rmontanana/BayesNet
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Fix tolerance hyperp error & gridsearch

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This commit is contained in:
Ricardo Montañana Gómez 2023-11-29 12:33:50 +01:00
parent 460d20a402
commit e3f6dc1e0b
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
3 changed files with 19 additions and 4 deletions
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20
src/BayesNet/BoostAODE.cc
View File

@ -12,7 +12,7 @@
namespace bayesnet {
BoostAODE::BoostAODE() : Ensemble()
{
validHyperparameters = { "repeatSparent", "maxModels", "ascending", "convergence", "threshold", "select_features" };
validHyperparameters = { "repeatSparent", "maxModels", "ascending", "convergence", "threshold", "select_features", "tolerance" };
}
void BoostAODE::buildModel(const torch::Tensor& weights)
@ -47,22 +47,32 @@ namespace bayesnet {
y_train = y_;
}
}
void BoostAODE::setHyperparameters(const nlohmann::json& hyperparameters)
void BoostAODE::setHyperparameters(const nlohmann::json& hyperparameters_)
{
auto hyperparameters = hyperparameters_;
if (hyperparameters.contains("repeatSparent")) {
repeatSparent = hyperparameters["repeatSparent"];
hyperparameters.erase("repeatSparent");
}
if (hyperparameters.contains("maxModels")) {
maxModels = hyperparameters["maxModels"];
hyperparameters.erase("maxModels");
}
if (hyperparameters.contains("ascending")) {
ascending = hyperparameters["ascending"];
hyperparameters.erase("ascending");
}
if (hyperparameters.contains("convergence")) {
convergence = hyperparameters["convergence"];
hyperparameters.erase("convergence");
}
if (hyperparameters.contains("threshold")) {
threshold = hyperparameters["threshold"];
hyperparameters.erase("threshold");
}
if (hyperparameters.contains("tolerance")) {
tolerance = hyperparameters["tolerance"];
hyperparameters.erase("tolerance");
}
if (hyperparameters.contains("select_features")) {
auto selectedAlgorithm = hyperparameters["select_features"];
@ -72,6 +82,10 @@ namespace bayesnet {
if (std::find(algos.begin(), algos.end(), selectedAlgorithm) == algos.end()) {
throw std::invalid_argument("Invalid selectFeatures value [IWSS, FCBF, CFS]");
}
hyperparameters.erase("select_features");
}
if (!hyperparameters.empty()) {
throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
}
}
std::unordered_set<int> BoostAODE::initializeModels()
@ -109,10 +123,8 @@ namespace bayesnet {
void BoostAODE::trainModel(const torch::Tensor& weights)
{
std::unordered_set<int> featuresUsed;
int tolerance = 5; // number of times the accuracy can be lower than the threshold
if (selectFeatures) {
featuresUsed = initializeModels();
tolerance = 0; // Remove tolerance if features are selected
}
if (maxModels == 0)
maxModels = .1 * n > 10 ? .1 * n : n;

1
src/BayesNet/BoostAODE.h
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@ -21,6 +21,7 @@ namespace bayesnet {
// Hyperparameters
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
bool convergence = false; //if true, stop when the model does not improve
bool selectFeatures = false; // if true, use feature selection

2
src/Platform/GridSearch.cc
View File

@ -89,6 +89,8 @@ namespace platform {
double bestScore = 0.0;
for (int nfold = 0; nfold < config.n_folds; nfold++) {
auto clf = Models::instance()->create(config.model);
auto valid = clf->getValidHyperparameters();
hyperparameters.check(valid, fileName);
clf->setHyperparameters(hyperparameters.get(fileName));
auto [train, test] = fold->getFold(nfold);
auto train_t = torch::tensor(train);