rmontanana/BayesNet
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change boostaode ascending hyperparameter to order {asc,desc,rand}

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This commit is contained in:
Ricardo Montañana Gómez 2024-02-26 17:07:57 +01:00
parent 43dc79a345
commit fc3d63b7db
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
4 changed files with 59 additions and 19 deletions
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6
CHANGELOG.md
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@ -5,6 +5,12 @@ 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]
### Added
- Change _ascending_ hyperparameter to _order_ with these possible values _{"asc", "desc", "rand"}_
## [1.0.3]
### Added

31
src/BoostAODE.cc
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@ -10,7 +10,7 @@
namespace bayesnet {
BoostAODE::BoostAODE(bool predict_voting) : Ensemble(predict_voting)
{
validHyperparameters = { "repeatSparent", "maxModels", "ascending", "convergence", "threshold", "select_features", "tolerance", "predict_voting" };
validHyperparameters = { "repeatSparent", "maxModels", "order", "convergence", "threshold", "select_features", "tolerance", "predict_voting" };
}
void BoostAODE::buildModel(const torch::Tensor& weights)
@ -57,9 +57,13 @@ namespace bayesnet {
maxModels = hyperparameters["maxModels"];
hyperparameters.erase("maxModels");
}
if (hyperparameters.contains("ascending")) {
ascending = hyperparameters["ascending"];
hyperparameters.erase("ascending");
if (hyperparameters.contains("order")) {
std::vector<std::string> algos = { "asc", "desc", "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]");
}
hyperparameters.erase("order");
}
if (hyperparameters.contains("convergence")) {
convergence = hyperparameters["convergence"];
@ -81,9 +85,9 @@ namespace bayesnet {
auto selectedAlgorithm = hyperparameters["select_features"];
std::vector<std::string> algos = { "IWSS", "FCBF", "CFS" };
selectFeatures = true;
algorithm = selectedAlgorithm;
select_features_algorithm = selectedAlgorithm;
if (std::find(algos.begin(), algos.end(), selectedAlgorithm) == algos.end()) {
throw std::invalid_argument("Invalid selectFeatures value [IWSS, FCBF, CFS]");
throw std::invalid_argument("Invalid selectFeatures value, valid values [IWSS, FCBF, CFS]");
}
hyperparameters.erase("select_features");
}
@ -96,14 +100,14 @@ namespace bayesnet {
std::unordered_set<int> featuresUsed;
torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
int maxFeatures = 0;
if (algorithm == "CFS") {
if (select_features_algorithm == "CFS") {
featureSelector = new CFS(dataset, features, className, maxFeatures, states.at(className).size(), weights_);
} else if (algorithm == "IWSS") {
} else if (select_features_algorithm == "IWSS") {
if (threshold < 0 || threshold >0.5) {
throw std::invalid_argument("Invalid threshold value for IWSS [0, 0.5]");
}
featureSelector = new IWSS(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
} else if (algorithm == "FCBF") {
} else if (select_features_algorithm == "FCBF") {
if (threshold < 1e-7 || threshold > 1) {
throw std::invalid_argument("Invalid threshold value [1e-7, 1]");
}
@ -120,7 +124,7 @@ namespace bayesnet {
significanceModels.push_back(1.0);
n_models++;
}
notes.push_back("Used features in initialization: " + std::to_string(featuresUsed.size()) + " of " + std::to_string(features.size()) + " with " + algorithm);
notes.push_back("Used features in initialization: " + std::to_string(featuresUsed.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
delete featureSelector;
return featuresUsed;
}
@ -150,10 +154,14 @@ namespace bayesnet {
// n_models == maxModels
// epsilon sub t > 0.5 => inverse the weights policy
// validation error is not decreasing
bool ascending = order_algorithm == "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
std::unique_ptr<Classifier> model;
if (order_algorithm == "rand") {
std::shuffle(featureSelection.begin(), featureSelection.end(), g);
}
auto feature = featureSelection[0];
if (!repeatSparent || featuresUsed.size() < featureSelection.size()) {
bool used = true;
@ -170,6 +178,7 @@ namespace bayesnet {
continue;
}
}
std::unique_ptr<Classifier> model;
model = std::make_unique<SPODE>(feature);
model->fit(dataset, features, className, states, weights_);
auto ypred = model->predict(X_train);

4
src/BoostAODE.h
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@ -22,10 +22,10 @@ namespace bayesnet {
bool repeatSparent = false; // if true, a feature can be selected more than once
int maxModels = 0;
int tolerance = 0;
bool ascending = false; //Process KBest features ascending or descending order
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 algorithm = ""; // Selected feature selection algorithm
std::string select_features_algorithm = ""; // Selected feature selection algorithm
FeatureSelect* featureSelector = nullptr;
double threshold = -1;
};

37
tests/TestBayesModels.cc
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@ -17,7 +17,7 @@ const std::string ACTUAL_VERSION = "1.0.3";
TEST_CASE("Test Bayesian Classifiers score & version", "[BayesNet]")
{
map <pair<std::string, std::string>, float> scores = {
map <pair<std::string, std::string>, float> scores{
// Diabetes
{{"diabetes", "AODE"}, 0.811198}, {{"diabetes", "KDB"}, 0.852865}, {{"diabetes", "SPODE"}, 0.802083}, {{"diabetes", "TAN"}, 0.821615},
{{"diabetes", "AODELd"}, 0.8138f}, {{"diabetes", "KDBLd"}, 0.80208f}, {{"diabetes", "SPODELd"}, 0.78646f}, {{"diabetes", "TANLd"}, 0.8099f}, {{"diabetes", "BoostAODE"}, 0.83984f},
@ -31,7 +31,7 @@ TEST_CASE("Test Bayesian Classifiers score & version", "[BayesNet]")
{{"iris", "AODE"}, 0.973333}, {{"iris", "KDB"}, 0.973333}, {{"iris", "SPODE"}, 0.973333}, {{"iris", "TAN"}, 0.973333},
{{"iris", "AODELd"}, 0.973333}, {{"iris", "KDBLd"}, 0.973333}, {{"iris", "SPODELd"}, 0.96f}, {{"iris", "TANLd"}, 0.97333f}, {{"iris", "BoostAODE"}, 0.98f}
};
std::map<std::string, bayesnet::BaseClassifier*> models = {
std::map<std::string, bayesnet::BaseClassifier*> models{
{"AODE", new bayesnet::AODE()}, {"AODELd", new bayesnet::AODELd()},
{"BoostAODE", new bayesnet::BoostAODE()},
{"KDB", new bayesnet::KDB(2)}, {"KDBLd", new bayesnet::KDBLd(2)},
@ -104,7 +104,7 @@ TEST_CASE("BoostAODE test used features in train note and score", "[BayesNet]")
auto raw = RawDatasets("diabetes", true);
auto clf = bayesnet::BoostAODE(true);
clf.setHyperparameters({
{"ascending",true},
{"order", "asc"},
{"convergence", true},
{"repeatSparent",true},
{"select_features","CFS"},
@ -168,8 +168,8 @@ TEST_CASE("Model predict_proba", "[BayesNet]")
{0, 1, 0},
{0, 1, 0}
});
std::map<std::string, std::vector<std::vector<double>>> res_prob = { {"TAN", res_prob_tan}, {"SPODE", res_prob_spode} , {"BoostAODEproba", res_prob_baode }, {"BoostAODEvoting", res_prob_voting } };
std::map<std::string, bayesnet::BaseClassifier*> models = { {"TAN", new bayesnet::TAN()}, {"SPODE", new bayesnet::SPODE(0)}, {"BoostAODEproba", new bayesnet::BoostAODE(false)}, {"BoostAODEvoting", new bayesnet::BoostAODE(true)} };
std::map<std::string, std::vector<std::vector<double>>> res_prob{ {"TAN", res_prob_tan}, {"SPODE", res_prob_spode} , {"BoostAODEproba", res_prob_baode }, {"BoostAODEvoting", res_prob_voting } };
std::map<std::string, bayesnet::BaseClassifier*> models{ {"TAN", new bayesnet::TAN()}, {"SPODE", new bayesnet::SPODE(0)}, {"BoostAODEproba", new bayesnet::BoostAODE(false)}, {"BoostAODEvoting", new bayesnet::BoostAODE(true)} };
int init_index = 78;
auto raw = RawDatasets("iris", true);
@ -178,9 +178,9 @@ TEST_CASE("Model predict_proba", "[BayesNet]")
auto clf = models[model];
clf->fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
auto y_pred_proba = clf->predict_proba(raw.Xv);
auto yt_pred_proba = clf->predict_proba(raw.Xt);
auto y_pred = clf->predict(raw.Xv);
auto yt_pred = clf->predict(raw.Xt);
auto yt_pred_proba = clf->predict_proba(raw.Xt);
REQUIRE(y_pred.size() == yt_pred.size(0));
REQUIRE(y_pred.size() == y_pred_proba.size());
REQUIRE(y_pred.size() == yt_pred_proba.size(0));
@ -193,6 +193,9 @@ TEST_CASE("Model predict_proba", "[BayesNet]")
REQUIRE(predictedClass == y_pred[i]);
// Check predict is coherent with predict_proba
REQUIRE(yt_pred_proba[i].argmax().item<int>() == y_pred[i]);
for (int j = 0; j < yt_pred_proba.size(1); j++) {
REQUIRE(yt_pred_proba[i][j].item<double>() == Catch::Approx(y_pred_proba[i][j]).epsilon(raw.epsilon));
}
}
// Check predict_proba values for vectors and tensors
for (int i = 0; i < res_prob.size(); i++) {
@ -222,3 +225,25 @@ TEST_CASE("BoostAODE voting-proba", "[BayesNet]")
REQUIRE(pred_voting[83][2] == Catch::Approx(0.552091).epsilon(raw.epsilon));
REQUIRE(pred_proba[83][2] == Catch::Approx(0.546017).epsilon(raw.epsilon));
}
TEST_CASE("BoostAODE order asc, desc & random", "[BayesNet]")
{
auto raw = RawDatasets("glass", true);
std::map<std::string, double> scores{
{"asc", 0.83178f }, { "desc", 0.84579f }, { "rand", 0.83645f }
};
for (const std::string& order : { "asc", "desc", "rand" }) {
auto clf = bayesnet::BoostAODE();
clf.setHyperparameters({
{"order", order},
});
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));
}
}