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refactor fit parameters

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This commit is contained in:
Ricardo Montañana Gómez
2023-11-11 11:19:33 +01:00
parent b6a3a05020
commit a3bf97e501
3 changed files with 58 additions and 40 deletions
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91
example/example.cc
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@@ -21,25 +21,19 @@ public:
} }
}; };
tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(const string& name, bool class_last) tuple<Tensor, Tensor> loadDataset(const string& name, bool class_last)
{ {
auto handler = ArffFiles(); auto handler = ArffFiles();
handler.load(Paths::datasets() + static_cast<string>(name) + ".arff", class_last); handler.load(Paths::datasets() + static_cast<string>(name) + ".arff", class_last);
// Get Dataset X, y // Get Dataset X, y
vector<vector<float>> X = handler.getX(); vector<vector<float>> X = handler.getX();
vector<int> y = handler.getY(); vector<int> y = handler.getY();
// Get className & Features
auto className = handler.getClassName();
vector<string> features;
auto attributes = handler.getAttributes();
transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
Tensor Xd; Tensor Xd;
auto states = map<string, vector<int>>();
Xd = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kFloat32); Xd = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kFloat32);
for (int i = 0; i < features.size(); ++i) { for (int i = 0; i < X.size(); ++i) {
Xd.index_put_({ i, "..." }, torch::tensor(X[i], torch::kFloat32)); Xd.index_put_({ i, "..." }, torch::tensor(X[i], torch::kFloat32));
} }
return { Xd, torch::tensor(y, torch::kInt32), features, className, states }; return { Xd, torch::tensor(y, torch::kInt32) };
} }
int main(int argc, char* argv[]) int main(int argc, char* argv[])
@@ -50,7 +44,7 @@ int main(int argc, char* argv[])
using namespace torch::indexing; using namespace torch::indexing;
auto datasetName = "iris"; auto datasetName = "iris";
bool class_last = true; bool class_last = true;
auto [X, y, features, className, states] = loadDataset(datasetName, class_last); auto [X, y] = loadDataset(datasetName, class_last);
auto m = y.size(0); auto m = y.size(0);
int train_split = m * .7; int train_split = m * .7;
auto Xtrain = X.index({ "...", Slice(0, train_split) }); auto Xtrain = X.index({ "...", Slice(0, train_split) });
@@ -60,38 +54,57 @@ int main(int argc, char* argv[])
cout << "Dataset: " << datasetName << endl; cout << "Dataset: " << datasetName << endl;
cout << "X: " << X.sizes() << endl; cout << "X: " << X.sizes() << endl;
cout << "y: " << y.sizes() << endl; cout << "y: " << y.sizes() << endl;
// auto clf = pywrap::STree(); cout << "Xtrain: " << Xtrain.sizes() << endl;
// auto stree = pywrap::STree(); cout << "ytrain: " << ytrain.sizes() << endl;
// auto hyperparameters = json::parse("{\"C\": 0.7, \"max_iter\": 10000, \"kernel\": \"rbf\", \"random_state\": 17}"); cout << "Xtest : " << Xtest.sizes() << endl;
// stree.setHyperparameters(hyperparameters); cout << "ytest : " << ytest.sizes() << endl;
// cout << "STree Version: " << clf.version() << endl; //
// auto svc = pywrap::SVC(); // STree
// cout << "SVC with hyperparameters" << endl; //
// svc.fit(X, y, features, className, states); auto clf = pywrap::STree();
// cout << "Graph: " << endl << clf.graph() << endl; clf.fit(X, y);
// double clf_score = clf.fit(X, y, features, className, states).score(X, y); double clf_score = clf.score(X, y);
// double stree_score = stree.fit(X, y, features, className, states).score(X, y); auto stree = pywrap::STree();
// auto prediction = clf.predict(X); auto hyperparameters = json::parse("{\"C\": 0.7, \"max_iter\": 10000, \"kernel\": \"rbf\", \"random_state\": 17}");
// cout << "Prediction: " << endl << "{"; stree.setHyperparameters(hyperparameters);
// for (int i = 0; i < prediction.size(0); ++i) { cout << "STree Version: " << clf.version() << endl;
// cout << prediction[i].item<int>() << ", "; auto prediction = clf.predict(X);
// } cout << "Prediction: " << endl << "{";
// cout << "}" << endl; for (int i = 0; i < prediction.size(0); ++i) {
// cout << "Building Random Forest" << endl; cout << prediction[i].item<int>() << ", ";
// auto rf = pywrap::RandomForest(); }
// rf.fit(X, y, features, className, states); cout << "}" << endl;
//
// SVC
//
auto svc = pywrap::SVC();
cout << "SVC with hyperparameters" << endl;
svc.fit(X, y);
//
// Random Forest
//
cout << "Building Random Forest" << endl;
auto rf = pywrap::RandomForest();
rf.fit(Xtrain, ytrain);
//
// XGBoost
//
cout << "Building XGBoost" << endl; cout << "Building XGBoost" << endl;
auto xg = pywrap::XGBoost(); auto xg = pywrap::XGBoost();
cout << "Fitting XGBoost" << endl; cout << "Fitting XGBoost" << endl;
xg.fit(Xtrain, ytrain, features, className, states); // xg.fit(Xtrain, ytrain);
cout << "Scoring dataset" << endl; // double xg_score = xg.score(Xtest, ytest);
double xg_score = xg.score(Xtest, ytest); //
// cout << "Scores:" << endl; // Scoring
// cout << "STree Score ......: " << clf_score << endl; //
// cout << "STree hyper score : " << stree_score << endl; cout << "Scoring dataset: " << datasetName << endl;
// cout << "RandomForest Score: " << rf.score(X, y) << endl; cout << "Scores:" << endl;
// cout << "SVC Score ........: " << svc.score(X, y) << endl; cout << "STree Score ......: " << clf_score << endl;
cout << "XGBoost Score ....: " << xg_score << endl; cout << "STree train/test .: " << clf.fit(Xtrain, ytrain).score(Xtest, ytest) << endl;
cout << "STree hyper score : " << stree.fit(Xtrain, ytrain).score(Xtest, ytest) << endl;
cout << "RandomForest Score: " << rf.score(Xtest, ytest) << endl;
cout << "SVC Score ........: " << svc.score(X, y) << endl;
// cout << "XGBoost Score ....: " << xg_score << endl;
} }
cout << "* End." << endl; cout << "* End." << endl;
} }

6
src/PyClassifier.cc
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@@ -35,7 +35,7 @@ namespace pywrap {
{ {
return pyWrap->callMethodString(id, method); return pyWrap->callMethodString(id, method);
} }
PyClassifier& PyClassifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) PyClassifier& PyClassifier::fit(torch::Tensor& X, torch::Tensor& y)
{ {
if (!fitted && hyperparameters.size() > 0) { if (!fitted && hyperparameters.size() > 0) {
pyWrap->setHyperparameters(id, hyperparameters); pyWrap->setHyperparameters(id, hyperparameters);
@@ -47,6 +47,10 @@ namespace pywrap {
fitted = true; fitted = true;
return *this; return *this;
} }
PyClassifier& PyClassifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
{
return fit(X, y);
}
torch::Tensor PyClassifier::predict(torch::Tensor& X) torch::Tensor PyClassifier::predict(torch::Tensor& X)
{ {
int dimension = X.size(1); int dimension = X.size(1);

1
src/PyClassifier.h
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@@ -18,6 +18,7 @@ namespace pywrap {
PyClassifier(const std::string& module, const std::string& className); PyClassifier(const std::string& module, const std::string& className);
virtual ~PyClassifier(); virtual ~PyClassifier();
PyClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states); PyClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states);
PyClassifier& fit(torch::Tensor& X, torch::Tensor& y);
torch::Tensor predict(torch::Tensor& X); torch::Tensor predict(torch::Tensor& X);
double score(torch::Tensor& X, torch::Tensor& y); double score(torch::Tensor& X, torch::Tensor& y);
std::string version(); std::string version();