com.github.lwhite1.tablesaw.api.ml.classification.RandomForest Maven / Gradle / Ivy

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package com.github.lwhite1.tablesaw.api.ml.classification;

import com.github.lwhite1.tablesaw.api.CategoryColumn;
import com.github.lwhite1.tablesaw.api.IntColumn;
import com.github.lwhite1.tablesaw.api.NumericColumn;
import com.github.lwhite1.tablesaw.api.ShortColumn;
import com.github.lwhite1.tablesaw.util.DoubleArrays;
import com.google.common.base.Preconditions;

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

/**
 *
 */
public class RandomForest extends AbstractClassifier {

  private final smile.classification.RandomForest classifierModel;


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

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

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

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

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

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

    SortedSet