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#include "opencv2/ml/ml.hpp"
#include "opencv2/core/core_c.h"
#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(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(_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;
}