rmontanana/BayesNet
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Fix Experiment

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This commit is contained in:
Ricardo Montañana Gómez 2023-07-26 14:11:49 +02:00
parent af7a1d2b40
commit 49a49a9dcd
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
2 changed files with 26 additions and 16 deletions
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31
src/Platform/Experiment.cc
View File

@ -25,9 +25,11 @@ Result cross_validation(Fold* fold, string model_name, Tensor& X, Tensor& y, vec
{ "SPODE", new bayesnet::SPODE(2) }, { "TAN", new bayesnet::TAN() }
}
);
auto Xt = torch::transpose(X, 0, 1);
auto result = Result();
auto k = fold->getNumberOfFolds();
auto accuracy = torch::zeros({ k }, kFloat64);
auto accuracy_test = torch::zeros({ k }, kFloat64);
auto accuracy_train = torch::zeros({ k }, kFloat64);
auto train_time = torch::zeros({ k }, kFloat64);
auto test_time = torch::zeros({ k }, kFloat64);
Timer train_timer, test_timer;
@ -37,9 +39,9 @@ Result cross_validation(Fold* fold, string model_name, Tensor& X, Tensor& y, vec
auto [train, test] = fold->getFold(i);
auto train_t = torch::tensor(train);
auto test_t = torch::tensor(test);
auto X_train = X.index({ train_t, "..." });
auto X_train = Xt.index({ "...", train_t });
auto y_train = y.index({ train_t });
auto X_test = X.index({ test_t, "..." });
auto X_test = Xt.index({ "...", test_t });
auto y_test = y.index({ test_t });
model->fit(X_train, y_train, features, className, states);
cout << "Training Fold " << i + 1 << endl;
@ -48,12 +50,15 @@ Result cross_validation(Fold* fold, string model_name, Tensor& X, Tensor& y, vec
cout << "X_test: " << X_test.sizes() << endl;
cout << "y_test: " << y_test.sizes() << endl;
train_time[i] = train_timer.getDuration();
auto accuracy_train_value = model->score(X_train, y_train);
test_timer.start();
auto acc = model->score(X_test, y_test);
auto accuracy_test_value = model->score(X_test, y_test);
test_time[i] = test_timer.getDuration();
accuracy[i] = acc;
accuracy_train[i] = accuracy_train_value;
accuracy_test[i] = accuracy_test_value;
}
result.setScore(torch::mean(accuracy).item<double>());
result.setScoreTest(torch::mean(accuracy_test).item<double>()).setScoreTrain(torch::mean(accuracy_train).item<double>());
result.setScoreTestStd(torch::std(accuracy_test).item<double>()).setScoreTrainStd(torch::std(accuracy_train).item<double>());
result.setTrainTime(torch::mean(train_time).item<double>()).setTestTime(torch::mean(test_time).item<double>());
return result;
}
@ -114,8 +119,9 @@ int main(int argc, char** argv)
catch (...) {
throw runtime_error("Number of folds must be an integer");
}});
program.add_argument("-s", "--seed").help("Random seed").default_value(-1).scan<'i', int>();
bool class_last, discretize_dataset, stratified;
int n_folds;
int n_folds, seed;
string model_name, file_name, path, complete_file_name;
try {
program.parse_args(argc, argv);
@ -125,6 +131,7 @@ int main(int argc, char** argv)
discretize_dataset = program.get<bool>("discretize");
stratified = program.get<bool>("stratified");
n_folds = program.get<int>("folds");
seed = program.get<int>("seed");
complete_file_name = path + file_name + ".arff";
class_last = datasets[file_name];
if (!file_exists(complete_file_name)) {
@ -142,18 +149,16 @@ int main(int argc, char** argv)
auto [X, y, features, className, states] = loadDataset(path, file_name, class_last, discretize_dataset);
Fold* fold;
if (stratified)
fold = new StratifiedKFold(n_folds, y, -1);
fold = new StratifiedKFold(n_folds, y, seed);
else
fold = new KFold(n_folds, y.numel(), -1);
fold = new KFold(n_folds, y.numel(), seed);
auto experiment = Experiment();
experiment.setDiscretized(discretize_dataset).setModel(model_name).setPlatform("cpp");
experiment.setStratified(stratified).setNFolds(5).addRandomSeed(271).setScoreName("accuracy");
experiment.setStratified(stratified).setNFolds(n_folds).addRandomSeed(seed).setScoreName("accuracy");
auto result = cross_validation(fold, model_name, X, y, features, className, states);
result.setDataset(file_name);
experiment.addResult(result);
experiment.save(path);
for (auto& item : states) {
cout << item.first << ": " << item.second.size() << endl;
}
experiment.show();
return 0;
}

11
src/Platform/Result.h
View File

@ -17,7 +17,7 @@ class Result {
private:
string dataset, hyperparameters;
int samples, features, classes;
float score, score_std, train_time, train_time_std, test_time, test_time_std;
float score_train, score_test, score_train_std, score_test_std, train_time, train_time_std, test_time, test_time_std;
public:
Result() = default;
Result& setDataset(string dataset) { this->dataset = dataset; return *this; }
@ -25,12 +25,16 @@ public:
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& setScore(float score) { this->score = score; return *this; }
Result& setScoreStd(float score_std) { this->score_std = score_std; 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; }
float get_score_train() { return score_train; }
float get_score_test() { return score_test; }
};
class Experiment {
private:
@ -53,5 +57,6 @@ public:
Experiment& addResult(Result result) { results.push_back(result); return *this; }
Experiment& addRandomSeed(int random_seed) { random_seeds.push_back(random_seed); return *this; }
void save(string path) { cout << "Saving experiment..." << endl; }
void show() { cout << "Showing experiment..." << "Score Test: " << results[0].get_score_test() << " Score Train: " << results[0].get_score_train() << endl; }
};
#endif