DfRegTraverseModel.java

/* file: DfRegTraverseModel.java */
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/*
 //  Content:
 //     Java example of decision forest regression model traversal
 //
 //     The program trains the decision forest regression model on a training
 //     datasetFileName and prints the trained model by its depth-first traversing.
 */

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 {
    /* Input data set parameters */
    private static final String trainDataset = "../data/batch/df_regression_train.csv";

    private static final int nFeatures     = 13;

    /* Decision forest regression algorithm parameters */
    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() {
        /* Retrieve the data from the input data sets */
        FileDataSource trainDataSource = new FileDataSource(context, trainDataset,
                DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
                DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);

        /* Create Numeric Tables for training data and labels */
        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);

        /* Retrieve the data from an input file */
        trainDataSource.loadDataBlock(mergedData);

        /* Set feature as categorical */
        trainData.getDictionary().setFeature(Float.class,3,DataFeatureUtils.FeatureType.DAAL_CATEGORICAL);

        /* Create algorithm objects to train the decision forest regression model */
        TrainingBatch algorithm = new TrainingBatch(context, Float.class, TrainingMethod.defaultDense);
        algorithm.parameter.setNTrees(nTrees);

        /* Pass a training data set and dependent values to the algorithm */
        algorithm.input.set(InputId.data, trainData);
        algorithm.input.set(InputId.dependentVariable, trainGroundTruth);

        /* Train the decision forest regression model */
        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);
        }
    }
}
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