如何在WEKA中打印交叉验证后的预测类别

Question

使用分类器完成 10 倍交叉验证后，如何打印出每个实例的预测类以及这些实例的分布？

J48 j48 = new J48();
Evaluation eval = new Evaluation(newData);
eval.crossValidateModel(j48, newData, 10, new Random(1));

当我尝试类似于下面的内容时，它说分类器未构建。

for (int i=0; i<data.numInstances(); i++){
   System.out.println(j48.distributionForInstance(newData.instance(i)));
 }

我想要做的功能与 WEKA GUI 中的功能相同，其中训练分类器后，我可以单击“可视化分类器错误”>“保存”，然后我将在中找到预测的类但现在我需要它在我自己的 Java 代码中工作，

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我尝试了如下所示：

J48 j48 = new J48();
Evaluation eval = new Evaluation(newData);
StringBuffer forPredictionsPrinting = new StringBuffer();
weka.core.Range attsToOutput = null;
Boolean outputDistribution = new Boolean(true);
eval.crossValidateModel(j48, newData, 10, new Random(1), forPredictionsPrinting, attsToOutput, outputDistribution);

但它提示了我错误：

Exception in thread "main" java.lang.ClassCastException: java.lang.StringBuffer cannot be cast to weka.classifiers.evaluation.output.prediction.AbstractOutput

Answer 1

crossValidateModel() 方法可以采用 forPredictionsPrinting varargs 参数，该参数是 weka.classifiers.evaluation.output.prediction.AbstractOutput< /代码> 实例。

其中重要的部分是一个 StringBuffer 来保存所有预测的字符串表示形式。以下代码是未经测试的 JRuby 代码，但您应该能够根据您的需要对其进行转换。

j48 = j48.new
eval = Evalution.new(newData)
predictions = java.lange.StringBuffer.new
eval.crossValidateModel(j48, newData, 10, Random.new(1), predictions, Range.new('1'), true)
# variable predictions now hold a string of all the individual predictions

Answer 2

前几天我被困住了。我想使用矩阵而不是从 arff 文件加载来评估 matlab 中的 Weka 分类器。我使用 http://www.mathworks.com/matlabcentral/fileexchange/21204 -matlab-weka-interface 和以下源代码。我希望这对其他人有帮助。

import weka.classifiers.*;

import java.util.*

wekaClassifier = javaObject('weka.classifiers.trees.J48');

wekaClassifier.buildClassifier(processed);%Loaded from loadARFF

e = javaObject('weka.classifiers.Evaluation',processed);%Loaded from loadARFF
myrand = Random(1);
plainText = javaObject('weka.classifiers.evaluation.output.prediction.PlainText');
buffer = javaObject('java.lang.StringBuffer');
plainText.setBuffer(buffer)
bool = javaObject('java.lang.Boolean',true);
range = javaObject('weka.core.Range','1');
array = javaArray('java.lang.Object',3);
array(1) = plainText;
array(2) = range;
array(3) = bool;
e.crossValidateModel(wekaClassifier,testing,10,myrand,array)
e.toClassDetailsString

阿斯德鲁巴尔·洛佩斯·乔

Answer 3

clc
clear
%Load from disk
fileDataset = 'cm1.arff';
myPath = 'C:\Users\Asdrubal\Google Drive\Respaldo\DoctoradoALCPC\Doctorado ALC PC\AlcMobile\AvTh\MyPapers\Papers2014\UnderOverSampling\data\Skewed\datasetsKeel\';
javaaddpath('C:\Users\Asdrubal\Google Drive\Respaldo\DoctoradoALCPC\Doctorado ALC PC\AlcMobile\JarsForExperiments\weka.jar');
wekaOBJ = loadARFF([myPath fileDataset]);
%Transform from data into Matlab
[data, featureNames, targetNDX, stringVals, relationName] = ... 
weka2matlab(wekaOBJ,'[]');
%Create testing and training sets in matlab format (this can be improved)
[tam, dim] = size(data);
idx = randperm(tam);
testIdx = idx(1 : tam*0.3);
trainIdx = idx(tam*0.3 + 1:end);
trainSet = data(trainIdx,:);
testSet = data(testIdx,:);
%Trasnform the training and the testing sets into the Weka format
testingWeka = matlab2weka('testing', featureNames, testSet);
trainingWeka = matlab2weka('training', featureNames, trainSet);
%Now evaluate classifier
import weka.classifiers.*;
import java.util.*
wekaClassifier = javaObject('weka.classifiers.trees.J48');
wekaClassifier.buildClassifier(trainingWeka);
e = javaObject('weka.classifiers.Evaluation',trainingWeka);
myrand = Random(1);
plainText = javaObject('weka.classifiers.evaluation.output.prediction.PlainText');
buffer = javaObject('java.lang.StringBuffer');
plainText.setBuffer(buffer)
bool = javaObject('java.lang.Boolean',true);
range = javaObject('weka.core.Range','1');
array = javaArray('java.lang.Object',3);
array(1) = plainText;
array(2) = range;
array(3) = bool;
e.crossValidateModel(wekaClassifier,testingWeka,10,myrand,array)%U
e.toClassDetailsString