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117 lines
3.9 KiB
C++
117 lines
3.9 KiB
C++
#include "opencv2/ml.hpp"
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#include "opencv2/core.hpp"
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#include "opencv2/core/utility.hpp"
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#include <stdio.h>
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#include <string>
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#include <map>
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using namespace cv;
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using namespace cv::ml;
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static void help(char** argv)
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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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"Usage:\n\t%s [-r=<response_column>] [-ts=type_spec] <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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"-ts= specifies the var type spec in the form ord[n1,n2-n3,n4-n5,...]cat[m1-m2,m3,m4-m5,...]\n"
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"<csv filename> is the name of training data file in comma-separated value format\n\n", argv[0]);
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}
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static void train_and_print_errs(Ptr<StatModel> model, const Ptr<TrainData>& data)
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{
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bool ok = model->train(data);
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if( !ok )
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{
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printf("Training failed\n");
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}
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else
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{
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printf( "train error: %f\n", model->calcError(data, false, noArray()) );
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printf( "test error: %f\n\n", model->calcError(data, true, noArray()) );
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}
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}
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int main(int argc, char** argv)
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{
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cv::CommandLineParser parser(argc, argv, "{ help h | | }{r | 0 | }{ts | | }{@input | | }");
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if (parser.has("help"))
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{
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help(argv);
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return 0;
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}
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std::string filename = parser.get<std::string>("@input");
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int response_idx;
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std::string typespec;
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response_idx = parser.get<int>("r");
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typespec = parser.get<std::string>("ts");
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if( filename.empty() || !parser.check() )
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{
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parser.printErrors();
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help(argv);
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return 0;
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}
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printf("\nReading in %s...\n\n",filename.c_str());
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const double train_test_split_ratio = 0.5;
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Ptr<TrainData> data = TrainData::loadFromCSV(filename, 0, response_idx, response_idx+1, typespec);
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if( data.empty() )
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{
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printf("ERROR: File %s can not be read\n", filename.c_str());
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return 0;
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}
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data->setTrainTestSplitRatio(train_test_split_ratio);
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std::cout << "Test/Train: " << data->getNTestSamples() << "/" << data->getNTrainSamples();
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printf("======DTREE=====\n");
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Ptr<DTrees> dtree = DTrees::create();
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dtree->setMaxDepth(10);
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dtree->setMinSampleCount(2);
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dtree->setRegressionAccuracy(0);
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dtree->setUseSurrogates(false);
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dtree->setMaxCategories(16);
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dtree->setCVFolds(0);
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dtree->setUse1SERule(false);
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dtree->setTruncatePrunedTree(false);
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dtree->setPriors(Mat());
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train_and_print_errs(dtree, data);
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if( (int)data->getClassLabels().total() <= 2 ) // regression or 2-class classification problem
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{
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printf("======BOOST=====\n");
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Ptr<Boost> boost = Boost::create();
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boost->setBoostType(Boost::GENTLE);
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boost->setWeakCount(100);
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boost->setWeightTrimRate(0.95);
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boost->setMaxDepth(2);
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boost->setUseSurrogates(false);
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boost->setPriors(Mat());
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train_and_print_errs(boost, data);
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}
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printf("======RTREES=====\n");
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Ptr<RTrees> rtrees = RTrees::create();
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rtrees->setMaxDepth(10);
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rtrees->setMinSampleCount(2);
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rtrees->setRegressionAccuracy(0);
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rtrees->setUseSurrogates(false);
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rtrees->setMaxCategories(16);
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rtrees->setPriors(Mat());
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rtrees->setCalculateVarImportance(true);
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rtrees->setActiveVarCount(0);
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rtrees->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 0));
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train_and_print_errs(rtrees, data);
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cv::Mat ref_labels = data->getClassLabels();
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cv::Mat test_data = data->getTestSampleIdx();
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cv::Mat predict_labels;
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rtrees->predict(data->getSamples(), predict_labels);
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cv::Mat variable_importance = rtrees->getVarImportance();
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std::cout << "Estimated variable importance" << std::endl;
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for (int i = 0; i < variable_importance.rows; i++) {
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std::cout << "Variable " << i << ": " << variable_importance.at<float>(i, 0) << std::endl;
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}
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return 0;
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}
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