com.cloudera.oryx.app.rdf.tree.DecisionForest.java Source code

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Here is the source code for com.cloudera.oryx.app.rdf.tree.DecisionForest.java

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/*
 * Copyright (c) 2014, Cloudera, Inc. All Rights Reserved.
 *
 * Cloudera, Inc. licenses this file to you 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
 *
 * This software 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.
 */

package com.cloudera.oryx.app.rdf.tree;

import java.util.Arrays;

import com.google.common.base.Function;
import com.google.common.collect.Lists;

import com.cloudera.oryx.app.rdf.predict.Prediction;
import com.cloudera.oryx.app.rdf.example.Example;
import com.cloudera.oryx.app.rdf.predict.WeightedPrediction;

/**
 * An ensemble classifier based on many {@link DecisionTree}s.
 *
 * @see DecisionTree
 */
public final class DecisionForest implements TreeBasedClassifier {

    private final DecisionTree[] trees;
    private final double[] weights;
    //private final double[] evaluations;
    private final double[] featureImportances;

    public DecisionForest(DecisionTree[] trees, double[] weights, double[] featureImportances) {
        this.trees = trees;
        this.weights = weights;
        //this.evaluations = new double[weights.length];
        this.featureImportances = featureImportances;
    }

    /**
     * @return {@link DecisionTree}s in the ensemble forest
     */
    public DecisionTree[] getTrees() {
        return trees;
    }

    public double[] getWeights() {
        return weights;
    }

    /*
    public double[] getEvaluations() {
      return evaluations;
    }
     */

    public double[] getFeatureImportances() {
        return featureImportances;
    }

    @Override
    public Prediction predict(Example test) {
        return WeightedPrediction
                .voteOnFeature(Lists.transform(Arrays.asList(trees), new TreeToPredictionFunction(test)), weights);
    }

    @Override
    public void update(Example train) {
        for (TreeBasedClassifier tree : trees) {
            tree.update(train);
        }
    }

    @Override
    public String toString() {
        StringBuilder result = new StringBuilder();
        for (DecisionTree tree : trees) {
            result.append(tree).append('\n');
        }
        return result.toString();
    }

    private static final class TreeToPredictionFunction implements Function<DecisionTree, Prediction> {
        private final Example test;

        TreeToPredictionFunction(Example test) {
            this.test = test;
        }

        @Override
        public Prediction apply(DecisionTree tree) {
            return tree.predict(test);
        }
    }
}