binaryprobabilisticdecisiontree.h

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// -*- C++ -*-

#ifndef _CLUS_BINARYPROBABILISTICDECISIONTREE_H_
#define _CLUS_BINARYPROBABILISTICDECISIONTREE_H_

#include "machine.h"
#include "binaryprobabilisticdecisiontreenode.h"
#include "dctraingen.h"
#include <iostream>

using namespace std;

namespace CLUS
{

template< class T_Splitter >
class BinaryProbabilisticDecisionTree : public Machine
{
protected:
    BinaryProbabilisticDecisionTreeNode< T_Splitter >* root;

    /// vector of discrete domain sizes
    const Vector<int>& dDomainSize;
    
    /// number of discrete split variables
    int dsplitDim;

    /// number of continuous split variables
    int csplitDim;

    /// the minimum mass (sum of weights) to continue splitting
    double minMass;

    /// the minimum value of the probability to belong to a partition to be considered
    double threshold;

    /// do we do bootstrapping
    bool bootstrapping;

    /// number of repetitions for bootstrapping
    int bootstrappingRepetitions;

public:
    BinaryProbabilisticDecisionTree(const Vector<int>& DDomainSize,
                                    int CsplitDim):
            Machine(CsplitDim,1),dDomainSize(DDomainSize),
            dsplitDim(DDomainSize.dim()-1),
            csplitDim(CsplitDim)
    {
        minMass=10.0;
        threshold=.01;
        root=NULL;
        bootstrapping=false;
        bootstrappingRepetitions=1000;
    }

    ~BinaryProbabilisticDecisionTree(void)
    {
        if (root!=0)
            delete root;
    }
    
    virtual int InDim(void)
    {
        return dsplitDim+csplitDim;
    }

    virtual string TypeName(void)
    {
        return string("BinaryProbabilisticDecisionTree");
    }

    virtual void Infer(void)
    {
        if (root==0)
            return;
        // translate the first dsplitDim inputs into ints
        int Dvars[MAX_VARIABLES];
        for(int i=0; i<dsplitDim; i++)
        {
            Dvars[i]=(int)(*input)[i];
        }

        double Cvars[MAX_VARIABLES];
        for (int i=0; i<csplitDim; i++)
        {
            Cvars[i]=(*input)[i+dsplitDim];
        }

        // ask the root what is the probability to have class label 0
        // if >.5 return 0 otherwise 1
        // this has a small bias for first class
        if (root->ProbabilityFirstClass(Dvars,Cvars,1.0,threshold)>.5)
            output[0]=0;
        else
            output[0]=1;

#ifdef DEBUG_PRINT

        cout << "\t\t";
        for(int i=0; i<dsplitDim; i++)
            cout << Dvars[i] << " ";
        cout << "\t";
        for (int i=0; i<csplitDim; i++)
            cout << Cvars[i] << " ";

        double pFC=root->ProbabilityFirstClass(Dvars,Cvars,1.0,threshold);
        cout << pFC << " - " << output[0] << endl;
#endif

    }

    virtual void Identify(void)
    {
        const Matrix<double>& ctrainData = training->GetTrainingData();
        const Matrix<int>& dtrainData = dynamic_cast< DCTrainingData* >( training )
                                        -> GetDiscreteTrainingData();

        int M=ctrainData.num_rows();

        // create the root and give it the Id 1
        root = new BinaryProbabilisticDecisionTreeNode< T_Splitter >
               (1, dDomainSize, csplitDim);

        do
        {
            root->StartLearningEpoch();
            for(int i=0; i<M; i++)
            {
                int classLbl=dtrainData[i][dsplitDim];
                root->LearnSample(dtrainData[i],ctrainData[i],classLbl,1.0,threshold);
            }
        }
        while (root->StopLearningEpoch(minMass));

        cout << "End Learning" << endl;
        // PrintSizeTree();
    }

    virtual void Prune(void)
    {
        const Matrix<double>& ctrainData = pruning->GetTrainingData();
        const Matrix<int>& dtrainData = dynamic_cast< DCTrainingData* >( pruning )
                                        -> GetDiscreteTrainingData();

        int M=ctrainData.num_rows();

        root->InitializePruningStatistics();
        for(int i=0; i<M; i++)
        {
            int classLbl=dtrainData[i][dsplitDim];
            root->UpdatePruningStatistics(dtrainData[i], ctrainData[i], classLbl, 1.0, threshold);
        }
        root->FinalizePruningStatistics();

        // now cut the tree to the right size
        double cost=root->PruneSubtree();
        cout << "RMSN after pruning is:" << cost/M << endl;
        // PrintSizeTree();
    }

    virtual int SetOption(char* name, char* val)
    {
        if (strcmp(name,"MinMass")==0)
            minMass = atof(val);
        else
            if (strcmp(name,"Threshold")==0)
                threshold = atof(val);
            else
                return Machine::SetOption(name,val);
        return 1;
    }

    virtual void SaveToStream(ostream& out)
    {
        out << TypeName() << " (  " << dsplitDim << " " << csplitDim << " ) { "  << endl;

        out << "[ ";
        for(int i=0; i<dsplitDim; i++)
            out << dDomainSize[i] << " ";
        out << "]" << endl;

        root->SaveToStream(out);

        out << '}' << endl;
    }
};

}

#endif // _CLUS_BINARYPROBABILISTICDECISIONTREE_H_

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