text: /* file: dt_cls_traverse_model.cpp */
  /*******************************************************************************
  * Copyright 2014 Intel Corporation
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
  * You may obtain a copy of the License at
  *
  *     http://www.apache.org/licenses/LICENSE-2.0
  *
  * Unless required by applicable law or agreed to in writing, software
  * distributed under the License is distributed on an "AS IS" BASIS,
  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
  *******************************************************************************/
  
  /*
  !  Content:
  !    C++ example of decision tree classification model traversal.
  !
  !    The program trains the decision tree classification model on a training
  !    datasetFileName and prints the trained model by its depth-first traversing.
  !******************************************************************************/
  
  /**
   * <a name="DAAL-EXAMPLE-CPP-DT_CLS_TRAVERSE_MODEL"></a>
   * \example dt_cls_traverse_model.cpp
   */
  
  #include "daal.h"
  #include "service.h"
  #include <cstdio>
  
  using namespace std;
  using namespace daal;
  using namespace daal::algorithms;
  using namespace daal::data_management;
  
  /* Input data set parameters */
  string trainDatasetFileName = "../data/batch/decision_tree_train.csv";
  string pruneDatasetFileName = "../data/batch/decision_tree_prune.csv";
  
  const size_t nFeatures = 5; /* Number of features in training and testing data sets */
  const size_t nClasses  = 5; /* Number of classes */
  
  decision_tree::classification::training::ResultPtr trainModel();
  void printModel(const daal::algorithms::decision_tree::classification::Model & m);
  
  int main(int argc, char * argv[])
  {
      checkArguments(argc, argv, 2, &trainDatasetFileName, &pruneDatasetFileName);
  
      decision_tree::classification::training::ResultPtr trainingResult = trainModel();
      printModel(*trainingResult->get(classifier::training::model));
  
      return 0;
  }
  
  decision_tree::classification::training::ResultPtr trainModel()
  {
      /* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
      FileDataSource<CSVFeatureManager> trainDataSource(trainDatasetFileName, DataSource::notAllocateNumericTable, DataSource::doDictionaryFromContext);
  
      /* Create Numeric Tables for training data and labels */
      NumericTablePtr trainData(new HomogenNumericTable<>(nFeatures, 0, NumericTable::notAllocate));
      NumericTablePtr trainGroundTruth(new HomogenNumericTable<>(1, 0, NumericTable::notAllocate));
      NumericTablePtr mergedData(new MergedNumericTable(trainData, trainGroundTruth));
  
      /* Retrieve the data from the input file */
      trainDataSource.loadDataBlock(mergedData.get());
  
      /* Initialize FileDataSource<CSVFeatureManager> to retrieve the pruning input data from a .csv file */
      FileDataSource<CSVFeatureManager> pruneDataSource(pruneDatasetFileName, DataSource::notAllocateNumericTable, DataSource::doDictionaryFromContext);
  
      /* Create Numeric Tables for pruning data and labels */
      NumericTablePtr pruneData(new HomogenNumericTable<>(nFeatures, 0, NumericTable::notAllocate));
      NumericTablePtr pruneGroundTruth(new HomogenNumericTable<>(1, 0, NumericTable::notAllocate));
      NumericTablePtr pruneMergedData(new MergedNumericTable(pruneData, pruneGroundTruth));
  
      /* Retrieve the data from the pruning input file */
      pruneDataSource.loadDataBlock(pruneMergedData.get());
  
      /* Create an algorithm object to train the Decision tree model */
      decision_tree::classification::training::Batch<> algorithm(nClasses);
  
      /* Pass the training data set, labels, and pruning dataset with labels to the algorithm */
      algorithm.input.set(classifier::training::data, trainData);
      algorithm.input.set(classifier::training::labels, trainGroundTruth);
      algorithm.input.set(decision_tree::classification::training::dataForPruning, pruneData);
      algorithm.input.set(decision_tree::classification::training::labelsForPruning, pruneGroundTruth);
  
      /* Train the Decision tree model */
      algorithm.compute();
  
      /* Retrieve the results of the training algorithm  */
      return algorithm.getResult();
  }
  
  /** Visitor class implementing TreeNodeVisitor interface, prints out tree nodes of the model when it is called back by model traversal method */
  class PrintNodeVisitor : public daal::algorithms::tree_utils::classification::TreeNodeVisitor
  {
  public:
      virtual bool onLeafNode(const tree_utils::classification::LeafNodeDescriptor & desc)
      {
          for (size_t i = 0; i < desc.level; ++i) std::cout << "  ";
          std::cout << "Level " << desc.level << ", leaf node. Response value = " << desc.label << ", Impurity = " << desc.impurity
                    << ", Number of samples = " << desc.nNodeSampleCount << std::endl;
          return true;
      }
  
      virtual bool onSplitNode(const tree_utils::classification::SplitNodeDescriptor & desc)
      {
          for (size_t i = 0; i < desc.level; ++i) std::cout << "  ";
          std::cout << "Level " << desc.level << ", split node. Feature index = " << desc.featureIndex << ", feature value = " << desc.featureValue
                    << ", Impurity = " << desc.impurity << ", Number of samples = " << desc.nNodeSampleCount << std::endl;
          return true;
      }
  };
  
  void printModel(const daal::algorithms::decision_tree::classification::Model & m)
  {
      PrintNodeVisitor visitor;
      m.traverseDFS(visitor);
  }
  