opencv/samples/cpp/tree_engine.cpp

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

using namespace cv;
using namespace cv::ml;

static void help()
{
    printf(
        "\nThis sample demonstrates how to use different decision trees and forests including boosting and random trees.\n"
        "Usage:\n\t./tree_engine [-r=<response_column>] [-ts=type_spec] <csv filename>\n"
        "where -r=<response_column> specified the 0-based index of the response (0 by default)\n"
        "-ts= specifies the var type spec in the form ord[n1,n2-n3,n4-n5,...]cat[m1-m2,m3,m4-m5,...]\n"
        "<csv filename> is the name of training data file in comma-separated value format\n\n");
}

static void train_and_print_errs(Ptr<StatModel> model, const Ptr<TrainData>& data)
{
    bool ok = model->train(data);
    if( !ok )
    {
        printf("Training failed\n");
    }
    else
    {
        printf( "train error: %f\n", model->calcError(data, false, noArray()) );
        printf( "test error: %f\n\n", model->calcError(data, true, noArray()) );
    }
}

int main(int argc, char** argv)
{
    cv::CommandLineParser parser(argc, argv, "{ help h | | }{r | 0 | }{ts | | }{@input | | }");
    if (parser.has("help"))
    {
        help();
        return 0;
    }
    std::string filename = parser.get<std::string>("@input");
    int response_idx;
    std::string typespec;
    response_idx = parser.get<int>("r");
    typespec = parser.get<std::string>("ts");
    if( filename.empty() || !parser.check() )
    {
        parser.printErrors();
        help();
        return 0;
    }
    printf("\nReading in %s...\n\n",filename.c_str());
    const double train_test_split_ratio = 0.5;

    Ptr<TrainData> data = TrainData::loadFromCSV(filename, 0, response_idx, response_idx+1, typespec);
    if( data.empty() )
    {
        printf("ERROR: File %s can not be read\n", filename.c_str());
        return 0;
    }

    data->setTrainTestSplitRatio(train_test_split_ratio);
    std::cout << "Test/Train: " << data->getNTestSamples() << "/" << data->getNTrainSamples();

    printf("======DTREE=====\n");
    Ptr<DTrees> dtree = DTrees::create();
    dtree->setMaxDepth(10);
    dtree->setMinSampleCount(2);
    dtree->setRegressionAccuracy(0);
    dtree->setUseSurrogates(false);
    dtree->setMaxCategories(16);
    dtree->setCVFolds(0);
    dtree->setUse1SERule(false);
    dtree->setTruncatePrunedTree(false);
    dtree->setPriors(Mat());
    train_and_print_errs(dtree, data);

    if( (int)data->getClassLabels().total() <= 2 ) // regression or 2-class classification problem
    {
        printf("======BOOST=====\n");
        Ptr<Boost> boost = Boost::create();
        boost->setBoostType(Boost::GENTLE);
        boost->setWeakCount(100);
        boost->setWeightTrimRate(0.95);
        boost->setMaxDepth(2);
        boost->setUseSurrogates(false);
        boost->setPriors(Mat());
        train_and_print_errs(boost, data);
    }

    printf("======RTREES=====\n");
    Ptr<RTrees> rtrees = RTrees::create();
    rtrees->setMaxDepth(10);
    rtrees->setMinSampleCount(2);
    rtrees->setRegressionAccuracy(0);
    rtrees->setUseSurrogates(false);
    rtrees->setMaxCategories(16);
    rtrees->setPriors(Mat());
    rtrees->setCalculateVarImportance(true);
    rtrees->setActiveVarCount(0);
    rtrees->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 0));
    train_and_print_errs(rtrees, data);
    cv::Mat ref_labels = data->getClassLabels();
    cv::Mat test_data = data->getTestSampleIdx();
    cv::Mat predict_labels;
    rtrees->predict(data->getSamples(), predict_labels);

    cv::Mat variable_importance = rtrees->getVarImportance();
    std::cout << "Estimated variable importance" << std::endl;
    for (int i = 0; i < variable_importance.rows; i++) {
        std::cout << "Variable " << i << ": " << variable_importance.at<float>(i, 0) << std::endl;
    }
    return 0;
}