org.apache.mahout.classifier.RegressionResultAnalyzer.java Source code

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Here is the source code for org.apache.mahout.classifier.RegressionResultAnalyzer.java

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/**
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF 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
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License 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 org.apache.mahout.classifier;

import java.text.DecimalFormat;
import java.text.NumberFormat;
import java.util.List;
import java.util.Locale;

import com.google.common.collect.Lists;
import org.apache.commons.lang3.StringUtils;

/**
 * ResultAnalyzer captures the classification statistics and displays in a tabular manner
 */
public class RegressionResultAnalyzer {

    private static class Result {
        private final double actual;
        private final double result;

        Result(double actual, double result) {
            this.actual = actual;
            this.result = result;
        }

        double getActual() {
            return actual;
        }

        double getResult() {
            return result;
        }
    }

    private List<Result> results;

    /**
     * 
     * @param actual
     *          The actual answer
     * @param result
     *          The regression result
     */
    public void addInstance(double actual, double result) {
        if (results == null) {
            results = Lists.newArrayList();
        }
        results.add(new Result(actual, result));
    }

    /**
     * 
     * @param results
     *          The results table
     */
    public void setInstances(double[][] results) {
        for (double[] res : results) {
            addInstance(res[0], res[1]);
        }
    }

    @Override
    public String toString() {
        double sumActual = 0.0;
        double sumActualSquared = 0.0;
        double sumResult = 0.0;
        double sumResultSquared = 0.0;
        double sumActualResult = 0.0;
        double sumAbsolute = 0.0;
        double sumAbsoluteSquared = 0.0;
        int predictable = 0;
        int unpredictable = 0;

        for (Result res : results) {
            double actual = res.getActual();
            double result = res.getResult();
            if (Double.isNaN(result)) {
                unpredictable++;
            } else {
                sumActual += actual;
                sumActualSquared += actual * actual;
                sumResult += result;
                sumResultSquared += result * result;
                sumActualResult += actual * result;
                double absolute = Math.abs(actual - result);
                sumAbsolute += absolute;
                sumAbsoluteSquared += absolute * absolute;
                predictable++;
            }
        }

        StringBuilder returnString = new StringBuilder();

        returnString.append("=======================================================\n");
        returnString.append("Summary\n");
        returnString.append("-------------------------------------------------------\n");

        if (predictable > 0) {
            double varActual = sumActualSquared - sumActual * sumActual / predictable;
            double varResult = sumResultSquared - sumResult * sumResult / predictable;
            double varCo = sumActualResult - sumActual * sumResult / predictable;

            double correlation;
            if (varActual * varResult <= 0) {
                correlation = 0.0;
            } else {
                correlation = varCo / Math.sqrt(varActual * varResult);
            }

            Locale.setDefault(Locale.US);
            NumberFormat decimalFormatter = new DecimalFormat("0.####");

            returnString.append(StringUtils.rightPad("Correlation coefficient", 40)).append(": ")
                    .append(StringUtils.leftPad(decimalFormatter.format(correlation), 10)).append('\n');
            returnString.append(StringUtils.rightPad("Mean absolute error", 40)).append(": ")
                    .append(StringUtils.leftPad(decimalFormatter.format(sumAbsolute / predictable), 10))
                    .append('\n');
            returnString.append(StringUtils.rightPad("Root mean squared error", 40)).append(": ")
                    .append(StringUtils
                            .leftPad(decimalFormatter.format(Math.sqrt(sumAbsoluteSquared / predictable)), 10))
                    .append('\n');
        }
        returnString.append(StringUtils.rightPad("Predictable Instances", 40)).append(": ")
                .append(StringUtils.leftPad(Integer.toString(predictable), 10)).append('\n');
        returnString.append(StringUtils.rightPad("Unpredictable Instances", 40)).append(": ")
                .append(StringUtils.leftPad(Integer.toString(unpredictable), 10)).append('\n');
        returnString.append(StringUtils.rightPad("Total Regressed Instances", 40)).append(": ")
                .append(StringUtils.leftPad(Integer.toString(results.size()), 10)).append('\n');
        returnString.append('\n');

        return returnString.toString();
    }
}