Files
PyWrap/example/example.cc

110 lines
3.7 KiB
C++

#include <torch/torch.h>
#include "ArffFiles.h"
#include <vector>
#include <string>
#include <iostream>
#include <map>
#include <tuple>
#include "STree.h"
#include "SVC.h"
#include "RandomForest.h"
#include "XGBoost.h"
using namespace std;
using namespace torch;
class Paths {
public:
static string datasets()
{
return "../discretizbench/datasets/";
}
};
tuple<Tensor, Tensor> loadDataset(const string& name, bool class_last)
{
auto handler = ArffFiles();
handler.load(Paths::datasets() + static_cast<string>(name) + ".arff", class_last);
// Get Dataset X, y
vector<vector<float>> X = handler.getX();
vector<int> y = handler.getY();
Tensor Xd;
Xd = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kFloat32);
for (int i = 0; i < X.size(); ++i) {
Xd.index_put_({ i, "..." }, torch::tensor(X[i], torch::kFloat32));
}
return { Xd, torch::tensor(y, torch::kInt32) };
}
int main(int argc, char* argv[])
{
using json = nlohmann::json;
cout << "* Begin." << endl;
{
using namespace torch::indexing;
auto datasetName = "iris";
bool class_last = true;
auto [X, y] = loadDataset(datasetName, class_last);
auto m = y.size(0);
int train_split = m * .7;
auto Xtrain = X.index({ "...", Slice(0, train_split) });
auto ytrain = y.index({ Slice(0, train_split) });
auto Xtest = X.index({ "...", Slice(train_split, m) });
auto ytest = y.index({ Slice(train_split, m) });
cout << "Dataset: " << datasetName << endl;
cout << "X: " << X.sizes() << endl;
cout << "y: " << y.sizes() << endl;
cout << "Xtrain: " << Xtrain.sizes() << endl;
cout << "ytrain: " << ytrain.sizes() << endl;
cout << "Xtest : " << Xtest.sizes() << endl;
cout << "ytest : " << ytest.sizes() << endl;
//
// STree
//
auto clf = pywrap::STree();
clf.fit(X, y);
double clf_score = clf.score(X, y);
auto stree = pywrap::STree();
auto hyperparameters = json::parse("{\"C\": 0.7, \"max_iter\": 10000, \"kernel\": \"rbf\", \"random_state\": 17}");
stree.setHyperparameters(hyperparameters);
cout << "STree Version: " << clf.version() << endl;
auto prediction = clf.predict(X);
cout << "Prediction: " << endl << "{";
for (int i = 0; i < prediction.size(0); ++i) {
cout << prediction[i].item<int>() << ", ";
}
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;
auto xg = pywrap::XGBoost();
cout << "Fitting XGBoost" << endl;
// xg.fit(Xtrain, ytrain);
// double xg_score = xg.score(Xtest, ytest);
//
// Scoring
//
cout << "Scoring dataset: " << datasetName << endl;
cout << "Scores:" << endl;
cout << "STree Score ......: " << clf_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;
}