bool TreeClassification::findBestSplit(size_t nodeID, std::vector<size_t>& possible_split_varIDs) {
size_t num_samples_node = sampleIDs[nodeID].size();
size_t num_classes = class_values->size();
double best_decrease = -1;
size_t best_varID = 0;
double best_value = 0;
size_t* class_counts = new size_t[num_classes]();
// Compute overall class counts
for (size_t i = 0; i < num_samples_node; ++i) {
size_t sampleID = sampleIDs[nodeID][i];
uint sample_classID = (*response_classIDs)[sampleID];
++class_counts[sample_classID];
}
// For all possible split variables
for (auto& varID : possible_split_varIDs) {
// Find best split value, if ordered consider all values as split values, else all 2-partitions
if ((*is_ordered_variable)[varID]) {
// Use memory saving method if option set
if (memory_saving_splitting) {
findBestSplitValueSmallQ(nodeID, varID, num_classes, class_counts, num_samples_node, best_value, best_varID,
best_decrease);
} else {
// Use faster method for both cases
double q = (double) num_samples_node / (double) data->getNumUniqueDataValues(varID);
if (q < Q_THRESHOLD) {
findBestSplitValueSmallQ(nodeID, varID, num_classes, class_counts, num_samples_node, best_value, best_varID,
best_decrease);
} else {
findBestSplitValueLargeQ(nodeID, varID, num_classes, class_counts, num_samples_node, best_value, best_varID,
best_decrease);
}
}
} else {
findBestSplitValueUnordered(nodeID, varID, num_classes, class_counts, num_samples_node, best_value, best_varID,
best_decrease);
}
}
delete[] class_counts;
// Stop if no good split found
if (best_decrease < 0) {
return true;
}
// Save best values
split_varIDs[nodeID] = best_varID;
split_values[nodeID] = best_value;
// Compute gini index for this node and to variable importance if needed
if (importance_mode == IMP_GINI) {
addGiniImportance(nodeID, best_varID, best_decrease);
}
return false;
}
bool TreeProbability::findBestSplit(size_t nodeID, std::vector<size_t>& possible_split_varIDs) {
size_t num_samples_node = sampleIDs[nodeID].size();
double best_decrease = -1;
size_t best_varID = 0;
double best_value = 0;
// Compute sum of responses in node
double sum_node = 0;
for (auto& sampleID : sampleIDs[nodeID]) {
sum_node += data->get(sampleID, dependent_varID);
}
// For all possible split variables
for (auto& varID : possible_split_varIDs) {
// Find best split value, if ordered consider all values as split values, else all 2-partitions
if ((*is_ordered_variable)[varID]) {
// Use memory saving method if option set
if (memory_saving_splitting) {
findBestSplitValueSmallQ(nodeID, varID, sum_node, num_samples_node, best_value, best_varID, best_decrease);
} else {
// Use faster method for both cases
double q = (double) num_samples_node / (double) data->getNumUniqueDataValues(varID);
if (q < Q_THRESHOLD) {
findBestSplitValueSmallQ(nodeID, varID, sum_node, num_samples_node, best_value, best_varID, best_decrease);
} else {
findBestSplitValueLargeQ(nodeID, varID, sum_node, num_samples_node, best_value, best_varID, best_decrease);
}
}
} else {
findBestSplitValueUnordered(nodeID, varID, sum_node, num_samples_node, best_value, best_varID, best_decrease);
}
}
// Stop if no good split found
if (best_decrease < 0) {
return true;
}
// Save best values
split_varIDs[nodeID] = best_varID;
split_values[nodeID] = best_value;
// Compute decrease of impurity for this node and add to variable importance if needed
if (importance_mode == IMP_GINI) {
addImpurityImportance(nodeID, best_varID, best_decrease);
}
return false;
}