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decision_tree_trainer.hpp
/*
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* Point Cloud Library (PCL) - www.pointclouds.org
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#pragma once
namespace pcl {
template <class FeatureType,
class DataSet,
class LabelType,
class ExampleIndex,
class NodeType>
DecisionTreeTrainer<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::
DecisionTreeTrainer() = default;
template <class FeatureType,
class DataSet,
class LabelType,
class ExampleIndex,
class NodeType>
DecisionTreeTrainer<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::
~DecisionTreeTrainer() = default;
template <class FeatureType,
class DataSet,
class LabelType,
class ExampleIndex,
class NodeType>
void
DecisionTreeTrainer<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::train(
pcl::DecisionTree<NodeType>& tree)
{
// create random features
std::vector<FeatureType> features;
if (!random_features_at_split_node_)
feature_handler_->createRandomFeatures(num_of_features_, features);
// recursively build decision tree
NodeType root_node;
tree.setRoot(root_node);
if (decision_tree_trainer_data_provider_) {
std::cerr << "use decision_tree_trainer_data_provider_" << std::endl;
decision_tree_trainer_data_provider_->getDatasetAndLabels(
data_set_, label_data_, examples_);
trainDecisionTreeNode(
features, examples_, label_data_, max_tree_depth_, tree.getRoot());
label_data_.clear();
data_set_.clear();
examples_.clear();
}
else {
trainDecisionTreeNode(
features, examples_, label_data_, max_tree_depth_, tree.getRoot());
}
}
template <class FeatureType,
class DataSet,
class LabelType,
class ExampleIndex,
class NodeType>
void
DecisionTreeTrainer<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::
trainDecisionTreeNode(std::vector<FeatureType>& features,
std::vector<ExampleIndex>& examples,
std::vector<LabelType>& label_data,
const std::size_t max_depth,
NodeType& node)
{
const std::size_t num_of_examples = examples.size();
if (num_of_examples == 0) {
PCL_ERROR(
"Reached invalid point in decision tree training: Number of examples is 0!\n");
return;
};
if (max_depth == 0) {
stats_estimator_->computeAndSetNodeStats(data_set_, examples, label_data, node);
return;
};
if (examples.size() < min_examples_for_split_) {
stats_estimator_->computeAndSetNodeStats(data_set_, examples, label_data, node);
return;
}
if (random_features_at_split_node_) {
features.clear();
feature_handler_->createRandomFeatures(num_of_features_, features);
}
std::vector<float> feature_results;
std::vector<unsigned char> flags;
feature_results.reserve(num_of_examples);
flags.reserve(num_of_examples);
// find best feature for split
int best_feature_index = -1;
float best_feature_threshold = 0.0f;
float best_feature_information_gain = 0.0f;
const std::size_t num_of_features = features.size();
for (std::size_t feature_index = 0; feature_index < num_of_features;
++feature_index) {
// evaluate features
feature_handler_->evaluateFeature(
features[feature_index], data_set_, examples, feature_results, flags);
// get list of thresholds
if (!thresholds_.empty()) {
// compute information gain for each threshold and store threshold with highest
// information gain
for (const float& threshold : thresholds_) {
const float information_gain = stats_estimator_->computeInformationGain(
data_set_, examples, label_data, feature_results, flags, threshold);
if (information_gain > best_feature_information_gain) {
best_feature_information_gain = information_gain;
best_feature_index = static_cast<int>(feature_index);
best_feature_threshold = threshold;
}
}
}
else {
std::vector<float> thresholds;
thresholds.reserve(num_of_thresholds_);
createThresholdsUniform(num_of_thresholds_, feature_results, thresholds);
// compute information gain for each threshold and store threshold with highest
// information gain
for (std::size_t threshold_index = 0; threshold_index < num_of_thresholds_;
++threshold_index) {
const float threshold = thresholds[threshold_index];
// compute information gain
const float information_gain = stats_estimator_->computeInformationGain(
data_set_, examples, label_data, feature_results, flags, threshold);
if (information_gain > best_feature_information_gain) {
best_feature_information_gain = information_gain;
best_feature_index = static_cast<int>(feature_index);
best_feature_threshold = threshold;
}
}
}
}
if (best_feature_index == -1) {
stats_estimator_->computeAndSetNodeStats(data_set_, examples, label_data, node);
return;
}
// get branch indices for best feature and best threshold
std::vector<unsigned char> branch_indices;
branch_indices.reserve(num_of_examples);
{
feature_handler_->evaluateFeature(
features[best_feature_index], data_set_, examples, feature_results, flags);
stats_estimator_->computeBranchIndices(
feature_results, flags, best_feature_threshold, branch_indices);
}
stats_estimator_->computeAndSetNodeStats(data_set_, examples, label_data, node);
// separate data
{
const std::size_t num_of_branches = stats_estimator_->getNumOfBranches();
std::vector<std::size_t> branch_counts(num_of_branches, 0);
for (std::size_t example_index = 0; example_index < num_of_examples;
++example_index) {
++branch_counts[branch_indices[example_index]];
}
node.feature = features[best_feature_index];
node.threshold = best_feature_threshold;
node.sub_nodes.resize(num_of_branches);
for (std::size_t branch_index = 0; branch_index < num_of_branches; ++branch_index) {
if (branch_counts[branch_index] == 0) {
NodeType branch_node;
stats_estimator_->computeAndSetNodeStats(
data_set_, examples, label_data, branch_node);
// branch_node->num_of_sub_nodes = 0;
node.sub_nodes[branch_index] = branch_node;
continue;
}
std::vector<LabelType> branch_labels;
std::vector<ExampleIndex> branch_examples;
branch_labels.reserve(branch_counts[branch_index]);
branch_examples.reserve(branch_counts[branch_index]);
for (std::size_t example_index = 0; example_index < num_of_examples;
++example_index) {
if (branch_indices[example_index] == branch_index) {
branch_examples.push_back(examples[example_index]);
branch_labels.push_back(label_data[example_index]);
}
}
trainDecisionTreeNode(features,
branch_examples,
branch_labels,
max_depth - 1,
node.sub_nodes[branch_index]);
}
}
}
template <class FeatureType,
class DataSet,
class LabelType,
class ExampleIndex,
class NodeType>
void
DecisionTreeTrainer<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::
createThresholdsUniform(const std::size_t num_of_thresholds,
std::vector<float>& values,
std::vector<float>& thresholds)
{
// estimate range of values
float min_value = ::std::numeric_limits<float>::max();
float max_value = -::std::numeric_limits<float>::max();
const std::size_t num_of_values = values.size();
for (std::size_t value_index = 0; value_index < num_of_values; ++value_index) {
const float value = values[value_index];
if (value < min_value)
min_value = value;
if (value > max_value)
max_value = value;
}
const float range = max_value - min_value;
const float step = range / static_cast<float>(num_of_thresholds + 2);
// compute thresholds
thresholds.resize(num_of_thresholds);
for (std::size_t threshold_index = 0; threshold_index < num_of_thresholds;
++threshold_index) {
thresholds[threshold_index] =
min_value + step * (static_cast<float>(threshold_index + 1));
}
}
} // namespace pcl
pcl::DecisionTree
Class representing a decision tree.
Definition decision_tree.h:49
pcl::DecisionTree::getRoot
NodeType & getRoot()
Returns the root node of the tree.
Definition decision_tree.h:69
pcl::DecisionTree::setRoot
void setRoot(const NodeType &root)
Sets the root node of the tree.
Definition decision_tree.h:62
pcl::DecisionTreeTrainer::createThresholdsUniform
static void createThresholdsUniform(const std::size_t num_of_thresholds, std::vector< float > &values, std::vector< float > &thresholds)
Creates uniformly distributed thresholds over the range of the supplied values.
Definition decision_tree_trainer.hpp:260
pcl::DecisionTreeTrainer::trainDecisionTreeNode
void trainDecisionTreeNode(std::vector< FeatureType > &features, std::vector< ExampleIndex > &examples, std::vector< LabelType > &label_data, std::size_t max_depth, NodeType &node)
Trains a decision tree node from the specified features, label data, and examples.
Definition decision_tree_trainer.hpp:101
pcl::DecisionTreeTrainer::train
void train(DecisionTree< NodeType > &tree)
Trains a decision tree using the set training data and settings.
Definition decision_tree_trainer.hpp:64
pcl::DecisionTreeTrainer::~DecisionTreeTrainer
virtual ~DecisionTreeTrainer()
Destructor.
pcl::DecisionTreeTrainer::DecisionTreeTrainer
DecisionTreeTrainer()
Constructor.
pcl
Definition convolution.h:46