rmontanana/BayesNet
rmontanana/BayesNet
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Fix some mistakes in timer and output format

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This commit is contained in:
Ricardo Montañana Gómez 2023-07-27 18:40:04 +02:00
parent 7f7ddad36a
commit b03e84044a
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
4 changed files with 28 additions and 24 deletions
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2
src/Platform/Experiment.cc
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@ -75,7 +75,7 @@ namespace platform {
void Experiment::save(string path)
{
json data = build_json();
ofstream file(path + get_file_name());
ofstream file(path + "/" + get_file_name());
file << data;
file.close();
}

43
src/Platform/Experiment.h
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@ -15,18 +15,23 @@ namespace platform {
using json = nlohmann::json;
class Timer {
private:
chrono::time_point<chrono::steady_clock> begin;
chrono::high_resolution_clock::time_point begin;
public:
Timer() = default;
~Timer() = default;
void start() { begin = chrono::high_resolution_clock::now(); }
float getDuration() { return chrono::duration_cast<chrono::milliseconds>(chrono::high_resolution_clock::now() - begin).count() / 1000; }
double getDuration()
{
chrono::high_resolution_clock::time_point end = chrono::high_resolution_clock::now();
chrono::duration<double> time_span = chrono::duration_cast<chrono::duration<double>>(end - begin);
return time_span.count();
}
};
class Result {
private:
string dataset, hyperparameters, model_version;
int samples, features, classes;
float score_train, score_test, score_train_std, score_test_std, train_time, train_time_std, test_time, test_time_std;
double score_train, score_test, score_train_std, score_test_std, train_time, train_time_std, test_time, test_time_std;
float nodes, leaves, depth;
public:
Result() = default;
@ -35,14 +40,14 @@ namespace platform {
Result& setSamples(int samples) { this->samples = samples; return *this; }
Result& setFeatures(int features) { this->features = features; return *this; }
Result& setClasses(int classes) { this->classes = classes; return *this; }
Result& setScoreTrain(float score) { this->score_train = score; return *this; }
Result& setScoreTest(float score) { this->score_test = score; return *this; }
Result& setScoreTrainStd(float score_std) { this->score_train_std = score_std; return *this; }
Result& setScoreTestStd(float score_std) { this->score_test_std = score_std; return *this; }
Result& setTrainTime(float train_time) { this->train_time = train_time; return *this; }
Result& setTrainTimeStd(float train_time_std) { this->train_time_std = train_time_std; return *this; }
Result& setTestTime(float test_time) { this->test_time = test_time; return *this; }
Result& setTestTimeStd(float test_time_std) { this->test_time_std = test_time_std; return *this; }
Result& setScoreTrain(double score) { this->score_train = score; return *this; }
Result& setScoreTest(double score) { this->score_test = score; return *this; }
Result& setScoreTrainStd(double score_std) { this->score_train_std = score_std; return *this; }
Result& setScoreTestStd(double score_std) { this->score_test_std = score_std; return *this; }
Result& setTrainTime(double train_time) { this->train_time = train_time; return *this; }
Result& setTrainTimeStd(double train_time_std) { this->train_time_std = train_time_std; return *this; }
Result& setTestTime(double test_time) { this->test_time = test_time; return *this; }
Result& setTestTimeStd(double test_time_std) { this->test_time_std = test_time_std; return *this; }
Result& setNodes(float nodes) { this->nodes = nodes; return *this; }
Result& setLeaves(float leaves) { this->leaves = leaves; return *this; }
Result& setDepth(float depth) { this->depth = depth; return *this; }
@ -54,14 +59,14 @@ namespace platform {
const int getSamples() const { return samples; }
const int getFeatures() const { return features; }
const int getClasses() const { return classes; }
const float getScoreTrain() const { return score_train; }
const float getScoreTest() const { return score_test; }
const float getScoreTrainStd() const { return score_train_std; }
const float getScoreTestStd() const { return score_test_std; }
const float getTrainTime() const { return train_time; }
const float getTrainTimeStd() const { return train_time_std; }
const float getTestTime() const { return test_time; }
const float getTestTimeStd() const { return test_time_std; }
const double getScoreTrain() const { return score_train; }
const double getScoreTest() const { return score_test; }
const double getScoreTrainStd() const { return score_train_std; }
const double getScoreTestStd() const { return score_test_std; }
const double getTrainTime() const { return train_time; }
const double getTrainTimeStd() const { return train_time_std; }
const double getTestTime() const { return test_time; }
const double getTestTimeStd() const { return test_time_std; }
const float getNodes() const { return nodes; }
const float getLeaves() const { return leaves; }
const float getDepth() const { return depth; }

1
src/Platform/Folding.cc
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@ -15,7 +15,6 @@ KFold::KFold(int k, int n, int seed) : Fold(k, n, seed)
}
pair<vector<int>, vector<int>> KFold::getFold(int nFold)
{
if (nFold >= k || nFold < 0) {
throw out_of_range("nFold (" + to_string(nFold) + ") must be less than k (" + to_string(k) + ")");
}

6
src/Platform/main.cc
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@ -102,20 +102,20 @@ int main(int argc, char** argv)
cout << "*** Starting experiment: " << title << " ***" << endl;
timer.start();
for (auto fileName : filesToProcess) {
cout << "- " << fileName << " ";
cout << "- " << setw(20) << left << fileName << " " << right << flush;
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 << " (" << samples << ", " << features.size() << ") " << flush;
cout << " (" << setw(5) << samples << "," << setw(3) << features.size() << ") " << flush;
Fold* fold;
if (stratified)
fold = new StratifiedKFold(n_folds, y, seed);
else
fold = new KFold(n_folds, samples, seed);
auto result = platform::cross_validation(fold, model_name, X, y, features, className, states);
result.setDataset(file_name);
result.setDataset(fileName);
experiment.setModelVersion(result.getModelVersion());
experiment.addResult(result);
delete fold;