MyCARTree.h

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/*
 * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (w) 2014 Parijat Mazumdar
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 * list of conditions and the following disclaimer.
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 * this list of conditions and the following disclaimer in the documentation
 * and/or other materials provided with the distribution.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
 * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *
 * The views and conclusions contained in the software and documentation are those
 * of the authors and should not be interpreted as representing official policies,
 * either expressed or implied, of the Shogun Development Team.
 */
 
 
#ifndef _MYCARTREE_H__
#define _MYCARTREE_H__
 
//#include <shogun/lib/config.h>
 
#include <shogun/multiclass/tree/TreeMachine.h>
#include <shogun/ensemble/MeanRule.h>
#include <shogun/features/DenseFeatures.h>
#include "MyCARTreeNodeData.h"
 
using std::vector;
namespace shogun
{
 
 
    class CMyCARTree : public CTreeMachine<MyCARTreeNodeData>
    {
    public:
        CMyCARTree();
 
        CMyCARTree(SGVector<bool> attribute_types, EProblemType prob_type=PT_MULTICLASS);
 
        CMyCARTree(SGVector<bool> attribute_types, EProblemType prob_type, int32_t num_folds, bool cv_prune);
 
        virtual ~CMyCARTree();
 
        virtual void set_labels(CLabels* lab);
 
        virtual const char* get_name() const;
 
        virtual EProblemType get_machine_problem_type() const;
 
        void set_machine_problem_type(EProblemType mode);
 
        virtual bool is_label_valid(CLabels* lab) const;
 
        virtual CBinaryLabels* apply_binary(CFeatures* data=NULL);
 
        virtual CMulticlassLabels* apply_multiclass(CFeatures* data=NULL);
 
        virtual CRegressionLabels* apply_regression(CFeatures* data=NULL);
 
        void prune_using_test_dataset(CDenseFeatures<float64_t>* feats, CLabels* gnd_truth, SGVector<float64_t> weights=SGVector<float64_t>());
 
        void set_weights(SGVector<float64_t> w);
 
        SGVector<float64_t> get_weights() const;
 
        void clear_weights();
 
        void set_feature_types(SGVector<bool> ft);
 
        SGVector<bool> get_feature_types() const;
 
        void clear_feature_types();
 
        int32_t get_num_folds() const;
 
        void set_num_folds(int32_t folds);
 
        int32_t get_max_depth() const;
 
        void set_max_depth(int32_t depth);
 
        int32_t get_min_node_size() const;
 
        void set_min_node_size(int32_t nsize);
 
        void set_cv_pruning(bool cv_pruning);
 
        float64_t get_label_epsilon();
 
        void set_label_epsilon(float64_t epsilon);
 
        void pre_sort_features(CFeatures* data, SGMatrix<float64_t>& sorted_feats, SGMatrix<index_t>& sorted_indices);
         
        void set_sorted_features(SGMatrix<float64_t>& sorted_feats, SGMatrix<index_t>& sorted_indices);
 
        std::vector<double> feature_importances();
        
        SGVector<float64_t> get_certainty_vector() const;
 
        void set_probabilities(CLabels* labels, CFeatures* data=NULL);  
    protected:
        virtual bool train_machine(CFeatures* data=NULL);
 
        virtual CBinaryTreeMachineNode<MyCARTreeNodeData>* CARTtrain(CFeatures* data, SGVector<float64_t> weights, CLabels* labels, int32_t level);
 
        SGVector<float64_t> get_unique_labels(SGVector<float64_t> labels_vec, int32_t &n_ulabels);
 
        virtual int32_t compute_best_attribute(const SGMatrix<float64_t>& mat, const SGVector<float64_t>& weights, CLabels* labels,
            SGVector<float64_t>& left, SGVector<float64_t>& right, SGVector<bool>& is_left_final, int32_t &num_missing,
            int32_t &count_left, int32_t &count_right, float64_t& IG, int32_t subset_size=0, const SGVector<int32_t>& active_indices=SGVector<index_t>());
 
 
        SGVector<bool> surrogate_split(SGMatrix<float64_t> data, SGVector<float64_t> weights, SGVector<bool> nm_left, int32_t attr);
 
 
        void handle_missing_vecs_for_continuous_surrogate(SGMatrix<float64_t> m, CDynamicArray<int32_t>* missing_vecs,
            CDynamicArray<float64_t>* association_index, CDynamicArray<int32_t>* intersect_vecs, SGVector<bool> is_left,
                                        SGVector<float64_t> weights, float64_t p, int32_t attr);
 
        void handle_missing_vecs_for_nominal_surrogate(SGMatrix<float64_t> m, CDynamicArray<int32_t>* missing_vecs,
            CDynamicArray<float64_t>* association_index, CDynamicArray<int32_t>* intersect_vecs, SGVector<bool> is_left,
                                        SGVector<float64_t> weights, float64_t p, int32_t attr);
 
        float64_t gain(SGVector<float64_t> wleft, SGVector<float64_t> wright, SGVector<float64_t> wtotal, SGVector<float64_t> labels);
 
        float64_t gain(const SGVector<float64_t>& wleft, const SGVector<float64_t>& wright, const SGVector<float64_t>& wtotal);
 
        float64_t gini_impurity_index(const SGVector<float64_t>& weighted_lab_classes, float64_t &total_weight);
 
        float64_t least_squares_deviation(const SGVector<float64_t>& labels, const SGVector<float64_t>& weights, float64_t &total_weight);
 
        CLabels* apply_from_current_node(CDenseFeatures<float64_t>* feats, bnode_t* current, 
                                         bool set_certainty=false);
 
        void prune_by_cross_validation(CDenseFeatures<float64_t>* data, int32_t folds);
 
        float64_t compute_error(CLabels* labels, CLabels* reference, SGVector<float64_t> weights);
 
        CDynamicObjectArray* prune_tree(CTreeMachine<MyCARTreeNodeData>* tree);
 
        float64_t find_weakest_alpha(bnode_t* node);
 
        void cut_weakest_link(bnode_t* node, float64_t alpha);
 
        void form_t1(bnode_t* node);
 
        void init();
 
        void get_importance(bnode_t* node, vector<double>& importances);
        
        
    public:
        static const float64_t MISSING;
 
        static const float64_t MIN_SPLIT_GAIN;
 
        static const float64_t EQ_DELTA;
 
    protected:
        float64_t m_label_epsilon;
 
        SGVector<bool> m_nominal;
 
        SGVector<float64_t> m_weights;
 
        SGMatrix<float64_t> m_sorted_features;
 
        SGMatrix<index_t> m_sorted_indices;
 
        bool m_pre_sort;
 
        bool m_types_set;
 
        bool m_weights_set;
 
        bool m_apply_cv_pruning;
 
        int32_t m_folds;
 
        EProblemType m_mode;
 
        CDynamicArray<float64_t>* m_alphas;
 
        int32_t m_max_depth;
 
        int32_t m_min_node_size;
        
        SGVector<float64_t> m_certainty;
    };
} 
 
#endif