rmontanana/BayesNet
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Fix some mistakes in tensors treatment

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This commit is contained in:
Ricardo Montañana Gómez
2023-07-26 01:39:01 +02:00
parent be06e475f0
commit 099b4bea09
18 changed files with 255 additions and 72 deletions
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23
src/Platform/Experiment.cc
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@@ -18,8 +18,13 @@
using namespace std;
Result cross_validation(Fold* fold, bayesnet::BaseClassifier* model, Tensor& X, Tensor& y, vector<string> features, string className, map<string, vector<int>> states)
Result cross_validation(Fold* fold, string model_name, Tensor& X, Tensor& y, vector<string> features, string className, map<string, vector<int>> states)
{
auto classifiers = map<string, bayesnet::BaseClassifier*>({
{ "AODE", new bayesnet::AODE() }, { "KDB", new bayesnet::KDB(2) },
{ "SPODE", new bayesnet::SPODE(2) }, { "TAN", new bayesnet::TAN() }
}
);
auto result = Result();
auto k = fold->getNumberOfFolds();
auto accuracy = torch::zeros({ k }, kFloat64);
@@ -27,6 +32,7 @@ Result cross_validation(Fold* fold, bayesnet::BaseClassifier* model, Tensor& X,
auto test_time = torch::zeros({ k }, kFloat64);
Timer train_timer, test_timer;
for (int i = 0; i < k; i++) {
bayesnet::BaseClassifier* model = classifiers[model_name];
train_timer.start();
auto [train, test] = fold->getFold(i);
auto train_t = torch::tensor(train);
@@ -43,8 +49,7 @@ Result cross_validation(Fold* fold, bayesnet::BaseClassifier* model, Tensor& X,
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 = 7;
auto acc = model->score(X_test, y_test);
test_time[i] = test_timer.getDuration();
accuracy[i] = acc;
}
@@ -140,18 +145,16 @@ int main(int argc, char** argv)
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() }
}
);
auto experiment = Experiment();
experiment.setDiscretized(discretize_dataset).setModel(model_name).setPlatform("cpp");
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_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;
}
return 0;
}