// Flext attribute setters
void dtree::set_training_mode(int training_mode)
{
bool success = grt_dtree.setTrainingMode(training_mode);
if (success == false)
{
error("unable to set training_mode, hint: must be a value between 0 and " + std::to_string(GRT::DecisionTree::NUM_TRAINING_MODES));
}
}
// Flext attribute setters
void dtree::set_training_mode(int training_mode)
{
GRT::Tree::TrainingMode training_mode_ = GRT::Tree::TrainingMode::BEST_ITERATIVE_SPILT;
try
{
training_mode_ = get_grt_training_mode(training_mode);
}
catch (std::exception& e)
{
error("unable to set training_mode, hint: must be a value between 0 and " + std::to_string(GRT::Tree::NUM_TRAINING_MODES));
return;
}
bool success = grt_dtree.setTrainingMode(training_mode_);
if (success == false)
{
error("unable to set training mode");
}
}
void dtree::get_remove_features_at_each_split(bool &remove_features_at_each_split) const
{
remove_features_at_each_split = grt_dtree.getRemoveFeaturesAtEachSpilt();
}
void dtree::get_max_depth(int &max_depth) const
{
max_depth = grt_dtree.getMaxDepth();
}
void dtree::get_min_samples_per_node(int &min_samples_per_node) const
{
min_samples_per_node = grt_dtree.getMinNumSamplesPerNode();
}
void dtree::get_num_splitting_steps(int &num_splitting_steps) const
{
num_splitting_steps = grt_dtree.getNumSplittingSteps();
}
// Flext attribute getters
void dtree::get_training_mode(int &training_mode) const
{
training_mode = grt_dtree.getTrainingMode();
}
void dtree::set_remove_features_at_each_split(bool remove_features_at_each_split)
{
grt_dtree.setRemoveFeaturesAtEachSpilt(remove_features_at_each_split);
}
void dtree::set_max_depth(int max_depth)
{
grt_dtree.setMaxDepth(max_depth);
}
void dtree::set_min_samples_per_node(int min_samples_per_node)
{
grt_dtree.setMinNumSamplesPerNode(min_samples_per_node);
}
void dtree::set_num_splitting_steps(int num_splitting_steps)
{
grt_dtree.setNumSplittingSteps(num_splitting_steps);
}
