hoeffding_tree.hpp

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#ifndef MLPACK_METHODS_HOEFFDING_TREES_HOEFFDING_TREE_HPP
#define MLPACK_METHODS_HOEFFDING_TREES_HOEFFDING_TREE_HPP

#include <mlpack/prereqs.hpp>
#include <mlpack/core/data/dataset_mapper.hpp>
#include "gini_impurity.hpp"
#include "hoeffding_numeric_split.hpp"
#include "hoeffding_categorical_split.hpp"

namespace mlpack {
namespace tree {

template<typename FitnessFunction = GiniImpurity,
         template<typename> class NumericSplitType =
             HoeffdingDoubleNumericSplit,
         template<typename> class CategoricalSplitType =
             HoeffdingCategoricalSplit
>
class HoeffdingTree
{
 public:
  typedef NumericSplitType<FitnessFunction> NumericSplit;
  typedef CategoricalSplitType<FitnessFunction> CategoricalSplit;

  template<typename MatType>
  HoeffdingTree(const MatType& data,
                const data::DatasetInfo& datasetInfo,
                const arma::Row<size_t>& labels,
                const size_t numClasses,
                const bool batchTraining = true,
                const double successProbability = 0.95,
                const size_t maxSamples = 0,
                const size_t checkInterval = 100,
                const size_t minSamples = 100,
                const CategoricalSplitType<FitnessFunction>& categoricalSplitIn
                    = CategoricalSplitType<FitnessFunction>(0, 0),
                const NumericSplitType<FitnessFunction>& numericSplitIn =
                    NumericSplitType<FitnessFunction>(0));

  HoeffdingTree(const data::DatasetInfo& datasetInfo,
                const size_t numClasses,
                const double successProbability = 0.95,
                const size_t maxSamples = 0,
                const size_t checkInterval = 100,
                const size_t minSamples = 100,
                const CategoricalSplitType<FitnessFunction>& categoricalSplitIn
                    = CategoricalSplitType<FitnessFunction>(0, 0),
                const NumericSplitType<FitnessFunction>& numericSplitIn =
                    NumericSplitType<FitnessFunction>(0),
                std::unordered_map<size_t, std::pair<size_t, size_t>>*
                    dimensionMappings = NULL,
                const bool copyDatasetInfo = true);

  HoeffdingTree();

  HoeffdingTree(const HoeffdingTree& other);

  HoeffdingTree(HoeffdingTree&& other);

  HoeffdingTree& operator=(const HoeffdingTree& other);

  HoeffdingTree& operator=(HoeffdingTree&& other);

  ~HoeffdingTree();

  template<typename MatType>
  void Train(const MatType& data,
             const arma::Row<size_t>& labels,
             const bool batchTraining = true,
             const bool resetTree = false,
             const size_t numClasses = 0);

  template<typename MatType>
  void Train(const MatType& data,
             const data::DatasetInfo& info,
             const arma::Row<size_t>& labels,
             const bool batchTraining = true,
             const size_t numClasses = 0);

  template<typename VecType>
  void Train(const VecType& point, const size_t label);

  size_t SplitCheck();

  size_t SplitDimension() const { return splitDimension; }

  size_t MajorityClass() const { return majorityClass; }
  size_t& MajorityClass() { return majorityClass; }

  double MajorityProbability() const { return majorityProbability; }
  double& MajorityProbability() { return majorityProbability; }

  size_t NumChildren() const { return children.size(); }

  const HoeffdingTree& Child(const size_t i) const { return *children[i]; }
  HoeffdingTree& Child(const size_t i) { return *children[i]; }

  double SuccessProbability() const { return successProbability; }
  void SuccessProbability(const double successProbability);

  size_t MinSamples() const { return minSamples; }
  void MinSamples(const size_t minSamples);

  size_t MaxSamples() const { return maxSamples; }
  void MaxSamples(const size_t maxSamples);

  size_t CheckInterval() const { return checkInterval; }
  void CheckInterval(const size_t checkInterval);

  template<typename VecType>
  size_t CalculateDirection(const VecType& point) const;

  template<typename VecType>
  size_t Classify(const VecType& point) const;

  size_t NumDescendants() const;

  template<typename VecType>
  void Classify(const VecType& point, size_t& prediction, double& probability)
      const;

  template<typename MatType>
  void Classify(const MatType& data, arma::Row<size_t>& predictions) const;

  template<typename MatType>
  void Classify(const MatType& data,
                arma::Row<size_t>& predictions,
                arma::rowvec& probabilities) const;

  void CreateChildren();

  template<typename Archive>
  void serialize(Archive& ar, const uint32_t /* version */);

 private:
  // We need to keep some information for before we have split.

  std::vector<NumericSplitType<FitnessFunction>> numericSplits;
  std::vector<CategoricalSplitType<FitnessFunction>> categoricalSplits;

  std::unordered_map<size_t, std::pair<size_t, size_t>>* dimensionMappings;
  bool ownsMappings;

  size_t numSamples;
  size_t numClasses;
  size_t maxSamples;
  size_t checkInterval;
  size_t minSamples;
  const data::DatasetInfo* datasetInfo;
  bool ownsInfo;
  double successProbability;

  // And we need to keep some information for after we have split.

  size_t splitDimension;
  size_t majorityClass;
  double majorityProbability;
  typename CategoricalSplitType<FitnessFunction>::SplitInfo categoricalSplit;
  typename NumericSplitType<FitnessFunction>::SplitInfo numericSplit;
  std::vector<HoeffdingTree*> children;

  template<typename MatType>
  void TrainInternal(const MatType& data,
                     const arma::Row<size_t>& labels,
                     const bool batchTraining);

  void ResetTree(
      const CategoricalSplitType<FitnessFunction>& categoricalSplitIn =
          CategoricalSplitType<FitnessFunction>(0, 0),
      const NumericSplitType<FitnessFunction>& numericSplitIn =
          NumericSplitType<FitnessFunction>(0));
};

} // namespace tree
} // namespace mlpack

#include "hoeffding_tree_impl.hpp"

#endif