initial commit

samples/cpp/tree_engine.cpp

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+#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;
+}