int main(int argc, char *argv[]) {
g_conf.number_of_feature = 3;
g_conf.max_depth = 4;
if (argc > 1) {
g_conf.max_depth = boost::lexical_cast<int>(argv[1]);
}
DataVector d;
bool r = LoadDataFromFile("../../data/train.dat", &d);
assert(r);
RegressionTree tree;
tree.Fit(&d);
std::ofstream model_output("../../data/model");
model_output << tree.Save();
RegressionTree tree2;
tree2.Load(tree.Save());
DataVector::iterator iter = d.begin();
PredictVector predict;
for ( ; iter != d.end(); ++iter) {
std::cout << (*iter)->ToString() << std::endl;
ValueType p = tree2.Predict(**iter);
predict.push_back(p);
std::cout << p << "," << tree.Predict(**iter) << std::endl;
}
std::cout << "rmse: " << RMSE(d, predict) << std::endl;
CleanDataVector(&d);
return 0;
}
double RMSE(const DataVector &data, const PredictVector &predict, size_t len) {
assert(data.size() >= len);
assert(predict.size() >= len);
double s = 0;
double c = 0;
for (size_t i = 0; i < data.size(); ++i) {
s += Squared(predict[i] - data[i]->label) * data[i]->weight;
c += data[i]->weight;
}
return std::sqrt(s / c);
}
int main(int argc, char *argv[]) {
std::srand ( unsigned ( std::time(0) ) );
g_conf.number_of_feature = 3;
g_conf.max_depth = 4;
g_conf.iterations = 100;
g_conf.shrinkage = 0.1F;
if (argc < 3) return -1;
std::string train_file(argv[1]);
std::string test_file(argv[2]);
if (argc > 3) {
g_conf.max_depth = boost::lexical_cast<int>(argv[3]);
}
if (argc > 4) {
g_conf.iterations = boost::lexical_cast<int>(argv[4]);
}
if (argc > 5) {
g_conf.shrinkage = boost::lexical_cast<float>(argv[5]);
}
if (argc > 6) {
g_conf.feature_sample_ratio = boost::lexical_cast<float>(argv[6]);
}
if (argc > 7) {
g_conf.data_sample_ratio = boost::lexical_cast<float>(argv[7]);
}
g_conf.debug = true;
// g_conf.loss = LOG_LIKELIHOOD;
g_conf.loss = SQUARED_ERROR;
DataVector d;
bool r = LoadDataFromFile(train_file, &d);
assert(r);
// g_conf.min_leaf_size = d.size() / 10;
std::cout << g_conf.ToString() << std::endl;
GBDT gbdt;
Elapsed elapsed;
gbdt.Fit(&d);
std::cout << "fit time: " << elapsed.Tell() << std::endl;
CleanDataVector(&d);
FreeVector(&d);
std::string model_file = train_file + ".model";
std::ofstream model_output(model_file.c_str());
model_output << gbdt.Save();
GBDT gbdt2;
gbdt2.Load(gbdt.Save());
DataVector d2;
r = LoadDataFromFile(test_file, &d2);
assert(r);
elapsed.Reset();
DataVector::iterator iter = d2.begin();
PredictVector predict;
for ( ; iter != d2.end(); ++iter) {
ValueType p;
if (g_conf.loss == SQUARED_ERROR) {
p = gbdt2.Predict(**iter);
predict.push_back(p);
} else if (g_conf.loss == LOG_LIKELIHOOD) {
p = gbdt2.Predict(**iter);
p = Logit(p);
if (p >= 0.5)
p = 1;
else
p = -1;
predict.push_back(p);
}
// std::cout << (*iter)->ToString() << std::endl
// << p << std::endl;
}
std::cout << "predict time: " << elapsed.Tell() << std::endl;
std::cout << "rmse: " << RMSE(d2, predict) << std::endl;
CleanDataVector(&d2);
return 0;
}
int main(int argc, char *argv[]) {
std::srand ( unsigned ( std::time(0) ) );
#ifdef USE_OPENMP
const int threads_wanted = 4;
omp_set_num_threads(threads_wanted);
#endif
g_conf.number_of_feature = 79;
g_conf.max_depth = 6;
g_conf.iterations = 10;
g_conf.shrinkage = 0.1F;
if (argc < 3) return -1;
std::string train_file(argv[1]);
std::string test_file(argv[2]);
if (argc > 3) {
g_conf.max_depth = boost::lexical_cast<int>(argv[3]);
}
if (argc > 4) {
g_conf.iterations = boost::lexical_cast<int>(argv[4]);
}
if (argc > 5) {
g_conf.shrinkage = boost::lexical_cast<float>(argv[5]);
}
if (argc > 6) {
g_conf.feature_sample_ratio = boost::lexical_cast<float>(argv[6]);
}
if (argc > 7) {
g_conf.data_sample_ratio = boost::lexical_cast<float>(argv[7]);
}
int debug = 0;
if (argc > 8) {
debug = boost::lexical_cast<int>(argv[8]);
}
g_conf.loss = LOG_LIKELIHOOD;
g_conf.debug = debug > 0? true : false;
DataVector d;
bool r = LoadDataFromFile(train_file, &d);
assert(r);
g_conf.min_leaf_size = d.size() / 40;
std::cout << "configure: " << std::endl
<< g_conf.ToString() << std::endl;
if (argc > 9) {
g_conf.LoadFeatureCost(argv[9]);
}
GBDT gbdt;
Elapsed elapsed;
gbdt.Fit(&d);
std::cout << "fit time: " << elapsed.Tell() << std::endl;
std::string model_file = train_file + ".model";
std::ofstream model_output(model_file.c_str());
model_output << gbdt.Save();
CleanDataVector(&d);
FreeVector(&d);
DataVector d2;
r = LoadDataFromFile(test_file, &d2);
assert(r);
elapsed.Reset();
DataVector::iterator iter = d2.begin();
PredictVector predict;
for ( ; iter != d2.end(); ++iter) {
ValueType p = Logit(gbdt.Predict(**iter));
predict.push_back(p);
}
std::cout << "predict time: " << elapsed.Tell() << std::endl;
std::string predict_file = test_file + ".predict";
std::ofstream predict_output(predict_file.c_str());
Auc auc;
for (size_t i = 0; i < d2.size(); ++i) {
predict_output << predict[i] << " " << d2[i]->ToString() << std::endl;
auc.Add(predict[i], d2[i]->label);
}
std::cout << "auc: " << auc.CalculateAuc() << std::endl;
auc.PrintConfusionTable();
CleanDataVector(&d2);
return 0;
}