TEST(SVRTest, train) {
DataSet dataset;
dataset.load_from_file(DATASET_DIR "test_dataset.txt");
// dataset.load_from_file(DATASET_DIR "E2006.train");
SvmParam param;
param.gamma = 0.25;
param.C = 10;
param.p = 0.1;
param.epsilon = 0.001;
param.nu = 0.5;
param.kernel_type = SvmParam::RBF;
param.svm_type = SvmParam::EPSILON_SVR;
SvmModel *model = new SVR();
model->train(dataset, param);
model->save_to_file(DATASET_DIR "test_dataset.txt.model");
SvmModel *new_model = new SVR();
new_model->load_from_file(DATASET_DIR "test_dataset.txt.model");
vector<float_type> predict_y;
predict_y = new_model->predict(dataset.instances(), 100);
float_type mse = 0;
for (unsigned i = 0; i < predict_y.size(); ++i) {
mse += (predict_y[i] - dataset.y()[i]) * (predict_y[i] - dataset.y()[i]);
}
mse /= predict_y.size();
LOG(INFO) << "MSE = " << mse;
EXPECT_NEAR(mse, 0.03097, 1e-4);
}
TEST(OneClassSVCTest, train) {
DataSet dataset;
dataset.load_from_file(DATASET_DIR "test_dataset.txt");
SvmParam param;
param.gamma = 0.5;
param.nu = 0.1;
param.epsilon = 0.001;
param.kernel_type = SvmParam::RBF;
param.svm_type = SvmParam::ONE_CLASS;
SvmModel *model = new OneClassSVC();
model->train(dataset, param);
model->save_to_file(DATASET_DIR "test_dataset.txt.model");
SvmModel *new_model = new OneClassSVC();
new_model->load_from_file(DATASET_DIR "test_dataset.txt.model");
vector<float_type> predict_y = new_model->predict(dataset.instances(), 100);
int n_pos = 0;
for (unsigned i = 0; i < predict_y.size(); ++i) {
if (predict_y[i] > 0)
n_pos++;
}
EXPECT_EQ(n_pos, 135);
delete model;
delete new_model;
}
//void DataSet_load_from_python(DataSet *dataset, float *y, char **x, int len) {dataset->load_from_python(y, x, len);}
void thundersvm_train_sub(DataSet& train_dataset, CMDParser& parser, char* model_file_path){
SvmModel *model = nullptr;
switch (parser.param_cmd.svm_type) {
case SvmParam::C_SVC:
model = new SVC();
break;
case SvmParam::NU_SVC:
model = new NuSVC();
break;
case SvmParam::ONE_CLASS:
model = new OneClassSVC();
break;
case SvmParam::EPSILON_SVR:
model = new SVR();
break;
case SvmParam::NU_SVR:
model = new NuSVR();
break;
}
//todo add this to check_parameter method
if (parser.param_cmd.svm_type == SvmParam::NU_SVC) {
train_dataset.group_classes();
for (int i = 0; i < train_dataset.n_classes(); ++i) {
int n1 = train_dataset.count()[i];
for (int j = i + 1; j < train_dataset.n_classes(); ++j) {
int n2 = train_dataset.count()[j];
if (parser.param_cmd.nu * (n1 + n2) / 2 > min(n1, n2)) {
printf("specified nu is infeasible\n");
return;
}
}
}
}
if (parser.param_cmd.kernel_type != SvmParam::LINEAR)
if (!parser.gamma_set) {
parser.param_cmd.gamma = 1.f / train_dataset.n_features();
}
#ifdef USE_CUDA
CUDA_CHECK(cudaSetDevice(parser.gpu_id));
#endif
vector<float_type> predict_y, test_y;
if (parser.do_cross_validation) {
predict_y = model->cross_validation(train_dataset, parser.param_cmd, parser.nr_fold);
} else {
model->train(train_dataset, parser.param_cmd);
model->save_to_file(model_file_path);
LOG(INFO) << "evaluating training score";
predict_y = model->predict(train_dataset.instances(), -1);
//predict_y = model->predict(train_dataset.instances(), 10000);
//test_y = train_dataset.y();
}
Metric *metric = nullptr;
switch (parser.param_cmd.svm_type) {
case SvmParam::C_SVC:
case SvmParam::NU_SVC: {
metric = new Accuracy();
break;
}
case SvmParam::EPSILON_SVR:
case SvmParam::NU_SVR: {
metric = new MSE();
break;
}
case SvmParam::ONE_CLASS: {
}
}
if (metric) {
LOG(INFO) << metric->name() << " = " << metric->score(predict_y, train_dataset.y()) << std::endl;
}
return;
}
void thundersvm_train_matlab(int argc, char **argv) {
CMDParser parser;
parser.parse_command_line(argc, argv);
/*
parser.param_cmd.svm_type = SvmParam::NU_SVC;
parser.param_cmd.kernel_type = SvmParam::RBF;
parser.param_cmd.C = 100;
parser.param_cmd.gamma = 0;
parser.param_cmd.nu = 0.1;
parser.param_cmd.epsilon = 0.001;
*/
DataSet train_dataset;
char input_file_path[1024] = DATASET_DIR;
char model_file_path[1024] = DATASET_DIR;
strcat(input_file_path, parser.svmtrain_input_file_name);
strcat(model_file_path, parser.model_file_name);
train_dataset.load_from_file(input_file_path);
SvmModel *model = nullptr;
switch (parser.param_cmd.svm_type) {
case SvmParam::C_SVC:
model = new SVC();
break;
case SvmParam::NU_SVC:
model = new NuSVC();
break;
case SvmParam::ONE_CLASS:
model = new OneClassSVC();
break;
case SvmParam::EPSILON_SVR:
model = new SVR();
break;
case SvmParam::NU_SVR:
model = new NuSVR();
break;
}
//todo add this to check_parameter method
if (parser.param_cmd.svm_type == SvmParam::NU_SVC) {
train_dataset.group_classes();
for (int i = 0; i < train_dataset.n_classes(); ++i) {
int n1 = train_dataset.count()[i];
for (int j = i + 1; j < train_dataset.n_classes(); ++j) {
int n2 = train_dataset.count()[j];
if (parser.param_cmd.nu * (n1 + n2) / 2 > min(n1, n2)) {
printf("specified nu is infeasible\n");
return;
}
}
}
}
#ifdef USE_CUDA
CUDA_CHECK(cudaSetDevice(parser.gpu_id));
#endif
vector<float_type> predict_y, test_y;
if (parser.do_cross_validation) {
vector<float_type> test_predict = model->cross_validation(train_dataset, parser.param_cmd, parser.nr_fold);
int dataset_size = test_predict.size() / 2;
test_y.insert(test_y.end(), test_predict.begin(), test_predict.begin() + dataset_size);
predict_y.insert(predict_y.end(), test_predict.begin() + dataset_size, test_predict.end());
} else {
model->train(train_dataset, parser.param_cmd);
model->save_to_file(model_file_path);
//predict_y = model->predict(train_dataset.instances(), 10000);
//test_y = train_dataset.y();
}
/*
//perform svm testing
Metric *metric = nullptr;
switch (parser.param_cmd.svm_type) {
case SvmParam::C_SVC:
case SvmParam::NU_SVC: {
metric = new Accuracy();
break;
}
case SvmParam::EPSILON_SVR:
case SvmParam::NU_SVR: {
metric = new MSE();
break;
}
case SvmParam::ONE_CLASS: {
}
}
if (metric) {
LOG(INFO) << metric->name() << " = " << metric->score(predict_y, test_y);
}
*/
return;
}
