OpenCV码源笔记——Decision Tree决策树

来自OpenCV2.3.1 sample/c/mushroom.cpp

 

1.首先读入agaricus-lepiota.data的训练样本。

   样本中第一项是e或p代表有毒或无毒的标志位；其他是特征，可以把每个样本看做一个特征向量；

   cvSeqPush( seq, el_ptr );读入序列seq中，每一项都存储一个样本即特征向量；

   之后，把特征向量与标志位分别读入CvMat* data与CvMat* reponses中

   还有一个CvMat* missing保留丢失位当前小于0位置；

 

2.训练样本

dtree = new CvDTree;
dtree->train( data, CV_ROW_SAMPLE, responses, 0, 0, var_type, missing,
CvDTreeParams( 8, // max depth
10, // min sample count 样本数小于10时，停止分裂
0, // regression accuracy: N/A here；回归树的限制精度
true, // compute surrogate split, as we have missing data；；为真时，计算missing data和变量的重要性
15, // max number of categories (use sub-optimal algorithm for larger numbers)类型上限以保证计算速度。树会以次优分裂（suboptimal split）的形式生长。只对2种取值以上的树有意义
10, // the number of cross-validation folds；If cv_folds > 1 then prune a tree with K-fold cross-validation where K is equal to cv_folds
true, // use 1SE rule => smaller tree；If true 修剪树. 这将使树更紧凑,更能抵抗训练数据噪声,但有点不太准确
true, // throw away the pruned tree branches
priors //错分类的代价我们判断的：有毒VS无毒 错误的代价比 the array of priors, the bigger p_weight, the more attention
// to the poisonous mushrooms
// (a mushroom will be judjed to be poisonous with bigger chance)
));

 

3.

double r = dtree->predict( &sample, &mask )->value;//使用predict来预测样本，结果为 CvDTreeNode结构,dtree->predict(sample,mask)->value是分类情况下的类别或回归情况下的函数估计值；

4.interactive_classification通过人工输入特征来判断。

 

#include "opencv2/core/core_c.h"
#include "opencv2/ml/ml.hpp"
#include <stdio.h>

void help()
{
    printf("\nThis program demonstrated the use of OpenCV's decision tree function for learning and predicting data\n"
        "Usage :\n"
        "./mushroom <path to agaricus-lepiota.data>\n"
        "\n"
        "The sample demonstrates how to build a decision tree for classifying mushrooms.\n"
        "It uses the sample base agaricus-lepiota.data from UCI Repository, here is the link:\n"
        "\n"
        "Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).\n"
        "UCI Repository of machine learning databases\n"
        "[http://www.ics.uci.edu/~mlearn/MLRepository.html].\n"
        "Irvine, CA: University of California, Department of Information and Computer Science.\n"
        "\n"
        "// loads the mushroom database, which is a text file, containing\n"
        "// one training sample per row, all the input variables and the output variable are categorical,\n"
        "// the values are encoded by characters.\n\n");
}

int mushroom_read_database( const char* filename, CvMat** data, CvMat** missing, CvMat** responses )
{
    const int M = 1024;
    FILE* f = fopen( filename, "rt" );
    CvMemStorage* storage;
    CvSeq* seq;
    char buf[M+2], *ptr;
    float* el_ptr;
    CvSeqReader reader;
    int i, j, var_count = 0;

    if( !f )
        return 0;

    // read the first line and determine the number of variables
    if( !fgets( buf, M, f ))
    {
        fclose(f);
        return 0;
    }

    for( ptr = buf; *ptr != '\0'; ptr++ )
        var_count += *ptr == ',';//计算每个样本的数量，每个样本一个“，”，样本数量=var_count+1;
    assert( ptr - buf == (var_count+1)*2 );

    // create temporary memory storage to store the whole database
    //把样本存入seq中，存储空间是storage；
    el_ptr = new float[var_count+1];
    storage = cvCreateMemStorage();
    seq = cvCreateSeq( 0, sizeof(*seq), (var_count+1)*sizeof(float), storage );//

    for(;;)
    {
        for( i = 0; i <= var_count; i++ )
        {
            int c = buf[i*2];
            el_ptr[i] = c == '?' ? -1.f : (float)c;
        }
        if( i != var_count+1 )
            break;
        cvSeqPush( seq, el_ptr );
        if( !fgets( buf, M, f ) || !strchr( buf, ',' ) )
            break;
    }
    fclose(f);

    // allocate the output matrices and copy the base there
    *data = cvCreateMat( seq->total, var_count, CV_32F );//行数：样本数量；列数：样本大小；
    *missing = cvCreateMat( seq->total, var_count, CV_8U );
    *responses = cvCreateMat( seq->total, 1, CV_32F );//样本标志；

    cvStartReadSeq( seq, &reader );

    for( i = 0; i < seq->total; i++ )
    {
        const float* sdata = (float*)reader.ptr + 1;
        float* ddata = data[0]->data.fl + var_count*i;
        float* dr = responses[0]->data.fl + i;
        uchar* dm = missing[0]->data.ptr + var_count*i;

        for( j = 0; j < var_count; j++ )
        {
            ddata[j] = sdata[j];
            dm[j] = sdata[j] < 0;
        }
        *dr = sdata[-1];//样本的第一个位置是标志；
        CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
    }

    cvReleaseMemStorage( &storage );
    delete el_ptr;
    return 1;
}


CvDTree* mushroom_create_dtree( const CvMat* data, const CvMat* missing,
    const CvMat* responses, float p_weight )
{
    CvDTree* dtree;
    CvMat* var_type;
    int i, hr1 = 0, hr2 = 0, p_total = 0;
    float priors[] = { 1, p_weight };

    var_type = cvCreateMat( data->cols + 1, 1, CV_8U );
    cvSet( var_type, cvScalarAll(CV_VAR_CATEGORICAL) ); // all the variables are categorical

    dtree = new CvDTree;

    dtree->train( data, CV_ROW_SAMPLE, responses, 0, 0, var_type, missing,
        CvDTreeParams( 8, // max depth
        10, // min sample count样本数小于10时，停止分裂
        0, // regression accuracy: N/A here；回归树的限制精度
        true, // compute surrogate split, as we have missing data；为真时，计算missing data和可变的重要性正确度
        15, // max number of categories (use sub-optimal algorithm for larger numbers)类型上限以保证计算速度。树会以次优分裂（suboptimal split）的形式生长。只对2种取值以上的树有意义
        10, // the number of cross-validation folds；If cv_folds > 1 then prune a tree with K-fold cross-validation
        true, // use 1SE rule => smaller treeIf true 修剪树. 这将使树更紧凑,更能抵抗训练数据噪声,但有点不太准确
        true, // throw away the pruned tree branches
        priors // the array of priors, the bigger p_weight, the more attention
        // to the poisonous mushrooms
        // (a mushroom will be judjed to be poisonous with bigger chance)
        ));

    // compute hit-rate on the training database, demonstrates predict usage.

    for( i = 0; i < data->rows; i++ )
    {
        CvMat sample, mask;
        cvGetRow( data, &sample, i );
        cvGetRow( missing, &mask, i );
        double r = dtree->predict( &sample, &mask )->value;//使用predict来预测样本，结果为 CvDTreeNode结构,dtree->predict(sample,mask)->value是分类情况下的类别或回归情况下的函数估计值；
        int d = fabs(r - responses->data.fl[i]) >= FLT_EPSILON;//大于阈值FLT_EPSILON被判断为误检
        if( d )
        {
            if( r != 'p' )
                hr1++;
            else
                hr2++;
        }
        p_total += responses->data.fl[i] == 'p';
    }

    printf( "Results on the training database:\n"
        "\tPoisonous mushrooms mis-predicted: %d (%g%%)\n"
        "\tFalse-alarms: %d (%g%%)\n", hr1, (double)hr1*100/p_total,
        hr2, (double)hr2*100/(data->rows - p_total) );

    cvReleaseMat( &var_type );

    return dtree;
}


static const char* var_desc[] =
{
    "cap shape (bell=b,conical=c,convex=x,flat=f)",
    "cap surface (fibrous=f,grooves=g,scaly=y,smooth=s)",
    "cap color (brown=n,buff=b,cinnamon=c,gray=g,green=r,\n\tpink=p,purple=u,red=e,white=w,yellow=y)",
    "bruises? (bruises=t,no=f)",
    "odor (almond=a,anise=l,creosote=c,fishy=y,foul=f,\n\tmusty=m,none=n,pungent=p,spicy=s)",
    "gill attachment (attached=a,descending=d,free=f,notched=n)",
    "gill spacing (close=c,crowded=w,distant=d)",
    "gill size (broad=b,narrow=n)",
    "gill color (black=k,brown=n,buff=b,chocolate=h,gray=g,\n\tgreen=r,orange=o,pink=p,purple=u,red=e,white=w,yellow=y)",
    "stalk shape (enlarging=e,tapering=t)",
    "stalk root (bulbous=b,club=c,cup=u,equal=e,rhizomorphs=z,rooted=r)",
    "stalk surface above ring (ibrous=f,scaly=y,silky=k,smooth=s)",
    "stalk surface below ring (ibrous=f,scaly=y,silky=k,smooth=s)",
    "stalk color above ring (brown=n,buff=b,cinnamon=c,gray=g,orange=o,\n\tpink=p,red=e,white=w,yellow=y)",
    "stalk color below ring (brown=n,buff=b,cinnamon=c,gray=g,orange=o,\n\tpink=p,red=e,white=w,yellow=y)",
    "veil type (partial=p,universal=u)",
    "veil color (brown=n,orange=o,white=w,yellow=y)",
    "ring number (none=n,one=o,two=t)",
    "ring type (cobwebby=c,evanescent=e,flaring=f,large=l,\n\tnone=n,pendant=p,sheathing=s,zone=z)",
    "spore print color (black=k,brown=n,buff=b,chocolate=h,green=r,\n\torange=o,purple=u,white=w,yellow=y)",
    "population (abundant=a,clustered=c,numerous=n,\n\tscattered=s,several=v,solitary=y)",
    "habitat (grasses=g,leaves=l,meadows=m,paths=p\n\turban=u,waste=w,woods=d)",
    0
};


void print_variable_importance( CvDTree* dtree, const char** var_desc )
{
    const CvMat* var_importance = dtree->get_var_importance();
    int i;
    char input[1000];

    if( !var_importance )
    {
        printf( "Error: Variable importance can not be retrieved\n" );
        return;
    }

    printf( "Print variable importance information? (y/n) " );
    scanf( "%1s", input );
    if( input[0] != 'y' && input[0] != 'Y' )
        return;

    for( i = 0; i < var_importance->cols*var_importance->rows; i++ )
    {
        double val = var_importance->data.db[i];
        if( var_desc )
        {
            char buf[100];
            int len = strchr( var_desc[i], '(' ) - var_desc[i] - 1;
            strncpy( buf, var_desc[i], len );
            buf[len] = '\0';
            printf( "%s", buf );
        }
        else
            printf( "var #%d", i );
        printf( ": %g%%\n", val*100. );
    }
}

void interactive_classification( CvDTree* dtree, const char** var_desc )
{
    char input[1000];
    const CvDTreeNode* root;
    CvDTreeTrainData* data;

    if( !dtree )
        return;

    root = dtree->get_root();
    data = dtree->get_data();

    for(;;)
    {
        const CvDTreeNode* node;

        printf( "Start/Proceed with interactive mushroom classification (y/n): " );
        scanf( "%1s", input );
        if( input[0] != 'y' && input[0] != 'Y' )
            break;
        printf( "Enter 1-letter answers, '?' for missing/unknown value...\n" );

        // custom version of predict
        //传统的预测方式;
        node = root;
        for(;;)
        {
            CvDTreeSplit* split = node->split;
            int dir = 0;

            if( !node->left || node->Tn <= dtree->get_pruned_tree_idx() || !node->split )
                break;

            for( ; split != 0; )
            {
                int vi = split->var_idx, j;
                int count = data->cat_count->data.i[vi];
                const int* map = data->cat_map->data.i + data->cat_ofs->data.i[vi];

                printf( "%s: ", var_desc[vi] );
                scanf( "%1s", input );

                if( input[0] == '?' )
                {
                    split = split->next;
                    continue;
                }

                // convert the input character to the normalized value of the variable
                for( j = 0; j < count; j++ )
                    if( map[j] == input[0] )
                        break;
                if( j < count )
                {
                    dir = (split->subset[j>>5] & (1 << (j&31))) ? -1 : 1;
                    if( split->inversed )
                        dir = -dir;
                    break;
                }
                else
                    printf( "Error: unrecognized value\n" );
            }

            if( !dir )
            {
                printf( "Impossible to classify the sample\n");
                node = 0;
                break;
            }
            node = dir < 0 ? node->left : node->right;
        }

        if( node )
            printf( "Prediction result: the mushroom is %s\n",
            node->class_idx == 0 ? "EDIBLE" : "POISONOUS" );
        printf( "\n-----------------------------\n" );
    }
}


int main( int argc, char** argv )
{
    CvMat *data = 0, *missing = 0, *responses = 0;
    CvDTree* dtree;
    const char* base_path = argc >= 2 ? argv[1] : "agaricus-lepiota.data";

    help();

    if( !mushroom_read_database( base_path, &data, &missing, &responses ) )
    {
        printf( "\nUnable to load the training database\n\n");
        help();
        return -1;
    }

    dtree = mushroom_create_dtree( data, missing, responses,
        10 // poisonous mushrooms will have 10x higher weight in the decision tree
        );
    cvReleaseMat( &data );
    cvReleaseMat( &missing );
    cvReleaseMat( &responses );

    print_variable_importance( dtree, var_desc );
    interactive_classification( dtree, var_desc );
    delete dtree;

    return 0;
}
//from: http://blog.csdn.net/yangtrees/article/details/7490852