001/*
002 * Copyright (c) 2015-2020, Oracle and/or its affiliates. All rights reserved.
003 *
004 * Licensed under the Apache License, Version 2.0 (the "License");
005 * you may not use this file except in compliance with the License.
006 * You may obtain a copy of the License at
007 *
008 *     http://www.apache.org/licenses/LICENSE-2.0
009 *
010 * Unless required by applicable law or agreed to in writing, software
011 * distributed under the License is distributed on an "AS IS" BASIS,
012 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express implied.
013 * See the License for the specific language governing permissions and
014 * limitations under the License.
015 */
016
017package org.tribuo.regression.rtree.impurity;
018
019import com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance;
020import com.oracle.labs.mlrg.olcut.provenance.impl.ConfiguredObjectProvenanceImpl;
021
022import java.util.List;
023
024/**
025 * Measures the mean absolute error over a set of inputs.
026 * <p>
027 * Used to calculate the impurity of a regression node.
028 */
029public class MeanAbsoluteError implements RegressorImpurity {
030
031    @Override
032    public double impurity(float[] targets, float[] weights) {
033        float weightedSum = 0.0f;
034        float weightSum = 0.0f;
035        for (int i = 0; i < targets.length; i++) {
036            weightedSum += targets[i]*weights[i];
037            weightSum += weights[i];
038        }
039        float mean = weightedSum / weightSum;
040
041        float absoluteError = 0.0f;
042
043        for (int i = 0; i < targets.length; i++) {
044            float error = Math.abs(mean - targets[i]);
045            absoluteError += error*weights[i];
046        }
047        return absoluteError / weightSum;
048    }
049
050    @Override
051    public ImpurityTuple impurityTuple(int[] indices, int indicesLength, float[] targets, float[] weights) {
052        if (indicesLength == 1) {
053            return new ImpurityTuple(0.0f,weights[indices[0]]);
054        } else {
055            float weightedSum = 0.0f;
056            float weightSum = 0.0f;
057            for (int i = 0; i < indicesLength; i++) {
058                int idx = indices[i];
059                weightedSum += targets[idx]*weights[idx];
060                weightSum += weights[idx];
061            }
062            float mean = weightedSum / weightSum;
063
064            float absoluteError = 0.0f;
065
066            for (int i = 0; i < indicesLength; i++) {
067                int idx = indices[i];
068                float error = Math.abs(mean - targets[idx]);
069                absoluteError += error*weights[idx];
070            }
071            return new ImpurityTuple(absoluteError,weightSum);
072        }
073    }
074
075    @Override
076    public ImpurityTuple impurityTuple(List<int[]> indices, float[] targets, float[] weights) {
077        float weightedSum = 0.0f;
078        float weightSum = 0.0f;
079        for (int[] curIndices : indices) {
080            for (int i = 0; i < curIndices.length; i++) {
081                int idx = curIndices[i];
082                weightedSum += targets[idx] * weights[idx];
083                weightSum += weights[idx];
084            }
085        }
086        float mean = weightedSum / weightSum;
087
088        float absoluteError = 0.0f;
089
090        for (int[] curIndices : indices) {
091            for (int i = 0; i < curIndices.length; i++) {
092                int idx = curIndices[i];
093                float error = Math.abs(mean - targets[idx]);
094                absoluteError += error * weights[idx];
095            }
096        }
097        return new ImpurityTuple(absoluteError,weightSum);
098    }
099
100    @Override
101    public String toString() {
102        return "MeanAbsoluteError";
103    }
104
105    @Override
106    public ConfiguredObjectProvenance getProvenance() {
107        return new ConfiguredObjectProvenanceImpl(this,"RegressorImpurity");
108    }
109}