tech.tablesaw.api.ml.classification.DecisionTree Maven / Gradle / Ivy

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package tech.tablesaw.api.ml.classification;

import com.google.common.base.Preconditions;

import tech.tablesaw.api.CategoryColumn;
import tech.tablesaw.api.IntColumn;
import tech.tablesaw.api.NumericColumn;
import tech.tablesaw.api.ShortColumn;
import tech.tablesaw.util.DoubleArrays;

import java.util.SortedSet;
import java.util.TreeSet;

/**
 *
 */
public class DecisionTree extends AbstractClassifier {

    private final smile.classification.DecisionTree classifierModel;


    private DecisionTree(int maxNodes, int[] classArray, NumericColumn... columns) {
        double[][] data = DoubleArrays.to2dArray(columns);
        this.classifierModel = new smile.classification.DecisionTree(data, classArray, maxNodes);
    }

    public static DecisionTree learn(int maxNodes, IntColumn classes, NumericColumn... columns) {
        int[] classArray = classes.data().toIntArray();
        return new DecisionTree(maxNodes, classArray, columns);
    }

    public static DecisionTree learn(int maxNodes, ShortColumn classes, NumericColumn... columns) {
        int[] classArray = classes.toIntArray();
        return new DecisionTree(maxNodes, classArray, columns);
    }

    public static DecisionTree learn(int nTrees, CategoryColumn classes, NumericColumn... columns) {
        int[] classArray = classes.data().toIntArray();
        return new DecisionTree(nTrees, classArray, columns);
    }

    public int predict(double[] data) {
        return classifierModel.predict(data);
    }

    public ConfusionMatrix predictMatrix(ShortColumn labels, NumericColumn... predictors) {
        Preconditions.checkArgument(predictors.length > 0);

        SortedSet