rmontanana/BayesNet
rmontanana/BayesNet
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Refactor crossvalidation to remove unneeded params

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This commit is contained in:
Ricardo Montañana Gómez 2023-07-29 16:49:06 +02:00
parent adc0ca238f
commit c4f3e6f19a
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
3 changed files with 17 additions and 14 deletions
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20
src/Platform/Experiment.cc
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@ -1,4 +1,5 @@
#include "Experiment.h" #include "Experiment.h"
#include "Datasets.h"
namespace platform { namespace platform {
using json = nlohmann::json; using json = nlohmann::json;
@ -88,16 +89,25 @@ namespace platform {
json data = build_json(); json data = build_json();
cout << data.dump(4) << endl; cout << data.dump(4) << endl;
} }
Result Experiment::cross_validation(string model_name, torch::Tensor& Xt, torch::Tensor& y, vector<string> features, string className, map<string, vector<int>> states) Result Experiment::cross_validation(const string& path, const string& fileName)
{ {
auto classifiers = map<string, bayesnet::BaseClassifier*>({ auto classifiers = map<string, bayesnet::BaseClassifier*>({
{ "AODE", new bayesnet::AODE() }, { "KDB", new bayesnet::KDB(2) }, { "AODE", new bayesnet::AODE() }, { "KDB", new bayesnet::KDB(2) },
{ "SPODE", new bayesnet::SPODE(2) }, { "TAN", new bayesnet::TAN() } { "SPODE", new bayesnet::SPODE(2) }, { "TAN", new bayesnet::TAN() }
} }
); );
auto datasets = platform::Datasets(path, true, platform::ARFF);
// Get dataset
auto [X, y] = datasets.getTensors(fileName);
auto states = datasets.getStates(fileName);
auto features = datasets.getFeatures(fileName);
auto samples = datasets.getNSamples(fileName);
auto className = datasets.getClassName(fileName);
cout << " (" << setw(5) << samples << "," << setw(3) << features.size() << ") " << flush;
// Prepare Result
auto result = Result(); auto result = Result();
auto [values, counts] = at::_unique(y); auto [values, counts] = at::_unique(y);;
result.setSamples(Xt.size(1)).setFeatures(Xt.size(0)).setClasses(values.size(0)); result.setSamples(X.size(1)).setFeatures(X.size(0)).setClasses(values.size(0));
int nResults = nfolds * static_cast<int>(randomSeeds.size()); int nResults = nfolds * static_cast<int>(randomSeeds.size());
auto accuracy_test = torch::zeros({ nResults }, torch::kFloat64); auto accuracy_test = torch::zeros({ nResults }, torch::kFloat64);
auto accuracy_train = torch::zeros({ nResults }, torch::kFloat64); auto accuracy_train = torch::zeros({ nResults }, torch::kFloat64);
@ -123,9 +133,9 @@ namespace platform {
auto [train, test] = fold->getFold(nfold); auto [train, test] = fold->getFold(nfold);
auto train_t = torch::tensor(train); auto train_t = torch::tensor(train);
auto test_t = torch::tensor(test); auto test_t = torch::tensor(test);
auto X_train = Xt.index({ "...", train_t }); auto X_train = X.index({ "...", train_t });
auto y_train = y.index({ train_t }); auto y_train = y.index({ train_t });
auto X_test = Xt.index({ "...", test_t }); auto X_test = X.index({ "...", test_t });
auto y_test = y.index({ test_t }); auto y_test = y.index({ test_t });
cout << nfold + 1 << ", " << flush; cout << nfold + 1 << ", " << flush;
clf->fit(X_train, y_train, features, className, states); clf->fit(X_train, y_train, features, className, states);

3
src/Platform/Experiment.h
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@ -105,8 +105,7 @@ namespace platform {
Experiment& setDuration(float duration) { this->duration = duration; return *this; } Experiment& setDuration(float duration) { this->duration = duration; return *this; }
string get_file_name(); string get_file_name();
void save(string path); void save(string path);
//Result cross_validation(const string& path, const string& fileName); Result cross_validation(const string& path, const string& fileName);
Result cross_validation(string model_name, torch::Tensor& X, torch::Tensor& y, vector<string> features, string className, map<string, vector<int>> states);
void show(); void show();
}; };
} }

8
src/Platform/main.cc
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@ -120,13 +120,7 @@ int main(int argc, char** argv)
timer.start(); timer.start();
for (auto fileName : filesToProcess) { for (auto fileName : filesToProcess) {
cout << "- " << setw(20) << left << fileName << " " << right << flush; cout << "- " << setw(20) << left << fileName << " " << right << flush;
auto [X, y] = datasets.getTensors(fileName); auto result = experiment.cross_validation(path, fileName);
auto states = datasets.getStates(fileName);
auto features = datasets.getFeatures(fileName);
auto samples = datasets.getNSamples(fileName);
auto className = datasets.getClassName(fileName);
cout << " (" << setw(5) << samples << "," << setw(3) << features.size() << ") " << flush;
auto result = experiment.cross_validation(model_name, X, y, features, className, states);
result.setDataset(fileName); result.setDataset(fileName);
experiment.addResult(result); experiment.addResult(result);
cout << endl; cout << endl;