139 lines
4.5 KiB
C++
139 lines
4.5 KiB
C++
#include "opencv2/ml/ml.hpp"
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#include "opencv2/core/core_c.h"
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#include <stdio.h>
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#include <map>
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static void help()
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{
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printf(
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"\nThis sample demonstrates how to use different decision trees and forests including boosting and random trees:\n"
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"CvDTree dtree;\n"
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"CvBoost boost;\n"
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"CvRTrees rtrees;\n"
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"CvERTrees ertrees;\n"
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"CvGBTrees gbtrees;\n"
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"Call:\n\t./tree_engine [-r <response_column>] [-c] <csv filename>\n"
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"where -r <response_column> specified the 0-based index of the response (0 by default)\n"
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"-c specifies that the response is categorical (it's ordered by default) and\n"
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"<csv filename> is the name of training data file in comma-separated value format\n\n");
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}
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static int count_classes(CvMLData& data)
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{
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cv::Mat r(data.get_responses());
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std::map<int, int> rmap;
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int i, n = (int)r.total();
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for( i = 0; i < n; i++ )
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{
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float val = r.at<float>(i);
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int ival = cvRound(val);
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if( ival != val )
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return -1;
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rmap[ival] = 1;
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}
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return (int)rmap.size();
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}
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static void print_result(float train_err, float test_err, const CvMat* _var_imp)
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{
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printf( "train error %f\n", train_err );
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printf( "test error %f\n\n", test_err );
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if (_var_imp)
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{
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cv::Mat var_imp(_var_imp), sorted_idx;
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cv::sortIdx(var_imp, sorted_idx, CV_SORT_EVERY_ROW + CV_SORT_DESCENDING);
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printf( "variable importance:\n" );
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int i, n = (int)var_imp.total();
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int type = var_imp.type();
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CV_Assert(type == CV_32F || type == CV_64F);
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for( i = 0; i < n; i++)
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{
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int k = sorted_idx.at<int>(i);
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printf( "%d\t%f\n", k, type == CV_32F ? var_imp.at<float>(k) : var_imp.at<double>(k));
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}
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}
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printf("\n");
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}
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int main(int argc, char** argv)
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{
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if(argc < 2)
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{
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help();
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return 0;
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}
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const char* filename = 0;
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int response_idx = 0;
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bool categorical_response = false;
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for(int i = 1; i < argc; i++)
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{
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if(strcmp(argv[i], "-r") == 0)
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sscanf(argv[++i], "%d", &response_idx);
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else if(strcmp(argv[i], "-c") == 0)
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categorical_response = true;
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else if(argv[i][0] != '-' )
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filename = argv[i];
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else
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{
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printf("Error. Invalid option %s\n", argv[i]);
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help();
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return -1;
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}
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}
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printf("\nReading in %s...\n\n",filename);
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CvDTree dtree;
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CvBoost boost;
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CvRTrees rtrees;
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CvERTrees ertrees;
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CvGBTrees gbtrees;
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CvMLData data;
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CvTrainTestSplit spl( 0.5f );
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if ( data.read_csv( filename ) == 0)
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{
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data.set_response_idx( response_idx );
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if(categorical_response)
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data.change_var_type( response_idx, CV_VAR_CATEGORICAL );
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data.set_train_test_split( &spl );
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printf("======DTREE=====\n");
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dtree.train( &data, CvDTreeParams( 10, 2, 0, false, 16, 0, false, false, 0 ));
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print_result( dtree.calc_error( &data, CV_TRAIN_ERROR), dtree.calc_error( &data, CV_TEST_ERROR ), dtree.get_var_importance() );
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if( categorical_response && count_classes(data) == 2 )
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{
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printf("======BOOST=====\n");
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boost.train( &data, CvBoostParams(CvBoost::DISCRETE, 100, 0.95, 2, false, 0));
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print_result( boost.calc_error( &data, CV_TRAIN_ERROR ), boost.calc_error( &data, CV_TEST_ERROR ), 0 ); //doesn't compute importance
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}
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printf("======RTREES=====\n");
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rtrees.train( &data, CvRTParams( 10, 2, 0, false, 16, 0, true, 0, 100, 0, CV_TERMCRIT_ITER ));
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print_result( rtrees.calc_error( &data, CV_TRAIN_ERROR), rtrees.calc_error( &data, CV_TEST_ERROR ), rtrees.get_var_importance() );
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printf("======ERTREES=====\n");
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ertrees.train( &data, CvRTParams( 18, 2, 0, false, 16, 0, true, 0, 100, 0, CV_TERMCRIT_ITER ));
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print_result( ertrees.calc_error( &data, CV_TRAIN_ERROR), ertrees.calc_error( &data, CV_TEST_ERROR ), ertrees.get_var_importance() );
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printf("======GBTREES=====\n");
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if (categorical_response)
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gbtrees.train( &data, CvGBTreesParams(CvGBTrees::DEVIANCE_LOSS, 100, 0.1f, 0.8f, 5, false));
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else
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gbtrees.train( &data, CvGBTreesParams(CvGBTrees::SQUARED_LOSS, 100, 0.1f, 0.8f, 5, false));
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print_result( gbtrees.calc_error( &data, CV_TRAIN_ERROR), gbtrees.calc_error( &data, CV_TEST_ERROR ), 0 ); //doesn't compute importance
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}
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else
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printf("File can not be read");
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return 0;
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}
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