rmontanana/BayesNet
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Refactor tensor2vector

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This commit is contained in:
Ricardo Montañana Gómez
2023-07-24 13:22:53 +02:00
parent c10ebca0e0
commit be06e475f0
5 changed files with 47 additions and 24 deletions
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50
src/Platform/Experiment.cc
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@@ -31,14 +31,20 @@ Result cross_validation(Fold* fold, bayesnet::BaseClassifier* model, Tensor& X,
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 = X.index({ train_t, "..." });
auto y_train = y.index({ train_t });
auto X_test = X.index({ test_t });
auto X_test = X.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;
cout << "X_train: " << X_train.sizes() << endl;
cout << "y_train: " << y_train.sizes() << endl;
cout << "X_test: " << X_test.sizes() << endl;
cout << "y_test: " << y_test.sizes() << endl;
train_time[i] = train_timer.getDuration();
test_timer.start();
auto acc = model->score(X_test, y_test);
//auto acc = model->score(X_test, y_test);
auto acc = 7;
test_time[i] = test_timer.getDuration();
accuracy[i] = acc;
}
@@ -64,7 +70,7 @@ int main(int argc, char** argv)
valid_datasets.push_back(dataset.first);
}
argparse::ArgumentParser program("BayesNetSample");
program.add_argument("-f", "--file")
program.add_argument("-d", "--dataset")
.help("Dataset file name")
.action([valid_datasets](const std::string& value) {
if (find(valid_datasets.begin(), valid_datasets.end(), value) != valid_datasets.end()) {
@@ -74,7 +80,7 @@ int main(int argc, char** argv)
}
);
program.add_argument("-p", "--path")
.help(" folder where the data files are located, default")
.help("folder where the data files are located, default")
.default_value(string{ PATH }
);
program.add_argument("-m", "--model")
@@ -87,15 +93,33 @@ int main(int argc, char** argv)
throw runtime_error("Model must be one of {AODE, KDB, SPODE, TAN}");
}
);
program.add_argument("--discretize").default_value(false).implicit_value(true);
bool class_last, discretize_dataset;
program.add_argument("--discretize").help("Discretize input dataset").default_value(false).implicit_value(true);
program.add_argument("--stratified").help("If Stratified KFold is to be done").default_value(false).implicit_value(true);
program.add_argument("-f", "--folds").help("Number of folds").default_value(5).scan<'i', int>().action([](const string& value) {
try {
auto k = stoi(value);
if (k < 2) {
throw runtime_error("Number of folds must be greater than 1");
}
return k;
}
catch (const runtime_error& err) {
throw runtime_error(err.what());
}
catch (...) {
throw runtime_error("Number of folds must be an integer");
}});
bool class_last, discretize_dataset, stratified;
int n_folds;
string model_name, file_name, path, complete_file_name;
try {
program.parse_args(argc, argv);
file_name = program.get<string>("file");
file_name = program.get<string>("dataset");
path = program.get<string>("path");
model_name = program.get<string>("model");
discretize_dataset = program.get<bool>("discretize");
stratified = program.get<bool>("stratified");
n_folds = program.get<int>("folds");
complete_file_name = path + file_name + ".arff";
class_last = datasets[file_name];
if (!file_exists(complete_file_name)) {
@@ -111,7 +135,11 @@ int main(int argc, char** argv)
* Begin Processing
*/
auto [X, y, features, className, states] = loadDataset(path, file_name, class_last, discretize_dataset);
auto fold = StratifiedKFold(5, y, -1);
Fold* fold;
if (stratified)
fold = new StratifiedKFold(n_folds, y, -1);
else
fold = new KFold(n_folds, y.numel(), -1);
auto classifiers = map<string, bayesnet::BaseClassifier*>({
{ "AODE", new bayesnet::AODE() }, { "KDB", new bayesnet::KDB(2) },
{ "SPODE", new bayesnet::SPODE(2) }, { "TAN", new bayesnet::TAN() }
@@ -119,9 +147,9 @@ int main(int argc, char** argv)
);
auto experiment = Experiment();
experiment.setDiscretized(discretize_dataset).setModel(model_name).setPlatform("cpp");
experiment.setStratified(true).setNFolds(5).addRandomSeed(271).setScoreName("accuracy");
experiment.setStratified(stratified).setNFolds(5).addRandomSeed(271).setScoreName("accuracy");
bayesnet::BaseClassifier* model = classifiers[model_name];
auto result = cross_validation(&fold, model, X, y, features, className, states);
auto result = cross_validation(fold, model, X, y, features, className, states);
result.setDataset(file_name);
experiment.addResult(result);
experiment.save(path);