std::vector<std::string> & featureNames, const std::string & className, const std::map<std::string,std::vector<int>> & states) : void
+fit(const torch::Tensor & X, const torch::Tensor & y, const torch::Tensor & weights, const std::vector<std::string> & featureNames, const std::string & className, const std::map<std::string,std::vector<int>> & states) : void
+fit(const std::vector<std::vector<int>> & input_data, const std::vector<int> & labels, const std::vector<double> & weights, const std::vector<std::string> & featureNames, const std::string & className, const std::map<std::string,std::vector<int>> & states) : void
+getClassName() const : std::string
+getClassNumStates() const : int
+getEdges() const : std::vector<std::pair<std::string,std::string>>
+getFeatures() const : std::vector<std::string>
+getMaxThreads() const : float
+getNodes() : std::map<std::string,std::unique_ptr<Node>> &
+getNumEdges() const : int
+getSamples() : torch::Tensor &
+getStates() const : int
+graph(const std::string & title) const : std::vector<std::string>
+initialize() : void
+predict(const std::vector<std::vector<int>> &) : std::vector<int>
+predict(const torch::Tensor &) : torch::Tensor
+predict_proba(const std::vector<std::vector<int>> &) : std::vector<std::vector<double>>
+predict_proba(const torch::Tensor &) : torch::Tensor
+predict_tensor(const torch::Tensor & samples, const bool proba) : torch::Tensor
+score(const std::vector<std::vector<int>> &, const std::vector<int> &) : double
+show() const : std::vector<std::string>
+topological_sort() : std::vector<std::string>
+version() : std::string
+__
+}
+enum "bayesnet::status_t" as C_0000738420730783851375
+enum C_0000738420730783851375 {
+NORMAL
+WARNING
+ERROR
+}
+abstract "bayesnet::BaseClassifier" as C_0000327135989451974539
+abstract C_0000327135989451974539 #aliceblue;line:blue;line.dotted;text:blue {
+~BaseClassifier() = default : void
+..
+{abstract} +dump_cpt() const = 0 : std::string
+{abstract} +fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) = 0 : BaseClassifier &
+{abstract} +fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) = 0 : BaseClassifier &
+{abstract} +fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const torch::Tensor & weights) = 0 : BaseClassifier &
+{abstract} +fit(std::vector<std::vector<int>> & X, std::vector<int> & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) = 0 : BaseClassifier &
+{abstract} +getClassNumStates() const = 0 : int
+{abstract} +getNotes() const = 0 : std::vector<std::string>
+{abstract} +getNumberOfEdges() const = 0 : int
+{abstract} +getNumberOfNodes() const = 0 : int
+{abstract} +getNumberOfStates() const = 0 : int
+{abstract} +getStatus() const = 0 : status_t
+getValidHyperparameters() : std::vector<std::string> &
+{abstract} +getVersion() = 0 : std::string
+{abstract} +graph(const std::string & title = "") const = 0 : std::vector<std::string>
+{abstract} +predict(std::vector<std::vector<int>> & X) = 0 : std::vector<int>
+{abstract} +predict(torch::Tensor & X) = 0 : torch::Tensor
+{abstract} +predict_proba(std::vector<std::vector<int>> & X) = 0 : std::vector<std::vector<double>>
+{abstract} +predict_proba(torch::Tensor & X) = 0 : torch::Tensor
+{abstract} +score(std::vector<std::vector<int>> & X, std::vector<int> & y) = 0 : float
+{abstract} +score(torch::Tensor & X, torch::Tensor & y) = 0 : float
+{abstract} +setHyperparameters(const nlohmann::json & hyperparameters) = 0 : void
+{abstract} +show() const = 0 : std::vector<std::string>
+{abstract} +topological_order() = 0 : std::vector<std::string>
+{abstract} #trainModel(const torch::Tensor & weights) = 0 : void
+__
+#validHyperparameters : std::vector<std::string>
+}
+abstract "bayesnet::Classifier" as C_0002043996622900301644
+abstract C_0002043996622900301644 #aliceblue;line:blue;line.dotted;text:blue {
+Classifier(Network model) : void
+~Classifier() = default : void
+..
+addNodes() : void
+#buildDataset(torch::Tensor & y) : void
+{abstract} #buildModel(const torch::Tensor & weights) = 0 : void
+#checkFitParameters() : void
+dump_cpt() const : std::string
+fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) : Classifier &
+fit(std::vector<std::vector<int>> & X, std::vector<int> & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) : Classifier &
+fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) : Classifier &
+fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const torch::Tensor & weights) : Classifier &
+getClassNumStates() const : int
+getNotes() const : std::vector<std::string>
+getNumberOfEdges() const : int
+getNumberOfNodes() const : int
+getNumberOfStates() const : int
+getStatus() const : status_t
+getVersion() : std::string
+predict(std::vector<std::vector<int>> & X) : std::vector<int>
+predict(torch::Tensor & X) : torch::Tensor
+predict_proba(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
+predict_proba(torch::Tensor & X) : torch::Tensor
+score(torch::Tensor & X, torch::Tensor & y) : float
+score(std::vector<std::vector<int>> & X, std::vector<int> & y) : float
+setHyperparameters(const nlohmann::json & hyperparameters) : void
+show() const : std::vector<std::string>
+topological_order() : std::vector<std::string>
+#trainModel(const torch::Tensor & weights) : void
+__
+#className : std::string
+#dataset : torch::Tensor
+#features : std::vector<std::string>
+#fitted : bool
+#m : unsigned int
+#metrics : Metrics
+#model : Network
+#n : unsigned int
+#notes : std::vector<std::string>
+#states : std::map<std::string,std::vector<int>>
+#status : status_t
+}
+class "bayesnet::KDB" as C_0001112865019015250005
+class C_0001112865019015250005 #aliceblue;line:blue;line.dotted;text:blue {
+KDB(int k, float theta = 0.03) : void
+~KDB() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
+graph(const std::string & name = "KDB") const : std::vector<std::string>
+setHyperparameters(const nlohmann::json & hyperparameters_) : void
+__
+}
+class "bayesnet::TAN" as C_0001760994424884323017
+class C_0001760994424884323017 #aliceblue;line:blue;line.dotted;text:blue {
+TAN() : void
+~TAN() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
+graph(const std::string & name = "TAN") const : std::vector<std::string>
+__
+}
+class "bayesnet::Proposal" as C_0002219995589162262979
+class C_0002219995589162262979 #aliceblue;line:blue;line.dotted;text:blue {
+Proposal(torch::Tensor & pDataset, std::vector<std::string> & features_, std::string & className_) : void
+~Proposal() : void
+..
+#checkInput(const torch::Tensor & X, const torch::Tensor & y) : void
+#fit_local_discretization(const torch::Tensor & y) : std::map<std::string,std::vector<int>>
+#localDiscretizationProposal(const std::map<std::string,std::vector<int>> & states, Network & model) : std::map<std::string,std::vector<int>>
+#prepareX(torch::Tensor & X) : torch::Tensor
+__
+#Xf : torch::Tensor
+#discretizers : map<std::string,mdlp::CPPFImdlp *>
+#y : torch::Tensor
+}
+class "bayesnet::TANLd" as C_0001668829096702037834
+class C_0001668829096702037834 #aliceblue;line:blue;line.dotted;text:blue {
+TANLd() : void
+~TANLd() = default : void
+..
+fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) : TANLd &
+graph(const std::string & name = "TAN") const : std::vector<std::string>
+predict(torch::Tensor & X) : torch::Tensor
+{static} +version() : std::string
+__
+}
+abstract "bayesnet::FeatureSelect" as C_0001695326193250580823
+abstract C_0001695326193250580823 #aliceblue;line:blue;line.dotted;text:blue {
+FeatureSelect(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights) : void
+~FeatureSelect() : void
+..
+#computeMeritCFS() : double
+#computeSuFeatures(const int a, const int b) : double
+#computeSuLabels() : void
+{abstract} +fit() = 0 : void
+getFeatures() const : std::vector<int>
+getScores() const : std::vector<double>
+#initialize() : void
+#symmetricalUncertainty(int a, int b) : double
+__
+#fitted : bool
+#maxFeatures : int
+#selectedFeatures : std::vector<int>
+#selectedScores : std::vector<double>
+#suFeatures : std::map<std::pair<int,int>,double>
+#suLabels : std::vector<double>
+#weights : const torch::Tensor &
+}
+class "bayesnet::CFS" as C_0000011627355691342494
+class C_0000011627355691342494 #aliceblue;line:blue;line.dotted;text:blue {
+CFS(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights) : void
+~CFS() : void
+..
+fit() : void
+__
+}
+class "bayesnet::FCBF" as C_0000144682015341746929
+class C_0000144682015341746929 #aliceblue;line:blue;line.dotted;text:blue {
+FCBF(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights, const double threshold) : void
+~FCBF() : void
+..
+fit() : void
+__
+}
+class "bayesnet::IWSS" as C_0000008268514674428553
+class C_0000008268514674428553 #aliceblue;line:blue;line.dotted;text:blue {
+IWSS(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights, const double threshold) : void
+~IWSS() : void
+..
+fit() : void
+__
+}
+class "bayesnet::SPODE" as C_0000512022813807538451
+class C_0000512022813807538451 #aliceblue;line:blue;line.dotted;text:blue {
+SPODE(int root) : void
+~SPODE() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
+graph(const std::string & name = "SPODE") const : std::vector<std::string>
+__
+}
+class "bayesnet::Ensemble" as C_0001985241386355360576
+class C_0001985241386355360576 #aliceblue;line:blue;line.dotted;text:blue {
+Ensemble(bool predict_voting = true) : void
+~Ensemble() = default : void
+..
+#compute_arg_max(std::vector<std::vector<double>> & X) : std::vector<int>
+#compute_arg_max(torch::Tensor & X) : torch::Tensor
+dump_cpt() const : std::string
+getNumberOfEdges() const : int
+getNumberOfNodes() const : int
+getNumberOfStates() const : int
+graph(const std::string & title) const : std::vector<std::string>
+predict(std::vector<std::vector<int>> & X) : std::vector<int>
+predict(torch::Tensor & X) : torch::Tensor
+#predict_average_proba(torch::Tensor & X) : torch::Tensor
+#predict_average_proba(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
+#predict_average_voting(torch::Tensor & X) : torch::Tensor
+#predict_average_voting(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
+predict_proba(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
+predict_proba(torch::Tensor & X) : torch::Tensor
+score(std::vector<std::vector<int>> & X, std::vector<int> & y) : float
+score(torch::Tensor & X, torch::Tensor & y) : float
+show() const : std::vector<std::string>
+topological_order() : std::vector<std::string>
+#trainModel(const torch::Tensor & weights) : void
+#voting(torch::Tensor & votes) : torch::Tensor
+__
+#models : std::vector<std::unique_ptr<Classifier>>
+#n_models : unsigned int
+#predict_voting : bool
+#significanceModels : std::vector<double>
+}
+class "bayesnet::(anonymous_45089536)" as C_0001186398587753535158
+class C_0001186398587753535158 #aliceblue;line:blue;line.dotted;text:blue {
+__
+CFS : std::string
+FCBF : std::string
+IWSS : std::string
+}
+class "bayesnet::(anonymous_45090163)" as C_0000602764946063116717
+class C_0000602764946063116717 #aliceblue;line:blue;line.dotted;text:blue {
+__
+ASC : std::string
+DESC : std::string
+RAND : std::string
+}
+class "bayesnet::BoostAODE" as C_0000358471592399852382
+class C_0000358471592399852382 #aliceblue;line:blue;line.dotted;text:blue {
+BoostAODE(bool predict_voting = false) : void
+~BoostAODE() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
+graph(const std::string & title = "BoostAODE") const : std::vector<std::string>
+setHyperparameters(const nlohmann::json & hyperparameters_) : void
+#trainModel(const torch::Tensor & weights) : void
+__
+}
+class "bayesnet::MST" as C_0000131858426172291700
+class C_0000131858426172291700 #aliceblue;line:blue;line.dotted;text:blue {
+MST() = default : void
+MST(const std::vector<std::string> & features, const torch::Tensor & weights, const int root) : void
+..
+maximumSpanningTree() : std::vector<std::pair<int,int>>
+__
+}
+class "bayesnet::Graph" as C_0001197041682001898467
+class C_0001197041682001898467 #aliceblue;line:blue;line.dotted;text:blue {
+Graph(int V) : void
+..
+addEdge(int u, int v, float wt) : void
+find_set(int i) : int
+get_mst() : std::vector<std::pair<float,std::pair<int,int>>>
+kruskal_algorithm() : void
+union_set(int u, int v) : void
+__
+}
+class "bayesnet::KDBLd" as C_0000344502277874806837
+class C_0000344502277874806837 #aliceblue;line:blue;line.dotted;text:blue {
+KDBLd(int k) : void
+~KDBLd() = default : void
+..
+fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) : KDBLd &
+graph(const std::string & name = "KDB") const : std::vector<std::string>
+predict(torch::Tensor & X) : torch::Tensor
+{static} +version() : std::string
+__
+}
+class "bayesnet::AODE" as C_0000786111576121788282
+class C_0000786111576121788282 #aliceblue;line:blue;line.dotted;text:blue {
+AODE(bool predict_voting = false) : void
+~AODE() : void
+..
+#buildModel(const torch::Tensor & weights) : void
+graph(const std::string & title = "AODE") const : std::vector<std::string>
+setHyperparameters(const nlohmann::json & hyperparameters) : void
+__
+}
+class "bayesnet::SPODELd" as C_0001369655639257755354
+class C_0001369655639257755354 #aliceblue;line:blue;line.dotted;text:blue {
+SPODELd(int root) : void
+~SPODELd() = default : void
+..
+commonFit(const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) : SPODELd &
+fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) : SPODELd &
+fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) : SPODELd &
+graph(const std::string & name = "SPODE") const : std::vector<std::string>
+predict(torch::Tensor & X) : torch::Tensor
+{static} +version() : std::string
+__
+}
+class "bayesnet::AODELd" as C_0000487273479333793647
+class C_0000487273479333793647 #aliceblue;line:blue;line.dotted;text:blue {
+AODELd(bool predict_voting = true) : void
+~AODELd() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
+fit(torch::Tensor & X_, torch::Tensor & y_, const std::vector<std::string> & features_, const std::string & className_, std::map<std::string,std::vector<int>> & states_) : AODELd &
+graph(const std::string & name = "AODELd") const : std::vector<std::string>
+#trainModel(const torch::Tensor & weights) : void
+__
+}
+C_0001303524929067080934 --> C_0001303524929067080934 : -parents
+C_0001303524929067080934 --> C_0001303524929067080934 : -children
+C_0001186707649890429575 o-- C_0001303524929067080934 : -nodes
+C_0000327135989451974539 ..> C_0000738420730783851375
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+C_0002043996622900301644 o-- C_0000738420730783851375 : #status
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+C_0001985241386355360576 o-- C_0002043996622900301644 : #models
+C_0002043996622900301644 <|-- C_0001985241386355360576
+C_0000358471592399852382 --> C_0001695326193250580823 : -featureSelector
+C_0001985241386355360576 <|-- C_0000358471592399852382
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+C_0000512022813807538451 <|-- C_0001369655639257755354
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+C_0001985241386355360576 <|-- C_0000487273479333793647
+C_0002219995589162262979 <|-- C_0000487273479333793647
+
+'Generated with clang-uml, version 0.5.1
+'LLVM version clang version 17.0.6 (Fedora 17.0.6-2.fc39)
+@enduml
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--- a/tests/TestBayesModels.cc
+++ b/tests/TestBayesModels.cc
@@ -20,7 +20,7 @@
 #include "bayesnet/ensembles/BoostAODE.h"
 #include "TestUtils.h"

-const std::string ACTUAL_VERSION = "1.0.4.1";
+const std::string ACTUAL_VERSION = "1.0.5";

 TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
 {
Author	SHA1	Message	Date
Ricardo Montañana Gómez	b956aa3873	Upgrade version number to 1.0.5 Fix dependency graph Remove loguru library	2024-04-20 18:00:40 +02:00
Ricardo Montañana Gómez	1f06631f69	Add check dependencies in make diagrams endpoint	2024-04-19 19:47:37 +02:00
Ricardo Montañana	6dd589bd61	Add diagram changes to CHANGELOG	2024-04-19 18:29:43 +02:00
Ricardo Montañana Gómez	6475f10825	Add class and dependency diagrams	2024-04-19 14:33:00 +02:00
Ricardo Montañana Gómez	7d906b24d1	Merge pull request 'block_update' (#26 ) from block_update into main Reviewed-on: #26	2024-04-15 10:26:50 +00:00