opencv/samples/c/tree_engine.cpp

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

static void help()
{
    printf(
        "\nThis sample demonstrates how to use different decision trees and forests including boosting and random trees:\n"
        "CvDTree dtree;\n"
        "CvBoost boost;\n"
        "CvRTrees rtrees;\n"
        "CvERTrees ertrees;\n"
        "CvGBTrees gbtrees;\n"
        "Call:\n\t./tree_engine [-r <response_column>] [-c] <csv filename>\n"
        "where -r <response_column> specified the 0-based index of the response (0 by default)\n"
        "-c specifies that the response is categorical (it's ordered by default) and\n"
        "<csv filename> is the name of training data file in comma-separated value format\n\n");
}


static int count_classes(CvMLData& data)
{
    cv::Mat r = cv::cvarrToMat(data.get_responses());
    std::map<int, int> rmap;
    int i, n = (int)r.total();
    for( i = 0; i < n; i++ )
    {
        float val = r.at<float>(i);
        int ival = cvRound(val);
        if( ival != val )
            return -1;
        rmap[ival] = 1;
    }
    return (int)rmap.size();
}

static void print_result(float train_err, float test_err, const CvMat* _var_imp)
{
    printf( "train error    %f\n", train_err );
    printf( "test error    %f\n\n", test_err );

    if (_var_imp)
    {
        cv::Mat var_imp = cv::cvarrToMat(_var_imp), sorted_idx;
        cv::sortIdx(var_imp, sorted_idx, CV_SORT_EVERY_ROW + CV_SORT_DESCENDING);

        printf( "variable importance:\n" );
        int i, n = (int)var_imp.total();
        int type = var_imp.type();
        CV_Assert(type == CV_32F || type == CV_64F);

        for( i = 0; i < n; i++)
        {
            int k = sorted_idx.at<int>(i);
            printf( "%d\t%f\n", k, type == CV_32F ? var_imp.at<float>(k) : var_imp.at<double>(k));
        }
    }
    printf("\n");
}

int main(int argc, char** argv)
{
    if(argc < 2)
    {
        help();
        return 0;
    }
    const char* filename = 0;
    int response_idx = 0;
    bool categorical_response = false;

    for(int i = 1; i < argc; i++)
    {
        if(strcmp(argv[i], "-r") == 0)
            sscanf(argv[++i], "%d", &response_idx);
        else if(strcmp(argv[i], "-c") == 0)
            categorical_response = true;
        else if(argv[i][0] != '-' )
            filename = argv[i];
        else
        {
            printf("Error. Invalid option %s\n", argv[i]);
            help();
            return -1;
        }
    }

    printf("\nReading in %s...\n\n",filename);
    CvDTree dtree;
    CvBoost boost;
    CvRTrees rtrees;
    CvERTrees ertrees;
    CvGBTrees gbtrees;

    CvMLData data;


    CvTrainTestSplit spl( 0.5f );

    if ( data.read_csv( filename ) == 0)
    {
        data.set_response_idx( response_idx );
        if(categorical_response)
            data.change_var_type( response_idx, CV_VAR_CATEGORICAL );
        data.set_train_test_split( &spl );

        printf("======DTREE=====\n");
        dtree.train( &data, CvDTreeParams( 10, 2, 0, false, 16, 0, false, false, 0 ));
        print_result( dtree.calc_error( &data, CV_TRAIN_ERROR), dtree.calc_error( &data, CV_TEST_ERROR ), dtree.get_var_importance() );

        if( categorical_response && count_classes(data) == 2 )
        {
        printf("======BOOST=====\n");
        boost.train( &data, CvBoostParams(CvBoost::DISCRETE, 100, 0.95, 2, false, 0));
        print_result( boost.calc_error( &data, CV_TRAIN_ERROR ), boost.calc_error( &data, CV_TEST_ERROR ), 0 ); //doesn't compute importance
        }

        printf("======RTREES=====\n");
        rtrees.train( &data, CvRTParams( 10, 2, 0, false, 16, 0, true, 0, 100, 0, CV_TERMCRIT_ITER ));
        print_result( rtrees.calc_error( &data, CV_TRAIN_ERROR), rtrees.calc_error( &data, CV_TEST_ERROR ), rtrees.get_var_importance() );

        printf("======ERTREES=====\n");
        ertrees.train( &data, CvRTParams( 18, 2, 0, false, 16, 0, true, 0, 100, 0, CV_TERMCRIT_ITER ));
        print_result( ertrees.calc_error( &data, CV_TRAIN_ERROR), ertrees.calc_error( &data, CV_TEST_ERROR ), ertrees.get_var_importance() );

        printf("======GBTREES=====\n");
        if (categorical_response)
            gbtrees.train( &data, CvGBTreesParams(CvGBTrees::DEVIANCE_LOSS, 100, 0.1f, 0.8f, 5, false));
        else
            gbtrees.train( &data, CvGBTreesParams(CvGBTrees::SQUARED_LOSS, 100, 0.1f, 0.8f, 5, false));
        print_result( gbtrees.calc_error( &data, CV_TRAIN_ERROR), gbtrees.calc_error( &data, CV_TEST_ERROR ), 0 ); //doesn't compute importance
    }
    else
        printf("File can not be read");

    return 0;
}