package com.intel.daal.examples.decision_forest;
import com.intel.daal.algorithms.tree_utils.regression.TreeNodeVisitor;
import com.intel.daal.algorithms.tree_utils.regression.LeafNodeDescriptor;
import com.intel.daal.algorithms.tree_utils.SplitNodeDescriptor;
import com.intel.daal.algorithms.decision_forest.regression.*;
import com.intel.daal.algorithms.decision_forest.regression.training.*;
import com.intel.daal.algorithms.decision_forest.*;
import com.intel.daal.data_management.data.NumericTable;
import com.intel.daal.data_management.data.HomogenNumericTable;
import com.intel.daal.data_management.data.MergedNumericTable;
import com.intel.daal.data_management.data_source.DataSource;
import com.intel.daal.data_management.data_source.FileDataSource;
import com.intel.daal.examples.utils.Service;
import com.intel.daal.services.DaalContext;
import com.intel.daal.data_management.data.*;
class DfRegPrintNodeVisitor extends TreeNodeVisitor {
@Override
public boolean onLeafNode(LeafNodeDescriptor desc) {
if(desc.level != 0)
printTab(desc.level);
System.out.println("Level " + desc.level + ", leaf node. Response value = " + desc.response +
", Impurity = " + desc.impurity + ", nNodeSampleCount = " + desc.nNodeSampleCount);
return true;
}
public boolean onSplitNode(SplitNodeDescriptor desc){
if(desc.level != 0)
printTab(desc.level);
System.out.println("Level " + desc.level + ", split node. Feature index = " + desc.featureIndex + ", feature value = " + desc.featureValue +
", Impurity = " + desc.impurity + ", nNodeSampleCount = " + desc.nNodeSampleCount);
return true;
}
private void printTab(long level) {
String s = "";
for (long i = 0; i < level; i++) {
s += " ";
}
System.out.print(s);
}
}
class DfRegTraverseModel {
private static final String trainDataset = "../data/batch/df_regression_train.csv";
private static final int nFeatures = 13;
private static final int nTrees = 2;
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
TrainingResult trainingResult = trainModel();
printModel(trainingResult);
context.dispose();
}
private static TrainingResult trainModel() {
FileDataSource trainDataSource = new FileDataSource(context, trainDataset,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);
NumericTable trainData = new HomogenNumericTable(context, Float.class, nFeatures, 0, NumericTable.AllocationFlag.NotAllocate);
NumericTable trainGroundTruth = new HomogenNumericTable(context, Float.class, 1, 0, NumericTable.AllocationFlag.NotAllocate);
MergedNumericTable mergedData = new MergedNumericTable(context);
mergedData.addNumericTable(trainData);
mergedData.addNumericTable(trainGroundTruth);
trainDataSource.loadDataBlock(mergedData);
trainData.getDictionary().setFeature(Float.class,3,DataFeatureUtils.FeatureType.DAAL_CATEGORICAL);
TrainingBatch algorithm = new TrainingBatch(context, Float.class, TrainingMethod.defaultDense);
algorithm.parameter.setNTrees(nTrees);
algorithm.input.set(InputId.data, trainData);
algorithm.input.set(InputId.dependentVariable, trainGroundTruth);
return algorithm.compute();
}
private static void printModel(TrainingResult trainingResult) {
Model m = trainingResult.get(TrainingResultId.model);
long nTrees = m.getNumberOfTrees();
System.out.println("Number of trees: " + nTrees);
DfRegPrintNodeVisitor visitor = new DfRegPrintNodeVisitor();
for (long i = 0; i < nTrees; i++) {
System.out.println("Tree #" + i);
m.traverseDFS(i, visitor);
}
}
}